Identifying challenges and future directions of flood hazards mitigation strategies in health facilities: a systematic literature review - Arefi, Farhad; Tavan, Asghar; Moradi, Seyed Mobin; Daneshi, Salman; Farahmandnia, Hojjat (BMC Emergency Medicine, 2025)

Performance Evaluation of Recently Constructed Ponds for Flood Mitigation in Erbil City and their Impacts on the Environment (Global NEST Journal, 2025)
This study investigates the performance of 35 recent ponds (which are under tendering, under construction, and finished in Erbil City), focusing on their role in flood mitigation across 11 distinct catchment areas. The total storage capacity of these ponds is approximately 9,926,394 m³, significantly enhancing the city's ability to manage stormwater runoff and reduce flood risks. The Watershed Modeling System (WMS), along with the Soil Conservation Service Curve Number (SCS-CN) method, was utilized for hydrological modeling to evaluate runoff behavior and water retention performance. Calculated Retention Capacity Ratio (RCR) values vary from as low as 21 % in the smallest system to 136 % in the Kasnazan catchment, with Chamarga similarly exceeding full capacity at 131 %. These over-capacity networks not only attenuate peak flows but also promote groundwater recharge, improve downstream water quality by trapping sediments and nutrients, and create valuable aquatic and riparian habitats. Our findings demonstrate the multifaceted benefits of high-capacity retention ponds and provide a replicable model for integrating green infrastructure into urban planning to build flood resilience and sustainable water management in rapidly urbanizing regions.

Climate changes and cancer risk: Key factors and emerging health threats - Sotomayor, Marina Rayciki; Campos, Aline Graciele Henriques; Filho, Altair Rodrigues Pires De Paula; Goulart, Ana Carolina Aredes; Vacario, Beatriz Geovana Leite; Orrutéa, Julia Fernandes Gois; Valentim, Juliana Maria Bitencourt De Morais; Fagundes, Tatiane Renata; Gaboardi, Shaiane Carla; Panis, Carolina (Hygiene and Environmental Health Advances, 2025)

Identifying challenges and future directions of flood hazards mitigation strategies in health facilities: a systematic literature review - Arefi, Farhad; Tavan, Asghar; Moradi, Seyed Mobin; Daneshi, Salman; Farahmandnia, Hojjat (BMC Emergency Medicine, 2025)

Multi-objective optimization of nature-based solutions in urban stormwater management: A scoping review - Bista, A.; Paus, K.A.H.; Seifert-Dähnn, I. (Environmental Modelling & Software, 2025)

Identifying challenges and future directions of flood hazards mitigation strategies in health facilities: a systematic literature review - Arefi, Farhad; Tavan, Asghar; Moradi, Seyed Mobin; Daneshi, Salman; Farahmandnia, Hojjat (BMC Emergency Medicine, 2025)

Climate changes and cancer risk: Key factors and emerging health threats - Sotomayor, Marina Rayciki; Campos, Aline Graciele Henriques; Filho, Altair Rodrigues Pires De Paula; Goulart, Ana Carolina Aredes; Vacario, Beatriz Geovana Leite; Orrutéa, Julia Fernandes Gois; Valentim, Juliana Maria Bitencourt De Morais; Fagundes, Tatiane Renata; Gaboardi, Shaiane Carla; Panis, Carolina (Hygiene and Environmental Health Advances, 2025)

Multi-objective optimization of nature-based solutions in urban stormwater management: A scoping review - Bista, A.; Paus, K.A.H.; Seifert-Dähnn, I. (Environmental Modelling & Software, 2025)

Natural Disasters and Rehabilitation: Post‐Disaster Aid, Corruption, Misallocation, and Mistargeting - Ahmad, Muhammad Irshad; Shen, Qiong; Boota, Muhammad Waseem; Liu, Ruifeng; Ma, Hengyun (Sustainable Development, 2025)
ABSTRACT The increasing frequency of natural disasters, such as floods, droughts, and tsunamis, has made vulnerable communities less resilient, pushing them toward long‐term poverty and food insecurity. Effective post‐disaster rehabilitation is critical to restoring livelihoods, infrastructure, and food security. However, challenges such as corruption, misallocation, and mistargeting undermine post‐disaster aid programs. This study systematically reviews 86 peer‐reviewed articles (1990–2023) using the preferred reporting items for systematic reviews and meta‐analyses (PRISMA) protocol to investigate aid inefficiencies in disaster recovery. The findings reveal that aid often fails to reach the most affected communities, being diverted to unaffected areas due to political influence and local elites, exacerbating inequalities. Corruption further hampers institutional performance and long‐term disaster resilience efforts. The study calls for transparent, accountable, and inclusive strategies for aid distribution, aligning with SDG 10 (reduced inequalities) and SDG 11 (sustainable cities and communities). Future research should focus on gender‐sensitive strategies, local governance, and technological innovations to enhance aid transparency and effectiveness.

Determinants and relationships of climate change, climate change hazards, mental health, and well-being: a systematic review - Rückle, Karolin; Rohrer, Mario; Mihók, Barbara; Johansson, Maria; Andersson, Hanna; Pomee, Muhammad Saleem; Vergadi, Eleni; Rouva, Glykeria; Agrawal, Apoorv; Balázs, Balint; Brattich, Erika; Carelli, Maria; et al. (Frontiers in Psychiatry, 2025)
Introduction Impacts of climate change on human health receive increasing attention. However, the connections of climate change with well-being and mental health are still poorly understood. Objective As part of the Horizon Europe project TRIGGER, we aim to deepen the understanding of the relationships between climate change and human mental health and well-being in Europe by focusing on environmental and socio-individual determinants. Methods This study is a systematic literature review based on the PRISMA guidelines using Embase, Medline and Web of Science. Results 143 records were retrieved. The results show that climate change and its specific hazards (air pollution, floods, wildfires, meteorological variables, and temperature extremes) impact human well-being and mental health. Discussion Mental health and well-being outcomes are complex, extremely individual, and can be long lasting. Determinants like the living surrounding, human’s life activities as well as socio-individual determinants alter the linkage between climate change and mental health. The same determinant can exert both a pathogenic and a salutogenic effect, depending on the outcome. Knowing the effects of the determinants is of high relevance to improve resilience. Several pathways were identified. For instance, higher level of education and female gender lead to perceiving climate change as a bigger threat but increase preparedness to climate hazards. Elderly, children and adolescents are at higher risks of mental health problems. On the other hand, social relation, cohesiveness and support from family and friends are generally protective. Green and blue spaces improve well-being and mental health. Overall, comparing the different hazard-outcome relationships is difficult due to varying definitions, measurement techniques, spatial and temporal range, scales, indicators and population samples. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/home , identifier CRD42023426758.

Identifying challenges and future directions of flood hazards mitigation strategies in health facilities: a systematic literature review - Arefi, Farhad; Tavan, Asghar; Moradi, Seyed Mobin; Daneshi, Salman; Farahmandnia, Hojjat (BMC Emergency Medicine, 2025)

Flood inundation amplified by large-scale ground subsidence funnel under the ongoing global climate change - He, Liming; Yin, Yanzhuang; Cai, Jiuyang; Wu, Lixin; Tang, Yongliang; He, Ronghua; Qu, Zhenglin; Liu, Yu; Pei, Panke; Wang, Qiao (Communications Earth & Environment, 2025)

The 2021 Henan flood increased citizen demand for government-led climate change adaptation in China - Shen, Shiran Victoria (Communications Earth & Environment, 2025)

Enhancing management of flood forecasting in Southern New Jersey: a HEC-HMS model development for Maurice River and Raccoon Creek Watersheds - Chowdhury, Rumman M.; Ahn, Jeong; Torlapati, Jagadish; Jahan, Kauser (Applied Water Science, 2025)

Quantitative assessment of exacerbating effect on urban inundation when river flood encounters heavy rainfall in an urbanized watershed - Zhang, Haijia; Liu, Jiahong; Zhou, Jinjun; Mei, Chao; Wang, Jia; Song, Tianxu (Science China Technological Sciences, 2025)

Integrating hydrological and economic assessments of soil infiltration enhancement for urban flood resilience - Ren, Xingwei; Guo, Zeyu; Li, Qingli; Zhou, Zhengzheng; Chen, Xin; Liao, Jiajie (Science of The Total Environment, 2025)

Enhanced spatiotemporal prediction of rainfall erosivity through flood season data integration - Qi, Fei; Wang, Yunpeng; Xu, Luyue; Liu, Xia; Zhang, Chunqiang; Fan, Zhaofei; Guo, Jiayu; Yang, Xing (CATENA, 2025)

Predicting current habitat suitability for intermediate snail hosts of urogenital and intestinal schistosomiasis in the Lower Shire Valley floodplain of southern Malawi - Nkolokosa, Clinton; Chirombo, James; Jones, Christopher M.; Mbewe, Rex B.; Kambewa, Eggrey Aisha; Makaula, Peter; Tangena, Julie-Anne Akiko; Stothard, J. Russell (Parasites & Vectors, 2025)
Abstract Background Relating the geographical distribution of intermediate freshwater snail hosts (viz. vectors of schistosomes) to local environmental attributes offers value for understanding the epidemiological landscape of schistosomiasis transmission in a changing aquatic environment. Schistosomiasis—both urogenital and intestinal—causes significant human suffering, affecting approximately 240 million people globally and grouped within the neglected tropical disease (NTD) umbrella. This study addresses the following questions: 1. Where are the most suitable habitats for intermediate host snails in the Lower Shire Valley (LSV) in Malawi? 2. Which environmental factors are strongly associated with the geographical distribution of such snails in the LSV? Methods This paper presents the first species distribution models (SDMs) for intermediate snail hosts for urogenital and intestinal schistosomiasis in Chikwawa and Nsanje Districts, which together form the LSV). The SDMs developed for this study are ensemble machine learning approaches based on Random Forest (RF), Support Vector Machines (SVM), and multilayer perceptron (MLP) and are specific to the Bulinus africanus group and Biomphalaria pfeifferi . The former transmits urogenital schistosomiasis ( Schistosoma haematobium ), while the latter transmits intestinal schistosomiasis ( Schistosoma mansoni ). Results The SDMs reveal the following: 1) currently, Bu. africanus group not only has a wide distribution across central Chikwawa and eastern Nsanje but is also concentrated in floodplains, and the LSV has few habitats that can support Bi. pfeifferi , and 2) vegetation cover is the most important predictor of Bu. africanus group distribution, whereas precipitation variables are most important for Bi. pfeifferi in the LSV. Thus, Bu. africanus group habitat is the most dominant and abundant, while Bi. pfeifferi suitable habitat is patchy and scarce. Conclusion The distribution of suitable habitats for potential urogenital and intestinal schistosomiasis transmission across LSV is not uniform and typically non-overlapping. Understanding the spatial and temporal distributions of these snails is important for controlling and eliminating schistosomiasis. Graphical Abstract

Rapid simulation for real-time flood depth prediction using support vector machine - Kim, Beom-Jin; Kim, Minkyu; Yoo, Jaehwan; Kim, Byunghyun (Scientific Reports, 2025)

Market-based instruments to fund nature-based solutions for flood risk management can disproportionately benefit affluent areas - Hill, Bartholomew; Marjoribanks, Tim; Moore, Harriet; Bosher, Lee; Gussy, Mark (Communications Earth & Environment, 2025)
Abstract Market-based instruments, including competitive tenders, are central to funding global environmental restoration and management projects. Recently, tenders have been utilised to fund Nature-based Solutions schemes for Natural Flood Management, with the explicit purpose of achieving co-benefits; flood management and reducing inequities. While multiple studies consider the efficacy of Nature-based Solutions for tackling inequities, no prior research has quantified whether the resource allocation for these projects has been conducted equitably. We analyse two national natural flood management programmes funded through competitive tenders in England to explore who benefits by considering the characteristics of projects, including socio-economic, geographical (e.g. rurality) and flood risk dynamics. Our results suggest that inequity occurs at both the application and funding stages of Nature-based Solutions projects for flood risk management. This reflects wider international challenges of using market-based instruments for environmental resource allocation. Competitive tenders have the potential to undermine the equitable benefits of Nature-based Solutions.

Urban growth or urban risk? Unraveling the flood paradox in Kumasi (Ghana) through the lens of natural factors and spatial decision modeling - Liwur, Stephen Biliyitorb; Tagnan, Jacob Nchagmado; Asamoah, Pearl Seyram (Journal of African Earth Sciences, 2025)

Research on Extreme Precipitation Risk Considering Physical-social-environmental Attributes - Zhang, Jianlei; Chu, Chenkun; Wang, Peng (Journal of Disaster Prevention and Mitigation Engineering, 2025)
This study aims to conduct a grid-scale extreme precipitation risk assessment in Xuanwu District, Nanjing, so as to fill the gaps in existing indicator systems and improve the precision of risk characterization. By integrating physical, social, and environmental indicators, a risk assessment framework was constructed to comprehensively represent the characteristics of extreme precipitation risk. This study applied the entropy weight method to calculate indicator weights, combined with ArcGIS technology and the K-means clustering algorithm, to analyze the spatial distribution characteristics of risk under a 100-year extreme precipitation scenario and to identify key influencing indicators across different risk levels. The results showed that extreme precipitation risk levels in Xuanwu District exhibited significant spatial heterogeneity, with an overall distribution pattern of low risk in the central area and high risk in the surrounding areas. The influence mechanisms of key indicators showed tiered response characteristics: the low-risk areas were mainly controlled by the submerged areas of urban and rural, industrial and mining, and residential lands, water body area, soil erosion level, and normalized difference vegetation index (NDVI). The medium-risk areas were influenced by the submerged areas of urban and rural, industrial and mining, residential lands, the submerged areas of forest land, emergency service response time to disaster-affected areas, soil erosion level, and NDVI. The high-risk areas were jointly dominated by the submerged areas of urban and rural, industrial and mining, residential lands, the submerged areas of forest land, and NDVI. The extremely high-risk areas were driven by three factors—the submerged areas of forest land, emergency service response time to disaster-affected areas, and the proportion of the largest patch to the landscape area. This study improves the indicator system for extreme precipitation risk assessment and clarifies the tiered response patterns of risk-driving indicators, providing a scientific basis for developing differentiated flood control strategies in Xuanwu District while offering important theoretical support for improving regional flood disaster resilience. © 2025 Editorial Office of Journal of Disaster Prevention and Mitigation Engineering. All rights reserved.
Keywords: Scopus_Alert_5_Septembre_2025

Integrated hydrologic-economic modeling for urban flood risk mitigation using SWMM, HEC-RAS, and HAZUS: a case study of the Bronx river watershed, NYC - Javidi Sabbaghian, Reza; Fereshtehpour, Mohammad; Goli Hosseinabad, Mohammad Reza (Sustainable Water Resources Management, 2025)
Rapid urban expansion has significantly altered land use patterns, resulting in a decrease in pervious surface areas and a disruption of hydrologic connectivity between surface water and groundwater systems. Combined with inadequate drainage systems and poorly managed runoff, these changes have intensified urban flooding, leading to fatalities and significant infrastructure damage in many rapidly growing and climate-vulnerable urban areas around the world. This study presents an integrated economic-hydrologic model to assess the effectiveness of Low Impact Development (LID) measures—specifically permeable pavement, infiltration trenches, bio-retention cells, and rain barrels—in mitigating flood damage in the Bronx river watershed, NYC. The Storm Water Management Model (SWMM) was employed to simulate flood events and assess the effectiveness of various LIDs, applied individually and in combination, in reducing peak discharge. Flood inundation maps generated using HEC-GeoRAS were integrated with the HAZUS damage estimation model to quantify potential flood damages. A benefit-to-cost (BC) ratio was then calculated by comparing the monetary savings from reduced flood damage against the implementation costs of LID measures. Results indicate that the combined LID scenario offers the highest peak flow reduction, with permeable pavement alone reducing flow by 57%, outperforming other techniques under equal area coverage. Among all individual options, permeable pavement yields the highest cumulative BC ratio under all scenarios (4.6), whereas rain barrels are the least effective (2.6). The proposed evaluation framework highlights the importance of economic efficiency in flood mitigation planning and provides a structured foundation for informed decision-making to enhance urban resilience through LID implementation. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.
Keywords: Scopus_Alert_5_Septembre_2025

River basin urban flood resilience: A multi-dimensional framework for risk mitigation to adaptive management and ecosystem protection under changing climate - Soomro, Shan-e-hyder; Wei, Huaibin; Boota, Muhammad Waseem; Soomro, Nishan-E-hyder; Faisal, Muhammad; Nazli, Sana; sarwari, Soraya; Shi, Xiaotao; Hu, Caihong; Guo, Jiali; Li, Yinghai (Ecological Informatics, 2025)
Study region: This study aims at the Kunhar River Basin, Pakistan, that has been facing repeated flood occurrences on a recurring basis. As the flood susceptibility of this area is high, its topographic complexity demands correct predictive modeling for strategic flood planning. Study focus: We developed a system of flood susceptibility mapping based on Geographic Information Systems (GIS), Principal Component Analysis (PCA), and Support Vector Machine (SVM) classification. Four kernel functions were applied, and the highest-performing was the Radial Basis Function (SVM-RBF). The model was validated and trained using historical flood inventories, morphometric parameters, and hydrologic variables, and feature dimensionality was reduced via PCA for increased efficiency. New hydrological insights: The SVM-RBF model recorded an AUC of 0.8341, 88.02% success, 84.97% predictability, 0.89 Kappa value, and F1-score of 0.86, all of which indicated high predictability. Error analysis yielded a PBIAS of +2.14%, indicating negligible overestimation bias but within limits acceptable in hydrological modeling. The results support the superiority of the SVM-RBF approach compared to conventional bivariate methods in modeling flood susceptibility over the complex terrain of mountains. The results can be applied in guiding evidence-based flood mitigation, land-use planning, and adaptive management in the Kunhar River Basin. © 2025 The Author(s)
Keywords: Scopus_Alert_5_Septembre_2025

Integrative strategies for urban flood resilience and risk: A meta-analysis of policy, infrastructural, and ecosystem-based interventions - Wang, Shitao; Bi, Xuehao (Physics and Chemistry of the Earth, 2025)
Urban flooding, intensified by climate change and rapid urbanization, demands robust and operationally effective resilience strategies. However, empirical evidence on the comparative effectiveness of such strategies remains limited. This study presents the first meta-analytic synthesis evaluating urban flood resilience interventions across institutional, infrastructural, and socio-ecological domains. By synthesizing data from 29 peer-reviewed studies (2000–2024), this study applies standardized effect sizes (Cohen's d) and meta-regression models to assess the effectiveness of different strategies. Results reveal a substantial overall effect (pooled d = 2.96, 95 % CI: [1.92, 3.99]) with high heterogeneity (I2 = 93.8 %). Institutional mechanisms, such as policy coordination, regulatory frameworks, and risk governance, consistently show the strongest and most statistically significant impacts (d ≈ 2.96). Low Impact Development (LID) demonstrates limited, non-significant effects (d ≈ 0.08). The study introduces a novel hierarchical resilience framework spanning different dimensions and establishes an evidence-based typology of urban flood resilience strategies. These findings highlight the importance of integrated, multi-level governance and context-specific planning in enhancing urban flood resilience. The study findings provides critical insights for implementing resilience strategies in flood-prone urban areas, and support the formulation of adaptive and sustainable urban policies. © 2025
Keywords: Scopus_Alert_5_Septembre_2025

Increasing tidal inundation does not buffer heat stress effects: Implications for benthic ecosystem responses to climate change - Thomas, Simon D.; Bartl, Ines; Thrush, Simon F. (Journal of Experimental Marine Biology and Ecology, 2025)
Intertidal soft sediments are on the frontline of climate change effects, subject to increasing atmospheric heatwave intensity alongside rising sea levels. Longer inundation periods can keep sediments more saturated with water, potentially providing a thermal buffer against atmospheric heatwaves and creating refugia for resident communities and the ecosystem functions they produce. To assess this effect, we set up a field experiment to simulate a heat stress event along a gradient of inundation time on an intertidal flat using open top heat chambers. We examined how heat stress and inundation interact to influence sediment temperature, and how this in turn affects CO₂ flux, a proxy for ecosystem metabolism, and its relationship to key ecological covariates. We found that in ambient conditions, maximum sediment temperature decreased with increasing inundation. However, under heat stress, this relationship reversed, with maximum sediment temperature increasing with inundation, suggesting that more saturated sediments conduct heat more efficiently and promote higher temperatures under prolonged heat stress. Despite this temperature anomaly, the effect of heat stress on ecosystem metabolism remained consistent across the inundation gradient, indicating that increasing inundation time does not provide refugia from heat stress, nor does the elevated temperature anomaly amplify metabolic responses. Resilience mechanisms such as functional redundancy and compensatory interactions in sediment communities may have buffered against temperature variability, therefore maintaining function under heat stress. Our findings highlight the complex interactions between heat stress and inundation and suggest that sea level rise will not provide straightforward thermal refugia for soft sediment ecosystem function. © 2025 The Authors
Keywords: Scopus_Alert_5_Septembre_2025

Enhancing the hydrological performance of Low Impact Development infrastructure through earthworm activity and vegetation dynamics for mitigating urban flooding - Lhamidi, Khalil; El Khattabi, Jamal (Ecological Engineering, 2025)
Urban soil sealing and anthropogenic activities, combined with the increasing intensity of rainfall due to climate change, is a threat to urban environments, exacerbating flood risks. To assess these challenges, Low Impact Development strategies, based on Nature-based solutions, are a key solution to mitigate urban flooding. To enhance the hydrological performance of LID infrastructure, and to meet the guideline requirements related to emptying time, specifically in low hydraulic conductivity soils, earthworm activity and vegetation dynamics can play a major role. The ETAGEP experimental site was built to study to address those challenges. 12 swales (10 m2 infiltration area for each swale) were monitored to evaluate the impact of earthworm activity (A. caliginosa and L. terrestris) and vegetation dynamics (Rye Grass, Petasites hybridus and Salix alba) to enhance the hydrological performance. The infiltration rate of the swales evolved in a differentiated manner, with an increase of 16.1 % to 310.8 % and draining times decrease of 13.9 % to 75.7, depending on initial soil hydro-physical properties and the impervious areas of the catchment which influence runoff volumes. The simulations on SWMM software showed similar results, with an enhancement of the hydraulic conductivity of N6 swales (60 m2 total catchment area) increasing from 18 mm h−1 to 25 mm h−1, and a reduction of drawdown time by 24.4 % (N6) and 20.8 % (N11–110 m2 active surface). A simulated storm event of 44.8 mm resulted in an overflow of 2.12 m3 for the N11 swale configuration, while no overflow was observed for N6. These results highlight the ecosystem services of earthworms for a sustainable stormwater management in urban environments, enhancing the hydrological performance of LID infrastructures and reducing therefore flood risks and limiting pressure on drainage network. © 2025 The Author(s)
Keywords: Scopus_Alert_5_Septembre_2025

Predictive Modeling and Strategic Planning for Urban Flood Risk Mitigation - Mitali, Patel; Patel, Nishit; Modi, Kshitish; Patel, Samir (Communications in Computer and Information Science, 2026)
Urban flooding threatens Indian cities and is made worse by rapid urbanization, climate change and poor infrastructure. Severe flooding occurred in cities such as Mumbai, Chennai and Ahmedabad. This has caused huge economic losses and displacement. This study addresses the limitations of traditional flood forecasting methods. It has to contend with the complex dynamics of urban flooding. We offer a deep learning approach which uses the network Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to improve flood risk prediction. Our CNN-LSTM model combines spatial data (water table, topography) and temporal data (historical model) to classify flood risk as low or high. This method includes collecting data pre-processing (MinMaxScaler, LabelEncoder) Modeling, Training and Evaluation. The results demonstrate the accuracy of flood risk predictions and provide insights into flexible strategies for urban flood management. This research highlights the role of data-driven approaches in improving urban planning to reduce flood risk in high-risk areas. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
Keywords: Scopus_Alert_5_Septembre_2025

Analysis of rainfall disaster-causing factors weight and hazard assessment of non-typhoon rainstorms in Fujian Province - Lin, Xin; Yang, Kaijia; Zhang, Rongyan (Torrential Rain and Disasters, 2025)
Conducting an importance analysis of rainfall disaster-causing factors and hazard assessment is crucial for preventing rainstorm disasters. Based on precipitation observation data from 66 national meteorological stations from 1981 to 2020 and 1,038 provincial meteorological stations from 2010 to 2020 in Fujian Province, the correlation coefficient weighting method was used to calculate and compare the weight coefficients of the rainfall disaster-causing factors of non-typhoon rainstorms in 5 seasons (early spring, rainy season, summer, autumn and winter) and typhoon rainstorms in 3 periods (early, common and late). Then, the comprehensive index method was employed to conduct the hazard assessment and validation of non-typhoon rainstorms. The results are as follows: (1) The spatial extent of heavy precipitation is the primary factor in causing disasters for non-typhoon rainstorms in all seasons and for late typhoon rainstorms. Short-duration intense precipitation is the major factor causing disasters in summer non-typhoon rainstorms, early typhoon rainstorms, and common typhoon rainstorms. (2) The results of the disaster risk assessment of non-typhoon rainstorms show that the hazard of the non-typhoon rainstorm event from June 14 to 26, 2010 was the highest. There is an overall spatial distribution indicating a pattern of being high in the east and west while low in the central area, with two contiguous high and very high risk areas in the northwest inland and southeast coastal areas, respectively. (3) The spatial distributions of disaster risk for three historical non-typhoon rainstorm events from 2010 to 2020 obtained from the non-typhoon rainstorm disaster assessment model are basically consistent with the disaster situation. © Editorial Office of Torrential Rain and Disasters. OA under CC BY−NC−ND 4.0.
Keywords: Scopus_Alert_5_Septembre_2025

Impact of Land Use Patterns on Flood Risk in the Chang-Zhu-Tan Urban Agglomeration, China - Zhang, Ting; Wu, Kai; Wang, Xiulian; Li, Xinai; Li, Long; Chen, Longqian (Remote Sensing, 2025)
Flood risk assessment is an effective tool for disaster prevention and mitigation. As land use is a key factor influencing flood disasters, studying the impact of different land use patterns on flood risk is crucial. This study evaluates flood risk in the Chang-Zhu-Tan (CZT) urban agglomeration by selecting 17 socioeconomic and natural environmental factors within a risk assessment framework encompassing hazard, exposure, vulnerability, and resilience. Additionally, the Patch-Generating Land Use Simulation (PLUS) and multilayer perceptron (MLP)/Bayesian network (BN) models were coupled to predict flood risks under three future land use scenarios: natural development, urban construction, and ecological protection. This integrated modeling framework combines MLP’s high-precision nonlinear fitting with BN’s probabilistic inference, effectively mitigating prediction uncertainty in traditional single-model approaches while preserving predictive accuracy and enhancing causal interpretability. The results indicate that high-risk flood zones are predominantly concentrated along the Xiang River, while medium-high- and medium-risk areas are mainly distributed on the periphery of high-risk zones, exhibiting a gradient decline. Low-risk areas are scattered in mountainous regions far from socioeconomic activities. Simulating future land use using the PLUS model with a Kappa coefficient of 0.78 and an overall accuracy of 0.87. Under all future scenarios, cropland decreases while construction land increases. Forestland decreases in all scenarios except for ecological protection, where it expands. In future risk predictions, the MLP model achieved a high accuracy of 97.83%, while the BN model reached 87.14%. Both models consistently indicated that the flood risk was minimized under the ecological protection scenario and maximized under the urban construction scenario. Therefore, adopting ecological protection measures can effectively mitigate flood risks, offering valuable guidance for future disaster prevention and mitigation strategies. © 2025 by the authors.
Keywords: Scopus_Alert_5_Septembre_2025

Significant Attribution of Urbanization to Triggering Extreme Rainfall in the Urban Core—A Case of Dallas–Fort Worth in North Texas - Ahmad, Junaid; Eisma, Jessica A.; Sajjad, Muhammad (Urban Science, 2025)
While rainfall occurs for several reasons, climate change and urbanization influence its frequency and geographical disparities. Although recent research suggests that urbanization may lead to increased rainfall, insights into how urbanization can trigger rainfall remain limited. We selected the Dallas–Fort Worth (DFW) metroplex, which has minimal orographic and coastal influences, to analyze the urban impact on rainfall. DFW was divided into 256 equal grids (10 km × 10 km) and grouped into four clusters using K-means clustering based on the urbanization ratio. Using Multi-Sensor Precipitation Estimator data (with a spatial resolution of 4 km), we examined rainfall exceeding the 95th percentile (i.e., extreme rainfall) on low synoptic days to highlight localized effects. The urban heat island (UHI) effect was estimated based on the average temperature difference between the urban core and the other three non-urban clusters. Multiple rainfall events were monitored on an hourly basis. Potential linkages between urbanization, the UHI, extreme rainfall, wind speed, wind direction, convective inhibition, and convective available potential energy were evaluated. An intense UHI within the DFW area triggered a tornado, resulting in maximum rainfall in the urban core area under high wind speeds and a dominant wind direction. Our findings further clarify the role of urbanization in generating extreme rainfall events, which is essential for developing better policies for urban planning in response to intensifying extreme events due to climate change. © 2025 by the authors.
Keywords: Scopus_Alert_5_Septembre_2025

An Attention-Enhanced Bivariate AI Model for Joint Prediction of Urban Flood Susceptibility and Inundation Depth - Le, Thuan Thanh; Vo, Tuong Quang; Kim, Jongho (Mathematics, 2025)
This study presents a novel bivariate-output deep learning framework based on LeNet-5 for the simultaneous prediction of urban flood susceptibility and inundation depth in Seoul, South Korea. Unlike previous studies that relied on single-output models, the proposed approach jointly learns classification and regression targets through a shared feature extraction structure, enhancing consistency and generalization. Among six tested architectures, the Le5SD_CBAM model—integrating a Convolutional Block Attention Module (CBAM)—achieved the best performance, with 83% accuracy, an Area Under the ROC Curve (AUC) of 0.91 for flood susceptibility classification, and a mean absolute error (MAE) of 0.12 m and root mean squared error (RMSE) of 0.18 m for depth estimation. The model’s spatial predictions aligned well with hydrological principles and past flood records, accurately identifying low-lying flood-prone zones and capturing localized inundation patterns influenced by infrastructure and micro-topography. Importantly, it detected spatial mismatches between susceptibility and depth, demonstrating the benefit of joint modeling. Variable importance analysis highlighted elevation as the dominant predictor, while distances to roads, rivers, and drainage systems were also key contributors. In contrast, secondary terrain attributes had limited influence, indicating that urban infrastructure has significantly altered natural flood flow dynamics. Although the model lacks dynamic forcings such as rainfall and upstream inflows, it remains a valuable tool for flood risk mapping in data-scarce settings. The bivariate-output framework improves computational efficiency and internal coherence compared to separate single-task models, supporting its integration into urban flood management and planning systems. © 2025 by the authors.
Keywords: Scopus_Alert_5_Septembre_2025

Enhanced Urban Flood Hazard Assessment by Stochastic Event Catalog - Yan, Haochen; Guo, Kaihua; Guan, Mingfu (Water Resources Research, 2025)
Assessing flood severity in urban areas is a pivotal task for urban resilience and climate adaptation. However, the lack of in situ measurements hinders direct spatial estimation of flood return periods, while conventional assumptions about rainstorm-flood consistency introduce significant uncertainties due to rainstorm spatiotemporal variability (STV). This study proposes a novel framework that utilizes multivariate frequency analysis of flood variables at the street level (50 m) through a stochastic rainstorm-flood event catalog. The rainstorm events in the catalog are generated by a random field generator and resampled to match the joint distribution of STV variables consistent with radar observations. Urban flood processes are then simulated by a hydrodynamic model for flood hazard assessment (FHA). We applied the framework to a rural-urban watershed using 3,000 cases randomly resampled from the catalog. Results reveal that inundation characteristics respond more rapidly to increasing rainfall intensities than downstream flood peaks, particularly during the early stages of rainstorms. The complex joint probability structures of rainstorm severity and STV variables obscure the mechanistic control of individual factors on flood response. A significant underestimation of street-level flood hazards occurs when assuming the same return periods (RPs) as those for watershed-level hazards. The inconsistency between rainstorm and flood severities results in widespread underestimation of street-level flood hazards in upstream regions, while traditional storm designs that neglect STV lead to overestimations in mid- and downstream areas. This study highlights the complex probabilistic behavior of spatially distributed flood hazards across multiple scales, enhancing the insights and methodologies for street-level FHA. © 2025 The Author(s).
Keywords: Scopus_Alert_5_Septembre_2025

Land Subsidence and Coastal Flood Impact Scenarios Based on Remote Sensing in Selangor, Malaysia - Batmanathan, Navakanesh M.; Pereira, Joy Jacqueline; Shah, Afroz Ahmad; Muhamad, Nurfashareena; Sian, Lim Choun (Journal of Marine Science and Engineering, 2025)
This study uses remote sensing data to assess susceptibility to hazards, which are then validated to model impact scenarios for land subsidence and coastal flooding in the Integrated Coastal Zone Management (ICZM) of Selangor, Malaysia, to support decision-making in urban planning and land management. Land subsidence and coastal floods affect a major proportion of the population in the ICZM, with subsidence being significant contributing factors, but information on the extent of susceptible areas, monitoring, and wide-area coverage is limited. Land subsidence distribution is demarcated using Interferometric Synthetic Aperture Radar (InSAR) time-series data (2015–2022), and integrated with coastal flood susceptibility derived from Analytic Hierarchy Process (AHP)-based weights to model impacts on land cover. Results indicate maximum subsidence rates of 46 mm/year (descending orbit) and 61 mm/year (ascending orbit); reflecting a gradual increase in subsidence trends with an average rate of 13 mm/year. In the worst-case scenario, within the ICZM area of 2262 km2, nearly 12% of the total built-up land cover with the highest population density is exposed to land subsidence, while exposure to coastal floods is relatively larger, covering nearly 34% of the built-up area. Almost 27% of the built-up area is exposed to the combined effects of both land subsidence and coastal floods, under present sea level conditions, with increasing risks of coastal floods over 2040, 2050 and 2100, due to both combinations. This research prioritizes areas for further study and provides a scientific foundation for resilience strategies aimed at ensuring sustainable coastal development within the ICZM. © 2025 by the authors.
Keywords: Scopus_Alert_5_Septembre_2025

