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Extreme rainfall events are frequent, particularly in economically underdeveloped hilly areas, where conventional hydrological models struggle to accurately simulate the formation of flash floods. Therefore, this study focuses on the Daxi River Basin in Guangdong Province. First, CMIP6 precipitation data is utilized to analyze the future precipitation variations on interannual and monthly scales. Compared to the baseline period, the annual precipitation increases under all three scenarios. Next, design storms with a return period greater than 2 years are allocated into rainfall patterns. By combining the accumulated precipitation with the soil moisture content, different distributed hydrological models are applied to calculate the corresponding flood discharges for different rainfall events. The results indicate that: 1) Precipitation under the SSP5-8.5 scenario is generally higher than under the SSP1-2.6 and SSP2-4.5 scenarios, with the SSP1-2.6 scenario showing the mildest increase. 2) The peak flood simulated by the CREST model are relatively low, at 235.4 m³/s, with fewer precipitation events covered, which is significantly lower than the simulation accuracy of the CNFF model. 3) The Daxi River Basin has a low probability of experiencing flash flood disasters exceeding the 10-year return period in the period from 2026 to 2070. The above research results will provide important references for flash flood disaster prevention in similar basins. [ABSTRACT FROM AUTHOR]

This paper presents methods for a UAV-based survey of the site "Kuklica" near Kratovo, North Macedonia. Kuklica is a rare natural complex with earth pyramids, and because of its exceptional scientific, educational, touristic, and cultural significance, it was proclaimed to be a Natural Monument in 2008. However, after the proclamation, the interest in visiting this site and the threats in terms of its potential degradation rapidly grew, increasing the need for a detailed survey of the site and monitoring. Given the site's small size (0.5 km2), the freely available satellite images and digital elevation models are not suitable for comprehensive analysis and monitoring of the site, especially in terms of the individual forms within the site. Instead, new tools are increasingly being used for such tasks, including UAVs (unmanned aerial vehicles) and LiDAR (Light Detection and Ranging). Since professional LiDAR is very expensive and still not readily available, we used a low-cost UAV (DJI Mini 4 Pro) to carry out a detailed survey. First, the flight path, the altitude of the UAV, the camera angle, and the photo recording intervals were precisely planned and defined. Also, the ground markers (checkpoints) were carefully selected. Then, the photos taken by the drone were aligned and processed using Agisoft Metashape software (v. 2.1.4), producing a digital elevation model and orthophoto imagery with a very high (sub-decimeter) resolution. Following this procedure, more than 140 earth pyramids were delineated, ranging in height from 1 to 2 m and to 30 m at their highest. At this stage, a very accurate UAV-based 3D model of the most remarkable earth pyramids was developed (the accuracy was checked using the iPhone 14 Pro LiDAR module), and their morphometrical properties were calculated. Also, the site's erosion rate and flash flood potential were calculated, showing high susceptibility to both. The final goal was to monitor the changes and to minimize the degradation of the unique landscape, thus better protecting the geosite and its value. [ABSTRACT FROM AUTHOR]

