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

Dürre und Flut in Stadt und Raum (WASSERWIRTSCHAFT, 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

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)

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

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.

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.

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

Current Status of Studying on Physiological Mechanisms of Rice Response to Flooding Stress and Flooding-Resistant Cultivation Regulation - Bu, Weicheng; Ahmad, Irshad; Fei, Han; Ibrahim, Muhi Eldeen Hussien; Xu, Yunji; Meng, Tianyao; Zuo, Qingsong; Lei, Tianjie; Zhou, Guisheng; Zhu, Guanglong (Plants, 2025)
Due to climate change, flooding stress has occurred more frequently and intensively than ever before, which has become one of the major abiotic stresses affecting rice production. In tropical regions around the world, southeastern coastal countries, and southern rice production areas of China, frequent flooding disaster usually takes place during the rainy season and heavy summer rainfall, which leads to great yield losses in rice production. Currently, only a few rice genotypes are flooding-tolerant, and the relevant flooding-resistant cultivation and regulation practices are still lacking. Therefore, this review highlighted the latest studies on the physiological mechanisms of rice response to flooding stress and flooding-resistant cultivation, particularly summarizing the effect of flooding stress on rice root system architecture, plant growth, reactive oxygen metabolism, energy metabolism, radiation use efficiency, endogenous hormone metabolism, nitrogen utilization efficiency, and yield formation. In addition, the breeding strategies and cultivation regulation approaches for alleviating the flooding stress of rice were analyzed. Finally, future research directions are outlined. This review comprehensively summarizes the rice growth performance and physiological traits response to flooding stress, and sums up some useful regulation strategies, which might assist in further interpreting the mechanisms of plants’ response to flooding stress and developing stress-resistant cultivation practices for rice production.
Keywords: Scopus_Alert_10_Octobre_2025

Informing Coastal Futures: Sea Level Rise and Ecosystem Service Disruption in Caribbean Biodiversity Hotspots - Hurtado-Valdivieso, Rafael; Mancera-Pineda, Jose Ernesto; Martin, Ariadna; Quintero, Paola; Ruíz, Daniel; Baggio, Jacopo (Current Climate Change Reports, 2025)

Informing Coastal Futures: Sea Level Rise and Ecosystem Service Disruption in Caribbean Biodiversity Hotspots - Hurtado-Valdivieso, Rafael; Mancera-Pineda, Jose Ernesto; Martin, Ariadna; Quintero, Paola; Ruíz, Daniel; Baggio, Jacopo (Current Climate Change Reports, 2025)
Keywords: Scopus_Alert_10_Octobre_2025

A state-of-the-art approach to assessing relative multi-dimensional vulnerabilities for urban flood resilience in the UK - Woods, Arthur C.; Gupta, Vijaya; Campos, Luiza C. (International Journal of Disaster Risk Reduction, 2025)
Keywords: Scopus_Alert_10_Octobre_2025

A risk-informed approach for combined functionality analysis of buildings and road network under flooding: A case study at Silchar, India - Sen, Mrinal Kanti; Kumar, Prem; Laskar, Jahir Iqbal; Dutta, Subhrajit (International Journal of Disaster Risk Reduction, 2025)
Keywords: Scopus_Alert_10_Octobre_2025

Flood vulnerability analysis in the Itang watershed, lower Baro-Akobo basin, Southwestern Ethiopia - Chengu, Shibiru; Assen, Mohammed; Gebeyehu, Elias (Discover Sustainability, 2025)
Keywords: Scopus_Alert_10_Octobre_2025

Practitioner Perspectives of Flood Source Area ( FSA ) Analysis for System‐Based Flood Risk Management - Dawson, David A.; O'Donnell, Emily; Bond, Stephanie; Willis, Thomas; Huck, Jonathan; Sherwood, Matthew; Moxon, Jonathan (Journal of Flood Risk Management, 2025)
ABSTRACT Urban Flood Risk Management (FRM) is a critical aspect of developing resilient environments for future generations to inhabit. It is now interconnected with the requirement to be more environmentally conscious through blue‐green infrastructure and the delivery of wider co‐benefits. The complexity of balancing urban growth with environmental drivers and increasing resilience is a key challenge for strategic urban decision‐making. Through computational modelling developments, new approaches to assess the spatial contribution of area to flood hazard are improving our understanding of the catchment response and our ability to develop multifunctional, multi‐beneficial projects. Yet at present, these approaches remain largely theoretical or are a ‘best intention’. This study uses an adapted ‘Unit Flood Response’ approach to generate Flood Source Area (FSA) maps for an urban catchment in the UK. A user‐focused engagement approach is applied using FSA outputs to generate key insight into its applicability from a practitioner perspective. The FSA modelling identified several hazard sources, from widespread contributions upstream to discrete contributions downstream. Stakeholders concluded that the FSA can support FRM at the pre‐planning stage by providing a clearer strategic vision across the catchment to support traditional ‘receptor‐led’ decision‐making. Improved identification and negotiation of project partners and the potential to support/identify wider scale options that integrate with existing and planned infrastructure in other sectors, for example, housing and transport, were additional benefits of this approach. While the computational aspects of FSA analyses could be improved for model robustness (e.g., calibration, validation), they must do so with a full understanding of the practicalities of applying these techniques on the ground, demonstrating the importance of co‐development of research with practitioners and decision‐makers.
Keywords: Scopus_Alert_10_Octobre_2025

Assessment of urban flood risk of the lower Tapi basin through an integrated approach using GIS based AHP - Jariwala, Aayushi; Agnihotri, Prasit (Journal of Earth System Science, 2025)
Keywords: Scopus_Alert_10_Octobre_2025

A Coupled Hydrological‐Hydrodynamic Modelling Approach for Assessing the Impacts of Multiple Natural Flood Management Interventions on Downstream Flooding - Zhu, Qiuyu; Klaar, Megan; Willis, Thomas; Holden, Joseph (Journal of Flood Risk Management, 2025)
ABSTRACT While natural flood management (NFM) as a flood mitigation strategy is becoming widely used, there remains a lack of evidence regarding the effectiveness of different NFM scenarios under high flow events. To demonstrate how different types and extents of NFM interventions interact to flood peaks at larger catchment scales, combined scenarios of existing NFM interventions and an ideal maximum woodland scenario were modelled in the Upper Aire, northern England, using a coupled model that integrates Spatially Distributed TOPMODEL (SD‐TOPMODEL) with a 2D hydrodynamic model (Flood Modeller 2D) at an 81.4 km 2 catchment. The coupled model exhibited a strong fit with observed data (NSE up to 0.95), effectively capturing flood peaks and peak shapes. Leaky dams were found to be more effective at delaying flood peaks with mean values ranging from 8.6 to 60 min than reducing peak discharge (mean values ranging from 0.53% to 1.84%), though these effects were inversely proportional and influenced by tributary characteristics such as channel gradient. Simulations applying multiple NFM interventions consistently demonstrated positive flood mitigation impacts, including reduced peak discharge up to 2.59% and delayed peaks up to 30 min, while inundation depths reduced by 0.5 m in most areas, with inundation extent reduction at critical points in an urban area. The study demonstrated the utility of the coupled model for evaluating NFM strategies while emphasising the need for further validation and exploration of systematic interventions at larger catchment scales. By providing insights into the interactions between NFM interventions and catchment characteristics, this research contributes to the optimisation of flood risk management strategies and informs future policy development.
Keywords: Scopus_Alert_10_Octobre_2025

Flood disaster experiences of elderly individuals living in the western black sea region of Türkiye: a phenomenological study - Atalay, Ezgi; Gündüz Zeybekoğlu, Fatma; Torpuş, Kemal; Usta, Galip (BMC Geriatrics, 2025)
Keywords: Scopus_Alert_10_Octobre_2025

Analyzing Synthetic Stage‐Discharge Rating Curves and Riverine Flood Inundation Maps Derived From Global‐Scale Hydrologic and Hydraulic Modeling - Gutenson, Joseph L.; Follum, Michael L.; Staebell, Kathleen A.; Ondich, Emily S.; Wahl, Mark D. (Journal of Flood Risk Management, 2025)
ABSTRACT Synthetic rating curves (SRCs) are often used to translate streamflow forecasts into flood inundation maps. Previous studies have investigated the development and errors in SRCs at local, regional, and continental scales. In this analysis, we used the latest global methodology and datasets to develop SRCs for use in flood inundation map forecasting. Using the Yellowstone River Basin and the 2022 floods that affected the region, we analyzed the error in the SRCs assessment of stage and water surface elevation (WSE). We then investigated the error in flood inundation maps produced using the SRCs. Comparing SRCs to locally derived rating curves from 29 U.S. Geological Survey (USGS) stream gages, median error in SRC stage ranged from 0.45 to 0.65 m and SRC error was greatest at higher magnitude streamflows. This error increased to a median of 1.98–2.30 m when converting the stage to a WSE. After using the SRC WSE estimates to create an estimated flood inundation map, the WSE error at observed high‐water marks (1.99 m) was nearly proportional to average WSE error at the stream gage locations along the same river reach. Our results provide the first regional assessment of globally derived SRCs that are used in flood inundation mapping.
Keywords: Scopus_Alert_10_Octobre_2025

Evaluation of climate change impact on future flood in the Bagmati river basin, Nepal using CMIP6 climate projections and HEC-RAS modeling - Malla, Sushmita; Ohgushi, Koichiro (Water Cycle, 2026)
Keywords: Scopus_Alert_10_Octobre_2025

Integrating Hydrodynamic Mechanisms and Deep Learning for Real-Time Flood Inundation Forecasting - Fang, Xin; Li, Heng; Zhang, Sherong; Liu, Kang; Wang, Xiaohua; Wang, Chao (Journal of Computing in Civil Engineering, 2026)
Keywords: Scopus_Alert_10_Octobre_2025

Systematic Review of Multidimensional Assessment of Coastal Infrastructure Resilience to Climate-Induced Flooding: Integrating Structural Vulnerability, System Capacity, and Organizational Preparedness - Mashwama, Nokulunga Xolile; Phesa, Mbulelo (Climate, 2025)
This study investigates the multifaceted factors influencing the success of government-funded construction projects and addresses the challenges posed by climate-induced flooding, proposing integrated solutions encompassing structural vulnerability, system capacity, and organizational preparedness. By examining the challenges faced by coastal infrastructure, such as aging infrastructure, sea-level rise, and extreme weather events, this research seeks to identify strategies that enhance resilience and minimize the impact of flooding on coastal communities. The study presents a systematic review of 80 scholarly articles integrating quantitative and qualitative findings. Utilizing the PRISMA guidelines, the review highlights structural analysis, hydraulic modeling, and organizational surveys, to assess the resilience of coastal infrastructure systems. The results of this study offer actionable insights for policymakers, infrastructure managers, and coastal communities, facilitating informed decision-making and promoting climate-resilient development. Coastal regions around the world are increasingly vulnerable to climate-induced hazards such as sea level rise, storm surges, and intense flooding events. Among the most at-risk assets are transport infrastructure and buildings, which serve as the backbone of urban and regional functionality. This research paper presents a multidimensional assessment framework that integrates structural vulnerability, system capacity, and organizational preparedness to evaluate the resilience of coastal infrastructure. Drawing upon principles of resilience such as robustness, redundancy, safe-to-fail design, and change-readiness, the study critically reviews and synthesizes existing literature, identifies gaps in current assessment models, and proposes a comprehensive methodology for resilience evaluation. By focusing on both transport systems and building infrastructure, the research aims to inform adaptive strategies and policy interventions that enhance infrastructure performance and continuity under future climate stressors.
Keywords: Scopus_Alert_10_Octobre_2025