Flood Exposure Assessment of Railway Infrastructure: A Case Study for Iowa - Alabbad, Yazeed; Cikmaz, Atiye Beyza; Yildirim, Enes; Demir, Ibrahim (Applied Sciences (Switzerland), 2025)
Floods pose a substantial risk to human well-being. These risks encompass economic losses, infrastructural damage, disruption of daily life, and potential loss of life. This study presents a state-wide and county-level spatial exposure assessment of the Iowa railway network, emphasizing the resilience and reliability of essential services during such disasters. In the United States, the railway network is vital for the distribution of goods and services. This research specifically targets the railway network in Iowa, a state where the impact of flooding on railways has not been extensively studied. We employ comprehensive GIS analysis to assess the vulnerability of the railway network, bridges, rail crossings, and facilities under 100- and 500-year flood scenarios at the state level. Additionally, we conducted a detailed investigation into the most flood-affected counties, focusing on the susceptibility of railway bridges. Our state-wide analysis reveals that, in a 100-year flood scenario, up to 9% of railroads, 8% of rail crossings, 58% of bridges, and 6% of facilities are impacted. In a 500-year flood scenario, these figures increase to 16%, 14%, 61%, and 13%, respectively. Furthermore, our secondary analysis using flood depth maps indicates that approximately half of the railway bridges in the flood zones of the studied counties could become non-functional in both flood scenarios. These findings are crucial for developing effective disaster risk management plans and strategies, ensuring adequate preparedness for the impacts of flooding on railway infrastructure. © 2025 by the authors.
Keywords: Scopus_Alert_5_Septembre_2025

AI-Enhanced Remote Sensing of Land Transformations for Climate-Related Financial Risk Assessment in Housing Markets: A Review - Zhang, Chuanrong; Li, Xinba (Land, 2025)
Amid accelerating climate change, climate-related hazards—such as floods, wildfires, hurricanes, and sea-level rise—increasingly drive land transformations and pose growing risks to housing markets by affecting property valuations, insurance availability, mortgage performance, and broader financial stability. This review synthesizes recent progress in two distinct domains and their linkage: (1) assessing climate-related financial risks in housing markets, and (2) applying AI-driven remote sensing for hazard detection and land transformation monitoring. While both areas have advanced significantly, important limitations remain. Existing housing finance studies often rely on static models and coarse spatial data, lacking integration with real-time environmental information, thereby reducing their predictive power and policy relevance. In parallel, remote sensing studies using AI primarily focus on detecting physical hazards and land surface changes, yet rarely connect these spatial transformations to financial outcomes. To address these gaps, this review proposes an integrative framework that combines AI-enhanced remote sensing technologies with financial econometric modeling to improve the accuracy, timeliness, and policy relevance of climate-related risk assessment in housing markets. By bridging environmental hazard data—including land-based indicators of exposure and damage—with financial indicators, the framework enables more granular, dynamic, and equitable assessments than conventional approaches. Nonetheless, its implementation faces technical and institutional barriers, including spatial and temporal mismatches between datasets, fragmented regulatory and behavioral inputs, and the limitations of current single-task AI models, which often lack transparency. Overcoming these challenges will require innovation in AI modeling, improved data-sharing infrastructures, and stronger cross-disciplinary collaboration. © 2025 by the authors.
Keywords: Scopus_Alert_5_Septembre_2025

Integrating climate change and land use change into storm flood impact analysis in coastal cities - Sun, Qinke; Reyns, Johan; Fang, Jiayi; Wang, Xiaoting; Wang, Zhe; Zhou, Liang; Liu, Min (Journal of Hydrology: Regional Studies, 2025)
Study region: Shanghai, China Study focus: This paper proposes a comprehensive framework for quantifying storm surge floods in coastal cities by incorporating the influences of both climate change and urbanization. The framework achieves a physically process-based numerical simulation of storm surge-induced flood hazards due to tropical cyclones in coastal cities by coupling the fast flood inundation model (SFINCS) and the land use change model (GeoSOS-FLUS), along with the numerical nested model for storm surges (Delft 3D Flow & Wave). Using a 1000-year tropical cyclone simulated by the STORM model as an example, this study analyzes and maps coastal flood impacts under the moderate climate scenario (SSPs245) and high emission scenario (SSPs585), and also evaluates the impact of land use changes on these scenarios. New hydrological insights for the region: Taking Shanghai, China as an example, the results show that by 2100, urban land use changes will lead to an increase in the extent of 1000-year TC flooding areas by 4.91–34.00 %, underestimating the inundation area of storm surges if future urban land use changes are not considered. Additionally, our predictions indicate the vulnerability of Chongming island and Changxing island to the impacts of climate change, despite the protective role of coastal embankments considered in the tropical cyclone storm surge simulation. The results of this study represent an important contribution to a better understanding of how future urban land use changes will affect storm surge flooding risks in and around Shanghai. The proposed methodology can be applied to coastal areas worldwide that are vulnerable to tropical cyclones, aiding in the formulation of hazard mitigation policies to alleviate flood impacts in these regions. © 2025 The Authors
Keywords: Scopus_Alert_5_Septembre_2025

Assessing subseasonal forecast skill for use in predicting US coastal inundation risk - Albers, John R.; Newman, Matthew; Balmaseda, Magdalena A.; Sweet, William; Wang, Yan; Xu, Tongtong (Ocean Science, 2025)
Abstract. Developing predictions of coastal flooding risk on subseasonal timescales (2–6 weeks in advance) is an emerging priority for the National Oceanic and Atmospheric Administration (NOAA). In this study, we assess the ability of two current operational forecast systems, the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (IFS) and the Centre National de Recherches Météorologiques climate model (CNRM), to make subseasonal ensemble predictions of the non-tidal residual component of coastal water levels at United States coastal gauge stations for the period 2000–2019. These models were chosen because they assimilate satellite altimetry at forecast initialization and attempt to predict the mean sea level, including a global mean component whose absence in other forecast systems complicates assessment of tide gauge reforecast skill. Both forecast systems have skill that exceeds damped persistence for forecast leads through 2–3 weeks, with IFS skill exceeding damped persistence for leads up to 6 weeks. Post-processing forecasts to include the inverse barometer effect, derived from mean sea level pressure forecasts, improves skill for relatively short forecast leads (1–3 weeks). Accounting for vertical land motion of each gauge primarily improves skill for longer leads (3–6 weeks), especially for the Alaskan and Gulf coasts; sea-level trends contribute to reforecast skill for both model and persistence forecasts, primarily for the East and Gulf coasts. Overall, we find that current forecast systems have sufficiently high levels of deterministic and probabilistic skill to be used in support of operational coastal flood guidance on subseasonal timescales.
Keywords: Scopus_Alert_5_Septembre_2025

Investigation of an extreme rainfall event during 8–12 December 2018 over central Vietnam – Part 2: An evaluation of predictability using a time-lagged cloud-resolving ensemble system - Wang, Chung-Chieh; Van Nguyen, Duc; Van Vu, Thang; Nga, Pham Thi Thanh; Chuang, Pi-Yu; Truong, Kien Ba (Natural Hazards and Earth System Sciences, 2025)
This is the second part of a two-part study that investigates an extreme rainfall event that occurred from 8 to 12 December 2018 over central Vietnam (referred to as the D18 event). In this part, the study aims to evaluate the practical predictability of the D18 event using the quantitative precipitation forecasts (QPFs) from a time-lagged cloud-resolving ensemble system. To do this, 29 time-lagged (8 d in forecast range) high-resolution (2.5 km) members were run, with the first member initialized at 12:00 UTC on 3 December and the last one at 12:00 UTC on 10 December 2018. Between the first and the last members are multiple members that were executed every 6 h. The evaluation results reveal that the cloud-resolving model (CReSS) predicted the rainfall fields in the short range (less than 3 d) well for 10 December (the rainiest day). Particularly, the CReSS model shows high skill in heavy-rainfall QPFs for this date with a similarity skill score (SSS) greater than 0.5 for both the last five members and the last nine members. The good results are due to the model having good predictions of relevant meteorological variables such as surface winds. However, the predictive skill is reduced at lead times longer than 3 d, and it is challenging to achieve good QPFs for rainfall thresholds greater than 100 mm at lead times longer than 6 d. These results also confirmed our scientific hypothesis that the cloud-resolving time-lagged ensemble system (using the CReSS model) improved the QPFs of this event in the short range. Furthermore, the results also demonstrated that a decent QPF can be made at a longer lead time (by a member initialized at 18:00 UTC on 4 December). In addition, the ensemble-based sensitivity analysis (ESA) of 24 h rainfall in central Vietnam shows that it is highly sensitive to initial conditions, not only at lower levels but also at upper levels. The rainfall is sensitive to both kinematics and moisture convergence at low levels, and such sensitivities decrease with increasing lead time. The ESA also facilitates a better understanding of the mechanisms in the D18 event, implying that it is meaningful to apply ESA to control initial conditions in the future. © Author(s) 2025.
Keywords: Scopus_Alert_5_Septembre_2025

Seismic Performance Assessment of Gravity Dams for Urban Flood Risk Mitigation Using the Scaled Boundary Finite Element Method (SBFEM) - Kim, Min-Koan; Xu, Dai (Hydrology, 2025)
Rapid urbanization and climate change have intensified urban flood risks, necessitating resilient upstream infrastructure to ensure metropolitan water security and effective flood mitigation. Gravity dams, as critical components of urban flood protection systems, regulate discharge to downstream urban areas. Gravity dams are critical for regulating flood discharge, yet their seismic vulnerability poses significant challenges, particularly under compound effects involving concurrent seismic loading and climate-induced elevated reservoir levels. This study introduces a novel seismic analysis framework for gravity dams using the scaled boundary finite element method (SBFEM), which efficiently models dam–water and dam–foundation interactions in infinite domains. A two-dimensional numerical model of a concrete gravity dam, subjected to realistic seismic loading, was developed and validated against analytical solutions and conventional finite element method (FEM) results, achieving discrepancies as low as 0.95% for static displacements and 0.21% for natural frequencies. The SBFEM approach accurately captures hydrodynamic pressures and radiation damping, revealing peak pressures at the dam heel during resonance and demonstrating computational efficiency with significantly reduced nodal requirements compared to FEM. These findings enhance understanding of dam behavior under extreme loading. The proposed framework supports climate-adaptive design standards and integrated hydrological–structural modeling. By addressing the seismic safety of flood-control dams, this research contributes to the development of resilient urban water management systems capable of protecting metropolitan areas from compound climatic and seismic extremes. © 2025 by the authors.
Keywords: Scopus_Alert_5_Septembre_2025

Spatial–Temporal Characteristics and Drivers of Summer Extreme Precipitation in the Poyang Lake City Group (PLCG) from 1971 to 2022 - Liu, Hua; Zhang, Ziqing; Liu, Bo (Remote Sensing, 2025)
Global warming has intensified the hydrological cycle, resulting in more frequent extreme precipitation events and altered spatiotemporal precipitation patterns in urban areas, thereby increasing the risk of urban flooding and threatening socio-economic and ecological security. This study investigates the characteristics of summer extreme precipitation in the Poyang Lake City Group (PLCG) from 1971 to 2022, utilizing the China Daily Precipitation Dataset and NCEP/NCAR reanalysis data. Nine extreme precipitation indices were examined through linear trend analysis, Mann–Kendall tests, wavelet transforms, and correlation methods to quantify trends, periodicity, and atmospheric drivers. The key findings include: (1) All indices exhibited increasing trends, with RX1Day and R95p exhibiting significant rises (p < 0.05). PRCPTOT, R20, and SDII also increased, indicating heightened precipitation intensity and frequency. (2) R50, RX1Day, and SDII demonstrated east-high-to-west-low spatial gradients, whereas PRCPTOT and R20 peaked in the eastern and western PLCG. More than over 88% of stations recorded rising trends in PRCPTOT and R95p. (3) Abrupt changes occurred during 1993–2009 for PRCPTOT, R50, and SDII. Wavelet analysis revealed dominant periodicities of 26–39 years, linked to atmospheric oscillations. (4) Strong subtropical highs, moisture convergence, and negative OLR anomalies were closely associated with extreme precipitation. Warmer SSTs in the eastern equatorial Pacific amplified precipitation in preceding seasons. This study provides a scientific basis for flood prevention and climate adaptation in the PLCG and highlighting the region’s vulnerability to monsoonal shifts under global warming. © 2025 by the authors.
Keywords: Scopus_Alert_5_Septembre_2025

Flood susceptibility mapping in arid urban areas using SHAP-enhanced stacked ensemble learning: A case study of Jeddah - Zerouali, Bilel; Almaliki, Abdulrazak H.; Santos, Celso Augusto Guimarães (Journal of Environmental Management, 2025)
Flooding is an escalating hazard in arid and rapidly urbanizing environments such as Jeddah, Saudi Arabia, where the lack of historical flood records and sparse monitoring systems challenge effective risk prediction. To address this gap, this study aims to develop an accurate and interpretable flood susceptibility-mapping framework tailored to data-scarce urban settings. The research integrates a stacked ensemble model—comprising machine learning: XGBoost, CatBoost, and Histogram-based Gradient Boosting (HGB)—with SHapley Additive exPlanations (SHAP) to enhance prediction accuracy and model transparency. Random Forest was excluded from the final model stack due to inferior classification performance. A diverse set of geospatial inputs, including digital elevation model, slope, flow direction, Curve Number, topographic indices, and LULC (from ESRI Sentinel-2) were used as predictors. Furthermore, 92 and 198 flooded and non-flooded points were used for model validation. The model achieved strong predictive performance (AUC = 0.92, Accuracy = 0.82) on the validation set. In the absence of official flood records, model outputs were intersected with road network data to identify 395 road points in highly susceptible zones. Although these points do not represent a formal validation dataset—due to the general lack of detailed flood event records in the region, particularly in relation to infrastructure—they provide a valuable proxy for identifying flood-prone road segments. SHAP explainability analysis revealed that TRI, TPI, and distance to rivers were the most globally influential features, while Curve Number and LULC were key drivers of high-risk predictions. The model mapped 139 km2 (8.7 %) of the area as very high flood susceptibility and 325 km2 (20.3 %) as high susceptibility, outperforming individual learners. These results confirm that stacked ensemble learning, paired with explainable AI and creative validation strategies, can produce reliable flood susceptibility maps even in data-constrained contexts. This framework offers a transferable and scalable solution for flood risk assessment in similar arid and urbanizing environments. © 2025 Elsevier Ltd
Keywords: Scopus_Alert_5_Septembre_2025

Revolutionizing urban water resilience: Innovative strategies and advancements in sustainable urban drainage systems (SuDS) - Pachouri, Vikrant; Kothari, Prafull; Kathuria, Samta; Gehlot, Anita; Singh, Rajesh; Thakur, Amit Kumar; Gupta, Lovi Raj; Dogra, Sudhanshu; Priyadarshi, Neeraj; Mohamed, Heba G. (Desalination and Water Treatment, 2025)
Climate change and rapid urbanisation are straining urban stormwater management further, with floods and water pollution becoming more intense. SUDS is a nature-based alternative that solves these issues because it replicates natural hydrologic processes to create urban resilience. This systematic review summarises recent trends in SUDS technologies, performance, and policy frameworks, and their potential to mitigate flood risks, improve water quality, and enhance climate resilience. By the PRISMA methodology, 90 peer-reviewed studies published between 2014 and 2025 were considered, dealing with SUDS performance, cost-effectiveness, and overall difficulties with large-scale implementation of these systems. Main results are that bio-retention systems, permeable pavements, and green roofs are effective in controlling surface runoff and enhancing water quality. Moreover, the development of IoT-based monitoring and smart technologies has also considerably increased the scalability and efficiency of a SUDS. The review recommends the standardisation of SUDS performance, the incorporation of smart technologies, and more attractive policy incentives to speed up the uptake of SUDS in urban planning. One of the main contributions that this research is likely to make to the discourse concerning urban water resilience is that it offers evidence-based suggestions to policymakers and urban developers, and these suggestions argue in favour of taking urgent action in the area of climate adaptation by using SUDS extensively. © 2025 The Authors
Keywords: Scopus_Alert_5_Septembre_2025

Producing Hydrological Projections Under Climate Change: A Groundwater-Inclusive Practical Guide - Mourot, Frédérique M.; Irvine, Dylan J.; Remenyi, Tomas A.; Hutley, Lindsay B.; Crosbie, Russell S.; Moore, Catherine R. (Earth's Future, 2025)
With global warming, the hydrological cycle is intensifying with more frequent and severe droughts and floods, placing water resources and their dependent communities under increasing stress. Guidance and insights into the projection of future water conditions are, therefore, increasingly needed to inform climate change adaptation. Hydrological projections can provide such insights when suitably designed for user needs, produced from the best available climate knowledge, and leverage appropriate hydrological models. However, producing such hydrological projections is a complex process that requires skills and knowledge spanning from the often-siloed disciplines of climate, hydrology, communication, and decision-making. Groundwater projections are still underrepresented compared to surface water projections, despite the importance of groundwater to sustain society and the environment. Accordingly, this paper bridges these silos and fills a gap by providing detailed guidance on the important steps and best practices to develop groundwater-inclusive hydrological projections that can effectively support decision-making. Using an extensive literature review and our practical experience as climate scientists, hydro(geo)logists, numerical modelers, uncertainty experts and decision-makers, here we provide: (a) an overview of climate change hydrological impacts as background knowledge; (b) a step-by-step guide to produce groundwater-inclusive hydrological projections under climate change, targeted to both scientists and water practitioners; (c) a summary of important considerations related to hydrological projection uncertainty; and (d) insights to use hydrological projections and their associated uncertainty for impactful communication and decision-making. By providing this practical guide, our paper addresses a critical interdisciplinary knowledge gap and supports enhanced decision-making and resilience to climate change threats. © 2025 Commonwealth of Australia. Earth Science New Zealand. Acclimatised Pty Ltd and The Author(s). Earth's Future published by Wiley Periodicals LLC on behalf of American Geophysical Union.
Keywords: Scopus_Alert_5_Septembre_2025

Interest Flooding Attacks in Named Data Networking and Mitigations: Recent Advances and Challenges - Ogunbunmi, Simeon; Chen, Yu; Zhao, Qi; Nagothu, Deeraj; Wei, Sixiao; Chen, Genshe; Blasch, Erik (Future Internet, 2025)
Named Data Networking (NDN) represents a promising Information-Centric Networking architecture that addresses limitations of traditional host-centric Internet protocols by emphasizing content names rather than host addresses for communication. While NDN offers advantages in content distribution, mobility support, and built-in security features, its stateful forwarding plane introduces significant vulnerabilities, particularly Interest Flooding Attacks (IFAs). These IFA attacks exploit the Pending Interest Table (PIT) by injecting malicious interest packets for non-existent or unsatisfiable content, leading to resource exhaustion and denial-of-service attacks against legitimate users. This survey examines research advances in IFA detection and mitigation from 2013 to 2024, analyzing seven relevant published detection and mitigation strategies to provide current insights into this evolving security challenge. We establish a taxonomy of attack variants, including Fake Interest, Unsatisfiable Interest, Interest Loop, and Collusive models, while examining their operational characteristics and network performance impacts. Our analysis categorizes defense mechanisms into five primary approaches: rate-limiting strategies, PIT management techniques, machine learning and artificial intelligence methods, reputation-based systems, and blockchain-enabled solutions. These approaches are evaluated for their effectiveness, computational requirements, and deployment feasibility. The survey extends to domain-specific implementations in resource-constrained environments, examining adaptations for Internet of Things deployments, wireless sensor networks, and high-mobility vehicular scenarios. Five critical research directions are proposed: adaptive defense mechanisms against sophisticated attackers, privacy-preserving detection techniques, real-time optimization for edge computing environments, standardized evaluation frameworks, and hybrid approaches combining multiple mitigation strategies. © 2025 by the authors.
Keywords: Scopus_Alert_5_Septembre_2025

Injectivity, Potential Wettability Alteration, and Mineral Dissolution in Low-Salinity Waterflood Applications: The Role of Salinity, Surfactants, Polymers, Nanomaterials, and Mineral Dissolution - Sarma, Hemanta K.; Awolayo, Adedapo N.; Olayiwola, Saheed O.; Fakir, Shasanowar H.; Belhaj, Ahmed F. (Processes, 2025)
Waterflooding, a key method for secondary hydrocarbon recovery, has been employed since the early 20th century. Over time, the role of water chemistry and ions in recovery has been studied extensively. Low-salinity water (LSW) injection, a common technique since the 1930s, improves oil recovery by altering the wettability of reservoir rocks and reducing residual oil saturation. Recent developments emphasize the integration of LSW with various recovery methods such as CO2 injections, surfactants, alkali, polymers, and nanoparticles (NPs). This article offers a comprehensive perspective on how LSW injection is combined with these enhanced oil recovery (EOR) techniques, with a focus on improving oil displacement and recovery efficiency. Surfactants enhance the effectiveness of LSW by lowering interfacial tension (IFT) and improving wettability, while ASP flooding helps reduce surfactant loss and promotes in situ soap formation. Polymer injections boost oil recovery by increasing fluid viscosity and improving sweep efficiency. Nevertheless, challenges such as fine migration and unstable flow persist, requiring additional optimization. The combination of LSW with nanoparticles has shown potential in modifying wettability, adjusting viscosity, and stabilizing emulsions through careful concentration management to prevent or reduce formation damage. Finally, building on discussions around the underlying mechanisms involved in improved oil recovery and the challenges associated with each approach, this article highlights their prospects for future research and field implementation. By combining LSW with advanced EOR techniques, the oil industry can improve recovery efficiency while addressing both environmental and operational challenges. © 2025 by the authors.
Keywords: Scopus_Alert_5_Septembre_2025

Establishing a Sea Level Rise-Adjusted Design Flood Elevation for Buildings: A Comparative Study of Methods - Meguro, Wendy; Briones, Josephine I.; Teeples, Eric; Fletcher, Charles H. (Water (Switzerland), 2025)
Coastal high tide flooding doubled in the U.S. between 2000 and 2022 and sea level rise (SLR) due to climate change will dramatically increase exposure and vulnerability to flooding in the future. However, standards for elevating buildings in flood hazard areas, such as base flood elevations set by the Federal Emergency Management Agency, are based on historical flood data and do not account for future SLR. To increase flood resilience in flood hazard areas, federal, state, regional, and municipal planning initiatives are developing guidance to increase elevation requirements for occupied spaces in buildings. However, methods to establish a flood elevation that specifically accounts for rising sea levels (or sea level rise-adjusted design flood elevation (SLR-DFE)) are not standardized. Many municipalities or designers lack clear guidance on developing or incorporating SLR-DFEs. This study compares guidance documents, policies, and methods for establishing an SLR-DFE. The authors found that the initiatives vary in author, water level measurement starting point, SLR scenario and timeframe, SLR adjustment, freeboard, design flood elevation, application (geography and building type), and whether it is required or recommended. The tables and graph compare the different initiatives, providing a useful summary for policymakers and practitioners to develop SLR-DFE standards. © 2025 by the authors.
Keywords: Scopus_Alert_5_Septembre_2025

Machine Learning for Flood Resiliency—Current Status and Unexplored Directions - Uddameri, Venkatesh; Hernandez, E. Annette (Environments - MDPI, 2025)
A systems-oriented review of machine learning (ML) over the entire flood management spectrum, encompassing fluvial flood control, pluvial flood management, and resiliency-risk characterization was undertaken. Deep learners like long short-term memory (LSTM) networks perform well in predicting reservoir inflows and outflows. Convolution neural networks (CNNs) and other object identification algorithms are being explored in assessing levee and flood wall failures. The use of ML methods in pump station operations is limited due to lack of public-domain datasets. Reinforcement learning (RL) has shown promise in controlling low-impact development (LID) systems for pluvial flood management. Resiliency is defined in terms of the vulnerability of a community to floods. Multi-criteria decision making (MCDM) and unsupervised ML methods are used to capture vulnerability. Supervised learning is used to model flooding hazards. Conventional approaches perform better than deep learners and ensemble methods for modeling flood hazards due to paucity of data and large inter-model predictive variability. Advances in satellite-based, drone-facilitated data collection and Internet of Things (IoT)-based low-cost sensors offer new research avenues to explore. Transfer learning at ungauged basins holds promise but is largely unexplored. Explainable artificial intelligence (XAI) is seeing increased use and helps the transition of ML models from black-box forecasters to knowledge-enhancing predictors. © 2025 by the authors.
Keywords: Scopus_Alert_5_Septembre_2025

Exploring the potential of coastal cities to address climate change towards an inclusive, equitable and politically engaged orientation - Monckeberg, Elisa; Gómez, Sílvia (Anthropocene Coasts, 2025)
In the context of the global climate crisis, the analysis and strengthening of adaptive capacities in coastal urban environments has become imperative. Nearly 40% of the global population lives within 100 km of the coastline, making them critical research hotspots due to their particular vulnerability. This qualitative literature review takes a transdisciplinary approach and prioritizes research that addresses specific challenges and solutions for these vulnerable environments, with an emphasis on resilience to phenomena such as sea level rise, flooding and extreme weather events. The review analyzes articles that offer a holistic view, encompassing green and blue infrastructures, community needs and governance dynamics. It highlights studies that propose innovative strategies to foster citizen participation and explicitly address aspects such as climate justice. By synthesizing interdisciplinary perspectives and local knowledge, this review aims to provide a comprehensive framework for climate adaptation in coastal urban areas. The findings have the potential to inform public policy and urban planning practices. © The Author(s) 2025.
Keywords: Scopus_Alert_5_Septembre_2025

Climate teleconnection-driven stochastic simulation for future water-related risk management - Lee, Taesam; Ouarda, Taha B. M. J. (Journal of Hydrology, 2025)
Water risk management has been adversely affected by climate variations, including recent climate change. Climate variations have highly impacted the hydrological cycles in the atmosphere and biosphere, and their impact can be defined with the teleconnection between climate signals and hydrological variables. Water managers should practice future risk management to mitigate risks, including the impact of teleconnection, and stochastically simulated scenarios can be employed as an effective tool to take advantage of water management preparation. A stochastic simulation model for hydrological variables teleconnected with climate signals is very useful for water managers. Therefore, the objective of the current study was to develop a novel stochastic simulation model for the simulation of synthetic series teleconnected with climate signals. By jointly decomposing the hydrological variables and a climate signal with bivariate empirical mode decomposition (BEMD), the bivariate nonstationary oscillation resampling (B-NSOR) model was applied to the significant components. The remaining components were simulated with the newly developed method of climate signal-led K-nearest neighbor-based local linear regression (CKLR). This entire approach is referred to as the climate signal-led hydrologic stochastic simulation (CSHS) model. The key statistics were estimated from the 200 simulated series and compared with the observed data, and the results showed that the CSHS model could reproduce the key statistics including extremes while the SML model showed slight underestimation in the skewness and maximum values. Additionally, the observed long-term variability of hydrological variables was reproduced well with the CSHS model by analyzing drought statistics. Moreover, the Hurst coefficient with slightly higher than 0.8 was fairly preserved by the CSHS model while the SML model is underestimated as 0.75. The overall results demonstrate that the proposed CSHS model outperformed the existing shifting mean level (SML) model, which has been used to simulate hydroclimatological variables. Future projections until 2100 were obtained with the CSHS model. The overall results indicated that the proposed CSHS model could represent a reasonable alternative to teleconnect climate signals with hydrological variables.
Keywords: Infolettre_Septembre_2025

Copula-based joint distribution modelling of precipitation, temperature and humidity events in the assessments of agricultural risks, with a case study in Morocco - Latif, Shahid; El Ouadi, Ihssan; Ouarda, Taha B. M. J. (Stochastic Environmental Research and Risk Assessment, 2025)
The agriculture sector is profoundly impacted by the abiotic stresses in arid or semi-arid regions that experience extreme weather patterns related to temperature (T), precipitation (P), humidity (H), and other factors. This study adopts a flexible approach that incorporates the D-vine copula density to analyze trivariate (and bivariate) joint and conditional hazard risk. The methodology was applied to a case study in the Ait Ben Yacoub region of Morocco. Monthly series for T, H, and P were modeled using the Weibull-2P and Weibull-3P models, selected based on fitness statistics. The survival BB8 copula was best described as joint dependence for pair T–P, rotated BB8 270 degrees copula for T–H, while rotated Joe 270 degrees copula for P–H. The analysis of joint probability stress focused on both primary joint scenarios (for OR and AND-hazard conditions) and conditional return periods (RPs) for trivariate and bivariate case. Lower univariate RPs resulted in higher marginal quantiles for T and lower for H and P events. Lower trivariate (and bivariate) AND-joint RPs (or higher concurrence probabilities) were associated with higher T with lower P and H quantiles. The occurrence of trivariate (and bivariate) events was less frequent in the AND-joint case compared to the OR-joint case. The conditional joint RP of T (or T with P, or T with H) was significantly affected by different P (at 10th and 25th percentile) and H (at 5th and 25th percentile) (or P, or H) conditions. Lower conditional RPs of T (or T with H, or T with P) had resulted at given low P and H (or low P, or low H levels). In conclusion, the estimated risk statistics are vital for the study region, highlighting the need for effective adaptation and resilience planning in agriculture crop management.
Keywords: Infolettre_Septembre_2025

Natural disasters and individual economic performance: a case study from the slave lake wildfire - Kabore, Philippe; Rivers, Nicholas; Armstrong, Catherine Deri (Climate Change Economics, 2025)
In May 2011, the municipality of Slave Lake, Alberta, was hit by a devastating wildfire; the second costliest natural disaster in Canada at the time. Residents of Slave Lake were forced to evacuate for at least a month. This case study uses longitudinal income tax data from 2004 to 2018 to estimate the short, medium, and long-term individual economic effects of this wildfire. Estimates suggest an average drop in total income of 10.5% relative to a counter-factual scenario with no wildfire over the 7 years following the wildfire, mainly driven by a decrease in employment income. The percentage of total income lost is similar for males and females. The largest effects are found for workers in the agriculture and forestry sectors. Back-of-the- envelope calculations suggest an aggregate loss in employment income of $150 million in the 7 years following the disaster, equivalent to over 13% of direct economic losses due to property damage, firefighting, and contemporaneous business closure.
Keywords: Infolettre_Septembre_2025

High-Resolution Daily XCH4 Prediction Using New Convolutional Neural Network Autoencoder Model and Remote Sensing Data - Awad, Mohamad M.; Homayouni, Saeid (Atmosphere, 2025)
Atmospheric methane (CH4) concentrations have increased to 2.5 times their pre-industrial levels, with a marked acceleration in recent decades. CH4 is responsible for approximately 30% of the global temperature rise since the Industrial Revolution. This growing concentration contributes to environmental degradation, including ocean acidification, accelerated climate change, and a rise in natural disasters. The column-averaged dry-air mole fraction of methane (XCH4) is a crucial indicator for assessing atmospheric CH4 levels. In this study, the Sentinel-5P TROPOMI instrument was employed to monitor, map, and estimate CH4 concentrations on both regional and global scales. However, TROPOMI data exhibits limitations such as spatial gaps and relatively coarse resolution, particularly at regional scales or over small areas. To mitigate these limitations, a novel Convolutional Neural Network Autoencoder (CNN-AE) model was developed. Validation was performed using the Total Carbon Column Observing Network (TCCON), providing a benchmark for evaluating the accuracy of various interpolation and prediction models. The CNN-AE model demonstrated the highest accuracy in regional-scale analysis, achieving a Mean Absolute Error (MAE) of 28.48 ppb and a Root Mean Square Error (RMSE) of 30.07 ppb. This was followed by the Random Forest (RF) regressor (MAE: 29.07 ppb; RMSE: 36.89 ppb), GridData Nearest Neighbor Interpolator (NNI) (MAE: 30.06 ppb; RMSE: 32.14 ppb), and the Radial Basis Function (RBF) Interpolator (MAE: 80.23 ppb; RMSE: 90.54 ppb). On a global scale, the CNN-AE again outperformed other methods, yielding the lowest MAE and RMSE (19.78 and 24.7 ppb, respectively), followed by RF (21.46 and 27.23 ppb), GridData NNI (25.3 and 32.62 ppb), and RBF (43.08 and 54.93 ppb).
Keywords: Infolettre_Septembre_2025

