Flood Risk Assessment Research Articles

Integrating Hydrological Models for Improved Flash Flood Risk Assessment and Mitigation Strategies in Northeastern Thailand

This study focuses on assessing flash flood risks in Northeastern Thailand, particularly within the Lam Saphung, Phrom, and Chern River Basins, which are highly susceptible to flash floods and debris flows. Using the HEC-RAS hydraulic model integrated with GIS tools, the research analyzes historical and scenario-based flood events to evaluate the impact of land use changes and hydrological dynamics. The model was calibrated and validated with statistical metrics such as R2 values ranging from 0.745 to 0.994 and NSE values between 0.653 and 0.893, indicating strong agreement with the observed data. This study also identified high-risk areas, with up to 5.49% and 5.50% increases in flood-prone areas in the Phrom and Chern River Basins, respectively, from 2006 to 2019. Key findings highlight the critical role of proactive risk management and targeted mitigation strategies in enhancing community resilience. The integration of advanced hydraulic modeling with detailed datasets enables precise flood hazard mapping, including flood depths exceeding 1.5 m in certain areas and high-risk zones covering up to 105.2 km2 during severe flood events. These results provide actionable insights for emergency response and land use planning. This research significantly contributes to hydrological risk assessments by advancing modeling techniques and delivering practical recommendations for sustainable flood management. The outcomes are particularly relevant for stakeholders, including urban planners, emergency management officials, and policymakers, who aim to strengthen resilience in vulnerable regions. By addressing the complexities of flash flood risk assessments with robust quantitative evidence, this study not only enhances the understanding of flood dynamics, but also lays the groundwork for developing adaptive strategies to mitigate the adverse impacts of flash floods, safeguarding both communities and infrastructure in the region.

Need for judicious selection of runoff inputs in a global flood model

Abstract Numerous flood hazard assessment and risk management studies depend on hydrodynamic flood models, which require detailed inputs. However, these models face challenges when assessing flood hazards and risks at national scales due to the unavailability of input data and high computational demands. Recent advancements in global flood models (GFMs) have emerged as promising solutions. These widely adopted GFMs, capable of producing flood characteristics, require runoff input typically derived from land surface models (LSMs) or global hydrological models (GHMs), which are prone to inherit cascading uncertainties. Moreover, the utilization of a single runoff input into a GFM can produce biased and misinterpreted flood hazards due to underestimation or overestimation of GFM outputs. To highlight these implications, the present study examines GFM simulations forced with eight state-of-the-art model runoff datasets, including LSMs, GHMs, and reanalysis observations, uncovering unsafe inter-model flood depth variation (IMDV). Focusing on the flood-prone Mahanadi River Basin (MRB) of India, the study observes that IMDV surpasses the self-help range of humans (0.2 m) for 65% of the MRB region, and exceeds human and vehicle safety thresholds (2 m) for 15% of the region, based on four past flood events from the Dartmouth Flood Observatory. These regions exhibiting high IMDV overlap with densely populated areas, potentially affecting 1.66–3.65 million people. Thus, the injudicious use of runoff in GFM for flood disaster planning can lead to inaccurate flood hazard and risk assessments, significantly affecting populous regions. An alternative approach is recommended, advocating for the use of multiple simulations incorporating diverse runoff datasets. This approach would generate conservative and optimistic flood scenarios, leveraging each model’s strengths. Such comprehensive hazard scenarios would enhance flood management and decision-making for policymakers by addressing the uncertainty and providing possible impacts through risk assessments.

Evaluation and Adjustment of Historical Hydroclimate Data: Improving the Representation of Current Hydroclimatic Conditions in Key California Watersheds

