Climate change has significantly intensified flood regimes in recent decades. While traditional flood management emphasizes disaster prevention through structural interventions, it often overlooks the ecological functions of flood pulses. This study first explores how flood frequency and phases affect riverine habitat quality, integrating two-dimensional hydrodynamic modeling with ecological habitat assessment. Using the Qingyi River as a case study, we quantified the habitat responses of multiple aquatic organisms under different flood frequencies and different hydrological stages. Results show that moderate floods (with a 10-year return period) enhanced fish habitat functionality (Weighted Usable Area, WUA) by up to 120% compared to multi-year average discharge, primarily by expanding shallow zones and improving lateral connectivity. Fish species reached peak habitat suitability during flood crests, while Benthic organisms favored recession phases, revealing phase-specific ecological responses. To further account for flood risks-often neglected in ecological evaluations-we introduced a Flood Assessment Method Coupling Ecology and Risk (FAMCER) and in this method we proposed the Flood Efficiency Index (FEI) to integrate ecological gains and hazard costs. This index identifies trade-offs between ecological benefits and flood risks, showing that 10-year floods achieve an optimal balance in our case study. The flexible FEI-based framework enables targeted evaluation across different conservation priorities and supports adaptive decision-making for ecological flow releases and floodplain restoration. These findings highlight the need to shift from purely defensive flood control to integrated flood management that balances ecological enhancement with risk mitigation.

ABSTRACT Changes in climate patterns and land use due to human activities and carbon emissions are driving and changing the frequency of flooding. Moreover, the ensemble method demonstrates reliable efficiency in preparing flood vulnerability maps when integrated with climate and land-use data. In exploring the impact of climate change and land-use changes in the future (2050) on future flood risk, the general circulation model (GCM) with representative concentration pathways of the 2.6 and 8.5 scenarios by 2050 was adopted to understand the impact on eight variable rainfall. The CA–Markov model was also applied to future land use in 2050. To validate it, the receiver operating characteristic–area under the curve (AUC) statistical analysis and other statistical analyses were carried out. The ensemble model showed a good AUC value (0.99) and consistent results across other statistical validation indices, outperforming standalone models. The areas with moderate to very high risk of flooding will increase in 2050. The proportion from the current distribution to 2050 in the RCP 2.6 scenario changes in the ensemble model. However, this change is more significant in the RCP 8.5 than the current scenario. The integration of ensemble modeling, climate projections, and land-use simulations provides a novel framework for improving future flood assessment and management strategies.

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

Abstract. Rapid and accurate flood assessment is crucial for effective disaster response, rehabilitation, and mitigation strategies. This study presents a fully automated framework for floodwater delineation and depth estimation using the Earth Observation Satellite 4 (EOS-04) (Radar Imaging Satellite, RISAT-1A) synthetic aperture radar (SAR) imagery and a digital elevation model (DEM). This is the first study to apply the established automatic-tile-based segmentation method and the height above the nearest drainage (HAND) tool to EOS-04 data for flood extent delineation. For flood depth estimation, this study introduces a novel application of the trend surface analysis (TSA) technique, enabling rapid and data-efficient assessment. Unlike traditional hydrodynamic models that demand extensive datasets and computational resources, TSA operates using only the inundated water layer and DEM, providing a highly data-efficient solution. The methodology is applied to flood-prone regions in Andhra Pradesh, Assam, Bihar, and Uttar Pradesh, India. Validation of flood extent against optical data demonstrates accuracy greater than 90 %. Flood depth estimation using TSA is validated by comparing water depths derived from river gauge stations with real-time field measurements and results from the floodwater depth estimation tool (FwDET). The TSA method achieves a root-mean-square error (RMSE) of 0.805, significantly outperforming FwDET's RMSE of 5.23. This integration of high-resolution SAR imagery and DEM represents a transformative, automated solution for real-time flood monitoring and depth estimation, enhancing disaster management capabilities.