Flooding stress influences productivity and modulates biodiversity effects in experimental grassland communities, shaping biodiversity–productivity relationships - Zhekov, Aleksandar; Bourgeois, Bérenger; Poulin, Monique (American Journal of Botany, 2025)
Abstract Premise Biodiversity loss and increasing extreme weather events disrupt the functioning of ecosystems and thus their ability to provide services. While the interplay among various climatic constraints, diversity and productivity has received increasing attention in the last decades, the role of flooding has been overlooked. Methods In a greenhouse experiment, we manipulated species richness and water regimes to evaluate the influence of flooding on species diversity–productivity relationships. We measured biomass production and partitioned net biodiversity effects into complementarity and selection effects. To link changes in biodiversity effects to underlying mechanisms, we evaluated the contribution of species richness, species identity, functional diversity and community‐level traits. Results Under flooding, biomass production decreased, and biodiversity effects were less frequently positive. By reducing the incidence of positive complementarity effects, flooding promoted a preponderance of selection effects. Flooding further favored competitive displacement by Phalaris arundinacea ; balanced contributions to selection effects from all functional groups at field capacity subsided under flooding when P. arundinacea became the single dominant species. As a result, its acquisitive leaf trait attributes contributed more to selection effects and biomass production under flooding, while root traits contributed less to complementarity effects at field capacity. Conclusions As an environmental stressor, flooding promoted the dominance of tolerant species and reduced the incidence of complementary species interactions in the experimental plant communities, clearly modulating the linkage between diversity and productivity.
Keywords: Infolettre_Septembre_2025

Projections of extreme rainfall and floods in Mediterranean basins from an ensemble of convection-permitting models - Poncet, Nils; Tramblay, Yves; Lucas-Picher, Philippe; Thirel, Guillaume; Caillaud, Cécile (Climatic Change, 2025)
Floods have major impacts on the Mediterranean region, but little is currently known about their potential evolution in the context of climate change. This is due in particular to the limited ability of climate models to reproduce extreme meteorological events such as heavy rains that lead to flash floods, especially at the local scale over smaller basins. This study is the first to explore future flood scenarios over 12 Mediterranean basins using an ensemble of 12 high-resolution convection-permitting climate models and the GR5H hourly rainfall-runoff model. The results indicate an overall increase in flood intensity across all basins, particularly for the most severe events, but also a strong spatial variability in the change signal depending on the geographic location. There is good agreement among the convection-permitting climate models on an increase in hourly and daily rainfall extremes in the Mediterranean, but these changes are not strongly correlated with changes in flood-peak intensity, indicating that change in rainfall intensity alone is a poor predictor of future flood hazards. At present, this type of analysis is hampered by the short duration of the available high-resolution climate simulations. Longer timeseries would be required to better assess the robustness of the projected changes against climate variability.
Keywords: Infolettre_Septembre_2025

Debating Disaster Risk: Ethical Dilemmas in the Era of Climate Change - Lizarralde, Gonzalo; Bornstein, Lisa; Dhar, Tapan (Columbia University Press, 2025)
Dealing with the risks of climate change and disaster is a political process. It produces winners and losers, mobility and permanence, radical change and continuity, relief and suffering. For some, it ultimately leads to life or death. Yet consultants, academics, humanitarian agents, and politicians often simply propose well-intentioned ideas—resilience, sustainability, community participation, emergency shelter, green development—while failing to perceive the blind spots and unintended consequences of such approaches.Debating Disaster Risk brings together leading global experts to explore the controversies that emerge—and the tough decisions that must be made—when cities, people, and the environment are at risk. Scholars and practitioners discuss the challenges of reducing vulnerability and rebuilding after destruction in an accessible and lively debate format, with commentary by researchers, students, and development workers from across the world. They emphasize the ethical consequences of decisions about how cities and communities should prepare for and react to disasters, considering issues such as housing, environmental protection, urban development, and infrastructure recovery.A valuable resource for scholars, students, and practitioners in a variety of fields, this book provides an in-depth analysis of the difficult choices we face in dealing with disasters. As climate change accelerates, Debating Disaster Risk invites readers to grapple with the most pressing controversies.
Keywords: Infolettre_Septembre_2025

Predicting synthetic rating curve adjustment factors with explainable machine learning for enhancing the United States operational flood inundation mapping framework - Baruah, Anupal; Spies, Ryan; Devi, Dipsikha; Cohen, Sagy; Aristizabal, Fernando; Nikrou, Parvaneh; Tian, Dan; Pruitt, Carson (Journal of Hydrology, 2025)
The increasing threats of global flood risk mandate rapid and accurate high-resolution flood modeling strategies over large scales. In the United States, the National Oceanic and Atmospheric Administration (NOAA) Office of Water Prediction (OWP) has operationalised a Flood Inundation Mapping (FIM) framework utilising the Height Above Nearest Drainage (HAND)-Synthetic Rating Curve (SRC) approach. It translates streamflow into stage and subsequently maps the inundation over the floodplain. It is a low-fidelity FIM framework, suitable for large-scale applications with much less computational effort. The SRCs are calculated for each river segment using Manning's equation; however, uncertainty in Manning's parameters and missing bathymetry impart bias in SRC calculation, and thus in FIM. An SRC adjustment factor (λsrc), introduced by OWP, calibrates SRCs against USGS rating curves, HEC-RAS 1D rating curves, and National Weather Service (NWS)-Categorical Flood Inundation Mapping (CatFIM) locations. Adjusted SRCs improve the FIM predictions but are limited to locations with the above data sources. In this paper, we develop machine learning models to predict the λsrc over the entire United States river network. Results show that the eXtreme Gradient Boosting model yielded the strongest predictability, with an R2 of 0.70. The impact of λsrc on FIM predictions is evaluated for Hurricane Matthew in North Carolina and synthetic flood events in 15 watersheds. For Hurricane Matthew flooding, the mean percentage improvements in Critical Success Index (CSI), Probability of Detection (POD), and F1 Score are 17.5%, 20% and 12.5%, while for synthetic events, the improvements are 2.59%, 4.93%, and 3.03%, respectively. © 2025 The Author(s)

Agent-based vulnerability model for pedestrians exposed to urban floodwaters - Li, Qijie; Xia, Junqiang; Xu, Zheng; Liang, Dongfang; Hinkelmann, Reinhard (Journal of Hydrology, 2025)
Climate change-induced floods will have a profound impact on densely populated urban areas. The survey results indicate that a substantial proportion of respondents engaged in evacuation behavior during urban flooding events. However, current assessment methods may underestimate the impact of human motions in floodwaters on pedestrian evacuation safety. To quantitively study the dynamic vulnerability of individuals exposed to flooding scenarios, an agent-based vulnerability model was proposed based on mechanics modelling and experimentally calibrating. A full-scale physical testing platform was constructed and utilized to calibrate the proposed model and to determine the stability limits of pedestrian safety in floodwaters. Spatial and temporal dynamic characteristics of pedestrians were analyzed and results reveal significant variations in pedestrian movement and stability. The general temporal trend of movement speed changing as a power function of the specific flood force has been validated. It is also found that pedestrian stability is notably affected by movement in floodwaters, particularly when walking against the flow, which intensifies the risk of instability, leading to vulnerability indices that increase by 123.2 % at a depth of 0.3 m and by 82.7 % at 0.5 m compared to still-water conditions. In contrast, moving with the flow reduces hydrodynamic forces, although the rate of this reduction decreases with greater water depths, dropping to 16.0 % at 0.5 m and 9.7 % at 0.7 m. Additionally, this work provides guidelines for assessing pedestrian evacuation vulnerability that enhances evacuation safety and supports flood management. © 2025 Elsevier B.V.

Optimizing ensemble learning for satellite-based multi-hazard monitoring and susceptibility assessment of landslides, land subsidence, floods, and wildfires - Razavi-Termeh, Seyed Vahid; Sadeghi-Niaraki, Abolghasem; Ali, Farman; Pradhan, Biswajeet; Choi, Soo-Mi (Scientific Reports, 2025)
The preparation of accurate multi-hazard susceptibility maps is essential to effective disaster risk management. Past studies have relied mainly on traditional machine learning models, but these models do not perform well for complex spatial patterns. To address this gap, this study uses two meta-heuristic algorithms (Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)) to provide an optimized Random Forest (RF) model with better predictive ability. We focus on four significant hazards—landslides, land subsidence, wildfires, and floods—in Kurdistan Province, Iran, using Sentinel-1 and Sentinel-2 satellite imagery collected between 2015 and 2022. Furthermore, two models of RF-GA and RF-PSO were utilized to create multi-hazard susceptibility, which were evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC). The RF-GA algorithm achieved 91.1% accuracy for flood hazards, 83.8% for wildfires, and 99.1% for landslide hazards. In contrast, utilizing RF-PSO resulted in a 95.9% accuracy for land subsidence hazards. The combined RF-GA algorithm demonstrated superior accuracy to individual RF modeling techniques. Furthermore, eastern regions are more prone to floods and land subsidence, whereas western areas face more significant risks from landslides and wildfires. Additionally, floods and land subsidence exhibit a considerable correlation, impacting each other’s occurrence, while wildfires and landslides demonstrate interacting dynamics, influencing each other’s likelihood of occurrence. © The Author(s) 2025.

Multi-criteria decision analysis for flood hazard mapping in the Itang watershed, lower Baro-Akobo Basin, Southwest Ethiopia - Chengu, Shibiru; Assen, Mohammed; Gebeyehu, Elias (Discover Environment, 2025)
Flooding remains a critical hydrological hazard in the Itang watershed within the Lower Baro-Akobo Basin, requiring an in-depth assessment of flood susceptibility. This study employs a multi-criteria evaluation method, integrating key geospatial and hydrological parameters such as topographic slope, elevation, land use/land cover, River proximity, drainage network density, precipitation intensity, and soil properties. Utilizing a Multi-Criteria Decision Analysis (MCDA) approach within the ArcMap 10.3.1 environment, a flood hazard zonation map was generated, classifying the watershed into five risk categories: Very high, high, moderate, low, and very low. The findings reveal that approximately 69.69% of the watershed falls within the high to very high flood risk zones, predominantly influenced by low-lying Elevation, gentle slopes, proximity to the river, land cover dynamics, high drainage density, and precipitation variability. These insights emphasize the necessity of integrating robust flood mitigation measures, early warning mechanisms, and sustainable watershed management interventions to enhance flood resilience and reduce hydrological risks in the study watershed. © The Author(s) 2025.

Black swans to gray rhinos: Robust decision making for Natech scenarios caused by floods - Hu, Xiangyang; Yang, Angbin; Wu, Shaohui; Tong, Ruipeng (Journal of Loss Prevention in the Process Industries, 2025)
Industrial facilities and critical infrastructure are affected by natural disasters with increasing probability, potentially resulting in serious health impacts, environmental pollution, and economic losses. Deep uncertainty about future scenarios leads to under-adaptation due to the inability of existing knowledge to cope with ambiguity and complexity. With scientific constraints, particularly in model limitations and scenario scarcity, estimating the likelihood of risk events and possible implications is challenging and error-prone. Using systems thinking to guide scenario planning, a Pressure-State-Response (PSR) model of Natech risk was developed to outline the uncertainty involved in the full course of the Natech event in this paper. Taking the flood-triggered Natech risks as an example, a robust decision-making (RDM) framework was adopted to analyze the impacts of future extreme rainfall scenarios on the city. Obtaining future rainfall scenarios through screening and quantitative analysis of uncertainties and their intervals of variability under the impact of climate change. By evaluating urban disaster curves that may be triggered in the future, an interpretive structural model (ISM) of the future urban response to the Natech accident scenario was constructed, and prioritized adaptation paths were selected to enhance urban resilience. © 2025 Elsevier Ltd

Risk identification and prevention of multi-level flood and typhoon prevention emergency drills - Cao, Feifeng; Chen, Changhui; Zhang, Conglin; Xing, Jianwu (Progress in Disaster Science, 2025)
Amid increasing extreme weather events driven by global climate change, pre-emptive emergency drills are vital for strengthening disaster resilience. This paper focuses on risk identification and prevention in multi-level flood and typhoon prevention emergency drills, aiming to achieve effective risk management across administrative levels. Through literature review and expert consultation, 24 risk factors were hierarchically identified. A quantitative risk assessment model was developed by integrating the risk matrix and cloud model eigenvalues. The results show that risks are the most serious at municipal-level drills, with 20 risk factors (79.17 % of the total) at Level-III and above, decreasing at lower administrative levels (where risk level are categorized into Level-I (Major), Level-II (Large), Level-III (General), and Level-IV (Low) based on the risk matrix integrating likelihood and consequence levels, and Level-III and above risks may trigger resource wastage, drill failure, or even personnel casualties). Temporally, 39 risk factors at Level-III and above were concentrated in preparation stages across all administrative levels, declining to 3 such risk factors during rectification stage. Spatially, the number of risk factors peaked during the municipal-level and county-level preparation stages (11 risk factors respectively at Level-III and above), with their quantity gradually decreasing as the administrative level decreases and drill stages advance. Based on these findings, a systematic risk prevention matrix is proposed to offer targeted guidance for multi-level flood and typhoon prevention emergency drills in addressing climate change-induced disaster challenges. © 2025 The Authors

Unveiling hidden risks in healthcare from flood-induced transportation disruption in Germany - Wassmer, Jonas; Bryant, Seth; Schimansky, Paul; Keegan, Lindsay T.; Pregnolato, Maria; Kurths, Jürgen; Marwan, Norbert; Merz, Bruno (Communications Earth and Environment, 2025)
Despite investments in disaster resilience, flooding continues to disrupt healthcare systems, both by limiting access and through failures in the surrounding transportation network. Existing models for mitigation planning often overlook critical dynamics, such as traffic rerouting, particularly at the national scales necessary for effective planning. Here we present a scalable method to identify hospitals at risk of emergency response delays and service disruptions caused by flood-induced traffic impacts. Our approach integrates a regional flood model with a gravity-based traffic model to simulate traffic flow from open-source road data. Our findings reveal hidden risks for hospitals located far from flood zones, showing how flood-related road disruptions and traffic rerouting can reduce access to critical healthcare services. In particular, we found 75 (of 2,475) hospitals at risk of patient surges beyond their regular capacity, driven solely by flood-related traffic disruptions. Of these, a third are more than 10 km from the nearest inundation, suggesting these facilities may be unaware and thus under-prepared — risks that have, until now, remained hidden from assessments. © The Author(s) 2025.

Optimization of emergency logistics for urban flooding with consideration of rainfall effects - Zhang, Peiwen; Zhang, Chenxing; Zhang, Pan; Yan, Xuxian; Yang, Huawei (Scientific Reports, 2025)
Urban flooding frequently causes significant damage to infrastructure and facilities, leading to critical supply shortages in affected regions. Ensuring rapid and efficient distribution of relief supplies remains a key challenge during disaster response operations. This study proposes a two-stage optimization framework for emergency logistics. First, a supply distribution model is developed by integrating resource scarcity indices and disaster severity indices, optimized through a simulated annealing algorithm. Second, a vehicle routing model accounting for rainfall and dynamic vehicle speeds is established, solved using a hybrid Genetic Simulated Annealing algorithm to enhance computational efficiency. Ultimately, through simulation with randomly generated calculation examples, it was found that for the supply distribution model, the allocation model that takes into account both the resource scarcity index and the disaster index is more suitable for scenarios with an uneven distribution of disaster severity. The results of the model that takes into account the resource scarcity index, disaster index and waiting time index shows an improvement of 4% over the model that doesn’t consider the resource scarcity index. The experimental results show that the proposed methodology not only adapts to varying disaster spatial patterns but also balances efficiency and equity under supply constraints, offering a scalable tool for designing resilient urban flood response systems. © The Author(s) 2025.

A novel and scalable flood risk assessment framework for cultural heritage based on unmanned aerial vehicle photogrammetry and multi-scale rain-on-grid hydraulic modeling - Alexopoulos, M.J.; Iliopoulou, T.; Modé, P.; Istrati, D.; Spyrakos, C.C.; Soile, S.; Verykokou, S.; Ioannidis, C.; Koutsoyiannis, D. (Science of the Total Environment, 2025)
This study presents a novel multi-scale flood risk assessment framework for cultural heritage sites, applied to the Temple of Apollo, Aegina Island, Greece. Three modeling configurations were developed and compared: (i) an island-wide Rain-on-Grid (RoG) hydraulic model at 5 m resolution, (ii) a site-only model driven by inflows from the island-scale simulation, and (iii) a high-resolution nested model coupling island-scale outputs with centimeter-scale site RoG simulations enabled by UAV photogrammetry. Simulations for 100-, 1000-, and 2000-year return periods revealed strong scale-dependent differences: island-wide inundation extents of 7.3–10.3 km2, site-specific inundation of 2–24 %, and water volumes of 92–1483 m3 depending on the model configuration and return period. Flow velocities remained below 1.0 m/s, indicating low erosive potential but possible material degradation. Limestone deterioration analysis showed 4–10 % compressive strength reduction, 3–9 % elastic modulus decrease, and mass losses of 0.64–26.08 kg after 24-h inundations. The nested approach provided more realistic water volume accumulation over the single-scale model and revealed critical micro-topographic controls on flood behavior. This scalable, built on readily accessible tools (HEC-RAS and UAV), framework supports rapid deployment to heritage sites globally, enabling quantitative risk assessments for adaptation planning and conservation prioritization. © 2025 The Authors

Multiscale semantic segmentation and damage assessment of village houses in post-flood scenarios using an enhanced DeepLabv3 with dual attention mechanism - Wu, Luyuan; Cheng, Yi; Su, Aifang; Zhang, Boyang; Wang, Zifa; Tong, Jingbo; Li, Meng; Zhang, Anqi (Advanced Engineering Informatics, 2025)
Floods can cause varying degrees of damage to village houses, and rapid assessment of houses damage is crucial for post-disaster safety evaluations. To address this issue, the paper proposes a DeepLabv3-Dual Attention (DDA) model incorporating improved ResNet-50, Atrous Spatial Pyramid Pooling (ASPP), Selective Kernel(SK)-Attention, and Self-Attention mechanisms. In the DDA model, the ASPP structure is enhanced with Self-Attention, while the backbone network employs SK-Attention improved ResNet-50, enabling more effective feature extraction across both spatial and channel dimensions. Validated on the “7.20” heavy rain disaster dataset from Zhengzhou, DDA achieves optimal performance (learning rate: 0.01, batch_size: 16, epoch: 88) with 87.5% accuracy, 98.5% global accuracy, 77.8% MIoU, and 86.5% F1-score, outperforming DeepLabv3+, Swin-Unet, U-Net, YOLO baseline models. Ablation studies confirm dual-attention synergy improves MIoU by 2.5%, precision by 2.5% over single mechanisms. Further, a damage quantification framework is developed using OpenCV and Canny edge detection to extract contours, coupled with a maximum tangent circle algorithm for pixel-level width measurement and skeletonization-based pixel-level length calculation. The results of damage quantification model show that the proportion of images with damage width quantification error ⩽ 20% are 85.29%. This study serves as a reference for intelligent assessment and risk classification of structural damage to buildings affected by flooding. © 2025 Elsevier Ltd

Editorial preface to special issue: Temporal and spatial patterns in Holocene floods under the influence of past global change, and their implications for forecasting “unpredecented” future events - Schulte, Lothar; Santisteban, Juan I.; Fuller, Ian C.; Ballesteros-Cánovas, Juan Antonio (Global and Planetary Change, 2025)
Floods constitute the most significant natural hazard to societies worldwide. Population growth and unchecked development have led to floodplain encroachment. Modelling suggests that climate change will regionally intensify the threat posed by future floods, with more people in harm's way. From a global change perspective, past flood events and their spatial-temporal patterns are of particular interest because they can be linked to former climate patterns, which can be used to guide future climate predictions. Millennial and centennial time series contain evidence of very rare extreme events, which are often considered by society as ‘unprecedented’. By understanding their timing, magnitude and frequency in conjunction with prevailing climate regime, we can better forecast their future occurrence. This Virtual Special Issue (VSI) entitled Temporal and spatial patterns in Holocene floods under the influence of past global change, and their implications for forecasting “unpredecented” future events comprises 14 papers that focus on how centennial and millennia-scale natural and documentary flood archives help improve future flood science. Specifically, documentation of large and very rare flood episodes challenges society's lack of imagination regarding the scale of flood disasters that are possible (what we term here, the “unknown unknowns”). Temporal and spatial flood behaviour and related climate patterns as well as the reconstruction of flood propagation in river systems are important foci of this VSI. These reconstructions are crucial for the provision of robust and reliable data sets, knowledge and baseline information for future flood scenarios and forecasting. We argue that it remains difficult to establish analogies for understanding flood risk during the current period of global warming. Most studies in this VSI suggest that the most severe flooding occurred during relatively cool climate periods, such as the Little Ice Age. However, flood patterns have been significantly altered by land use and river management in many catchments and floodplains over the last two centuries, thereby obscuring the climate signal. When the largest floods in instrumental records are compared with paleoflood records reconstructed from natural and documentary archives, it becomes clear that precedent floods should have been considered in many cases of flood frequency analysis and flood risk modelling in hydraulic infrastructure. Finally, numerical geomorphological analysis and hydrological simulations show great potential for testing and improving our understanding of the processes and factors involved in the temporal and spatial behaviour of floods. © 2025 The Authors

Developing rejuvenation strategies and artificial flood mitigation plans for Indrani River: a case study - Muhammed, Shaz P.; Holla, B.R.K.; Balakrishna, K.; Warrier, Anish Kumar (Scientific Reports, 2025)
Artificial flooding of rainwater is most common in urban areas due to various reasons, such as improper drainage systems, obstruction of natural drainage by building constructions, and encroachment of stormwater nallahs. Flash floods lead to significant losses, disrupt transportation, and cause inconvenience to the public. Udupi, characterized by its porous lateritic strata, undulating topography, and proximity to the sea, experiences artificial flooding during the peak monsoon season in its low-lying areas, primarily due to the overflow of the Indrani River, which is also a potential water resource for Udupi, Karnataka. Currently, the river faces significant challenges due to increasing anthropogenic activities. Revitalizing the Indrani River offers numerous benefits, including its potential use as a drinking water source during periods of water scarcity. This study aims to propose flood and stormwater management measures for the river catchment and to evaluate selected water quality parameters (pH, dissolved oxygen, and conductivity) at fifteen strategic locations along the river course. Higher conductivity observed at downstream stations is attributed to sewage discharge from urban settlements and a sewage treatment plant. The study suggests short-term measures such as targeted clean-up operations and stricter enforcement of pollution control regulations. Additionally, it recommends long-term strategies, including the development of a comprehensive river basin management plan, community engagement initiatives, and improvements to wastewater treatment infrastructure. To maintain the health of the Indrani River, this research emphasizes the importance of continuous monitoring and the implementation of integrated management practices. © The Author(s) 2025.

Effects of long-term wetland variations on flood risk assessments in the Yangtze River Basin - Guo, Ziying; Shi, Xiaogang; Zhang, Dingfan; Zhao, Qunshan (Environmental Impact Assessment Review, 2026)
Flooding is the most frequent natural disaster in the Yangtze River Basin (YRB), causing significant socio-economic damages. In recent decades, abundant wetland resources in the YRB have experienced substantial changes and played a significant role in strengthening the hydrological resilience to flood risks. However, wetland-related approaches remain underdeveloped for mitigating flood risks in the YRB due to the lack of considering long-term wetland effects in the flood risk assessment. Therefore, this study develops an wetland-related GIS-based spatial multi-index flood risk assessment model by incorporating the effects of wetland variations, to investigate the long-term implications of wetland variations on flood risks, to identify dominant flood risk indicators under wetland effects, and to provide wetland-related flood risk management suggestions. These findings indicate that wetlands in the Taihu Lake Basin, Wanjiang Plain, Poyang Lake Basin, and Dongting and Honghu Lake Basin could enhance flood control capacity and reduce flood risks in most years between 1985 and 2021 except years with extreme flood disasters. Wetlands in the Sichuan Basin have aggravated but limited impacts on flood risks. Precipitation in the Taihu Lake Basin and Poyang Lake Basin, runoff and vegetation cover in the Wanjiang Plain, GDP in the Taihu Lake Basin, population density in the Taihu lake Basin, Dongting and Honghu Lake Basin, and the Sichuan Basin are dominant flood risk indicators under wetland effects. Reasonably managing wetlands, maximizing stormwater storage capacity, increasing vegetation coverage in urbanized and precipitated regions are feasible suggestions for developing wetland-related flood resilience strategies in the YRB. © 2025 The Authors

Development and validation of a predictive flux regression model for tubular membrane treatment of Polymer-Flooding produced water - Xu, Jun; Wu, Man; Hao, Jiahou; Lu, Jing; Zhao, Dongsheng; Zheng, Wenyu; Jiang, Gengmin; Yu, Shuili; Wang, Linna (Separation and Purification Technology, 2025)
This study established and validated a regression model for predicting membrane flux (Jv) during treatment of polymer-flooding produced water (PFPW) using PVDF ultrafiltration membranes hydrophilized by Nano-Al2O3 particles. A factorial crossover experimental design was employed to investigate the temporal flux variations and 24-hour average flux (Jv24) under different membrane surface velocities and transmembrane pressure differences. Based on Taylor's mathematical principles, a regression model for predicting Jv in PFPW treatment was developed using the experimental results. The coefficients, contour plots and response surface of the model were obtained through MATLAB software. Theoretical and experimental validations were conducted to assess the accuracy of the model. Theoretical validation demonstrated that the standard residuals of the model and predicted values were within ± 2.00, with the predicted and measured Jv exhibiting similar statistical distribution characteristics. Experimental validation revealed that, except for the relative errors at the endpoints of the model's independent variable intervals (approximately 7 %) of the model, all other errors were below 4 %, indicating high accuracy of the regression model. This work provides a methodological and theoretical basis for optimizing operational parameters in PFPW membrane treatment, and predicting Jv under specified conditions to support energy savings and consumption reduction. © 2025 Elsevier B.V.

Intrinsic drivers of urban flood disasters from the resilience perspective in China - Li, Xiaojuan; Chen, Rixin; Ren, Yifei; Jim, C.Y. (Reliability Engineering and System Safety, 2026)
Urban flood disasters pose substantial threats to public safety and urban development, with climate change exacerbating the intensity, frequency, and consequences of such events. While existing research has predominantly concentrated on flood control and disaster response, limited attention has been paid to the underlying drivers and evolutionary mechanisms of urban flood resilience. This study applies the resilience framework to develop an integrated methodology for assessing urban flood resilience. Focusing on three coastal provinces in China that frequently experience severe flooding, the study identifies fifteen key resilience drivers to construct a compound driver system. The evolution of flood resilience is examined through the lens of the Pressure-State-Response (PSR) model, which categorizes the drivers into three distinct dimensions. The Decision Making Trial and Evaluation Laboratory (DEMATEL) and Interpretative Structural Model (ISM) methods are employed to analyze the interrelationships and hierarchical structure among drivers. In parallel, a system dynamics (SD) modeling approach is used to construct causal-loop and stock-flow diagrams, revealing the complex interdependencies and critical pathways across resilience dimensions. The analysis identifies rainfall intensity as the most influential driver in shaping urban flood resilience. Scenario simulations based on the SD model explore variations in resilience performance under three developmental pathways. Findings suggest that enhancing response resilience is crucial under current flood control trajectories. This study contributes novel conceptual and methodological insights into the measurement and evolution of urban flood resilience. It offers actionable guidance for policymakers aiming to strengthen flood risk governance and urban safety. © 2025 Elsevier Ltd

Enhancing flood prediction in the Lower Mekong River Basin by scale-independent interpretable deep learning model - Qiu, Yangzi; Shi, Xiaogang; He, Xiaogang (Environmental Impact Assessment Review, 2026)
Climate change has increased the frequency and intensity of extreme floods in the Lower Mekong River Basin (LMB). This study leverages the Long Short-Term Memory (LSTM) model to evaluate its performance in predicting river discharge across the LMB and to identify the key variables contributing to flood prediction through SHapley Additive exPlanation (SHAP) and Universal Multifractal (UM) analyses, in a scale-dependent and scale-independent manner, respectively. The performance of the LSTM model is satisfactory, with Nash–Sutcliffe Efficiency (NSE) values exceeding 0.9 for all subbasins when using all input features. The model tends to underestimate the largest peak flows in the midstream subbasins that experienced extreme rainfall events. According to SHAP, soil-related variables are important contributors to discharge prediction, with their impacts partially manifested through interactions with precipitation and runoff. Furthermore, the dominant contributing variables influencing flood prediction vary over time: soil-related variables and vegetation-related variables played a more significant role in earlier years, whereas hydrometeorological variables became more dominant after 2017. The UM analysis investigates the scaling behaviours of contributing variables, showing that hydrometeorological-related variables have a greater influence on predicting extreme discharge across the small temporal scales. Additionally, the UM analysis indicates that the model's performance improves as the temporal variability in extremes of the combined features decreases across 1 to 16 days. Overall, this study provides a comprehensive assessment of the LSTM model's performance in discharge prediction, emphasising the impact of the variability in the extremes of combined features through the scale-independent interpretation. These findings will offer valuable insights for stakeholders to improve flood risk management across the LMB. © 2025 The Authors

Reservoir Potential Unlocked: Synergies Between Low-Salinity Water Flooding, Nanoparticles and Surfactants in Enhanced Oil Recovery─A Review - Mumbere, Walidi; Sagala, Farad; Gupta, Uttam; Bbosa, Denis (ACS Omega, 2025)
This review explores the synergistic application of low-salinity water flooding (LSWF), nanoparticles, and surfactants in enhanced oil recovery (EOR), emphasizing their combined potential to address modern oil production challenges. It highlights the ability of LSWF to alter reservoir wettability, improve sweep efficiency, nanoparticles to enhance fluid mobility, and reduce interfacial tension, and surfactants to lower the interfacial tension and promote emulsification. All of this aim to provide practical, easy-to-use, and sustainable solutions for oil recovery. The review discusses existing research gaps in the long-term stability of nanoparticle-surfactant complexes, site-specific differences in rock-fluid interactions, and economic viability across diverse reservoirs. The review summarizes experimental findings, demonstrating recovery rates of 45–55% for nanoparticles, 50–60% for surfactants, and 40–50% for LSWF with improvements of 75–82% through this integrated approach. Additionally, the review addresses technological advancements, environmental considerations, and economic feasibility, highlighting challenges such as nanoparticle agglomeration and surfactant adsorption. A bibliographic analysis of global research trends, mechanisms, and field applications underscores the importance of interdisciplinary collaboration in optimizing this strategy for sustainable and efficient EOR processes. The findings aim to guide researchers and industry professionals in leveraging the synergistic effects of these methods to maximize oil recovery and reduce environmental footprints with a proposition to develop advanced characterization techniques, and verifying practicality through field implementation studies © 2025 The Authors. Published by American Chemical Society

Impact of Climate Change on Human Health in Thailand: A Literature Review - Ratanawong, Pitcha; Nunthaitaweekul, Pachanat; Huynh, Phuong Trang; Weesakul, Uruya (Journal of Disaster Research, 2025)
This study aims to find and summarize published studies that examined the effects of climate change on human health and diseases in Thailand by conducting aliterature review using the preferred reporting items for systematic reviews and meta-analysis guidelines between October 17, 2023, and January 31, 2024. We earched PubMed and OvidSP for relevant research. We included studies that were written in English orThai; primary research focused on climate change or its subsets (natural disasters or climate issues, such as rising temperatures and altered weather patterns that increase the frequency, intensity, and severity of manynatural disasters and climate issues); focused on human health; indexed by PubMed or OvidSP; available as published research with full-text journal articles; and published in 2013 or later. Our search yielded 53 relevant articles. These articles identified five main categories of climate issues: temperature, rainfall/precipitation, humidity, wind speed, and flooding. We identified five categories of health issues: dengue, respiratory diseases and infections, malaria, skin diseases/symptoms, and other health issues. The most studied relationship is between temperature and dengue. Most articles reported the harmful effects of climate issues on health, although four reported opposite effects, and seven reported no significant associations. Among the 53 articles, ten utilized prediction models. The main goal of this review is to summarize current research to guide future studies and assist policymakers in prioritizing climate-related health policies in Thailand. Study limitations include the use of only two databases, the restriction to articles from 2013 onwards, and the inclusion of only articles in English and Thai, which may have limited the number of articles found for this literature review. © 2025, Fuji Technology Press. All rights reserved.

Non-Aquatic Fauna of Amazonian Floodplain Forests and its Responses to Seasonal Flooding - Pedroza, Diego; Cohn-Haft, Mario; Haugaasen, Torbjørn (Wetlands, 2025)
The non-aquatic fauna (e.g. insects, birds, mammals) that occupies seasonally flooded floodplain forests in the Amazon is a major component of the region’s biodiversity, and the responses portrayed to cope with this inundation are varied. However, no systematic review of these species, including specialist species (exclusive to this environment), and their responses to seasonal inundation has yet been performed. Here, we provide an up-to-date and thorough examination of research on non-aquatic fauna that utilize Amazonian floodplain forests and their responses to seasonal flooding. We conducted a survey of published and unpublished studies from 1853 to 2023 through the Web of Science and Google Scholar platforms. We found a total of 445 studies, including 11,513 species of non-aquatic animals that inhabit floodplain forests. We identified ten main types of responses to flooding, the three most common being vertical migration, occupation of floating substrates and eggs submerged in a dormant state. Results suggest great behavioral, morphological and physiological plasticity among non-aquatic species, including those that are not floodplain forest specialists. Several types of responses occur independently in widely distinct taxonomic groups, emphasizing convergent strategies to deal with seasonal flooding. Our findings underline the uniqueness of the floodplain fauna and its importance for the regional biodiversity conservation agenda. © The Author(s), under exclusive licence to Society of Wetland Scientists 2025.