Flash floods, which are influenced by hydro-meteorological conditions, are increasingly being triggered in the Uttarakhand Himalayas due to human-induced environmental and climatic changes. The catchment areas of Himalayan rivers in Uttarakhand experience flash floods every year, primarily caused by heavy rainfall and glacial lake outburst floods (GLOFs). Despite the severity of the issue, few studies have used optimized deep learning methods for robust flash flood susceptibility modeling (FFSM) and mapping. Therefore, this study aims to introduce a novel approach for FFSM using optimized deep learning (DL) models and to identify the most influential factors for prediction using SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDPs). In this study, two optimized DL models, namely the Deep Neural Network (DNN) and Convolutional Neural Network (CNN), were trained for FFSM. A spatial database was constructed using 320 past flash flood and non-flash flood sample and twelve flash flood influencing factors: Elevation, Slope, Curvature, Normalized Difference Vegetation Index (NDVI), Topographic Ruggedness Index (TRI), Stream Power Index (SPI), Land Use and Land Cover (LULC), Distance from River, Drainage Density, Topographic Wetness Index (TWI), Annual Rainfall, and Geology. The predictive performance of the models was validated and compared using statistical evaluation metrics, including the Receiver Operating Characteristic (ROC) curve, Precision-Recall Curves (PRC), accuracy, precision, recall, and F1 score. The results show that 4 to 6% of the areas were predicted as being in the very high flood susceptibility zone in both models, demonstrating high accuracy with strong areas under the curve (AUC) for both the ROC and PRC. The DNN model achieved an AUC of 0.91 and 0.94 for prediction, with accuracy, precision, recall, and F1 scores of 0.8265, 0.8723, 0.7885, and 0.8283, respectively. The CNN model achieved an AUC of 0.92 and 0.95, with corresponding accuracy, precision, recall, and F1 scores of 0.8776, 0.8704, 0.9038, and 0.8868. SHAP and PDP analyses revealed that Elevation, Slope, Annual Rainfall, Drainage Density, LULC, NDVI, Distance from River, and TWI were the most influential factors for the trained FFSM. This prediction accuracy emphasises the potential of these models as reliable tools for the strategic planning of flood protection measures. This research thus demonstrates that the use of optimized DL models can significantly improve flash flood susceptibility mapping and provide a quantitative and methodologically sound approach to mitigating the negative impacts of flash floods. The results can help stakeholders to make informed decisions to reduce the risks of flash floods and ensure the safety of people and infrastructure in vulnerable areas. [ABSTRACT FROM AUTHOR]

Since the onset of the Industrial Revolution, significant climatic shifts have led to various environmental imbalances globally, notably increasing the frequency of flash floods, especially in vulnerable regions like the Assaka watershed in southwestern Morocco. This study aims to enhance flash flood risk prediction by integrating machine learning (ML) algorithms with geographic information system (GIS) technology. The random forest (RF) algorithm was employed to analyze over eight million data points, using fourteen predictors categorized into topographic (e.g., altitude, slope, topographic wetness index (TWI)), climatic (e.g., land surface temperature (LST), soil moisture index (SMI)), and geological factors (e.g., drainage density, soil type, lithology). These variables were derived from remotely sensed data and geospatial analyses. The RF model classified the Assaka watershed into five flood susceptibility levels: lowest, low, medium, high, and highest. The results indicated that the most vulnerable areas are near the watershed outlet and the main tributaries, Essayed and Oum Laachar Wadis. These regions are characterized by high land surface temperatures, low drainage density, poor soil moisture, and specific geological conditions, all of which contribute to heightened flood risk. The model's performance was evaluated using multiple metrics, achieving precision (0.968), recall (0.967), accuracy (0.967), F1 score (0.965), Kappa statistic (0.839), and an AUC of 1.0, highlighting its robustness and predictive capabilities. The originality of this study lies in its comprehensive integration of ML with GIS to develop a highly reliable flood susceptibility map for the Assaka watershed. This framework addresses existing gaps in flood risk assessment, offering a significant advancement over traditional methods through its use of advanced data-driven modeling techniques. The findings provide essential insights for prioritizing conservation and flood management strategies, contributing to better preparedness against flash floods in the Guelmim region and potentially other similar environments globally. [ABSTRACT FROM AUTHOR]

This research aimed to analyze the level of flood vulnerability in Masamba urban area, using quantitative and qualitative descriptive approaches. Data analysis techniques were carried out by weighting, scoring, and overlaying for each flood-causing parameter map, including; slope map, rainfall, land elevation, soil type, and land use. Delineation of flood-prone areas is determined based on spatial analysis between the detailed spatial plan map of Masamba urban area and the maps of these parameters, obtaining the area and classification of the level of vulnerability to flood disasters in the Masamba urban area. The results of the analysis show that the low flood vulnerability level is located in the Kamiri village, Masamba village and part of the Kappuna village with an area of 83.39 Ha or 7.22%, the medium flood vulnerability level covers the Radda and Baliase villages with an area of 824.75 Ha or 71.58%, while the Bone and Bone Tua Villages are included in the high vulnerability classification category with an area of 244.07 Ha or 21.18% of the Masamba urban detailed spatial plan (RDTR) area. On the basis of the research findings, mitigation efforts are needed in spatial planning in North Luwu Regency. Directions for spatial utilization in the flood-prone Masamba urban area, including; development of a flood disaster mitigation system with structural and non-structural methods. Recommendations for spatial utilization in Masamba urban area to minimize similar flash flood events in the future are reviewing the RDTR of Masamba urban area, revising the urban drainage system master plan, and modeling the classification of flood-prone areas. With the availability of flood vulnerability data in North Luwu Regency, it can be an input for the government and the community as a basis for decision making in spatial planning, spatial utilization and spatial control to create a resilient and sustainable Masamba City. [ABSTRACT FROM AUTHOR]