The water regime in Pakistan’s northern region has experienced significant changes regarding hydrological extremes like floods because of climate change. Coupling hydrological models with remote sensing data can be valuable for flow simulation in data-scarce regions. This study focused on simulating the snow- and glacier-melt runoff using the snowmelt runoff model (SRM) in the Gilgit and Kachura River Basins of the upper Indus basin (UIB). The SRM was applied by coupling it with in situ and improved cloud-free MODIS snow and glacier composite satellite data (MOYDGL06) to simulate the flow under current and future climate scenarios. The SRM showed significant results: the Nash–Sutcliffe coefficient (NSE) for the calibration and validation period was between 0.93 and 0.97, and the difference in volume (between the simulated and observed flow) was in the range of −1.5 to 2.8% for both catchments. The flow tends to increase by 0.3–10.8% for both regions (with a higher increase in Gilgit) under mid- and late-21st-century climate scenarios. The Gilgit Basin’s higher hydrological sensitivity to climate change, compared to the Kachura Basin, stems from its lower mean elevation, seasonal snow dominance, and greater temperature-induced melt exposure. This study concludes that the simple temperature-based models, such as the SRM, coupled with improved satellite snow cover data, are reliable in simulating the current and future flows from the data-scarce mountainous catchments of Pakistan. The outcomes are valuable and can be used to anticipate and lessen any threat of flooding to the local community and the environment under the changing climate. This study may support flood assessment and mapping models in future flood risk reduction plans.

ABSTRACT Flash floods pose a significant menace to urban and periurban areas where rapid urbanization increases flood risk. Accurate flood susceptibility modelling is essential for effective risk management. This paper compares the performance of the conventional analytic network process (ANP) and two ANP-based ensembles, fuzzy logic ANP (F-ANP) and Monte Carlo simulations ANP (MC-ANP), in predicting flash flood susceptibility in Béjaïa, Algeria. Eight flood-conditioning factor layers were prepared in GIS. Flash flood susceptibility maps for the three models were generated using the weighted linear combination, with factor weights assigned through expert-based pairwise comparisons and connections. The modelling accuracy was tested using the receiver operating characteristic model. Results revealed that MC-ANP achieved the best performance with an AUC of 91.2%, followed by F-ANP and ANP with 88.1% and 87.8%, respectively. Overall, the ANP-based ensembles effectively improved modelling performance by better handling expert judgement uncertainty, supporting their use in reliable flash flood assessment.

Floods, as one of the most destructive natural disasters, impose extensive human and economic losses on communities annually. This study pursues two primary objectives by introducing an innovative hybrid framework: (1) identifying flood-prone areas in Iran’s Kashkan River Basin using the Maximum Entropy (MaxEnt) model, and (2) prioritizing critical sub-basins based on the Borda method in game theory. Variable selection was performed using the Random Forest algorithm, resulting in the identification of nine key factors influencing flood occurrence. Important variables affecting flooding include aspects, slope, distance from stream, drainage density, lithology, land use, precipitation, soil texture, and topographic wetness index. The MaxEnt model subsequently predicted high-risk areas with exceptional accuracy (AUC = 0.945 for training; 0.906 for validation), while the Borda method ranked the sub-watersheds through parameter weighting. According to the findings, flood vulnerability was most influenced by distance from streams—30.9%—then by slope at 23.2%. The most important parameter found, based on Borda method results from the game theory model, was maximum 24-h rainfall with a 25-year return time. Following this were parameters of agricultural land usage and the average slope %. Sub-basin code 2221 ranked highest in choosing and prioritizing important sub-watersheds depending on flood susceptibility inside the Kashkan basin. The unprecedented integration of MaxEnt and the Borda method provides a quantitative–qualitative strategy for flood assessment that overcomes the limitations of single-model approaches. Proposed solutions include the construction of sedimentation basins in Sub-basin 2221, the reinforcement of the channel walls in Sub-basin 2222, and the implementation of flood spreading projects in Sub-basin 2223. The integration of the MaxEnt model with game theory represents a strategic innovation in risk analysis and complex decision-making. This approach combines quantitative risk assessment data with competitive strategies and collective decision-making processes, enabling managers and policymakers to adopt optimized, coordinated strategies against natural threats such as floods, based on scientific evidence.