Harnessing Vegetation as a Nature-Based Solution for Integrated River Corridor Management: A Path Towards Harmonized Human–Nature Synergy - Maurya, Om Prakash; Nandi, Ketan Kumar; Dutta, Subashisa (Ecohydrology, 2025)
This review examines the role of vegetation as a nature-based solution (NBS) for sustainable river corridor management, integrating a wide range of interdisciplinary domains. It synthesizes studies addressing global challenges in river systems, the worldwide adoption of vegetation-based solutions and location-specific field observations from major Indian rivers such as the Brahmaputra and Ganga. This paper also reviews flume-scale experiments on vegetation–flow interactions and explores the biomechanical properties of vegetation, such as root reinforcement that contribute to riverbank stability. In addition, it discusses the selection of suitable species based on specific climatic regions, as reported in the literature. Building on this interdisciplinary understanding, this review highlights the vital role of vegetation in mitigating bank erosion, regulating sediment transport, attenuating floods and enhancing the overall health and resilience of riverine ecosystems and communities. It proposes an integrated framework that combines vegetation with biodegradable materials such as bamboo fencing and geo-bags and conventional engineering measures to address high-flow conditions and ensure long-term riverbank stability. Additionally, a flume-scale physical model study was conducted to investigate near-bank hydrodynamics in the presence of a series of three spurs and a combination of rigid and flexible vegetation. The results indicate that vegetation significantly reduces streamwise velocity near the bank, achieving performance comparable to that of the spur arrangement. This study identifies key challenges, including appropriate species selection, long-term maintenance of vegetation-based solutions and the need for adaptive management strategies. It further emphasizes the importance of stakeholder engagement for successful and sustainable implementation. © 2025 John Wiley & Sons Ltd.

Managing floods and droughts synergistically: A transformative governance approach - Sánchez, Ana Núñez; Koehler, Johanna; Van Loon, Anne F. (PLOS Water, 2025)
Floods and droughts cause large economic and environmental impacts and incalculable human suffering. Despite growing evidence of important synergies in their management, floods and droughts tend to be mostly managed in silos. The synergistic management of flood and drought risk is limited by the inability of current governance systems to change at the scope, depth and speed required to address the emerging challenges of climate change induced hydroclimatic risks. Building on the concept of continuous transformational change and combining key elements across sectoral governance frameworks, this paper proposes a transformative governance conceptual framework that enables national governments to work across silos in a whole of government approach to lead a whole of society effort to manage the whole hydroclimatic spectrum. Spain, a country with an advanced hydroclimatic risk management system, is presented as an illustrative example to explore the possible idiosyncrasies of implementing the proposed changes on the ground. © 2025 Núñez Sánchez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

A systematic literature review of digital elevation models and hydrological models integration for advanced flood risk management - Ahmad, Raheel; Abdul Maulud, Khairul Nizam; Bin Zamir, Umair; Mohd Razali, Siti Fatin; Yaseen, Zaher Mundher; Pradhan, Biswajeet; Khan, Mohd Nazish; Eshquvvatov, Bekzod (Geomatics, Natural Hazards and Risk, 2025)
Floods are one of the most prevalent natural disasters, and advancements in geospatial technologies have revolutionized flood management, particularly the use of Digital Elevation Models (DEMs) in hydrological modelling. However, a comprehensive analysis DEMs integration in flood risk management is lacking. This study addresses this gap through a thorough Systematic Literature Review focusing on the combined application of DEMs and hydrological models in flood mitigation and risk management. The SLR scrutinized 21 articles, revealing eight key themes: DEM data sources and characteristics, DEM integration with hydrological models, flood hazard mapping applications, terrain impact assessment, model performance evaluation, machine learning in flood management, ecosystem services and resilience, and policy and governance implications. These findings emphasize the importance of precise DEM selection and correction for successful flood modelling, highlighting Advanced Land Observing Satellite as the most effective freely available DEM for use with the HEC-RAS unsteady flood model. This integration significantly enhances flood mitigation efforts and strengthens management strategies. Finally, this study underscores the pivotal role of DEM integration in crafting effective flood mitigation strategies, especially in addressing climate change challenges and bolstering community and ecosystem resilience. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Climate-smart public health for global health resilience - Golden, Christopher D; Childs, Marissa L; Mudele, Oladimeji E; Andriamizarasoa, F Adolphe; Bouley, Timothy A; De Nicola, Giacomo; Fontaine, Max A; Huybers, Peter J; Mahatante, Paubert T; Rabemananjara, Rija; Rakotoarison, Norohasina; Ramambason, H Rakoto; et al. (The Lancet Planetary Health, 2025)
Climate change poses urgent public health risks from rising global temperatures and extreme weather events, including heatwaves, droughts, and floods, which disproportionately affect vulnerable populations. To address the current silos embedded in climate, environmental, and public health monitoring and surveillance systems, climate-smart public health (CSPH) creates an integrated platform for action across these sectors, enabling more rapid and efficient responses to climate-related public health challenges. In this Personal View, we introduce the concept of CSPH, a data-driven framework designed to monitor, assess, and adapt to climate-related health impacts. CSPH incorporates surveillance, risk assessment, early warning systems, and resilient health-care infrastructure to address the evolving challenges of climate change. The framework adopts an iterative, community-centred model that responds to local needs and incorporates feedback from health-care providers and policy makers. CSPH also leverages data science and artificial intelligence to address a wide range of health concerns, including infectious diseases, non-communicable diseases, nutrition, and mental health. We applied this framework in Madagascar, a region highly vulnerable to climate impacts, where poverty, malnutrition, and frequent extreme weather events make climate adaptation particularly urgent. Early data analysis has shown strong climate sensitivity in important diseases such as malaria and diarrhoea, which could enable preparedness efforts to target some regions more efficiently. CSPH provides a pathway to enhance resilience in such settings by improving the capacity of public health systems to withstand and respond to climate-related stressors. © 2025 The Author(s)

Role of the internet of things in flood risk management: a critical review on current practices and future directions - Bakhtiari, Vahid; Kerchi, Hamed Darabi; Piadeh, Farzad; Behzadian, Kourosh; Nasirzadeh, Farnad (Natural Hazards, 2025)
The Internet of Things (IoT) has become increasingly important in flood risk management (FRM). This trend emerged as climate change intensified flooding events, driving the urgent need for localised early warning systems. Previous studies demonstrated the effectiveness of IoT sensors in forecasting potential floods and supporting flood modelling. However, comprehensive research addressing all FRM stages - prevention, mitigation, preparedness, response, and recovery - has remained limited. To address this research gap, this study identified five key IoT sensor categories: water quantity, water quality, rainfall intensity, weather conditions, and catchment characteristics. The roles, objectives, and applications of these sensors across FRM stages were then investigated. Results showed that water quantity sensors were the most common, accounting for 48% of documented IoT applications. Weather condition sensors (27%) and rainfall intensity sensors (21%) were also widely used, especially after 2021. Additionally, IoT-based FRM had three primary Objectives flood modelling (61%), alerting (25%), and visualisation (14%). Most cases (42%) focused on the preparedness stage, while prevention (8%) and recovery (5%) were underrepresented, highlighting clear gaps in existing research. The review also revealed several overlooked sensor types, including groundwater level, biochemical oxygen demand, and nitrite sensors. Despite their potential to enhance quality-based flood modelling, these sensors were rarely utilised. Consequently, the study emphasised the need for broader integration of IoT sensors throughout all FRM stages. Such integration could support more resilient, data-driven flood management strategies, particularly in regions where IoT deployment has remained limited. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

Extreme and compound events in lakes - Woolway, R. Iestyn; Zhang, Yunlin; Jennings, Eleanor; Zohary, Tamar; Jane, Stephen F.; Jansen, Joachim; Weyhenmeyer, Gesa A.; Long, Di; Fleischmann, Ayan; Feng, Lian; Qin, Boqiang; Shi, Kun; et al. (Nature Reviews Earth and Environment, 2025)
Extreme and compound events disrupt lake ecosystems worldwide, with their frequency, intensity and duration increasing in response to climate change. In this Review we outline evidence of the occurrence, drivers and impact of extreme and compound events in lakes. Univariate extremes, which include lake heatwaves, droughts and floods, underwater dimming episodes and hypoxia, can occur concurrently, sequentially or simultaneously at different locations to form multivariate, temporal or spatial compound events, respectively. The probability of extreme and compound events is increasing owing to climate warming, declining lake water levels in half of lakes globally, and basin-scale anthropogenic stressors, such as nutrient pollution. Most in-lake extreme events are inherently compound in nature owing to tightly coupled physical, chemical and biological underlying processes. The cascading effects of compound events propagate or dissipate through lakes. For example, a heatwave might trigger stratification and oxygen depletion, subsequently leading to fish mortality or the proliferation of harmful algal blooms. Interactions between extremes are increasingly observed and can trigger feedback loops that exacerbate harmful algal blooms and fishery declines, leading to severe ecological and socio-economic consequences. Managing the increasing risk of compound events requires integrated models, coordinated monitoring and proactive adaptation strategies tailored to the vulnerabilities of lake ecosystems. © Springer Nature Limited 2025.

Solastalgies. Changements climatiques et deuils inégaux - Uhl, Magali; Niemeyer, Katharina (Frontières, 2025)
Le présent numéro de la revue Frontières se penche sur le concept et les expériences de la solastalgie en les liant aux changements climatiques et aux inégalités sociales et géographiques subséquentes devant le deuil. Par conséquent, la solastalgie se conjugue ici au pluriel pour témoigner des manières différenciées de la vivre et de la penser. Le numéro accueille des articles provenant de plusieurs pays, en sciences sociales, en humanités environnementales, en histoire de l’art ainsi qu’en études des médias et de la communication afin de nourrir un regard transdisciplinaire et international sur le sujet. Partant de corpus ou de cas d’études empiriques ou artistiques, la variété des contributions publiées souligne l’étendue actuelle des recherches sur la solastalgie et sur le deuil écologique.

L’espace vécu sur Tshitassinu : cartographie participative et représentations du territoire des jeunes Pekuakamiulnuatsh (Québec) - Hirt, Irène; Desbiens, Caroline; Boivin, Hélène; Nepton, Michel (Revue d’études autochtones, 2025)
Au Canada, les Premières Nations accordent une importance croissante aux points de vue des jeunes dans leurs projets d’autodétermination politique et territoriale. Cet article présente un atelier de cartographie participative d’une journée mis en oeuvre dans le cadre du partenariat de recherche « Tshishipiminu » (2011 à 2019) entre des géographes de l’Université Laval et Pekuakamiulnuatsh Takuhikan, autorité politique de la Nation Ilnu de Mashteuiatsh (Québec). L’atelier a été réalisé en avril 2016 avec des jeunes Pekuakamiulnuatsh de 15 et 16 ans pour documenter leurs représentations et pratiques du territoire. L’activité a mobilisé cartes et objets comme supports de discussions collectives et la production de cartes mentales comme outils d’expression des espaces vécus. L’atelier montre que si les jeunes n’ont pas tous et toutes une vision politique de leur territoire, ils et elles continuent généralement à y pratiquer les activités liées à la culture ilnu (chasse, pêche, artisanat, etc.). Les transformations coloniale et industrielle du territoire ne les empêchent pas, en outre, d’éprouver des sentiments d’appartenance à l’égard de celui-ci. Enfin, ils et elles se réapproprient des espaces d’origine coloniale, dont l’ilnu assi (la « réserve »), devenu un marqueur d’identification.

First Main Report - IPBC (2025)

1st Main Report - Volume 1 now available! - IPBC (2025)
First Main Report“Drivers of behavioral change and non change in transition times” – First MAIN REPORT – VOLUME 1 IS NOW AVAILABLE! Download the Vol.1 of First Main Report   The call for publication for this first volume that you are about to discover received an important number of proposals for chapters, with over 45 authors from […]

AI-Enhanced Coastal Flood Risk Assessment: A Real-Time Web Platform with Multi-Source Integration and Chesapeake Bay Case Study - Magoulick, Paul (Water (Switzerland), 2025)
A critical gap exists between coastal communities’ need for accessible flood risk assessment tools and the availability of sophisticated modeling, which remains limited by technical barriers and computational demands. This study introduces three key innovations through Coastal Defense Pro: (1) the first operational web-based AI ensemble for coastal flood risk assessment integrating real-time multi-agency data, (2) an automated regional calibration system that corrects systematic model biases through machine learning, and (3) browser-accessible implementation of research-grade modeling previously requiring specialized computational resources. The system combines Bayesian neural networks with optional LSTM and attention-based models, implementing automatic regional calibration and multi-source elevation consensus through a modular Python architecture. Real-time API integration achieves >99% system uptime with sub-3-second response times via intelligent caching. Validation against Hurricane Isabel (2003) demonstrates correction from 197% overprediction (6.92 m predicted vs. 2.33 m observed) to accurate prediction through automated identification of a Chesapeake Bay-specific reduction factor of 0.337. Comprehensive validation against 15 major storms (1992–2024) shows substantial improvement over standard methods (RMSE = 0.436 m vs. 2.267 m; R2 = 0.934 vs. −0.786). Economic assessment using NACCS fragility curves demonstrates 12.7-year payback periods for flood protection investments. The open-source Streamlit implementation democratizes access to research-grade risk assessment, transforming months-long specialist analyses into immediate browser-based tools without compromising scientific rigor. © 2025 by the author.
Keywords: Scopus_Alert_22_Aout_2025

Research on the Comprehensive Evaluation Model of Risk in Flood Disaster Environments - Yu, Yan; Zhou, Tianhua (Water (Switzerland), 2025)
Losses from floods and the wide range of impacts have been at the forefront of hazard-triggered disasters in China. Affected by large-scale human activities and the environmental evolution, China’s defense flood situation is undergoing significant changes. This paper constructs a comprehensive flood disaster risk assessment model through systematic analysis of four key factors—hazard (H), exposure (E), susceptibility/sensitivity (S), and disaster prevention capabilities (C)—and establishes an evaluation index system. Using the Analytic Hierarchy Process (AHP), we determined indicator weights and quantified flood risk via the following formula R = H × E × V × C. After we applied this model to 16 towns in coastal Zhejiang Province, the results reveal three distinct risk tiers: low (R < 0.04), medium (0.04 ≤ R ≤ 0.1), and high (R > 0.1). High-risk areas (e.g., Longxi and Shitang towns) are primarily constrained by natural hazards and socioeconomic vulnerability, while low-risk towns benefit from a robust disaster mitigation capacity. Risk typology analysis further classifies towns into natural, social–structural, capacity-driven, or mixed profiles, providing granular insights for targeted flood management. The spatial risk distribution offers a scientific basis for optimizing flood control planning and resource allocation in the district. © 2025 by the authors.
Keywords: Scopus_Alert_22_Aout_2025

Assessing Community Perception, Preparedness, and Adaptation to Urban Flood Risks in Malaysia - Kumaresen, Maniyammai; Teo, Fang Yenn; Selvarajoo, Anurita; Sivapalan, Subarna; Falconer, Roger A. (Water (Switzerland), 2025)
Urban flooding has significantly impacted the livelihoods of households and communities worldwide. It highlights the urgency of focusing on both flood preparedness and adaptation strategies to understand the community’s perception and adaptive capacity. This study investigates the levels of risk perception, flood preparedness, and adaptive capacity, while also exploring the inter-relationships among these factors within the context of urban flooding in Malaysia. A quantitative approach was employed, involving a structured questionnaire administered to residents in flood-prone urban areas across Greater Kuala Lumpur, Malaysia. A total of 212 responses were analysed using descriptive statistics, categorical index classification, and Spearman correlation analysis. The findings indicate that residents generally reported high levels of risk perception and preparedness, although adaptive capacity exhibited greater variability, with a mean score of 3.97 (SD = 0.64). Positive associations were found among risk perception, flood preparedness, and adaptive capacity. This study contributes to the existing knowledge by providing evidence on community resilience and highlighting key factors that can guide flood management policies and encourage adaptive planning at the community level. © 2025 by the authors.
Keywords: Scopus_Alert_22_Aout_2025

Assessing Hydropower Impacts on Flood and Drought Hazards in the Lancang–Mekong River Using CNN-LSTM Machine Learning - Zhang, Muzi; Chi, Boying; Gu, Hongbin; Zhou, Jian; Chen, Honggang; Wang, Weiwei; Wang, Yicheng; Chen, Juanjuan; Yang, Xueqian; Zhang, Xuan (Water (Switzerland), 2025)
The efficient and rational development of hydropower in the Lancang–Mekong River Basin can promote green energy transition, reduce carbon emissions, prevent and mitigate flood and drought disasters, and ensure the sustainable development of the entire basin. In this study, based on publicly available hydrometeorological observation data and satellite remote sensing monitoring data from 2001 to 2020, a machine learning model of the Lancang–Mekong Basin was developed to reconstruct the basin’s hydrological processes, and identify the occurrence patterns and influencing mechanisms of water-related hazards. The results show that, against the background of climate change, the Lancang–Mekong Basin is affected by the increasing frequency and intensity of extreme precipitation events. In particular, Rx1day, Rx5day, R10mm, and R95p (extreme precipitation indicators determined by the World Meteorological Organization’s Expert Group on Climate Change Monitoring and Extreme Climate Events) in the northwestern part of the Mekong River Basin show upward trends, with the average maximum daily rainfall increasing by 1.8 mm/year and the total extreme precipitation increasing by 18 mm/year on average. The risks of flood and drought disasters will continue to rise. The flood peak period is mainly concentrated in August and September, with the annual maximum flood peak ranging from 5600 to 8500 m3/s. The Stung Treng Station exhibits longer drought duration, greater severity, and higher peak intensity than the Chiang Saen and Pakse Stations. At the Pakse Station, climate change and hydropower development have altered the non-drought proportion by −12.50% and +15.90%, respectively. For the Chiang Saen Station, the fragmentation degree of the drought index time series under the baseline, naturalized, and hydropower development scenarios is 0.901, 1.16, and 0.775, respectively. These results indicate that hydropower development has effectively reduced the frequency of rapid drought–flood transitions within the basin, thereby alleviating pressure on drought management efforts. The regulatory role of the cascade reservoirs in the Lancang River can mitigate risks posed by climate change, weaken adverse effects, reduce flood peak flows, alleviate hydrological droughts in the dry season, and decrease flash drought–flood transitions in the basin. The research findings can enable basin managers to proactively address climate change, develop science-based technical pathways for hydropower dispatch, and formulate adaptive disaster prevention and mitigation strategies. © 2025 by the authors.
Keywords: Scopus_Alert_22_Aout_2025

The Impact of Land Use Change on Improving Surface Runoff, Peak Flood Discharge, and Sedimentation in the Maros Watershed - Mansida, Amrullah; Gaffar, Farida; Zainuddin, Muh Amir; Syamsuri, Andi Makbul (Engineering, Technology and Applied Science Research, 2025)
The present study aims to analyze how land use changes affect surface runoff, flood peak discharge, and sedimentation. Moreover, it aims to assess the specific impact of these changes on the flood peak discharge and to propose effective strategies for reducing flood risks. Visual interpretation of the land use changes was utilized based on Landsat imagery from 2010, 2017, and 2021, along with SPOT 4 satellite data. Soil samples were collected to measure the erosion rates, water discharge, and sediment loads (both suspended and bedload). The findings showed a significant reduction in the secondary dryland forest, which shrank by 52.71 km² (a 7.99% decrease), while shrub and agricultural areas expanded by 51.03 km² (a 7.73% increase). This shift contributed to a greater surface runoff and an increased erosion, especially in dryland-shrubland areas, where erosion reached 4,248.33 tons/ha/year. The flood peak discharges rose sharply in areas converted to agriculture and settlements, halving the flood return period from 50 years to just 25 years. During the wet season, the sediment loads peaked at 782.17 tons/day (equivalent to 377,293.48 m³ per year), while the dry season sedimentation—mostly driven by quarrying—reached 10.45 tons/day. To address these issues, the current study proposes adopting adaptive spatial planning, restoring the watershed, and applying nature-based solutions, such as Biopore Absorption Holes (BAH), Rainwater Infiltration Wells (RIW), and similar technologies. © by the authors.
Keywords: Scopus_Alert_22_Aout_2025

Identification of Critical Exposed Elements and Strategies for Mitigating Secondary Hazards in Flood-Induced Coal Mine Accidents - Yang, Xue; Liu, Chen; Pan, Langxuan; Su, Xiaona; He, Ke; Mao, Ziyu (Water (Switzerland), 2025)
Natech events, involving multi-hazard coupling and cascading effects, pose serious threats to coal mine safety. This paper addresses flood-induced Natech scenarios in coal mining and introduces a two-stage cascading analysis framework based on hazard systems theory. A tri-layered network—comprising natural hazards, exposed elements, and secondary hazards—models hazard propagation. In Stage 1, an improved adjacency information entropy algorithm with multi-hazard coupling coefficients identifies critical exposed elements. In Stage 2, Dijkstra’s algorithm extracts key risk transmission paths. A dual-dimensional classification method, based on entropy and transmission risk, is then applied to prioritize emergency responses. This method integrates the criticality of exposed elements with the risk levels associated with secondary disaster propagation paths. Case studies validate the framework, revealing: (1) Hierarchical heterogeneity in the network, with surface facilities and surrounding hydrological systems as central hubs; shaft and tunnel systems and surrounding geological systems are significantly affected by propagation from these core nodes, exhibiting marked instability. (2) Strong risk polarization in secondary hazard propagation, with core-node-originated paths being more efficient and urgent. (3) The entropy-risk classification enables targeted hazard control, improving efficiency. The study proposes chain-breaking strategies for precise, hierarchical, and timely emergency management, enhancing coal mine resilience to flood-induced Natech events. © 2025 by the authors.
Keywords: Scopus_Alert_22_Aout_2025

Response of Grain Yield to Extreme Precipitation in Major Grain-Producing Areas of China Against the Background of Climate Change—A Case Study of Henan Province - Sheng, Keding; Li, Rui; Zhang, Fengqiuli; Chen, Tongde; Liu, Peng; Hu, Yanan; Li, Bingyin; Song, Zhiyuan (Water (Switzerland), 2025)
Based on the panel data of daily meteorological stations and winter wheat yield in Henan Province from 2000 to 2023, this study comprehensively used the Mann–Kendall trend test, wavelet coherence analysis (WTC), and other methods to reveal the temporal and spatial evolution of extreme precipitation and its multi-scale stress mechanism on grain yield. The results showed the following: (1) Extreme precipitation showed the characteristics of ‘frequent fluctuation-gentle trend-strong spatial heterogeneity’, and the maximum daily precipitation in spring (RX1DAY) showed a significant uplift. The increase in rainstorm events (R95p/R99p) in the southern region during the summer is particularly prominent; at the same time, the number of consecutive drought days (CDDs > 15 d) in the middle of autumn was significantly prolonged. It was also found that 2010 is a significant mutation node. Since then, the synergistic effect of ‘increasing drought days–increasing rainstorm frequency’ has begun to appear, and the short-period coherence of super-strong precipitation (R99p) has risen to more than 0.8. (2) The spatial pattern of winter wheat in Henan is characterized by the three-level differentiation of ‘stable core area, sensitive transition zone and shrinking suburban area’, and the stability of winter wheat has improved but there are still local risks. (3) There is a multi-scale stress mechanism of extreme precipitation on winter wheat yield. The long-period (4–8 years) drought and flood events drive the system risk through a 1–2-year lag effect (short-period (0.5–2 years) medium rainstorm intensity directly impacted the production system). This study proposes a ‘sub-scale governance’ strategy, using a 1–2-year lag window to establish a rainstorm warning mechanism, and optimizing drainage facilities for high-risk areas of floods in the south to improve the climate resilience of the agricultural system against the background of climate change. © 2025 by the authors.
Keywords: Scopus_Alert_22_Aout_2025

Empirical Modelling of Ice-Jam Flood Hazards Along the Mackenzie River in a Changing Climate - Lindenschmidt, Karl-Erich; Gomez, Sergio; Saade, Jad; Perry, Brian; Das, Apurba (Water (Switzerland), 2025)
This study introduces a novel methodology for assessing ice-jam flood hazards along river channels. It employs empirical equations that relate non-dimensional ice-jam stage to discharge, enabling the generation of an ensemble of longitudinal profiles of ice-jam backwater levels through Monte-Carlo simulations. These simulations produce non-exceedance probability profiles, which indicate the likelihood of various flood levels occurring due to ice jams. The flood levels associated with specific return periods were validated using historical gauge records. The empirical equations require input parameters such as channel width, slope, and thalweg elevation, which were obtained from bathymetric surveys. This approach is applied to assess ice-jam flood hazards by extrapolating data from a gauged reach at Fort Simpson to an ungauged reach at Jean Marie River along the Mackenzie River in Canada’s Northwest Territories. The analysis further suggests that climate change is likely to increase the severity of ice-jam flood hazards in both reaches by the end of the century. This methodology is applicable to other cold-region rivers in Canada and northern Europe, provided similar fluvial geomorphological and hydro-meteorological data are available, making it a valuable tool for ice-jam flood risk assessment in other ungauged areas. © 2025 by the authors.
Keywords: Scopus_Alert_22_Aout_2025

Hydrodynamic and Sedimentological Impacts of Intake Gate Opening Adjustments for Flood Mitigation in Water Systems - Rahmawati; Tumpu, Miswar; Yunianta, Andung (Engineering, Technology and Applied Science Research, 2025)
Sediment management poses a significant challenge in hydraulic systems, affecting the water flow efficiency, structural durability, and operational reliability. The operation of the intake gate greatly influences the sediment characteristics, including the transport, deposition, and distribution patterns. This study investigates how different intake gate openings impact the sediment behavior in hydraulic systems to improve the operational strategies and reduce sediment-related problems. An experimental method was employed using a scaled physical model in controlled laboratory conditions, where various intake gate configurations were tested at consistent flow rates to simulate real-world hydraulic structures. Sediment samples were analyzed for grain size distribution, deposition patterns, and transport process dynamics. Data were gathered through direct measurements and video recordings, and then processed using sediment analysis software. The results showed that larger intake openings promote the sediment transport downstream and reduce the localized deposition near the intake. Conversely, smaller openings lead to sediment accumulation at the gate, increasing the risk of blockage and operational inefficiencies. Over time, these patterns evolve, potentially causing long-term sediment accumulation or channel scouring depending on the frequency and the way the gate is adjusted. Based on these findings, the study proposes adaptive, long-term sediment management approaches, including periodic gate operation adjustments, sediment flushing protocols, and comprehensive monitoring systems. These strategies aim to balance the sediment transport and deposition over extended operational periods, enhancing the performance and sustainability of hydraulic infrastructure, such as irrigation channels, reservoirs, and hydropower plants. © 2025, Dr D. Pylarinos. All rights reserved.
Keywords: Scopus_Alert_22_Aout_2025

Analysis of the Availability of Residential Locations Based on Flood Mitigation in Momunu District, Buol Regency - Assiddiqi, Moh. Hasbi; Lanuru, Mahatma; Tumpu, Miswar (Engineering, Technology and Applied Science Research, 2025)
The population growth and limited land availability for housing have forced some communities to reside in disaster-prone areas, particularly those vulnerable to flooding. This study, presents a spatial-based assessment that integrates physical and regulatory criteria to identify safe and appropriate residential zones. Using spatial analysis through map overlays, scoring, and weighting techniques, the research evaluates seven key physical variables: land slope, drainage, erosion, land use, road accessibility, access to essential facilities, and flood hazard vulnerability. The novelty of this study lies in the integration of flood mitigation into residential suitability mapping that is aligned with the Regional Spatial Plan (RTRW), offering a comprehensive and policy-relevant framework. The findings reveal that 20.85% of the study area is suitable for housing, 61.83% is conditionally suitable, and 17.32% is unsuitable. Based on the land availability and RTRW compliance, residential land is categorized into Available Location I (47 hectares) and Available Location II (423 hectares). These results provide not only a technical basis for guiding safe settlement, but also a strategic reference for planners and policymakers. The study proposes that future research incorporates socio-economic variables and real-time flood data for dynamic risk assessment. Furthermore, stakeholder engagement and community-based mapping are proposed to enhance the local resilience and ensure participatory planning. Ultimately, this research contributes to sustainable urban development by supporting informed decision-making for safer, flood-resilient settlements. © by the authors.
Keywords: Scopus_Alert_22_Aout_2025

Assessing Flooding from Changes in Extreme Rainfall: Using the Design Rainfall Approach in Hydrologic Modeling - Jalowska, Anna M.; Line, Daniel E.; Spero, Tanya L.; Kurki-Fox, J. Jack; Doll, Barbara A.; Bowden, Jared H.; Gray, Geneva M. E. (Water (Switzerland), 2025)
Quantifying future changes in extreme events and associated flooding is challenging yet fundamental for stormwater managers. Along the U.S. Atlantic Coast, Eastern North Carolina (ENC) is frequently exposed to catastrophic floods from extreme rainfall that is typically associated with tropical cyclones. This study presents a novel approach that uses rainfall data from five dynamically and statistically downscaled (DD and SD) global climate models under two scenarios to visualize a potential future extent of flooding in ENC. Here, we use DD data (at 36-km grid spacing) to compute future changes in precipitation intensity–duration–frequency (PIDF) curves at the end of the 21st century. These PIDF curves are further applied to observed rainfall from Hurricane Matthew—a landfalling storm that created widespread flooding across ENC in 2016—to project versions of “Matthew 2100” that reflect changes in extreme precipitation under those scenarios. Each Matthew-2100 rainfall distribution was then used in hydrologic models (HEC-HMS and HEC-RAS) to simulate “2100” discharges and flooding extents in the Neuse River Basin (4686 km2) in ENC. The results show that DD datasets better represented historical changes in extreme rainfall than SD datasets. The projected changes in ENC rainfall (up to 112%) exceed values published for the U.S. but do not exceed historical values. The peak discharges for Matthew-2100 could increase by 23–69%, with 0.4–3 m increases in water surface elevation and 8–57% increases in flooded area. The projected increases in flooding would threaten people, ecosystems, agriculture, infrastructure, and the economy throughout ENC. © 2025 by the authors.
Keywords: Scopus_Alert_22_Aout_2025

Machine learning-based ensemble of Global climate models and trend analysis for projecting extreme precipitation indices under future climate scenarios - Kumar, G. Praveen; Dwarakish, G.S. (Environmental Monitoring and Assessment, 2025)
Monitoring changes in climatic extremes is vital, as they influence current and future climate while significantly impacting ecosystems and society. This study examines trends in extreme precipitation indices over an Indian tropical river basin, analyzing and ranking 28 Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs) based on their performance against India Meteorological Department (IMD) data. The top five performing GCMs were selected to construct multi-model ensembles (MMEs) using Machine Learning (ML) algorithms, Random Forest (RF), Support Vector Machine (SVM), Multiple Linear Regression (MLR), and the Arithmetic Mean. Statistical metrics reveal that the application of an RF model for ensembling performs better than other models. The analysis focused on six IMD-convention indices and eight indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI). Future projections were examined for three timeframes: near future (2025–2050), mid-future (2051–2075), and far future (2076–2100) for SSP245 and SSP585 scenarios. Statistical trend analysis, the Mann-Kendall test, Sen’s Slope estimator, and Innovative Trend Analysis (ITA), were applied to the MME to assess variability and detect changes in extreme precipitation trends. Compared to SSP245, in the SSP585 scenario, Total Precipitation (PRCPTOT) shows a significant decreasing trend in the near future, mid-future, and far future and Moderate Rain (MR) shows a decreasing trend in the near future and far future of monsoon season. The findings reveal significant future trends in extreme precipitation, impacting Sustainable Development Goals (SDGs) achievement and providing crucial insights for sustainable water resource management and policy planning in the Kali River basin. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.
Keywords: Scopus_Alert_22_Aout_2025

Multi-Sensor Flood Mapping in Urban and Agricultural Landscapes of the Netherlands Using SAR and Optical Data with Random Forest Classifier - Narin, Omer Gokberk; Sekertekin, Aliihsan; Bayik, Caglar; Bektas Balcik, Filiz; Arıkan, Mahmut; Sanli, Fusun Balik; Abdikan, Saygin (Remote Sensing, 2025)
Floods stand as one of the most harmful natural disasters, which have become more dangerous because of climate change effects on urban structures and agricultural fields. This research presents a comprehensive flood mapping approach that combines multi-sensor satellite data with a machine learning method to evaluate the July 2021 flood in the Netherlands. The research developed 25 different feature scenarios through the combination of Sentinel-1, Landsat-8, and Radarsat-2 imagery data by using backscattering coefficients together with optical Normalized Difference Water Index (NDWI) and Hue, Saturation, and Value (HSV) images and Synthetic Aperture Radar (SAR)-derived Grey Level Co-occurrence Matrix (GLCM) texture features. The Random Forest (RF) classifier was optimized before its application based on two different flood-prone regions, which included Zutphen’s urban area and Heijen’s agricultural land. Results demonstrated that the multi-sensor fusion scenarios (S18, S20, and S25) achieved the highest classification performance, with overall accuracy reaching 96.4% (Kappa = 0.906–0.949) in Zutphen and 87.5% (Kappa = 0.754–0.833) in Heijen. For the flood class F1 scores of all scenarios, they varied from 0.742 to 0.969 in Zutphen and from 0.626 to 0.969 in Heijen. Eventually, the addition of SAR texture metrics enhanced flood boundary identification throughout both urban and agricultural settings. Radarsat-2 provided limited benefits to the overall results, since Sentinel-1 and Landsat-8 data proved more effective despite being freely available. This study demonstrates that using SAR and optical features together with texture information creates a powerful and expandable flood mapping system, and RF classification performs well in diverse landscape settings. © 2025 by the authors.
Keywords: Scopus_Alert_22_Aout_2025

