Political development predicts reduced human cost of flooding - Vesco, Paola; Von Uexkull, Nina; Vestby, Jonas; Buhaug, Halvard (Nature Communications, 2025)
Abstract Societal impacts from extreme climate and weather events depend not only on hazard magnitude but also on the vulnerability of the affected population. Existing research suggests that adverse socioeconomic conditions are associated with higher baseline vulnerability to many types of risk, but comparatively little attention has been paid to political drivers of vulnerability. Focusing on floods, the most frequent climate-related hazard, this article evaluates the impact of political development on flood mortality. Findings from a Bayesian predictive analysis of global flood impacts from 2000 to 2018 suggest that democracy, institutional quality, and peace reduce the predicted human cost of flooding. The effect of a breakdown of peace on predicted flood mortality is especially pronounced. These results indicate that promoting peace, justice, and strong institutions (Sustainable Development Goal 16) can help to mitigate disaster risks and support effective climate change adaptation.
Keywords: Scopus_Alert_05_Decembre_2025

Combining Climate Forcings and Initial Hydrological Conditions for Enhanced Subseasonal to Seasonal Flood Forecasting - Jiang, Lulu; Wu, Huan; Li, Chaoqun; Kimball, John; Gu, Guojun; Hu, Ying; Chen, Sirong; Hu, Yamin; Dong, Shaorou; Zhou, Xiuhua (Journal of Hydrometeorology, 2025)
Abstract This study is motivated by the desire to enhance the understanding of subseasonal to seasonal (S2S) streamflow and flood predictability by addressing the challenges posed by S2S climate forcings (SCFs). Based on six numerical weather prediction (NWP) models and a calibrated hydrological model, we assess the predictability of precipitation, streamflow, and flooding across 24 river stations in the rainfall-dominated Pearl River basin, South China. Various model configurations are conducted to address uncertainties related to initial hydrological conditions (IHCs) and SCFs, with ensemble streamflow prediction (ESP) serving as a benchmark for comparison. Key findings are as follows: 1) NWP-driven hydrological forecasts significantly improve streamflow prediction accuracy, achieving Kling–Gupta efficiency (KGE) values above 0.5 for up to 44 days, while precipitation forecasts reach KGE values above 0.2 for only 10 days; 2) NWP-based streamflow forecasts consistently outperform ESP, with KGE exceeding 0.6 for 21 days, compared to 5 days for ESP, and the critical success index (CSI) for flood event detection remaining above 0.3 for 3 weeks, compared to 1 week for ESP; 3) both SCFs and IHCs are essential for S2S streamflow predictability, with IHCs governing short-lead skill (especially in the first week), and improved SCFs providing a sustained source of predictability that extends skill to longer leads and, via more accurate state evolution between initializations, also strengthens IHCs at subsequent initializations. These results highlight the importance of considering both factors to achieve reliable long-term flood predictions.
Keywords: Scopus_Alert_05_Decembre_2025

A model for assessing urban resilience of coastal areas against natural flooding risk - Gemaila, Shady Hafez; Al‑Attar, Abdullah Farouk; El‑Barmelgy, Hesham Mohamed (Renewable Energy and Sustainable Development, 2025)
Keywords: Scopus_Alert_05_Decembre_2025

Exploring the relationship between urban morphology types and household-level flood vulnerability profiles in Ho Chi Minh city - Tu, Jiachang; Downes, Nigel K.; Warth, Gebhard; Obaitor, Olabisi Sakirat; Reimuth, Andrea; Sairam, Nivedita; Hoai Tan, Do Ly; Nguyen, Hong Quan; Kreibich, Heidi; Garschagen, Matthias (International Journal of Disaster Risk Reduction, 2025)
Keywords: Scopus_Alert_05_Decembre_2025

Assessing transport network resilience under flood scenarios using the capability approach: A GIS-Based case study of Ennis urban centre, Ireland - Sangeeta; Sarma, Hrishikesh Dev; Martinez-Pastor, Beatriz; McHenry, Helen; Teixeira, Rui (International Journal of Disaster Risk Reduction, 2025)
Keywords: Scopus_Alert_05_Decembre_2025

Probabilistic assessment of human instability in urban areas exposed to flood events - Bernardini, Gabriele; Lagona, Francesco; Mingione, Marco; Postacchini, Matteo (Scientific Reports, 2025)
Abstract Floods in an urban environment are often associated with casualties among pedestrians who move to reach a safe area, due to body stability issues. Pedestrian instability is a random event that depends on the interaction between individual movement and floodwater. Randomness is however often overlooked by current methods, which assess instability risk by deterministic models and provide simplified thresholds that decision-makers then use in emergency planning. Here, instability risk is estimated by a logistic regression model where the toppling probability is a function of water depth and flow speed. The proposal depends on parameters that can be rigorously estimated by experimental data using standard statistical methods. It can be straightforwardly extended to allow for multiple instability mechanisms and subject-specific biometrical information. It includes previous proposals as particular cases, providing a general framework where different thresholds can be compared. It therefore provides a novel rigorous probabilistic integration of instability mechanisms into a unified logistic regression framework, calibrated with experimental data, and applied at urban scale.
Keywords: Scopus_Alert_05_Decembre_2025

Flood dynamic monitoring and XGBoost-SHAP based risk assessment: A case study of the 23·7 extreme rainstorm in BTH region, China - Li, Siyi; Ge, Xiaosan; Jin, Guowang; Lou, Zhengfang; Yang, He (Environmental and Sustainability Indicators, 2025)
Keywords: Scopus_Alert_05_Decembre_2025

Enhancement of coastal flood mitigation by implementing hybrid flood defences integrating hard engineering and nature-based solutions - Matsoukis, Constantinos; Payo, Marta Payo; Brown, Jennifer; Amoudry, Laurent (Science of The Total Environment, 2025)
Keywords: Scopus_Alert_05_Decembre_2025

SMF-Net: Spatiotemporal Motion Former Network for Ship Flooding Time Prediction - Dongmei, Yang; Ze, Li; Kanjun, Chen; Churong, Lai (Neural Information Processing, 2026)
Series Title: Communications in Computer and Information Science
Keywords: Scopus_Alert_05_Decembre_2025

The analysis of road stormwater runoff pollution in the typical coastal city of Lianyungang, East China - Leng, H.; Luo, H.; Han, R.; Wu, W.; Song, B.; Zhao, Y.; He, B.-J. (International Journal of Environmental Science and Technology, 2026)
Keywords: Scopus_Alert_05_Decembre_2025

How Close Are Opportunistic Rainfall Observations to Providing Societal Benefit? - Olsson, Jonas; Horváth-Varga, Laura; Van De Beek, Remco; Graf, Maximilian; Overeem, Aart; Szaton, Magdalena; Bareš, Vojtěch; Bezak, Nejc; Chwala, Christian; De Michele, Carlo; Fencl, Martin; Seidel, Jochen; et al. (Journal of Hydrometeorology, 2025)
Abstract Mitigation of water-related hazards as well as sustainable water resource management is conditioned on accurate and detailed spatiotemporal rainfall observations. Today, water authorities like National Meteorological and Hydrological Services (NMHS) in developed countries operate observation systems consisting of meteorological stations and weather radars. These observations provide state-of-the-art precipitation products, but they remain error prone due to device-specific limitations. This has driven growing interest in opportunistic sensors (OS) of rainfall, primarily commercial microwave links (CML) and personal weather stations (PWS). In the Global South, where meteorological station networks are usually very sparse, OS rainfall data conceivably have an even higher potential to provide an added value. However, although numerous studies have demonstrated the capability and potential of accurate rainfall estimation by OS, no dedicated investigation has been made with regard to their application for operational monitoring and prediction. How close are OS rainfall data to providing societal benefit, e.g., by widespread integration in existing hydrometeorological observation and prediction systems? We address this question by 1) making a review of studies that use OS rainfall data in applications (rainfall mapping, nowcasting, and hydrological prediction), 2) providing a status report on the transition from research to operational usage from the perspective of European Cooperation in Science and Technology (EU COST) Action Opportunistic Precipitation Sensing Network (OpenSense), and 3) discussing the challenges NMHS face in deploying OS rainfall data in operational services. We conclude that while distinct challenges still remain, in terms of both access and processing, the applicability of OS rainfall data is well scientifically supported and operation is under way in several countries. Significance Statement Accurate and detailed rainfall measurements are crucial for many applications, not least for rainfall forecasting as well as flood risk and water resource management. To complement official rainfall measurements, recently so-called opportunistic sensors (OS) have been advocated. The two main OS types are commercial microwave links, where rainfall intensity is derived from signal disturbances between cell phone towers, and personal weather stations, low-cost gauges connected to a web platform. In this study, we summarize scientific papers on applications of OS data, we investigate their current use in routine operation, and we discuss remaining efforts required. Overall, we find not only a solid scientific support and ongoing activities in several European countries but also remaining challenges in terms of acquisition and quality assurance.
Keywords: Scopus_Alert_05_Decembre_2025

Evacuation and Transportation Barriers Among Vulnerable Populations in Natural Hazard-Related Disasters: A Scoping Review - Matsuo, Yuriko; Kietzman, Kathryn; Hays, Ron D.; Song, Yeonsu (International Journal of Environmental Research and Public Health, 2025)
Background and Aim: Natural hazard-related disasters such as wildfires, hurricanes, earthquakes, and floods pose significant risks to older adults, individuals with disabilities, and those with chronic health conditions. Transportation-related challenges during and after evacuation can severely impact their safety, mobility, and recovery. This scoping review examines the current evidence to identify research gaps and inform strategies to improve evacuation outcomes and long-term resilience for these at-risk groups. The STEPS framework (Spatial, Temporal, Economic, Physiological, Social) was applied to guide the analysis and interpretation of findings. Methods: This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines and searched five databases, including PubMed, APA PsycINFO, CINAHL Complete, EMBASE, and Web of Science for primary studies on transportation and disaster evacuation among vulnerable populations. Results: Twenty studies were included. Four key areas of concern were identified: (1) immediate transportation barriers during evacuation, (2) prolonged transportation disruptions post-disaster, (3) anticipated logistical challenges in future evacuation planning, and (4) inconsistent and inaccessible communication of transportation-related information during emergencies. These challenges intersected with all five STEPS dimensions. Conclusions: Transportation barriers remain a persistent and under-addressed risk factor in disaster contexts for vulnerable groups. The STEPS framework helped reveal the multidimensional nature of these issues, emphasizing the need for integrated planning, assistive transport options, inclusive communication systems, and stronger public–private coordination. Future research should focus on collecting disaggregated data, evaluating interventions, and supporting inclusive policy reforms tailored to each type of disaster.
Keywords: Scopus_Alert_05_Decembre_2025

A global systematic review of socio-economic impact assessments of high-tide flooding - Xu, Yicheng; Sirianni, Hannah; Wade, Eric; Hoover, Fushcia-Ann; Mukherji, Anuradha; Narayan, Siddharth (Frontiers in Human Dynamics, 2025)
High-tide flooding (HTF) is an increasingly frequent and disruptive phenomenon in coastal regions worldwide, driven by rising local sea levels. Despite recent attention to the drivers of this emerging flood hazard, the scope and extent of socio-economic impacts of HTF impacts are not well understood. This study presents a global systematic review of studies that quantitatively assess the socio-economic impacts of HTF. A total of 993 peer-reviewed English-language studies published between 1985 and October 2024 were identified through Scopus and Google Scholar. Of these, 194 focus on HTF events, of which 26% address socio-economic impacts. The review shows that the vast majority of HTF impact studies, 72%, are focused on U.S. geographies with Indonesia having the second largest number of HTF impact studies (20%). In the United States, socioeconomic impacts from HTF events have been assessed in 24 cities across the Atlantic, Gulf and Pacific coasts with Norfolk, VA and Miami, FL on the Atlantic having the most number of assessments. HTF impacts affect multiple socio-economic sectors, including public infrastructure, private sectors, and environmental health. However, even among studies in the same sector, the use of different baselines, scales, and HTF event threshold definitions makes it difficult to compare HTF impacts across locations. From these findings our review identifies four points for further research on the socio-economic impacts of HTF to improve collective understanding of these emerging coastal risks.
Keywords: Scopus_Alert_05_Decembre_2025

The impacts of extreme weather events on health services and systems: A systematic review of reviews - Valente, Martina; Del Prete, Clara; Facci, Giulia; Musso, Francesco; Cenati, Stefano; Calligaro, Sara; Ragazzoni, Luca; Barone-Adesi, Francesco (Public Health, 2026)
Keywords: Scopus_Alert_05_Decembre_2025

Urban Stormwater and Groundwater Quality: Pathways, Risks, and Green Infrastructure Solutions - Motlagh, Amir (Environments, 2025)
The development of urban areas and the proliferation of impervious surfaces have significantly altered natural hydrological cycles, resulting in an increase in stormwater runoff and substantial risks to groundwater quality. This review synthesizes current research on the transport mechanisms of stormwater contaminants, including toxic elements, nutrients, pathogens, and emerging pollutants such as microplastics and pharmaceuticals, into aquifers. This study analyzes the physicochemical and biological processes that affect pollutant mobility and retention in urban soils, emphasizing the vulnerability of groundwater systems, particularly in areas with permeable soils and shallow water tables. The article evaluates a range of green infrastructure (GI) and low-impact development (LID) strategies—including rain gardens, bioswales, infiltration basins, constructed wetlands, and urban forestry—to assess how effectively they can mitigate stormwater pollution and improve groundwater protection. Case studies from North America illustrate the practical implementation and performance of GI systems, emphasizing the importance of site-specific design, monitoring, and adaptive management. The review also discusses global policy frameworks and community engagement strategies that support sustainable stormwater management. Ultimately, it advocates for an integrated, multidisciplinary approach that combines engineering, ecological science, and public policy to safeguard groundwater resources in the face of climate variability and urban expansion.
Keywords: Scopus_Alert_05_Decembre_2025

Requirements for Flood-Driven Forecasting Systems for Small and Medium-Sized Catchments in Germany - Leandro, Jorge; Althoff, Ingrid; Fischer, Svenja; Mudersbach, Christoph; Lesny, Kerstin (Water, 2025)
Unlike most other measures in flood risk management, flood forecasting stands out because it is not designed to address a pre-defined return period. In principle it is applicable to a whole range of possible events and can be operated continuously in real time. This makes flood forecasting an effective non-structural measure for saving lives and property, even in the face of increased hydro-meteorological variability and extremes. In Germany, a series of regional and transregional flood forecasting centres and services have been established that cover the entire national territory. For large basins, the existing forecasting centres are well equipped to provide accurate real-time forecasts. Nevertheless, there are remaining challenges that need to be met when the focus is on small to medium-sized catchments. This study focuses on discussing the capabilities of six state-of-the-art flood forecasting centres and derives the most important requirements for significant improvements in flood forecasting capabilities for small to medium-sized catchment areas. We emphasise that future research must focus on flood-driven predictions, including the prediction of flood inundation and consequences for buildings and infrastructure, as well as geotechnical failure mechanisms in Germany.
Keywords: Scopus_Alert_05_Decembre_2025

An overview of low-oxygen sensing and flooding responses of tomato - Eerdekens, Niels; Kabak, Elif Nur; Geldhof, Batist; Vaughan-Hirsch, John; Chavez, César Antonio; Mignolli, Francesco; Vidoz, Maria Laura; Van De Poel, Bram (Journal of Experimental Botany, 2025)
Abstract Tomato (Solanum lycopersicum) is a globally significant and widely consumed vegetable crop. However, the productivity of tomato cultivation is increasingly threatened by flooding events, which are predicted to escalate in both frequency and severity due to climate change. During waterlogging, plants experience acute hypoxic stress, which can be lethal if prolonged. This review examines the mechanisms by which plants sense and signal low-oxygen stress, with a focus on the role of group VII ethylene response factors and the N-degron pathway, including their regulation. A comparative analysis of these low-oxygen signaling pathways between Arabidopsis and tomato reveals considerable conservation across species, although understudied in tomato. Furthermore, this review elucidates how hypoxia triggers various adaptation strategies in tomato. We highlight the physiological, morphological, metabolic, and hormonal responses, including modifications in plant transpiration and photosynthesis, the development of aerenchyma and adventitious roots, the induction of epinasty, and the reprogramming of energy metabolism. The review also provides insights into the hormonal signaling cascades that play a pivotal role in flooding stress responses. We aim to provide an in-depth understanding of how tomato plants deal with flooding-induced hypoxic stress. Additionally, we aim to provide insights that can be leveraged for breeding more flood-tolerant and climate-resilient tomato cultivars.
Keywords: Scopus_Alert_05_Decembre_2025

Open problems in uncertainty quantification for flood modelling: A systematic review - Idowu, Jamiu Adekunle; Alfahid, Ayman (Environmental Modelling & Software, 2026)
Keywords: Scopus_Alert_05_Decembre_2025

Analysis of Consecutive Dry Days in the MATOPIBA Region During the Rainy and Dry Seasons - Rodrigues, Daniele Tôrres; Batista, Flavia Ferreira; Andrade, Lara De Melo Barbosa; Da Silva, Helder José Farias; Cabral Júnior, Jório Bezerra; Ribeiro, Marcos Samuel Matias; Dos Reis, Jean Souza; Santos Silva, Josiel Dos; Dos Santos Silva, Fabrício Daniel; Santos E Silva, Claudio Moisés (Atmosphere, 2025)
Climate change and its impacts on precipitation patterns have intensified the occurrence of prolonged dry periods in agricultural regions of Brazil, particularly in the MATOPIBA region (comprising the states of Maranhão, Tocantins, Piauí, and Bahia). This study analyzes the seasonal variability and trends of the Consecutive Dry Days (CDDs) index in the MATOPIBA region from 1981 to 2023. Daily precipitation data from the Brazilian Daily Weather Gridded Data (BR-DWGD) dataset were used for the analysis. The novelty of this work lies in its focus on the seasonal characterization of CDD across the entire MATOPIBA field of agriculture, addressing the following main research question: how have the frequency and persistence of dry spells evolved during the rainy and dry seasons over the past four decades? The methodology involved trend detection using the Mann–Kendall test and Sen’s Slope estimator. The results indicated that during the rainy season, the average CDD ranged from 20 to 60 days, with higher values concentrated in the states of Piauí and Bahia. In contrast, during the dry period, averages exceeded 100 days across most of the region. Trend analysis revealed a significant increase in CDD over extensive areas, particularly in Tocantins and Southern Bahia. The increasing trends were estimated at 1 to 4 days per decade during the rainy season and 4 to 14 days per decade in the dry period. Although a decreasing CDD trend was observed in small areas of Northern Maranhão, possibly associated with the influence of the Intertropical Convergence Zone, the overall scenario indicates a greater persistence of long dry spells. This pattern suggests an increase in vulnerability to water scarcity and agricultural losses. These findings highlight the need for implementing adaptation strategies, such as the use of drought-tolerant cultivars, conservation management practices, irrigation expansion, and public policies aimed at promoting climate resilience in the MATOPIBA region.
Keywords: Scopus_Alert_05_Decembre_2025

Tail-Aware Forecasting of Precipitation Extremes Using STL-GEV and LSTM Neural Networks - Niu, Haoyu; Murray, Samantha; Jaber, Fouad; Heidari, Bardia; Duffield, Nick (Hydrology, 2025)
Accurate prediction of extreme precipitation events remains a critical challenge in hydrological forecasting due to their rare occurrence and complex statistical behavior. These extreme events are becoming more frequent and intense under the influence of climate change. Their unpredictability not only hampers water resource management and disaster preparedness but also leads to disproportionate impacts on vulnerable communities and critical infrastructure. Therefore, in this article, we introduce a hybrid modeling framework that combines Generalized Extreme Value (GEV) distribution fitting with deep learning models to forecast monthly maximum precipitation extremes. Long Short-term Memory models (LSTMs) are proposed to predict the cumulative distribution (CDF) values of the GEV-fitted remainder series. This approach transforms the forecasting problem into a bounded probabilistic learning task, improving model stability and interpretability. Crucially, a tail-weighted loss function is designed to emphasize rare but high-impact events in the training process, addressing the inherent class imbalance in extreme precipitation predictions. Results demonstrate strong predictive performance in both the CDF and residual domains, with the proposed model accurately identifying anomalously high precipitation months. This hybrid GEV–deep learning approach offers a promising solution for early warning systems and long-term climate resilience planning in hydrologically sensitive regions.
Keywords: Scopus_Alert_05_Decembre_2025

Extreme precipitation scaling under synoptic air mass regimes - Obarein, Omon A.; Lee, Cameron C. (Atmospheric Research, 2026)
Keywords: Scopus_Alert_05_Decembre_2025

Sea level rise: A plea for taking the viewpoint of the ocean - Casati, Roberto; Portier, Augustin; Delor, Clément Astruc; Baird-Zars, Bernadette; Cerino, Christophe; Esteban-Cantillo, Oscar Julian; Eveillard, Damien; Malatesta, Stefano; Di Friedberg, Marcella Schmidt (Marine Policy, 2026)
Keywords: Scopus_Alert_05_Decembre_2025

Assessment of precipitation and its extreme precipitation changes over the Himalayan Bhilangana River Basin, India - Joshi, Bhupendra; Singh, Vishal; Chandola, V. K.; Singh, Atar (Theoretical and Applied Climatology, 2025)
Keywords: Scopus_Alert_28_Aout_2025

Assessment of flood risk in jinsha river basin based on land use value and PSR model - Chen, Xiao; Zhai, Ran; Fang, Wen; Xu, Liwen; Liang, Lili; Yang, Juanru; Man, Hanlin; Lv, Zhenyu; Wang, Chao; Liu, Deng; Cui, Mengqing (Physics and Chemistry of the Earth, Parts A/B/C, 2025)
Keywords: Scopus_Alert_28_Aout_2025