The assumption of stationarity in historical hydroclimatic data, fundamental to traditional water resource planning models, is increasingly challenged by the impacts of climate change. This discrepancy can lead to inaccurate model outputs and misinformed management decisions. This study addresses this challenge by developing a novel monthly data adjustment approach, the Runoff Curve Year–Type–Monthly (RC-YTM) method. The application of this method is exemplified at five key California watersheds. The RC-YTM method accounts for the increasing variability and shifts in seasonal runoff timing observed in the historical data (1922–2021), aligning it with the contemporary climate conditions represented by the period from 1992 to 2021 at the study watersheds. This method adjusts both annual and monthly streamflow values using a combination of precipitation–runoff relationships, quantile mapping, and water year classification. The adjusted data, reflecting current climatic conditions more accurately than the raw historical data, serve as valuable inputs for operational water resource planning models like CalSim3, commonly used in California for water management. This approach, demonstrably effective in capturing the observed climate change impacts on streamflow at monthly timesteps, enhances the reliability of model simulations representing contemporary conditions, which can lead to better-informed decision-making in water management, infrastructure investment, drought and flood risk assessment, and adaptation strategies. While focused on specific California watersheds, this study’s findings and the adaptable RC-YTM method hold significant implications for water resource management in other regions facing similar hydroclimatic challenges in a changing climate.

Enhanced streamflow forecasting using hybrid modelling integrating glacio-hydrological outputs, deep learning and wavelet transformation

Understanding snow and ice melt dynamics is vital for flood risk assessment and effective water resource management in populated river basins sourced in inaccessible high-mountains. This study provides an AI-enabled hybrid approach integrating glacio-hydrological model outputs (GSM-SOCONT), with different machine learning and deep learning techniques framed as alternative ‘computational scenarios, leveraging both physical processes and data-driven insights for enhanced predictive capabilities. The standalone deep learning model (CNN-LSTM), relying solely on meteorological data, outperformed its counterpart machine learning and glacio-hydrological model equivalents. Hybrid models (CNN-LSTM1 to CNN-LSTM15) were trained using meteorological data augmented with glacio-hydrological model outputs representing ice and snow-melt contributions to streamflow. The hybrid model (CNN-LSTM14), using only glacier-derived features, performed best with high NSE (0.86), KGE (0.80), and R (0.93) values during calibration, and the highest NSE (0.83), KGE (0.88), R (0.91), and lowest RMSE (892) and MAE (544) during validation. Finally, a multi-scale analysis using different feature permutations was explored using wavelet transformation theory, integrating these into the final hybrid model (CNN-LSTM19), which significantly enhances predictive accuracy, particularly for high-flow events, as evidenced by improved NSE (from 0.83 to 0.97) and reduced RMSE (from 892 to 442) during validation. The comparative analysis illustrates how AI-enhanced hydrological models improve the accuracy of runoff forecasting and provide more reliable and actionable insights for managing water resources and mitigating flood risks - despite the paucity of direct measurements.

Urban Flooding Disaster Risk Assessment Based on Combined Empowerment-Fuzzy Mathematics

Purpose In recent years, flooding disasters have occurred frequently in major cities, in order to improve urban safety and reduce flooding disaster losses. With four urban areas in Zhengzhou City as the research object, it aims to provide a reference basis for the risk management and control of heavy rainfall and flooding disaster in Zhengzhou City. Methods From the four aspects of urban flooding disaster causative factor risk, sensitivity of the breeding environment, vulnerability of the disaster-bearing body and disaster prevention and mitigation capacity, 12 evaluation indicators were selected to construct the Zhengzhou city flood disaster risk assessment system. Based on the hierarchical analysis method (AHP) and entropy weight method (EWM) to get the weights of the combination of indicators, and using fuzzy mathematical analysis method to analyse the risk of urban flooding disaster. Results The assessment results were analysed to conclude that Jinshui District has the highest urban flood risk disaster as high risk, followed by Erqi District, Guancheng District and Zhongyuan District as medium risk, and Huiji District has the lowest risk as low risk. Conclusion Develop a set of flood risk assessment model, and accordingly draw a flood risk coefficient map of Zhengzhou City, and then derive the engineering and non-engineering measures to deal with the flood disaster in Zhengzhou City.