Enhancing water security through integrated storage mechanisms and rainwater harvesting for sustainable development - Alao, Joseph Omeiza; Eze, Stanley U.; Onyenweife, Geraldine I.; Ibe, Amarachukwu A.; Otorkpa, Oche J.; Ayejoto, Daniel A.; Abubakar, Fahad; Abdulsalami, Momohjimoh; Abdulmalik, Danga Onimisi (Discover Sustainability, 2025)
Keywords: Scopus_Alert_10_Octobre_2025

Unlocking Financial Potential for Urban Nature‐Based Solutions: A PRISMA ‐Based Systematic Review - Ahmad, Maruf; Du, Jinfeng (Sustainable Development, 2025)
ABSTRACT Contemporary urbanization has intensified environmental, social, and economic challenges, underscoring the need for Nature‐Based Solutions (NBS) to address issues of urban carbon emission, flooding, stormwater management, heatwaves, and biodiversity loss. However, securing funding for NBS implementation and maintenance remains a critical barrier. This study systematically reviews 121 financial‐related scholarly literature on urban NBS, applying the PESTLE (political, economic, social, technological, legal, and environmental) method to identify barriers to financial innovation and implementation. To ensure reliability, countermeasures cited in at least three independent sources were included. Key financial barriers for urban NBS include unstandardized valuation metrics (19%), insufficient financing mechanisms (15%), and regulatory limits (11%). This review also highlights countermeasures such as economic valuation (8%), innovative finance modalities (8%), tax benefits, and community workshops (7%) that can address these barriers. The study's uniqueness lies in how financial resource alignment and regulatory support, guided by Resource‐Based View and Institutional Theory , can promote sustainable NBS adoption. It also calls for standardized valuation metrics and adaptive monitoring systems to improve the social, environmental, and economic impact of NBS, making them more attractive to investors and ensuring long‐term sustainability. These findings provide actionable insights for financial restructuring and urban planners to effectively bridge funding gaps and promote sustainable environmental financing.
Keywords: Scopus_Alert_10_Octobre_2025

Dynamic flood risk modeling in urban metro systems considering station configuration - Liang, Chen; Guan, Mingfu; Guo, Kaihua; Yu, Dapeng; Yin, Jie (Reliability Engineering & System Safety, 2026)
Keywords: Scopus_Alert_3_Octobre_2025

Real-time coastal flood risk assessment using IoT-integrated satellite data and machine learning models for predicting flooding events and informing resilient coastal planning for Durban coastal region, South Africa - Vadivel, M.; Vijaya Saraswathi, R.; Sree Lakshmi, P.; Merugu, R Rajaramesh; T., Subbulakshmi; Vivek, S. (Journal of African Earth Sciences, 2026)
Keywords: Scopus_Alert_3_Octobre_2025

Does flood risk matter in shaping climate gentrification? Evidence from Brisbane (2006–2021) - Gao, Xiaoyong; Yi, Xianjian; Kwan, Mei-Po; Li, Huan (Cities, 2026)
Keywords: Scopus_Alert_3_Octobre_2025

A data-driven and expert flood knowledge model based on the development of the Huber loss function for flood forecasting - Malik, Haider; Feng, Jun; Shao, Pingping; Abduljabbar, Zaid Ameen (Water Research, 2026)
Keywords: Scopus_Alert_3_Octobre_2025

Flood vulnerability and risk mapping in Arba minch city using remote sensing, GIS and AHP - Kasahun, Melion; Diriba, Dechasa; Lemma, Tesfaye; Karuppannan, Shankar; Kanko, Niguse (Scientific African, 2025)
Keywords: Scopus_Alert_3_Octobre_2025

Complementing expertise: Q-visualization method for community-participatory flood loss assessment of Mihe Town, Zhengzhou, China - Feng, Xiao; Zhao, Zhenbin; Zhang, Jian; Li, Yangyang; Zhang, Yuqian (Applied Geography, 2025)
Keywords: Scopus_Alert_3_Octobre_2025

Spatial Modeling of Flood Hazard in Addis Ababa Using Geographic Information System ( GIS ) and Information Gain Ratio ( IGR ) Method - Bekalo, Desta Jula; Bojer, Amanuel Kumsa; Debelee, Taye Girma; Fadhil Al‐Quraishi, Ayad M.; Negera, Worku Gachena; Gebissa, Kena Wendimu; Nadarajah, Saralees; Woldesillasie, Firesew Feyiso (Journal of Flood Risk Management, 2025)
ABSTRACT Floods are one of nature's most disturbing catastrophes, resulting in infrastructure damage, property devastation, and mortality. In Addis Ababa, flooding has significantly impacted residents and caused millions' worth of property damage in the last decade alone. It is continuously threatening and affecting city residents. This study focused on the spatial modeling of floods and the identification of areas susceptible to flood hazards in the city. Geographic information system (GIS) techniques combined with the information gain ratio (IGR) method were employed in this study. Five major flood hazard factors were identified: elevation, slope, rainfall, drainage density, and distance from drainage channels. The results show that 1.3% (7.1 km 2 ) of the area is highly susceptible to floods, 29.4% (159 km 2 ) is highly susceptible to heavy rains, 56% (302 km 2 ) of the area is moderately susceptible, 12.5% (67.3 km 2 ) of the area has low susceptibility, and less than 1% (4.2 km 2 ) has very low susceptibility. Slope is the most influential factor (42.74%), followed by drainage density (28.21%), distance from drainage channels (18.8%), rainfall (7.69%), and elevation (2.56%). The sub‐cities of Nifas Silk Lafto and Akaki Kality are the most susceptible to flood hazards; areas with steep slopes trigger high runoff during heavy rainy periods and cause flood hazards on gentle slope surfaces. It is recommended that to improve the accuracy of identifying susceptible flood‐hazard locations, flooding simulation should be performed in conjunction with other variables and rainfall data (such as rainfall duration and intensity). Nevertheless, this research provides recommendations to municipal administration decision‐makers regarding strategic management in the prioritization of flood‐hazard zones.
Keywords: Scopus_Alert_3_Octobre_2025

Reduction of the uncertainty of flood projection under a future climate by focusing on similarities among multiple SSP-RCP scenarios - Kimura, Y.; Hirabayashi, Y.; Yamazaki, D. (Scientific Reports, 2025)
Abstract Climate projections contain the uncertainty due to the internal variability of the climate system, including its chaotic nature. While the uncertainty due to the internal variability can be theoretically mitigated by executing large ensemble simulations with perturbed initial conditions, only a limited number of large-ensemble experiments are available in CMIP6 future scenario dataset. Here we propose a method that increases the effective ensemble sampling size in evaluations of future projection by integrating multiple SSP-RCPs for a period corresponding to a specific increase in temperature from the preindustrial level (i.e., X°C warming). The success of the method was assessed by investigating whether the uncertainty due to small number of ensemble members could be reasonably reduced. First, we confirmed that the spatial distributions of the future flood magnitude change were similar under a 2 °C warming in all SSP-RCP scenarios. Additionally, the uncertainty due to the different SSP-RCPs (5–10%) was smaller than the differences between different warming levels such as between 2 and 3 °C (around 20–50%), suggesting differences among SSP-RCPs as to future flood discharge change are relatively small. These results suggested that integrating SSP-RCPs to increase the effective ensemble size was a reasonable approach, reducing unbiased variance among GCMs in about 70% of land grid points comparing to the result using SSP5-RCP8.5 alone.
Keywords: Scopus_Alert_3_Octobre_2025

Human health risk assessment of heavy metals in fish and shellfishes from heavy monsoon flood affected coastal zones of Tuticorin, Southeastern India - Yogeshwaran, Arumugam; Anandhan, Krishnan; Sanjeev, Chandrasekar; Rajaram, Rajendran; Muralisankar, Thirunavukkarasu (Food and Humanity, 2025)
Keywords: Scopus_Alert_3_Octobre_2025

Towards measuring community resilience to flood risk: A baseline analysis - Awah, Lum Sonita; Belle, Johanes Amate; Orimoloye, Israel Ropo; Nyam, Yong Sebastian (Scientific African, 2025)
Keywords: Scopus_Alert_3_Octobre_2025

Ferric sulfate-modified biochar as a dual-function amendment for methane mitigation and heavy metal risk control in flooded rice systems - Zhang, Zhiwei; Bo, Xiaomeng; Lu, Mingwang; Mai, Xiaofeng; Li, Zhutao; Guo, Shumin; Shen, Mengxue; Han, Zhaoqiang; Wu, Shuang; Wang, Jinyang; Zou, Jianwen (Environmental Technology & Innovation, 2025)
Keywords: Scopus_Alert_3_Octobre_2025

Multi-decade analysis of flood risks to community infrastructure in Philadelphia - Son, Youngjun; Sun, Ning; Reesman, Cade; Judi, David; Li, Xue (International Journal of Disaster Risk Reduction, 2025)
Keywords: Scopus_Alert_3_Octobre_2025

Between flood vulnerability and watery pedagogies: A critical review of children and young people’s voices and experiences - Verlie, Blanche; Rousell, David; De Kleyn, Lisa; Hartup, Michael; Rickards, Lauren; St Clair, Jeanti; Bayes, Chantelle; Widdop Quinton, Helen; Blom, Simone; Hotko, Katie; Cutter-Mackenzie-Knowles, Amy; De Rivera, Liberty; et al. (Geoforum, 2025)
Keywords: Scopus_Alert_3_Octobre_2025

Dynamic urban flood risk assessment based on human activity patterns: An IFAHP-EWM-TOPSIS approach - He, Weijie; Zeng, Jian; Zhang, Jiahao; Namaiti, Aihemaiti; Shi, Weihao; Song, Yuanzhen; Tian, Jian (Sustainable Cities and Society, 2025)
Keywords: Scopus_Alert_3_Octobre_2025

Key Drivers in Flood and Drought Risk Assessment: Unveiling Risk Factors through a Multi-Metric Network Approach - Fasihi, Siavash; Lim, Wen Zyn; Wu, Wenyan; Proverbs, David (Water Resources, 2025)
Keywords: Scopus_Alert_3_Octobre_2025

Flood risk analysis of strategically important structures to river coasts: case study of wastewater disposal facility - Akpınar, Özkan; Anıl, Özgür (Environmental Earth Sciences, 2025)
Keywords: Scopus_Alert_3_Octobre_2025

The effects of flood disasters on urban farmers in Ghana: a case-based perspective - Owusu, Doreen; Mensah, Henry; Simpeh, Eric Kwame; Guodaar, Lawrence; Asare-Baafi, Louisa (SN Social Sciences, 2025)
Keywords: Scopus_Alert_3_Octobre_2025