Floods, exacerbated by climate change, necessitate timely and accurate situational awareness to support effective disaster response. While electro-optical (EO) satellite imagery has been widely employed for flood assessment, its utility is significantly limited under conditions such as cloud cover or nighttime. Synthetic Aperture Radar (SAR) provides consistent imaging regardless of weather or lighting conditions but it remains challenging for human analysts to interpret. To bridge this modality gap, we present diffusion-based SAR-to-EO image translation (DSE), a novel framework designed specifically for enhancing the interpretability of SAR imagery in flood scenarios. Unlike conventional GAN-based approaches, our DSE leverages the Brownian Bridge Diffusion Model to achieve stable and high-fidelity EO synthesis. Furthermore, it integrates a self-supervised SAR denoising module to effectively suppress SAR-specific speckle noise, thereby improving the quality of the translated outputs. Quantitative experiments on the SEN12-FLOOD dataset show that our method improves PSNR by 3.23 dB and SSIM by 0.10 over conventional SAR-to-EO baselines. Additionally, a user study with SAR experts revealed that flood segmentation performance using synthetic EO (SynEO) paired with SAR was nearly equivalent to using true EO–SAR pairs, with only a 0.0068 IoU difference. These results confirm the practicality of the DSE framework as an effective solution for EO image synthesis and flood interpretation in SAR-only environments.

Climate change is altering flood patterns in India, emphasising accurate flood assessment for effective resource management through flood frequency analysis. This study evaluates three probability distribution methods: log-normal, Gumbel’s extreme value (GEV), and log-Pearson type 3 (LP3) using annual maximum discharge data (1973–2018) from the Dhanera gauging station in the Rel River basin. The most suitable model is determined by way of Kolmogorov–Smirnov, Anderson–Darling and chi-squared tests at a 5% significance level, with GEV identified as the best fit. Predicted peak discharge values for return periods of 2, 5, 10, 25, 50, 100 and 200 years were integrated into a two-dimensional hydraulic model, validated against observed flood depths from July 2017. Maximum discharge and averaged digital elevation model data were utilised in the Hydrologic Engineering Center river analysis system (HEC-RAS) model to predict inundation, processed with ArcGIS to create flood inundation maps. The 200 year return period simulation revealed areas also affected by the historic 2017 floods. This research offers crucial insights into flood depths and characteristics, aiding government authorities and stakeholders in making informed decisions for risk-based mitigation post the floods of 2015 and 2017.

Flooding refers to the adverse effects caused by rivers overflowing their banks due to various reasons, affecting surrounding land, residential areas, and infrastructure. At the watershed scale, particularly in cases where flow monitoring stations are absent, hydrographs must be generated to analyze rainfall-runoff relationships for flood assessments. This study aims to generate synthetic hydrographs, analyze rainfall-runoff relationships, and estimate flood discharges for different return periods in a predominantly forested sub-watershed located in the Sarıyer district of Istanbul. The study analyzed extreme rainfall by calculating 24-hour maximum values for return periods of 2 to 100 years using four common probability distribution functions: Normal, Log-Normal, Log-Pearson Type III, and Gumbel. Among these methods, Log-Pearson Type III yielded higher rainfall values, and given the extreme nature of floods, it was preferred for discharge calculations. In the second stage of the study, flood hydrographs specific to the watershed were generated for different return periods using the DSI, Mockus, and Snyder unit hydrograph methods, incorporating watershed physical characteristics and dimensionless unit hydrograph coordinates. The results indicated that the DSI and Mockus methods produced similar and higher peak discharge values (Qₘₐₓ = 67.44 and 63.76 m³/s, T=100 years), whereas the Snyder method resulted in lower peak discharge (Qₘₐₓ = 32.17 m³/s for T = 100 years) but a longer hydrograph duration. Overall, it was concluded that the DSI and Mockus methods are more suitable for flood analysis in forested and relatively small watersheds (≈10 km²) due to their effectiveness in generating hydrographs for flood assessments. This study contributes to the literature by offering a comparative evaluation of three widely used synthetic unit hydrograph methods, specifically tailored for a forest-dominated ungauged basin in an urbanizing region of Istanbul, providing actionable insights for flood estimation in data-scarce, forested urban catchments.