Predictive Flood Uncertainty Associated with the Overtopping Rates of Vertical Seawall on Coral Reef Topography - Zhang, Hongqian; Lu, Bin; Geng, Yumei; Liu, Ye (Water (Switzerland), 2025)
Accurate prediction of wave overtopping rates is essential for flood risk assessment along coral reef coastlines. This study quantifies the uncertainty sources affecting overtopping rates for vertical seawalls on reef flats, using ensemble simulations with a validated non-hydrostatic SWASH model. By generating extensive random wave sequences, we identify spectral resolution, wave spectral width, and wave groupiness as the dominant controls on the uncertainty. Statistical metrics, including the Coefficient of Variation ((Formula presented.)) and Range Uncertainty Level ((Formula presented.)), demonstrate that overtopping rates exhibit substantial variability under randomized wave conditions, with (Formula presented.) exceeding 40% for low spectral resolutions (50–100 bins), while achieving statistical convergence ((Formula presented.) around 20%) requires at least 700 frequency bins, far surpassing conventional standards. The (Formula presented.), which describes the ratio of extreme to minimal overtopping rates, also decreases markedly as the number of frequency bins increases from 50 to 700. It is found that the overtopping rate follows a normal distribution with 700 frequency bins in wave generation. Simulations further demonstrate that overtopping rates increase by a factor of 2–4 as the JONSWAP spectrum peak enhancement factor ((Formula presented.)) increases from 1 to 7. The wave groupiness factor ((Formula presented.)) emerges as a predictor of overtopping variability, enabling a more efficient experimental design through reduction in groupiness-guided replication. These findings establish practical thresholds for experimental design and highlight the critical role of spectral parameters in hazard assessment. © 2025 by the authors.
Keywords: Scopus_Alert_22_Aout_2025

Innovative Approaches to Flood Hazard Assessment in Semi-Arid Environments: A Comparative Analysis of Multi-Criteria and Geospatial Techniques - Chomani, Kaifi; Al-Shrafany, Dleen M. (Iraqi Geological Journal, 2025)
Flood intensity has significantly increased globally, which poses significant challenges for the environment and urban areas. This research aimed to evaluate the performance of different flood hazard assessment approaches to identify flood risk areas in Erbil city, Kurdistan region of Iraq. The analytical hierarchy process and the new ArcGIS pro flood simulation tool were compared to identify flood-risk areas and assess their performance based on historical flood events. Multiple factors such as rainfall, aspect, topographic wetness index, elevation, flow accumulation, lithology, soil data, normalized difference vegetation index, normalized difference built-up index, land use land cover, slope, stream power index, drainage density, evapotranspiration, infiltration rates, and distance to roads were considered to identify flood risk areas. Using the Analytical Hierarchy Process, areas spanning over 35 km² (3.9%) and 74 km² (27%) of Erbil city were identified as very high and high flood susceptible, respectively. However, the results of AFS indicated that an area of 66.3 km² (7.3%) of Erbil city will be inundated during rainfall intensity of 60 mm/day. The receiver operating characteristic area under the curve assessments showed the accuracy of AFS to be 95.3% and that of the Analytical Hierarchy Process to be 92.2%. The comparison analysis emphasized the effectiveness of ArcGIS Pro flood simulation in terms of accurate flood inundation assessments. This research provides significant insights into suitable approaches to flood hazard assessment by considering different scales and data availability, helping policymakers and urban planners understand floods better and implement appropriate mitigation strategies accordingly. © 2025, Union of Iraqi Geologists. All rights reserved.
Keywords: Scopus_Alert_22_Aout_2025

Toward optimal rainfall for flood prediction in headwater basins – improving soil moisture initialization to close the water budget within observational uncertainty - Liao, Mochi; Barros, Ana P. (Journal of Hydrology: Regional Studies, 2025)
Keywords: Scopus_Alert_22_Aout_2025

A multi-hazard assessment approach to reveal and compare heat- and flood-related risks and inequalities in London, Boston, and Sydney - Longato, Davide; Maragno, Denis (International Journal of Disaster Risk Reduction, 2025)
Keywords: Scopus_Alert_22_Aout_2025

Floods, land loss, and gendered vulnerability: Assessing the social impacts of small hydropower in Vietnam - Swe, Khin Nilar; Phuong, Ho Thi; Thinh, Tran Duc; Chan, Nyien (Environmental Development, 2025)
Keywords: Scopus_Alert_22_Aout_2025

Pervious concrete pavements for climate change adaptation and mitigation: a systematic review of performance, sustainability, and future perspectives - Wu, Yinglong; López-Carreño, Rubén-Daniel; Aidarov, Stanislav; Álvarez, Pablo Pujadas (Road Materials and Pavement Design, 2025)
Keywords: Scopus_Alert_22_Aout_2025

Hydrology and Climate Change in Africa: Contemporary Challenges, and Future Resilience Pathways - Adeyeri, Oluwafemi E. (Water, 2025)
African hydrological systems are incredibly complex and highly sensitive to climate variability. This review synthesizes observational data, remote sensing, and climate modeling to understand the interactions between fluvial processes, water cycle dynamics, and anthropogenic pressures. Currently, these systems are experiencing accelerating warming (+0.3 °C/decade), leading to more intense hydrological extremes and regionally varied responses. For example, East Africa has shown reversed temperature–moisture correlations since the Holocene onset, while West African rivers demonstrate nonlinear runoff sensitivity (a threefold reduction per unit decline in rainfall). Land-use and land-cover changes (LULCC) are as impactful as climate change, with analysis from 1959–2014 revealing extensive conversion of primary non-forest land and a more than sixfold increase in the intensity of pastureland expansion by the early 21st century. Future projections, exemplified by studies in basins like Ethiopia’s Gilgel Gibe and Ghana’s Vea, indicate escalating aridity with significant reductions in surface runoff and groundwater recharge, increasing aquifer stress. These findings underscore the need for integrated adaptation strategies that leverage remote sensing, nature-based solutions, and transboundary governance to build resilient water futures across Africa’s diverse basins.
Keywords: Scopus_Alert_22_Aout_2025

Current Research on Quantifying Cotton Yield Responses to Waterlogging Stress: Indicators and Yield Vulnerability - Qian, Long; Luo, Yunying; Duan, Kai (Plants, 2025)
Cotton (Gossypium spp.) is an important industrial crop, but it is vulnerable to waterlogging stress. The relationship between cotton yields and waterlogging indicators (CY-WI) is fundamental for waterlogging disaster reduction. This review systematically summarized and analyzed literature containing CY-WI relations across 1970s–2020s. China conducted the most CY-WI experiments (67%), followed by Australia (17%). Recent decades (2010s, 2000s) contributed the highest proportion of CY-WI works (49%, 15%). Surface waterlogging form is mostly employed (74%) much more than sub-surface waterlogging. The flowering and boll-forming stage, followed by the budding stage, performed the most CY-WI experiments (55%), and they showed stronger negative relations of CY-WI than other stages. Some compound stresses enhance negative relations of CY-WI, such as accompanying high temperatures, low temperatures, and shade conditions, whereas some others weaken the negative CY-WI relations, such as prior/post drought and waterlogging. Anti-waterlogging applications significantly weaken negative CY-WI relations. Regional-scale CY-WI research is increasing now, and they verified the influence of compound stresses. In future CI-WI works, we should emphasize the influence of compound stresses, establish regional CY-WI relations regarding cotton growth features, examine more updated cotton cultivars, focus on initial and late cotton stages, and explore the consequence of high-deep submergence.
Keywords: Scopus_Alert_22_Aout_2025

Nature-Based Solutions for Water Management in Europe: What Works, What Does Not, and What’s Next? - Santos, Eleonora (Water, 2025)
Nature-based solutions (NbS) are increasingly recognized as strategic alternatives and complements to grey infrastructure for addressing water-related challenges in the context of climate change, urbanization, and biodiversity decline. This article presents a critical, theory-informed review of the state of NbS implementation in European water management, drawing on a structured synthesis of empirical evidence from regional case studies and policy frameworks. The analysis found that while NbS are effective in reducing surface runoff, mitigating floods, and improving water quality under low- to moderate-intensity events, their performance remains uncertain under extreme climate scenarios. Key gaps identified include the lack of long-term monitoring data, limited assessment of NbS under future climate conditions, and weak integration into mainstream planning and financing systems. Existing evaluation frameworks are critiqued for treating NbS as static interventions, overlooking their ecological dynamics and temporal variability. In response, a dynamic, climate-resilient assessment model is proposed—grounded in systems thinking, backcasting, and participatory scenario planning—to evaluate NbS adaptively. Emerging innovations, such as hybrid green–grey infrastructure, adaptive governance models, and novel financing mechanisms, are highlighted as key enablers for scaling NbS. The article contributes to the scientific literature by bridging theoretical and empirical insights, offering region-specific findings and recommendations based on a comparative analysis across diverse European contexts. These findings provide conceptual and methodological tools to better design, evaluate, and scale NbS for transformative, equitable, and climate-resilient water governance.
Keywords: Scopus_Alert_22_Aout_2025

The Paradigm Shift in Scientific Interest on Flood Risk: From Hydraulic Analysis to Integrated Land Use Planning Approaches - Franco, Ángela; García-Ayllón, Salvador (Water, 2025)
Floods are natural hazards that have the greatest socioeconomic impact worldwide, given that 23% of the global population live in urban areas at risk of flooding. In this field of research, the analysis of flood risk has traditionally been studied based mainly on approaches specific to civil engineering such as hydraulics and hydrology. However, these patterns of approaching the problem in research seem to be changing in recent years. During the last few years, a growing trend has been observed towards the use of methodology-based approaches oriented towards urban planning and land use management. In this context, this study analyzes the evolution of these research patterns in the field by developing a bibliometric meta-analysis of 2694 scientific publications on this topic published in recent decades. Evaluating keyword co-occurrence using VOSviewer software version 1.6.20, we analyzed how phenomena such as climate change have modified the way of addressing the study of this problem, giving growing weight to the use of integrated approaches improving territorial planning or implementing adaptive strategies, as opposed to the more traditional vision of previous decades, which only focused on the construction of hydraulic infrastructures for flood control.
Keywords: Scopus_Alert_22_Aout_2025

Exploring Waterlogging Challenges, Causes and Mitigating Strategies in Maize ( Zea mays L.) - Behera, Supriya; Kar, Ipsita; Yadav, Ashok; Sahu, Ankita (Journal of Agronomy and Crop Science, 2025)
ABSTRACT Waterlogging is a critical abiotic stress factor severely affecting maize, one of the World's most widely cultivated cereal crops. Globally, maize is a crucial food crop, grown in diverse agro‐climatic zones, from subtropical to temperate climates. Waterlogging, resulting from flooding, intense rainfall and inefficient drainage systems, continues to be a major abiotic stress factor influencing crop productivity globally. Prolonged exposure to excess soil moisture leads to oxygen depletion in the root zone, resulting in restricted aerobic respiration, impaired nutrient uptake and disruption of physiological processes. This review aims to provide a comprehensive overview of the morphological, physiological and biochemical changes maize undergoes in response to waterlogging stress. Key aspects such as root system adaptation, reduction in photosynthetic efficiency, accumulation of reactive oxygen species (ROS) and hormonal imbalances are systematically examined. Furthermore, we delve into the metabolic shifts that enable maize to survive under anaerobic conditions, including alterations in energy metabolism, carbohydrate partitioning, and activating antioxidant defence mechanisms. The role of key signalling molecules such as ethylene is explored, highlighting their involvement in regulating stress responses. Additionally, the review discusses agronomic and genetic approaches for improving waterlogging tolerance in maize, including the development of stress‐resilient cultivars through breeding and biotechnological interventions. By synthesising recent advances in understanding maize's response to waterlogging, this paper identifies gaps and proposes future research directions, focusing on the integration of molecular and field‐based strategies. The insights from this review are crucial for developing sustainable agricultural practices aimed at mitigating the adverse impacts of waterlogging on maize productivity, particularly in flood‐prone areas. Breeding for waterlogging resilience integrates the creation of robust varieties, plant morphology optimisation, and utilisation of tolerant secondary traits through combined conventional and biotechnological breeding strategies.
Keywords: Scopus_Alert_22_Aout_2025

Hydrological Analysis and Impacts of Natural Flood Management Strategies: A Systematic Review - Bagheri‐Gavkosh, Mehdi; Panici, Diego; Puttock, Alan; Dauben, Tom; Brazier, Richard E. (Journal of Flood Risk Management, 2025)
ABSTRACT Natural flood management strategies (NFMs) encompass a variety of measures implemented across catchments to mitigate flood risks while providing multiple benefits. In recent years, NFMs have gained increasing attention from researchers and policymakers. However, despite the growing body of research, there remains a lack of a critical review that quantitatively synthesises the reported performance of different NFMs by analysing their effects on key hydrological parameters. To address this gap, we conducted a systematic review of NFMs based on 145 peer‐reviewed papers covering 216 case studies across 37 countries, following the preferred reporting items for systematic reviews and meta‐analyses (PRISMA) guidelines. Our analysis moves from a descriptive overview of the evidence base to a novel, quantitative investigation of three critical themes: the characteristics of studied NFM schemes, the methodologies used for their assessment, and their quantitative hydrological performance and its influencing factors. Results indicate that 31% of the studies identified flood peak reduction as the most commonly targeted hydrological objective. A significant positive correlation was found between intervention diversity and intensity (Spearman's ρ = 0.53). Furthermore, our methodological analysis reveals a critical trade‐off in the literature, with empirical monitoring typically used in small catchments over shorter durations, while modelling is used to assess a greater diversity of interventions at larger scales, with truly combined approaches being notably rare (11%). Notably, river and floodplain management (RFM) demonstrated higher effectiveness, achieving an average flood peak reduction of 30%, particularly in larger catchments. Bearing the often multi‐faceted aims of NFMs in mind, this paper provides key suggestions for future research.
Keywords: Scopus_Alert_22_Aout_2025

Geoinformatics-based watershed-level flood hazard analysis using fuzzy analytic hierarchy process across the Ganga–Brahmaputra basin - Kaushik, Kavita; Pandey, Arvind Chandra; Parida, Bikash Ranjan; Dwivedi, Chandra Shekhar (Journal of Earth System Science, 2025)
The flood disasters are prevalent in the Ganga–Brahmaputra (GB) basin with recurrent occurrences and severe impacts across the major watersheds. The present study analyses the vulnerability of 44 watersheds to flood inundation and its impact on cropland, urban areas, and population. The Sentinel-1 dataset was utilised to analyse flood extent and frequency from 2015 to 2022, enabling the identification of flood-prone watersheds in the Ganga–Brahmaputra basin. The analysis revealed that 7 watersheds in the Ganga basin and 12 watersheds in the Brahmaputra basin are particularly vulnerable to flooding. The flood hazard analysis was performed using fuzzy-AHP (Analytic Hierarchy Process), focusing on six parameters, including topographic wetness index (TWI), elevation, precipitation, drainage density, distance from river, and NDVI for the selected 19 watersheds. The inundation analysis from 2015 to 2022 revealed that the maximum flood extent was observed in 2020, with an affected area of 33,537.6 km2 and 34,937.9 km2 in the Ganga–Brahmaputra basin, respectively. The flood hazard analysis identified Upper Ganga (8877.52 km2), Ghaghara (18573.9 km2) and Teesta (1543.06 km2) as having the highest proportion of their geographical area under very high-hazard zone and the highest percentage in the very low hazard zones were observed in Jamuneshwary (1093.55 km2), Atreyee (4410.42 km2), and Kulsi (1273.89 km2). By first mapping these watersheds with precision and then using various parameters for flood hazard analysis, it ensures accurate identification of flood-prone areas, offering valuable insights for flood management and mitigation in a critical region. © Indian Academy of Sciences 2025.
Keywords: Scopus_Alert_15_Aout_2025

Quantifying the spatiotemporal dynamics of urban flooding susceptibility in the greater bay area under shared socio-economic pathways using the SD-PLUS-LightGBM framework - Zhou, Shiqi; Jia, Weiyi; Geng, Xiwen; Xu, Haowen; Diao, Haifeng; Liu, Zhiyu; Wang, Mo; Fu, Xiaoping; Wu, Yuwei; Qiao, Renlu; Wu, Zhiqiang (Resources, Conservation and Recycling, 2025)
Urbanization and climate change keep intensifying extreme rainfall events. Previous studies have explored urban flood susceptibility, yet a comprehensive approach that unifies these perspectives has remained underdeveloped. This study established a holistic framework using the SD-PLUS-LightGBM model with multiple variables under three SSP-RCP scenarios to predict spatial-temporal dynamics of flood susceptibility in the Greater Bay Area between 2030 and 2050. Compared with traditional models, LightGBM established superior predictive accuracy and operational reliability for urban flood susceptibility mapping. The results indicated a non-linear expansion of high-susceptibility zones, with SSP5–8.5 projections showing a two-fold increase in vulnerable areas by 2050 relative to 2020 baselines. Regions experiencing pronounced susceptibility transitions were expected to grow significantly (0.23 % of the total area), concentrated in historic urban cores and peri‑urban interfaces. This study offered an in-depth approach to stormwater management along with targeted recommendations for sustainable urban planning and design. © 2025
Keywords: Scopus_Alert_15_Aout_2025

Deep learning-enhanced flood damage prediction: A DFNN-based hybrid approach with simplified inputs - Mok, Ji-Yoon; Moon, Hyeon-Tae; Kim, Gil-Ho; Kim, Kyung-Tak; Moon, Young-Il (International Journal of Disaster Risk Reduction, 2025)
This study proposes a hybrid urban flood damage prediction framework that integrates a Deep Feed-Forward Neural Network (DFNN) with a Rainfall-Runoff (R-R) model and the Korean Flood Risk Assessment Model (K-FRM). The model predicts 10 types of flood risk indicators (FRIs), including damage to residential and non-residential buildings, using only simplified rainfall variables (SRVs), eliminating the need for complex hydrodynamic simulations. Synthetic rainfall scenarios were generated for training and fed into the R-R model, whose outputs were processed through K-FRM to produce training data for the DFNN model. The optimized DFNN model was validated by comparing its predictions with flood damage estimates from K-FRM, demonstrating a Nash-Sutcliffe Efficiency (NSE) of up to 0.87 and an R2 of up to 0.88, indicating strong predictive performance across flood risk indicators. These results highlight the effectiveness of the DFNN-based hybrid approach in capturing flood damage patterns and providing rapid predictions using forecasted rainfall data. The proposed method offers a practical and computationally efficient tool for urban flood risk management and disaster mitigation planning. © 2025 The Authors
Keywords: Scopus_Alert_15_Aout_2025

Seasonal Influences of Precipitation and River Stage on Groundwater Levels in Platte River Watersheds Vulnerable to Spring Floods - Kim, Incheol; Koya, Sinan Rasiya; Roy, Tirthankar; Eun, Jongwan (Journal of Hydrologic Engineering, 2025)
Spring floods occurring in the Midwest US are often aggravated by meteorological and hydrological conditions. In this study, the seasonal influences of precipitation (PRCP) and river stage (RS) on groundwater level (GWL) fluctuations were analyzed for the Middle and Lower Platte watersheds along the Platte River in a Midwestern catchment that is vulnerable to spring floods. Statistical analysis was conducted to simulate GWL with a moving average-based time lag consideration approach by using multiple hydrological data sets for 25 study sites. The results showed that the time lag consideration approach appropriately provided the regional information of water infiltration characteristics among GWL, PRCP, and RS for each study site. Also, the correlations of GWL with PRCP and RS were found to vary depending on the season. Especially in the early spring season, the correlation of GWL with PRCP is very weak (correlation coefficient=0.001 to 0.198). This may be due to entirely or partially frozen ground, which prevents rainwater from penetrating into the aquifer, causing large amounts of runoff and contributing to frequent flooding in early spring. In addition, statistical analysis showed that accounting for the time lag of PRCP and RS improved GWL simulation performance, and their influence varied by season. © 2025 American Society of Civil Engineers.
Keywords: Scopus_Alert_15_Aout_2025

Comparative assessment of flood risk to critical infrastructure: The case of Utah, USA - Clark, Austin S.; Collins, Tim (International Journal of Disaster Risk Reduction, 2025)
During and after a disaster, selected services and systems are needed to recover and maintain important functions of society. These are deemed critical infrastructure (CI). When these services are disrupted due to the impacts of a disaster, response and recovery may be slowed or halted. As flooding events are occurring more often across larger geographic extents, advancing methods for assessing risks of flooding to CI is vital. We use Utah, USA as a case study to demonstrate a novel, transferable approach for assessing fine-scale flood risks to CI across large geographic areas. Specifically, our assessment approach integrates high-resolution building footprints of schools, first responder facilities, and hospitals, and flood risk maps from a state-of-the-art big data flood model and the U.S. Federal Emergency Management Agency (FEMA). We show that 94 CI facilities across Utah are at risk of severe flooding, and that those risks to CI are almost entirely overlooked by FEMA flood risk maps. Though nearly every CI building is located outside of FEMA flood zones, FEMA maps inaccurately and incompletely represent flood risks, indicating that future flood risk assessment approaches should use flood risk maps from other sources. The approach we introduce can be used to assess flood risks to CI elsewhere, and case study results can be applied to inform flood risk reduction efforts in Utah. © 2025 Elsevier Ltd
Keywords: Scopus_Alert_15_Aout_2025

Identification of flood vulnerability areas using analytical hierarchy process techniques in the Wuseta watershed, Upper Blue Nile Basin, Ethiopia - Mulu, Arega; Kassa, Samuel Berihun; Wossene, Mindesilew Lakew; Adefris, Sintayehu; Meshesha, Taye Minchil (Scientific Reports, 2025)
Flooding, caused by the excessive accumulation of water on land, disrupts activities in floodplain regions, particularly during the rainy season. The main objective is to map Flood vulnerability areas and identify regions most vulnerable to flooding to inform effective flood management strategies using an integrated approach that combines remote sensing, geographic information systems (GIS), and the analytical hierarchy process (AHP) to assess Flood vulnerability in the Wuseta Watershed. The research was conducted in three phases: pre-fieldwork, fieldwork, and post-fieldwork. Key factors influencing Flood vulnerability such as drainage density, elevation, land use/land cover, and slope were hierarchically weighted to produce a Flood vulnerability map. Rainfall distribution was not considered as a contributing factor the Ethiopian Meteorological Agency has installed only one weather station in the study area, located in Wuseta watershed. As a result, the rainfall distribution is considered uniform throughout the watershed, making it unsuitable for flood susceptibility assessment. The Flood vulnerability map categorizes the watershed into five zones: very high (0.07 km2), high (4.65 km2), moderate (7.86 km2), slight (4.41 km2), and very slight (0.001 km2). The results show that the upstream, northern, northwestern, and northeastern areas of the watershed face slight to very slight Flood vulnerability, while the southern region is highly vulnerable to flooding. These findings provide valuable insights for policymakers and local communities, aiding in the development of targeted mitigation strategies and raising awareness of flood-prone areas. This study underscores the value of integrating geospatial technologies and multi-criteria decision analysis in flood risk assessment, particularly in data-scarce regions, to enhance disaster preparedness and climate resilience. © The Author(s) 2025.
Keywords: Scopus_Alert_15_Aout_2025

Flood risk mapping in an urbanized tropical river basin in India using MCDA-AHP: a post-storm event evaluation - Ajin, Rajendran Shobha; Senan, Chandini Padmanabha Panicker Chandra; Devi, B. R. Anaswara; Costache, Romulus; Nagar, Jitendra K.; Rajaneesh, A.; Sajinkumar, K.S. (Smart Construction and Sustainable Cities, 2025)
Flooding is a persistent hazard in tropical regions of India, primarily driven by intense precipitation and further aggravated by anthropogenic activities. Despite ongoing efforts, a gap persists in the development of comprehensive risk models that integrate hazard, vulnerability, and exposure components at a watershed level. This research seeks to bridge that gap by implementing a multi-criteria decision analysis (MCDA) technique, specifically the Analytical Hierarchy Process (AHP), to generate a risk map for the tropical Meenachil River Basin (MRB), originating in the Western Ghats, southwest India. Nine conditioning factors (CFs) were evaluated to assess hazard, and the resulting hazard layer was integrated with vulnerability data and different exposure factors (EFs), such as built-up height, built-up surface, built-up volume, population, and total exposure, to produce a risk map. Validation of the hazard model utilizing the Receiver Operating Characteristic (ROC) curve achieved an excellent Area Under Curve (AUC) of 0.825, along with high accuracy (0.818), F1-score (0.802), precision (0.812), and recall (0.793). Approximately 11% of the MRB lies in a very high hazard zone and 1.51% in a very high risk zone. These results advocate for sustainable flood management by identifying key risk zones, thereby facilitating the implementation of focused site-specific mitigation strategies. © The Author(s) 2025.
Keywords: Scopus_Alert_15_Aout_2025

A holistic methodology for evaluating flood vulnerability, generating flood risk map and conducting detailed flood inundation assessment - Devi, Kamalini; Reddy, Chundi Chenna; Rahul, Kandakatla; Khuntia, Jnana Ranjan; Das, Bhabani Shankar (Scientific Reports, 2025)
Flood risk assessment (FRA) is a process of evaluating potential flood damage by considering vulnerability of exposed elements and consequences of flood events through risk analysis which recommends the mitigation measures to reduce the impact of floods. This flood risk analysis is a technique used to identify and rank the level of flood risk through modeling and spatial analysis. In the present study, Musi River in the Osmansagar basin is taken in to consideration to evaluate the flood risk, which is located at Hyderabad. The input data collected for the study encompasses Hydrological and Meteorological datasets from Gandipet Guage station in Hyderabad, raster grid data for Osmansagar basin along with several indicators data influencing flood vulnerability. The primary research objective is to conduct a quantitative assessment of the Flood vulnerability index (FVI), to develop a comprehensive flood risk map and to evaluate the magnitude of damaging flood parameters, inundated volume and to analyze the regions inundated in the study area. In risk analysis, FVI determines the degree of which an area is susceptible to the negative impact of flood through various influencing indicators, Flood hazard map segregate the regions based on flood risk level through spatial analysis in Arc-GIS. A part of this study includes an integrated methodology for assessing flood inundation using Quantum Geographic Information Systems (QGIS) data modelling for spatial analysis, Hydraulic Engineering Center’s River Analysis System (HEC-RAS) hydraulic modelling for unsteady flow analysis and a machine learning technique i.e. XGBoost, to enhance the accuracy and efficiency of flood risk assessment. Subsequently, inundation map produced using HEC-RAS is superimposed with building footprints to identify vulnerable structures. The results obtained by risk analysis using hydraulic modeling, GIS analysis, and machine learning technique illustrates the flood vulnerability, areas having high flood risk and inundated volume along with predicted flood levels for next 10 years. These findings demonstrate the efficiency of the holistic approach in identifying vulnerability, flood-prone areas and evaluating potential impacts on infrastructure and communities. The outcomes of the study assist the decision-makers to gain valuable insights into flood risk management strategies. © The Author(s) 2025.
Keywords: Scopus_Alert_15_Aout_2025

Impact of climate change scenario on sea level rise and future coastal flooding in major coastal cities of India - Chakrabortty, Rabin; Ali, Tarig; Atabay, Serter; Roy, Paramita; Pande, Chaitanya Baliram (Scientific Reports, 2025)
This study evaluates the impacts of projected sea level rise (SLR) on coastal flooding across major Indian cities: Mumbai, Kolkata, Chennai, Visakhapatnam, Surat, Kochi, Thiruvananthapuram, and Mangaluru. Machine learning models, including Long Short-Term Memory (LSTM), Random Forest (RF), and Gradient Boosting (GB), has been employed to assess flood risks under four Shared Socioeconomic Pathways (SSP 126, 245, 370, and 585) emission scenarios. The research utilized these models because they demonstrate high performance in handling difficult data relationships and both temporal patterns and sophisticated environmental data. SLR projections provided by computers generate forecasts that combine with digital elevation models (DEMs) to determine coastal flooding risks and locate flood-prone areas. Results reveal that Mumbai and Kolkata face the highest flood risks, particularly under high emission scenarios, while Kochi and Mangaluru exhibit moderate exposure. Model performance is validated using residual analysis and Receiver Operating Characteristic (ROC) curves, confirming reliable predictive accuracy. These findings provide essential information for urban planners and policymakers to prioritize climate adaptation strategies in vulnerable coastal cities. © The Author(s) 2025.
Keywords: Scopus_Alert_15_Aout_2025

Multi-Criteria Decision Analysis for Managed Aquifer Recharge (MAR): A Flood-Responsive Approach in Milpa Alta, Mexico City - Areu-Rangel, Omar S.; Singh, Amrie; Bonasia, Rosanna (Environmental Quality Management, 2025)
Milpa Alta, located southeast of Mexico City, is a key region for environmental sustainability due to its volcanic soil, biodiversity, and critical role in aquifer recharge, which supports the city's water supply. However, rapid urbanization has severely impacted the area, causing reduced vegetation cover, increased runoff, and diminished groundwater recharge, which intensify flooding, soil erosion, and water scarcity. This study aims to identify optimal sites for managed aquifer recharge (MAR) structures in Milpa Alta through a multi-criteria analysis incorporating criteria such as topography, land use, proximity to urban areas, and drainage networks. Uniquely, hydraulic simulations of flood scenarios were integrated into the analysis to improve the precision of site selection. Geographic information systems (GIS) were used to assess and combine these criteria, providing a spatial evaluation of suitability. Results indicate that the central and northern regions of Milpa Alta, particularly around San Francisco Tecoxpa and San Antonio Tecómitl, are most suitable for MAR implementation due to their permeable soils, gentle slopes, and proximity to agricultural lands and drainage networks. These MAR structures can enhance groundwater recharge and mitigate flood risks during extreme rainfall events, with the potential to capture up to 300,000 m3 of surface runoff during a single high-intensity storm event. Despite its strengths, the study acknowledges limitations such as the absence of detailed water quality analyses and the need for sensitivity testing of the criteria weighting. This research provides an innovative approach to MAR site selection by integrating flood simulations, offering a replicable model for similar regions. Successful implementation of MAR in Milpa Alta requires addressing water quality concerns, engaging stakeholders, and ensuring compliance with regulatory frameworks. The findings emphasize MAR's potential to balance urbanization pressures with sustainable water management and flood mitigation strategies in Mexico City's rapidly developing areas. © 2025 Wiley Periodicals LLC.
Keywords: Scopus_Alert_15_Aout_2025

HyEco: A hybrid hydrodynamic-cum-ecological modelling framework to demystify flood and human-health risks over flood-prone metropolis - Deopa, Rahul; Mohanty, Mohit Prakash (Water Research X, 2025)
Urban flooding, exacerbated by climate change and unregulated urbanization, poses significant risks to the built environment. In addition to physical damage, floodwaters often mobilize a hazardous mix of untreated sewage, industrial effluents, and undesirable pollutants, leading to severe microbial contamination in the floodwaters. This study introduces “HyEco”-an integrated framework comprising high-fidelity 3-way coupled hydrodynamic and ecological modelling with an aim to capture the “unhidden tangible flood risks” and “hidden intangible public-health risks” in tandem. Focusing on Delhi, India, a densely populated metropolis prone to recurrent urban flooding and associated health crises, the framework simulates the 2023 mega-flood event. Results show that approximately 63.5 % of the region is categorized under ‘high’ to ‘very high’ flood risk zones, with over 20 % of these areas housing around 15 % of the city's dense population. The hydrodynamic model outputs were forced into the ecological model to simulate the fate and transport of microbial contamination in floodwaters. Escherichia coli concentrations ranged from 772,868 to 790,000 MPN/100 mL, far exceeding established safety thresholds. A Quantitative Microbial Risk Assessment (QMRA) reveals elevated infection probabilities, particularly among children, with risks up to 2.60×10⁻³ under repeated exposure and 8.38×10⁻⁴ to 8.57×10⁻⁴ for pedestrian splash exposures. Unlike prior approaches that examine flood and microbial risks in isolation or depend on static datasets, HyEco overcomes key methodological gaps by dynamically integrating flood and microbial processes at high spatio-temporal resolution. The HyEco framework offers a scalable and actionable tool for integrated flood risk management and climate-resilient public health planning. © 2025
Keywords: Scopus_Alert_15_Aout_2025

Pedestrian vulnerability assessment of underground staircases in urban flooding - Wu, Wenqi; Zhu, Zhongfan; Zheng, Xinyu; Liu, Shuyou; Zhang, Dawei (Sustainable Cities and Society, 2025)
Urban underground flooding occurs frequently worldwide in the context of climate change and rapid urbanization, posing a serious threat to the travel safety of urban populations; in particular, staircases play an important role in the emergency evacuation of pedestrians in floodwater. Previous studies have identified the staircase configuration as a crucial factor influencing the evacuation difficulty of pedestrians in floodwater. However, the correlation between the geometric characteristics of staircase structures and pedestrian vulnerability is not fully understood, and few parametric studies have focused on the effects of wind intrusion into underground spaces and the effects of various engineering measures. This study thereby aims to assess pedestrian vulnerability in underground staircase floodwater as the width of the lower segment of the staircase increases, the effect of wind in the staircase is incorporated, and several flood prevention and/or windproof measures are implemented. Computational fluid dynamics (CFD) simulations were performed to reproduce the mean floodwater flow field and wind flow field on underground staircases. The findings show that (i) a variation in the width of the lower staircase after the rest platform affects pedestrian vulnerability, i.e., an increasing lower staircase width decreases the pedestrian risk to a certain degree; however, the jet behind the rest platform intensifies it; (ii) the incorporation of the wind effect when it intrudes the staircase obviously deteriorates the pedestrian vulnerability, and a discrepancy exists in the evacuation speeds of different pedestrian groups under a joint effect of wind and floodwater; and (iii) some engineering measures, especially windproof measures, have the potential to mitigate pedestrian vulnerability. These findings can serve as a reference for policymakers and stakeholders in coping with urban underground flooding hazards and guiding the emergency evacuation of people trapped in floodwater in the context of resilient city construction. © 2025 Elsevier Ltd
Keywords: Scopus_Alert_15_Aout_2025