Climate Change and Population Aging Exacerbate Flood Risk to the Elderly in European Regions - Yang, Wenyu; Zhao, Ziyong; Wang, Zhenyu; Wang, Xuan; Li, Ruifei; Hua, Pei; Cheng, Xiangju; Liu, Yungang; Wang, Haijun; Krebs, Peter; Zhang, Jin (Earth's Future, 2025)
Abstract Floods threaten human lives globally, yet the flood risk to the elderly (above 65) remains uncertain within warming climates and population aging. Hence, this study incorporated the General Circulation Model and Shared Socioeconomic Pathway projections into the hydraulic modeling framework, to analyze the flood risk to the elderly in Europe under climatic and socioeconomic changes. Results demonstrated that central Europe has experienced an increase in both surface runoff and streamflow (exceeding 50%), which have jointly contributed to intensified flooding in the major basins (Loire, Rhine, Elbe, and Danube) within the region. Among them, the Elbe basin exhibited a significant increase in 100‐year flood peak (∼107%) and elderly population (∼15%), resulting in 51,300 (CI: 45,300–60,500) of the elderly population being exposed to high‐hazard floods under a high greenhouse gas scenario (SSP5‐8.5), with at most 58% (29,800, CI: 25,100–33,700) of them being densely settled or in low‐& middle‐income groups. Among aging cities, Prague was severely affected by floods, with 40%–54% of the exposed elderly in high‐risk areas. Followed by Dresden and Hamburg, where up to 18% of the exposed elderly were threatened by high‐risk floods. This study revealed regional inequalities induced by flood exposure within the context of warming climates and population aging. The methods and findings are expected to provide additional insights into sustainable flood risk management under global change. , Plain Language Summary Floods are threatening people around the world. As the climate gets warmer and the population gets older, it's important to understand how floods might affect the elderly (older than 65). In this study, we investigated how floods could impact the elderly in Europe by 2100. We coupled climate and socioeconomic projections with numerical simulations to analyze the flood risk in different future scenarios. We found that the flood risk could spatiotemporally vary across European basins and cities. In the Elbe River basin, flood peaks could increase by more than 100%, and the elderly population could grow by 15%, resulting in a maximum of 51,300 elderly population being exposed to floods, and up to 29,800 of them are highly vulnerable because of densely settled or in low‐& middle‐income groups. In aging cities like Prague, Dresden, and Hamburg, at most 54% of the exposed elderly can be in high‐risk areas. This research revealed that the vulnerable groups (elderly, low‐income) are disproportionately threatened by high‐risk floods. Here, we advocate for incorporating social vulnerability into flood risk evaluation and management, thereby helping mitigate regional inequalities and contribute to the United Nations Sustainable Development Goals. , Key Points Flood risk to the elderly across European regions was analyzed by coupling climatic and socioeconomic projections with hydraulic modeling Flood risk spatiotemporally varied in Europe, with Elbe basin showing an increase in floods (∼107%) and elderly population (∼15%) in future Over 51,300 of the elderly were exposed to floods in Elbe basin, with 58% of them being densely settled or in low‐& middle‐income groups
Keywords: Scopus_Alert_3_Octobre_2025

Testing the durability of hydraulic lime-based renders against extreme weather: Experimental aging tests for compound events in a changing climate - Romero-Gómez, M.I.; Flores-Colen, I.; Hooper, F.; Cardoso, R.; Pereira, M.F.C.; Bravo, M.; Mendes, M.P. (Journal of Building Engineering, 2025)
Keywords: Scopus_Alert_3_Octobre_2025

Impact of spatial resolution on multi-scenario WRF-ARW simulations driven by the CMIP6 MPI-ESM1-2-HR global model: a focus on precipitation distribution over Italy - Struglia, Maria Vittoria; Anav, Alessandro; Antonelli, Marta; Calmanti, Sandro; Catalano, Franco; Dell'Aquila, Alessandro; Pichelli, Emanuela; Pisacane, Giovanna (Geoscientific Model Development, 2025)
Abstract. We present the results of downscaling CMIP6 global climate projections to local scales for the Mediterranean and Italian regions, aiming to produce high-resolution climate information for the assessment of climate change signals, with a focus on precipitation extreme events. We performed hindcast (i.e., ERA5-driven) and historical simulations (driven by the MPI-ESM1-2-HR model) to simulate the present (1980–2014) and future (2015–2100) climate under three different emission scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5). For each experiment, a double-nesting approach is adopted to dynamically downscale global data to the regional domain of interest, firstly over the Europe (EURO) COordinated Regional climate Downscaling Experiment (CORDEX) domain, at a spatial resolution of 15 km, and then further refined (second nesting) over Italy and the northwestern Mediterranean at a resolution of 5 km, i.e., in the so-called gray zone (5–10 km), close to the convection-permitting (CP) limit. Besides validating the experimental protocol, this work potentially questions the need for climate simulations to always resort to deep convection parameterizations when spatial refinement is increased up to the limit of the CP scale, yet convective processes are still not explicitly resolved. Analyses of air temperature and precipitation are presented, with a focus on the spatial distribution of precipitation, its probability density function, and the statistics of extreme events for both current climate and far-end scenarios. By the end of the century for all the scenarios and seasons there is a projected general warming along with an intensification of the hydrological cycle over most of continental Europe and mean precipitation reduction over the Mediterranean region accompanied, over the Italian Peninsula, by a strong increase in the intensity of extreme precipitation events, particularly relevant for the SSP5-8.5 scenario during autumn.
Keywords: Scopus_Alert_3_Octobre_2025

Have river flow droughts become more severe? A review of the evidence from the UK – a data-rich, temperate environment - Hannaford, Jamie; Turner, Stephen; Chevuturi, Amulya; Chan, Wilson; Barker, Lucy J.; Tanguy, Maliko; Parry, Simon; Allen, Stuart (Hydrology and Earth System Sciences, 2025)
Abstract. When extreme hydrological events (floods and droughts) occur, there is inevitably speculation that such events are a manifestation of anthropogenic global warming. The UK is generally held to be a wet country, but recent drought events in the UK have led to growing concerns around droughts becoming more severe – for sound scientific reasons, given physical reasoning and projections for the future. In this extended review, we ask whether such claims are reasonable for hydrological droughts in the UK using a combination of literature review and extended analysis. The UK has a well-established monitoring programme and a very dense body of research to call on and, hence, provides the basis for a good international case study for addressing this question. We firstly assess the evidence for changes in the well-gauged post-1960 period before considering centennial-scale changes using published reconstructions. We then seek to provide a synthesis of the state of the art in our understanding of the drivers of change, both climatic and in terms of direct human disturbances to river catchments (e.g. changing patterns of water withdrawals, impoundments, land use changes). These latter impacts confound the identification of climate-driven changes, and yet, human influences are themselves being increasingly recognised as potential agents of changing drought regimes. We find little evidence of compelling changes towards worsening drought, apparently at odds with climate projections for the relatively near future and with widely held assumptions regarding the role of human disturbances in intensifying droughts. Scientifically, this is perhaps unsurprising (given uncertainties in future projections; the challenge of identifying signals in short, noisy records; and a lack of datasets to quantify human impacts), but it presents challenges to water managers and policymakers. We dissect some of the reasons for this apparent discrepancy and set out recommendations for guiding research and policy alike. While our focus is the UK, we envisage that the themes within this study will resonate with the international community, and we conclude with ways in which our findings are relevant more broadly, as well as how the UK can learn from the global community.
Keywords: Scopus_Alert_3_Octobre_2025

A systematic investigation of flood resilience measures in the Mekong River Basin - Ho, Thanh Phuoc; Garschagen, Matthias; Tri, Van Pham Dang; Yang, Liang Emlyn (Environmental Science & Policy, 2025)
Keywords: Scopus_Alert_3_Octobre_2025

A systematic review on climate change-induced flood susceptibility, vulnerability and risk: future research perspective - Ali, Rayees; Sajjad, Haroon; Rahaman, Md Hibjur; Saha, Tamal Kanti; Masroor, Md; Roshani; Sharma, Aastha (Environmental Monitoring and Assessment, 2025)
Keywords: Scopus_Alert_3_Octobre_2025

A review of the evolution of satellite radar altimetry for hydrological Monitoring: Water level and discharge estimation - Kumar, Barun; Bhattacharjee, Rajarshi; Dwivedi, Shyam Bihari; Gaur, Shishir; Chander, Shard; Ohri, Anurag (Physics and Chemistry of the Earth, Parts A/B/C, 2025)
Keywords: Scopus_Alert_3_Octobre_2025

Lessons from the catastrophic floods in southern Brazil: geographic and environmental factors, vulnerabilities, and the resilience of nephrology services during natural disasters - Silva, Dirceu Reis; Luz, Lucas Gobetti Da; Moura-Neto, José A. (Current Opinion in Nephrology & Hypertension, 2025)
Purpose of review In April and May of 2024, the state of Rio Grande do Sul in southern Brazil faced one of the most devastating natural disasters in its history. Torrential rains caused catastrophic flooding, affecting over 2.4 million people and damaging critical infrastructure. Among the most vulnerable were individuals with chronic kidney disease, particularly those dependent on dialysis or immunosuppressive therapy following kidney transplantation. This narrative report describes the impact of the floods on nephrology services, highlights systemic vulnerabilities, and outlines the responses by the Brazilian Society of Nephrology, healthcare providers, government authorities and other stakeholders. Recent findings Drawing on field reports and institutional data, the article details breakdowns in transportation, communication, water and power supply, and the cascading challenges affecting dialysis provision. It also documents mitigation efforts, including patient relocation, resource reallocation, inter-institutional collaboration, and telemedicine deployment. Although no deaths due to lack of dialysis were reported, the crisis exposed the fragility of health service continuity in extreme events. Summary This event underscores the urgent need to incorporate disaster preparedness into nephrology service planning. Strategies must be tailored to the specific geographic and infrastructural contexts of each region and integrated into broader public health emergency frameworks. The lessons learned offer valuable insights for strengthening healthcare system resilience in the face of climate-related disasters.
Keywords: Scopus_Alert_3_Octobre_2025

Artificial Intelligence in Sustainable Drainage Water Reuse: A Comprehensive Review - Abdulameer, Layth Saeed; Abdalrahem, Mushtaq K.; Al Maimuri, Najah M. L.; Al-Dujaili, Ahmed N.; Al-Shammari, Musa (Water Conservation Science and Engineering, 2025)
Keywords: Scopus_Alert_3_Octobre_2025

Extreme weather events and early childhood growth in low- and middle-income regions: A systematic review and meta-analysis - Wang, Xuechun; Lan, Lu; Xing, Xing; Yang, Wenyu; Zhu, He; Ding, Ruoxi (Environmental Research, 2025)
Keywords: Scopus_Alert_3_Octobre_2025

Use of Internet of Things in water resources applications: challenges and future directions: a critical review - Ansari, Sajad Ali; Vidyarthi, Vikas Kumar (Discover Internet of Things, 2025)
Keywords: Scopus_Alert_3_Octobre_2025