Towards More Efficient Urban Flood Assessment: Issue of Spatial Resolution in Urban Flood Hydrodynamic Modeling from Flood Exposure Perspective

The increasing frequency and intensity of flooding, driven by climate change, are having a detrimental impact on Phnom Penh’s urban ecosystem. Disaster managers and decision-makers increasingly recognize the value of tools that can directly translate flood hazards—specifically, the extent of inundation during flood events—into estimates of expected socio-economic impacts, including affected populations and economic losses. While previous studies have often focused on broad-scale flood impacts at local or regional levels, this paper presents a straightforward approach for estimating impacts at a finer, more localized scale within the affected area, using flood hazard data and damage functions. This study aims to assess flood inundation areas and the direct economic losses resulting from flood events in the southern and southwestern parts of Phnom Penh, the capital city of Cambodia. The methodology involves applying Google Earth Engine (GEE) with Sentinel-1 synthetic aperture radar (SAR) data to assess flooding, including the generation of flood extent and depth maps. These maps are then used as input for an impact assessment module, which translates the estimated flood extents and depths into quantitative assessments of socio-economic impacts. The results indicate that the inundation areas in the southern and southwestern urban areas of Phnom Penh reached 1,638 ha, with flood depths ranging from 0.5 to 6 m, during the October 2020 flood event. The total estimated economic damage was approximately USD 20 million. Agricultural areas were the most affected, followed by residential and commercial areas. This study provides a valuable tool for rapid assessment of urban flood impacts, offering crucial data on flood extent, depth, and economic consequences for urban policymakers and planners.

Flooding is a recurring environmental hazard with devastating socio-economic and ecological impacts, especially in vulnerable regions like Bayelsa State, Nigeria. The state’s low-lying terrain, dense river networks, and poor drainage infrastructure exacerbate its flood susceptibility. This study employs morphometric analysis to assess flood-prone areas across major river basins using Shuttle Radar Topographic Mission (SRTM) data, Geographic Information Systems (GIS), and remote sensing techniques. Key morphometric parameters stream order, drainage density (2.41-3.57 km/km²), bifurcation ratio (1.84-2.84), relief ratio (0.03-0.15), stream frequency (5.00-11.71 streams/km²), infiltration number, and form factor (0.64-1.04) were extracted and analyzed using ArcGIS 10.5, Arc Hydro tools, and Python. Results indicate significant spatial heterogeneity in flood susceptibility. The Forcados River catchment recorded the highest flood risk, with a priority score of 3.4/5, a relief ratio of 0.15, drainage density of 3.57 km/km², and stream frequency of 11.71 streams/km². This aligns with 78% of historical flood event locations. Conversely, the Ekole and Seibri catchments exhibited the lowest susceptibility, with priority scores of 2.0-2.1, relief ratios below 0.05, and drainage densities under 0.9 km/km². The Nun River catchment showed moderate risk (priority score: 2.4), with a stream frequency of 3.2/km² and elongation ratio of 0.6. To enhance predictive capacity, machine learning models were employed. The Random Forest classifier achieved 89% accuracy and an AUC-ROC of 0.93, outperforming the Support Vector Machine model. This study offers a scalable flood assessment framework for data-scarce regions and recommends targeted structural interventions and nature-based solutions tailored to each catchment’s flood profile.