Flooding can cause considerable damage to flexible pavements and negatively affects their performance over time. This study aims to evaluate the flooding effects on the flexible pavement performance and the potential resiliency of the pavement network through a mechanistic-empirical (ME) pavement analysis approach. The Unified Pavement Distress Analysis and Prediction System (UPDAPS) programme, which includes ME analysis models, was used to develop the UPDAPS-Flood programme. A total of 7,655 flexible pavement sections were extracted from the Highway Pavement Management System (HPMS) database in three United States regions. Finally, the resiliency of the pavement network to flood events was quantified using the loss of pavement service life based on the international roughness index (IRI). The results showed that flooding has the highest impact on rutting performance, followed by IRI and fatigue cracking. It has been found that a proper drainage system can reduce the flooding impact by a factor of two. The predicted pavement performance was found to be very sensitive to the coefficients of the rutting model, which brings the need for further studies to re-calibrate the rutting prediction model at higher moisture content levels. [ABSTRACT FROM AUTHOR]

Solid material sources, such as sediment, large wood, and vehicles, intensify flash flood hazards. This paper provides a detailed review of processes involving the recruitment, entrainment, transport, and blockage dynamics of various solid material sources. Results indicate that sediment supplied by processes like landslides and debris flows can obstruct river channels, leading to a sudden increase in flash flood levels. The failure of a barrier dam results in an expansion of downstream inundation areas. Large wood and floating vehicles transported by flash floods and debris flows may directly impact and destroy built structures or form blockages at built structures. Blockages lead to a backwater rise, and the sudden amplification of flow during the failure of these blockages causes more severe disasters. Based on these analyses, the paper proposes future research directions primarily focusing on the changes in sediment burial processes caused by the sheltering effects of building groups. Furthermore, the study aims to investigate the flow amplification effects of large wood and vehicle blockage. [ABSTRACT FROM AUTHOR]

With global warming and the increase in extreme precipitation events, floods are becoming more frequent in mountainous areas, and the safety of lives and property of people is seriously threatened. However, understanding of the flooding process in uninformative mountainous areas is limited due to the lack of high-quality hydrometeorological data. Hence, this study adopts the MIKE21 model to simulate flood inundation in the Shadai River basin in the Qilian Mountain region of the northern Tibetan Plateau as an example. This validates the modelsimulated flow and inundation extent using the flow data obtained from the calculation of the flood trace points, extent of inundation, and high-resolution remote sensing images. The results show that the flash flood inundation mainly occurs at 12:00-01:00 AM on 18 August 2022, and the simulated and actual maximum inundation areas are 7.9 and 9.5 km², respectively. The fitted F-statistic value is 0.81, and the relative error between the calculated flow rate of the flood trace point and the model-simulated flow rate is 8%, indicating good consistency. Furthermore, an in-depth exploration of the model parameter sensitivity reveals that the use of distributed Manning's roughness coefficient value has higher simulation accuracy. [ABSTRACT FROM AUTHOR]

This study assesses and maps the vulnerability index of Bangladesh at the subdistrict level to a range of natural and climate-induced disasters. Four vulnerability index maps are created using principal component analysis and categorized into five risk levels: (1) no/very low risk, (2) low risk, (3) moderate risk, (4) high risk and (5) very high risk for each sub-district. The results reveal that the south east region is highly vulnerable to cyclones, Haor region stands out as the most vulnerable area for flash floods, with numerous subdistricts facing very high to high risk levels; northern and north-eastern regions are prone to cold waves, while the western part of Bangladesh is highly vulnerable to heat waves. This comprehensive spatial analysis provides critical information for disaster risk reduction and adaptation strategies, assisting decision-makers in identifying the most vulnerable areas and prioritizing interventions. The findings of this study might be useful for policymakers as well as planners. [ABSTRACT FROM AUTHOR]