Flood Risk Assessment and Zoning for Niamey and Lokoja Metropolises in Niger and Nigeria

With the increasing frequency of floods in recent decades, particularly in West Africa, many regions have faced unusual and recurrent flooding events. Communities in flood-prone areas experience heightened insecurity, loss of property, and, in some cases, serious injuries or fatalities. Consequently, flood risk assessment and mitigation have become essential. This comparative study between Niamey and Lokoja employs Geographic Information Systems (GIS) and the Analytical Hierarchy Process (AHP) to delineate flood susceptibility, vulnerability, and risk zones. The study utilized a comprehensive range of thematic layers, with weight percentages assigned to each parameter as follows: 29% for elevation, 24% for slope, 15% for the Topographic Wetness Index (TWI), 9% for drainage density, 9% for distance from rivers, 4% for both precipitation and the Normalized Difference Water Index (NDWI), and 2% each for the Normalized Difference Vegetation Index (NDVI) and soil type. To validate these weightings, a consistency ratio was calculated, ensuring it remained below 10%. The findings reveal that 32% of the Niamey study area is at risk of flooding, compared to approximately 15% in Lokoja. The results highlight a very high flood potential, particularly in areas near the Niger River, with this potential decreasing as elevation increases. Given the current prevalence of extreme weather events in West Africa, it is crucial to employ effective tools to mitigate their adverse impacts. This research will assist decision-makers in quantifying the spatial vulnerability of flood-prone areas and developing effective flood risk assessment and mitigation strategies in the region.

Urban flood risk assessment and evacuation planning: a bi-level optimization model for sustainable high-density coastal areas

ABSTRACT Flooding caused by extreme climate change is becoming increasingly severe, especially in high-density coastal areas worldwide. Although many studies have conducted risk assessments of urban floods, most have not formed a comprehensive evacuation plan considering population distribution and flood disaster risk. To further enhance urban flood planning and emergency management for coastal areas, this study uses Victoria Harbor in Hong Kong, a typical flood-prone region, as a research area. The study first conducts a flood exposure risk assessment and classifies different regions according to flood risk levels. Then, by combining evacuation ability with the changing flood disaster and road evacuation flows, a novel bi-level optimization model is proposed to allocate zones for the citizens day and night. With the upper level using a genetic algorithm to minimize the total system evacuation time and the lower level applying a user equilibrium model for evacuee allocation, this model forms an evacuation that considers the distribution of population hotspots and the impact of flood risks on the road network. The findings of the study show that functional urban areas with high pedestrian flow, tourist spots, commercial centres, and schools are exposed to higher flood risk. Besides, the evacuation simulation of the bi-level optimization model proposed in this study matches the zoning results of actual urban activities and can effectively achieve the goal of evacuating 480,000 people within 12–18 minutes. This study innovatively proposes an effective evacuation plan that can reference the government’s emergency evacuation planning work.

Multi-Criteria Assessment of Flood Risk on Railroads Using a Machine Learning Approach: A Case Study of Railroads in Minas Gerais

Multi-Criteria Assessment of Flood Risk on Railroads Using a Machine Learning Approach: A Case Study of Railroads in Minas Gerais

Satellite precipitation product assessment and correction technique selection at sub-basin scale for maximum annual events. Case study: Acaponeta River basin

Satellite precipitation products (SPP) are increasingly being used for detailed hydrological studies due to scarce and discontinuous precipitation observations at different spatial and temporal scales. However, to evaluate its full utility, it is necessary to assess and correct the bias between estimated and observed precipitation (OP). The aim of this paper is to evaluate the CHIRPSv2.0 product for maximum annual events and different climatological conditions based on in-situ observations, using statistical metrics and selecting from linear scaling (LS), local intensity scaling (LOCI) and power transformation (PT) the appropriate bias correction technique (CT), at point and sub-basin scale, improving the maximum annual precipitation records for the period 2001-2020 in the Acaponeta River basin, Mexico. Previous applications of bias CT have focused on broader temporal scales rather than specific maximum events. Differences in the performance of the correction methods were identified between point and sub-basin scales. PT presented a good performance at the point scale, in contrast to percentual bias (PBIAS), which resulted in a great overestimation at the sub-basin scale in the upper zone for the average and dry years, while for the wet year, it overestimated in the lower part. Although LS and LOCI generally observed a good PBIAS reduction at the gauge stations, LS overestimated at the sub-basin scale overall. LOCI showed better SPP corrections in the middle and lower zones and a wider range of overestimation for the upper basins in the middle and wet years. The corrected annual maximum estimated values for the revised period are useful for hydrological analysis in the context of flood risk assessment.