Integrated surface-subterranean hydrodynamic modelling and evacuation risk assessment for urban metro systems under extreme rainfall scenarios - Li, Qijie; Xia, Junqiang; Liang, Dongfang; Hinkelmann, Reinhard (Journal of Cleaner Production, 2025)
Keywords: Scopus_Alert_28_Aout_2025

SWAT-based analysis of flood regulation service dynamics in the Pearl River Basin (2006–2018): Mechanisms from hydrological modelling to flood events - Yang, Heng; Tao, Jun; Li, Jiawei; Lu, Jingyi; Shi, Shuyi; Meng, Yaobin; Zhang, Hua (Journal of Cleaner Production, 2025)
Keywords: Scopus_Alert_28_Aout_2025

Flood hazard mapping and disaster prevention recommendations based on detailed topographical analysis in Khovd City, Western Mongolia - Narangerel, Serdyanjiv; Suzuki, Yasuhiro; Hasegawa, Tomonori; Takaichi, Yoshiyuki (Natural Hazards and Earth System Sciences, 2025)
Abstract. The impacts of climate change manifest heterogeneously across regions, and in Khovd City, a semi-arid area in Western Mongolia, the escalating threat of flooding is evident through the occurrence of 10 flash floods in the last 30 years. The risk zone, encompassing rivers and flash floods, endangers ca. 32 000 residents, with 750–1800 traditional nomadic dwellings (gers) located on the floodplain of the Buyant River during the summer. Furthermore, prolonged rains pose a flash flood risk to households in the province's center. Therefore, flood disaster prevention strategies must be tailored to semi-arid regions, as they differ significantly from those employed for river flooding in humid areas. In Khovd, residential areas are limited, and land use is not highly dense. In addition, since flood water levels are not high, knowledge of the location and direction of flood flow paths and places where water is likely to be collected in advance is essential for disaster risk reduction. Under these conditions, mapping using detailed DEM and identifying the extent of past floods using satellite images are important. We measured by Real-Time Kinematic on 22 Ground Control Points and collected 15 206 aerial photos for drone mapping under Unmanned Aerial Vehicle in Khovd City. This is an example of how to create a hazard map for a remote area based on a topographical analysis of detailed Digital Elevation Model, and discusses a methodology for using this map to help prevent flooding disasters. The resulting flood hazard map identified four flood risk areas based on flood flow direction and topographical features.
Keywords: Scopus_Alert_28_Aout_2025

Flood impact on urban transport networks considering the flooding propagation - Liu, Yimeng; Ford, Alistair; Dawson, Richard; Pyatkova, Katya; Tang, Jiting; Wan, Yingying; Yang, Saini (Journal of Environmental Management, 2025)
Keywords: Scopus_Alert_28_Aout_2025

Data Mining and Machine Learning for Predicting and Managing Flooding Disasters in Coastal and Riverine Areas - Tamilselvan, K.; Jena, Suvendu Kumar; Prabu, S.; Leopauline, S.; Deepika, J.; Srinivasan, J.M.E. (Disaster Advances, 2025)
Coastal lowlands and river floodplains still rank among the places most likely to swallow lives and highways alike during a single storm. Scientists who track these disasters continue to notice that climate change has nudged both the frequency and intensity of inundations toward increasingly extreme levels. Because of that shift, the old siren systems and water gauges no longer give people enough notice or confidence. This research study asks whether data-mining routines and the newly fashionable family of machine-learning tricks can fill that warning gap. Rain maps, river sticks, hillside surveys, sod moisture snapshots and centuries of logbook commas are fed into algorithms in hopes they will whisper a danger sign long before the water rises. Alongside the supervised staples, Random Forests, SVMs, etc., the K-Means unsupervised stage sifts the output to categorize neighborhoods into low, medium and high risk. A quick-hitter feature examination, borrowed from information theory, enables engineers to rank the flood drivers worth keeping and the noise worth ditching. That shortlist shuffles into geo-geo-dashboards that paint crimson where tomorrow's traffic could grind to a standstill. Field tests on Caribbean watersheds and midwestern floodplains push overall hit rates past the 90-percent mark and give emergency crews a map rather than a mantra. Analysis of the experimental results indicates that the fusion of data mining techniques with advanced predictive analytics markedly improves the readiness of emergency services, optimizes the distribution of limited resources and bolsters the resilience of affected communities. Such work feeds into the expanding literature on intelligent disaster-management systems and highlights, once more, the role of machine-learning algorithms in curbing the economic and social toll wrought by natural hazards on at-risk regions.
Keywords: Scopus_Alert_28_Aout_2025

Including dynamics in a network-based stochastic multihazard model: A virtual testbed for volcanic ashfall and flood risk assessment - Bebbington, Mark; Dunant, Alexandre; Harte, David; Mead, Stuart; Whitehead, Melody (International Journal of Disaster Risk Reduction, 2025)
Keywords: Scopus_Alert_28_Aout_2025

Multi-criteria analysis of ecosystem-based adaptation strategies for flood risk reduction in Irish, Italian, and Turkish Coastal City Living Labs - Tiwari, Ananya; Rodrigues, Luís Campos; Caruso, Rochelle; Ensenado, Elena Marie; Makousiari, Elina; Gharbia, Salem (Sustainable Futures, 2025)
Keywords: Scopus_Alert_28_Aout_2025

A novel approach to developing local flood vulnerability scenarios based on the Shared Socioeconomic Pathways framework: Sectoral risks and policy implications - Vu, Bien Thanh; Obaitor, Olabisi S.; Katzschner, Antje; Grobusch, Lena C.; Sett, Dominic; Ortiz-Vargas, Andrea; Hagenlocher, Michael; Schinkel, Ulrike; Bachofer, Felix; Nguyen, Linh Khanh Hoang; Garschagen, Matthias (Global Environmental Change, 2025)
Keywords: Scopus_Alert_28_Aout_2025

Key drivers of extreme precipitation and runoff and their trend for Indian cities in climatic variation under shared socioeconomic pathways - Rentachintala, Lakshmi Raghu Nagendra Prasad (Sādhanā, 2025)
Keywords: Scopus_Alert_28_Aout_2025

Quantitative microbial risk assessment of E. coli and Vibrio cholerae in drinking water sources of flood-affected districts in Baluchistan, Pakistan - Arain, Ghulam Murtaza; Sattar, Nazia; Khatoon, Sumaira; Khan, Nabeel Ali; Rasheed, Hifza; Naz, Farah; Badshah, Shakeel (Regional Studies in Marine Science, 2025)
Keywords: Scopus_Alert_28_Aout_2025

Flood Rescue: An Integrated GIS and Remote Sensing-Based Decision Support System for Flood Inundation Warning and Relief - Rinku, Dhruva R.; Sree, Parimi Hema; Nagajyothi, D.; Kulkarni, Anita (ICT Analysis and Applications, 2026)
Series Title: Lecture Notes in Networks and Systems
Keywords: Scopus_Alert_28_Aout_2025

Flood Risk Management in Development Projects: A Review of Malaysian Perspective within the Sendai Framework for Disaster Risk Reduction 2015-2030 - Saad, Mohammad Syamsyul Hairi; Ali, Mohamad Idris; Razi, Putri Zulaiha; Ramli, Noram Irwan (CONSTRUCTION, 2024)
This study rigorously assesses Malaysia's development projects' alignment with the Sendai Framework for Disaster Risk Reduction 2015-2030 to identify synergies that can be improved, with a focus on flood risk management challenges and opportunities. The primary goal is to establish a flexible framework that integrates national and grassroots initiatives to advance economic, social, and environmental responsibility in conjunction with development objectives. As Malaysia undergoes rapid development, it is increasingly exposed to flood risks, exacerbated by climate change and urbanization. The Sendai Framework provides guidelines to mitigate these escalating threats. Nonetheless, there is a discernible gap in the existing literature that neither adequately evaluates the development patterns amplifying flood risks nor scrutinizes Malaysia's conformity with the Sendai Framework's objectives. Utilizing a systematic and scoping literature review in conjunction with a qualitative approach. The findings underscore that despite notable progress in Malaysia's flood risk management, significant deficiencies persist in policy execution, financial allocation, and public cognizance. This research refined the flood risk management conceptual framework to adapt to changing environmental contexts. Crucially, the proactive involvement of the federal government, local authorities from various Malaysian states, and the Department of Irrigation and Drainage Malaysia is indispensable in flood risk mitigation. While frameworks like the National Physical Plan 4 (NPP4) have improved, local planning must adapt them to local needs. Every development project should mandatorily embed a comprehensive flood risk assessment, ensuring alignment with national guidelines. This increases compliance and construction sector interest, elevating flood disaster risk reduction. This enables a future where innovation, professionalism, and knowledge improve life quality while prioritizing safety and sustainability.
Keywords: Scopus_Alert_28_Aout_2025

Enhancing multiple ecosystem services: Nature-based measures outperform technical interventions in river management - Schmidt, Stefan; Albert, Christian (Ecosystem Services, 2025)
Keywords: Scopus_Alert_28_Aout_2025

Compound flooding: A review on assessment framework - Akter, Aysha; Ataullah, S. M. (Journal of Coastal Conservation, 2025)
Keywords: Scopus_Alert_28_Aout_2025

Impacts of global climate change on floods in Pakistan with current trends challenges and future perspectives - Khoso, Waheed Ali; Tanoli, Muhammad Ashraf; Bashir, Muhammad Tariq; Khan, Md. Munir Hayet; Waseem, Muhammad; Alrowais, Raid; Wahab, Abdul; Leta, Megersa Kebede (Discover Environment, 2025)
Keywords: Scopus_Alert_28_Aout_2025

An integrated framework to quantitatively analyze the potential threat of landslide-triggered outburst flood hazard chain, a case study in Danba, China - Liu, Dingzhu; Liu, Daochuan; He, Chunhui; Zhang, Chong; Jin, Wen; Sa, Wenqi; Xing, Xinfu; Wu, Chunhao; Tan, Chunping; Qin, Yang; Xiang, Bo; Shao, Jiang (International Journal of Disaster Risk Reduction, 2025)
Keywords: Scopus_Alert_28_Aout_2025

Quantitative evaluation of urban flooding originating from multiple sources using flood risk indices - Shin, Eun Taek; Lee, Seung Oh; Rhee, Dong Sop; Song, Chang Geun (International Journal of Disaster Risk Reduction, 2025)
Keywords: Scopus_Alert_28_Aout_2025

A multi-scale framework for assessing the suitability of low impact development in a dense flood-prone urban area - Şenocak, İlksen and Şimşek, Gül and Afshar, Mehdi H. and Tuna, Aysun and Alp, Sermin Çakıcı and Bektaş, Hayrettin Onur and Alp, Emre (International Journal of Disaster Risk Reduction, 2025)
Keywords: Scopus_Alert_28_Aout_2025

Enhancing Urban Flood Vulnerability Mapping with Multi-Criteria Decision Analysis - Department of Civil Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia; Muhammad Sukri, Nur Syakila; Abdul Maulud, Khairul Nizam; Department of Civil Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia; Earth Observation Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia; Syed Abdul Rahman, Syed Ahmad Fadhli; Earth Observation Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia; Department of Survey and Mapping, Perak Darul Ridzuan, Jalan Dato’ Seri Ahmad Said, Greentown,30450, Ipoh, Perak, Malaysia; Wan Mohd Jaafar, Wan Shafrina; Earth Observation Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia; Wan Mohtar, Wan Hanna Melini; Department of Civil Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia; et al. (Jurnal Kejuruteraan, 2025)
Urbanization, climate change, and hydrological variability have significantly heightened flood risks in metropolitan areas, particularly in low-lying and highly developed regions. The increasing frequency and severity of urban floods necessitate advanced spatial decision-making techniques for effective flood vulnerability mapping. This study applies Multi-Criteria Decision Analysis (MCDA) integrated with Geographic Information Systems (GIS) to assess urban flood susceptibility in Taman Sri Muda, Shah Alam, Malaysia, an area frequently affected by flash floods. The study aims to identify key factors contributing to urban flooding, develop a spatial flood hazard model, and propose mitigation strategies. Six critical parameters including land use and land cover, distance from the main channel, drainage density, rainfall distribution, elevation and slope were reclassified and assigned weights. The weighted overlay technique was employed to generate the flood vulnerability map, which was validated using historical flood records. The results indicate that 37.8% of the study area is classified as high flood risk, while 24.8% falls under moderate risk, 27.6% under low risk, and 9.8% under very low risk. The findings provide valuable insights for urban planners, emergency response teams, and policymakers in developing flood mitigation strategies, improving drainage infrastructure, and enhancing flood resilience. This study underscores the necessity for integrating real-time hydrological data and machine learning models to further improve flood prediction accuracy in future research.
Keywords: Scopus_Alert_28_Aout_2025

Resilience-oriented emergency resource dispatch for power–transportation coupled networks under rainstorm-induced flood hazards - Lian, Xianglong; Song, Chenkai; Li, Zepeng; Qiu, Lei; Liu, Lijun (Applied Energy, 2026)
Keywords: Scopus_Alert_21_Novembre_2025

Revealing the causes of inequality in rainstorm flood disaster resilience between town and rural areas in southwestern China: Based on natural and social system dimensions - Li, Zhangjie; Yang, Lihong; Zhu, Ling; Tan, Jing; Xie, Zhiqiang; Wang, Yudong; Zhang, Hong; Xie, Jinhang; Cui, Songyun; Bai, Shihan; Hou, Zhiqun (Journal of Rural Studies, 2026)
Keywords: Scopus_Alert_21_Novembre_2025

Extreme rainfall south of the Yangtze River in China during June 2024: Observational diagnosis and dynamical downscaling prediction - Zhao, Chongbo; Dong, Lili; Xie, Bing; Shi, Wen; Wang, Anran; Wu, Jie; Li, Qingquan (Atmospheric Research, 2026)
Keywords: Scopus_Alert_21_Novembre_2025

Machine learning prediction of summer extreme precipitation days in the middle and lower Yangtze River with SHAP explanation - Xiao, Chunyan; Duan, Anmin; Tang, Yuheng; Tang, Bin; Wang, Qilu; Yang, Xianyi (Atmospheric Research, 2026)
Keywords: Scopus_Alert_21_Novembre_2025

Impacts of the urban heat island on the convective initiation and propagation of an extreme precipitation event in the coastal megacity of Shanghai - Fu, Xinshu; Gao, Yanqing; Peng, Jie; Guo, Wei; Lu, Yiwen; Wang, Xiaofeng; Chu, Hai; Zhang, Yanyan; Wang, Huiying (Atmospheric Research, 2026)
Keywords: Scopus_Alert_21_Novembre_2025

Assessing flood potential prediction under joint LULC and climate change in a mountainous watershed, Northwest Iran - Irani, Tayebeh; Abghari, Hirad; Rasouli, Ali Akbar (Modeling Earth Systems and Environment, 2026)
Keywords: Scopus_Alert_21_Novembre_2025

High-precision flood change detection with lightweight SAR transformer network and context-aware attention for enriched-diverse and complex flooding scenarios - Du, Menghao; Shao, Zhenfeng; Xiao, Xiongwu; Zhang, Jindou; Zhu, Duowang; Wang, Jinyang; Balz, Timo; Li, Deren (ISPRS Journal of Photogrammetry and Remote Sensing, 2026)
Keywords: Scopus_Alert_21_Novembre_2025

Flood Forecasting Unveiled: Harnessing the Power of Sentinel-1A Imagery and ESA World Cover Through Multi-data Integration - Veedu, Jayasree Thazhath; Reghunadhan, Rajesh (Proceedings of Data Analytics and Management, 2026)
Series Title: Lecture Notes in Networks and Systems
Keywords: Scopus_Alert_21_Novembre_2025

Hybrid Support Vector Machine Model with Harris Hawks Optimization is a Successive Tool for Flood Prediction: A Case Study - Samantaray, Sandeep; Lal Mohiddin, S. K.; Panda, Debi Prasad; Dalai, Chitaranjan; Satapathy, Deba Prakash; Sahoo, Abinash; Mohanta, Nihar R. (Proceedings of International Conference on Information Technology and Intelligence, 2026)
Series Title: Lecture Notes in Networks and Systems
Keywords: Scopus_Alert_21_Novembre_2025

Hydropower vulnerability to drought-flood abrupt alternation under climate change - Huang, Yifan; Tan, Xuejin; Fu, Jianyu; Deng, Zhihong; Liu, Bingjun (Energy, 2025)
Keywords: Scopus_Alert_21_Novembre_2025

Multi-level enhanced visualization of virtual geographic scenes for improving risk perception: A case study of flood disaster scenes - Zhu, Jun; Wu, Jianlin; Guo, Yukun; Zhang, Jinbin; Guo, Zhihao; Dang, Pei; Li, Weilian (Environmental Modelling and Software, 2026)
As a vital application for geographic visualization, virtual geographic scenes can help the public enhance their risk awareness. However, existing research on virtual geographic scenes has excessively focused on visual effects, resulting in limited content diversity, inadequate expression of key information, and low cognitive efficiency for users. To address these issues, this article proposes a multi-level enhanced visualization method for virtual geographic scenes. First, a presentation-level enhancement method tailored for extended reality is developed. Second, an analysis-level enhancement method is designed by integrating collaborative semantic constraints and multiple visual variables. Third, an exploration-level enhancement method driven by interactive feedback is proposed to strengthen users' understanding of scenes and their cognitive exploration capabilities. Finally, a flood disaster was selected as a case study, and a prototype system was developed to support experimental analysis. The results indicate that the display-level enhancement significantly improves information transmission efficiency, the analysis-level enhancement markedly enhances cognitive accuracy and efficiency, and the exploration-level enhancement improves users’ decision-making support capabilities and cognitive engagement. These findings demonstrate that the proposed method enables users to capture key information in complex geographic scenes, enhances disaster risk perception, and provides scientific support for disaster management and emergency response. © 2025 Elsevier Ltd
Keywords: Scopus_Alert_21_Novembre_2025

Large-scale urban flood modeling with SPH-DEM: From simulation to resilience evaluation - Xiong, Hao; Cai, Jiayong; Xia, Changqing; Chen, Xiangsheng (Tunnelling and Underground Space Technology, 2026)
Keywords: Scopus_Alert_21_Novembre_2025

Scenario deduction for urban rainstorm-induced waterlogging disaster chain based on dynamic Bayesian network - Huang, Jing; Wang, Ziqing; Sun, Dianchen; Wang, Huimin (Reliability Engineering & System Safety, 2026)
Keywords: Scopus_Alert_21_Novembre_2025

Towards Accurate Flood Forecasting: Integrating Satellite Data, Hydrological Modeling, and Deep Learning - Pourentezari, Saeideh; Salehi, Hossein; Haghighi, Alireza Razeghi; Sadeghi, Mojtaba; Faridhosseini, Alireza (Journal of Flood Risk Management, 2025)
ABSTRACT In hydrologic modeling, forecasting peak floods is a complex and high‐priority task, particularly in regions with limited in situ observations. This research investigates the potential of utilizing satellite‐based precipitation products with near real‐time updates (NRT‐SbPP), together with deep learning, to improve peak flow estimation and flood forecasting. Using Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement (IMERG), the Final version as a reference, four NRT‐SbPPs were evaluated: Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)‐Cloud Classification System (CCS), PERSIANN‐Dynamic‐Infrared–RainRate (PDIR), the IMERG‐Early, and IMERG‐Late. This study evaluated the performance of these datasets for streamflow simulation using the Hydrologic Modeling System (HEC‐HMS) in a sub‐basin of the Russian River watershed. Bias correction was performed using Long Short‐Term Memory (LSTM) networks, with IMERG‐Final serving as the reference precipitation dataset. This correction improved the quality of the input rainfall data and led to more accurate streamflow simulations. For example, the Root Mean Square Error of IMERG‐Late decreased from 1.98 to 1.48 mm with LSTM, resulting in performance metrics similar to those of observed discharge (Nash‐Sutcliffe Efficiency: 0.82). Most importantly, PDIR exhibited significant enhancement, with a 36% correlation coefficient increase (from 0.52 to 0.81), as well as high rates for extreme event detection. The further findings establish the potential of employing LSTM methods and using IMERG‐Final as a reference to incorporate NRT‐SbPPs within real‐time flood forecasting and early warning frameworks. This method leverages the new technologies of satellite‐based meteorological data and offers an efficient, cost‐effective option to enhance flood prediction and disaster risk management, especially in data‐scarce regions.
Keywords: Scopus_Alert_21_Novembre_2025

Exploring spatiotemporal dynamics of urban flood socioeconomic exposure risk and driving factors under future precipitation change and urbanization scenarios - Liu, Wen; Feng, Qi; Liang, Yuwei; Cui, Yanqiang; Xu, Long; Engel, Bernard A. (Journal of Environmental Management, 2025)
Keywords: Scopus_Alert_21_Novembre_2025

Flooding, nonlinear scaling and Jensen’s inequality: Analyzing changes in inundation extent with river discharge nonstationarity - Baruah, Anupal; Hinge, Gilbert; Dhital, Supath; Kilicarslan, Berina Mina; Wani, Omar (Advances in Water Resources, 2025)
Keywords: Scopus_Alert_21_Novembre_2025

Urban flood intensification by storm clustering: The impact of rainstorm ‘train effect’ - Guo, Kaihua; Guan, Mingfu; Liang, Chen; Chen, Xunlai; Yu, Dapeng (Urban Climate, 2025)
Keywords: Scopus_Alert_21_Novembre_2025

A cross-city transferable convolutional neural network framework for assessing street-scale flood risks in urban networks - Wang, Mo; Zhuang, Ji'an; Zhao, Jiayu; Sun, Chuanhao; Li, Jingyao; Zhou, Luqi; Fan, Haowen; Zhou, Shiqi; Qi, Jinda (Journal of Environmental Management, 2025)
Keywords: Scopus_Alert_21_Novembre_2025