European extreme events climate index (E3CI): a climate service for weather induced hazard - Giugliano, Giuseppe; Pugliese, Alessandro; De Vivo, Carmela; Barbato, Giuliana; Ellena, Marta; Tirri, Antonio; Lo Conti, Francesco; Mortini, Laura; Gallo, Ethel; Bonfiglio, Alessandro; Mercogliano, Paola; Rianna, Guido (Frontiers in Climate, 2025)
In recent years, Europe has experienced a notable increase in the frequency and severity of extreme weather events, including heatwaves, floods, droughts, storms, and wildfires. These events have had profound impacts on human life, infrastructure, ecosystems, and economies, highlighting the urgent need to develop effective climate services to prevent, manage and timely respond to associated impacts. The aim of this paper is to describe and validate the European Extreme Climate Index (E3CI), designed as a climate service that allows monitoring the occurrence and severity of extreme weather dynamics across Europe. Starting from data provided by ERA5, the fifth generation of atmospheric reanalyzes produced by the European Center for Medium-Range Weather Forecasts (ECMWF), which covers the entire globe from 1940, E3CI provides insights regarding variations and trends in seven specific dynamics associated with extreme weather events: Extreme Maximum Temperatures, Extreme Minimum Temperatures, Extreme Precipitations, Extreme Winds, Hail, Droughts, and Fires. For each of these, the E3CI collects information at monthly scale in terms of anomalies compared to a reference climatological time span (1981–2010), therefore enabling back-analyses on specific territories, highlighting areas subject to significant weather-induced criticalities. E3CI has proven effective in identifying the spatial distribution and intensity of the extreme precipitation and extreme wind events in the areas affected by powerful storms Dudley, Eunice, and Franklin, as well as the significant heat waves in the summer of 2022 and the extreme precipitation in Emilia Romagna in May 2023. Such index offers numerous application opportunities for businesses, institutions, and communities, in the vein of profound innovation in climate risk management and extreme weather events policies. Its flexible and dynamic nature allows for the production of customizable analysis based on the specific user needs including the ability to refine geographic areas to a radius of approximately 30 km. The data are made available for visualization and download through a dedicated portal, which is updated monthly. Additionally, a Newsletter for subscribers provides information about major events across Europe and their assessment by the corresponding components of E3CI.
Keywords: Scopus_Alert_3_Octobre_2025

Concurrent modes of climate variability linked to spatially compounding wind and precipitation extremes in the Northern Hemisphere - François, Bastien; Teber, Khalil; Brett, Lou; Leeding, Richard; Gimeno-Sotelo, Luis; Domeisen, Daniela I. V.; Suarez-Gutierrez, Laura; Bevacqua, Emanuele (Earth System Dynamics, 2025)
Abstract. Compound wind and precipitation (CWP) extremes often cause severe impacts on human society and ecosystems, such as damage to crops and infrastructure. Spatially compounding events with multiple regions affected by CWP extremes in the same winter can impact the global economy and reinsurance industry; however, our understanding of these events is limited. While climate variability modes such as El Niño Southern Oscillation (ENSO) can influence the frequency of precipitation and wind extremes, their individual and combined effects on spatial co-occurrences of CWP extremes across the Northern Hemisphere have not been systematically examined. Here, by combining reanalysis data and climate model simulations, we investigate how two oceanic and two atmospheric variability modes – ENSO, the Atlantic Multidecadal Variability (AMV), the North Atlantic Oscillation (NAO), and the Pacific North American (PNA) – amplify the wintertime (December–February) frequency of daily CWP extremes and associated spatial co-occurrences across the Northern Hemisphere. We find many hotspot regions where concurrent variability mode anomalies significantly amplify wintertime CWP extreme event frequencies compared to single variability modes. By examining the relationships between frequencies of wintertime CWP extremes across regions, we identify dependencies enabling extreme spatially compounding events, that is winters with many regions experiencing CWP extremes. While ENSO is the most influential variability mode for such extreme spatially compounding events, the occurrence of these events increases further when multiple modes of variability are in anomalous phases. In particular, combinations of modes increase both the number of regions and the population exposed to daily CWP extremes in the same winter. For example, combined ENSO- and NAO+ nearly doubles the number of affected regions compared to neutral conditions on average. Our analysis highlights the importance of considering the interplay between variability modes to improve risk management and adapt to the impacts of spatially compounding CWP extremes.
Keywords: Scopus_Alert_3_Octobre_2025

Keyword Analysis and Systematic Review of China’s Sponge City Policy and Flood Management Research - Lu, Yichen; Huang, Muge; Xiao, Haixin; Lu, Zekun; Xie, Mingjing; Chen, Kaida (Atmosphere, 2025)
With the acceleration of climate change and urbanisation, Chinese cities are facing increasingly severe flood risks. To address this challenge, China began implementing its sponge city policy in 2013, leveraging low-impact development, green infrastructure construction, and integrated water resource management to enhance urban resilience to floods and improve water security. This study utilises the Web of Science database as a reference, retrieving 201 relevant literature sources. From these, 61 studies closely related to China’s sponge city policy and urban flood management were selected. CiteSpace was employed to conduct keyword co-occurrence and temporal evolution analyses, comprehensively outlining the research hotspots and developmental trajectory of this field. The results indicate that research content has gradually shifted from early engineering-based flood control models to multi-objective, interdisciplinary comprehensive management, encompassing flood risk assessment, policy implementation mechanisms, integration of green infrastructure, and economic feasibility analysis. Based on this, this paper constructs an analytical framework incorporating technical, environmental, institutional, and social dimensions to integrate existing research findings, while identifying gaps in cross-scale coordination, smart management, and public participation. The research conclusions can provide valuable references for future policy optimisation and urban sustainable development.
Keywords: Scopus_Alert_3_Octobre_2025

Advancing High‐Resolution Precipitation Analysis for Operational Hydrological Applications in Australia: Data Sources, Methodologies, and Recommendations - Zhang, Yuhang; Wang, Quan J.; Frost, Andrew J.; Trewin, Blair; Pudashine, Jayaram; Velasco‐Forero, Carlos; Su, Chun‐Hsu; Villani, Vincent (WIREs Water, 2025)
ABSTRACT High‐resolution precipitation products (e.g., km‐scale and hourly) are essential for capturing extreme rainfall dynamics and improving hydrological models for flood forecasting and related applications. However, single‐source estimates—whether from gauges, radar, or satellites—suffer from inherent limitations such as sparse spatial coverage, limited observational extent, and retrieval uncertainties. Multi‐source precipitation combinations can potentially provide a more accurate estimate by using the strengths of each source. This review presents globally relevant methodologies for constructing high‐resolution precipitation datasets, with a focus on sub‐daily temporal and km‐scale spatial resolutions. Particular emphasis is placed on their applicability to the development of a new multi‐source precipitation dataset for Australia (BRAIN: Blended Rain). We first compile and assess precipitation data sources specific to Australia, particularly those readily available to the Australian Bureau of Meteorology. These include ground‐based gauge observations, radar estimates, satellite‐derived products, reanalysis datasets, and numerical weather prediction outputs. While the primary emphasis is on Australian datasets, many sources provide global coverage, enhancing the broader relevance of this work. We then review globally applicable blending techniques, highlighting methods such as weighted averaging, multifractal blending, data assimilation, and machine learning. Key challenges, including latency, quality control, spatial heterogeneity, and validation, are discussed alongside opportunities for advancing multi‐source precipitation blending. Finally, we recommend specific datasets and blending methods for trial and assessment, considering both current Australian data availability and potential future upgrades. The insights provided aim to support the development of robust high‐resolution precipitation products for hydrological applications in Australia and other regions with similar data integration challenges. This article is categorized under: Science of Water > Methods
Keywords: Scopus_Alert_3_Octobre_2025

Experimental Modeling of Three-Dimensional (3D) Partial Dam-Break Flows: A Review - Meng, Chuke; Zhao, Weiyang; Niu, Zhipan; Lin, Pengzhi (Water, 2025)
The growing threat of dam-break events, fueled by aging infrastructure and climate change, necessitates comprehensive risk management and mitigation strategies. Experimental studies on partial dam-break flows are pivotal for understanding the complex dynamics of these events, particularly in assessing flood risk and refining predictive models. This review synthesizes current experimental investigations on three-dimensional (3D) partial dam-break flows, with an emphasis on breach dynamics, wave impacts, and the role of urban structures. It highlights the challenges in capturing high-resolution 3D flow characteristics and the advancements in measurement techniques such as particle tracking velocimetry and ultrasonic distance meters. The paper discusses the integration of experimental data with numerical models to validate and improve predictive capabilities, stressing the need for continuous refinement of experimental setups and computational approaches. Gaps in the current literature, including the under-representation of irregular breach geometries and complex terrain, are identified, and future research directions are proposed to address these shortcomings. This work underscores the importance of hybrid measurement techniques and interdisciplinary collaboration to enhance dam-break modeling accuracy and flood risk mitigation.
Keywords: Scopus_Alert_3_Octobre_2025

A Systematic Review of Forest Cover for Catchment‐Scale Flood Mitigation: A Nature‐Based Solution - Herath, Prabhasri; Croke, Barry; Prinsley, Roslyn; Vaze, Jai; Pollino, Carmel (Journal of Flood Risk Management, 2025)
ABSTRACT Forest cover within catchments is a widely adopted Nature‐based Solution (NbS) for flood mitigation, offering hydrological benefits such as rainfall interception, enhanced infiltration, and reduced overland flow. Despite its recognized potential, quantitative reviews remain limited, especially at the catchment scale, with effectiveness varying by spatial scale, forest type, and climate. This review synthesizes 50 international case studies involving forest‐based NbS, selected through structured screening based on intervention type, catchment characteristics, and availability of quantitative flood metrics, and presents a detailed bibliometric and content analysis. Forest cover consistently impacts peak flow across catchments of all sizes, with a generalized linear relationship where the effect magnitude is approximately half the forest cover change. For example, a 20% increase in forest cover tends to reduce peak flow by 10% across small, medium, and large catchments. Across a range of catchment sizes, there are only minor differences in the mean peak flow reductions for different event intensities (up to 1% AEP). An asymmetric hydrological response is evident: deforestation consistently increases peak flows, whereas afforestation yields gradual reductions, which are shaped by forest maturity, spatial distribution, and modeling assumptions. Upstream distributed forest placements offer distinct hydrological benefits. These outcomes highlight the importance of conserving mature forests, preventing deforestation, and optimizing forest placement, while acknowledging potential adverse impacts on water availability during dry periods.
Keywords: Scopus_Alert_3_Octobre_2025

Sex and Gender Influences on the Impacts of Disasters: A Rapid Review of Evidence - Muñoz-Nieves, Carol; Greaves, Lorraine; Huber, Ella; Brabete, Andreea C.; Wolfson, Lindsay; Poole, Nancy (International Journal of Environmental Research and Public Health, 2025)
Both sex-related factors and gender-related factors affect the immediate and long term mental and physical health impacts of disasters, including those resulting from public health emergencies, climate-related events, and naturally occurring phenomena. These include sex-specific biological, physiological and genetic processes, mechanisms underlying reproduction, disease outcomes, and stress, immune, and trauma responses. Gendered factors such as roles, relations, identity, and institutional policies that have an impact on caregiving, occupation, gender-based violence, and access to healthcare, also influence the impacts of disasters and emergencies. Sex/gender factors interact with a range of social determinants to affect the equitability of impacts. A rapid review was conducted to examine evidence from Australia, Canada, countries from the European Union, New Zealand, the United Kingdom (UK), and the United States of America (USA) on the influence of sex- and gender-related factors in the context of disasters, such as COVID-19, earthquakes, floods, hurricanes, and wildfires. This article describes and categorizes this evidence with attention to real-world impacts of the interactions between sex, gender, and other equity related factors. Broad considerations for improving research and practices to support more sex and gender research in this area and ultimately, to improve emergency and disaster management, are discussed.
Keywords: Scopus_Alert_3_Octobre_2025