Combining UAS LiDAR, Sonar, and Radar Altimetry for River Hydraulic Characterization

Accurate river hydraulic characterization is fundamental to assess flood risk, parametrize flood forecasting models, and develop river maintenance workflows. River hydraulic roughness and riverbed/floodplain geometry are the main factors controlling inundation extent and water levels. However, gauging stations providing hydrometric observations are declining worldwide, and they provide point measurements only. To describe hydraulic processes, spatially distributed data are required. In situ surveys are costly and time-consuming, and they are sometimes limited by local accessibility conditions. Satellite earth observation (EO) techniques can be used to measure spatially distributed hydrometric variables, reducing the time and cost of traditional surveys. Satellite EO provides high temporal and spatial frequency, but it can only measure large rivers (wider than ca. 50 m) and only provides water surface elevation (WSE), water surface slope (WSS), and surface water width data. UAS hydrometry can provide WSE, WSS, water surface velocity and riverbed geometry at a high spatial resolution, making it suitable for rivers of all sizes. The use of UAS hydrometry can enhance river management, with cost-effective surveys offering large coverage and high-resolution data, which are fundamental in flood risk assessment, especially in areas that difficult to access. In this study, we proposed a combination of UAS hydrometry techniques to fully characterize the hydraulic parameters of a river. The land elevation adjacent to the river channel was measured with LiDAR, the riverbed elevation was measured with a sonar payload, and the WSE was measured with a UAS radar altimetry payload. The survey provided 57 river cross-sections with riverbed elevation, and 8 km of WSE and land elevation and took around 2 days of survey work in the field. Simulated WSE values were compared to radar altimetry observations to fit hydraulic roughness, which cannot be directly observed. The riverbed elevation cross-sections have an average error of 32 cm relative to RTK GNSS ground-truth measurements. This error was a consequence of the dense vegetation on land that prevents the LiDAR signal from reaching the ground and underwater vegetation, which has an impact on the quality of the sonar measurements and could be mitigated by performing surveys during winter, when submerged vegetation is less prevalent. Despite the error of the riverbed elevation cross-sections, the hydraulic model gave good estimates of the WSE, with an RMSE below 3 cm. The estimated roughness is also in good agreement with the values measured at a gauging station, with a Gauckler–Manning–Strickler coefficient of M = 16–17 m1/3/s. Hydraulic modeling results demonstrate that both bathymetry and roughness measurements are necessary to obtain a unique and robust hydraulic characterization of the river.

Assessment of flood risk under Polder-Type flood control measure using improved projection pursuit model

Assessment of flood risk under Polder-Type flood control measure using improved projection pursuit model

Gis tools and numeric model to forecast the floods extend along the Rhouireg river, city of Taza, Morocco

Combining GIS tool with physical-based model is one of the most effective ways to locate flood-prone zones. In this study, we used a 1D hydrodynamic model to predict the extent of future floods and eventually present the floodable areas on a satellite image covering a densely populated section of the Rhouireg river, located in the eastern part of Taza city. We fed the model with peak flows calculated using the Rational method, estimated variables using standard tables (Manning's coefficient), and other field-measured parameters. We calibrated the model with minor adjustments that were necessary to accurately match the observed floods. Our findings revealed that the covered canal was sufficient to evacuate water during 10- and 20-year floods. The 50- and 100-year floods overflowed the canal and affected National Road No. 6, posing a danger to road traffic. Like this section, the upstream part demonstrated that the decadal frequency flowed through the channel without overflowing its banks. The centennial flood inundated the riverbeds, spreading large amounts of water into inhabited areas and cultivated fields. These results align with the flood that occurred on January 14, 2010, and confirm our previous observations, indicating that the model precisely anticipated the river's behavior. The numeric simulations have also proven to be an incredibly powerful tool for conducting a detailed assessment of flood risks along this small wadi.