Climate change and flood susceptibility in Bocas del Toro, Panama: A multi-criteria spatial analysis approach - Petiangma, Dereck Mbeh; Singh, Gerald G.; Quesada-Román, Adolfo; Hidalgo, Hugo; Blake, Sandra; Gonzalez, Alison; McFarlin, Aurelio; Collin, Rachel (Journal of Environmental Management, 2025)
Keywords: Scopus_Alert_21_Novembre_2025

Geospatial evaluation of flood susceptibility and vulnerability for sustainable urban planning in Sululta town, Oromia region, Ethiopia: A GIS and remote sensing perspective - Debele, Abu Duguma; Tessema, Israel (Scientific African, 2025)
Keywords: Scopus_Alert_21_Novembre_2025

Complex network reveals propagation and moisture dynamics of Indian monsoon precipitation extremes - Raghuvanshi, Akash Singh; Agarwal, Ankit (Climate Dynamics, 2025)
Keywords: Scopus_Alert_21_Novembre_2025

Exploring the Influence of Sea Surface Temperature Extremes on Precipitation Extremes Across India's Climate Zones: A Complex Network Approach - Reddy, Venuthurla Manohar; Ray, Litan Kumar; Manikanta, Velpuri (International Journal of Climatology, 2025)
ABSTRACT The present study investigates the impact of Sea Surface Temperature (SST) extremes on precipitation extremes in India's six homogeneous climate regions from 1981 to 2020. A wavelet‐based complex network analysis was used to explore the connections between precipitation extremes and SST extremes. The Maximum Overlap Discrete Wavelet Transform (MODWT) technique was employed for time series decomposition to identify these links across different temporal scales. Spearman correlation was then used to determine the relationships between SST extremes and precipitation extremes at the grid level. Complex networks were then constructed for each of India's climate regions, focusing on both positive and negative correlations with various global sea regions. Results reveal that precipitation extremes at shorter time scales are predominantly driven by proximal oceanic regions such as the Bay of Bengal (BOB), Arabian Sea (ARS), and Eastern Indian Ocean (EIO), reflecting strong monsoon–SST coupling through moisture convergence and synoptic convection processes. As the time scale increases, remote SST influences from the Atlantic (NAO, EAO), Pacific (EPO, NPO, SPO), and Southern Hemisphere oceans (SIO, SAO, SOO) become increasingly dominant, highlighting the role of atmospheric teleconnections, jet stream modulation, and cross‐equatorial moisture transport. Interannual scales show the widest and most diverse SST control, whilst decadal‐scale influence is generally weak and region‐specific. The regional analysis demonstrates distinct propagation pathways of SST influence, with West Central India showing consistent multiscale sensitivity, Central Northeast and Northeast India transitioning from local to global control, and South Peninsular India retaining strong regional dominance. These insights underscore the necessity of integrating both regional and global SST indices into forecasting frameworks for improved prediction and climate adaptation strategies in India.
Keywords: Scopus_Alert_21_Novembre_2025

Assessment of Historical and Future Mean and Extreme Precipitation Over Sub‐Saharan Africa Using NEX ‐ GDDP ‐ CMIP6 : Part II —Future Changes - Samuel, Sydney; Tsidu, Gizaw Mengistu; Dosio, Alessandro; Mphale, Kgakgamatso (International Journal of Climatology, 2025)
ABSTRACT This study utilised a multi‐model ensemble (MME) of 26 NASA Earth Exchange Global Daily Downscaled Projections (NEX‐GDDP) to assess future changes in mean and extreme precipitation over sub‐Saharan Africa at both seasonal and annual scales under three Shared Socioeconomic Pathway scenarios: SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5. The changes are examined for two distinct future periods, specifically the near future (2031–2060) and the far future (2061–2090), relative to 1985–2014. Nine precipitation indices are utilised to characterise extreme precipitation. The results show that mean precipitation is expected to increase in northern sub‐Saharan Africa, while a decrease is expected in the southern region. Additionally, the duration of dry spell (CDD) is expected to decrease, while the duration of wet spell (CWD) and precipitation frequency (RR1) are projected to increase in the northern region. Conversely, CDD is expected to increase, and CWD and RR1 are expected to decrease in the southern region. These trends become more pronounced in the far future compared to the near future, particularly under the high‐emission scenario SSP5‐8.5. However, there are few localised regions where at least 80% of the models agree with the MME on the changes in CDD, CWD and RR1 under all scenarios for both time frames. Precipitation intensity is expected to increase across most of sub‐Saharan Africa in both time frames, regardless of the scenario, leading to more frequent heavy precipitation and extreme wet events. This increase is expected to be more pronounced under the SSP5‐8.5 scenario, particularly in the far future. Specifically, at least 80% of the models project an increase in heavy and extreme wet events across most of northern sub‐Saharan Africa under all scenarios for both time frames. These findings emphasise the urgent need to develop effective adaptation strategies for sub‐Saharan Africa to mitigate the potential impacts of these projected changes in precipitation characteristics.
Keywords: Scopus_Alert_21_Novembre_2025

Drowning and Submersion Deaths in Bathtubs and Associated Factors: A Descriptive and Ecological Study in Japan, 1995–2020 - Tai, Yoshiaki; Obayashi, Kenji; Yamagami, Yuki; Saeki, Keigo (Journal of Epidemiology, 2025)
Keywords: Scopus_Alert_21_Novembre_2025

Geometry of Clairaut Riemannian warped product submersions - Roy, Arkadeepta; Meena, Kiran; Shah, Hemangi Madhusudan (Bulletin des Sciences Mathématiques, 2026)
Keywords: Scopus_Alert_21_Novembre_2025

A review of current trends and evolution of irrigation water management practices on greenhouse gas emissions - Darko, R. O.; Odoi-Yorke, F.; Abbey, A. A.; Agyekum, E. B.; Sam-Amoah, L. K. (International Journal of Energy and Water Resources, 2026)
Keywords: Scopus_Alert_21_Novembre_2025

Flood susceptibility mapping in urban areas based on analytical hierarchy process: A decade-long systematic literature review - Bouchikhi, Sara; Chourak, Mimoun; Boushaba, Farid; El Baida, Maelaynayn (Journal of African Earth Sciences, 2026)
Keywords: Scopus_Alert_21_Novembre_2025

Monitoring surface water in floodplains by satellites: Progress, challenges, and perspectives - Lin, Yaling; Song, Chunqiao (Journal of Hydrology, 2026)
Keywords: Scopus_Alert_21_Novembre_2025

Integrated assessment of urban flooding and heat island interactions: A systematic review of geospatial technologies, machine learning approaches, and microclimate dynamics - Imroz, Mohammad; Akhtar, M.P.; Sharma, Meena Kumari; Alshehri, Fahad (Journal of Environmental Management, 2025)
Keywords: Scopus_Alert_21_Novembre_2025

Effectiveness of nature-based solutions to address water challenges in the Greater Mekong Subregion: a systematic mapping - Lwin, Khaing Su; Saluja, Ridhi; Mandal, Sushmita; Krittasudthacheewa, Chayanis (Nature-Based Solutions, 2025)
Keywords: Scopus_Alert_21_Novembre_2025

The role of emotional responses in climate perception and mitigation behaviour among flood victims: A systematic review - Cheah, Winnie; Bono, Suzanna Awang; Karpudewan, Mageswary; Samsurijan, Mohamad Shaharudin (Journal of Environmental Psychology, 2025)
Keywords: Scopus_Alert_21_Novembre_2025

Characterization of Drop Size Distribution in Urban Heavy Rainfall Event Using Doppler Weather Radar and Laser Precipitation Monitor - Nandana, V.; Subrahmanyam, Kandula V.; Rao, V. Babu; Ratna, K. Naga; Nayak, R. K.; Ramana, M. V.; Rao, G. Srinivasa (Remote Sensing in Earth Systems Sciences, 2026)
Keywords: Scopus_Alert_21_Novembre_2025

Non-stationary stochastic modelling of precipitation extremes in the changing climate - Bhadra, Rituraj; Pandey, Mahesh D. (Reliability Engineering & System Safety, 2026)
Keywords: Scopus_Alert_21_Novembre_2025

Respective impacts of global warming, permafrost thaw and urban sprawl on debris-flow risk: Insights from the Dorfbach Torrent, Swiss Alps - Da Silva Correia, Gabriel; Favillier, Adrien; Stoffel, Markus; Graf, Christoph; Farvacque, Manon; Corona, Christophe (Geomorphology, 2026)
Keywords: Scopus_Alert_21_Novembre_2025

Mobility of disadvantaged communities exhibits lower sensitivity to extreme weather - Ermagun, Alireza; Zhang, Ella (Travel Behaviour and Society, 2026)
Keywords: Scopus_Alert_21_Novembre_2025

A five-century tree-ring record from Spain reveals recent intensification of western Mediterranean precipitation extremes - Marín-Martín, Marcos; Tejedor, Ernesto; Benito, Gerardo; Saz, Miguel A.; Barriendos, Mariano; Martínez Del Castillo, Edurne; Esper, Jan; De Luis, Martín (Climate of the Past, 2025)
Abstract. The Mediterranean basin, a recognized climate change hotspot, faces increasing hydroclimatic pressures, particularly from severe drought and precipitation events. To assess contemporary changes and potentially manage future impacts, it is crucial to understand the long-term context of this variability beyond the relatively short instrumental record. This study utilizes tree-ring records to reconstruct past hydroclimate in the Iberian Range of eastern Spain, a water-sensitive Mediterranean environment. We present a well-replicated tree-ring width chronology from Pinus sylvestris and Pinus nigra trees that calibrates and verifies significantly against cumulative instrumental precipitation over a 320 d period ending in June (r = 0.749; p < 0.01). The resulting 520-year reconstruction reveals substantial multi-centennial variability in precipitation and reveals an increase in the frequency and intensity of hydroclimatic extremes (both wet and dry) during the late 20th and early 21st centuries compared to the longer-term baseline. The reconstruction has a spatial representativeness centred over eastern and central Iberia and covaries with independent historical drought indices derived from rogation ceremony records during the late 18th and early 19th centuries. The documented intensification of hydroclimatic extremes is consistent with climate change projections and provides a baseline for evaluating ecosystem resilience and water resource vulnerability.
Keywords: Scopus_Alert_21_Novembre_2025

Investigating the Spatiotemporal Dynamics of Temperature and Precipitation Extremes Across the Loess Plateau Region in China During 1982–2023 - Rashid, Abdur; Xinyu, Zhang; Qixiang, Wang (International Journal of Climatology, 2025)
ABSTRACT The Loess Plateau Region (LPR) in China, identified as one of the world's most severely affected areas by soil erosion, contains fragile ecosystems highly susceptible to climate variability. This study examines the long‐term trends and abrupt shifts in temperature and precipitation extremes across the LPR from 1982 to 2023, offering a comprehensive assessment of climate change in this sensitive region. Homogenised daily data for the mean ( T mean ), maximum ( T max ) and minimum ( T min ) temperatures, along with precipitation (PPT), were collected from 160 meteorological stations. Data quality control and homogenisation were performed using RHtestsV3. The datasets were interpolated onto a 0.75° × 0.75° grid via ordinary Kriging in ArcGIS. Monotonic trends were analysed using the Mann–Kendall test and Sen's slope, with iterative pre‐whitening to mitigate autocorrelation bias. Abrupt shifts were detected using the Mann–Whitney U test, with significance assessed via Z ‐values and Monte Carlo simulations. We found a significant regional warming of annual T mean at 0.0177°C/year (≈0.71°C since 1982). Annual T max changed little (0.002°C/year, non‐significant), whereas T min decreased overall (−0.051°C/year), despite spring–summer T min increases and winter T min cooling (−0.018°C/year). Consequently, the diurnal temperature range widened, especially in winter. The strongest warming occurred in winter and spring, with over 83% of grid cells exhibiting significant trends ( p < 0.05). Precipitation increased significantly (4.81 mm/year; 200 mm over the study period), predominantly during the rainy season (+3.02 mm/year). Spatial variability was notable, where 41% of grid cells showed precipitation increases, while 13% exhibited declines, particularly in the southeastern LPR. Abrupt shifts affected 27% of temperature and 18.5% of precipitation grid cells, predominantly around 1993–1997 and 2009–2012; however, these shifts were localised and not statistically significant at the regional scale. Persistent warming and moderately increasing precipitation characterise the LPR's recent climate, underscoring the necessity for continued monitoring and adaptive strategies to address the impacts of temperature and precipitation extremes in this vulnerable region.
Keywords: Scopus_Alert_21_Novembre_2025

Modelling groundwater/surface-water connectivity fluxes using cross-correlation analysis and flood rating curves, Szentendre Island floodplain, Hungary - Kurgatt, Geoffrey; Langat, Philip Kibet; Koech, Richard; Tóth, Andrea (Hydrogeology Journal, 2025)
Keywords: Scopus_Alert_21_Novembre_2025

Synergies Between Observed Warming and ENSO Episodes on Extreme Events - Estrada, Francisco; Perron, Pierre; Yamamoto, Yohei (Annals of the New York Academy of Sciences, 2025)
ABSTRACT El Niño/Southern Oscillation (ENSO) is the dominant interannual variability mode of the global climate system with significant effects on a variety of weather conditions, including extremes. Past events illustrate the severe societal consequences this phenomenon has through weather disasters, food security, health, economic growth, migration, and conflict. ENSO's interactions with global warming are not well understood, although they can lead to significant changes in the characteristics of extreme events. Climate conditions in 2024/2025 may favor widespread severe extreme events with global temperature anomalies nearing or surpassing 1.5°C and a transition from strong El Niño to La Niña conditions. Here, we show that current warming has amplified the effects of ENSO on temperature and precipitation extremes worldwide. Results show that warming has produced a considerable amplification of the effects of ENSO episodes over such extremes, as well as extensively modified spatial patterns. We show that considerable shares of the population, gross domestic product, agriculture, and ecosystems now face a higher risk from extreme events due to the interactions between increased anthropogenic forcing and ENSO.
Keywords: Scopus_Alert_21_Novembre_2025

Real-Time Flood Alert System in Parking Lot Using IoT and Machine Learning - Nivetha, R.; Saravanan, S.; Ilango, V.; Dharmaraj, T. (2025 9th International Conference on Inventive Systems and Control (ICISC), 2025)
Keywords: Scopus_Alert_21_Novembre_2025

Nature-Based Solutions for Urban Buildings—The Potential of Vertical Greenery: A Brief Review of Benefits and Challenges of Implementation - Theodoridou, Ifigeneia; Vatitsi, Katerina; Stefanidou, Maria; Vanian, Vachan; Fanaradelli, Theodora; Macha, Makrini; Zapris, Adamantis; Kytinou, Violetta; Voutetaki, Maristella; Rousakis, Theodoros; Mallinis, Giorgos; Chalioris, Constantin (Urban Science, 2025)
The global rapid urbanization intensifies environmental challenges related to climate change, such as air pollution and the urban heat island (UHI) effect in built environments. The need to optimize nature-based solutions (NBSs) is imperative to mitigate climate change and adapt to extreme weather phenomena. Against this background, this review offers an analysis regarding the integration of vertical greenery systems (VGSs) into urban environments so as to capitalize on their environmental, social, and economic benefits. Key aspects of the review include the positive role of VGSs in UHI mitigation, air quality improvement, stormwater management, and biodiversity enhancement, while examining social aspects (i.e., improved well-being and mental health, noise reduction, and urban built aesthetics). Finally, parameters related to economic benefits and energy efficiency are assessed. The submission further analyses the significant challenges that VGSs face, such as high maintenance costs, structural risks, plant health issues, fire hazards, and other limitations (legislative and technical). The crucial need for interdisciplinary collaborations among urban planners, architects, environmental engineers, and stakeholders is highlighted, in order to successfully integrate VGSs into urban buildings. Thus, this paper aims to identify key strategies for optimizing VGSs’ implementation and provide valuable insights for policymakers and researchers aiming to enhance urban sustainability through vertical greening.
Keywords: Scopus_Alert_7_Novembre_2025

Economic Assessment of Building Adaptation to Climate Change: A Systematic Review of Cost Evaluation Methods - Felicioni, Licia; Klepačová, Kateřina; Hejtmánková, Barbora (Smart Cities, 2025)
Climate change is intensifying the frequency and severity of extreme weather events, threatening the resilience of buildings and urban infrastructure. While technical solutions for climate adaptation in buildings are well documented, their economic viability remains a critical, yet underexplored, dimension of decision-making. This novel systematic review analyzes publications with an exclusive focus on climate adaptation strategies for buildings using cost-based evaluation methods. This review categorises the literature into three methodological clusters: Cost–Benefit Analysis (CBA), Life Cycle Costing (LCC), and alternative methods including artificial intelligence, simulation, and multi-criteria approaches. CBA emerges as the most frequently used and versatile tool, often applied to evaluate micro-scale flood protection and nature-based solutions. LCC is valuable for assessing long-term investment efficiency, particularly in retrofit strategies targeting energy and thermal performance. Advanced methods, such as genetic algorithms and AI-driven models, are gaining traction but face challenges in data availability and transparency. Most studies focus on residential buildings and flood-related hazards, with a growing interest in heatwaves, wildfires, and compound risk scenarios. Despite methodological advancements, challenges persist—including uncertainties in climate projections, valuation of non-market benefits, and limited cost data. This review highlights the need for integrated frameworks that combine economic, environmental, and social metrics, and emphasises the importance of stakeholder-inclusive, context-sensitive decision-making. Ultimately, aligning building adaptation with financial feasibility and long-term sustainability is achievable through improved data quality, flexible methodologies, and supportive policy instruments.
Keywords: Scopus_Alert_7_Novembre_2025

Flood Risk Modelling Based on Machine Learning Using Google Earth Engine in Hulu Sungai Utara Regency - Aditya, Krisna; Ridwan, Ichsan; ., Nurlina (Nature Environment and Pollution Technology, 2025)
Flood risk modeling is essential for effective disaster mitigation, particularly in flood-prone areas such as Hulu Sungai Utara Regency, Indonesia. This study leverages Google Earth Engine (GEE) to integrate multi-source satellite data and machine learning techniques for flood susceptibility mapping. Key geospatial variables, including the Normalized Difference Vegetation Index (NDVI), elevation, distance from rivers, and the Topographic Position Index (TPI), were analyzed using a weighted overlay method within GEE. A supervised classification approach was employed to enhance accuracy, and validation was performed using historical flood event data. The results indicate that 51.66% (47,875.86 ha) of the study area falls into the low-risk category, 42.90% (39,763.08 ha) is at moderate risk, and 5.44% (5,040.36 ha) is highly susceptible to flooding. This study highlights the advantages of GEE in large-scale flood risk assessments by enabling real-time processing, high computational efficiency, and seamless integration of geospatial datasets. The findings provide critical insights for local governments and disaster management agencies to develop proactive flood mitigation strategies.
Keywords: Scopus_Alert_7_Novembre_2025

Debris flow risk characteristics and potential spatial mitigation strategies under extreme rainfall events - Chen, Tzu-Ling; Wu, Yu-Hsuan; Chiu, Yin-Hao (Climate Services, 2025)
Keywords: Scopus_Alert_7_Novembre_2025

Can runoff modeled at coarse resolution simulate floods at finer resolutions? A case study over the Ohio River Basin - Troy, Tara J.; Devineni, Naresh; Lima, Carlos H.R.; Lall, Upmanu (Advances in Water Resources, 2025)
Keywords: Scopus_Alert_7_Novembre_2025

Evaluation and projection of extreme rainfall from a large ensemble of high–resolution regional climate models in Australia - Jayaweera, Lalani; Wasko, Conrad; Nathan, Rory; Syktus, Jozef; Eccles, Rohan (Weather and Climate Extremes, 2025)
Keywords: Scopus_Alert_7_Novembre_2025

Evaluating the combined impact of climate change and urbanization patterns on flood risks - Ali, Syed Shujaat; Shah, Syed Akhtar Ali; Ahmed, Fahad; Isha, Sabahat; Hussain, Qudeer; Yamoat, Nirat; Yanghaipol, Piyakorn (Engineering Research Express, 2025)
Abstract Urban flooding exacerbated by climate change and urban expansion poses a significant challenge in developing countries like Pakistan. This study assesses the combined impacts of urbanization and climate change on flooding in eight identified hotspots in Peshawar based on reports from the Water and Sanitation Services Peshawar (WSSP). Climate projections utilize precipitation data from CMIP6 under SSP2-4.5 and SSP5-8.5 scenarios (1980–2100). Bias correction through quantile mapping and performance evaluation identified BCC-CSM2-MR as the top-performing Global Circulation Model (GCM), with the five best models used for future projections. Land cover analysis using Landsat imagery (2000 and 2024) from the USGS was processed in GIS and classified into built-up areas, barren land, cropland, and water bodies, revealing significant urban expansion. Saturation years were estimated based on annual growth rates for each hotspot. Rainfall-runoff modeling in WinTR-20 was conducted for land cover scenarios in 2000, 2024, and projected saturation years, as well as climate projections, to estimate peak flows. Results indicate rapid urbanization, with saturation expected by 2025 in Hotel Grand and Madina Colony and by 2034 in National Bank Colony and Warsak Road. Under SSP5-8.5, precipitation could increase by up to 81% with a 200-year return period. Hydrological modeling predicts rising peak flows across all catchments, with NB Colony reaching 112 m 3 s −1 under urbanization and extreme climate scenarios, approximately 114% higher than the peak value in the year 2024. These findings underscore the need to integrate climate and land cover dynamics into sustainable urban drainage planning to mitigate future flood risks effectively.
Keywords: Scopus_Alert_7_Novembre_2025