Rapid-Mapping Maximum Water Depth Map of Urban Flood Using a Highly Adaptable Machine Learning Based Model - Li, Jingru; Pan, Guiying; Chen, Yangyu; Wang, Xiaoling; Huang, Peizhi; Zhang, Li; Zhou, Haijun (Journal of Flood Risk Management, 2025)
Rapid urban flood mapping is crucial for timely risk alerts and emergency relief. Machine learning (ML)-based mapping models emerge as a promising approach for fast, accurate inundation forecasts. However, current ML models often use precipitation features as inputs and predict maximum flood depth for all grid cells of a specific region simultaneously. This special design improves their prediction efficiency but limits their application in new regions. This study aims to create a highly adaptable, rapid urban maximum flood water depth mapping model based on the random forest regression algorithm and the extreme gradient boosting algorithm. Our mapping model additionally incorporates terrain and land-use features, besides the precipitation feature, as input variables and generates the maximum water depth only for a grid cell in each mapping. Thus, it can be unchangeably applied to the grid cells in a new area when the model is fully trained. In the case study of Shenzhen, China, our ML-based mapping model demonstrated excellent mapping ability in both training and validation sets. The coefficient of determination (R2) is consistently greater than or close to 95%. Furthermore, it revealed good generalization ability when directly applied to a new rainfall event (R2 = 0.875) and a new area (R2 = 0.810). Meanwhile, the time cost of the mapping model is less than 3 s, meeting the requirement for real-time mapping. These results indicate that this highly adaptable model, once appropriately trained, can be applied to rapid urban flood severity mapping, which significantly reduces its use cost in urban flood management. © 2025 The Author(s). Journal of Flood Risk Management published by Chartered Institution of Water and Environmental Management and John Wiley & Sons Ltd.
Keywords: Scopus_Alert_15_Aout_2025

Extreme precipitation risk assessment with improved WOA-Optimized copula model under composite conditions - Wang, Zhaocai; Ma, Chao; Wu, Junhao; Wu, Tunhua (Theoretical and Applied Climatology, 2025)
An accurate study of extreme precipitation risk events is crucial for flood control, urban planning, and engineering design. The Copula function effectively handles uncertainties and nonlinear interactions among multiple hydrological variables, capturing complex correlations in extreme precipitation events for more precise risk assessments. Selecting the parameters of the Copula function is critical, as it defines the function’s shape and the dependence structure between variables, influencing its application. Traditional parameterization methods, like maximum likelihood estimation and least squares, often require large datasets and distribution assumptions, making them cumbersome for high-dimensional data. This research presents a model using an enhanced whale optimization algorithm to estimate Copula parameters (CLCWOA-Copula), aiming to assess return period (RP) and failure probability associated with extreme precipitation risk events. Monthly precipitation data from Zhengzhou, China, from 1950 to 2024 is analyzed, using the 90th percentile as the extreme precipitation threshold. Marginal distributions are fitted using the P-III and gamma distributions, etc., which are then combined using CLCWOA-Copula to analyze coincident RP, joint RP, Kendall RP, and failure probability under composite conditions. The results demonstrate that this optimization method possesses strong global search capabilities and parallel computing abilities, yielding optimal Copula parameter values within few iterations and selecting the best-fit Copula function based on AIC, R², and RMSE. The Kendall RP and failure probability offer more accurate tools for extreme precipitation risk analysis; when Pmax reaches 540 mm, P90 reaches 1080 mm, or R90 reaches 0.83, a one-in-a-century extreme precipitation event is indicated. This study provides important insights for risk metrics applicable to extreme weather. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2025.
Keywords: Scopus_Alert_15_Aout_2025

Climate-smart water management in rice paddies: a meta-synthesis on greenhouse gas emissions and yield impacts - Minamikawa, Kazunori (Paddy and Water Environment, 2025)
Water management practices in rice paddies, particularly alternate wetting and drying and midseason drainage followed by intermittent irrigation, are widely recognized for reducing methane (CH4) emissions and irrigation water use compared to continuous flooding (CF). However, these practices also increase nitrous oxide (N2O) emissions and their effect on rice yield remains unclear, especially in the context of technology dissemination to farmers. This study (1) reviews 11 recent meta-analyses on CH4 and N2O emissions and rice yield and (2) synthesizes their reported effects on rice growth and yield. Aggregated data show that CH4 emissions decreased by 31–62% (n = 10), while N2O emissions increased by 37–445% (n = 7), relative to CF. Rice yield change ranged from − 5.4% to + 11% with a mean of + 1.3% (n = 8). The impact of water management on rice yield varied depending on the timing and intensity of drainage events, with excessive water stress—particularly during the heading stage—and prolonged reductive soil conditions being key risk factors. Results indicate that mild-intensity drainage practices, such as ‘safe AWD,’ not only avoid yield penalties but can significantly enhance rice productivity when tailored to favorable environmental and agronomic conditions. For effective dissemination of these practices, leveraging yield improvement as an incentive for farmers is essential. Optimizing drainage schedules in accordance with rice physiological stages and local conditions is critical. With appropriate localization, water management can serve as a climate-smart strategy that simultaneously improves water efficiency, reduces greenhouse gas emissions, and maintains or increases rice productivity. © The Author(s) 2025.
Keywords: Scopus_Alert_15_Aout_2025

A REVIEW OF THE IMPACT OF EXTREME WEATHER EVENTS ON FRESHWATER, TERRESTRIAL AND MARINE ECOSYSTEMS - Eichelmann, Elke; Naber, Nadeem; Battamo, Ashenafi Yohannes; O’Sullivan, John J.; Salauddin; Kelly-Quinn, Mary (Biology and Environment, 2025)
Extreme weather events (EWEs), including floods, droughts, heatwaves and storms, are increasingly recognised as major drivers of biodiversity loss and ecosystem degradation. In this systematic review, we synthesise 251 studies documenting the impacts of extreme weather events on freshwater, terrestrial and marine ecosystems, with the goal of informing effective conservation and management strategies for areas of special conservation or protection focus in Ireland.Twenty-two of the reviewed studies included Irish ecosystems. In freshwater systems, flooding (34 studies) was the most studied EWE, often linked to declines in species richness, abundance and ecosystem function. In terrestrial ecosystems, studies predominantly addressed droughts (60 studies) and extreme temperatures (48 studies), with impacts including increase in mortality, decline in growth and shift in species composition. Marine and coastal studies focused largely on storm events (33 studies), highlighting physical damages linked to wave actions, behavioural changes in macrofauna, changes in species composition and distribution, and loss in habitat cover. Results indicate that most EWEs lead to negative ecological responses, although responses are context specific.While positive responses to EWEs are rare, species with adaptive traits displayed some resilience, especially in ecosystems with high biodiversity or refuge areas.These findings underscore the need for conservation strategies that incorporate EWE projections, particularly for protected habitats and species. © 2025 Royal Irish Academy. All rights reserved.
Keywords: Scopus_Alert_15_Aout_2025

Systematic review of flood resilience strategies in Lagos Metropolis: pathways toward the 2030 sustainable development agenda - Orimoogunje, Oluwagbenga; Aniramu, Opeyemi (Frontiers in Climate, 2025)
Flooding presents a significant challenge in the Lagos metropolis, driven by rapid urbanization, poor drainage infrastructure, and climate change. This study evaluates flood resilience strategies in Lagos, analyzing their effectiveness in mitigating flood risks and their alignment with the 2030 Agenda. The research utilizes the PICO (Population, Intervention, Control, and Outcomes) framework to refine research questions and follows PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines for study selection, search strategies, and data extraction. A thorough search across databases such as Google Scholar, SCOPUS, and government data repositories was conducted to ensure the inclusion of relevant studies while minimizing selection bias. The study emphasizes the severe impacts of flooding, referencing the 2022–2023 flood event which resulted in USD 262,500 damages and displaced 8000 residents in Lagos State. Current flood resilience strategies are inadequate to meet the Sustainable Development Goals (SDGs) due to insufficient urban flood infrastructure, poor waste disposal practices, and worsening climatic conditions. The livelihoods, income, health, and overall survival of vulnerable communities are at significant risk. Key gaps identified include the weak enforcement of urban planning regulations, limited community engagement, ineffective early warning systems, and poor intervention initiatives. This study suggests a multi-stakeholder approach that enhances both structural and non-structural flood resilience. Improving drainage systems, promoting sustainable waste management, improving climate adaptation policies, and fostering community-based flood mitigation strategies are crucial for achieving long-term urban resilience. These findings offer valuable insights for policymakers, urban planners, and climate resilience advocates working toward the Sustainable Development Agenda in Lagos metropolis. Copyright © 2025 Orimoogunje and Aniramu.
Keywords: Scopus_Alert_15_Aout_2025

Advances in flow and civil structures - Wang, Benshuo; Zhang, Xuelin; Li, Cruz Y.; Chen, Zengshun; Tse, Tim K. T. (Physics of Fluids, 2025)
The Flow and Civil Structures special collection is one of the earliest and largest efforts to consolidate transformative research bridging fluid mechanics and civil engineering. It addresses mutual and escalating challenges posed by extreme environmental loads and rapid urbanization, linking these two massive research fields. With nearly 200 papers, contributions span fluid-structure interactions in bridges, building, and high-speed railways; hydrodynamic resilience of offshore infrastructure; granular flows in urban drainage systems; turbulence-driven pollutant dispersion; and much beyond. The collection features advances in experiments, numerical simulations, field measurements, and analytical methods to improve predictions of wind-induced vibrations, optimize wave-resistant design, and mitigate urban flooding hazards. By integrating artificial intelligence and machine learning analysis, it advances infrastructure resilience for compound hazards in an increasingly dynamic climate, addressing both global and local scales. © 2025 Author(s).
Keywords: Scopus_Alert_15_Aout_2025

Projections of extreme weather events according to climate change scenarios and populations at-risk in Brazil - Dantas, Leydson Galvíncio; Ferreira, Andrêa Jacqueline Fortes; Pinto Junior, Jony Arrais; Cortes, Taísa Rodrigues; Neves, Danielson Jorge Delgado; de Oliveira, Beatriz Fátima Alves; da Silveira, Ismael Henrique (Climatic Change, 2025)
Extreme weather events, such as heat waves, heavy rainfall and droughts, have become more frequent and intense in Brazil. According to climate change scenarios, this trend, which has a negative impact on people’s health and living conditions, will continue. Here, we analysed indicators for extreme weather events resulting from climate change, projected for the 21st century, alongside socio-demographic indicators for Brazilian municipalities, in an attempt to identify populations exposed to the risks of the climate crisis. We calculated the values of indicators for extreme air temperature and precipitation events, based on NEX-GDDP-CMIP6 data, for a reference period and for the future, as well as socio-demographic indicators based on recent census data. Using Spearman’s coefficient, we then calculated anomaly indicators for the future time intervals and analysed correlations with the socio-demographic indicators. Our results indicate a reduction in cold days and an increase in hot days and heat waves in both scenarios (SSP2-4.5 and SSP5-8.5), with the most changes occurring in the highest emission scenario. The extreme precipitation indicators suggest both an increase and a reduction in intense precipitation and droughts in a number of the country’s regions. The projected changes are more intense in the highest emission scenario, and in the North and Northeast regions. We noted a trend for greatest occurrence of extreme events in locations with a higher proportion of Black, Parda/Brown, Indigenous and Quilombola populations, and the socially vulnerable. We recommend that policies to adapt and mitigate the impacts of climate change focus on reducing inequalities and promoting climate justice. © The Author(s) 2025.
Keywords: Scopus_Alert_15_Aout_2025

Future Projection of Rainstorm and Flood Disaster Risk in Sichuan-Chongqing Region under CMIP6 Different Climate Change Scenarios - Yao, Ying; Li, Xiehui; Wang, Lei; Li, Hongying (Plateau Meteorology, 2025)
In recent years，rapid urbanization and global warming have led to frequent and severe rainstorm and flood disasters in the Sichuan-Chongqing region. This change will not only have a serious impact on the ecological environment and socio-economic development of the area，but also significantly increase the pressure on urban infrastructure and threaten the safety of people's lives and property. Therefore，it is particularly important to scientifically and accurately analyze the disaster risk of rainstorm and flood in Sichuan-Chongqing region in the past and future. This paper utilized daily precipitation data from 50 selected meteorological stations in the Sichuan-Chongqing region，precipitation data from 5 CMIP6 models，gridded population and economic data under Shared Socioeconomic Pathways（SSPs），as well as DEM and land use remote sensing data. Firstly，using Taylor diagrams，quantitative indices（S），and standardized anomaly sequences，the study evaluated the simulation performance of 5 individual CMIP6 models，an equal-weighted aggregation of 5 models（EWA-5），and unequally-weighted aggregations of 5 models（UEWA-5）for five selected extreme precipitation indices. Then，by building a comprehensive risk assessment model of rainstorm and flood disaster based on disaster risk and vulnerability of disaster bearing body，the study conducted risk assessments，future projections，and comparative analyses of rainstorm and flood disasters during baseline（1995-2014）and future near-term（2025-2044）and long-term （2045-2064）periods under three different climate change scenarios（SSP1-2. 6，SSP2-4. 5，SSP5-8. 5）. Results indicated：（1）The EC-Earth3 model performed best in simulating the five extreme precipitation indices，with correlation coefficients between simulated and observed values of 0. 78 for R95p，0. 90 for RX1day，and 0. 77 for RX5day. Overall，the simulation performance of UEWA-5 exceeded that of EWA-5.（2）During the baseline period，central Sichuan exhibited high values for the five extreme precipitation indices，followed by eastern Sichuan and Chongqing，while western Sichuan showed lower values. The year 1998 recorded peak values for all five indices，with a maximum single-day precipitation of 86 mm for RX1day and an intensity（SDII）value of 11. 3 mm·d-1.（3）In future periods，the five extreme precipitation indices display a spatial distribution characterized by higher values in central regions and lower values around the periphery. Higher levels of social vulnerability and radiative forcing correlate with larger values of extreme precipitation indices. Comparing the two future periods，values of the indices are larger in the long term，notably with R95p averaging 846. 8 mm，an increase of 169. 2 mm compared to the near term.（4）During historical periods，areas with higher comprehensive risk of rainstorm and flood disasters were concentrated in central Sichuan and downtown Chongqing. In the two future periods，the high and moderately high-risk areas in central Sichuan are expected to expand，while the moderate-risk areas will shrink. The range of low-risk areas in the western Sichuan Plateau will also decrease，and the risk levels in southern Sichuan and eastern Sichuan-Chongqing border areas will respectively decrease to moderate-low and low-risk zones. Comparing the two future periods，the range of moderately high and moderate-risk areas in central Sichuan is expected to expand，while southwestern Chongqing will transition to a moderate-risk area in the long term. Other regions will generally maintain their original risk levels. Changes in disaster risk levels in the Sichuan-Chongqing region are less pronounced with increasing social vulnerability and radiative forcing，especially in the western Sichuan Plateau and northeastern Sichuan，where changes in disaster risk levels are minimal. The study results can provide important references for reducing disaster risks，enhancing emergency response capabilities，and making scientifically informed decisions for disaster prevention in the Sichuan-Chongqing region. © Editorial Department of Plateau Meteorology.
Keywords: Scopus_Alert_15_Aout_2025

Interactive Effects of Climate Change and Contaminants in Aquatic Ecosystems on Environmental-Human Health - Yu, Kaifeng; Mohapatra, Sanjeeb; Chen, Yihan; Jiang, Peng; Tong, Xuneng (Current Pollution Reports, 2025)
Purpose of the Review: Climate change is intensifying the pressures on aquatic ecosystems by altering the dynamics of contaminants, with cascading effects on ecological and human health. This review synthesizes recent evidence on how rising temperatures, altered precipitation patterns, and extreme weather events influence chemical and microbial contaminant dynamics in aquatic environments. Recent Findings: Key findings reveal that elevated temperatures enhance phosphorus pollution and algal blooms, increase heavy metal release from sediments, and promote the mobilization of organic pollutants. Concurrently, climate change exacerbates microbial contamination by facilitating the spread of waterborne microbial contaminants, especially posing more pressure to antimicrobial resistance-related contaminants through temperature-driven horizontal gene transfer and extreme precipitation events. Complex interactions between chemical and microbial contaminants like heavy metals co-selecting for antibiotic resistance further amplify risks. The compounded effects of climate change and contaminants threaten water quality, ecosystem resilience, and public health, particularly through increased toxicant exposure via seafood and waterborne disease outbreaks. Despite growing recognition of these interactions, critical gaps remain in understanding their synergistic mechanisms, especially in data-scarce regions. Summary: This review highlights the urgent need for integrated monitoring, predictive modeling, and adaptive policies under a One Health framework to mitigate the multifaceted impacts of climate-driven contamination. Future research should prioritize real-world assessments of temperature effects, urban overflow dynamics during extreme weather, and the socio-behavioral dimensions of contaminant spread to inform effective mitigation strategies. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.
Keywords: Scopus_Alert_15_Aout_2025

Entendre et comprendre les expériences de désastre par la recherche narrative - Leclerc, Typhaine; Lessard, Lily; Saint-Charles, Johanne (Intervention, 2024)
Les événements météorologiques extrêmes (EME) et les désastres qu’ils entrainent provoquent des conséquences psychosociales qui sont modulées en fonction de différents facteurs sociaux. On constate aussi que les récits médiatiques et culturels qui circulent au sujet des EME ne sont pas représentatifs de l’ensemble des expériences de personnes sinistrées : celles qui en subissent les conséquences les plus sévères tendent aussi à être celles qu’on « entend » le moins dans l’espace public. Ces personnes sont ainsi susceptibles de vivre de l’injustice épistémique, ce qui a des effets délétères sur le soutien qu’elles reçoivent. Face à ces constats s’impose la nécessité de mieux comprendre la diversité des expériences d’EME et d’explorer des stratégies pour soutenir l’ensemble des personnes sinistrées dans leur rétablissement psychosocial. Cet article soutient que la recherche narrative peut contribuer à répondre à ces objectifs. En dépeignant des réalités multiples, la recherche narrative centrée sur les récits de personnes sinistrées présente aussi un intérêt significatif pour l’amélioration des pratiques d’intervention en contexte de désastre. , Extreme weather events (EWE) and their resulting disasters cause psychosocial consequences that are moderated by different social factors. Media and cultural accounts of EWEs do not represent the full range of disaster survivor experiences, that is, those who experienced the most severe consequences also tend to be those least “heard” in the public arena. These people are therefore most likely to experience forms of epistemic injustice that negatively impact the support offered to cope with disaster. Considering these findings, there is a need to better understand the diversity of EWE experiences and explore strategies for supporting all disaster survivors in their psychosocial recovery. This article argues that narrative research can help meet these needs. By portraying the multiple realities of people affected by EWEs, narrative research focusing on the stories of disaster survivors is also of significant interest for improving intervention practices in this context.

A novel explainable stacking ensemble model for estimating design floods: A data-driven approach for ungauged regions - Kanani-Sadat, Yousef; Safari, Abdolreza; Nasseri, Mohsen; Homayouni, Saeid (Advanced Engineering Informatics, 2025)
Keywords: Infolettre_Ete_2025

Planetary-Scale Dynamics of Extreme Precipitation Regimes: Precursors and Contributors to Intensity and Longevity - Low, Yeechian; Gyakum, John R. (Monthly Weather Review, 2025)
Abstract Extreme precipitation is often challenging to predict but has substantial societal impacts, especially when it is persistent and affects a large region. We analyze Rossby wave packets, jet streams, atmospheric blocking, Madden–Julian oscillation (MJO), and El Niño–Southern Oscillation (ENSO) and elucidate the associated large-scale physical mechanisms contributing to the occurrence and persistence of extreme precipitation regimes (EPRs) in eastern North America as identified in an earlier study. The large temporal and spatial scales of EPRs, as well as the climatological study of EPRs, distinguish this study from previous precipitation studies, which are mostly on shorter-duration events. EPRs are characterized by an unusually slow-moving and persistent large-scale synoptic-scale circulation structure favorable for the southerly flow of subtropical moisture into eastern North America. The strength of the southerly flow is critical in producing large precipitation rates. The favorable synoptic structure is established by the start of the EPR, moves very slowly eastward until the middle of the EPR, and then travels faster eastward by the end of the EPR. The persistence of midlatitude ridges and the long-wavelength and slow-moving nature of the synoptic structure are critical to the longevity of EPRs. The latent heat release associated with moisture transport and ascent in cyclones provides a feedback mechanism contributing to the persistence. MJO phase 3 is favored before the EPR start, while phases 4 and 5 are favored during the EPR. During EPRs, there is no significant preference for El Niño or La Niña conditions, but a negative Pacific decadal oscillation (PDO) is favored. Significance Statement Cool-season extreme precipitation regimes often lead to flooding and other societal impacts and represent a significant forecast challenge. We analyze large-scale weather patterns and physical mechanisms in the North Pacific and North America contributing to the occurrence and persistence of extreme precipitation regimes. Recognizing them could promote their predictability since the North Pacific is a climatologically favored area for persistent anomalous large-scale weather patterns. The regimes are characterized by an unusually slow-moving and persistent large-scale weather pattern favoring the southerly flow of subtropical moisture into eastern North America. The persistence, size, and slow-moving nature of the weather pattern are critical to the regimes’ longevity. Storms tracking on the west of high pressure areas provide a feedback mechanism that helps maintain the regimes.
Keywords: Infolettre_Ete_2025

Performance and durability of passive direct formate fuel cells with Nafion- or PTFE-based cathodes - Shyu, Jin-Cherng; Khoiroh, Ikhwatinah; He, Jie-An (Fuel, 2025)
Keywords: Infolettre_Ete_2025

Enhancing Research-to-Operations in Hydrological Forecasting: Innovations across Scales and Horizons - Pechlivanidis, Ilias G.; Du, Yiheng; Bennett, James; Boucher, Marie-Amélie; Chang, Annie Y. Y.; Crochemore, Louise; Dasgupta, Antara; Di Baldassarre, Giuliano; Luterbacher, Jürg; Pappenberger, Florian; Ramos, Maria-Helena; Slater, Louise; et al. (Bulletin of the American Meteorological Society, 2025)
Abstract Over the past 20 years, the Hydrological Ensemble Prediction Experiment (HEPEX) international community of practice has advanced the science and practice of hydrological ensemble prediction and its application in impact- and risk-based decision-making, fostering innovations through cutting-edge techniques and data that enhance water-related sectors. Here, we present insights from those 20 years on the key priorities for (co)creating broadly applicable hydrological forecasting systems that add value across spatial scales and time horizons. We highlight the advancement of hydrological forecasting chains through rigorous data management that incorporates diverse, high-quality data sources, data assimilation techniques, and the application of artificial intelligence (AI) to improve predictive accuracy. HEPEX has played a critical role in enhancing the reliability of water resources and water-related risk management globally by standardizing ensemble forecasting. This effort complements HEPEX’s broader initiative to strengthen research to operations, making innovative forecasting solutions both practical and accessible. Additionally, efforts have been made toward supporting the United Nations Early Warnings for All initiative through developing robust and reliable early warning systems by means of global training, education and capacity development, and the sharing of technology. Finally, we note that the integration of advanced science, user-centric methods, and global collaboration can provide a solid framework for improving the prediction and management of hydrological extremes, aligning forecasting systems with the dynamic needs of water resource and risk management in a changing climate. To effectively meet future demands, it is crucial to accelerate the integration of innovative science within operational frameworks, fostering adaptable and resilient hydrological forecasting systems globally.
Keywords: Infolettre_Ete_2025

HurricaneLog: A serious game for data collection and analysis of hurricane preparedness and response operations - Correia Pereira, Thiago; Aloise, Daniel; Rancourt, Marie-Ève (International Journal of Disaster Risk Reduction, 2025)
Keywords: Infolettre_Ete_2025

The Needs, Challenges, and Priorities for Advancing Global Flood Research - Samadi, Vidya; Fowler, Hayley J.; Lamond, Jessica; Wagener, Thorsten; Brunner, Manuela; Gourley, Jonathan; Moradkhani, Hamid; Popescu, Ioana; Wasko, Conrad; Wright, Daniel; Wu, Huan; Zhang, Ke; et al. (WIREs Water, 2025)
ABSTRACT In recent years, numerous flood events have caused loss of life, widespread disruption, and damage across the globe. These devastating impacts highlight the importance of a better understanding of flood generating processes, their impacts, and their variability under climate and landscape changes. Here, we argue that the ability to better model flooding is underpinned by the grand challenge of understanding flood generation mechanisms and potential impacts. To address this challenge, the World Meteorological Organization‐Global Energy and Water Exchanges (GEWEX) Hydrometeorology Panel (GHP) aims to establish a Global Flood Crosscutting project to propagate flood modeling and research knowledge across regions and to synthesize results at the global scale. This paper outlines a framework for understanding the dynamics and impacts of runoff generation processes and a rationale for the role of a Global Flood Crosscutting project to address these challenges. Within this Global Flood Crosscutting project, we will establish a common terminology and methods to enable the global research community to exchange knowledge and experiences, and to design experiments toward developing actionable recommendations for more effective flood management practices and policies for improved resilience. This harmonization of rich perspectives across disciplines will foster the co‐production of knowledge primed to advance flood research, particularly in the current period of heightened climate variability and rapid change. It will create a new transdisciplinary paradigm for flood science, wherein different dimensions of mechanistic understanding and processes are rigorously considered alongside socioeconomic impacts, early warning communications, and longer‐term adaptation to alleviate flood risks in society.
Keywords: Infolettre_Ete_2025

Projecting climate and socioeconomic contributions to global flood-induced displacements using a data-driven approach - Del Rio Amador, Lenin; Boudreault, Mathieu; Carozza, David A. (Natural Hazards, 2025)
Keywords: Infolettre_Ete_2025

Full-scale characterization of the effects of a bioretention system on water quality and quantity following the replacement of a mixed stormwater and combined sewer system - Le Cauchois, Pia; Doucet, Samuel; Bouattour, Ons; McQuaid, Natasha; Beral, Henry; Kõiv-Vainik, Margit; Bichai, Françoise; McCarthy, David; St-Laurent, Julien; Dagenais, Danielle; Bennekrela, Nesrine; Guerra, Juan; et al. (Blue-Green Systems, 2025)
ABSTRACT Urbanization is leading to more frequent flooding as cities have more impervious surfaces and runoff exceeds the capacity of combined sewer systems. In heavy rainfall, contaminated excess water is discharged into the natural environment, damaging ecosystems and threatening drinking water sources. To address these challenges aggravated by climate change, urban blue-green water management systems, such as bioretention cells, are increasingly being adopted. Bioretention cells use substrate and plants adapted to the climate to manage rainwater. They form shallow depressions, allowing infiltration, storage, and gradual evacuation of runoff. In 2018, the City of Trois-Rivières (Québec, Canada) installed 54 bioretention cells along a residential street, several of which were equipped with access points to monitor performance. Groundwater quality was monitored through the installation of piezometers to detect potential contamination. This large-scale project aimed to improve stormwater quality and reduce sewer flows. The studied bioretention cells reduced the flow and generally improved water quality entering the sewer system, as well as the quality of stormwater, with some exceptions. Higher outflow concentrations were observed for contaminants such as manganese and nitrate. The results of this initiative provide useful recommendations for similar projects for urban climate change adaptation.
Keywords: Infolettre_Ete_2025

Impacts of Adjusting Solid Precipitation Amounts in the Canadian Precipitation Analysis System - Aubry, Catherine; Bélair, Stéphane; Thériault, Julie M.; Mekis, Eva; Feng, Pei-Ning; Lespinas, Franck; Khedhaouiria, Dikra; Beaudry, Florence (Journal of Applied Meteorology and Climatology, 2025)
Abstract Real-time precipitation data are essential for weather forecasting, flood prediction, drought monitoring, irrigation, fire prevention, and hydroelectric management. To optimize these activities, reliable precipitation estimates are crucial. Environment and Climate Change Canada (ECCC) leads the Canadian Precipitation Analysis (CaPA) project, providing near-real-time precipitation estimates across North America. However, during winter, CaPA’s 6-hourly accuracy is limited because many automatic surface observations are not assimilated due to wind-induced gauge undercatch. The objective of this study is to evaluate the added value of adjusted hourly precipitation amounts for gauge undercatch due to wind speed in CaPA. A recent ECCC dataset of hourly precipitation measurements from automatic precipitation gauges across Canada is included in CaPA as part of this study. Precipitation amounts are adjusted based on several types of transfer functions, which convert measured precipitation into what high-quality equipment would have measured with reduced undercatch. First, there are no notable differences in CaPA when comparing the performance of the universal transfer function with that of several climate-specific transfer functions based on wind speed and air temperature. However, increasing solid precipitation amounts using a specific type of transfer function that depends on snowfall intensity rather than near-surface air temperature is more likely to improve CaPA’s precipitation estimates during the winter season. This improvement is more evident when the objective evaluation is performed with direct comparison with the Adjusted Daily Rainfall and Snowfall (AdjDlyRS) dataset.
Keywords: Infolettre_Ete_2025

Non-stationary and multivariate spring floods estimation of the Saint John River (eastern Canada) - Yousfi, Nawres; El Adlouni, Salah; Gachon, Philippe (Stochastic Environmental Research and Risk Assessment, 2025)
Keywords: Infolettre_Ete_2025

The vertical premium: Relationship between relative height of properties from real flood elevation and housing prices - Kim, Youjung; Koo, Bon Woo; Lee, Sungmin (Cities, 2025)
Keywords: Infolettre_Ete_2025

Météo: les Américains rendent nos cieux plus incertains - Soleil, Pascale Lévesque, Le (Le Soleil, 2025)
Orages violents, pluies diluviennes, records battus à Québec comme à Montréal: l'été 2025 rappelle combien les prévisions météo sont essentielles. Selon des experts, les récentes coupes budgétaires dans les agences américaines pourraient être de mauvais augure chez nous.
Keywords: Infolettre_Ete_2025

The anatomy of a flash flood: Why the Texas flood was so deadly - Bonakdari, Hossein (The Conversation, 2025)
Understanding the combination of meteorological, geomorphological and hydrological factors that led to the Texas flood could help prevent future disasters.
Keywords: Infolettre_Ete_2025

Les enjeux actuels sont mondiaux. Pour y faire face, il faut créer de véritables partenariats en recherche - Quirion, Rémi (The Conversation, 2025)
Dans un monde de plus en plus polarisé, où la diplomatie scientifique devient parfois la dernière ligne de dialogue entre nations, les partenariats entre chercheurs jouent un rôle critique.
Keywords: Infolettre_Ete_2025

The Impact of Flood Adaptation Measures on Affected Population’s Mental Health: A mixed method Scoping Review - El-Mousawi, Fatima; Ortiz, Ariel Mundo; Berkat, Rawda; Nasri, Bouchra (Cold Spring Harbor Laboratory, 2023)
AbstractThe frequency and severity of floods has increased in different regions of the world due to climate change. Although the impact of floods on human health has been extensively studied, the increase in the segments of the population that are likely to be impacted by floods in the future makes it necessary to examine how adaptation measures impact the mental health of individuals affected by these natural disasters. The goal of this scoping review is to document the existing studies on flood adaptation measures and their impact on the mental health of affected populations, in order to identify the best preventive strategies as well as limitations that deserve further exploration. This study employed the methodology of the PRISMA-ScR extension for scoping reviews to systematically search the databases Medline and Web of Science to identify studies that examined the impact of adaptation measures on the mental health of flood victims. The database queries resulted in a total of 857 records from both databases. Following two rounds of screening, 9 studies were included for full-text analysis. Most of the analyzed studies sought to identify the factors that drive resilience in flood victims, particularly in the context of social capital (6 studies), whereas the remaining studies analyzed the impact of external interventions on the mental health of flood victims, either from preventive or post-disaster measures (3 studies). There is a very limited number of studies that analyze the impact of adaptation measures on the mental health of populations and individuals affected by floods, which complicates the generalizability of their findings. There is a need for public health policies and guidelines for the development of flood adaptation measures that adequately consider a social component that can be used to support the mental health of flood victims.

« J’ai juste besoin de parler » : les histoires de femmes sinistrées au coeur de la recherche sur les conséquences psychosociales des inondations - Leclerc, Typhaine; Lessard, Lily; Saint-Charles, Johanne (Le Climatoscope, 2024)
Les récits médiatiques et culturels qui circulent sur les événements météorologiques extrêmes (EME) ne sont pas représentatifs de l’ensemble des expériences de personnes sinistrées. Les groupes qui en subissent les conséquences les plus sévères tendent à être ceux que l’on « entend » le moins dans l’espace public. L’approche de recherche narrative permet de documenter une diversité d’expériences d’EME pour en tracer un panorama plus complet. Adoptant une approche narrative féministe, notre recherche a été menée auprès de femmes touchées par des inondations en Beauce. Des extraits d’entrevues semi-directives menées avec des femmes sinistrées offrent une illustration des conséquences psychosociales entrainées par les inondations. Les forces des participantes et certains défis rencontrés en lien avec leurs rôles dans la famille et la communauté sont aussi abordés. La méthode adoptée a permis de collecter des récits d’expérience riches et singuliers qui rendent plus tangibles les effets différenciés des EME. Tenir compte de cette diversité d’expériences favoriserait une prise en charge plus équitable des personnes sinistrées à court, moyen et long terme.