The Bioremediation of Nutrients and Heavy Metals in Watersheds: The Role of Floating Treatment Wetlands - Kumar, Nirmal; Singh, Bhupinder; Chen, Yuanze; Kafle, Abishek; Zhu, Weihang; Ray, Ram L.; Kumar, Sandeep; Shan, Xiaonan; Balan, Venkatesh (Applied Sciences, 2025)
Floating treatment wetlands (FTWs) are engineered systems that utilize floating platforms planted with aquatic vegetation to treat polluted water such as stormwater, agricultural runoff, and wastewater. FTWs have emerged as promising and environmentally sustainable solutions for water purification. This review synthesizes the current knowledge on FTW design, plant selection, and performance evaluation. It highlights key factors influencing nutrient and heavy metal removal, including the hydraulic retention time, mat thickness, and types of plant species. Recent findings on the roles of root architecture, microbial interactions, and seasonal variability in treatment efficiency are also discussed. Additionally, the review explores advanced analytical methods for monitoring water quality and assessing plant growth and contaminant uptake. Case studies from both laboratory- and field-scale experiments illustrate how variation in FTW configurations impacts pollutant removal efficiency. The review concludes by identifying critical research gaps, including the need for standardized monitoring protocols, strategies to enhance long-term performance, and the integration of FTWs with complementary treatment technologies to improve effectiveness across diverse aquatic environments.
Keywords: Scopus_Alert_3_Octobre_2025

Enhancing streamflow ensemble forecasting: evaluating pre- and post-processing strategies for a spatially distributed model - Askarinejad, Alireza; Trudel, Mélanie; Boucher, Marie-Amélie (Canadian Water Resources Journal / Revue canadienne des ressources hydriques, 2025)
Keywords: Infolettre_Octobre_2025

Evaluating the Hydrological Performance of a Water Square with 2D Modeling and In Situ Monitoring - Institut national de la recherche scientifique-INRS; Ossa Ossa, Juan Esteban; Duchesne, Sophie; Institut national de la recherche scientifique-INRS; Pelletier, Geneviève; Université Laval; Rokhzadi, Arman; Institut national de la recherche scientifique-INRS (Journal of Water Management Modeling, 2025)
In situ monitoring and numerical modeling tools are essential to quantify the advantages of stormwater management infrastructures and to improve their future designs. Such infrastructures offer effective solutions for decreasing runoff volume and mitigating surface sewer overflow issues in urban environments. This paper assesses the hydraulic and hydrologic performance of a stormwater management infrastructure using numerical modeling and in situ monitoring for a water square called Place des Fleurs-de-Macadam, located in a dense urban area in Montreal, Canada. This floodable park comprises a detention pond and bioretention cells that redirect and accumulate surface water from adjacent streets, enabling water infiltration and preventing excess water from surcharging the sewer system. The performance of the water square was evaluated with a coupled 1D/2D model in PCSWMM, as well as in situ monitoring, which was carried out by a flood test on site. The associated rainfall event was a three-hour rainfall of 62 mm, equivalent to a 100-year event for this area. Results obtained by numerical modeling and in situ monitoring indicated that urban stormwater runoff can be effectively mitigated. For the tested rainfall event, simulation and in situ monitoring results showed that the water square reduced the runoff volume by 98% and delayed outflows by two hours.
Keywords: Infolettre_Octobre_2025

JAX-WSPM: A GPU-accelerated parallel framework based on the JAX library for modeling water flow and solute transport in unsaturated porous media using an implicit finite element method - Toutlini, Nour-Eddine; Soulaïmani, Azzeddine; Beljadid, Abdelaziz (Computer Physics Communications, 2026)
Keywords: Infolettre_Octobre_2025

Global flood extent monitoring using SAR satellite and hydrological data: A multi-scale and multi-source approach - Zhu, Yuting; Yoshimura, Kei; Liu, Yingying; Fu, Haohuan (Journal of Hydrology, 2025)
Keywords: Scopus_Alert_26_Septembre_2025

An integrated Bayesian hydrodynamic framework for quantifying rating curve uncertainty in design flood estimation and flood hazard mapping in a data-scarce urban area - Das, Tanmoy; Das, Subhasish (Water Research, 2026)
Keywords: Scopus_Alert_26_Septembre_2025

Short-Term Streamflow and Flood Forecasting Based on Graph Convolutional Recurrent Neural Network and Residual Error Learning - Pan, Xiyu; Mohammadi, Neda; Taylor, John E. (Journal of Computing in Civil Engineering, 2026)
Keywords: Scopus_Alert_26_Septembre_2025

Satellite-based monitoring and hazard assessment of multiple flooding types in the Haihe river Basin induced by the July 2023 extreme rainstorm - Xu, Ziteng; Yang, Wentao; Zhang, Xiya; Gu, Changjun; Shen, Lingling; Hu, Haibo (Journal of Hydrology, 2025)
Keywords: Scopus_Alert_26_Septembre_2025

Comprehensive performance assessment of state-of-the-art metaheuristic algorithms for multi-scenario reservoir flood control optimization - Wang, Wen-chuan; Tian, Wei-can; Zang, Hongfei; Zhang, Xu-tong (Results in Engineering, 2025)
Keywords: Scopus_Alert_26_Septembre_2025

Development of probabilistic flood forecast based on ensemble weather forecast and historical flood simulation database for resource-constrained area. Case study: Semarang City, Indonesia - Suwarman, Rusmawan; Farid, Mohammad; Abdillah, Muhammad Rais; Wahid, Ahmad Nur; Hadi, Tri Wahyu; Riawan, Edi; Fajary, Faiz Rohman; Simanjuntak, Yogi; Azizah, Siti; Sirait, Rinaldi; Adityawan, Mohammad Bagus; Roesbianto, Azman Syah Barran; et al. (Environmental Challenges, 2025)
Keywords: Scopus_Alert_26_Septembre_2025

Integrated use of the analytical hierarchy process method for mapping areas susceptible to flooding in the urban area in a city in southwest Bahia, Brazil - Oliveira Lima, Klaus Cardoso; Ferraz, Lorena Lima; Santana, Gregório Matheus; Gomes, Ronaldo Lima; Silva Santos, Carlos Amilton; Pereira, Thais Barros; De Jesus, Raildo Mota (Journal of South American Earth Sciences, 2025)
Keywords: Scopus_Alert_26_Septembre_2025

Critical overflow of the main combined sewer in the city of Lviv: A case study of recent extreme flooding - Matlai, Ivan; Popadiuk, Ihor; Vovk, Lesya; Zhuk, Volodymyr (Case Studies in Chemical and Environmental Engineering, 2025)
Keywords: Scopus_Alert_26_Septembre_2025

Comparative Flood Risk Mapping Using Knowledge-Driven, Data-Driven, and Ensemble Models in a Humid Tropical River Basin in India - Senan, Chandini Padmanabha Panicker Chandr; Ajin, Rajendran Shobha; Devi, B. R. Anaswara; Rajaneesh, A.; Nagar, Jitendra K.; Sajinkumar, K. S. (Water Conservation Science and Engineering, 2025)
Keywords: Scopus_Alert_26_Septembre_2025

Evidence of increasing coastal flood risk along mainland China from 1985 to 2050: Perspectives on climate change and urban expansion - Bian, Wanchao; Fang, Jiayi; Du, Shiqiang; Hu, Tangao (Ocean & Coastal Management, 2025)
Keywords: Scopus_Alert_26_Septembre_2025

Quantifying uncertainties in data and model: a prediction model for extreme rainfall events with application to Beijing subway - Mu, Liang; Kang, Yurui; Yan, Zixu; Yang, Xiaobao; Zhu, Guangyu (Accident Analysis & Prevention, 2025)
Keywords: Scopus_Alert_26_Septembre_2025

How do flood risk–socioeconomic interactions influence household income dynamics in shrinking cities of the Rust Belt in the US? - Kim, Young Eun; Lee, Jaekyung; Kim, Hyungkyoo (International Journal of Disaster Risk Reduction, 2025)
Keywords: Scopus_Alert_26_Septembre_2025

Climate change-driven coastal flooding in the Mid Adriatic Sea and adaptation of coastal defense structures - Baldoni, Agnese; Marini, Francesco; Filomena, Giorgio; Parlani, Stefano; Brocchini, Maurizio (Estuarine, Coastal and Shelf Science, 2025)
Keywords: Scopus_Alert_26_Septembre_2025

Advancing Flood Detection and Mapping: A Review of Earth Observation Services, 3D Data Integration, and AI-Based Techniques - Destefanis, Tommaso; Guliyeva, Sona; Boccardo, Piero; Fissore, Vanina (Remote Sensing, 2025)
Floods are among the most frequent and damaging hazards worldwide, with impacts intensified by climate change and rapid urban growth. This review analyzes how satellite-based Earth Observation (EO) technologies are evolving to meet operational needs in flood detection and water depth estimation, with a focus on the Copernicus Emergency Management Service (CEMS) as a mature and widely adopted European framework. We compare the capabilities of conventional EO datasets—optical and Synthetic Aperture Radar (SAR)—with 3D geospatial datasets such as high-resolution Digital Elevation Models (DEMs) and Light Detection and Ranging (LiDAR). While 2D EO imagery is essential for rapid surface water mapping, 3D datasets add volumetric context, enabling improved flood depth estimation and urban impact assessment. LiDAR, in particular, can capture microtopography between high-rise structures, but its operational use is constrained by cost, data availability, and update frequency. We also review how artificial intelligence (AI), including machine learning and deep learning, is enhancing automation, generalization, and near-real-time processing in flood mapping. Persistent gaps remain in model transferability, uncertainty quantification, and the integration of scarce high-resolution topographic data. We conclude by outlining a roadmap towards hybrid frameworks that combine EO observations, 3D datasets, and physics-informed AI, bridging the gap between current technological capabilities and the demands of real-world emergency management.
Keywords: Scopus_Alert_26_Septembre_2025

Digital Twin Technology for Urban Flood Risk Management: A Systematic Review of Remote Sensing Applications and Early Warning Systems - Hlal, Mohammed; Baraka Munyaka, Jean-Claude; Chenal, Jérôme; Azmi, Rida; Diop, El Bachir; Bounabi, Mariem; Ebnou Abdem, Seyid Abdellahi; Almouctar, Mohamed Adou Sidi; Adraoui, Meriem (Remote Sensing, 2025)
Digital Twin (DT) technology has emerged as a transformative tool in urban flood risk management (UFRM), enabling real-time data integration, predictive modeling, and decision support. This systematic review synthesizes existing literature to evaluate the scientific impact, technological advancements, and practical applications of DTs in UFRM. Using the PRISMA 2020 framework, we retrieved 1085 records (Scopus = 85; Web of Science = 1000), merged and deduplicated them using DOI and fuzzy-matched titles, screened titles/abstracts, and assessed full texts. This process yielded 85 unique peer-reviewed studies published between 2018 and 2025. Key findings highlight the role of remote sensing (e.g., satellite imagery, IoT sensors) in enhancing DT accuracy, the integration of machine learning for predictive analytics, and case studies demonstrating reduced flood response times by up to 40%. Challenges such as data interoperability and computational demands are discussed, alongside future directions for scalable, AI-driven DT frameworks. This review identifies key technical and governance challenges while recommending the development of modular, AI-driven DT frameworks, particularly tailored for resource-constrained regions.
Keywords: Scopus_Alert_26_Septembre_2025