Flood risk assessment of cultural heritage across countries and spatial scales

Flood risk assessment of cultural heritage across countries and spatial scales

In search of non-stationary dependence between estuarine river discharge and storm surge based on large-scale climate teleconnections

Compound floods may happen in low-lying estuarine environments when sea level above normal tide co-occurs with high river flow. Thus, comprehensive flood risk assessments for estuaries should not only account for the individual hazard arising from each environmental variable in isolation, but also for the case of bivariate hazard. Characterization of the dependence structure of the two flood drivers becomes then crucial, especially under climatic variability and/or change that may affect their relationship. In this article, we demonstrate our search for evidence of non-stationarity in the dependence between river discharge and storm surge along the East and Gulf coasts of the United States, driven by large-scale climate variability, particularly El-Niño Southern Oscillation and North Atlantic Oscillation (NAO). Leveraging prolonged overlapping observational records and copula theory, we recover parameters of both stationary and dynamic copulas using state-of-the-art Markov Chain Monte Carlo methods. Physics-informed copulas are developed by modeling the magnitude of dependence as a linear function of large-scale climate indices, i.e., Oceanic Niño Index or NAO index. After model comparison via suitable Bayesian metrics, we find no strong indication of such non-stationarity for most estuaries included in our analysis. However, when non-stationarity due to these climate modes cannot be neglected, this work highlights the importance of appropriately characterizing bivariate hazard under non-stationarity assumption. As an example, we find that during a strong El-Niño year, Galveston Bay, TX, is much more likely to experience a coincidence of abnormal sea level and elevated river stage.

Flood Hazard Assessment Using Weather Radar Data in Athens, Greece

Weather radar plays a critical role in flash flood forecasting, providing an effective and comprehensive guide for the identification of possible flood-prone areas. However, the utilization of radar precipitation data remains limited in current research and applications, particularly in addressing flash flood hazards in complex environments such as in Athens, Greece. To address this gap, this study introduces the Gridded Flash Flood Guidance (GFFG) method, a short-term flash flood forecasting and warning technology based on radar precipitation and hydrological model coupling, and implements it in the region of Athens, Greece. The GFFG system improves upon the traditional flash flood guidance (FFG) concept by better integrating the weather radar dataset’s spatial and temporal flexibility, leading to increased resolution results. Results from six flood events underscore its ability to identify high-risk areas dynamically, with urban regions frequently flagged for flooding unless initial soil moisture conditions are low. Moreover, the sensitivity analysis of the system showed that the most crucial parameter apart from rainfall input is the soil moisture conditions, which define the amount of effective rainfall. This study highlights the significance of incorporating radar precipitation and real-time soil moisture assessments to improve flood prediction accuracy and provide valuable flood risk assessments.

Urban flood risk assessment using fuzzy logic and real-time flood simulation model – a geomatics techniques

Urban flood risk assessment using fuzzy logic and real-time flood simulation model – a geomatics techniques

Enhanced Flood Monitoring in the Pearl River Basin via GAIN-Reconstructed GRACE Terrestrial Water Storage Anomalies

Floods are a significant and pervasive threat globally, exacerbated by climate change and increasing extreme weather events. The Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission (GRACE-FO) provide crucial insights into terrestrial water storage anomalies (TWSA), which are vital for understanding flood dynamics. However, the observational gap between these missions presents challenges for flood monitoring, affecting the estimation of long-term trends and limiting the analysis of interannual variability, thereby impacting overall analysis accuracy. Reconstructing the missing data between GRACE and GRACE-FO is essential for systematically understanding the spatiotemporal distribution characteristics and driving mechanisms of interannual changes in regional water reserves. In this study, the Generative Adversarial Imputation Network (GAIN) is applied to improve the monitoring capability for flood events in the Pearl River Basin (PRB). First, the GRACE/GRACE-FO TWSA data gap is imputed with GAIN and compared with long short-term memory (LSTM) and k-Nearest Neighbors (KNN) methods. Using the reconstructed data, we develop the Flood Potential Index (FPI) by integrating GRACE-based TWSA with precipitation data and analyze key characteristics of FPI variability against actual flood events. The results indicate that GAIN effectively predicts the GRACE/GRACE-FO TWSA gap, with an average improvement of approximately 50.94% over LSTM and 68.27% over KNN. The reconstructed FPI proves effective in monitoring flood events in the PRB, validating the reliability of the reconstructed TWSA. Additionally, the FPI achieves a predictive accuracy of 79.7% for real flood events, indicating that short-term flood characteristics are better captured using TWSA. This study demonstrates the effectiveness of GAIN in enhancing data continuity, providing a reliable framework for large-scale flood risk assessment and offering valuable insights for flood management in vulnerable regions.