Éduquer aux risques dès l’école primaire: de la représentation à la conscientisation - Julien, Marie-Pierre; Bédouret, David; Chalmeau, Raphaël; Mainar, Christine Vergnolle; Léna, Jean-Yves; Calvet, Anne (VertigO - la revue électronique en sciences de l'environnement, 2021)
Le territoire de la vallée du Gave de Gavarnie a connu un épisode d’inondation/crue particulièrement catastrophique en 2013, ayant entrainé de forts dégâts matériels et des pertes humaines. Dans ce contexte, la culture du risque est un enjeu tant pour les acteurs de la gestion de ce territoire que pour les citoyens, d’autant plus que les risques présents y sont multiples (avalanches, glissements de terrain et séismes). Dans cette perspective, l’école peut jouer un rôle déterminant à travers la mise en place de projets d’éducations au(x) risque(s). Ce type d’éducation doit commencer par la perception et la conscience du (des) risque(s), rendues possibles par le vécu et/ou par la culture du groupe dans lequel l’élève vit. Cette étude a pour objectif d’examiner les représentations et la perception du risque des élèves d’une école élémentaire française située sur une commune fortement impactée par cette crue, et l’évolution de ces représentations et cette perception un an après la mise en œuvre du projet éducatif. Les résultats montrent une représentation plurielle du risque par les élèves avec des différences entre classes. La classe de CP-CE (enfants âgés de 6 à 8 ans) associe essentiellement le risque à l’aléa naturel (avalanche, inondation…) alors que les élèves en CM (enfants âgés de 9 à 10 ans) sont centrés sur ce qui pourrait leur arriver (accident, maladie…). Le risque inondation/crue est dans un premier temps très peu évoqué dans les représentations des élèves, mais lorsque les activités pédagogiques permettent de contextualiser cette notion sur leur territoire, il est alors plus fortement perçu.

Development of an under-ice river discharge forecasting system in Delft-Flood Early Warning System (Delft-FEWS) for the Chaudière River based on a coupled hydrological-hydrodynamic modelling approach - Usman, Kh Rahat; Montero, Rodolfo Alvarado; Ghobrial, Tadros; Anctil, François; van Loenen, Arnejan (Geoscientific Model Development Discussions, 2024)
Abstract. Year-round river discharge estimation and forecasting is a critical component of sustainable water resource management. However, in cold climate regions such as Canada, this basic task gets intricated due to the challenge of river ice conditions. River ice conditions are dynamic and can change quickly in a short period of time. This dynamic nature makes river ice conditions difficult to forecast. Moreover, the observation of under-ice river discharge also remains a challenge since no reliable method for its estimation has been developed till date. It is therefore an active field of research and development. The integration of river ice hydraulic models in forecasting systems has remained relatively uncommon. The current study has two main objectives: first is to demonstrate the development and capabilities of a river ice forecasting system based on coupled hydrological and hydraulic modelling approach for the Chaudière River in Québec; and second is to assess its functionality over selected winter events. The forecasting system is developed within a well-known operational forecasting platform: the Delft Flood Early Warning System (Delft-FEWS). The current configuration of the systems integrates (i) meteorological products such as the Regional Ensemble Prediction System (REPS); (ii) a hydrological module implemented through the HydrOlOgical Prediction LAboratory (HOOPLA), a multi-model based hydrological modelling framework; and (iii) hydraulic module implemented through a 1D steady and unsteady HEC-RAS river ice models. The system produces ensemble forecasts for discharge and water level and provides flexibility to modify various dynamic parameters within the modelling chain such as discharge timeseries, ice thickness, ice roughness as well as carryout hindcasting experiments in a batch production way. Performance of the coupled modelling approach was assessed using “Perfect forecast” over winter events between 2020 and 2023 winter seasons. The root mean square error (RMSE) and percent bias (Pbias) metrics were calculated. The hydrologic module of the system showed significant deviations from the observations. These deviations could be explained by the inherent uncertainty in the under-ice discharge estimates as well as uncertainty in the modelling chain. The hydraulic module of the system performed better and the Pbias was within ±10 %.

Flood Risk and Community Resilience: An Interdisciplinary Approach - McEwen, Lindsey (2024)
This book details the impact of flooding on our environment, and the ways in which communities, and those that work with them, can act to manage the associated risks. Flooding is an increasingly significant environmental hazard which inflicts major costs to the economies and livelihoods of developed countries. This book explores how local communities can identify, manage, and adapt to the ever-increasing damage flooding causes. Focusing on the future role of local communities, the benefits and c

A Flood Risk Management Model to Identify Optimal Defence Policies in Coastal Areas Considering Uncertainties in Climate Projections - Cioffi, Francesco; De Bonis Trapella, Alessandro; Giannini, Mario; Lall, Upmanu (Water, 2022)
Coastal areas are particularly vulnerable to flooding from heavy rainfall, sea storm surge, or a combination of the two. Recent studies project higher intensity and frequency of heavy rains, and progressive sea level rise continuing over the next decades. Pre-emptive and optimal flood defense policies that adaptively address climate change are needed. However, future climate projections have significant uncertainty due to multiple factors: (a) future CO2 emission scenarios; (b) uncertainties in climate modelling; (c) discount factor changes due to market fluctuations; (d) uncertain migration and population growth dynamics. Here, a methodology is proposed to identify the optimal design and timing of flood defense structures in which uncertainties in 21st century climate projections are explicitly considered probabilistically. A multi-objective optimization model is developed to minimize both the cost of the flood defence infrastructure system and the flooding hydraulic risk expressed by Expected Annual Damage (EAD). The decision variables of the multi-objective optimization problem are the size of defence system and the timing of implementation. The model accounts for the joint probability density functions of extreme rainfall, storm surge and sea level rise, as well as the damages, which are determined dynamically by the defence system state considering the probability and consequences of system failure, using a water depth–damage curve related to the land use (Corine Land Cover); water depth due to flooding are calculated by hydraulic model. A new dominant sorting genetic algorithm (NSGAII) is used to solve the multi-objective problem optimization. A case study is presented for the Pontina Plain (Lazio Italy), a coastal region, originally a swamp reclaimed about a hundred years ago, that is rich in urban centers and farms. A set of optimal adaptation policies, quantifying size and timing of flood defence constructions for different climate scenarios and belonging to the Pareto curve obtained by the NSGAII are identified for such a case study to mitigate the risk of flooding and to aid decision makers.

Robust Flood Risk Management Strategies Through Bayesian Estimation and Multi-objective Optimization - Sobhaniyeh, Zahra; Niksokhan, Mohammad Hossein; Omidvar, Babak; Gaskin, Susan (International Journal of Environmental Research, 2021)
Management and control of flood hazards, the most frequent natural disaster worldwide, has become a greater challenge due to the increasingly unpredictable precipitation and runoff due to climate change. As many rural areas in Iran are vulnerable to flash floods occurring mainly in the spring, more accurate plans are needed to help reduce the risk of related damage. To address this concern, a robust methodology using multi-objective optimization is proposed, which incorporates the large uncertainties in the modeling parameters defining the risk of flooding. The proposed framework has been implemented in the upper catchment of the Taleghanrood river in the Taleghan district in Iran, which is vulnerable to flooding. The results provide a detailed performance assessment of alternative infrastructure designs, which will help to increase the efficiency of flood management strategies. The optimization uses multi-criteria optimization evolutionary algorithms (MOEA) and Bayesian estimation concepts. The resulting specific design plans, as levees’ height increases over a 50-year time horizon, for controlling floods under given scenarios reflect the uncertainty in the parameters.

Investigating incomplete mixing models in cross junctions under real-world conditions of water distribution networks - Yousefian, Reza; Duchesne, Sophie; Schwarz, Pierre-Olivier (Water Supply, 2024)
Questions have been raised about the correctness of water quality models with complete mixing assumptions in cross junctions of water distribution systems. Recent developments in the mixing phenomenon within cross junctions of water distribution networks (WDNs) have heightened the need for evaluating the existing incomplete mixing models under real-world conditions. Therefore, in this study, two cross junctions with pipe diameters of 100 Â 100 Â 100 Â 100 mm and 150 Â 150 Â 150 Â 150 mm were employed in laboratory experiments to evaluate six existing incomplete mixing models for 25 ﬂow rate scenarios ranging between 1.5 and 3.0 L/s. It was observed that within the same ﬂow rate scenario, the degree of mixing in a cross junction with a pipe relative roughness of 6.00 Â 10À5 (pipe diameter of 25 mm) was higher than that in a cross junction with a pipe relative roughness of 3.00 Â 10À5 (pipe diameter of 50 mm) and smaller. Considering the real-world size of pipes in evaluating the incomplete mixing models showed that two incomplete mixing models, AZRED and the one by Shao et al., had the best accordance with the results of the laboratory experiments.

Integration of Incomplete Mixing at Junctions of Water Distribution Networks in Contamination Source Identification Tools - Yousefian, Reza; Émond, Laurent; Duchesne, Sophie (Journal of Water Resources Planning and Management, 2025)
The water quality models used in contamination source identification (CSI) tools assume complete mixing at the junctions of drinking water distribution networks. Two extensions of the contamination status algorithm (CSA)—a CSI tool that employs water quality models in a reverse-time manner—were accordingly developed in this study, one assuming complete mixing (CSA-CMX) and the other assuming incomplete mixing (CSA-IMX) at cross-junctions. Both algorithms identified contamination sources based on the results of grab sampling at iteratively suggested locations. The performances of CSA-CMX and CSA-IMX were evaluated through laboratory experiments using three contamination identification problems: CSA-IMX identified the contamination source in all three problems, whereas CSA-CMX identified the contamination source in only one. Furthermore, the specificity (i.e., the ability to distinguish the real contamination source from other possible contamination sources) was higher for CSA-IMX than for CSA-CMX in two of the three problems. Therefore, the incomplete mixing assumption was confirmed to be a crucial factor in CSI tools.

Evaluating real-time control of stormwater drainage network and green stormwater infrastructure for enhancing flooding resilience under future rainfall projections - Li, Jiada; Burian, Steven J. (Resources, Conservation and Recycling, 2023)
Traditional stormwater control measures are designed to handle system loadings induced by fixed-size storm events. However, climate change is predicted to alter the frequency and intensity of flooding events, stimulating the need to explore another more adaptive flooding solution like real-time control (RTC). This study assesses the performance of RTC to mitigate impacts of climate change on urban flooding resilience. A simulated, yet realistic, urban drainage system in Salt Lake City, Utah, USA, shows that RTC improves the flooding resilience by up to 17% under climatic rainfall changes. Compared with green stormwater infrastructure (GSI), RTC exhibits a lower resistibility, lower flooding failure level, and higher recovery rate in system performance curves. Results articulate that keeping RTC's performance consistent under ‘back-to-back’ storms requires a tradeoff between upstream dynamical operation and downstream flooding functionality loss. This research suggests that RTC provides a path towards smart and resilient stormwater management strategy.

Adaptation of Dual Drainage to Control Flooding and Enhance Combined Sewer Systems in Highly Urbanized Areas - Ossa Ossa, Juan Esteban; Duchesne, Sophie; Pelletier, Geneviève (2024)
Combined sewer surcharges in densely urbanized areas have become more frequent due to the expansion of impervious surfaces and intensified precipitation caused by climate change. These surcharges can generate system overflows, causing urban flooding and pollution of urban areas. This paper presents a novel methodology to mitigate sewer system surcharges and control surface water. In this methodology, flow control devices and urban landscape retrofitting are proposed as strategies to reduce water inflow into the sewer network and manage excess water on the surface during extreme rainfall events. For this purpose, a 1D/2D dual drainage model was developed for two case studies located in Montreal, Canada. Applying the proposed methodology to these two sites led to a reduction of the volume of wastewater overflows by 100% and 86%, and a decrease in the number of surface overflows by 100% and 71%, respectively, at the two sites for a 100-year return period 3-h Chicago design rainfall. It also controlled the extent of flooding, reduced the volume of uncontrolled surface floods by 78% and 80% and decreased flooded areas by 68% and 42%, respectively, at the two sites for the same design rainfall.

Understanding Flood Risk Perception: A Case Study from Canada - Oubennaceur, Khalid; Chokmani, Karem; Lessard, Florence; Gauthier, Yves; Baltazar, Catherine; Toussaint, Jean-Patrick (Sustainability, 2022)
In recent years, understanding and improving the perception of flood risk has become an important aspect of flood risk management and flood risk reduction policies. The aim of this study was to explore perceptions of flood risk in the Petite Nation River watershed, located in southern Quebec, Canada. A survey was conducted with 130 residents living on a floodplain in this river watershed, which had been affected by floods in the spring of 2017. Participants were asked about different aspects related to flood risk, such as the flood hazard experience, the physical changes occurring in the environment, climate change, information accessibility, flood risk governance, adaptation measures, and finally the perception of losses. An analysis of these factors provided perspectives for improving flood risk communication and increasing the public awareness of flood risk. The results indicated that the analyzed aspects are potentially important in terms of risk perception and showed that the flood risk perceptions varied for each aspect analyzed. In general, the information regarding flood risk management is available and generally understandable, and the level of confidence was good towards most authorities. However, the experiences of flood risk and the consequences of climate change on floods were not clear among the respondents. Regarding the adaptation measures, the majority of participants tended to consider non-structural adaptation measures as being more relevant than structural ones. Moreover, the long-term consequences of flooding on property values are of highest concern. These results provide a snapshot of citizens’ risk perceptions and their opinions on topics that are directly related to such risks.

Flood Risk Communication Using ArcGIS StoryMaps - Oubennaceur, Khalid; Chokmani, Karem; El Alem, Anas; Gauthier, Yves (Hydrology, 2021)
In Canada, flooding is the most common and costly natural hazard. Flooding events significantly impact communities, damage infrastructures and threaten public security. Communication, as part of a flood risk management strategy, is an essential means of countering these threats. It is therefore important to develop new and innovative tools to communicate the flood risk with citizens. From this perspective, the use of story maps can be very effectively implemented for a broad audience, particularly to stakeholders. This paper details how an interactive web-based story map was set up to communicate current and future flood risks in the Petite-Nation River watershed, Quebec (Canada). This web technology application combines informative texts and interactive maps on current and future flood risks in the Petite-Nation River watershed. Flood risk and climate maps were generated using the GARI tool, implemented using a geographic information system (GIS) supported by ArcGIS Online (Esri). Three climate change scenarios developed by the Hydroclimatic Atlas of Southern Quebec were used to visualize potential future impacts. This study concluded that our story map is an efficient flood hazard communication tool. The assets of this interactive web mapping tool are numerous, namely user-friendly mapping, use and interaction, and customizable displays.

Flood inundation modeling in ungauged basins using Unmanned Aerial Vehicles imagery - Yavari, Adel; Homayouni, Saeid; Oubennaceur, Khalid; Chokmani, Karem (Earth Observation and Geomatics Engineering, 2020)
This paper presents a new framework for floodplain inundation modeling in an ungauged basin using unmanned aerial vehicles (UAVs) imagery. This method is based on the integrated analysis of high-resolution ortho-images and elevation data produced by the structure from motion (SfM) technology. To this end, the Flood-Level Marks (FLMs) were created from high-resolution UAV ortho-images and compared to the flood inundated areas simulated using the HEC-RAS hydraulic model. The flood quantiles for 25, 50, 100, and 200 return periods were then estimated by synthetic hydrographs using the Natural Resources Conservation Service (NRCS). The proposed method was applied to UAV image data collected from the Khosban village, in Taleghan County, Iran, in the ungauged sub-basin of the Khosban River. The study area is located along one kilometre of the river in the middle of the village. The results showed that the flood inundation areas modeled by the HEC-RAS were 33%, 19%, and 8% less than those estimated from the UAV’s FLMs for 25, 50, and 100 years return periods, respectively. For return periods of 200 years, this difference was overestimated by more than 6%, compared to the UAV’s FLM. The maximum flood depth in our four proposed scenarios of hydraulic models varied between 2.33 to 2.83 meters. These analyses showed that this method, based on the UAV imagery, is well suited to improve the hydraulic modeling for seasonal inundation in ungauged rivers, thus providing reliable support to flood mitigation strategies

Imaging Floods and Glacier Geohazards with Remote Sensing - Cigna, Francesca; Xie, Hongjie (Remote Sensing, 2020)
Geohazards associated with the dynamics of the liquid and solid water of the Earth’s hydrosphere, such as floods and glacial processes, may pose significant risks to populations, activities and properties [...]

Development of an approach based on historical Landsat data for delineating Canadian flood zones at different return periods - Chokmani, Karem; Zidi, Haythem; El Alem, Anas; Gill-Fortin, Jasmin (EGU General Assembly Conference Abstracts, 2024)
The study addresses the need for flood risk anticipation and planning, through the development of a flood zone mapping approach for different return periods, in order to best prevent and protect populations. Today, traditional methods are too costly, too slow or require too many requirements to be applied over large areas. As part of a project funded by the Canadian Space Agency, Geosapiens and the Institut National de la Recherche Scientifique set themselves the goal of designing an automatic process to generate water presence maps for different return periods at a resolution of 30 m, based on the historical database of Landsat missions from 1982 to the present day. This involved the design, implementation and training of a deep learning algorithm model based on the U-Net architecture for the detection of water pixels in Landsat imagery. The resulting maps were used as the basis for applying a frequency analysis model to fit a probability of occurrence function for the presence of water at each pixel. The frequency analysis data were then used to obtain maps of water occurrence at different return preiods such as 2, 5 and 20 years.

Flood Models: An Exploratory Analysis and Research Trends - Morante-Carballo, Fernando; Montalván-Burbano, Néstor; Arias-Hidalgo, Mijaíl; Domínguez-Granda, Luis; Apolo-Masache, Boris; Carrión-Mero, Paúl (Water, 2022)
Floods can be caused by heavy rainfall and the consequent overflow of rivers, causing low-lying areas to be affected. Populated regions close to riverbeds are the sectors most affected by these disasters, which requires modelling studies to generate different scenarios. The work focuses on the bibliometric analysis of the search for topics such as flood modelling focused on the research, risk, and assessment of these catastrophes, aiming to determine new trends and tools for their application in the prevention of these natural disasters. The methodology consists of: (i) search criteria and database selection, (ii) pre-processing of the selected data and software, and (iii) analysis and interpretation of the results. The results show a wide range of studies for dimensional analysis in different flood scenarios, which greatly benefit the development of flood prevention and risk strategies. In addition, this work provides insight into the different types of software and modelling for flood analysis and simulation and the various trends and applications for future modelling.

A PCA spatial pattern based artificial neural network downscaling model for urban flood hazard assessment - Carreau, J.; Guinot, V. (Advances in Water Resources, 2021)

Impacts et coûts indirects des stresseurs secondaires sur la santé biopsychosociale des sinistrés des inondations de 2019 - Maltais, Danielle; Bourdeau-Brien, Michaël; Gilbert, Simon; Normandin, Julie-Maude; Tchassem Pinlap, Jonas; Généreux, Mélissa; Landaverde, Elsa; Boudreault, Mathieu (2023)
Les inondations de 2017 et 2019 au Québec ont affecté respectivement 293 et 240 municipalités. Ces inondations ont généré une cascade d’évènements stressants (stresseurs primaires et secondaires) qui ont eu des effets sur la santé mentale de la population et retardé le processus de rétablissement des individus. Cette période de rétablissement peut s’échelonner sur plusieurs mois voire plusieurs années. Cette étude s’inscrit dans la spécificité de la recherche mixte mise de l’avant à travers trois stratégies de recherche, réalisées de façon séquentielle : 1) sondage populationnelle réalisé auprès de 680 personnes, 2) analyse de documents produits par les organisations participant au processus de rétablissement social des sinistrés, ou sur des analyses externes portant sur ces interventions de rétablissement et 3) entrevues semi-dirigées auprès de 15 propriétaires occupants ayant complété une demande d’indemnisation à la suite des inondations de 2019 et auprès de 11 professionnels et gestionnaires participant au processus de rétablissement social. Les entrevues semi-dirigées et les questionnaires complétés par les personnes sinistrées lors des inondations de 2019 démontrent que les principales sources de stress ayant des impacts sur la santé et le bien-être des répondants sont : 1) l’absence d’avertissement et la vitesse de la montée des eaux; 2) l’obligation de se relocaliser et la peur d’être victime de pillage; 3) le manque de solidarité et d’empathie de la part de certains employés du MSP; 4) la gestion des conflits familiaux; 5) la gestion de problèmes de santé nouveaux ou préexistants; 6) la complexité des demandes d’indemnisation; 7) la lourdeur et les délais des travaux de nettoyage ou de restauration; 8) les indemnités inférieures aux coûts engendrés par l’inondation; 9) les pertes matérielles subies, particulièrement ceux d’une valeur de plus de 50 000 $; et 10) la diminution anticipée de la valeur de sa résidence. À cela s’ajoute l’insatisfaction à l’égard du programme d’indemnisation du gouvernement du Québec (PGIAF) qui fait plus que doubler la prévalence des symptômes de stress post-traumatique. Les inondations entraînent également une perte de satisfaction ou de bien-être statistiquement significative. La valeur monétaire de cette perte de jouissance peut être exprimée en équivalent salaires. En moyenne, cette diminution du bien-être équivaut à une baisse de salaire de 60 000$ pour les individus ayant vécu une première inondation et à 100 000$ pour les individus ayant vécu de multiples inondations. Ces résultats suggèrent que les coûts indirects et intangibles représentent une part importante des dommages découlant des inondations. Ce projet de recherche vise également à analyser l’application du PGIAF et son influence sur les stresseurs vécus par les sinistrés dans le contexte de la pandémie de COVID-19. La principale recommandation de cette étude repose sur une analyse de documents, un sondage populationnel et des entrevues semi-dirigées. Ainsi, s’attaquer à la réduction de principaux stresseurs nécessite 1) d’améliorer la gouvernance du risque d’inondation, 2) d’intensifier la communication et le support aux sinistrés, et 3) de revoir les mécanismes d’indemnisation existants.

Global Asymmetries in the Influence of ENSO on Flood Risk Based on 1,600 Years of Hybrid Simulations - Del Rio Amador, L.; Boudreault, M.; Carozza, D. A. (Geophysical Research Letters, 2023)
AbstractEl Niño‐Southern Oscillation (ENSO) is often considered as a source of long‐term predictability for extreme events via its teleconnection patterns. However, given that its characteristic cycle varies from two to 7 years, it is difficult to obtain statistically significant conclusions based on observational periods spanning only a few decades. To overcome this, we apply the global flood risk modeling framework developed by Carozza and Boudreault to an equivalent of 1,600 years of bias‐corrected General Circulation Model outputs. The results show substantial anomalies in flood occurrences and impacts for El Niño and La Niña when compared to the all‐year baseline. We were able to obtain a larger global coverage of statistically significant results than previous studies limited to observational data. Asymmetries in anomalies for both ENSO phases show a larger global influence of El Niño than La Niña on flood hazard and risk.

A data science approach to climate change risk assessment applied to pluvial flood occurrences for the United States and Canada - Bourget, Mathilde; Boudreault, Mathieu; Carozza, David A.; Boudreault, Jérémie; Raymond, Sébastien (ASTIN Bulletin: The Journal of the IAA, 2024)
There is mounting pressure on (re)insurers to quantify the impacts of climate change, notably on the frequency and severity of claims due to weather events such as flooding. This is however a very challenging task for (re)insurers as it requires modeling at the scale of a portfolio and at a high enough spatial resolution to incorporate local climate change effects. In this paper, we introduce a data science approach to climate change risk assessment of pluvial flooding for insurance portfolios over Canada and the United States (US). The underlying flood occurrence model quantifies the financial impacts of short-term (12–48 h) precipitation dynamics over the present (2010–2030) and future climate (2040–2060) by leveraging statistical/machine learning and regional climate models. The flood occurrence model is designed for applications that do not require street-level precision as is often the case for scenario and trend analyses. It is applied at the full scale of Canada and the US over 10–25 km grids. Our analyses show that climate change and urbanization will typically increase losses over Canada and the US, while impacts are strongly heterogeneous from one state or province to another, or even within a territory. Portfolio applications highlight the importance for a (re)insurer to differentiate between future changes in hazard and exposure, as the latter may magnify or attenuate the impacts of climate change on losses.

Adapting to Rising Flood Risk: An Analysis of Insurance Solutions for Canada - Task Force on Flood Insurance and Relocation (Public Safety Canada, 2022)

Incentivizing Flood Risk Reduction for Homeowners - Boudreault, M (Global Risk Institute, 2024)
Read the latest report from UQAM and GRI, looking at options to reduce flood risk for Canadian homeowners living in flood-prone areas.

A Global Flood Risk Modeling Framework Built With Climate Models and Machine Learning - Carozza, David A.; Boudreault, Mathieu (Journal of Advances in Modeling Earth Systems, 2021)
AbstractLarge scale flood risk analyses are fundamental to many applications requiring national or international overviews of flood risk. While large‐scale climate patterns such as teleconnections and climate change become important at this scale, it remains a challenge to represent the local hydrological cycle over various watersheds in a manner that is physically consistent with climate. As a result, global models tend to suffer from a lack of available scenarios and flexibility that are key for planners, relief organizations, regulators, and the financial services industry to analyze the socioeconomic, demographic, and climatic factors affecting exposure. Here we introduce a data‐driven, global, fast, flexible, and climate‐consistent flood risk modeling framework for applications that do not necessarily require high‐resolution flood mapping. We use statistical and machine learning methods to examine the relationship between historical flood occurrence and impact from the Dartmouth Flood Observatory (1985–2017), and climatic, watershed, and socioeconomic factors for 4,734 HydroSHEDS watersheds globally. Using bias‐corrected output from the NCAR CESM Large Ensemble (1980–2020), and the fitted statistical relationships, we simulate 1 million years of events worldwide along with the population displaced in each event. We discuss potential applications of the model and present global flood hazard and risk maps. The main value of this global flood model lies in its ability to quickly simulate realistic flood events at a resolution that is useful for large‐scale socioeconomic and financial planning, yet we expect it to be useful to climate and natural hazard scientists who are interested in socioeconomic impacts of climate.

Climate, Spatial Dependence and Flood Risk: A U.S. - Erhardt, R; Boudreault, M; Carozza, D; Yu, K (Case Study. e-forum, Casualty Actuarial Society, 2022)

Multi-tracer approach to understand nitrate contamination and groundwater-surface water interactions in the Mediterranean coastal area of Guerbes-Senhadja, Algeria - Boumaiza, Lamine; Walter, Julien; Chesnaux, Romain; Huneau, Frédéric; Garel, Émilie; Erostate, Mélanie; Johannesson, Karen H.; Vystavna, Yuliya; Bougherira, Nabil; Bordeleau, Geneviève; Stotler, Randy L.; Blarasin, Mónica; et al. (Journal of Contaminant Hydrology, 2022)

Evaluating potential impacts of industrial activities at depth on shallow groundwater: Holistic integration of results from multidisciplinary research in eastern Canada - Rivard, C.; Lavoie, D.; Bordeleau, G.; Huchet, F.; Lefebvre, R.; Duchesne, M.J.; Pinet, N.; Séjourné, S.; Crow, H.; Bellefleur, G.; Brake, V.; Hinds, S. (Groundwater for Sustainable Development, 2024)

Saline and hydrocarbon-bearing fluids detected in shallow aquifers of southern New Brunswick, Canada: Natural occurrence, or deep migration along faults and industrial wellbores? - Bordeleau, G.; Lavoie, D.; Rivard, C.; Pinet, N.; Barton, D.; Hinds, S.; Al, Tom (Science of The Total Environment, 2024)

Large‐scale turbulent mixing at a mesoscale confluence assessed through drone imagery and eddy‐resolved modelling - Duguay, Jason; Biron, Pascale; Buffin‐Bélanger, Thomas (Earth Surface Processes and Landforms, 2022)
Abstract Confluences are sites of intense turbulent mixing in fluvial systems. The large‐scale turbulent structures largely responsible for this mixing have been proposed to fall into three main classes: vertically orientated (Kelvin–Helmholtz) vortices, secondary flow helical cells and smaller, strongly coherent streamwise‐orientated vortices. Little is known concerning the prevalence and causal mechanisms of each class, their interactions with one another and their respective contributions to mixing. Historically, mixing processes have largely been interpreted through statistical moments derived from sparse pointwise flow field and passive scalar transport measurements, causing the contribution of the instantaneous flow field to be largely overlooked. To overcome the limited spatiotemporal resolution of traditional methods, herein we analyse aerial video of large‐scale turbulent structures made visible by turbidity gradients present along the mixing interface of a mesoscale confluence and complement our findings with eddy‐resolved numerical modelling. The fast, shallow main channel (Mitis) separates over the crest of the scour hole's avalanche face prior to colliding with the slow, deep tributary (Neigette), resulting in a streamwise‐orientated separation cell in the lee of the avalanche face. Nascent large‐scale Kelvin–Helmholtz instabilities form along the collision zone and expand as the high‐momentum, separated near‐surface flow of the Mitis pushes into them. Simultaneously, the strong downwelling of the Mitis is accompanied by strong upwelling of the Neigette. The upwelling Neigette results in ∼50% of the Neigette's discharge crossing the mixing interface over the short collision zone. Helical cells were not observed at the confluence. However, the downwelling Mitis, upwelling Neigette and separation cell interact to generate considerable streamwise vorticity on the Mitis side of the mixing interface. This streamwise vorticity is strongly coupled to the large‐scale Kelvin–Helmholtz instabilities, which greatly enhances mixing. Comparably complex interactions between large‐scale Kelvin–Helmholtz instabilities and coherent streamwise vortices are expected at other typical asymmetric confluences exhibiting a pronounced scour hole.

Climate‐Driven Variations in Nitrogen Retention From a Riverine Submerged Aquatic Vegetation Meadow - Botrel, M.; Hudon, C.; Heffernan, J. B.; Biron, P. M.; Maranger, R. (Water Resources Research, 2022)
Abstract Large rivers can retain a substantial amount of nitrogen (N), particularly in submerged aquatic vegetation (SAV) meadows that may act as disproportionate control points for N retention. However, the temporal variation of N retention in large rivers remains unknown since past measurements were snapshots in time. Using high‐frequency plants and NO 3 − measurements over the summers 2012–2017, we investigated how the climate variation influenced N retention in a SAV meadow (∼10 km 2 ) at the confluence zone of two agricultural tributaries entering the St. Lawrence River. Distinctive combinations of water temperature and level were recorded between years, ranging from extreme hot‐low (2012) and cold‐high (2017) summers (2°C and 1.4 m interannual range). Using an indicator of SAV biomass, we found that these extreme hot‐low and cold‐high years had reduced biomass compared to hot summers with intermediate levels. In addition, changes in main stem water levels were asynchronous with the tributary discharges that controlled NO 3 − inputs at the confluence. We estimated daily N uptake rates from a moored NO 3 − sensor and partitioned these into assimilatory and dissimilatory pathways. Measured rates were variable but among the highest reported in rivers (median 576 mg N m −2 d −1 , range 60–3,893 mg N m −2 d −1 ) and SAV biomass promoted greater proportional retention and permanent N loss through denitrification. We estimated that the SAV meadow could retain up to 0.8 kt N per year and 87% of N inputs, but this valuable ecosystem service is contingent on how climate variations modulate both N loads and SAV biomass. , Plain Language Summary Large rivers remove significant amounts of nitrogen pollution generated by humans in waste waters and from fertilizers applied to agricultural lands. Underwater meadows of aquatic plants remove nitrogen particularly well. To keep the river clean, plants use the nitrogen themselves and promote conditions where bacteria can convert this pollution into a gas typically found in air. Measuring nitrogen removal in rivers is really difficult, and we do not know how climate conditions influence this removal or plant abundance. We successfully measured nitrogen pollution removal from an underwater plant meadow in a large river over six summers. We found that plant abundance and river nitrogen inputs were critical to determine how much pollution was removed, and that these were controlled by climatic conditions. Plant abundance was controlled by both water temperatures and levels. When water was warm and levels were neither too high nor too low, conditions were perfect for lots of plants to grow, which mainly stimulated bacteria that permanently eliminated nitrogen. We showed that the amount of nitrogen pollution removed over the summer by the meadow changes with climatic conditions but in general represents the amount produced by a city of half a million people. , Key Points Nitrogen retention and biomass were measured at a high resolution over six summers in a submerged aquatic vegetation meadow of a large river Among the highest riverine, nitrate uptake rates were recorded, and 47%–87% of loads were retained with plants favoring denitrification Interannual climate variations influenced nitrate retention by altering water levels, temperature, plant biomass, and tributary nitrate load

Combining quadrat, rake, and echosounding to estimate submerged aquatic vegetation biomass at the ecosystem scale - Botrel, Morgan; Hudon, Christiane; Biron, Pascale M.; Maranger, Roxane (Limnology and Oceanography: Methods, 2023)
Abstract Measuring freshwater submerged aquatic vegetation (SAV) biomass at large spatial scales is challenging, and no single technique can cost effectively accomplish this while maintaining accuracy. We propose to combine and intercalibrate accurate quadrat‐scuba diver technique, fast rake sampling, and large‐scale echosounding. We found that the overall relationship between quadrat and rake biomass is moderately strong (pseudo R 2 = 0.61) and varies with substrate type and SAV growth form. Rake biomass was also successfully estimated from biovolume (pseudo R 2 = 0.57), a biomass proxy derived from echosounding. In addition, the relationship was affected, in decreasing relevance, by SAV growth form, flow velocity, acoustic data quality, depth, and wind conditions. Sequential application of calibrations yielded predictions in agreement with quadrat observations, but echosounding predictions underestimated biomass in shallow areas (< 1 m) while outperforming point estimation in deep areas (> 3 m). Whole‐system quadrat‐equivalent biomass from echosounding differed by a factor of two from point survey estimates, suggesting echosounding is more accurate at larger scales owing to the increased sample size and better representation of spatial heterogeneity. To decide when an individual or a combination of techniques is profitable, we developed a step‐by‐step guideline. Given the risks of quadrat‐scuba diver technique, we recommend developing a one‐time quadrat–rake calibration, followed by the use of rake and echosounding when sampling at larger spatial and temporal scales. In this case, rake sampling becomes a valid ground truthing method for echosounding, also providing valuable species information and estimates in shallow waters where echosounding is inappropriate.