Enhancing Preparedness and Response to Flood Disasters: A Scoping Review of Training Activities for Response Staff - Kokoç, Mehmet; Şimşek, Perihan; Gündüz, Abdülkadir; Akyıldız, Salih (Disaster Medicine and Public Health Preparedness, 2025)
Abstract Objective This study examines the scope and trends of empirical research on training activities for flood disaster response teams. Methods A scoping review, adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines, was conducted in June 2024 across four electronic databases and grey literature. The analysis included experimental and quasi-experimental studies published since 2005. An initial pool of 1193 studies was identified, 18 met the eligibility criteria and were included in the final analysis. Results These studies revealed three key themes: 1) evaluation and improvement of operational procedures, 2) preparation of response teams, and 3) management of health-related issues. Conclusion The results highlight the limited availability of empirical evidence in this area, reflecting the relatively small number of studies focusing specifically on training activities for flood disaster response teams. However, research in this field has shown growth since 2010, with a notable integration of educational technologies in most studies. Key training topics include psychological first aid and the evaluation of operational plans. Given the increasing frequency and severity of flood disasters due to global warming, further research is essential to develop and implement effective training programs, enabling response teams to address such emergencies more efficiently and comprehensively.
Keywords: Scopus_Alert_26_Septembre_2025

Towards wildfire risk reduction goals and targets for Europe – Opportunities and challenges - Berchtold, C.; Petersen, K.; Kaskara, M.; Pettinari, M.L.; Vinders, J.; Schlierkamp, J.; Kalapodis, N.; Sakkas, G.; Brunet, P.; Soldatos, J.; Lazarou, A.; Casciano, D.; et al. (Climate Risk Management, 2025)
Keywords: Scopus_Alert_26_Septembre_2025

Nature-Based Solutions for Urban Drainage: A Systematic Review of Sizing and Monitoring Methods - Cansian, André Ricardo; Guzmán, Diego A.; Rosa, Altair; De Toledo Machado, Juliana (Water, 2025)
Urban areas face escalating hydrological risks due to climate change, urban sprawl, and aging stormwater infrastructures. In this context, Nature-Based Solutions (NbSs), especially Sustainable Urban Drainage Systems (SUDSs), have emerged as viable strategies to enhance water resilience and sustainability. However, the literature still lacks standardized and scalable methodologies for their design and performance monitoring. This study conducts a systematic review following the PRISMA protocol, combined with bibliometric and co-occurrence analyses, to identify prevailing approaches in the sizing and monitoring of NbS-based SUDSs. Based on the peer-reviewed literature indexed in Scopus and Web of Science from 2020 to 2024, the findings reveal an increasing integration of hydrological modeling with artificial intelligence, remote sensing, and IoT-based real-time monitoring. Despite this progress, challenges remain in methodology validation, data availability, and system adaptability. The review underscores the need for hybrid, context-sensitive frameworks that integrate empirical and simulated data to support decision-making in urban drainage planning and management.
Keywords: Scopus_Alert_26_Septembre_2025

Network Governance: What Two Decades of Social and Institutional Network Research Tell Us About Flood Risk Management - Ziga‐Abortta, Fafali R.; Espinosa, Julio César Millán; Ziervogel, Gina; Kruse, Sylvia (WIREs Water, 2025)
ABSTRACT Despite increasing scholarly attention to flood risk management (FRM), there remains limited systematic understanding of how network governance arrangements influence FRM outcomes. This knowledge gap is particularly critical given the frequency and severity of floods, causing significant socio‐economic and environmental damages globally. Through a systematic literature review analyzing 38 peer‐reviewed publications from 2000 to 2023, this paper investigates network governance in FRM using a three‐component analytical framework examining actor constellations, operational activities, and governance outcomes. Our analysis explores the nature of network actor constellations in FRM research, the exchanges characterizing their operational activities, and the resultant governance outcomes that emerge from their interactions. The findings identify distinct network configurations: formal state agencies at different governance scales, informal local networks and bottom‐up initiatives, and hybrid constellations incorporating both state and non‐state actors. Operational activities are shaped by complex interdependencies including altruistic support, resource exchanges, knowledge flows, power dynamics, formal collaborations, and trust‐based sociability. Our results reveal both desired outcomes, such as social learning and innovation, and undesired outcomes stemming from structural weaknesses and pragmatic constraints. For improving FRM practice and governance outcomes, we suggest, where possible, shifting more responsibility to highly effective non‐state actors, managing negative interdependencies, and leveraging network governance throughout FRM phases. Our findings provide practical guidance for policymakers and practitioners for optimizing FRM governance arrangements while contributing theoretically to understanding how different network types influence flood resilience outcomes across developed and developing contexts. This article is categorized under: Human Water > Water Governance
Keywords: Scopus_Alert_26_Septembre_2025

Understanding South Africa’s Flood Vulnerabilities and Resilience Pathways: A Comprehensive Overview - Byaruhanga, Nicholas; Kibirige, Daniel; Mkhonta, Glen (Water, 2025)
This review examines South Africa’s escalating flood vulnerability through a synthesis of over 80 peer-reviewed articles, historical records, policy reports, and case studies. Using a PRISMA-guided analysis, the study identifies key climatic drivers, including extreme rainfall from tropical–temperate interactions, cut-off lows, and La Niña conditions that interact with structural weaknesses such as inadequate drainage, poorly maintained stormwater systems, and rapid urban expansion. Apartheid-era spatial planning has further entrenched risk by locating marginalised communities in floodplains. Governance failures like weak disaster risk reduction (DRR) policies, fragmented institutional coordination, and insufficient early warning systems intensify flood vulnerabilities. Catastrophic events in KwaZulu-Natal (KZN) and the Western Cape (WC) illustrate the consequences exemplified by the April 2022 KZN floods alone, which caused over 450 deaths, displaced more than 40,000 people, and generated damages exceeding ZAR 17 billion. Nationally, more than 1500 flood-related fatalities have been documented in the past two decades. Emerging resilience pathways include ecosystem-based adaptation, green infrastructure, participatory governance, integration of Indigenous knowledge, improved hydrological forecasting, and stricter land-use enforcement. These approaches can simultaneously reduce physical risks and address entrenched socio-economic inequalities. However, significant gaps remain in spatial flood modelling, gender-sensitive responses, urban–rural disparities, and policy implementation. The review concludes that South Africa urgently requires integrated, multi-scalar strategies that combine scientific innovation, policy reform, and community-based action. Embedding these insights into disaster management policy and planning is essential to curb escalating losses and build long-term resilience in the face of climate change.
Keywords: Scopus_Alert_26_Septembre_2025

Impacts of a Reduced AMOC on the South America Mean Climate and Extremes - Meccia, Virna L.; Blázquez, Josefina (Journal of Geophysical Research: Atmospheres, 2025)
Abstract The Atlantic meridional overturning circulation (AMOC) plays a crucial role in regulating global and regional climate by distributing heat meridionally across the planet. Future projections indicate a weakening of the AMOC in response to increased greenhouse gas concentrations. In this study, we investigate the impact of AMOC weakening on South America's mean climate, as well as temperature and precipitation extremes. We analyze targeted sensitivity experiments using the EC‐Earth3 climate model. Our findings reveal that AMOC weakening induces cooling and wetting in the tropics of South America, contrasted by warming and dryness in the extratropics. This pattern weakens the meridional temperature gradient, shifts the intertropical convergence zone and the high‐latitude winter and summer jets southward, and reduces midlatitude storm tracks, particularly in austral winter. We analyze extreme events in two representative regions—La Plata Basin and northeast Brazil—which exhibit contrasting responses. In La Plata Basin, summer heat waves become more frequent, intense, and prolonged, while winter cold spells occur more frequently but with decreased intensity and duration when the AMOC reduces. In contrast, northeast Brazil experiences an increase in the frequency and intensity of heat waves, while cold spells become less frequent but more intense. Extreme precipitation events weaken in the La Plata Basin but intensify in northeast Brazil in both seasons under AMOC weakening. Some of these climate impacts may exacerbate global warming effects, while others may partially offset them. These findings underscore the critical need to refine climate models' representation of AMOC dynamics to improve predictions of future climate scenarios. , Plain Language Summary The Atlantic meridional overturning circulation (AMOC) helps to regulate the Earth's climate by transporting heat around the globe. Scientists expect it to weaken in the future due to global warming. This study looks at how a weaker AMOC could affect South America's climate, including temperature and rainfall extremes. Using a climate model, the researchers found that a weaker AMOC would make tropical South America cooler and wetter, while the extratropical regions would get warmer and drier. This change is associated with modifications in weather patterns like jet streams and storm tracks. They focused on two important regions: the La Plata Basin and northeast Brazil to analyze extreme events. In the La Plata Basin, summers would have more intense and longer‐lasting heat waves, while winters would have more frequent but milder cold spells. Northeast Brazil, on the other hand, would see stronger and more frequent heat waves, and less frequent but harsher cold spells. Rainfall extremes would decrease in the La Plata Basin but increase in northeast Brazil. Some of these changes could make global warming amplificated, while others might ease its effects a bit. The study highlights the importance of improving how climate models simulate AMOC to better predict future climate changes. , Key Points The Atlantic meridional overturning circulation is projected to weaken, and this affects the South America mean climate and extreme events A weaker AMOC would induce cooling and wetting in tropical South America, whereas warming and drying in the extratropics Some climate impacts of a weakened AMOC in South America may amplify the effects of global warming, while others could counteract them
Keywords: Scopus_Alert_26_Septembre_2025