Convolutional Neural Network-Based Method for Identifying Floods in Urban Environments - Gomathi, B.; Manimegalai, R.; Harshini, K.; Vishnu Priya, V. (Smart System for Integrated Computing and Communication, 2026)
Series Title: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Keywords: Scopus_Alert_7_Novembre_2025

Cumulative effects of severe childhood maltreatment and flood-related residential displacement on PTSD risk among pregnant women in southern Brazil - Viola, Thiago W.; Giuberti, Pedro; Garcia, Miguel G.; Baldi, Bernardo B.C.; Berlaver, Sofia T.; Lodi, Martina A.; Machado, Gabriela C.; Grassi-Oliveira, Rodrigo (Child Abuse & Neglect, 2025)
Keywords: Scopus_Alert_7_Novembre_2025

Advancing Flood Forecasting With Wavelet‐ LSTM : The Role of Nonlinearity in Discharge Prediction - Khazaeiathar, Mahshid; Schmalz, Britta (Journal of Flood Risk Management, 2025)
ABSTRACT Discharge modeling utilizing novel deep learning techniques is highly recommended due to their high efficacy in modeling nonlinear time series. In this study, a hybrid discharge model is developed, termed wavelet‐based long short‐term memory (WLSTM), by integrating wavelet transform and Long Short‐Term Memory (LSTM). This technique focuses on improving discharge prediction by effectively denoising the input data and amplifying the most relevant temporal patterns for the model. However, since LSTM models depend on underlying data patterns, their performance can be significantly affected by the intensity of nonlinearity in hydrological time series. To address this, we introduce a novel method using ApEn (Approximate Entropy) to quantify nonlinearity intensity. Then, we applied Fuzzy Clustering to classify nonlinearity into weak, moderate, and high nonlinearity categories. The performance of both LSTM and WLSTM is evaluated using daily discharge data from 16 hydrometric stations in Hesse, Germany, for the period 2000–2017. The results notably show a remarkable reduction of 66.43% for Root Mean Squared Error (RMSE) and of 45.49% for Mean Absolute Percentage Error (MAPE) for WLSTM performance compared to LSTM. Furthermore, WLSTM increased R ‐squared ( R 2 ) by 2.06%. This research acknowledges that there is a direct correlation between the streamflow nonlinearity and WLSTM accuracy. With increasing nonlinearity intensity, WLSTM captures the complexity of streamflow patterns more effectively. RMSE is 0.1194, 0.0836, 0.0547 and R 2 is 0.9976, 0.9990, 0.9994 for weak, moderate, and high nonlinearity groups, respectively. This study highlights the importance of streamflow nonlinearity analysis in improving flood forecasting and risk management.
Keywords: Scopus_Alert_7_Novembre_2025

Integrating Remote Sensing, Machine Learning, and Local Knowledge for Innovative Flood Susceptibility and Vulnerability Mapping - Nasiri Khiavi, Ali; Vafakhah, Mehdi; Kim, Dongkun; Jun, Changhyun; Bateni, Sayed M. (Journal of Flood Risk Management, 2025)
ABSTRACT This study develops a comprehensive framework for mapping flood susceptibility and vulnerability in the Cheshmeh‐Kileh forest watershed in northern Iran by integrating remote sensing (RS), local knowledge, and machine learning (ML) algorithms. This was accomplished through the application of various MLs, such as K‐nearest neighbor (KNN), random forest (RF), support vector regression (SVR), and Naive Bayes. In this study, flood susceptibility refers to the physical propensity of an area to experience flooding, influenced by geo‐environmental factors, while flood vulnerability captures the socio‐economic and institutional dimensions that determine a community's ability to cope with and recover from flood events. This research first identified critical geo‐environmental factors influencing flood susceptibility and utilized remote sensing to locate areas prone to runoff generation. Flood risk zoning was then implemented using machine learning techniques in Python. To assess flood vulnerability, data were collected from local residents via questionnaires, focusing on economic, infrastructural‐physical, institutional‐policy, and social‐cultural aspects. The flood vulnerability map was created by integrating these survey results with population density data to identify areas where high social exposure coincides with high physical susceptibility. Findings indicated that the combined remote sensing‐SVR model was the most effective for sensitivity classification, identifying sub‐watersheds 2 and 8 in the Sehezar River (a major basin within the study area) as the areas with the highest and lowest flooding susceptibility, respectively, with sub‐watershed 10 in the Dohezar River (another major basin) being the most vulnerable. The estimated values for Mean Absolute Error (0.041), Mean Square Error (0.042), Root Mean Square Error (0.205), and Area Under the Curve (0.980) demonstrated high model accuracy. The Friedman statistical test showed that the average scores for the different dimensions of vulnerability decreased in the order of: economic (0.48), social‐cultural (0.44), infrastructural‐physical (0.34), and institutional‐policy (0.28). Consequently, the economic dimension was prioritized for its highest score. Flood vulnerability mapping revealed that sub‐watersheds 5, 11, 14, and 15, which had higher population densities, were naturally more vulnerable to floods. This finding reflects a direct relationship between population density and flood vulnerability. Overall, this study underscores the urgent need for effective planning and preventive strategies to mitigate flood risks and enhance resilience in the region.
Keywords: Scopus_Alert_7_Novembre_2025

Multiple driving pathways for the coupled and coordinated development of urbanization and flood disasters: A time differencing qualitative comparative analysis - Li, Pengfei; Tan, Shizheng; Li, Wei; Xue, Ye; Liang, Jialiang; Wang, Mengxin (Urban Climate, 2025)
Keywords: Scopus_Alert_7_Novembre_2025

An Operational Flood Risk Assessment System for Better Resilience Against Rain‐Induced Impacts Under Climate Change in Hong Kong - Tam, Hiu‐ching; Yeung, Hon‐yin; Lai, Ka‐yan; Lo, Ka‐wai; Leung, Ka‐fai; Chong, Sze‐ning (Meteorological Applications, 2025)
ABSTRACT Under the background of climate change, extreme weather events are apparently becoming more frequent. In Hong Kong, a record‐breaking ‘Black’ Rainstorm on 7–8 September 2023 brought widespread flooding and caused landslides, paralysing the entire community. To enhance the community's response and resilience in coping with extreme weather, the Hong Kong Observatory has developed the Flood Risk Assessment System (FRAS), which embraces an impact‐based forecasting method and a risk‐based warning strategy. The main inputs are real‐time rainfall data from rain gauges and the in‐house developed probabilistic rainfall nowcast. The process from rain falling through the air to flooding observed on the ground is complicated, involving many non‐meteorological and random factors. As a result, the corresponding impact assessment is highly non‐trivial. The key technique adopted by FRAS is the use of a district‐scale ‘rainfall‐flooding impact’ statistical model, developed through in‐depth study of historical flood reports and rainfall records. The risk‐based warning strategy is designed largely based on the risk matrix recommended by the World Meteorological Organization. The performance of FRAS has been optimised in accordance with users' operational needs under the premises of a high safety margin and early alert. FRAS was launched in May 2024 for trial by government departments/bureaux, operating continuously in real‐time and offering automatic flood risk assessment for all districts every minute during rainy seasons. This paper briefly presents the design, key techniques, and warning products of FRAS. Its performance as an early warning service is also examined through objective verification results and user feedback.
Keywords: Scopus_Alert_7_Novembre_2025

Database System and Model for Predicting Risk Level of Flood That Damages Rice Farming in Thailand - Susutti, Wittawin; Dilokpatpongsa, Pirun; Kaemawichanurat, Pawaton; Saleewong, Teerapol; Dhakonlayodhin, Boonkong; Supapakorn, Thidaporn; Watthayu, Wiboonsak (Statistics, Optimization & Information Computing, 2025)
Rice is always an important economic crop of Thailand as it is not only the staple in every family of the entire country but it also earns the extremely large income among all the Thai crop exports. However, Thai farmers are considered starve and still have to face many difficulties, in particular, flood problem. The flood problem has destroyed rice farming areas over the past decade until now. Risk and severity assessments mainly contribute the government promptly subsidizing the farmers. In any case, the updated and reliable database systems are the main ingredients to develop the model of these assessments. In this paper, we develop a database system from surveying with 5,000 samples over the whole country. All the raw data has been managed to clean and prepare in order to develop model that is used to predict risk level. The model achieves 87.24 percent accuracy with significant level of 0.05. In addition, the model is able to select variables that have a statistically significant effect on the risk level forecast, and these variables could be used to improve the quality and data structure for developing Web Application (WebApp). The WebApp of our research group for individual risk assessment of the rice farmers has been developed by Javascript to the front end while the back end is run by Python. The WebApp was evaluated satisfactions by over 370 farmers from three public hearings. The satisfaction average scores are over 4 to maximum 5 in all categories.
Keywords: Scopus_Alert_7_Novembre_2025

Enhancing urban pluvial flood modeling through graph reconstruction of incomplete sewer networks - Li, Ruidong; Liu, Jiapei; Sun, Ting; Shao, Jian; Tian, Fuqiang; Ni, Guangheng (Hydrology and Earth System Sciences, 2025)
Abstract. This work presents an efficient graph-reconstruction-based approach for generating physical sewer models from incomplete information, addressing the challenge of representing the sewer drainage effect in urban pluvial flood simulations. The approach utilizes graph-based topological analysis and hydraulic design constraints to derive gravitational flow directions and nodal invert elevations in decentralized sewer networks with multiple outfalls. By incorporating linearized programming formulation to solve reconstruction problems, this approach can achieve high computational efficiency, making it suitable for application to city-scale sewer networks with thousands of nodes and links. Tested in Yinchuan, China, the approach integrates with a 1D/2D coupled hydrologic–hydrodynamic model and accurately reproduces maximum inundation depths (R2=0.95) when the complete network layout and regulated facilities are available. Simplifications, such as the adoption of road-based layouts and the omission of regulated facilities, can degrade simulation performance for extreme rainfall events compared to calibrated equifinal methods. However, design rainfall analysis demonstrates that the physical reconstruction approach can reliably outperform equifinal methods, achieving reduced variation and higher accuracy in simulating inundation areas. However, proper configuration of regulated facilities and network connectivity remains crucial, particularly for simulating local inundation during extreme rainfall. Thus, it is recommended that the proposed algorithm be integrated with targeted field investigations to further improve urban pluvial flood simulation performance in data-scarce regions.
Keywords: Scopus_Alert_7_Novembre_2025

Unbalanced relationship between flood risk perception and flood preparedness from the perspective of response intention and socio-economic factors: a case study of Nanjing, China - Li, Yabo; Wang, Peng (Natural Hazards and Earth System Sciences, 2025)
Abstract. Perceived flood risk and flood preparedness are critical components of effective flood management. However, perceived flood risk does not always translate into flood preparedness. This study investigated the potential association between risk perception and flood preparedness in Nanjing by designing and carrying out a questionnaire survey. The results indicated that participants exhibited moderate perceptions of flood risk but demonstrated higher levels of flood preparedness. Higher risk perception was observed in individuals who were older, exercised regularly, had lower levels of education or had lived in the area for a longer period of time. Higher levels of flood preparedness were evident among females, the elderly and those with higher levels of education. Participants relied more on threat appraisal to perceive risk, but this failed to trigger sufficient coping appraisal. Inadequate risk perception resulted in a significant transformation towards flood preparedness, leading to an unbalanced relationship. Groups with distinct socio-economic characteristics showed different preferences for achieving risk perception and flood preparedness. Path analysis suggests that threat appraisal can transform into flood preparedness under the influence of response intention and socio-economic features. Individuals with higher levels of education or poor health could be more likely to perceive risk and engage in preventive behavior. These findings provide critical insights into intervention strategies designed to improve public flood preparedness in flood management.
Keywords: Scopus_Alert_7_Novembre_2025

Flood exposure in Rotterdam's unembanked areas from 1970 to 2150: sensitivities to urban development, sea level rise, and adaptation - Oerlemans, Cees; Van Den Boomen, Martine; Rijcken, Ties; Kok, Matthijs (Natural Hazards and Earth System Sciences, 2025)
Abstract. Uncertainties in the rate of sea level rise, coupled with ongoing urban expansion, are a challenge for city planners designing flood risk adaptation strategies. This study quantifies flood exposure in Rotterdam's unembanked areas from 1970 to 2150. We modelled flood hazards for 10-, 100-, and 1000-year return periods under both low- (RCP2.6) and high-emission (RCP8.5) scenarios while assessing exposure using historic and planned urban development data. Temporal variations in exposure rates are attributed to three factors: urban development, sea level rise, and the construction of the Maeslant storm surge barrier. Without adaptation measures, flood exposure in Rotterdam's unembanked areas is projected to increase. Under RCP8.5, exposure rates for 10-year flood events are expected to increase 7-fold by 2150 compared to 2020. For RCP2.6, a 3-fold increase is projected for 10-year events, reflecting uncertainties in long-term sea level rise. A retrospective analysis reveals a decrease in flood exposure: exposure levels observed in 2020 were approximately half those observed in 1996, due to construction of the Maeslant barrier. Exposure rates are primarily influenced by the Maeslant barrier, followed by sea level rise and urban development. Understanding the interplay of these three factors is crucial for urban planning and flood risk management in delta cities.
Keywords: Scopus_Alert_7_Novembre_2025

Designing Effective Flood Early Warning Systems: A Review of Barriers, Best Practices, and Key Characteristics - Painter, Patrick; Semmens, Kathryn; Maxfield, Keri; Cattoën, Céline; Carr, Rachel Hogan (Journal of Flood Risk Management, 2025)
ABSTRACT Flooding is a disruptive and devastating natural hazard for communities all around the world. To combat the negative effects of flooding, it has become a global priority to implement and research flood early warning systems (FEWS). However, previous research did not comprehensively examine both the technological and social dimensions of FEWS nor the knowledge of their availability and status around the world. To address these gaps, this paper completes a narrative review and synthesizes best practices in FEWS warning and system design, and introduces a “FEWS Around the World” repository which catalogs 3–4 examples per region across six continents. Our analysis shows that while most FEWS include design features outlined in the literature such as timeliness, human capacity, and integration, fewer implement novel components related to impact‐based warnings, participatory science, bottom‐up approaches, advanced technology, and maintaining a preparedness fund, highlighting gaps and opportunities for improvement. Examples demonstrate how these characteristics manifest in diverse contexts, from community‐based systems in Nepal to AI‐driven systems like Google's Flood Hub. By bridging design principles with observed global practices, this paper is designed to aid researchers, practitioners, and communities in people‐centered FEWS development, implementation, and operation, improving resilient flood risk management.
Keywords: Scopus_Alert_7_Novembre_2025

Uncertainty in estimating the relative change of design floods under climate change: a stylized experiment with process-based, deep learning, and hybrid models - Poudel, Sandeep; Najibi, Nasser; Steinschneider, Scott (Journal of Hydrology, 2026)
Keywords: Scopus_Alert_7_Novembre_2025

A flood susceptibility prediction method for climate change scenarios driven by coupled land simulation and spatiotemporal dual convolution synergy - Wang, Rongyao; Chen, Yangbo; Wu, Hanfei; Liu, Jun; Wang, Meiying; Duan, Jiajun (Journal of Hydrology, 2026)
Keywords: Scopus_Alert_7_Novembre_2025

Knowledge-guided and data-driven flood prediction based on Besov space optimization - Shao, Pingping; Feng, Jun; Cai, Yang; Zhang, Jianxin; Hou, Aizhong (Information Sciences, 2026)
Keywords: Scopus_Alert_7_Novembre_2025

Mountainous Flood Resilience: A Comprehensive Systematic Review of Flood Analysis Methods - Rijal, Madhab; Luo, Pingping; Mishra, Binaya Kumar; Thakuri, Sudeep; Zhang, Yubin; Zhao, Yang (Archives of Computational Methods in Engineering, 2025)
Keywords: Scopus_Alert_7_Novembre_2025

Behaviors of Highway Culverts Subjected to Flooding: A Comprehensive Review - Zeyrek, Omer; Wang, Fei; Xu, Jun (Water, 2025)
Highway culverts are essential components of transportation infrastructure, designed to convey water beneath highways and protect embankments from flooding. However, extreme flood events often impose hydraulic loads, overtopping, and debris accumulation that can trigger erosion, scour, blockage, and in severe cases, catastrophic washout. This paper presents a comprehensive review of highway culvert behavior under flooding conditions, integrating insights from hydraulics, geotechnical engineering, and structural performance. The review is organized around four themes: (1) types of flooding and their interactions with culverts; (2) hydraulic performance during flood events; (3) common failure modes, including scour, debris blockage, and structural instability; and (4) mitigation strategies to enhance resilience. Advances in hydraulic modeling, including 1D, 2D, 3D, and CFD approaches, are summarized, with attention to their accuracy, applicability limits, and validation needs. Representative experimental, numerical, and empirical studies are grouped by common properties to highlight key findings and constraints. Finally, emerging research opportunities are discussed, including the need for quantitative relationships between culvert geometry and flood intensity, methods to assess structural capacity loss during flooding, and the integration of artificial intelligence and computer vision for rapid post-flood inspection. This synthesis establishes a foundation for more robust evaluation, design, and maintenance strategies, supporting the long-term resilience of highway culverts in an era of increasingly frequent and severe floods.
Keywords: Scopus_Alert_7_Novembre_2025

From Flood to Drip Irrigation: A Review of Irrigation Modernization Trade-Offs - Santini, Alessandra; Masiero, Mauro; Amato, Giulia; Pettenella, Davide Matteo (Water, 2025)
Water scarcity, climate change, population growth, and rising water demand highlight the urgency of adopting effective water conservation measures. The transition from traditional irrigation systems, such as flood irrigation, to modern ones, like drip irrigation, is often seen as a panacea to improve irrigation efficiency and address water shortages. Despite the flourishing literature on the efficiency gains achieved by shifting to drip irrigation, trade-offs associated with replacing traditional irrigation systems with modern irrigation technologies remain unexplored. Building on this gap, this paper provides a systematic literature review to analyze the current state of knowledge and research on the trade-offs associated with this transition. The review analyses not only the possible effects on agricultural productivity and irrigation efficiency at the farm scale, but also the environmental implications and socio–economic consequences that may emerge at a larger scale. We found that while studies conducted at the field-level emphasize clear benefits associated with the adoption of drip irrigation, including higher crop yields and improved water use efficiency, basin-scale analyses reveal drawbacks, including increased consumptive use, reduced return flows for ecosystem processes, and more generally limited real water savings. Overall, our findings stress the need for more holistic, multi-scale, and interdisciplinary approaches to assess the impact of irrigation modernization, along with the need for policy frameworks that balance agricultural productivity gains with sustainable water management.
Keywords: Scopus_Alert_7_Novembre_2025

Ice Jam Flooding of the Drying Peace-Athabasca Delta: Hindsight on the Accuracy of the Traditional Knowledge and Historical Flood Record - Beltaos, Spyros (Environments, 2025)
The Peace-Athabasca Delta (PAD) in northern Alberta, Canada, is one of the world’s largest inland freshwater deltas and is largely located within the Wood Buffalo National Park, a UNESCO World Heritage Site. Owing to its ecological and socioeconomic significance, the PAD has been designated a Ramsar wetland of international importance. A paucity of large-scale Peace River ice jam flooding and concurrent drying trend during the past five decades has motivated various studies on relevant processes and on possible remedial action. In turn, many of these studies are informed by a flood record that was compiled in 1995, based on Historical information and Traditional Knowledge (H-TK flood record). Later work has expressed occasional reservations regarding the accuracy of this record, while much more is now known about the physical and hydroclimatic controls of PAD ice jams. This paper examines the 20th century portion of the H-TK record in the light of recent scientific advances made since the 1990s and of a wealth of hydrometric and climatic indicators, along with eyewitness corroborations, that extend back to the early 1900s. Systematic observational data and monitoring reports that have become available since the 1990s have also provided valuable documentation of PAD flooding. It is concluded that the record of major ice-jam floods is reliable, while the possibility of “missed” events cannot be precluded. The record of minor ice jam floods, which is largely inferred from reversed tributary flows entering Lake Athabasca, may not be reliable because more than half of the reported events might not have occurred at all. The value of the H-TK record is primarily in the major events, which generate overland inundation and can amply recharge various ponds, lakes, and wetlands of the PAD. Implications of the results for pre- and post-regulation flood frequencies and for future park management are discussed.
Keywords: Scopus_Alert_7_Novembre_2025

Review article: Rethinking preparedness for coastal compound flooding: insights from a systematic review - Gomez Rave, Dina Vanessa; Scolobig, Anna; Del Jesus, Manuel (Natural Hazards and Earth System Sciences, 2025)
Abstract. Tackling the growing risks of compound flooding (CF) requires transformative preparedness strategies, particularly in estuarine and coastal regions where the interaction of drivers such as storm surges, rainfall, and river discharge exacerbates impacts. Despite progress, fragmented governance, weak cross-sectoral coordination, and the limited integration of scientific insights hinder effective responses. This systematic review draws on 49 studies to explore how preparedness strategies are evolving to integrate technical, environmental, and social dimensions while evaluating the role of governance and collaboration in enhancing adaptive approaches. Hybrid early warning systems – combining statistical and hydrodynamic models with real-time data – are critical for forecasting accuracy and timely decision-making. Similarly, the balanced implementation of green, blue, and grey infrastructure provides sustainable responses, with nature-based solutions complementing traditional engineering. Our results show that strengthening governance and communication is essential to improving preparedness. Involving communities in land-use planning, building regulations, and communication ensures that measures are both actionable and context specific. Incorporating psychological and behavioural data into preparedness frameworks and models helps to strengthen the link between awareness and behaviours. Enhanced coordination across sectors and levels of government is also vital to addressing the systemic nature of CF risks, moving beyond siloed single-hazard responses.
Keywords: Scopus_Alert_7_Novembre_2025