Glacial Lake Outburst Floods (GLOFs) have emerged as a critical threat to high-mountain communities and ecosystems, driven by accelerated glacier retreat and lake expansion under climate change. This review synthesizes advancements in remote sensing technologies and methodologies for GLOF monitoring, risk assessment, and mitigation. Through a Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA)-guided systematic literature review and bibliometric analysis of studies from 2010 to 2025, we evaluate the transformative role of remote sensing in overcoming traditional field-based limitations. Central to this review is the exploration of multi-sensor data fusion for high-resolution lake dynamics mapping, machine learning algorithms for predictive risk modelling, and hydrodynamic simulations for flood propagation analysis. This review underscores the importance of these technologies in improving GLOF risk assessments and supporting early warning systems, which are crucial for safeguarding vulnerable high-mountain communities. It addresses existing challenges, such as data integration and model calibration, and advocates for collaborative efforts between scientists, policymakers, and local stakeholders to translate technological advancements into effective mitigation strategies, ensuring the sustainability of these at-risk regions.

Demak is a coastal regency located in Central Java, Indonesia, faces increasing vulnerability to flooding due to its low-lying topography and the impacts of climate change. The region experiences frequent inundation events, which pose significant threats not only to infrastructure but also to agricultural productivity and the livelihoods of local communities. This study aims to assess the effectiveness of various water indices in evaluating flood inundation in Demak by utilizing multitemporal satellite imagery captured before and during flood events. Three notable variations of water indices: McFeeters’ Normalized Difference Water Index (NDWImc), Gao’s Normalized Difference Water Index (NDWIGao) and the Modified Normalized Difference Water Index (MNDWI) are compared to determine their efficacy in identifying flood-prone areas within the region. The results indicate that water indices can effectively identify areas inundated by floods, demonstrating their potential as a reliable tool for rapid flood assessment by satellite imagery. Additionally, this study emphasizes the role of water indices in the quick monitoring of submerged regions, particularly within the agricultural sector, which is vital for sustaining local livelihoods and food security. The findings highlight the necessity for efficient methodologies to estimate flood-related losses, thereby enhancing preparedness and response strategies in flood-prone areas, particularly in Demak.

Clay and grass are natural building materials for flood defences that has been used in the Netherlands for centuries. However, the erosion process of the clay layer under wave loads is not well understood and is therefore usually not included in design and safety assessment methods in the Netherlands. Full scale physical model tests have been performed in the Delta Flume – the large scale wave flume at Deltares - with an undisturbed layer of clay and grass from sea dikes. Three measurement campaigns have been performed with 9 clay types and different geometries: slope steepness between 1:4 – 1:7 with and without a berm. The erosion process on the outer slope by wave impacts has been studied for significant wave heights up to 2 m and varying water levels. The results show that the erosion process of the clay revetment after erosion of the grass cover consists of two phases. During the first phase, the erosion mainly develops in depth. This erosion process shows a fast increase in erosion depth with a relatively small increase in eroded volume as the additional strength of the grass cover roots in this upper clay layer slows the growth of the erosion hole towards the dike crest. At a depth of around 50 cm, the grass cover roots have negligible influence on the erosion process allowing the erosion hole to grow faster towards the crest. A cliff is formed in the erosion hole and the second phase starts. During the second phase, the waves impact on the cliff resulting in cliff erosion and migration of the cliff towards the crest. The results of the physical model tests can be used to determine the erodibility of the different clay types and to develop formulas for the design and assessment of coastal flood defences built with local and natural materials.

Under the dual pressures of global climate change and rapid urbanization, urban drainage systems (UDS) face severe challenges caused by extreme precipitation events and altered surface hydrological processes. The drainage paradigm is shifting toward resilient systems integrating grey and green infrastructure, necessitating a comprehensive review of the design and operation of grey infrastructure. This study systematically summarizes advances in urban stormwater process-wide regulation, focusing on drainage network design optimization, siting and control strategies for flow control devices (FCDs), and coordinated management of Quasi-Detention Basins (QDBs). Through graph theory-driven topological design, real-time control (RTC) technologies, and multi-objective optimization algorithms (e.g., genetic algorithms, particle swarm optimization), the research demonstrates that decentralized network layouts, dynamic gate regulation, and stormwater resource utilization significantly enhance system resilience and storage redundancy. Additionally, deep learning applications in flow prediction, flood assessment, and intelligent control exhibit potential to overcome limitations of traditional models. Future research should prioritize improving computational efficiency, optimizing hybrid infrastructure synergies, and integrating deep learning with RTC to establish more resilient and adaptive urban stormwater management frameworks.