This research seeks to develop and test a rapid mapping approach using unmanned aerial vehicles (UAVs) and terrestrial laser scanning to provide precise, high-resolution spatial data for urban areas right after disasters. This mapping aims to support efforts to protect the population and infrastructure while analyzing the situation in affected areas. It focuses on flood-prone regions lacking modern hydrological data and where regular monitoring is absent. This study was conducted in resort villages and adjacent catchments in Bulgaria's southern Black Sea coast with leading maritime tourism features, after a flash flood on 5 September 2023 caused human casualties and severe material damage. The resulting field data with a spatial resolution of 3 to 5 cm/px were used to trace the effects of the flood on topographic surface changes and structural disturbances. Flood simulation using UAV data and a digital elevation model was performed. The appropriateness of contemporary land use forms and infrastructure location in catchments is discussed. The role of spatial data in the analysis of genetic factors in risk assessment is commented on. The results confirm the applicability of rapid mapping in informing the activities of responders in a period of increased vulnerability following a flood. The results were used by Bulgaria's Ministry of Environment and Water to analyze the situation shortly after the disaster. [ABSTRACT FROM AUTHOR]

Flash flooding is one of the most noteworthy natural disasters in arid regions, especially in urban areas. The Helwan-Atfih area is a heavily populated region and part of the Eastern Desert drylands of Egypt. It is characterized by ten drainage basins that dissect it and drain toward the Nile River (Wadies of Degla, Hof, Al-Gebbu, Garawy, Hera, Al-Hay, Al-Werg, Al-Nowya, Al-Reshrash, and AL-Atfehe). Landsat-8, STRM-DEM, and CFSR remote sensing satellite data of 15 m, 30 m, and 0.3-degree resolution, respectively, were prepared and utilized to evaluate flooding hazards within the study area using the GIS-weighted overlay technique. Weighted overlay analysis is a GIS-based multi-criteria decision-making technique. This technique was performed to delineate the most vulnerable areas for flooding, depending on 14 thematic layers representing the multi-class factors that influence flood hazard (nine morphometric parameters, slope, relief, lineament density, surface lithology, and surface runoff). According to the morphometric parameters, the basins of the study area are characterized by moderate drainage densities, and moderately permeable subsoil. Limestone occupies 83.41% of the total lithological units within the basins' area, which indicates a high flooding potential. Steep slopes are primarily observed in the southern basins, especially in the Al-Reshrash basin. Wadi Al-Atfehe and Wadi Al-Reshrash have the lowest lineament density areas, reflecting a higher flooding hazard. The total runoff volume ranges between 2.42 × 106 and 12.08 × 106 m3. Based on the results, Wadi Al-Reshrash receives the highest runoff volume (12.08 × 106 m3) and has the highest slope degree (57○-71○). 85.4% of its area is covered with limestone and it has a low to moderate lineament concentration. Accordingly, Wadi Al-Reshrash is the most prone basin to flooding within the study area, followed by Wadi Al-Werg, while the other basins show a moderate flood hazard degree. [ABSTRACT FROM AUTHOR]

The extreme precipitation resulting from climate change has been causing increasingly serious damage in populated areas over the past 10–15 years. The torrents of flash floods cause significant financial damage to both the natural environment and man-made structures (such as roads and bridges). The determination of the physical geographic parameters of this phenomenon (e.g. the amount of runoff water) is significantly affected by technical uncertainties, usually due to the lack of monitoring systems. However, the application of modern geospatial tools can improve the quality of input data needed for runoff modelling. In the present study, an existing runoff model (the Stowe model) developed by ESRI was further enhanced with field measurements, soil parameters, GIS, and remote sensing data, resulting in the creation of the model named ME-Hydrograph. Finally, the two models were compared, and we examined the capacity of an urban stormwater drainage system through surface runoff modelling. The aim of the research was to create a runoff model that can be easily and quickly used. The application of this geospatial model presented in the study can be useful not only in the examination of urban stormwater drainage but also in contributing to the understanding and management of flash floods that occur in Hungary. Additionally, it can aid in the development of risk mapping related to flash floods in the country.