A Systematic Review of Monitoring Approaches to Assess Hydrological Conditions in Small Catchments With Natural Flood Management - Jones, Anthony D.; Knapp, Julia L. A.; Reaney, Sim M.; Pattison, Ian (Journal of Flood Risk Management, 2025)
ABSTRACT Natural Flood Management (NFM) has gained prominence as a sustainable approach to flood risk reduction, particularly in small catchments where traditional grey infrastructure is less viable. However, understanding the effectiveness of NFM is closely tied to the quantity and quality of hydrological monitoring. In small catchments, this monitoring remains inconsistent, whereas high‐quality, high‐frequency networks maximise the likelihood of detecting NFM effects. This is the first systematic review to analyse current approaches to streamflow and rainfall monitoring used to assess NFM performance in small catchments (defined as < 25 km 2 ), consolidating data from 33 studies (65 catchments) into a practitioner‐oriented decision matrix that links site conditions, cost and certainty to method selection. The reviewed dataset consolidates example NFM interventions and associated monitoring approaches, highlighting the benefits and limitations of each method in a single, accessible resource. The review also highlights gaps, including limited baseline data, short monitoring durations, and infrequent reporting of costs and methods. A decision matrix is presented to support practitioners in selecting streamflow monitoring methods based on site conditions and resources for small catchments. Recommendations to improve standardisation, reporting, and the adoption of low‐cost, scalable techniques, including community‐led and non‐contact approaches (remote sensing and drone imagery) are also given.
Keywords: Scopus_Alert_7_Novembre_2025

Integrating statistical distributions with machine learning to model IDF curve shifts under future climate pathways - Bakheit Taha, Abubakr Taha; Aldrees, Ali; Mustafa Mohamed, Abdeliazim; Hayder, Gasim; Babur, Muhammad; Haq, Shay (Frontiers in Environmental Science, 2025)
Climate change has intensified rainfall variability, increasing urban flooding risks in arid regions like Makkah and Riyadh. This study develops Intensity-Duration-Frequency (IDF) curves to analyze rainfall intensities for various storm durations and return periods, supporting urban planning and water resource management. Historical precipitation data (1950–2020) and future projections from two Shared Socioeconomic Pathway scenarios (2021–2100) were used to construct IDF curves for Makkah and Riyadh to assess precipitation extremes and support hydrological and infrastructure planning. Downscaling and bias correction were applied to five Global Climate Models, followed by feature engineering using CatBoost and LightGBM. Multi-Model Ensemble (MME) predictions were then evaluated using machine learning algorithms, including AdaBoost, CatBoost, and XGBoost, with XGBoost achieving the highest accuracy. For precipitation modeling, Gamma and Log-Pearson 3 distributions were identified as the best fits for observed and projected data in Makkah and Riyadh, respectively, underscoring the importance of selecting appropriate probability distributions to accurately capture precipitation extremes. The study offers a predictive tool in terms of climate resilience of urban areas within arid zones, which strengthens climate projections to aid decision-making.
Keywords: Scopus_Alert_7_Novembre_2025

Response of Tropical Climate and Extreme Precipitation to Ocean Temperature in Convection‐Permitting Aquaplanet Simulations - Rios‐Berrios, Rosimar; Emanuel, Kerry; Bryan, George H.; Medeiros, Brian; Done, James (Journal of Advances in Modeling Earth Systems, 2025)
Abstract Tropical climate and weather systems are integral to the global hydrological cycle. Yet, their detected and projected trends are highly uncertain in part due to shortcomings of Earth system models. This study uses novel aquaplanet simulations with different sea‐surface temperature and with convection‐permitting resolution in the tropics to simulate the response of tropical phenomena to globally uniform warming. The results show a complex response within the tropical circulation. With warming, the tropical troposphere warms and expands vertically alongside a weaker and broader Hadley cell. Simultaneously, the Intertropical Convergence Zone (ITCZ) narrows and strengthens, though its location shows an unclear response to warming. Both dynamic and thermodynamic processes play a role in the ITCZ response, exhibiting patterned changes in vertical velocity and magnitude changes in water vapor. Clouds also show a complex response, particularly with tropical cloud ice showing a non‐linear response to tropical warming and likely causing non‐linear changes in the radiation budget. At finer scales, tropical precipitation extremes increase non‐linearly with warming, exceeding Clausius‐Clapeyron scaling at higher percentiles. The prevalence of non‐linear responses highlights the necessity of considering multiple scenarios when investigating the response of tropical phenomena to globally uniform warming. This research provides valuable insights into tropical climate sensitivity in a framework that captures multi‐scale processes from global to convective scales. , Plain Language Summary As our planet warms, weather patterns worldwide may also shift in frequency and intensity. However, predicting the specifics of these changes is challenging, especially in the tropical atmosphere where towering clouds and heavy rain are essential parts of weather systems. A key reason for this uncertainty is that current climate models struggle to accurately represent these towering clouds and heavy rainfall, overlooking crucial rainfall‐producing features in the tropics. In this study, we utilize innovative model simulations that capture these fine details in the tropics while also representing the global climate. We present three simulations that differ only in their ocean temperature—one simulation mimics our current climate, while the other two simulate cooler and warmer climates. Our findings highlight the importance of capturing small‐scale tropical processes, as the climate exhibits various responses to uniform warming. Tropical weather systems generate more intense extreme rainfall, likely connected to changes in the overall tropical environment. Future research should leverage these simulations to explore how specific weather systems, such as tropical cyclones, evolve in a warmer climate. , Key Points We present a set of aquaplanet experiments with different sea‐surface temperatures and with convection‐permitting resolution in the tropics The simulations enable studies on the response of atmospheric phenomena—from planetary to convective scales—to globally uniform ocean warming The Hadley cell, Intertropical Convergence Zone, and tropical precipitation extremes respond non‐linearly to uniform ocean warming
Keywords: Scopus_Alert_7_Novembre_2025

Impacts of solar radiation modification on precipitation extremes in Central China - Feng, Zeqian; Tan, Mou Leong; Mahamud, Mohd Amirul; Cheng, Shiyuan; Hou, Chenglei (Climate Dynamics, 2025)
Keywords: Scopus_Alert_7_Novembre_2025

A first run-of-river hydropower plant development in a permafrost-rich subarctic Canadian region: short-term fate of mercury and carbon - St-Aubin, Mariane; Lapierre, Jean-François; Ponton, Dominic E.; Sliger, Michel; Gaudreault, Jeanne; Atagotaaluk, Éric; Fortier, Daniel; Amyot, Marc (Environmental Science: Processes & Impacts, 2025)
Minimal research exists on the environmental impact of hydroelectricity in permafrost areas. This study reports increases in methylmercury in flooded soils, surface waters and first trophic levels following impoundment. , A first run-of-river power plant built in Nunavik (QC, Canada) lies in a continuous permafrost zone, a substantial carbon (C) rich mercury (Hg) reservoir. Its impoundment could promote permafrost thaw and remobilize Hg and lead to the enhanced production of highly toxic methylmercury (MeHg). To elucidate how RORs can influence C and Hg dynamics and transformations in a subarctic landscape, soils, water and benthic invertebrates were sampled shortly before and after the flooding. Soil Hg concentrations were higher in the organic active layer than in frozen ground. Three months after river impoundment, MeHg concentrations and proportions in the surface organic layer of flooded soils were seven and four times higher, respectively. A similar increase was observed in surface waters of the newly created bay, where MeHg concentration and proportion were, respectively, ∼10 and four times higher. Biological MeHg concentrations increased in low trophic level organisms associated with this flooded environment, namely primary consumers (∼4×) and omnivores (∼3×). However, these rises were limited to the small (<1 km 2 ) newly created bay, highlighting spatial heterogeneity in the production and trophic transfer of MeHg at the river scale in response to recent impoundment.
Keywords: Infolettre_Decembre_2025; Infolettre_Novembre_2025

DNA Methylation, Prenatal Maternal Stress and the Broad Autism Phenotype in Adolescence: Project Ice Storm - Cao-Lei, Lei; Elgbeili, Guillaume; Laplante, David Paul; Szyf, Moshe; King, Suzanne (Biology and Life Sciences, 2025)
Background/Objectives. Prenatal maternal stress (PNMS) predicts risk for autism spectrum disorders (ASD) although the mechanisms are unknown. The aim of this study was to determine the extent to which DNA methylation mediates the effects of PNMS from a natural disaster on autistic-like traits in offspring assessed during adolescence. Methods. Five months following the 1998 ice storm in Quebec, Canada we recruited women who had been pregnant during the crisis and assessed their PNMS: objective hardship, subjective distress, and cognitive appraisal. At age 13, their children provided blood samples for DNA. At ages 15, 16 and 19, the youth self-reported their own autistic-like traits using the Broad Autism Phenotype Questionnaire. Results. Results showed that mothers’ disaster-related objective hardship and their negative cognitive appraisal of the disaster predicted DNA methylation at age 13, which then predicted the severity of their children’s Aloof Personality and Pragmatic Language Deficits, but not Rigid Personality, at ages 15, 16 and 19. Mediation was significant particularly through genes within the PI3K/AKT/mTOR pathway which has been implicated in various neurodevelopmental disorders, including ASD. Interestingly, while greater PNMS predicted more severe ASD traits, the epigenetics effects were for less severe traits. Conclusions. These results suggest that DNA methylation, assessed in early adolescence, may protect against ASD traits at later ages, particularly when there is a mismatch between the prenatal environment (disaster) and the postnatal environment (absence of disaster). The interpretation of these results in the context of fetal programming and the predictive adaptive response are discussed.
Keywords: Infolettre_Novembre_2025

PROTOCOL: The Impact of Relocation Processes on Populations Facing Socio‐Territorial Inequities: A Scoping Review - Chagnon, Pascale; Pierre‐Paul, Audate; Cloutier, Geneviève; Demers‐Desmarais, Marianne (Campbell Systematic Reviews, 2025)
ABSTRACT Relocation processes are increasingly considered local adaptations to flooding. While relocation can offer many benefits, it also leads to socio‐economic and socio‐psychological consequences. Moreover, it tends to place greater pressure on populations experiencing socio‐territorial inequities compared to other households. It is therefore important to assess the direct and indirect impacts of relocation to consider its application from a more just and equitable perspective. The objective of this scoping review is to document the impacts of flood‐induced residential relocation processes on populations facing socio‐territorial inequities in North America and Europe. It also seeks to categorize the challenges faced by planners in relocation processes. This will help us better assess the relevance of relocation as an adaptation measure to climate change for populations facing socio‐territorial inequities. This scoping review will be conducted in accordance with the methodological guide JBI Manual for Evidence Synthesis (Aromataris and Munn 2020). A systematic search will first be developed in collaboration with an expert librarian. Five databases will be searched: Erudit, Cairn, Web of Science, GreenFILE (EBSCO), and GeoBase (Engineering Village). Gray literature will be collected from Policy Commons, Open Access Theses and Dissertations, Google Scholar, Google, and relevant governmental websites. A screening of the studies and documents obtained will then be carried out by two independent reviewers to retain the relevant documents for the scoping review. If needed, the involvement of a third reviewer will be solicited. The data relevant to our question will then be extracted, using a tool created by the authors, and then analyzed and presented in narrative and tabular formats. The results of this scoping review will be used to discuss and share insights and lessons learned. It will allow us to draw conclusions on the impacts of residential relocation processes on populations facing socio‐territorial inequities and to identify best practices for land‐use planning in the context of climate change.
Keywords: Infolettre_Novembre_2025

Joint use of the Sentinel-1–derived Kennaugh parameters and Sentinel-2 data for temporal landcover dynamics over the Indian Sundarbans region - Mukherjee, Sukanya; Dey, Subhadip; Kamilya, Dibyendu; Mondal, Sandipan; Homayouni, Saeid; Mahdianpari, Masoud (Journal of Applied Remote Sensing, 2025)
Keywords: Infolettre_Novembre_2025

Three-dimensional collaborative hydraulic fracturing control for thick hard roofs: mechanism, simulation, and field application in Yadian Coal Mine - Ren, Shuo; Li, Xuehua; Liang, Shun; He, Jie; Li, Zhongwei; Li, Changru; Luo, Hongye (Geomechanics and Geophysics for Geo-Energy and Geo-Resources, 2025)
Keywords: Infolettre_Novembre_2025

Exploring the ability of LSTM-based hydrological models to simulate streamflow time series for flood frequency analysis - Martel, Jean-Luc; Arsenault, Richard; Turcotte, Richard; Castañeda-Gonzalez, Mariana; Brissette, François; Armstrong, William; Mailhot, Edouard; Pelletier-Dumont, Jasmine; Lachance-Cloutier, Simon; Rondeau-Genesse, Gabriel; Caron, Louis-Philippe (Hydrology and Earth System Sciences, 2025)
Abstract. An increasing number of studies have shown the prowess of long short-term memory (LSTM) networks for hydrological modelling and forecasting. One drawback of these methods is the requirement for large amounts of training data to properly reproduce streamflow events. For maximum annual streamflow, this can be problematic since they are by definition less common than middle or low flows, leading to under-representation in the model's training set. This study investigates six methods to improve the peak-streamflow simulation skill of LSTM models used for flood frequency analysis (FFA) in ungauged catchments. These include adding meteorological data variables, providing streamflow simulations from a distributed hydrological model, oversampling peak-streamflow events, adding multi-head attention mechanisms, adding data from a large set of “donor” catchments, and combining some of these elements in a single model. Furthermore, results are compared to those obtained by the distributed hydrological model HYDROTEL. The study is performed on 88 catchments in the province of Quebec using a leave-one-out cross-validation implementation, and an FFA is applied using observations, as well as model simulations. Results show that LSTM-based models are able to simulate peak streamflow as well as (for a simple LSTM model implementation) or better than (with hybrid LSTM–hydrological model implementations) the distributed hydrological model. Multiple pathways forward to further improve the LSTM-based model's ability to predict peak streamflow are provided and discussed.
Keywords: Infolettre_Novembre_2025

Varying flood exposure due to uncertain data of flood hazard and population distribution - Shao, Wendi; Dong, Jiaqi; Li, Jingwei; Li, Mengmeng; Shen, Ju; Wu, Yijing; Du, Shiqiang (Environmental Research Letters, 2025)
Abstract Gridded population and flood hazard data are crucial for flood exposure assessments. However, current assessments incorporate uncertainties related to data selection, yet the mechanisms through which subjective data selection propagate uncertainties in exposure models remain poorly understood. To address this gap, this study conducted a comparative assessment of flood exposure in China using five population datasets and five flood hazard datasets. Furthermore, it explored the absolute and relative impacts of data uncertainties on 100 year return period flood exposure and discussed the underlying causes. Results exhibit substantial variations in flood exposure when different data combinations are employed. Specifically, there is a significant difference of 333 million individuals within the exposure range, with the highest estimate being 2.82 times the lowest one. Overall, the exposure variation was primarily from differences in flood hazards rather than population patterns, but their relative importance differed spatially depending on factors of slope, altitude, and artificial surface coverage. Despite the differences, all 25 data combinations revealed a disproportional larger share of population in floodplains, which was 2.28–3.49 times the share of floodplains. These findings are significant for understanding the uncertainties of flood exposure and can shed lights on informed policies for risk management.
Keywords: Scopus_Alert_31_Octobre_2025

Comparative Evaluation of Deep Learning–Based Super–Resolution Models for Urban Flood Mapping - Choi, Hyeonjin; Woo, Hyuna; Ryu, Hyungon; Rhee, Dong Sop; Noh, Seong Jin (Journal of Flood Risk Management, 2025)
ABSTRACT Urban flood forecasting benefits from high‐resolution inundation maps, but fine‐grid hydrodynamic simulations are computationally costly. We compared three CNN–based super–resolution (SR) models, ResUNet, EDSR, and RCAN, for downscaling physics–based simulations in downtown Portland, Oregon, using paired flood maps at 1 m (HR) and both 4 and 8 m (LR). Performance was assessed using image level metrics (PSNR, SSIM) and flood specific indicators: CSI for flood extent, RMSE for water depth accuracy, and a depth–based severity classification. At 4× upscaling, all SR models outperformed the LR baseline; RCAN performed best (PSNR +57%, SSIM +31%, RMSE −73%, CSI +53%), followed by EDSR (PSNR +50%, SSIM +30%, RMSE −64%, CSI +45%) and ResUNet (RMSE −55%, CSI +40%). Analysis of class–wise recall showed RCAN leading for non–flood (98.06%, +6.59 pp) and severe flood (96.48%, +16.90 pp), while EDSR led for mild flood class (97.95%, +6.49 pp). Errors were most pronounced along wet–dry boundaries and in complex urban geometries, where RCAN and EDSR reduced error magnitude more effectively than ResUNet. Models with larger numbers of parameters required longer training times. Furthermore, the computational cost further increased with more training epochs and especially at 4× upscaling relative to 8×, reflecting differences in model complexity and scaling configuration. Taken together, these findings support SR as a practical complement to physics–based modeling for real time forecasting and planning, while also providing guidance for selecting architectures under varying computational budgets.
Keywords: Scopus_Alert_31_Octobre_2025

Estimating urban flooding depth by integrating multimodal image-text data: A segment-level direct preference optimization-based multimodal large language model - Chu, Tianyou; Chen, Yumin; Zhu, Rui; Zeng, Fei (ISPRS Journal of Photogrammetry and Remote Sensing, 2025)
Keywords: Scopus_Alert_31_Octobre_2025

Resilient coasts: A holistic approach to flood risk assessment and mitigation on the East Coast of Ghana - Kwang, Clement; Yeboah, Emmanuel; Mensah, Augustine O. K. N.; Sarfo, Isaac; Nikoi, Ebenezer; Batame, Michael; Okrah, Abraham; Anaba, Clement Issaka; Antwi, Asante Vincent; Amoako, Joachim (Journal of Coastal Conservation, 2025)
Keywords: Scopus_Alert_31_Octobre_2025

Flood risk and vulnerability assessment due to dam break - Al-Omari, Saif; Al-Weshah, Radwan; Al-Salahat, Murad (Sustainable Water Resources Management, 2025)
Keywords: Scopus_Alert_31_Octobre_2025

Estimating flash flood surface flow velocity in heritage city using citizen-recorded videos - Asif, Muhammad; Sasso, Silvano Fortunato Dal; Lacava, Teodosio; Albano, Raffaele (Progress in Disaster Science, 2025)
Keywords: Scopus_Alert_31_Octobre_2025

Assessment and evolution analysis of urban infrastructure resilience under flood disaster scenarios based on the PSR model and extension catastrophe progression - Yang, Chenxin; Jin, Zhigang; Yin, Hao (Scientific Reports, 2025)
Keywords: Scopus_Alert_31_Octobre_2025

Evaluating coastal vulnerability: mapping inundation risk in Tamil Nadu with and without coastal protection - Mayamanikandan, T.; Usha, Tune; Dash, S. K.; Raju, S. K.; Mahendra, R. S.; Murthy, M. V. Ramana (Journal of Coastal Conservation, 2025)
Keywords: Scopus_Alert_31_Octobre_2025

Mitigating urban rainstorm waterlogging disasters in China through enhanced vegetation coverage and sponge city construction - Chen, Peng; Yu, Huanhuan; Sun, Yingyue (Scientific Reports, 2025)
Keywords: Scopus_Alert_31_Octobre_2025

Two sides of the same coin? Hydrometeorological uncertainties in impact-based flood warning systems and climate change sensitivity of floodplains - Mosimann, Markus; Martius, Olivia; Zischg, Andreas Paul (Weather and Climate Extremes, 2025)
Keywords: Scopus_Alert_31_Octobre_2025

Local-scale flood hazard projections in historically vulnerable communities - Bista, Sunil; Bhattarai, Saurav; Sharma, Sanjib; Talchabhadel, Rocky (Modeling Earth Systems and Environment, 2026)
Keywords: Scopus_Alert_31_Octobre_2025

A physically informed domain-independent data-driven inundation forecast model - Schmid, Felix; Müller, Leonie; Leandro, Jorge (Water Research, 2026)
Keywords: Scopus_Alert_31_Octobre_2025

Urban Floodplain Development, Building Damage Exposure, and the Economic Feasibility of Flood Mitigation - Shultz, Steven (Natural Hazards Review, 2026)
Keywords: Scopus_Alert_31_Octobre_2025

Comprehensive portfolio of adaptation measures to safeguard against evolving flood risks in a changing climate - Azhar, Mohammed; Kane, Bergen; Vahedifard, Farshid; AghaKouchak, Amir (Communications Earth & Environment, 2025)
Keywords: Scopus_Alert_31_Octobre_2025

Study on Hydroaccumulation Patterns in V-Shaped Slope Tunnels of Subways During Floods - Dong, Wenxuan; Xia, Xiujiang; Huang, Hong; Zhong, Maohua; Hu, Yifan; Yang, Taoyuan (Urban Rail Transit, 2025)
Abstract Under the broad consensus on reinforcing flood resilience in underground spaces, the hydraulic properties of metro tunnels have not been thoroughly examined. V-slope configurations are widely adopted as a standard feature in metro tunnel systems. This study aims to enhance the understanding of water propagation mechanisms in such tunnels to optimize the response of metro systems to upcoming floods. Through a combination of scaled physical model experiments and VOF numerical simulations, the research reveals key stages and patterns of water accumulation in V-shaped slope tunnels. The flood propagation process is divided into four stages: downhill flow on a single slope, uphill flow undergoing deceleration and accumulation, emergence of hydraulic jump, and wave reflection and oscillation. By investigating hydraulic jump characteristics and the evolution of submersion under varying conditions, the research highlights the local flow field discontinuity and identifies the incompatibility of existing hydraulic models with metro tunnel flooding prediction. It emphasizes the importance of considering detailed flood front movements and the surge of water depth for early flood warning in metro tunnels. The findings enhance predictive accuracy for inundation timing and dynamic flood progression.
Keywords: Scopus_Alert_31_Octobre_2025