Currently, regional flood research often lacks a synergistic assessment of both flood occurrence risk and flood duration, limiting the comprehensive understanding needed for sustainable disaster risk reduction. To address this gap, this study applies advanced machine learning approaches to assess flood hazards in the Yangtze River Delta, one of China’s most economically and environmentally significant regions. Specifically, XGBoost is employed to evaluate flood occurrence risk, while LSTM is used to predict flood duration. A novel flood risk index (FRI) is proposed to quantify the integrated risk by combining these two dimensions, supporting more sustainable and effective flood risk management strategies. Furthermore, SHAP analysis is conducted to identify the most critical factors contributing to flooding. The results demonstrate that XGBoost delivers strong predictive performance, with average precision, recall, F1-score, accuracy, and AUC values of 0.823398, 0.831667, 0.827090, 0.826435, and 0.871062, respectively. Areas with high flood risk, long duration, and elevated FRI values are mainly concentrated in major river basins and coastal zones. The range of flood risk spans from 0.000073 to 0.998483 (mean: 0.237031), flood duration from 0.223598 to 2.077040 (mean: 0.940050), and FRI from 0 to 0.934256 (mean: 0.091711). Cities with over 40% of their areas falling in medium to high FRI zones include Suzhou (48.99%), Jiaxing (48.07%), Yangzhou (46.87%), Suqian (44.19%), Changzhou (43.43%), Wuxi (43.20%), Lianyungang (42.21%), Yancheng (40.88%), Huai’an (40.73%), and Bengbu (40.06%). SHAP analysis reveals that elevation and rainfall are the most critical factors influencing flood occurrence, underscoring the importance of integrating environmental variables into sustainable flood risk governance.

Floods and droughts, common global natural disasters, threaten the human safety, food security, and socioeconomic development. This study proposed a multiscale approach using climate data, SPI, and SPEI to analyze the spatial and temporal characteristics of droughts and floods in Henan Province, China, in the past decades. At the same time, Sentinel-1 SAR data of pre- and in-flood period of the “7·20 Extreme Rainstorm” event occurring in July 17-23, 2021 were applied to assess the impacts of floods on farmland and urban environment in Pingdingshan City as a local-scale assessment and verification of the regional-scale analysis. The results show that SPEI is closer to the historical record than SPI in terms of the extent of flood events, and the identified local-scale floods in the pilot site Pingdingshan by a decision-tree classification with a high overall accuracy (OA > 95%) and Kappa Coefficient (KC > 0.85) reached 41.7 km2. Our study found that farmlands and built-up areas were most affected by flooding. It highlights the effectiveness of multiscale research for climate-related disaster assessment, offering a replicable approach for global applications. Recommendations were provided to local governments for taking disaster prevention measures and sustainable land management.

The experiment was conducted during September to December 2024 at SCS College of Agriculture, AAU, Dhubri, Assam, India using secondary data collected from Sentinel-1A satellite 10-meter spatial resolution for study year 2019–2024. The objective of the study was to assess the spatial and temporal variability of flood inundation and submergence of agricultural land in the district using Remote Sensing (RS) and Geographic Information Systems (GIS). The flood incidents at block levels in the entire Dhubri district over the period of 2020 to 2024 were considered for the study. For this purpose, Sentinel-1 SAR imagery of the study area and the period were extracted using Google Earth Engine. Significant spatial and temporal variations in flood inundations were observed during the years from 2020 to 2024 in all the blocks under the Dhubri district. Birsing Jarua block emerged as the most vulnerable one, with flooded areas increasing from 19.24% in 2022 to 49.12% in 2024, leading to a two and half times rise in cropland inundation. Nayeralga, Mahamaya, and Chapor-Salkocha blocks also showed high vulnerability towards intensified flooding. Conversely, Rupshi, Bilasipara, and Golaganj blocks exhibited declining flood extents over the period of study. The study highlighted the need for block level integrated contingency plan in high-risk zones to mitigate future agricultural damage caused by flood in the district.