Représentations des risques d’inondation, adaptation et évacuation en Île-de-France - Rufat, Samuel; Comby, Emeline; Santoni, Victor; Plattard, Odile; Lhomme, Serge; Enderlin, Eric (Cybergeo, 2025)
L’étude des déterminants des représentations liées aux risques et leurs effets sur les niveaux de préparation des populations exposées conduit à des conclusions souvent contradictoires d’une étude de cas à une autre. De nombreuses théories et hypothèses concurrentes ont été formulées dans des disciplines différentes, mais aucune n’est parvenue à s’imposer empiriquement. La validation croisée des résultats est complexe et le champ des risk perception s’apparente souvent à une collection d’études de cas indépendantes et peu comparables. À partir d’une enquête par questionnaire sur les représentations liées au risque inondation à Paris et en Île-de-France sur un échantillon de grande taille (n=2976), cet article propose une réflexion théorique et méthodologique pour consolider le positionnement de la géographie dans le champ des représentations des risques, pour mieux démêler les facteurs sociaux et territoriaux. Les questions de recherche se structurent autour du rôle de l’exposition aux aléas, de l’expérience préalable, des situations de vulnérabilité et des territoires de résidence sur les représentations puis sur les comportements. L’hypothèse principale est que les résultats contradictoires publiés dans la littérature internationale pour cerner les déterminants de ces représentations découlent d’approches disciplinaires et de théories divergentes, ainsi que de la place parfois peu assurée de la géographie dans le champ international des risk perception. , The study of risk perception determinants and their impacts on the levels of preparedness of exposed populations often leads to inconclusive results across case studies. Numerous competing theories and hypotheses have been formulated across different disciplines, but their empirical validation remains a challenge. The current fragmentation of research on risk perception hampers the cross-validation of previous results, which might make the field appear as a collection of independent case studies in some instances. We use a flood risk perception and emergency behaviour survey in Paris, France(n=2976), to discuss a theoretical and methodological approach to consolidating the position of geography in the field, better disentangling social and spatial factors. The research questions are structured around the role of hazard exposure, prior experience, place of residence, and social vulnerability on risk perception and emergency behaviour. The main hypothesis is that previous inconclusive results published in the literature on risk perception determinants stem from divergent disciplinary approaches and theories, as well as from the at times uncertain place of geography in the field. , El estudio de las determinantes de la percepción del riesgo y sus efectos sobre los niveles de preparación de la población expuesta, entregan resultados contradictorios según el caso analizado. Si bien han sido propuestas una amplia gama de hipótesis y teorías, éstas restan contrapuestas entre disciplinas y ninguna ha podido prevalecer empíricamente. La validación cruzada de los resultados resulta compleja, y el campo de la percepción del riesgo se asocia generalmente a una colección de estudios de casos independientes y con una baja capacidad de hacerlos comparables. A partir de una encuesta realizada a 2976 personas referente a las representaciones del riesgo de inundación aplicada en la ciudad de París y en la región de Île-de-France, este artículo propone un enfoque teórico y metodológico en vías de consolidar la posición de la geografía en el campo de las representaciones del riesgo, con el fin de dilucidar de manera más robusta los factores sociales y territoriales. Las preguntas de investigación se estructuran en torno al rol de la exposición al peligro, la experiencia previa, las situaciones de vulnerabilidad, el lugar de residencia en las representaciones y los comportamientos. La hipótesis principal expresa que los resultados publicados en la literatura internacional para identificar las determinantes de estas representaciones son contradictorios, lo cuales derivan tanto de enfoques y teorías disciplinariamente divergentes, como del rol en ocasiones incierto que toma la geografía en el ámbito internacional de la percepción del riesgo.
Keywords: Scopus_Alert_26_Septembre_2025

Comparison of Factors of Spatiotemporal Variability of 7-Day Low-Flow Timing in Southern Quebec - Assani, Ali Arkamose (Atmosphere, 2025)
The objective of this article is to analyze the impacts of climatic, physiographic, and land use/cover factors on the spatiotemporal variability of 7-day low-flow occurrence dates for 17 rivers during the period 1950–2023 in winter and summer in southern Quebec. Regarding spatial variability, correlation analysis revealed that these occurrence dates are primarily negatively correlated with agricultural surface area (early occurrence) during both seasons. In winter, they are also negatively correlated with total rainfall and daily mean maximum temperatures, but positively correlated with forest area and mean watershed slopes. Regarding temporal variability, the application of three Mann–Kendall tests showed that in summer, 7-day low flows tend to occur late in the season due to increased rainfall, particularly in the most agricultural watersheds. In contrast, in winter, very few significant changes were observed in the long-term trend of the analyzed hydrological series. Correlation analysis using redundancy analysis between eight climate indices and the occurrence dates of 7-day low flows showed that in summer, these dates are positively correlated with the global warming climate index, while they are not correlated with any climate index in winter. This study demonstrated that the spatiotemporal variability of the occurrence dates and magnitude of 7-day low flows are not influenced by the same factors in southern Quebec, except for the global warming climate index in summer. Finally, this study shows that the timing is much less sensitive to changes in climate change than the magnitude of low flows in southern Quebec.
Keywords: Infolettre_Octobre_2025

Prediction of flood susceptibility in an inter-fluvial region of Northern India using machine learning algorithms - Ghosh, Arijit; Siddiqui, Azizur Rahman (Natural Hazards Research, 2024)
Keywords: Scopus_Alert_19_Septembre_2025

From vulnerability to resilience: Addressing the causes, impacts, and solutions for recurrent flash floods in the Philippines - Rivera, Alice T.; Dela Vega, Jim Boy G. (Tropical Cyclone Research and Review, 2025)
Keywords: Scopus_Alert_19_Septembre_2025

How Responsive Are Mortgage Lending Conditions to Flood Risk? The Case of the Netherlands - Götz, Laura Naomi; Mager, Ferdinand; Zietz, Joachim (European Financial Management, 2025)
ABSTRACT We study the link between flood risk and mortgage lending in the Netherlands, a country where approximately 60% of the population resides in flood‐prone areas. Using over 100,000 securitised mortgages issued from 2013 to 2023, our study concludes that credit terms are indistinguishable between areas with and without flood risk. When we use the 2021 river flood disaster in a diff‐in‐diff setting, our results suggest that lenders temporarily (for about 6 months) raised interest premiums and partially reduced LTVs. It appears that regulatory mandates are needed if central banks want commercial banks to price climate risk into their lending.
Keywords: Scopus_Alert_19_Septembre_2025

Modelling flood map during Alex hurricane: Simulation enforced by multisensory precipitation in the city of Monterrey, Mexico - Department of Civil and Environmental Engineering, Lamar University, Beaumont, Texas, USA; Stella, Juan M. (Tecnología y ciencias del agua, 2025)
Alex hurricane was one of the most intense tropical cyclones in the North Atlantic that caused fatalities and loses in the Northeast of Mexico due to the flash floods. Flood hazard mapping is a vital tool to assess inundation areas, which can be simulated using hydraulic and hydrologic models. This study describes the modelling of a flood event during Alex hurricane in the Santa Catarina River Watershed, Northeast of Mexico, applying HEC-HMS and two dimensional (2D) HEC-RAS models forced with Multi Radar Multi Sensor-Quantitative Precipitation Estimation (MRMS-QPE). A HEC-HMS model was developed forced by (MRMS-QPE) as input to simulate discharges along the Santa Catarina River. The simulated discharges were introduced as border conditions along the mainstream of the Santa Catarina River inside a HEC-RAS 2D model to simulate a flood map along the mainstream of the Santa Catrina River. The observed against the simulated peak discharges achieved a r2 of 0.97 and a Nash-Sutcliffe coefficient of 0.97. The observed against the simulated accumulated discharges achieved a r2 of 0.99 and a Nash-Sutcliffe coefficient of 1.0. The observed against the simulated stages achieved a r2 of 0.74 and, a Nash-Sutcliffe coefficient of 0.68. The use of HEC-HMS and HEC-RAS 2D models coupled with MRMS-QPE shows that these models are user friendly to setup, the model has stability and the capacity to simulate flood maps along the whole mainstream of the Santa Catarina River with good results.

Farmers' Vulnerability to Floods in Ghana - Yorose, W. A.; Boatbil, C. S.; Apwah, F. (AFRICAN JOURNAL OF APPLIED RESEARCH, 2025)
Purpose: This study assesses the degree of farmers’ vulnerability to floods in the Upper East Region of Ghana. It also determined the adaptation strategies employed by farmers to cope with the floods. Design/Methodology/Approach: The mixed-methods research design was employed for this study. A multi-stage sampling technique was employed to select 343 farmers. Data was collected using a questionnaire, an in-depth interview guide and a focus group discussion guide. The data gathered were analysed using both quantitative and qualitative methods. Descriptive statistics, cross-tabulations, and chi-square tests of independence were used to present quantitative data, while qualitative data were transcribed and analysed manually using thematic analysis. Research Limitation: The study was limited by its inability to constitute a representative sample through random sampling procedures, which may affect the generalizability of the findings. Findings: The results showed that farmers were highly vulnerable to floods. It was also revealed that floods in the study area have occurred more often than ten years ago. Additionally, a statistically significant relationship was found between educational level and vulnerability to floods in the study area. Practical Implication: The study's findings provide actionable strategies for coping with floods in the study area and beyond. Social Implication: The study emphasises how efficient flood adaptation in the study area may have a positive impact on farmers’ livelihoods. Originality/Value: The uniqueness of this paper aligns with the on-going debate about the threat of floods in Ghana, which has led to the destruction of household assets and ecosystems.
Keywords: Scopus_Alert_19_Septembre_2025

Modelling flood map during Alex hurricane: Simulation enforced by multisensory precipitation in the city of Monterrey, Mexico - Department of Civil and Environmental Engineering, Lamar University, Beaumont, Texas, USA; Stella, Juan M. (Tecnología y ciencias del agua, 2025)
Alex hurricane was one of the most intense tropical cyclones in the North Atlantic that caused fatalities and loses in the Northeast of Mexico due to the flash floods. Flood hazard mapping is a vital tool to assess inundation areas, which can be simulated using hydraulic and hydrologic models. This study describes the modelling of a flood event during Alex hurricane in the Santa Catarina River Watershed, Northeast of Mexico, applying HEC-HMS and two dimensional (2D) HEC-RAS models forced with Multi Radar Multi Sensor-Quantitative Precipitation Estimation (MRMS-QPE). A HEC-HMS model was developed forced by (MRMS-QPE) as input to simulate discharges along the Santa Catarina River. The simulated discharges were introduced as border conditions along the mainstream of the Santa Catarina River inside a HEC-RAS 2D model to simulate a flood map along the mainstream of the Santa Catrina River. The observed against the simulated peak discharges achieved a r2 of 0.97 and a Nash-Sutcliffe coefficient of 0.97. The observed against the simulated accumulated discharges achieved a r2 of 0.99 and a Nash-Sutcliffe coefficient of 1.0. The observed against the simulated stages achieved a r2 of 0.74 and, a Nash-Sutcliffe coefficient of 0.68. The use of HEC-HMS and HEC-RAS 2D models coupled with MRMS-QPE shows that these models are user friendly to setup, the model has stability and the capacity to simulate flood maps along the whole mainstream of the Santa Catarina River with good results.
Keywords: Scopus_Alert_19_Septembre_2025

NLP-driven crowdsourcing for urban flood monitoring: insights from mumbai - Navalkar, Aniket; Tripathy, Shrabani S.; Gupta, Mayank; Basu, Sanghita; Tripathy, Puja; Banerjee, Archismita; Sekharan, Sheeba; Murtugudde, Raghu; Ghosh, Subimal (Sustainable Cities and Society, 2025)
Keywords: Scopus_Alert_19_Septembre_2025

Assess the impacts of climatic change and human activities on streamflow and floods by using a hybrid-physics-data (HPD) model: A case study in the Lancang-Mekong River Basin - Liu, Binxiao; Yun, Xiaobo; Pan, Baoxiang; Xu, Ximeng; Gaffney, Paul P.J.; Lu, Hui; Luo, Lifeng; Sun, Gaohu; Tang, Qiuhong (Journal of Hydrology: Regional Studies, 2025)
Keywords: Scopus_Alert_19_Septembre_2025