Comprehensive Evaluation of Precipitation Reanalysis Products and CMIP6 Models Using Statistical and Machine Learning Techniques With Nature‐Inspired Optimization - Choudhary, Sourav; Pingale, Santosh Murlidhar; Khare, Deepak; Patidar, Ruchir; Krishan, Radha (International Journal of Climatology, 2025)
ABSTRACT A comprehensive strategy that incorporates trend analysis, machine learning (ML), and climate model review is needed to improve water resource forecasts and evaluate hydroclimatic variability. The present study effectively combined various forms of categorical and continuous performance metrics for the CMIP6 and the reanalysis datasets in the Upper Godavari Sub‐basin area (UGSB) (India). MERRA2 reanalysis datasets demonstrated the highest accuracy for precipitation forecasting, achieving a POD of 0.82 and CSI of 0.71, while JRA‐55 closely followed with a CSI of 0.69. CMIP6 models exhibited overestimation tendencies, with a mean FAR of 0.34, highlighting their limitations in capturing precipitation extremes. Thereafter, to understand the long‐term variability of the best reanalysis product, trend analysis was also performed using the Mann‐Kendall test, Pettitt's test, Van Neumann ratio (VNR), and Innovative Trend Analysis (ITA). This analysis revealed properly the spatial variability of the precipitation, showing increasing (1.5–2.3 mm/year) and decreasing rates for various stations inside the UGSB. Thereafter, the temporal frequency and the intensity were captured by the Continuous Wavelet Transform (CWT) analysis, which further identified shifts in hydroclimatic variability towards higher frequencies after 2000. Thereafter, the prediction accuracy of prediction datasets of various ML models, which included Random Forest (RF), Multi‐Layer Perceptron (MLP), Long Short‐Term Memory (LSTM), and XGBoost models, were optimised by The Harris Hawks Optimization (HHO) algorithm, and the best optimised model, RF‐HHO, showed reducing RMSE to 4.92 at Ambajogai, 4.81 at Bodhegaon, and 5.21 at Ranjni. The study highlights the importance of combining reanalysis products, trend analysis, and optimised ML models to improve future precipitation predictions and support effective water resource management.
Keywords: Scopus_Alert_31_Octobre_2025

Survival outcomes with carboplatin versus cisplatin and the impact of COVID-19 on platinum choice: A nationwide Netherlands registry study in small cell lung cancer patients (CACIS) - Houda, Ilias; Naves, Dwayne; Ulas, Ezgi B.; Lissenberg-Witte, Birgit I.; Barlo, Nicole P.; Kastelijn, Elisabeth A.; Bouhaddou, Hicham; Shaikh, Rimsha; Dickhoff, Chris; Radonic, Teodora; Schneiders, Famke L.; Senan, Suresh; et al. (European Journal of Cancer, 2025)
Keywords: Scopus_Alert_31_Octobre_2025

A Risk‐Risk Assessment of Climate Extremes: Comparing Greenhouse Gas Warming and Stratospheric Aerosol Injection in UKESM1 - Wells, Alice F.; Haywood, James M. (Earth's Future, 2025)
Abstract This study investigates the potential of Stratospheric Aerosol Injection (SAI), a solar climate intervention strategy, to mitigate climate extremes driven by greenhouse gas (GHG) emissions, comparing its effects to those of GHG‐induced warming under the SSP5‐8.5 scenario. Using the UKESM1 climate model and the GeoMIP G6controller scenario, we examine extreme temperature, precipitation, and fire risk indices in a risk‐risk framework. The multi‐latitude G6controller strategy, an improvement on the equatorial injection strategy G6sulfur, reduces global mean temperature from SSP5‐8.5 to SSP2‐4.5, significantly reducing temperature and precipitation extremes. Results show that G6controller effectively reduces temperature extremes relative to SSP5‐8.5, especially in populated areas like Europe and South America, and reduces fire risk in high‐risk areas, such as South America and southern Africa. While both scenarios project broad precipitation increases, G6controller moderates these without introducing new drying relative to SSP5‐8.5, particularly in Southeast Asia. This study highlights G6controller's potential to lessen the magnitude of extreme climate events, offering insights into SAI's regional efficacy and highlighting the trade‐offs between GHG warming with and without solar climate intervention. , Plain Language Summary This study looks at how injecting reflective particles into the middle atmosphere, a process called Stratospheric Aerosol Injection (SAI), might help reduce the severe impacts of climate change, like heatwaves, heavy rainfall, and wildfires. It compares two future scenarios: one where greenhouse gas (GHG) emissions continue to rise and another where SAI is used to cool the planet. The results show that SAI can significantly reduce extreme heat and wildfire risks in many regions while also lessening the intensity of heavy rains. However, the strategy doesn't work equally well everywhere and comes with trade‐offs. This research provides valuable insights for understanding how SAI could help manage climate risks in the future while emphasizing the continued importance of cutting GHG emissions. , Key Points This study compares the impacts on extreme events under climate change with and without a multi‐latitude Stratospheric Aerosol Injection strategy in UKESM1 G6controller reduces extreme heat relative to GHG warming, particularly in populated regions, reducing warming trends by nearly half Fire danger days decrease under G6controller compared to SSP5‐8.5, mitigating wildfire risks by reducing precipitation changes
Keywords: Scopus_Alert_31_Octobre_2025

Reliability of Satellite Data in Capturing Spatiotemporal Changes of Precipitation Extremes in the Middle Reaches of the Yellow River Basin - Yang, Qianxi; Xie, Qiuyu; Xu, Ximeng (Remote Sensing, 2025)
Extreme precipitation in the Middle Reaches of the Yellow River Basin (MRYRB) has increased significantly and unevenly, heightening the urgency for rapid and accurate monitoring of such extremes. Satellite precipitation data have proved effective in capturing precipitation extremes but have not been validated in the MRYRB. Thus, station-interpolated data were used to validate the reliability of satellite data (GPM IMERG) in characterizing spatiotemporal changes in nine extreme precipitation indices across the entire MRYRB and its ten sub-basins from 2001 to 2022. The results show that all frequency, intensity, and cumulative amount indices exhibit significantly increasing trends. Spatially, extreme precipitation exhibits a clear southeast–northwest gradient. The higher values occur in the southeastern sub-basins. Characterized by high-intensity, short-duration precipitation, the central sub-basins exhibit the lower values of extreme precipitation indices, yet have experienced the most rapid upward trends in those indices. The comparative analysis demonstrates that GPM reliably reproduces indices such as the number of days and amounts with precipitation above a threshold (R10, R20, R95p), maximum precipitation over five days (RX5day), and total precipitation (PRCPTOT) (with regression slopes close to 1, coefficient of determination R2 and Nash-Sutcliffe efficiency (NSE) greater than 0.7, and residual sum of squares ratio (RSR) less than 0.6, with negligible relative bias), particularly in the southern sub-basins. However, it tends to underestimate continuous wet days (CWD) and total precipitation when precipitation is over the 99th percentile (R99p). These findings advance current understanding of GPM applicability at watershed scales and offer actionable insight for water-sediment prediction under the world’s changing climate.
Keywords: Scopus_Alert_31_Octobre_2025

Snow droughts, deluge, and reservation systems interact to drive recreation access at Yosemite National Park - Jenkins, Jeffrey S.; Marshall, Adrienne M.; Shiflett, Sheri A.; Mattos, Rachel F.; Sanchez, Isaac T.; Athearn, Nicole D. (Scientific Reports, 2025)
Abstract Changes in snow extremes can have important but as yet underexplored impacts on the 1.2-trillion dollar outdoor recreation industry, impacting essential ecosystem services. These hydroclimate conditions interact with reservation systems used by park managers to manage potential impacts to park resources and visitor experiences. Climate change extremes and associated hazards limit and enable access in different ways: snowpack from extreme wet years can prolong road closures at higher elevations, while extreme snow drought enables early season access. Few studies have attempted to assess and compare the influence of snow conditions and reservation systems among a population of visitors, as we do at Yosemite National Park. Roads were still closed into the 2023 peak season from a record extreme snow deluge, yet entry was unrestricted due to lack of a parkwide day use reservation system. This combination led to higher overall visitation levels but spatially constrained visitor mobility and resulted in more crowded conditions and traffic congestion. We assess the efficacy of the day use reservation system in limiting use by comparing differences from the annual mean and monthly volume and timing of visitors. Results show that annual use levels are influenced more by managed access than by climate extremes, and that results differ by overnight use types and traffic count locations with seasonal access limitations. These results demonstrate a novel aspect of the impacts of changing climate extremes with human use of natural resources and suggest that appropriately managed reservation systems are an essential tool for managing recreation resources in a changing climate.
Keywords: Scopus_Alert_31_Octobre_2025

Quantile-based evaluation of climate change impacts on extreme events and their lagged connections with large-scale climate oscillations - Wang, Siyu; Huang, Guohe (Climate Dynamics, 2025)
Keywords: Scopus_Alert_31_Octobre_2025

European Flood Risk Management: A Comparative Analysis from German–French Flood Mitigation Policy Regime - Nguyen, Hung T.; Nguyen, Tuyet-Nga T.; Van, Dao Thi Bich (Proceedings of the 16th Asian Urbanization Conference; AUC2024; 11–13 January; Binh Duong Province–Ho Chi Minh City, Vietnam, 2025)
Series Title: Advances in 21st Century Human Settlements
Keywords: Scopus_Alert_31_Octobre_2025

Flood Early Warning Systems in the White Volta Basin, Ghana: Challenges and Opportunities - Katsekpor, Josephine Thywill; Greve, Klaus; Yamba, Edmund I.; Amoah, Ebenezer Gyampoh (Journal of Flood Risk Management, 2025)
ABSTRACT Flooding in Ghana's White Volta basin has caused widespread displacement, fatalities, and damage to infrastructure and livelihoods in agriculturally dependent communities. Despite the presence of national agencies such as the Ghana Meteorological Agency (GMet) and Ghana Hydrological Authority (GHA), early warning capabilities remain constrained by limited real‐time data, outdated infrastructure, and weak coordination. As a result, many residents continue to rely on traditional knowledge and informal coping strategies. This study qualitatively assesses the operational state of Flood Early Warning Systems (FEWS) in the White Volta basin, focusing on their effectiveness, limitations, and opportunities for improvement. Using semi‐structured interviews with 18 key stakeholders spanning government agencies, technical experts, and community leaders, we analysed the institutional and technical dynamics of Ghana's FEWS through thematic analysis. Findings reveal that although the myDEWETRA‐VOLTALARM platform offers 5‐day flood forecasts through social media, SMS, and radio, its warnings are often mistrusted or inaccessible to rural populations. Thematic analysis identified four critical gaps: institutional fragmentation, exclusion of local knowledge, inadequate data infrastructure, and last‐mile communication failures. These are complicated by the basin's unique environmental conditions, including transboundary dam releases, intense seasonal rainfall, flat terrain, and poor drainage. We conclude that the current FEWS framework remains insufficient for proactive flood risk governance. Strengthening institutional coordination, integrating community‐based adaptation practices, and investing in localized data and communication infrastructure are essential to improving system legitimacy and resilience. The study contributes to broader discourses on early warning systems in resource‐constrained settings.
Keywords: Scopus_Alert_31_Octobre_2025

Optimization of passenger evacuation path in flood scenarios considering companion behaviors - Yang, Xiaoxia; Wan, Jiahui; Zhu, Haojie; Xie, Chuan-Zhi (Thomas); Zhang, Botao (Simulation Modelling Practice and Theory, 2025)
Keywords: Scopus_Alert_31_Octobre_2025

The impact of sea level rise on nesting sea turtles: a global meta-analysis to highlight current understanding and knowledge gaps - Staines, Melissa N.; Booth, David T.; Laloë, Jacques-Olivier; Hays, Graeme C. (Marine Biology, 2025)
Abstract Sea turtles face a number of threats from climate change. One pertinent threat is the impact of sea level rise (SLR), which can lead to a loss of nesting habitat and reduced survival of embryos due to inundation and erosion. Here we review the impacts of SLR on sea turtles. We examined 34 studies (66 assessments) on SLR impacts across six species and 40 sites globally, with 62.1% of assessments located in the Americas. Overall, 78.8% of the assessments showed declining trends in historical and projected estimates of nesting habitat area and or nest survival from inundation. Assessments with projected nesting habitat area showed appreciable loss across all site types (coral island, elevated island, barrier island and mainland), regardless of the SLR scenario. The projected percentage of habitat loss and nest flooding was greatest in island sites compared to the mainland sites, highlighting that SLR is likely to be most acute for low-lying islands with no nearby alternative nesting areas. We reviewed the predicted extent of nesting beach loss and examined how natural processes and conservation interventions might mitigate this threat. However, we require more empirical data on the extent of historical nesting habitat loss. There is also little known about a population’s ability to colonise new nesting areas once a site becomes unsuitable. By tracking their routes towards the breeding sites, there is potential to show how resilient sea turtles are to environmental change, as females may be exposed to other suitable nesting sites on their migration route.
Keywords: Scopus_Alert_26_Octobre_2025

Prediction of Flood Level Using LSTM and Watershed Hydrological Data - Kim, Hyun‐il; Jang, Se‐Dong; Choi, Hehun; Kim, Tae‐Hyung; Kim, Byunghyun (Journal of Flood Risk Management, 2025)
ABSTRACT Accurate flood level prediction is crucial for mitigating flood damage caused by typhoons or localized heavy rainfall. However, predicting flood levels is challenging due to changes in river environments and external factors, such as dam or weir operations. To address these challenges, this study proposes a methodology for constructing an optimal combination of input data using basic hydrological information and predicting flood levels in real time through a deep learning model. The study focuses on identifying the best input data combination tailored to each river basin's characteristics, considering both natural runoff rivers and those influenced by dam discharges. The Long Short‐Term Memory (LSTM) model, known for its superior performance in time‐series forecasting, was employed. The results demonstrate high accuracy in flood level prediction, particularly within a 3‐h lead time.
Keywords: Scopus_Alert_26_Octobre_2025

Improved XGBoost model for predicting minimum miscibility pressure in CO2 flooding - Yang, Yuxin; Zhang, Yizhong; Qin, Bowen; Guo, Jianhong; Zhang, Maolin (Scientific Reports, 2025)
Keywords: Scopus_Alert_26_Octobre_2025

Flood Forecasting of the Tajan Watershed Using the Output of the Numerical Weather Prediction Model (GFS) and the HEC-HMS Hydrological Model - Mehrpourbernety, Fatemeh; Fazloula, Ramin; Emadi, Alireza; Javaheri, Nasrollah; Gholami Sefidkouh, Mohammad Ali (Journal of Watershed Management Research, 2026)
Keywords: Scopus_Alert_26_Octobre_2025

Assessment of Flood Damage Costs for Different Storm Surge, Climate Change, and Drainage Scenarios in Busan, South Korea - Shin, Seoyoung; Kim, Jieun; Park, Daeryong; Kang, Taesoon; Jung, Kichul (JAWRA Journal of the American Water Resources Association, 2025)
ABSTRACT Sea level rise and storm surges resulting from climate change are expected to increase the frequency and intensity of floods. Aging and/or inefficient drainage systems can significantly exacerbate flood damage. Effective flood defense policies must categorize flood damage and provide a detailed assessment of each contributing factor. In this study, we evaluated the cost of flood damage in Busan—the second most populous city on the coast of South Korea. Flood damage costs were analyzed based on an object‐based approach across 10 scenarios, which accounted for variations in storm surge frequency, climate change impacts, and drainage efficiency. Flood damage costs for four categories, namely buildings, agriculture, human casualties, and vehicles, were examined across different administrative regions. Results reveal that flood damage costs increase with higher storm surge frequencies and climate change effects, while reduced drainage efficiency further amplifies these costs. Given that damage costs are predominantly concentrated in coastal areas, future data‐based flood defense policies should be developed to reflect the specific vulnerabilities and damage patterns of each administrative region.
Keywords: Scopus_Alert_26_Octobre_2025

Leveraging geospatial technology for flood inundation mapping and assessing vulnerability and risk in Agartala City, India - Jaman, Toushif; Nongkynrih, Jenita Mary; Sumanth, B. C.; Kurbah, Hunlinshisha; Gogoi, Rekha Bharali; Sarma, K. K.; Aggarwal, S. P. (GeoJournal, 2025)
Keywords: Scopus_Alert_26_Octobre_2025

Integrated morphometric and machine learning-based flood risk assessment in the Rangit river sub-watersheds, Eastern Himalayas - Das, Pintu; Sarkar, Debabrata; Saha, Sunil; Saikh, Nur Islam; Mondal, Prolay (Discover Applied Sciences, 2025)
Keywords: Scopus_Alert_26_Octobre_2025

The spatiotemporal movement ecology of shortfin eels and entrainment risk of yellow eels at a flood relief pump station with gravity bypass - Mahlum, Shad; Baker, Cindy; Lake, Mike; White, Emily; Williams, Alicia; Williams, Peter; Franklin, Paul (Ecological Solutions and Evidence, 2025)
Abstract Land drainage and flood protection infrastructure such as pump stations, tide/flood gates and levees are used to mitigate flood hazards and maintain productive land. However, such infrastructure fragments aquatic ecosystems, often altering the hydrological, geomorphic and physico‐chemical characteristics of waterways. To better understand the risks to eels in the vicinity of flood relief pump stations, we undertook a passive integrated transponder tag study evaluating the movements of 332 shortfin eels ( Anguilla australis ) across 2.5 years at a pump station with a gravity bypass in New Zealand. We observed clear diel and seasonal patterns in eel movements, with increased eel activity at dusk and during late spring and summer. In addition, we observed a unimodal relationship between daily eel activity and water temperature with peak activity occurring between 14°C and 15°C. The gravity bypass was the primary route used by eels for both downstream (45%) and upstream (20%) movements, while direct passage through the pump was uncommon, occurring in only 28 individuals. Among more than 12,000 recorded encounters with the flood pump and gravity bypass, the most frequent outcomes were avoidance of the bypass and obstruction at the pump (42% and 58% of the encounters across individuals, respectively), whereas direct or delayed entrainment through the flood pump occurred rarely (13.5% and 15.2% of the encounters across individuals, respectively). No eels were entrained in the flood pumps during the autumn downstream migration due to low flows throughout this period that precluded the need for active pump operations. In contrast, we observed eel entrainment during winter and spring, when the pumps were most frequently operating, suggesting that non‐migratory (yellow) eels may have been affected. Practical implications . These results highlight that the risk to eels of pump entrainment and mortality extend beyond the main period of downstream migration that has been the focus of most studies. Pump station design and operational management must allow for bidirectional movements of eels to provide effective functional connectivity.
Keywords: Scopus_Alert_26_Octobre_2025

Evolution and prediction of drought-flood abrupt alternation in mainland China using an improved index - Tong, Yiming; Chen, Yu; Qu, Yanping; Bento, Virgílio A.; Song, Hongquan; Qiu, Han; Shui, Wei; Zeng, Jingyu; Wang, Qianfeng (Climate Dynamics, 2025)
Keywords: Scopus_Alert_26_Octobre_2025

Autonomous Decision-Making Enhancing Natural Disaster Management through Open World Machine Learning: A Systematic Review - Gerolimos, Nikitas; Alevizos, Vasileios; Edralin, Sabrina; Xu, Clark; Priniotakis, Georgios; Papakostas, George A.; Yue, Zongliang (Human-Centric Intelligent Systems, 2025)
Abstract Natural disasters can be fatal and necessitate critical thinking in decision-making processes; thus, simulations of multiple scenarios must be evaluated to prepare for such imminent events. An Open World Machine Learning (OWML) framework has been proposed as a focal tool to improve decision-making in natural disaster management. To better understand and formulate a comprehensive foundation, a systematic review of different cases was conducted. In this study, four types of natural disasters-earthquakes, floods, wildfires, and hurricanes-were analyzed using machine learning techniques within the OWML framework. The essence of this approach lies in its ability to accumulate knowledge from various sources and adapt to new, unknown event types. Moreover, by explicitly incorporating both qualitative and quantitative characteristics, the framework enhances predictive accuracy and adaptability. The results demonstrate that OWML models can effectively process large geospatial datasets and respond to looming threats with greater precision. Albeit some limitations, such as data quality and model complexity, the findings suggest that OWML can serve as a foundational tool for governments to reduce expenditure and improve evacuation strategies. The evolutionary nature of the OWML framework allows for continuous learning and adaptation, which is crucial as natural disasters escalate in frequency and intensity due to climate change. In essence, this study provides a significant contribution to natural disaster management by explicitly demonstrating the potential of OWML frameworks in enhancing decision-making processes.
Keywords: Scopus_Alert_26_Octobre_2025

Artificial intelligence in wastewater treatment plants: a review of current trends and adaptation strategies in the face of climate-driven rainfall fluctuations - Hocaoglu, Selda Murat; Roghani, Bardia; Gulcan, Hande; Magna, Débora Jareta; Aydöner, Cihangir; Barros, Virginia Grace; Koyunluoglu Aynur, Sebnem; Ratnaweera, Harsha; Fatone, Francesco; Eusebi, Anna Laura; Maletskyi, Zakhar (Journal of Water and Climate Change, 2025)
ABSTRACT Wastewater treatment plants (WWTPs) are encountering challenges related to sustainability, efficiency, and economic viability due to stricter discharge standards and the impact of climate change, including heavy rainfall. Enhancing the resilience and performance of WWTPs is therefore crucial. This paper reviews the application of artificial intelligence (AI) models that can improve WWTP resilience in response to extreme rainfall events. It provides an overview of publications on digitalization of WWTPs over the past 33 years, proposes a new classification approach for AI models, and reviews existing AI applications aimed at improving the performance of WWTPs in response to extreme rainfall events. These applications highlight the importance of developing hybrid models that integrate AI with knowledge-driven models to improve accuracy. Furthermore, future AI research in WWTPs should incorporate hydrological forecasting to enable advance estimation of flow rates, and facilitate effective management.
Keywords: Scopus_Alert_26_Octobre_2025