This research investigates the efficacy of the cloud-resolving Weather Research and Forecasting (WRF) model in reproducing convective cells associated with flash-flooding heavy rainfall near Peja, Northeast Kosovo, on June 24, 2023. Employing two distinct dynamical cores and a unique numerical setup for the Kosovo domain, numerical experiments were conducted. The study employed a triply nested WRF-ARW model with a high resolution of 3 km horizontal grid spacing, integrating conventional analysis data. Additionally, experiments using the WRF-NMM core with 3 km for a larger domain covering Southeast Europe and Kosovo domain were executed to simulate the specific event. The WRF model accurately simulated the initiation of isolated thunderstorms, convective band formation, cloud cluster, and squall line at the opportune time. While precipitation distribution was reasonably replicated, there was a slight underestimation in the amount. Hydrological analysis of precipitation, including river discharge rates provided from ECMWF ERA5 reanalysis, identified a unique storm category with intense precipitation production, registering an intensity of approximately 54.6 mm in 1 h, leading to sudden flash flooding. [ABSTRACT FROM AUTHOR]

A flash flood is a natural phenomenon generally occurring in regions with dense and compact rainfall. The arid Far-North Region of Cameroon (FNRC) is subject to such climate conditions which result in recurrent flash flood events. Those events often cause numerous deaths and important property damage. This article aims at mitigating and reducing flood risks in the FNRC using a GIS-based multicriteria decision-making technique. For this, data were collected from the radar sensor ALOS PALSAR 2, the optical sensor Landsat 9 Operational Land Imager (OLI), and WorldClim 2. From the aforementioned datasets, ten influencing layers, namely curvature, drainage density, elevation, distance to rivers, distance to lakes, land use/land cover (LULC), rainfall, slope, stream power index (SPI) and topographic witness index (TWI) were prepared, normalized, and combined on a GIS environment. The resulting map of the flood susceptible zones (FSZ) reveals two-fifths of the FNRC is seriously threatened by flash flood events. FSZ are clearly demarcated and mapped, and this map is of paramount importance for sustaining safe settlements in the FNRC. In the context of scarce ground data, as in the FNRC where there is a single rain gauge located at the airport, a combined remote sensing-analytical hierarchy process is effective for flash flood investigation. This approach can help in flash flood analysis in other regions of the world. [ABSTRACT FROM AUTHOR]

Flash floods rank among the most catastrophic natural disasters worldwide, inflicting severe socio-economic, environmental, and human impacts. Consequently, accurately identifying areas at potential risk is of paramount importance. This study investigates the efficacy of Deep 1D-Convolutional Neural Networks (Deep 1D-CNN) in spatially predicting flash floods, with a specific focus on the frequent tropical cyclone-induced flash floods in Thanh Hoa province, North Central Vietnam. The Deep 1D-CNN was structured with four convolutional layers, two pooling layers, one flattened layer, and two fully connected layers, employing the ADAM algorithm for optimization and Mean Squared Error (MSE) for loss calculation. A geodatabase containing 2540 flash flood locations and 12 influencing factors was compiled using multi-source geospatial data. The database was used to train and check the model. The results indicate that the Deep 1D-CNN model achieved high predictive accuracy (90.2%), along with a Kappa value of 0.804 and an AUC (Area Under the Curve) of 0.969, surpassing the benchmark models such as SVM (Support Vector Machine) and LR (Logistic Regression). The study concludes that the Deep 1D-CNN model is a highly effective tool for modeling flash floods. [ABSTRACT FROM AUTHOR]