Density effects on streamwise-orientated vorticity at river confluences: a laboratory investigation - Duguay, J.M.; Biron, P.M.; Lacey, R.W.J. (Journal of Fluid Mechanics, 2023)
Small differences in the densities of a river confluence's tributaries (i.e. 0.5 kg m $^{-3}$ ) have been proposed to cause coherent streamwise-oriented vortices (SOVs) in its mixing interface. These secondary flow structures are thought to result from density-driven gravity currents being laterally confined between the converging flows. However, empirical evidence for density SOVs and the confined gravity current mechanism is lacking. To this end, experiments are carried out in a laboratory confluence permitting a spectrum of thermal density differences between its tributaries. Particle image velocimetry and laser-induced fluorescence are used simultaneously to study the mixing interface's dynamics. The sensitivity of the mixing interface's secondary flow structure to the confluence's momentum ratio and the magnitude of the density difference is evaluated. Density SOVs are confirmed in the mixing interface and are caused by the gravity currents being confined laterally as the opposing flows merge and accelerate downstream. The SOVs are largest and most coherent when the momentum of the dense channel is greater than that of the light channel. The dynamics of these secondary flow structures is strongly coupled to periodic vertically orientated Kelvin–Helmholtz instabilities. The striking similarities between the empirically reproduced SOVs herein and those recently observed at the Coaticook-Massawippi confluence (Quebec, Canada), despite a two-order magnitude difference in physical scale, suggest density SOVs are a scale-independent flow structure at confluences when specific, yet relatively common, hydraulic and density conditions align.

Trajectoires et visées de l'hydrogéomorphologie au Québec - Buffin‐Bélanger, Thomas; Lachapelle, Félix; Biron, Pascale; Boivin, Maxime (Canadian Geographies / Géographies canadiennes, 2024)
Résumé L'hydrogéomorphologie étudie la dynamique des rivières en se concentrant sur les interactions liant la structure des écoulements, la mobilisation et le transport des sédiments et les morphologies qui caractérisent les cours d'eau et leur bassin‐versant. Elle offre un cadre d'analyse et des outils pour une meilleure intégration des connaissances sur la dynamique des rivières pour la gestion des cours d'eau au sens large, et plus spécifiquement, pour leur restauration, leur aménagement et pour l'évaluation et la prévention des risques liés aux aléas fluviaux. Au Québec, l'hydrogéomorphologie émerge comme contribution significative dans les approches de gestion et d'évaluation du risque et se trouve au cœur d'un changement de paradigme dans la gestion des cours d'eau par lequel la restauration des processus vise à augmenter la résilience des systèmes et des sociétés et à améliorer la qualité des environnements fluviaux. Cette contribution expose la trajectoire de l'hydrogéomorphologie au Québec à partir des publications scientifiques de géographes du Québec et discute des visées de la discipline en recherche et en intégration des connaissances pour la gestion des cours d'eau . , Abstract Hydrogeomorphology studies river dynamics, focusing on the interactions between flow structure, sediment transport, and the morphologies that characterize rivers and their watersheds. It provides an analytical framework and tools for better integrating knowledge of river dynamics into river management in the broadest sense, and more specifically, into river restoration as well as into the assessment and prevention of risks associated with fluvial hazards. In Quebec, hydrogeomorphology is emerging as a significant contribution to risk assessment and management approaches, and is at the heart of a paradigm shift in river management whereby process restoration aims to increase the resilience of fluvial systems and societies, and improve the quality of fluvial environments. This contribution outlines the trajectory of hydrogeomorphology in Quebec, based on scientific publications by Quebec geographers, and discusses the discipline's aims in research and knowledge integration for river management . , Messages clés Les géographes du Québec ont contribué fortement au développement des connaissances et outils de l'hydrogéomorphologie. L'hydrogéomorphologie a évolué d'une science fondamentale à une science où les connaissances fondamentales sont au service de la gestion des cours d'eau. L'hydrogéomorphologie et le cortège de connaissances et d'outils qu'elle promeut font de cette discipline une partenaire clé pour une gestion holistique des cours d'eau.

Societal drivers for the integration of hydrogeomorphology and human benefits in river restoration projects - Gariépy‐Girouard, Étienne; Buffin‐Bélanger, Thomas; Biron, Pascale M. (River Research and Applications, 2025)
Abstract Integrating hydrogeomorphological (HGM) principles into the restoration of degraded rivers can achieve sustainable results and provide various human benefits. HGM principles mainly involve understanding the context and processes that shape a fluvial system before any intervention, in order to support its dynamism and to align with its potential functioning and uses. Despite recent management approaches inspired by HGM principles, most restoration projects carried out in Quebec (Canada) are not process‐based and target specific one‐dimensional objectives. Although there is an overall lack of post‐project monitoring, several projects appear to have failed or had mixed success. This research aims to shed light on the diversity of societal drivers behind river restoration projects and to examine how they influence the integration of HGM principles and human benefits. Four restoration projects were characterized through participant observation and interviews with the organizations running them. Representatives of two ministries involved in river restoration and management were also interviewed. The results show that projects were mainly shaped by public acceptance disregarding HGM principles, which can lead to poorly‐informed action. Project funding and stakeholders' expertise have also challenged project implementation and played a key role in defining their objectives. The addition of these components improve the current analytical frameworks for identifying river restoration objectives. Depending on specific sociocultural, political and legislative contexts, funding programs and stakeholders' expertise may either facilitate or restrict the integration of HGM principles and human benefits in the projects. Recognizing these key drivers reframes river restoration as a fundamentally social activity and enlightens how they could impel innovative approaches towards more sustainable results.

Données d’utilisation d’un autotraitement guidé en ligne pour promouvoir la résilience après une catastrophe naturelle - Lebel, Jessica; Flores-Tremblay, Thalie; Binet, Émilie; Ouellet, Marie-Christine; Belleville, Geneviève (Santé mentale au Québec, 2021)
Objectifs Malgré l’existence de plusieurs traitements en ligne pour les personnes avec un trouble de stress posttraumatique (TSPT), peu d’études se sont penchées sur les données d’utilisation d’une telle intervention. Étant donné le potentiel de la modalité en ligne à pallier les obstacles limitant l’accès à l’aide psychologique, il importe de documenter les interactions des usagers avec ces outils en lien avec l’amélioration des symptômes ciblés. L’objectif de cette étude est de documenter les données d’utilisation de la plateforme de traitement en ligne RESILIENT par les personnes évacuées des feux de Fort McMurray, Alberta (Canada), et d’examiner leur association avec l’efficacité du traitement sur les symptômes de trouble de stress posttraumatique (TSPT), d’insomnie et de dépression, et l’adhésion au traitement, mesurée par le nombre de modules consultés par les participants. Méthode Quatre-vingt-dix-sept personnes évacuées des feux de Fort McMurray présentant des symptômes de TSPT, d’insomnie et de dépression sont incluses dans la présente étude. Les participants étaient invités à utiliser la plateforme RESILIENT, un autotraitement en ligne guidé par un thérapeute qui cible les symptômes de TSPT, le sommeil et l’humeur, et comprend 12 modules offrant des stratégies de thérapies cognitives et comportementales (TCC) basées sur les données probantes. Des données d’utilisation objectives (p. ex. nombre de modules consultés) et subjectives (p. ex. niveau d’efforts investis) ont été recueillies. Résultats Afin de prédire la réduction des symptômes de TSPT, de dépression et d’insomnie, ainsi que le nombre de modules consultés par les participants, des modèles de régressions séquentielles ont été effectués, avec un contrôle statistique pour les symptômes prétraitement, l’âge et le genre. Les modèles finaux ont révélé qu’une réduction des symptômes de TSPT, de dépression et d’insomnie était prédite significativement par le nombre de modules consultés (β = - 0,41 ; - 0,53 ; - 0,49 respectivement, tous p < 0,001) ainsi que par le niveau d’efforts moyen autorapporté au module 7 (mi-parcours) (β = - 0,43 ; p < 0,001 ; β = - 0,38 ; p = 0,005 et β = - 0,36 ; p = 0,007 respectivement). Le nombre de modules consultés, par ailleurs, était prédit significativement par le nombre de mots dans le 4 e module (β = 0,34 ; p < 0,001) et dans le 7 e module (β = 0,44 ; p < 0,001), ainsi que par le nombre d’entrées dans le journal du sommeil (β = 0,28 ; p < 0,001). Conclusion Les résultats ont confirmé qu’une plus grande interaction avec la plateforme influence positivement l’efficacité du traitement et qu’une utilisation accrue en début de traitement semble être un bon prédicteur de l’achèvement de celui-ci. Cette étude confirme l’importance de soutenir l’engagement des participants envers le traitement en ligne afin d’optimiser son efficacité. , Objectives Despite the existence of several online treatments for people with posttraumatic stress disorder (PTSD), few studies have examined usage data for such interventions. Given the potential of the online modality to alleviate barriers limiting access to psychological help, it is important to document users’ interactions with these tools in relation to the improvement of targeted symptoms. The objective of this study is to document usage data of the online treatment platform RESILIENT by people evacuated from the Fort McMurray, Alberta (Canada) fires, and to examine their association with the effectiveness of treatment on symptoms of posttraumatic stress disorder (PTSD), insomnia and depression, and adherence to treatment, as measured by the number of modules accessed by participants. Methods Ninety-seven people evacuated from the Fort McMurray fires with symptoms of PTSD, insomnia and depression were included in this study. Participants were invited to use the RESILIENT platform, an online therapist-assisted self-help treatment program that targets PTSD symptoms, sleep and mood, and includes 12 modules offering evidence-based cognitive-behavioural therapy (CBT) strategies. Both objective (e.g., number of modules accessed) and subjective (e.g., level of effort invested) usage data were collected. Results In order to predict the reduction in PTSD, depression and insomnia symptoms, as well as the number of modules accessed by participants, sequential regression models were conducted, with statistical control for pretreatment symptoms, age and gender. The final models revealed that a reduction in PTSD, depression and insomnia symptoms was significantly predicted by the number of modules accessed (β = -.41; -.53; -.49 respectively, all p <.001) as well as the mean self-reported level of effort at module 7 (midway) (β = -.43; p <.001; β = -.38; p = .005 and β = -.36; p = .007 respectively). The number of modules accessed, on the other hand, was significantly predicted by the number of words in the 4th module (β = .34; p <.001) and 7th module (β = .44; p <.001) and the number of sleep diary entries (β = .28; p <.001). Conclusion These results confirmed that increased interaction with the platform positively influences treatment effectiveness and that increased use at the beginning of treatment appears to be a good predictor of treatment completion. This study confirms the importance of sustaining participants’ commitment to online treatment in order to optimize its effectiveness.

La communication de crise et de risque en contexte de catastrophes météorologiques : Synthèse de littérature - Cissé, Iliassou (Présenté au Réseau Inondations InterSectoriel du Québec, 2024)

Determination of critical hydraulic parameters affecting erosion in spillways using experimental and numerical analyses - Motahari Moghadam, Elaheh (Université du Québec à Chicoutimi, 2025)
Dam spillways are susceptible to a range of engineering challenges including structural deficiencies, insufficient discharge capacity, and mechanical failures; however, a particularly significant issue is hydraulic erosion, which poses a significant threat to dam infrastructure. This necessitates a comprehensive assessment of both hydraulic and rock mechanical parameters to ensure structural integrity and operational resilience. In the rock mechanical aspect of hydraulic erosion, the resistive capacity of the material holds great importance, while in the hydraulic aspect, the erosive force of water plays a pivotal role. Hence, neglecting these incidents would increase the risk of overtopping and subsequent downstream flooding, thereby impacting the overall safety and operational reliability of the dam. This study focuses on investigating the hydraulic parameters of a smooth surface unlined open channel spillway. By utilizing both numerical modeling and experimental analysis, we aim to explore how variations in these parameters impact erosion in dams’ spillways. The research centers on the Romaine 4 dam spillway, situated in the northeastern region of Quebec in Canada as a representative case study. The physical model of this spillway was constructed at the Université du Québec à Chicoutimi, where we carried out the experimental analyses. In this research, we also conducted a comprehensive numerical analysis using Finite Volume Method (FVM), enabling a detailed examination of three-dimensional flow behavior within the spillway. This enabled a precise monitoring of the fluid motion patterns. Moreover, an experimental approach was utilized to enhance the accuracy and reliability of the results. This involved conducting detailed tests on the reduced-scale model using a XYZ robotic system capable of movement in X,Y,Z directions and capturing position, velocity and pressure. The results of numerical and experimental analyses reveal that the numerical model effectively captures the overall flow characteristics, closely predicting the average velocity throughout the channel. However, it indicates limitations in accurately predicting extreme velocities, such as maximum and minimum values. The results show that the maximum discrepancies between experimental and numerical data primarily concern extreme velocities, with the numerical model underestimating maximum velocities and overestimating minimum velocities, with errors more pronounced at higher flow rates and upstream. This discrepancy can reach up to 60% in certain areas. Furthermore, the study examined the effects of gates on variability of hydraulic parameters like flow depth and velocity. The analysis of a number of gate configurations revealed that double-gate spillways maintain more consistent flow depths across all significant cross-sections. By explaining the complex interaction between hydraulic behavior and spillway design, this research attempts to advance our understanding of hydraulic-prone erosion areas in dam spillways and ensure the long-term resilience of dam infrastructure. Les évacuateurs de crues des barrages sont sujets à divers défis d'ingénierie, incluant des défaillances structurelles, une capacité d'évacuation insuffisante et des pannes mécaniques; cependant, l'érosion hydraulique constitue une problématique particulièrement importante qui menace l'infrastructure des barrages. Il est donc nécessaire d’évaluer de manière approfondie les paramètres hydrauliques et mécaniques des roches afin d’assurer l’intégrité structurelle et la résilience opérationnelle. Dans l’aspect mécanique des roches concernant l’érosion hydraulique, la capacité de résistance du matériau revêt une grande importance, tandis que dans l’aspect hydraulique, la force érosive de l’eau joue un rôle essentiel. Par conséquent, ignorer ces phénomènes augmenterait le risque de débordement et d’inondation en aval, impactant ainsi la sécurité et la fiabilité opérationnelle globale du barrage. Cette étude se concentre sur l’analyse des paramètres hydrauliques d'un évacuateur de crues à canal ouvert non revêtu et à surface lisse. En utilisant à la fois la modélisation numérique et l’analyse expérimentale, nous visons à explorer comment les variations de ces paramètres influencent l’érosion dans les évacuateurs de crues des barrages. La recherche porte sur l’évacuateur de crues du barrage Romaine 4, situé dans la région nord-est du Québec au Canada, en tant qu’étude de cas représentative. Le modèle physique de cet évacuateur a été construit à l’Université du Québec à Chicoutimi, où nous avons effectué les analyses expérimentales. Dans cette recherche, nous avons également réalisé une analyse numérique complète en utilisant la méthode des volumes finis (FVM), permettant un examen détaillé du comportement tridimensionnel de l’écoulement dans l’évacuateur. Cela a permis un suivi précis des schémas de mouvement du fluide. En outre, une approche expérimentale a été utilisée pour accroître la précision et la fiabilité des résultats, en réalisant des tests détaillés sur le modèle réduit à l’aide d’un système robotisé XYZ qui est capable de se déplacer dans trois directions (X, Y, Z), pour effectuer des prises de mesures de position, vitesse et pression. Les résultats des analyses numériques et expérimentales révèlent que le modèle numérique capture efficacement les caractéristiques générales de l’écoulement, prédisant de manière précise la vitesse moyenne dans le canal. Cependant, il présente des limitations dans la prédiction précise des pression dynamique et statique extrêmes comme les valeurs maximales et minimales. Les résultats montrent que les écarts maximaux entre les données expérimentales et numériques concernent principalement les vitesses extrêmes, le modèle numérique sous-estimant les vitesses maximales et surestimant les minimales, avec des erreurs plus marquées aux débits élevés et en amont. Cet écart peut aller jusqu’aux 60% à certains endroits. Par ailleurs, l’étude a examiné les effets des vannes sur la variabilité des paramètres hydrauliques tels que la profondeur de l’écoulement et la vitesse. L’analyse de plusieurs configurations de vannes a révélé que les évacuateurs à double vanne maintiennent des profondeurs d’écoulement plus constantes à travers toutes les sections transversales significatives. En expliquant l’interaction complexe entre le comportement hydraulique et la conception des évacuateurs de crues, cette recherche vise à améliorer notre compréhension des zones sujettes à l’érosion hydraulique dans les évacuateurs de barrages et à assurer la résilience à long terme de l’infrastructure des barrages.

Earth Observation of Ecosystem Structure and Function in Ecosystem Service Models - Ferguson, David (2025)

Évaluation préliminaire du risque lié aux aléas HGM des petits cours d'eau dans la région du Bas-Saint-Laurent - Bilé, Dahoua Virginie (Université du Québec à Rimouski, 2025)
RÉSUMÉ : Peu d’outils permettent de faire un audit des aléas hydrogéomorphologiques (HGM) auxquels les petits cours d’eau (PCE) sont sujets, pourtant à ces PCE sont associés plusieurs aléas HGM qui compromettent l’intégrité d’enjeux qui y sont exposés. L’objectif du projet vise à appliquer des outils SIG (ExZeco et Geomorphon landform) employés avec un modèle numérique d’élévation à haute résolution, pour l’évaluation préliminaire du risque lié aux aléas HGM des PCE. Cette évaluation préliminaire ne tient pas compte de la probabilité d’occurrence de l’aléa. Elle est plutôt basée sur la position des enjeux par rapport aux aléas HGM simulés sur les cônes alluviaux et les plaines d’inondations. Pour ce faire, quatre bassins versants de la région du Bas-Saint-Laurent ont été identifiés comme sites d’étude. La méthodologie se base dans un premier temps sur l’établissement d’un portrait d’aléa HGM, puis dans un deuxième temps sur la caractérisation de ces aléas en évaluant leur intensité, leur étendue et leur distribution spatiale et finalement, l’évaluation de l’exposition des enjeux vis-à-vis des aléas. Les résultats montrent que des enjeux (parcelle agricole, route, résidences) sont exposés à l’aléa torrentiel sur deux cônes alluviaux cartographiés dans les bassins versants pilotes, et des résidences sont exposées à un débordement probable de cours d’eau. L’analyse du risque a été réalisée avec obstruction probable des ponceaux sur le LiDAR et sans obstruction des ponceaux. L’aléa est de forte intensité dans le risque probable lié aux obstructions, et les enjeux exposés sont notamment les résidences, les routes et des portions de terre agricole. Des témoignages de riverains ont permis de valider l’intensité des aléas prédits par ExZeco. -- Mot(s) clé(s) en français : Aléa HGM, Évaluation préliminaire, cône alluvial, plaine d’inondation, LiDAR. -- ABSTRACT : Few tools are available for auditing the hydrogeomorphic hazards (HGM) to which small watercourses (PCE) are subject, yet these PCE are associated with several HGM hazards that compromise the integrity of issues exposed to them. The aim of the project is to apply GIS tools (ExZeco and Geomorphon landform) used in conjunction with a high-resolution digital elevation model, for the preliminary assessment of the risk associated with HGM hazards in PCE. This is an assessment based solely on the position of issues in relation to the HGM hazards to which alluvial fans and floodplains are subject. To this end, four watersheds in the Lower St. Lawrence region were identified as study sites. The methodology is based firstly on the establishment of a HGM hazard profile, then on the characterization of these hazards by assessing their intensity, extent and spatial distribution, and finally on the assessment of the level of exposure of issues to these hazards. The results show that issues (agricultural plots, roads, residences) are exposed to torrential hazards on two alluvial fans mapped in the pilot watersheds, and residences are exposed to probable river overflow. The risk analysis was carried out with probable culvert obstructions on the LiDAR and without culvert obstructions. The probable risk associated with obstructions is of high intensity, and the stakes exposed include residences, roads and portions of agricultural land. Testimonials from residents validated the intensity of the hazards predicted by ExZeco. -- Mot(s) clé(s) en anglais : HGM Hazard, Preliminary Assessment, Alluvial Fan, Floodplain, LiDAR.

Understanding Hydrogeomorphological Impacts of Artisanal Aggregate Mining on Alluvial Streams and Fluvial Landscape of the Rusine Subwatershed in Northern Rwanda. - Dos Santos, Nicolas (2025)

Modelling and Simulation of Extreme Waves in Floods and Mudflows - Yu, Boyuan (2024)

Comportement en cisaillement de ponceaux rectangulaires en béton armé avec des barres en polymère renforcé de fibre (PRF) - Elnady, Ahmed (2025)
Les ponceaux en béton armé sont l’un des éléments clés de la construction des autoroutes, facilitant le passage de l’eau et des eaux de drainage sous les routes et les voies ferrées. La structure des ponceaux joue également un rôle essentiel dans la gestion des risques d’inondation en tant qu’outil intégral de prévention des inondations qui protège les villes et les vies humaines. Les ponceaux sont soumis à des expositions agressives; l’eau qui coule et le sol environnant représentent un environnement difficile pour la structure du ponceau. L’eau peut contenir des acides, des déchets industriels, des produits chimiques et des chlorures. De plus, dans des régions comme l’Amérique du Nord, l’utilisation de sels de déglaçage accélère la corrosion de l’armature en acier. Cette étude a porté sur le comportement structurel et les performances de seize ponceaux rectangulaires en béton en vraie grandeur soumis à des charges concentrées verticales. Treize spécimens avaient une travée de 1500 mm, une flèche de 1500 mm, une épaisseur de dalle de 150 mm et une épaisseur de paroi de 150 mm. Les trois autres spécimens avaient une travée de 1800 mm, une flèche de 1500 mm et une épaisseur de dalle et de mur de 180 mm. Tous les ponceaux rectangulaires avaient des arêtiers aux raccordements entre les dalles et les murs, qui avaient les mêmes dimensions que les murs latéraux. La longueur du joint était de 1219 mm pour tous les spécimens et la couverture de béton a été maintenue constante à 25 mm. Les paramètres examinés étaient le ratio de renforcement longitudinal-FRP (0,83 %, 1,25 % et 1,67 %), le matériau de renforcement (PRFV, PRFB et acier), l’emplacement de la charge concentrée, la résistance à la compression du béton, le matériau d’assise de l’essai et l’effet de taille. Les résultats des essais ont été analysés en termes de résistance au cisaillement dans les deux directions, de mode de défaillance, de fléchissement de la dalle supérieure, de déplacement latéral des parois latérales et de déformations du béton et des armatures. L’effet de chaque paramètre sur le comportement a été évalué et discuté. Une étude théorique a été menée pour modifier l’équation de l’action du cisaillement bidirectionnel de la norme CAN/CSA S6:19 afin de prendre en compte les caractéristiques du matériau de renforcement PRF. L’équation modifiée a montré de bonnes prédictions de la capacité du cisaillement bidirectionnel expérimental des ponceaux en béton testés. En outre, une autre approche théorique a été menée de manière innovante pour proposerune nouvelle formule permettant de prédire avec précision la capacité de cisaillement dans les deux directions de la dalle supérieure des ponceaux rectangulaires en béton renforcés par des PRF. La formule proposée a montré d’excellentes prédictions de la capacité de cisaillement expérimentale dans les deux directions. En outre, la formule proposée a été développée pour prédire la capacité de cisaillement dans les deux directions des dalles en béton renforcé par des PRFV avec retenue des bords, représentant les dalles de pont en béton renforcé par des PRFV. Les analyses théoriques ont été soutenues par un modèle de régression linéaire et évaluées par des moyens statistiques.

Dynamique de l'aléa torrentiel au long de la rivière Chaudière : analyse historique et modélisation - Constantineau, Jo-Annie (UQAM, 2025)
Dans le bassin-versant de la rivière Chaudière, les inondations sont partie intégrante de la réalité territoriale. En effet, de nombreuses études ont été réalisées dans les dernières années concernant les inondations en eau libre et par embâcle. Néanmoins, on observe que les inondations torrentielles, ou crues torrentielles, bien qu’omniprésentes dans la région, sont très peu documentées à ce jour. Le présent mémoire s’intéresse à la dynamique spatio-temporelle de ces évènements et aux facteurs aggravants présents sur le territoire qui tendent à amplifier le phénomène. Par une double approche qualitative et quantitative, qui combine une recension historique, la caractérisation des sous-bassins-versants et la modélisation des facteurs de vulnérabilité environnementale, nous avons dressé un premier portrait de l’aléa torrentiel à l’échelle du bassin-versant de la rivière Chaudière. Ainsi, nous avons pu répertorier 53 évènements à caractère torrentiel pour la période de 1900 à aujourd’hui. La collecte des informations liées aux crues torrentielles, soit les facteurs météorologiques, anthropiques et géomorphologiques ont permis d’établir des constats généraux quant à l’occurrence de celles-ci. L’occurrence des évènements à caractère torrentiel semble premièrement liée aux passages d’évènements météorologiques extrêmes. Les facteurs aggravants consistent en un aménagement du territoire qui accroît le ruissellement (augmentation des surfaces minéralisées et diminution des forêts, prairies et milieux humides) et une disposition géomorphologique des tributaires (forte pente et compacité) qui provoque une amplification du ruissellement lors de fortes précipitations. L’analyse multicritère repose sur l’addition d’indices amplifiant le ruissellement lors de fortes précipitations (pente, occupation du sol et potentiel de ruissellement). La comparaison entre les sous-bassins-versants présentant les valeurs les plus élevées et ceux ayant connu le plus d'événements d'inondation selon la recension historique a démontré la pertinence de cette méthode pour identifier, de manière préliminaire, les sous-bassins-versants potentiellement vulnérables à l’aléa torrentiel. Cette étude se veut donc un premier jalon dans l’acquisition de connaissances sur la dynamique torrentielle dans le bassin-versant de la rivière Chaudière. _____________________________________________________________________________ MOTS-CLÉS DE L’AUTEUR : inondation, torrents, crues torrentielles, pluies torrentielles, aléas torrentiels, bassin-versant de la rivière Chaudière, facteurs aggravants, conditions hydrométéorologiques

Information Seeking and Information Sharing Related to Hurricane Harvey - Yang, Janet Z.; Zhuang, Jun (Journalism & Mass Communication Quarterly, 2020)
This study is focused on social cognitive variables that motivate information seeking and information sharing related to Hurricane Harvey. Survey results from a nationally representative sample and a quota sample of Houston residents indicate that climate change beliefs and issue salience are consistent predictors of risk perception, which increases individuals’ negative emotions and information insufficiency. This need for information subsequently motivates information seeking and information sharing. Informational subjective norms are significantly related to seeking and sharing in the Houston sample, whereas perceived information gathering capacity and trust in media are significant predictors in the national sample.

Twitter communication during winter storm Uri in San Antonio, Texas - Implications for climate resiliency planning - Terracciano, Emmalyn; Han, Albert Tonghoon (Cities, 2023)

Review of social influence in crisis communications and evacuation decision-making - Sadri, Arif Mohaimin; Ukkusuri, Satish V.; Ahmed, Md Ashraf (Transportation Research Interdisciplinary Perspectives, 2021)

A Meta-Cognitive Approach to Predicting Hurricane Evacuation Behavior - Goldberg, Matthew H.; Marlon, Jennifer R.; Rosenthal, Seth A.; Leiserowitz, Anthony (Environmental Communication, 2020)

Flood Risk Management via Risk Communication, Cognitive Appraisal, Collective Efficacy, and Community Action - Lin, Carolyn A. (Sustainability, 2023)
Climate change and more frequent severe storms have caused persistent flooding, storm surges, and erosion in the northeastern coastal region of the United States. These weather-related disasters have continued to generate negative environmental consequences across many communities. This study examined how coastal residents’ exposure to flood risk information and information seeking behavior were related to their threat appraisal, threat-coping efficacy, and participation in community action in the context of building social resilience. A random sample of residents of a coastal community in the Northeastern United States was selected to participate in an online survey (N = 302). Key study results suggested that while offline news exposure was weakly related to flood vulnerability perception, online news exposure and mobile app use were both weakly associated with flood-risk information seeking. As flood vulnerability perception was strongly connected to flood severity perception but weakly linked to lower self-efficacy beliefs, flood severity perception was weakly and moderately associated with response-efficacy beliefs and information seeking, respectively. Furthermore, self-efficacy beliefs, response efficacy beliefs, and flood-risk information seeking were each a weak or moderate predictor of collective efficacy beliefs. Lastly, flood risk information-seeking was a strong predictor and collective efficacy beliefs were a weak predictor of community action for flood-risk management. This study tested a conceptual model that integrated the constructs from risk communication, information seeking, and protection motivation theory. Based on the modeling results reflecting a set of first-time findings, theoretical and practical implications are discussed.

The role of public relations in building community resilience to natural disasters: perspectives from Sri Lanka and New Zealand - Dharmasena, M. K. Gayadini Imesha; Toledano, Margalit; Weaver, C. Kay (Journal of Communication Management, 2020)
The paper identifies a role for public relations in disaster management by analysing disaster and communication managers' understanding of community resilience and their use of communication in the context of two different cultural environments.,The research study comprised 51 in-depth qualitative interviews with disaster managers in Sri Lanka and New Zealand, which were thematically analysed using the software programme NVivo 10.,The study identified cultural differences in Sri Lanka and New Zealand that impact on how managers' communicate in natural disaster situations. The findings indicated that public relations’ understanding of communities’ cultures, their communication, networking and lobbying skills could further enhance the effectiveness of efforts to build community resilience to disasters.,Nations are complex multicultural realities; the findings cannot be generalized to make claims about how natural disasters are managed in different national contexts.,The paper identifies the unrealized potential of public relations’ expertise in communication, community relations, networking and lobbying to contribute to building community resilience to natural disasters.,By supporting efforts to build community resilience to disasters, public relations practitioners can contribute to social well-being in times of catastrophic natural disasters.,The paper adds an innovative perspective to public relations crisis literature by identifying the potential contribution of public relations’ concepts and practices to build community resilience to natural disasters. It demonstrates how sociocultural differences may affect disaster communication strategies.

When We Don't Want to Know More: Information Sufficiency and The Case Of Swedish Flood Risks - Lakew, Yuliya; Olausson, Ulrika (Journal of International Crisis and Risk Communication Research, 2023)
This study investigates the phenomenon of information (in)sufficiency in the context of flood risks. Individuals’ perception of how much risk information they need is a major trigger and driver of information-seeking behavior, and therefore it is an important part of creating effective preventive risk-communication campaigns. To understand factors that contribute to individuals’ sense of information (in)sufficiency, the roles played by prior experiences of floods and general risk sensitivity were analyzed using survey data from residents in flood-risk zones. The findings highlight that every third respondent reported a state of information sufficiency. Residents with prior experience evaluate their information sufficiency level based on their perception of consequences of future floods. But it is general risk sensitivity that best explains need for more information.

How to Exchange Stories of Local Flood Resilience From Flood Rich Areas to the Flooded Areas of the Future - Holmes, Andrew; McEwen, Lindsey (Environmental Communication, 2020)

Personal Stories Can Shift Climate Change Beliefs and Risk Perceptions: The Mediating Role of Emotion - Gustafson, Abel; Ballew, Matthew T.; Goldberg, Matthew H.; Cutler, Matthew J.; Rosenthal, Seth A.; Leiserowitz, Anthony (Communication Reports, 2020)

Addressing the climate change adaptation puzzle: a psychological science perspective - Bechtoldt, Myriam N.; Götmann, Alexander; Moslener, Ulf; Pauw, W. Pieter (Climate Policy, 2021)

The Usefulness of Global and Regional Precipitation and Temperature Reanalyses for Flood Modeling at the Catchment Scale - Valdez, Emixi; Anctil, Francois; Ramos, Maria-Helena (AGU Fall Meeting Abstracts, 2022)
Atmospheric reanalysis data provides a numerical description of global and regional water cycles by combining models and observations. These datasets are increasingly valuable as a substitute for observations in regions where these are scarce. They could significantly contribute to reducing losses by feeding flood early warning systems that can inform the population and guide civil security action. We assessed the suitability of two different precipitation and temperature reanalysis products readily available for predicting historic flooding of the La Chaudière River in Quebec: 1) Environment and Climate Change Canada's Regional Deterministic Reanalysis System (RDRS-v2) and 2) ERA5 from the Copernicus Climate Change Service. We exploited a multi-model hydrological ensemble prediction system that considers three sources of uncertainty: initial conditions, model structure, and weather forcing to produce streamflow forecasts up to 5 days into the future with a time step of 3 hours. These results are compared to a provincial reference product based on gauge measurements of the Ministère de l'Environnement et de la Lutte contre les Changements Climatiques. Then, five conceptual hydrological models were calibrated with three different meteorological datasets (RDRS-v2, ERA5, and observational gridded) and fed with two ensemble weather forecast products: 1) the Regional Ensemble Prediction System (REPS) from the Environment and Climate Change Canada and 2) the ensemble forecast issued by the European Centre for Medium-Range Weather Forecasts (ECMWF). Results reveal that the calibration of the model with reanalysis data as input delivered a higher accuracy in the streamflow simulation providing a useful resource for flood modeling where no other data is available. However, although the selection of the reanalysis is a determinant of capturing the flood volumes, selecting weather forecasts is more critical in anticipating discharge threshold exceedances.