Nexus between climate change and maternal mortality: a review of literature - Haq, Shah Md Atiqul; Mawa, Jannatul; Tasneem, Nishat; Saha, Bijoya; Ahmed, Mufti Nadimul Quamar; Ahmed, Khandaker Jafor (SN Social Sciences, 2025)
Abstract Climate change presents a significant threat to global health, with both immediate and long-term consequences, especially in low-income countries with limited adaptive capacity. Pregnant women, developing fetuses, and youth are among the most vulnerable populations, yet the specific risks they face remain underexplored. This literature review examines the impact of climate change on maternal mortality, focusing on both direct and indirect pathways. We analyzed peer-reviewed studies that address how climate-related events, such as severe droughts, water scarcity, wildfires, rising sea levels, frequent floods, melting polar ice caps, catastrophic storms, and biodiversity loss contribute to complications during pregnancy and childbirth. The evidence suggests that climate change can indirectly increase maternal health risks and mortality through social, economic, and psychological pathways, including inadequate sanitation, population shifts, disrupted healthcare access, malnutrition, and disease transmission. Our findings reveal a consistent pattern of heightened risk for maternal complications and mortality linked to climate-related events. We recommend conducting more country-specific and comprehensive studies to better understand these relationships and inform effective interventions. Policymakers should prioritize efforts to reduce maternal mortality and improve the health of expectant mothers and their newborns in the context of a changing climate.
Keywords: Scopus_Alert_26_Octobre_2025

Meteorological Tsunamis: From Local Hazard to Global Relevance - Vilibić, Ivica; Zemunik Selak, Petra; Šepić, Jadranka (Reviews of Geophysics, 2025)
Abstract Research on meteorological tsunamis or meteotsunamis—long ocean waves in the tsunami frequency band generated by propagating atmospheric disturbances which resonantly enhance ocean waves—has grown significantly in recent decades. This expansion is due to progress in (a) ocean and atmospheric measurements, including advanced instrumentation with higher precision and smaller sampling time steps, as well as installation of meteotsunami tracking measurement networks, (b) ocean and atmospheric data products, including those related to the upper atmosphere and ionosphere, and (c) supercomputing capabilities and sophisticated atmosphere‐ocean models that successfully simulate both atmospheric planetary processes and mesoscale systems capable of generating meteotsunamis, as well as sea level response to these. Meteotsunamis can induce multi‐meter sea level oscillations in harbors and low‐lying areas, leading to severe flooding, infrastructure damage, injuries, and sometimes fatalities. Traditionally, meteotsunami research focused on individual event analyses using available sea level and lower‐layer atmospheric observations. Recently, efforts have shifted toward global hazard mapping, the development of forecast and early‐warning systems, and toward quantifying projected meteotsunamis intensity and frequency, using climate models. The January 2022 eruption of the Hunga Tonga‐Hunga Ha'apai volcano, which generated acoustic‐gravity waves that circled the globe, has spurred research of planetary meteotsunami waves and their potential to pose coastal hazards worldwide. Additionally, meteotsunamis radiate acoustic‐gravity waves vertically, creating ionospheric oscillations detectable through electron content variations. This review will cover the mentioned developments and conclude with a discussion of research gaps and potential directions for further studies. , Plain Language Summary Sea level extremes are an emerging topic in the era of climate change, as most coastal damage and impacts occur during such events. One of the underrated phenomena contributing to these extremes are meteorological tsunamis or meteotsunamis—long ocean waves in the tsunami frequency band resonantly generated by traveling air pressure/wind disturbances—which are known to substantially impact certain coastlines and locations, causing deaths, injuries, and damage to coastal infrastructure, ships and yachts, buildings, and households, as well as affecting navigational safety. Until about a decade ago, research on meteotsunamis was mostly localized, focusing on specific meteotsunami hotspots. However, in recent years, research has become more global. This was made possible by the development of high‐precision oceanic and atmospheric instruments that collect data at minute or higher resolutions, as well as ultra‐high‐resolution atmosphere‐ocean models capable of reproducing meteotsunamis, supported by high‐performance computing facilities. This review summarizes all aspects of meteotsunamis, from available data, products, and tools that can be used for research, to an in‐depth exploration of the science behind the phenomenon, and its impact on coastal regions. It also discusses the development of forecasting and early‐warning systems. Finally, the review provides recommendations for future research directions on this hazardous phenomenon. , Key Points Minute‐scale sea level oscillations, including meteotsunamis, significantly contribute to extreme sea levels worldwide Generated though atmosphere‐ocean resonances, meteotsunamis can have from local to global impact and be destructive at certain locations Reliable estimates of meteotsunami hazard in present and future climates, along with effective forecasting and warning, remain a challenge
Keywords: Scopus_Alert_26_Octobre_2025

Machine learning-based hydrological models for flash floods: a systematic literature review - Santos, Leonardo B. L.; Escobar-Silva, Elton V.; Satolo, Luiz F.; Oyarzabal, Ricardo S.; Diniz, Michael M.; Negri, Rogério G.; Lima, Glauston R. T.; Stephany, Stephan; Soares, Jaqueline A. J. P.; Duque, Johan S.; Saraiva Filho, Fernando L.; Caseri, Angélica N.; et al. (Smart Construction and Sustainable Cities, 2025)
Abstract Flash floods are critical events for emergency management, yet their modeling remains highly challenging, even in smart cities approaches. Physically based hydrological models are often unsuitable at small spatiotemporal scales due to their computational complexity and dependence on detailed local parameters, which are rarely available during flash floods. With the growing availability of hydrological data, machine learning (ML) has emerged as a promising alternative. This work performs a Systematic Literature Review (SLR) to improve our understanding of the research landscape on ML applications for flash flood forecasting, a significant subset of flash flood modeling. From more than 1,200 papers published until January 2024 in Web of Science, SCOPUS/Elsevier, Springer/Nature, and Wiley, 50 were selected following PRISMA guidelines. The inclusion and exclusion criteria removed reviews, retractions, papers focused on post-flood damage assessment (not forecasting), and those with time resolutions of 6 hours or more, retaining only studies with fine-scale temporal data (<6 hours). For each paper, we extracted information on forecasting horizon, study area size, input data, ML techniques, and outcomes (regression or classification). Results show a sharp rise in ML-based flash flood research, with China leading (38%). Nearly all studies rely on rainfall, discharge, and water level data - often in combination. Long short-term memory (LSTM) networks dominate (60%). Unfortunately, only 10% of the selected studies provide access to their datasets. This lack of transparency poses a major barrier to reproducibility, inhibits fair comparative evaluation of models, and ultimately slows methodological progress in flash flood forecasting. Furthermore, our review highlights that no method consistently outperforms others. This variability in performance is likely influenced by factors such as regional hydrological characteristics (e.g., differences between arid and tropical basins), variations in input data quality, and the length of the forecast horizon (e.g., 1- vs. 6-hour prediction). Lastly, we recommend advancing this field through integration with early warning systems, creation of benchmarks, open data practices, and stronger multidisciplinary collaboration.
Keywords: Scopus_Alert_26_Octobre_2025

A systematic review of climate change adaptation in vegetable farming systems in Africa - Obossou, Esdras; Totin, Edmond; Singh, Chandni; Segnon, Alcade C. (Communications Earth & Environment, 2025)
Keywords: Scopus_Alert_26_Octobre_2025

Do Public and Farmer Preferences for Natural Flood Management Align? - King, Phoebe; Bark, Rosalind H.; Lovett, Andrew (Journal of Flood Risk Management, 2025)
ABSTRACT The demand for catchment‐based flood management to adapt to climate change is growing, with natural flood management (NFM) receiving increasing attention. NFM has implications for the ‘providers’ of land for measures upstream (the farmers) and the ‘beneficiaries’ of flood reduction downstream (the public). The misalignment of interests from these stakeholder groups may pose a challenge for flood risk managers during the delivery of NFM at the catchment scale. Considering this, a rapid evidence assessment (REA) of 60 peer‐reviewed articles was undertaken. This REA provides an overview of catchment perspectives, compares farmer and public preferences for NFM design, and explores key determinants of scheme acceptance. The public expressed positive perceptions and willingness to pay for NFM, with preferences for measures with large water storage capacity that deliver co‐benefits alongside flood management objectives. For farmers, NFM schemes that contributed to on‐farm conditions, for example, soil stability, were seen as positive, but overall, their willingness to adopt measures was limited. Nevertheless, knowledge of NFM among both groups strongly determined its acceptance. This suggests that resolving misaligned values will require policymakers and practitioners to work with these stakeholders on NFM design and farmer incentives to secure the delivery of future schemes.
Keywords: Scopus_Alert_26_Octobre_2025

Flood Impacts on Civil Infrastructure Systems: A Comprehensive Review of Impacts, Modeling Tools, Emerging Technologies, and Mitigation Strategies - Abegaz, Ruth; Xu, Jun; Wang, Fei (Natural Hazards Review, 2026)
Keywords: Scopus_Alert_26_Octobre_2025

Forecasting extreme events and storms and prediction of coastal flooding along microtidal Coast of El Djamila Bay, Algeria - Berkani, Moussaab; Otmani, Housseyn; Mezouar, Khoudir; Saf, Brahim (Journal of Coastal Conservation, 2025)
Keywords: Scopus_Alert_26_Octobre_2025

Mapping of Sea-Level Rise Impacts on the Cultural Heritage of the Apulian Gulf of Manfredonia and Ionian Coast - Lobascio, Arianna; Fioriello, Custode S.; Scardino, Giovanni; Scicchitano, Giovanni; Zingaro, Marina (Geographic Approaches to Climate Change and Mitigation: Urban and Rural Perspectives (Volume 2), 2025)
Series Title: Advances in Science, Technology & Innovation
Keywords: Scopus_Alert_26_Octobre_2025

Morphometric analyses of a deltaic strandplain inferred from LiDAR data and old maps: A contribution to the coastal risk mapping of the Tiber Delta (Italy) - Sarrau, Justine; Salomon, Ferréol (Journal of Geographical Sciences, 2025)
Keywords: Scopus_Alert_26_Octobre_2025

Multi-Hazard Scenarios of Extreme Compounded Events at the Local Scale Under Climate Change - Sfetsos, Athanasios; Politi, Nadia; Vlachogiannis, Diamando (Atmosphere, 2025)
As local risk assessments are fundamental for risk management and mitigation strategies, this work introduces a methodology for assessing multi-hazard scenarios of extreme compounded events and their duration using daily time series of surface variables from high-resolution climate simulations during historical and future periods under RCP8.5. The aim was to investigate the return level extremes of 20- and 50-year periods of hazards occurring within specific durations and concurrent extreme values of other surface variables, for selected locations in Greece. In addition, future changes in the temporal occurrence of compounded hazards involving precipitation and wind with temperature extremes were performed based on temperature extreme percentiles. The assessment revealed the geographical dependence in the projected occurrence, intensity, and duration of compounded multi-hazard extremes, emphasising the need for high spatial resolution climate data for their investigation. The highlights of the findings include a significant increasing trend of compounded multi-hazard extremes, e.g., hot days and tropical nights, milder winter minimum temperatures with lower rainfall extremes, hotter and windier events of shorter duration, and longer precipitation extremes with increased extreme temperatures. The projections showcased the impact of climate change on extreme compounds with a multitude of interesting findings associated with significant changes in their duration, intensity, and temporal occurrence.
Keywords: Scopus_Alert_17_Octobre_2025

Increasing compound heat and precipitation extremes and population exposure in a warming Vietnam - Nguyen-Duy, Tung; Ngo-Duc, Thanh; Nguyen-Le, Dzung; Nguyen-Xuan, Thanh; Tran-Anh, Quan (Natural Hazards, 2025)
Keywords: Scopus_Alert_17_Octobre_2025

Flood insurance purchasing behavior in two Gulf Coast cities - Cass, Evan; Shao, Wanyun; Hao, Feng (Cities, 2026)
Keywords: Scopus_Alert_17_Octobre_2025

Assessing subway station flood risks with a resilience Framework: Combining weighting methods and two-dimensional cloud model - Guo, De-Sai; Yang, Xin-Xin; Yang, Jin-Chen; Wu, Huai-Na; Meng, Fan-Yan; Chen, Ren-Peng (Tunnelling and Underground Space Technology, 2026)
Keywords: Scopus_Alert_17_Octobre_2025

GIS-driven hybrid multicriteria model for flood susceptibility assessment in a coastal metropolis of Ghana - Danso, Samuel Yaw (Geosystems and Geoenvironment, 2026)
Keywords: Scopus_Alert_17_Octobre_2025

Multi-model verification and evaluation of precipitation forecasts and Southwest China Vortex Rainstorms in Eastern Tibetan Plateau - Wu, Zhipeng; Zhang, Yan; Hu, Chunmei; Deng, Chengzhi; Luo, Juan; Wu, Shenggang; Zhou, Yingying; Zhang, Hong; Liao, Zhiyi; Wang, Huan; Pang, Yue (Atmospheric Research, 2026)
Keywords: Scopus_Alert_17_Octobre_2025

Serious Game Design for Resilience Enhancement of Transportation Infrastructure to Cyber Threats and/or Flood Hazards - Gordan, Meisam; Ko, Ili; Soroudi, Mona; McCrum, Daniel; König, Sandra; Schauer, Stefan; Connolly, Lorcan; Carroll, Páraic (Transport Transitions: Advancing Sustainable and Inclusive Mobility, 2026)
Abstract It is challenging to predict the precise location and severity of flood events, which can cause major disturbance to different public services, such as disruption to and the closure of critical transportation infrastructure. Therefore, approaches to enhance the resilience of these infrastructures are required. Serious games are new computing tools that have been applied in various fields with a combination of gameplay, learning and training. However, the application of serious games in intelligent transportation systems remains underutilized. As a result, the concept of a serious game is developed in this study to significantly improve transportation infrastructure resilience in an example of a flooding event. The serious game concept presented in this paper is being developed as part of the H2020-funded PRECINCT project ( www.precinct.info ).
Series Title: Lecture Notes in Mobility
Keywords: Scopus_Alert_17_Octobre_2025

The impact of risk control options in reducing/preventing risk in case of a flooding event - Mauro, Francesco; Paterson, Donald; Vassalos, Dracos (Ocean Engineering, 2025)
Keywords: Scopus_Alert_17_Octobre_2025

Design storm estimation for flood risk assessment in the temperate Himalayan basin using hydrological modelling - Attar, Mohmmad Idrees; Khan, Junaid Nazir; Altaf, Yasir; Alsubih, Majed; Naseer, Sameena; Kumar, Rohitashw; Bhat, Owais Ahmad; Bangroo, Shabir Ahmad; Sharma, M. K. (Scientific Reports, 2025)
Keywords: Scopus_Alert_17_Octobre_2025

Conceptual model of spatial level structure for developing multi-functional parks to reduce urban flood risk: A case study in Ho Chi Minh city - Duy, Phan Nhut; Tuan, Pham Anh; Nguyen, Nguyen Phuong Thao (City and Environment Interactions, 2025)
Keywords: Scopus_Alert_17_Octobre_2025

Accumulating climate change influences on extreme coastal, fluvial, and compound flooding in the upper transition zone - Mita, Kazi Samsunnahar; Orton, Philip; Montalto, Franco; Anbessie, Tsega (Journal of Hydrology, 2025)
Keywords: Scopus_Alert_17_Octobre_2025

Exploring characteristics and drivers of flood hazard under different urban development patterns - Zhong, Ming; Chen, Tailin; Zhuo, Lu; Wang, Zeqiang; Ling, Feng; Han, Dawei (Environmental and Sustainability Indicators, 2025)
Keywords: Scopus_Alert_17_Octobre_2025

Assessing blue-green infrastructures for urban flood and drought mitigation under changing climate scenarios - Wu, Xuan; Willems, Patrick (Journal of Hydrology: Regional Studies, 2025)
Keywords: Scopus_Alert_17_Octobre_2025

City-scale digital twin framework for flood impact analysis: Integrating urban infrastructure and real-time data analytics - Kaynak, Sümeyye; Kaynak, Baran; Mermer, Omer; Demir, Ibrahim (Urban Climate, 2025)
Keywords: Scopus_Alert_17_Octobre_2025

Interception reduction from deforestation and forest fire increases large-scale fluvial flooding risk - Kang, Tae-Ho; Sharma, Ashish; Marshall, Lucy; Kim, Young-Oh (Communications Earth & Environment, 2025)
Abstract Catastrophic flooding has been noted to occur with greater frequency following deforestation, but limited observations have been available to test this connection over large spatial scales. Here we used the data of mega forest fires impacting a region of 25,000 km 2 in Australia exhibiting rapid loss in forest canopy, where the runoff generation has been carefully observed with minimum anthropogenic influences for more than half a century. This provides a unique opportunity to assess the impact of the forest canopy loss on large-scale fluvial flooding. A state-controlled hypothesis test, with the climate and watershed states controlled to enhance robustness, shows a statistically significant increase in annual maximum flows resulting from the forest loss treatment. The reasoning for this natural experiment is that the forest loss impact on the interception potential of forest canopy, fallen leaves, and root-zone soils in wide region could have a recognizable impact on the fluvial flood.
Keywords: Scopus_Alert_17_Octobre_2025

Land tenure (in) security in the context of urban flood risk in Ghana - Ibrahim, Abdul-Salam; Isaac, Marney E.; Kepe, Thembela (Land Use Policy, 2025)
Keywords: Scopus_Alert_17_Octobre_2025

Optimizing the allocation of flood control investments for effective disaster risk reduction - Chang, Kuo-Hao; Hsu, Yueh-Ning; Li, Hsin-Chi (Computers & Industrial Engineering, 2025)
Keywords: Scopus_Alert_17_Octobre_2025

An integrated methodology for flood risk assessment of road networks: a case study of Ordu, Türkiye - Yılmaz, Muhammet; Alemdar, Kadir Diler (Modeling Earth Systems and Environment, 2025)
Keywords: Scopus_Alert_17_Octobre_2025

Adaptive flood risk management: A decision support system integrating deep learning, digital twins, and economic risk assessment - Chabot, Miia; Bertrand, Jean-Louis (Global Environmental Change, 2025)
Keywords: Scopus_Alert_17_Octobre_2025

Flood risk from small mountain lakes - Sattar, Ashim; Emmer, Adam; Lhazom, Tashi; Rai, Shashi Kant; Azam, Mohd. Farooq (Communications Earth & Environment, 2025)
Keywords: Scopus_Alert_17_Octobre_2025

Flood evacuation shelter location-allocation considering vulnerability identification in the context of pre-requisition - Gong, Zaiwu; Guo, Xinrui; Wei, Guo; Luo, Xinxin; Xu, Xiaoxia (Climate Services, 2025)
Keywords: Scopus_Alert_17_Octobre_2025

Urban Flooding in Tier-2 Indian Cities: A Qualitative Exploration of Causes, Impacts and Mitigation Strategies - Samantaray, Sadashiv; Sar, Ashok (Disaster Advances, 2025)
Urban flooding has emerged as a recurring challenge in Indian cities, with even minimal rainfall events triggering waterlogging and severe disruptions. This study investigates the phenomenon of frequent urban flooding in five tier-2 Indian cities: Puri, Raipur, Ranchi, Vizag and Bhopal through a qualitative lens. Using focus group discussions (FGDs) with a cross-section of stakeholders including residents, municipal officials, urban planners and environmental activists, the study aims to generate in-depth insights into the patterns of flooding, its multifaceted impacts, perceived causes and proposed mitigation strategies. The data, collected through semi-structured FGDs and analysed thematically, reveals that unplanned urban expansion, poor maintenance of drainage infrastructure, encroachment on natural waterways and inadequate storm-water management are recurrent factors contributing to urban flooding. Participants reported substantial health risks, property damage, traffic paralysis and income loss, especially among marginalized communities. This research adds to the limited qualitative evidence based on urban flooding in non-metro Indian contexts. By foregrounding lived experiences and stakeholder perspectives, the study offers valuable insights for localized disaster risk reduction strategies and urban policy reforms. It underscores the urgency of integrated urban planning, community-based resilience-building and proactive governance to mitigate the growing threat of urban flooding in emerging Indian cities.
Keywords: Scopus_Alert_17_Octobre_2025

Anammox in nature-based systems for urban water management: A review and Bayesian assessment of environmental drivers - Tang, Yinghui; Prodanovic, Veljko; O'Carroll, Denis M.; Zheng, Min; Zhang, Kefeng (Journal of Environmental Management, 2025)
Keywords: Scopus_Alert_17_Octobre_2025

Roles and nature of public participation in climate change adaptation in coastal South Asia - Siddiqui, Md Rezwan; Ishtiaque, Asif (Discover Environment, 2025)
Abstract This paper presents a systematic review of the roles and nature of public participation in climate change adaptation in coastal South Asia. In this region, coastal communities face significant climate-related risks due to their inherent vulnerability from population density, rapid changes in landuse, and extensive shoreline. They are particularly vulnerable to hazards such as sea-level rise, tropical cyclones, and storm surges. Governments in the region have initiated adaptation strategies, emphasizing the inclusion of local stakeholders. However, the extent and impact of public participation in these strategies vary across case studies. Drawing on 42 published peer review articles, this review synthesizes participatory approaches in coastal adaptation strategies. We found that public participation strengthens community cohesion, improves adaptation outcomes, and is a mandatory tool for identifying local vulnerabilities and capacities. Participation through grassroots organizations may result in wider sustainability for the vulnerable coastal communities. Additionally, the review identifies several barriers, including the prevalence of elite capture and token participation, externally driven participation processes, and unclear task definitions. We also found that to enhance the effectiveness of public participation in climate change adaptation, it is crucial to adopt inclusive, bottom-up approaches, empower marginalized groups, and ensure that participation is contextually grounded and representative. This study’s insights are applicable not only to South Asia but also to other vulnerable coastal regions in the Global South.
Keywords: Scopus_Alert_17_Octobre_2025