The extreme precipitation resulting from climate change has been causing increasingly serious damage in populated areas over the past 10-15 years. The torrents of flash floods cause significant financial damage to both the natural environment and man-made structures (such as roads and bridges). The determination of the physical geographic parameters of this phenomenon (e.g. the amount of runoff water) is significantly affected by technical uncertainties, usually due to the lack of monitoring systems. However, the application of modern geospatial tools can improve the quality of input data needed for runoff modelling. In the present study, an existing runoff model (the Stowe model) developed by ESRI was further enhanced with field measurements, soil parameters, GIS, and remote sensing data, resulting in the creation of the model named ME-Hydrograph. Finally, the two models were compared, and we examined the capacity of an urban stormwater drainage system through surface runoff modelling. The aim of the research was to create a runoff model that can be easily and quickly used. The application of this geospatial model presented in the study can be useful not only in the examination of urban stormwater drainage but also in contributing to the understanding and management of flash floods that occur in Hungary. Additionally, it can aid in the development of risk mapping related to flash floods in the country. [ABSTRACT FROM AUTHOR]

Flash flooding has become more prominent under climate change, threatening people's life and property. Post‐event investigations of recent events emphasize the role of floating debris, such as vehicles, in exacerbating damage. Few modeling methods and tools have been developed to simulate the full‐process dynamics of floating debris driven by large‐scale flood waves in real world. In this work, a fully coupled model is developed for simulating the full‐process interactive movements of vehicles driven by flash flood hydrodynamics, from entrainment, transport to deposition. The proposed coupled modeling system consists of a finite volume shock‐capturing hydrodynamic model solving the 2D shallow water equations and a 3D discrete element method (DEM) model. The proposed two‐way coupling approach estimates the hydrostatic and hydrodynamic forces acting on solid objects using the water depth and velocity predicted by the hydrodynamic model; the resulting counter forces on the fluid flow are then considered by adding extra source terms in the hydrodynamic model. A multi‐sphere method is further embedded in the DEM model to better represent vehicle shapes. New calculation modules are further implemented to represent the vehicle entrainment, contact and stopping motions. The coupled model is applied to reproduce a flash flood event hit Boscastle in the UK in 2004. Over 100 vehicles were moved and carried downstream by the highly transient flood flow. The model well predicts the hydrodynamics, interactive transport process and the final locations of vehicles. The proposed coupled model provides a new tool for simulating large‐scale flash flooding processes, including debris dynamics. Key Points: A new coupled model for simulation of entrainment, transport and deposition of vehicles driven by and interacting with flood hydrodynamicsThe model is used to reproduce a flash flood event that moved over 100 vehicles, with results consistent with post‐event report and surveyIncreasing number of floating vehicles alters flood hydrodynamics and intensifies debris‐debris and debris‐fluid interactions [ABSTRACT FROM AUTHOR]

This study assesses the effectiveness of a distributed physically based hydrological model (MARINE) to investigate erosion estimation during flash floods compared with other widely used empirical models derived from the Universal Soil Loss Equation (USLE) like Revised Universal Soil Loss Equation (RUSLE) and Modified Universal Soil Loss Equation (MUSLE). It is carried out on a small catchment in south‐eastern France, the Claduègne catchment. To compare the erosion volumes simulated by the three models, MARINE, MUSLE and RUSLE, a sensitivity analysis on the model parameters is carried out. According to physics‐based simulations, flood events fall into two categories: those dominated by raindrop erosion and those dominated by shear stress erosion. The results show that the erosion simulated by the three methods are comparable, except for events dominated by raindrop erosion suggesting that further research is needed to improve raindrop erosion within MARINE. Simulations from the MARINE model provide access to the spatio‐temporal variability of erosion dynamics during the event and can also be used to produce erosion/deposition maps, which are useful for environmental decision‐makers and planners in identifying areas at risk from erosion and deposition hazards. [ABSTRACT FROM AUTHOR]

The current study seeks to investigate the temporal and spatial distribution of flood events and casualties in the Yamuna River basin from 1978 to 2014. The data were compiled from the published reports on disastrous weather events and analyzed with simple quantitative methods (sums, averages, and percentages). The analysis revealed the occurrence of a total of 1886 flood events, which resulted in 5481 casualties (148 per year). The years 1995, 2003, 1993, and 1996 were observed as the most devastating years, contributing about 30 percent of the total flood events, while 1978, 1980, 1981, 1995, 2006, and 2013 were the deadliest years in terms of casualties (47 percent). The largest number of flood events and casualties were recorded during June–September months. Considering the event/casualty density and rates, the states of Himachal Pradesh and Uttarakhand experienced the highest event density (0.036 and 0.091) and casualty rates (2.1 and 1.9 persons). These findings will help in identifying and demarcating the worst flood-affected areas over the Yamuna River basin, which can subsequently help in reducing the flood risk and human casualties in these regions after initiating suitable flood mitigation measures. [ABSTRACT FROM AUTHOR]