A multi-scenario framework for quantifying flood hazard and exposure accounting for runoff-driven uncertainty in global flood models - Parmar, Jayesh; Karmakar, Subhankar (Science of The Total Environment, 2025)
Keywords: Scopus_Alert_19_Septembre_2025

Assessment of flood risk using integrated GIS and analytic hierarchy process in the Mekerra basin, Northwestern Algeria - Berrezel, Yacine Abdelbaset; Abdelbaki, Cherifa; Megnounif, Abdesselam; Saber, Mohamed; Benabdelkrim, Mohammed El Amin; Kumar, Navneet (Journal of African Earth Sciences, 2026)
Keywords: Scopus_Alert_19_Septembre_2025

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

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

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

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

Climate changes and cancer risk: Key factors and emerging health threats - Sotomayor, Marina Rayciki; Campos, Aline Graciele Henriques; Filho, Altair Rodrigues Pires De Paula; Goulart, Ana Carolina Aredes; Vacario, Beatriz Geovana Leite; Orrutéa, Julia Fernandes Gois; Valentim, Juliana Maria Bitencourt De Morais; Fagundes, Tatiane Renata; Gaboardi, Shaiane Carla; Panis, Carolina (Hygiene and Environmental Health Advances, 2025)
Keywords: Scopus_Alert_12_Septembre_2025

Multi-objective optimization of nature-based solutions in urban stormwater management: A scoping review - Bista, A.; Paus, K.A.H.; Seifert-Dähnn, I. (Environmental Modelling & Software, 2025)
Keywords: Scopus_Alert_12_Septembre_2025

Natural Disasters and Rehabilitation: Post‐Disaster Aid, Corruption, Misallocation, and Mistargeting - Ahmad, Muhammad Irshad; Shen, Qiong; Boota, Muhammad Waseem; Liu, Ruifeng; Ma, Hengyun (Sustainable Development, 2025)
ABSTRACT The increasing frequency of natural disasters, such as floods, droughts, and tsunamis, has made vulnerable communities less resilient, pushing them toward long‐term poverty and food insecurity. Effective post‐disaster rehabilitation is critical to restoring livelihoods, infrastructure, and food security. However, challenges such as corruption, misallocation, and mistargeting undermine post‐disaster aid programs. This study systematically reviews 86 peer‐reviewed articles (1990–2023) using the preferred reporting items for systematic reviews and meta‐analyses (PRISMA) protocol to investigate aid inefficiencies in disaster recovery. The findings reveal that aid often fails to reach the most affected communities, being diverted to unaffected areas due to political influence and local elites, exacerbating inequalities. Corruption further hampers institutional performance and long‐term disaster resilience efforts. The study calls for transparent, accountable, and inclusive strategies for aid distribution, aligning with SDG 10 (reduced inequalities) and SDG 11 (sustainable cities and communities). Future research should focus on gender‐sensitive strategies, local governance, and technological innovations to enhance aid transparency and effectiveness.
Keywords: Scopus_Alert_12_Septembre_2025

Determinants and relationships of climate change, climate change hazards, mental health, and well-being: a systematic review - Rückle, Karolin; Rohrer, Mario; Mihók, Barbara; Johansson, Maria; Andersson, Hanna; Pomee, Muhammad Saleem; Vergadi, Eleni; Rouva, Glykeria; Agrawal, Apoorv; Balázs, Balint; Brattich, Erika; Carelli, Maria; et al. (Frontiers in Psychiatry, 2025)
Introduction Impacts of climate change on human health receive increasing attention. However, the connections of climate change with well-being and mental health are still poorly understood. Objective As part of the Horizon Europe project TRIGGER, we aim to deepen the understanding of the relationships between climate change and human mental health and well-being in Europe by focusing on environmental and socio-individual determinants. Methods This study is a systematic literature review based on the PRISMA guidelines using Embase, Medline and Web of Science. Results 143 records were retrieved. The results show that climate change and its specific hazards (air pollution, floods, wildfires, meteorological variables, and temperature extremes) impact human well-being and mental health. Discussion Mental health and well-being outcomes are complex, extremely individual, and can be long lasting. Determinants like the living surrounding, human’s life activities as well as socio-individual determinants alter the linkage between climate change and mental health. The same determinant can exert both a pathogenic and a salutogenic effect, depending on the outcome. Knowing the effects of the determinants is of high relevance to improve resilience. Several pathways were identified. For instance, higher level of education and female gender lead to perceiving climate change as a bigger threat but increase preparedness to climate hazards. Elderly, children and adolescents are at higher risks of mental health problems. On the other hand, social relation, cohesiveness and support from family and friends are generally protective. Green and blue spaces improve well-being and mental health. Overall, comparing the different hazard-outcome relationships is difficult due to varying definitions, measurement techniques, spatial and temporal range, scales, indicators and population samples. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/home , identifier CRD42023426758.
Keywords: Scopus_Alert_12_Septembre_2025

Identifying challenges and future directions of flood hazards mitigation strategies in health facilities: a systematic literature review - Arefi, Farhad; Tavan, Asghar; Moradi, Seyed Mobin; Daneshi, Salman; Farahmandnia, Hojjat (BMC Emergency Medicine, 2025)
Keywords: Scopus_Alert_12_Septembre_2025

Flood inundation amplified by large-scale ground subsidence funnel under the ongoing global climate change - He, Liming; Yin, Yanzhuang; Cai, Jiuyang; Wu, Lixin; Tang, Yongliang; He, Ronghua; Qu, Zhenglin; Liu, Yu; Pei, Panke; Wang, Qiao (Communications Earth & Environment, 2025)
Keywords: Scopus_Alert_12_Septembre_2025

The 2021 Henan flood increased citizen demand for government-led climate change adaptation in China - Shen, Shiran Victoria (Communications Earth & Environment, 2025)
Keywords: Scopus_Alert_12_Septembre_2025

Enhancing management of flood forecasting in Southern New Jersey: a HEC-HMS model development for Maurice River and Raccoon Creek Watersheds - Chowdhury, Rumman M.; Ahn, Jeong; Torlapati, Jagadish; Jahan, Kauser (Applied Water Science, 2025)
Keywords: Scopus_Alert_12_Septembre_2025

Quantitative assessment of exacerbating effect on urban inundation when river flood encounters heavy rainfall in an urbanized watershed - Zhang, Haijia; Liu, Jiahong; Zhou, Jinjun; Mei, Chao; Wang, Jia; Song, Tianxu (Science China Technological Sciences, 2025)
Keywords: Scopus_Alert_12_Septembre_2025

Integrating hydrological and economic assessments of soil infiltration enhancement for urban flood resilience - Ren, Xingwei; Guo, Zeyu; Li, Qingli; Zhou, Zhengzheng; Chen, Xin; Liao, Jiajie (Science of The Total Environment, 2025)
Keywords: Scopus_Alert_12_Septembre_2025

Enhanced spatiotemporal prediction of rainfall erosivity through flood season data integration - Qi, Fei; Wang, Yunpeng; Xu, Luyue; Liu, Xia; Zhang, Chunqiang; Fan, Zhaofei; Guo, Jiayu; Yang, Xing (CATENA, 2025)
Keywords: Scopus_Alert_12_Septembre_2025

Predicting current habitat suitability for intermediate snail hosts of urogenital and intestinal schistosomiasis in the Lower Shire Valley floodplain of southern Malawi - Nkolokosa, Clinton; Chirombo, James; Jones, Christopher M.; Mbewe, Rex B.; Kambewa, Eggrey Aisha; Makaula, Peter; Tangena, Julie-Anne Akiko; Stothard, J. Russell (Parasites & Vectors, 2025)
Abstract Background Relating the geographical distribution of intermediate freshwater snail hosts (viz. vectors of schistosomes) to local environmental attributes offers value for understanding the epidemiological landscape of schistosomiasis transmission in a changing aquatic environment. Schistosomiasis—both urogenital and intestinal—causes significant human suffering, affecting approximately 240 million people globally and grouped within the neglected tropical disease (NTD) umbrella. This study addresses the following questions: 1. Where are the most suitable habitats for intermediate host snails in the Lower Shire Valley (LSV) in Malawi? 2. Which environmental factors are strongly associated with the geographical distribution of such snails in the LSV? Methods This paper presents the first species distribution models (SDMs) for intermediate snail hosts for urogenital and intestinal schistosomiasis in Chikwawa and Nsanje Districts, which together form the LSV). The SDMs developed for this study are ensemble machine learning approaches based on Random Forest (RF), Support Vector Machines (SVM), and multilayer perceptron (MLP) and are specific to the Bulinus africanus group and Biomphalaria pfeifferi . The former transmits urogenital schistosomiasis ( Schistosoma haematobium ), while the latter transmits intestinal schistosomiasis ( Schistosoma mansoni ). Results The SDMs reveal the following: 1) currently, Bu. africanus group not only has a wide distribution across central Chikwawa and eastern Nsanje but is also concentrated in floodplains, and the LSV has few habitats that can support Bi. pfeifferi , and 2) vegetation cover is the most important predictor of Bu. africanus group distribution, whereas precipitation variables are most important for Bi. pfeifferi in the LSV. Thus, Bu. africanus group habitat is the most dominant and abundant, while Bi. pfeifferi suitable habitat is patchy and scarce. Conclusion The distribution of suitable habitats for potential urogenital and intestinal schistosomiasis transmission across LSV is not uniform and typically non-overlapping. Understanding the spatial and temporal distributions of these snails is important for controlling and eliminating schistosomiasis. Graphical Abstract
Keywords: Scopus_Alert_12_Septembre_2025

Rapid simulation for real-time flood depth prediction using support vector machine - Kim, Beom-Jin; Kim, Minkyu; Yoo, Jaehwan; Kim, Byunghyun (Scientific Reports, 2025)
Keywords: Scopus_Alert_12_Septembre_2025

Market-based instruments to fund nature-based solutions for flood risk management can disproportionately benefit affluent areas - Hill, Bartholomew; Marjoribanks, Tim; Moore, Harriet; Bosher, Lee; Gussy, Mark (Communications Earth & Environment, 2025)
Abstract Market-based instruments, including competitive tenders, are central to funding global environmental restoration and management projects. Recently, tenders have been utilised to fund Nature-based Solutions schemes for Natural Flood Management, with the explicit purpose of achieving co-benefits; flood management and reducing inequities. While multiple studies consider the efficacy of Nature-based Solutions for tackling inequities, no prior research has quantified whether the resource allocation for these projects has been conducted equitably. We analyse two national natural flood management programmes funded through competitive tenders in England to explore who benefits by considering the characteristics of projects, including socio-economic, geographical (e.g. rurality) and flood risk dynamics. Our results suggest that inequity occurs at both the application and funding stages of Nature-based Solutions projects for flood risk management. This reflects wider international challenges of using market-based instruments for environmental resource allocation. Competitive tenders have the potential to undermine the equitable benefits of Nature-based Solutions.
Keywords: Scopus_Alert_12_Septembre_2025

Urban growth or urban risk? Unraveling the flood paradox in Kumasi (Ghana) through the lens of natural factors and spatial decision modeling - Liwur, Stephen Biliyitorb; Tagnan, Jacob Nchagmado; Asamoah, Pearl Seyram (Journal of African Earth Sciences, 2025)
Keywords: Scopus_Alert_12_Septembre_2025

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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