A semantic notation for comparing global high-resolution coastal flooding studies

IntroductionGlobal coastal flooding maps are now achieving a level of detail suitable for local applications. The resolution of these maps, derived from widely available open data sources, is approaching that of local flooding maps (0.5–100 m), increasing the need for a standardized approach to evaluate underlying assumptions and indicators for local applications.MethodsThis study introduces the Waterlevel, Elevation, Protection, Flood, Impact, Future (WEPFIF) notation, a structured notation for documenting and comparing key methodological choices and data variations across global coastal flooding studies. This approach enhances the understanding and explanation of the fitness-for- purpose of flood maps. This notation builds on commonly used methodological choices, dataset variations, and model approaches in global flooding risk research. Analysis of these workflows identifies common elements and highlights the need for a more structured reporting approach to improve comparability.ResultsApplying the WEPFIF notation to a case study in the Netherlands reveals significant variations in flood risk assessments originating from differences in Digital Elevation Model (DEM) and water level selection, and inclusion of protective infrastructure.DiscussionWEPFIF, by annotating these methodological variations, enables more informed comparisons between local and global flood studies. This allows researchers and practitioners to select appropriate data and models, based on their specific research objectives. The study proposes tailored approaches for three common types of flood studies: raising concern, optimizing flood protection investments, and representing the state of coastal risk.

Characteristics and Comparative Assessment of Flash Flood Hazard Evaluation Techniques: Insights from Wadi Haily Basin, Eastern Red Sea Coast, Saudi Arabia

The Wadi Haily basin in southwest Saudi Arabia, which runs along the Red Sea coast, serves as an ideal natural laboratory for understanding flash flood dynamics in this region. However, limited morphometric and hydrological data are currently available in this area. This study aims to analyze key morphometric effective parameters to examine and assess flash flood risk potential within the basin. Using remote sensing, GIS, geological, and topographical datasets, this research combines advanced modeling and GIS tools to produce detailed flood hazard maps and risk assessments. This study examines 15 sub-basins of varying sizes, characterized by primary stream orders ranging from 4th to 8th. Based on morphometric analysis, the basins are categorized by flood susceptibility: four basins have a low flood risk, five exhibit moderate risk, and six are highly susceptible to flooding. Key findings indicate that the study area features a vast drainage area, high grid cell values of the drainage frequency, moderate drainage density, elongated basin shapes, low infiltration rates, and long overland flow distances, all suggesting a heightened flood hazard. Additional indicators include high values in gradient ratios, slopes, ruggedness numbers, relief ratios, and basin relief, reinforcing the basin’s flash flood vulnerability. This study provides a comprehensive morphological framework that can support strategic flood management and hazard mitigation planning for the Wadi Haily region.

Evaluation of Flood Hazards and Impacts on the Environment: A Case Study of Louisville, Kentucky, USA, Using Remote Sensing and Geographic Information Systems (GIS)

This study evaluates flood hazards and environmental impacts in Louisville, Kentucky, using remote sensing and Geographic Information Systems (GIS). A comprehensive flood risk assessment framework was developed utilizing various data, including: digital elevation model, land use/land cover, soil, boundary, stream network, precipitation, and road data. These data were used for delineation, discretization, parameterization, and simulation of flooding events-a flood hazard map identified high, medium, and low-risk areas, with infrastructure at risk evaluated. Notably, high-hazard regions are located near stream networks. The demographic analysis revealed that males reside predominantly in high and medium hazard areas, Ages 22-29 and 30-39 are concentrated in southwest high and medium-hazard regions; Younger populations (<5, 5-17, 18-21) and older adults (40-49, 50-64) reside in central and northwest medium and low-hazard areas. This study shows flood water distribution is influenced by bottlenecks and critical points during specific discharges, impacting local populations in the environment. Key findings inform flood risk management strategies, infrastructure planning, and emergency response initiatives, ultimately enhancing resilience in Louisville.