Advances in artificial intelligence to model the impact of El Niño–Southern Oscillation on crop yield variability - Wannasingha, Usa Humphries; Waqas, Muhammad; Wangwongchai, Angkool; Hlaing, Phyo Thandar; Dechpichai, Porntip; Ahmad, Shakeel (MethodsX, 2025)
Keywords: Scopus_Alert_17_Octobre_2025

Risks to the future health and productivity of tropical estuaries under climate change and increasing human development - Burford, M.A.; Brooks, A.; Cartwright, P.; Faggotter, S.J.; Irvine, D.J.; Waltham, N.J. (Marine Pollution Bulletin, 2026)
Keywords: Scopus_Alert_17_Octobre_2025

Leveraging Coupled Hydrodynamic with Data-Driven GeoAI Models for Advancing Systemic Compound Flood Risk Evaluation in Coastal Urban Areas - Atmaja, Tri; Fukushi, Kensuke; Lee, Duu-Jong (Archives of Computational Methods in Engineering, 2025)
Abstract Compound flood (CF) represents a complex hazard that often leads to severe impacts. CF propagates across interconnected systems, generating systemic societal and environmental risks, particularly in coastal cities. Despite progress in data science and remote sensing, a comprehensive review of coupled hydrodynamics with the data-driven GeoAI—an integration of geospatial analysis and artificial intelligence (AI)—for systemic CF risk remains scarce. This review summarizes foundational data-driven and numerical approaches in CF modeling. It then synthesizes emergence, utilization modes, and advancements of coupled hydrodynamic-GeoAI frameworks for CF prediction and systemic impact quantification. A systematic review follows the PRISMA protocol, examining 403 articles from the Web of Science and Scopus databases. The concept of the coupled hydrodynamics-GeoAI model synergizes physics-based simulations with data-driven computational learning, enhancing predictive accuracy and spatially detailed flood risk while explicitly embedding geographic features into the framework. The model offers three utilization modes: (i) direct coupling, (ii) surrogate modeling, and (iii) stochastic statistical-hydrodynamic-ML framework. To enhance comprehensive and robust risk assessment, the review proposes four key model advancements: (1) implementing an active learning framework, (2) integration with physics-guided data-driven, (3) dynamically coupling CF drivers with external factors, and (4) incorporating spatiotemporal analysis under changing climate and socioeconomic conditions. We further advocate for integrating the quantification of both tangible and intangible cascading impacts into systemic CF risk assessments. This review synthesizes computational strategies integrating physics-based hydrodynamics with GeoAI, providing a foundation for systemic CF risk evaluation and guiding future advances in computational hydrology and resilient urban flood management. Graphical Abstract This graphical abstract visually encapsulates the core concept of leveraging coupled hydrodynamic with data-driven GeoAI models for systemic compound flood (CF) risk evaluation in coastal urban areas. The abstract integrates the three main flood drivers—coastal, pluvial, and fluvial—interacting to form the CF. The diagram flows from left to right, where the first section depicts the urban coastal zone, followed by a combination of flood drivers that illustrate the complexity and interconnectedness of factors leading to CF. On the right panel, the graphical focus shifts to the “Coupled Hydrodynamic with Data-Driven GeoAI Model” as the proposed approach for predicting the CF event, encompassing probabilistic analysis, flood propagation, risk assessment, real-time forecasting, and emergency response. The approach integrates physics-based simulations with data-driven geographic data analysis, known as GeoAI. On the bottom left panel, the illustration emphasizes the utilization of the approach through three key strategies: (i) direct coupling through ML-derived boundary conditions, (ii) surrogate modeling to emulate hydrodynamic outputs, and (iii) stochastic statistical-hydrodynamic-ML framework. These strategies demonstrate how GeoAI enhances hydrodynamic simulations to produce more accurate, timely, and spatially detailed flood predictions in various ways. The model’s advancements are also highlighted, addressing the critical need for the utilization of an active learning framework, integration with physics-guided data-driven, dynamically coupling CF drivers with external factors, and the importance of considering spatiotemporal analysis under climate and socioeconomic change. The graphic in the bottom right panel displays the CF output results from the model, which can be further utilized to assess the cascading impact on various aspects. Together, the elements of this graphical abstract convey a sophisticated, interdisciplinary approach to CF risk evaluation, focusing on integrating hydrodynamic with data-driven GeoAI models to better manage the complex challenges of systemic CF risks in urban coastal zones.
Keywords: Scopus_Alert_17_Octobre_2025

Optimal Configuration of a Convection-Permitting Regional Climate Model in Simulating Precipitation Extremes: The Saguenay Flood - Ghosh, Soumik; Lucas-Picher, Philippe; Roy, Philippe; Gachon, Philippe; Di Luca, Alejandro (Journal of Hydrometeorology, 2025)
Abstract This case study concerns a major flood event occurred in July 1996 in the Saguenay region (Québec, Canada) induced by heavy and persistent rainfall over this river basin. Various configurations of the CRCM6/GEM5 regional climate model (RCM) using 12-km (0.11° x 0.11°) and convection-permitting (CP) 2.5-km (0.0225° x 0.0225°) resolutions are used to evaluate added value from CP simulations on the simulated extreme precipitation characteristics. The effects of spectral nudging (SN) and initial soil moisture conditions (ISMCs) on surface are also tested on the simulated rainfall. The evaluation of all simulations shows a significant improvement in reproducing precipitation extremes with the convection-permitting model (CPM) at 2.5-km, and substantial influences from SN, and ISMCs. The SN in the CP simulation improved the spatial and temporal patterns of precipitation extremes. Additionally, forced ISMC from a long-term simulations at a 12-km resolution significantly enhanced the model’s ability to capture rainfall intensity, using rainfall observed stations as a reference dataset. This research contributes to the understanding of extreme precipitation events and its reliability as simulated by various configurations of our RCM, and the need to apply higher resolution and accurate surface conditions in the CRCM6/GEM5 for future projections, and its use in design infrastructures, and flood risk management strategies.
Keywords: Infolettre_Decembre_2025; Scopus_Alert_05_Decembre_2025

Flood relief logistics planning for coastal cities: a case study in Shanghai, China - Liang, Pujun; Yin, Jie; Wang, Dandan; Lu, Yi; Yang, Yuhan; Gao, Dan; Mai, Jianfeng (Natural Hazards and Earth System Sciences, 2025)
Abstract. Coastal cities are becoming more vulnerable to flood risks due to climate change, rising sea levels, intense storm surges, population growth and land subsidence. Developing emergency preparedness and response strategies can reduce the impact of coastal flooding and improve a city's resilience. This article presents a flood relief logistics planning approach aimed at providing decision-makers with a feasible framework. The framework integrates geographic information system (GIS) network analysis and resource allocation optimisation models. Considering the equity of resource allocation, a bi-objective allocation model that minimises the total transportation cost and maximum unsatisfied rate is developed. This flood relief logistics planning approach is applied to Shanghai, China, to present feasible distribution strategies. The case study indicates that the current spatial distribution of emergency reserve warehouses (ERWs) and emergency flood shelters (EFSs) in Shanghai may be vulnerable to extreme flood events. Under a 1000-year coastal flood scenario, the existing emergency resources are insufficient to meet the needs of the affected elderly population. In situations of resource scarcity, reducing the maximum unsatisfied rate can help to improve the equity of resource allocation. Furthermore, incorporating private warehouse clubs (WHCs) into government emergency logistics through public–private collaboration could reduce the governmental burden and improve system efficiency and resilience. This study provides a scientific reference for developing flood relief logistics plans in Shanghai, and it presents a transferable framework that is applicable to other coastal cities.
Keywords: Scopus_Alert_10_Octobre_2025

Machine Learning-Based Flood Risk Assessment in Urban Watershed: Mapping Flood Susceptibility in Charlotte, North Carolina - Shrestha, Sujan; Dahal, Dewasis; Bhattarai, Nishan; Regmi, Sunil; Sewa, Roshan; Kalra, Ajay (Geographies, 2025)
Flood impacts are intensifying due to the increasing frequency and severity of factors such as severe weather events, climate change, and unplanned urbanization. This study focuses on Briar Creek in Charlotte, North Carolina, an area historically affected by flooding. Three machine learning algorithms —bagging (random forest), extreme gradient boosting (XGBoost), and logistic regression—were used to develop a flood susceptibility model that incorporates topographical, hydrological, and meteorological variables. Key predictors included slope, aspect, curvature, flow velocity, flow concentration, discharge, and 8 years of rainfall data. A flood inventory of 750 data points was compiled from historic flood records. The dataset was divided into training (70%) and testing (30%) subsets, and model performance was evaluated using accuracy metrics, confusion matrices, and classification reports. The results indicate that logistic regression outperformed both XGBoost and bagging in terms of predictive accuracy. According to the logistic regression model, the study area was classified into five flood risk zones: 5.55% as very high risk, 8.66% as high risk, 12.04% as moderate risk, 21.56% as low risk, and 52.20% as very low risk. The resulting flood susceptibility map constitutes a valuable tool for emergency preparedness and infrastructure planning in high-risk zones.
Keywords: Scopus_Alert_10_Octobre_2025

Analysis of Risk Factors for Tunnel Flooding Disasters Based on DEMATEL - Fang, Yongxiang; Zhang, Yanmei; Zhu, Yanchang; Tao, Yingying; Zhang, Rui; Wang, Qikai (Water, 2025)
The growing frequency of extreme rainstorms has increasingly exposed tunnels to flooding risks, underscoring the urgent need for effective flood prevention and drainage measures. In this context, an evaluation framework for tunnel flood hazards was developed based on three criteria—hazard-inducing factors, hazard-formative environment, and disaster-bearing body—encompassing nine specific indicators. This study employs the Decision Making Trial and Evaluation Laboratory (DEMATEL) method to construct a causal analysis model and assess the interrelationships and influence levels of risk factors associated with tunnel flooding disasters. Rainfall intensity (C1), rainfall duration (C2), ground elevation (C4), road slope (C5), and impervious surface area (C6) exhibit high causal values, acting as external input factors that drive the occurrence of tunnel flooding incidents. Conversely, water depth (C3), tunnel drainage capacity (C7), emergency flood control measures (C8), and infrastructure aging (C9) display high centrality values, serving as internal factors that reflect the tunnel’s flood prevention capability and determine the extent of disaster losses. Simply enhancing tunnel drainage capacity from the perspective of internal factors alone is insufficient; optimizing the tunnel’s flood resilience requires a combined consideration of both internal and external factors.
Keywords: Scopus_Alert_10_Octobre_2025

Impact of Grated Inlet Clogging on Urban Pluvial Flooding - Russo, Beniamino; Beiró, Viviane; Lopez-Julian, Pedro Luis; Acero, Alejandro (Hydrology, 2025)
This study aims to analyse the effect of partially clogged inlets on the behaviour of urban drainage systems at the city scale, particularly regarding intercepted volumes and flood depths. The main challenges were to represent the inlet network in detail at a rather large scale and to avoid the effect of sewer network surcharging on the draining capacity of inlets. This goal has been achieved through a 1D/2D coupled hydraulic model of the whole urban drainage system in La Almunia de Doña Godina (Zaragoza, Spain). The model focuses on the interaction between grated drain inlets and the sewer network under partial clogging conditions. The model is fed with data obtained on field surveys. These surveys identified 948 inlets, classified into 43 types based on geometry and grouped into 7 categories for modelling purposes. Clogging patterns were derived from field observations or estimated using progressive clogging trends. The hydrological model combines a semi-distributed approach for micro-catchments (buildings and courtyards) and a distributed “rain-on-grid” approach for public spaces (streets, squares). The model assesses the impact of inlet clogging on network performance and surface flooding during four rainfall scenarios. Results include inlet interception volumes, flooded surface areas, and flow hydrographs intercepted by single inlets. Specifically, the reduction in intercepted volume ranged from approximately 7% under a mild inlet clogging condition to nearly 50% under severe clogging conditions. Also, the model results show the significant influence of the 2D mesh detail on flood depths. For instance, a mesh with high resolution and break lines representing streets curbs showed a 38% increase in urban areas with flood depths above 1 cm compared to a scenario with a lower-resolution 2D mesh and no curbs. The findings highlight how inlet clogging significantly affects the efficiency of urban drainage systems and increases the surface flood hazard. Further novelties of this work are the extent of the analysis (city scale) and the approach to improve the 2D mesh to assess flood depth.
Keywords: Scopus_Alert_10_Octobre_2025

Generalized Methodology for Two-Dimensional Flood Depth Prediction Using ML-Based Models - Soliman, Mohamed; Morsy, Mohamed M.; Radwan, Hany G. (Hydrology, 2025)
Floods are among the most devastating natural disasters; predicting their depth and extent remains a global challenge. Machine Learning (ML) models have demonstrated improved accuracy over traditional probabilistic flood mapping approaches. While previous studies have developed ML-based models for specific local regions, this study aims to establish a methodology for estimating flood depth on a global scale using ML algorithms and freely available datasets—a challenging yet critical task. To support model generalization, 45 catchments from diverse geographic regions were selected based on elevation, land use, land cover, and soil type variations. The datasets were meticulously preprocessed, ensuring normality, eliminating outliers, and scaling. These preprocessed data were then split into subgroups: 75% for training and 25% for testing, with six additional unseen catchments from the USA reserved for validation. A sensitivity analysis was performed across several ML models (ANN, CNN, RNN, LSTM, Random Forest, XGBoost), leading to the selection of the Random Forest (RF) algorithm for both flood inundation classification and flood depth regression models. Three regression models were assessed for flood depth prediction. The pixel-based regression model achieved an R2 of 91% for training and 69% for testing. Introducing a pixel clustering regression model improved the testing R2 to 75%, with an overall validation (for unseen catchments) R2 of 64%. The catchment-based clustering regression model yielded the most robust performance, with an R2 of 83% for testing and 82% for validation. The developed ML model demonstrates breakthrough computational efficiency, generating complete flood depth predictions in just 6 min—a 225× speed improvement (90–95% time reduction) over conventional HEC-RAS 6.3 simulations. This rapid processing enables the practical implementation of flood early warning systems. Despite the dramatic speed gains, the solution maintains high predictive accuracy, evidenced by statistically robust 95% confidence intervals and strong spatial agreement with HEC-RAS benchmark maps. These findings highlight the critical role of the spatial variability of dependencies in enhancing model accuracy, representing a meaningful approach forward in scalable modeling frameworks with potential for global generalization of flood depth.
Keywords: Scopus_Alert_10_Octobre_2025

A Novel Multimodal Large Language Model-Based Approach for Urban Flood Detection Using Open-Access Closed Circuit Television in Bandung, Indonesia - Yang, Tsun-Hua; Wijaya, Obaja Triputera; Ardianto, Sandy; Christian, Albert Budi (Water, 2025)
Monitoring urban pluvial floods remains a challenge, particularly in dense city environments where drainage overflows are localized, and sensor-based systems are often impractical. Physical sensors can be costly, prone to theft, and difficult to maintain in areas with high human activity. To address this, we developed an innovative flood detection framework that utilizes publicly accessible CCTV imagery and large language models (LLMs) to classify flooding conditions directly from images using natural language prompts. The system was tested in Bandung, Indonesia, across 340 CCTV locations over a one-year period. Four multimodal LLMs, ChatGPT-4.1, Gemini 2.5 Pro, Mistral Pixtral, and DeepSeek-VL Janus, were evaluated based on classification accuracy and operational cost. ChatGPT-4.1 achieved the highest overall accuracy at 85%, with higher performance during the daytime (89%) and lower accuracy at night (78%). A cost analysis showed that deploying GPT-4.1 every 15 min across all locations would require approximately USD 59,568 per year. However, using compact models like GPT-4 nano could reduce costs by up to seven times, with minimal loss of accuracy. These results highlight the trade-off between performance and affordability, especially in developing regions. This approach offers a scalable, passive flood monitoring solution that can be integrated into early warning systems. Future improvements may include multi-frame image analysis, automated confidence filtering, and multi-level flood classification for enhanced situational awareness.
Keywords: Scopus_Alert_10_Octobre_2025

Evaluating Infiltration Methods for the Assessment of Flooding in Urban Areas - Bianucci, Paola; Fernández-Fidalgo, Javier; Kyaw, Kay Khaing; Soriano, Enrique; Mediero, Luis (Water, 2025)
Urban flooding caused by short and high-intensity rainfall events presents increasing challenges for cities, threatening infrastructure, public safety and economic activity. Accurately representing infiltration processes in hydrodynamic models is critical, as oversimplifying infiltration can lead to significant errors in predicted flood extents and water depths. This study systematically compares two widely used infiltration models—Green-Ampt and Curve Number—implemented within two leading 2D hydraulic models, HEC-RAS and IBER, to assess their influence on urban flood predictions. Simulations were conducted for 26 rainfall events, including both observed and synthetic hyetographs, across two urban neighbourhoods in Pamplona metropolitan area, Spain. Model performance was evaluated using root mean square error, mean absolute error and confusion matrix-derived metrics such as precision, accuracy, specificity, sensitivity and negative predictive value. Results indicate that the choice of infiltration method significantly affects both water depths and inundation extents: while Green-Ampt yields more conservative water depth estimates, Curve Number tends to underestimate flood extents. The comparison between the two hydraulic models has shown that IBER simulates broader flood extents and lower water depth errors compared to HEC-RAS. The findings highlight the importance of selecting appropriate infiltration methods and hydraulic models for reliable urban flood risk assessment, as well as providing guidance for model selection in urban inundation studies.
Keywords: Scopus_Alert_10_Octobre_2025

Learning predictable and informative dynamical drivers of extreme precipitation using variational autoencoders - Spuler, Fiona R.; Kretschmer, Marlene; Balmaseda, Magdalena Alonso; Kovalchuk, Yevgeniya; Shepherd, Theodore G. (Weather and Climate Dynamics, 2025)
Abstract. Large-scale atmospheric dynamics modulate the occurrence of extreme precipitation events and provide sources of predictability of these events on timescales ranging from days to decades. In the midlatitudes, regional dynamical drivers are frequently represented as discrete, persistent and recurrent circulation regimes. However, available methods identify circulation regimes which are either predictable but not necessarily informative of the relevant local-scale impact studied, or targeted to a local-scale impact but no longer as predictable. In this paper, we introduce a generative machine learning method based on variational autoencoders for identifying probabilistic circulation regimes targeted to spatial patterns of precipitation. The method, CMM-VAE, combines targeted dimensionality reduction and probabilistic clustering in a coherent statistical model and extends a previous architecture published by the authors to allow for categorical target variables. We investigate the trade-off between regime informativeness of local precipitation extremes and predictability of the regimes at subseasonal lead times. In an application to study drivers of extreme precipitation over Morocco, we find that the targeted CMM-VAE regimes are more informative of the impact variable of interest, compared to two well-established linear approaches, while maintaining the predictability of conventional non-targeted circulation regimes in subseasonal hindcasts, hence resolving the trade-off identified in previous studies. Furthermore, the targeted regimes and their predictability are physically interpretable in terms of known subseasonal teleconnections relevant to the region, the Madden-Julian Oscillation and variability of the stratospheric polar vortex. The proposed method therefore allows to identify predictable, interpretable and locally relevant representations of regional dynamical drivers given a target variable of interest. These results highlight the potential of the method for a variety of applications, ranging from subseasonal forecasting to attribution and statistical downscaling.
Keywords: Scopus_Alert_10_Octobre_2025

Future forest conditions under alternative management and hydrological scenarios in the Upper Mississippi River floodplain - Trumper, Matthew L.; De Jager, Nathan R.; Van Appledorn, Molly; Meier, Andrew R. (Landscape Ecology, 2025)
Abstract Context Floodplain forests are being transformed by multiple pressures, prompting widespread management and restoration efforts. It is uncertain how disturbances, including hydrologic change, and management actions will interact to influence the ecology of these threatened forests. Objectives This study examined the effects of alternative management and hydrologic regimes on forest succession at an Upper Mississippi River floodplain site with a restoration project in planning. Methods We used the spatially explicit forest landscape model, LANDIS-II, to simulate forest succession for 100 years under four hydrogeomorphic management scenarios, three forest management scenarios, and two scenarios of future hydrologic conditions. We evaluated changes in forest biomass and composition over time and assessed the relative importance of management actions and hydrologic change on succession. Results Forest aboveground biomass decreased in all management-hydrology scenarios, especially in the wetter hydrological scenario. Intensified hydrogeomorphic and forest management scenarios reduced the magnitude and extent of biomass declines; however, they were unable to prevent overall declines in biomass or cause large shifts in tree species composition. Silver maple ( Acer saccharinum ) was projected to decrease in biomass, while increases in biomass were projected for several late-successional species including swamp white oak ( Quercus bicolor ). Among the factors influencing variation in biomass, forest management had the largest influence in the first 50 years of our simulations, but hydrological regime became the most important factor by the end of the century. Conclusions Our simulations indicate that management actions could play an important role in the conservation of floodplain forests, but their effectiveness will likely be limited if recent upward trends in flooding conditions in this system continue in the future. Thus, our results highlight both the potential benefits and limitations of management actions in the face of hydrologic change.
Keywords: Scopus_Alert_10_Octobre_2025