Flash floods can carry substantial sediment, posing significant sedimentation hazards in hilly cities. The sedimentation hazard map can reproduce the sediment thickness and extent of an extreme events scenario, playing an important role in sediment risk management. However, current research primarily focuses on modeling the inundation area and depth of floods, while studying sedimentation hazard caused by flash floods in urban areas remains insufficient. This paper aims to address this gap by utilizing a numerical model that simulates hyperconcentrated flow in hilly urban areas using the two‐dimensional hydro‐sediment‐morphological model to compile the cumulative sedimentation hazard map. The model, built upon the open‐source TELEMAC‐MASCARET framework, incorporates Zhang Hongwu's formula to simulate sediment‐carrying capacity, particularly suitable for hyper‐sediment concentration near the riverbed. This paper uses the data of extreme flash flood events in the Wuding River basin in 2017 to simulate and compile the cumulative sedimentation hazard map. The hazard map delineates the sedimentation hazard extent and level attributable to overbank floodplain sedimentation. Notably, the sediment thickness is highest in areas near the levees on both sides of the Dali River. Moreover, the map illustrates the extent of channel erosion resulting from hyperconcentrated floods, which could jeopardize bank stability. [ABSTRACT FROM AUTHOR]

Extremely rapid rates of rise in level and discharge in a subset of flash floods ('abrupt wave front floods', AWF) are separate hazards from peak level. Such flood events are investigated for Pennine catchments in northern England using both gauged and historical information. Gauged level and flow digital records at 15‐min intervals provide recent data. Historical information for 122 AWF events is extracted from a chronology of flash floods for Britain. Historical AWF events are mapped and found to occur on every major Pennine catchment; catchment descriptors are derived as a basis for assessing catchment vulnerability. We discuss the disputed origin of AWF. Using gauged data, we contrast the rising limb of AWF and 'normal' floods. We investigate time series of historical AWF, noting a puzzling peak in the late 19th century. Current rainfall and river monitoring does not provide a reliable basis for understanding AWF processes or for operational response and we suggest improvements. Similarly, current models for design flood estimation and forecasting do not generate the observed rapid increase in level in AWF floods. [ABSTRACT FROM AUTHOR]

A geographic information system (GIS) is a tool and technology capable of addressing the effects and challenges of natural disasters, particularly flash floods. GIS applications are used to generate flood risk maps to tackle flood issues. However, various challenges and problems arise when employing GIS to manage flash flood disasters in Shah Alam, Malaysia. Hence, this study aims to identify these challenges and gaps in GIS utilisation by Malaysian agencies for flash flood management in Shah Alam. Using the quadruple helix model technique, informal interviews were conducted as part of the study's qualitative methodology. Five respondents were chosen from each of the four main sectors for primary data collection: government, academia, business, and community/NGO. The data were analysed using Taguette qualitative theme analysis. The findings reveal that the primary challenges lie in government management, particularly in providing equipment and access to GIS for all stakeholders, including the public. This challenge is attributed to the high costs and complexity associated with GIS data usage, limiting accessibility. Furthermore, there is a lack of expertise and research on GIS in Malaysian universities concerning flash flood management. The government should take proactive steps to improve flash flood management in Shah Alam, Malaysia, in order to solve these issues. Specifically, GIS training should be given to stakeholders, particularly those in the government and academic sectors, in order to develop GIS specialists who will be necessary for efficient flood management in Malaysia. [ABSTRACT FROM AUTHOR]

Floods have become increasingly frequent and devastating in recent decades, posing unignorable risks as highly destructive natural hazards. To effectively manage and mitigate these risks, accurate flood hazard mapping is crucial. Machine learning models have emerged as valuable approaches for flood hazard assessment. In this study, six machine learning (ML) models, including Maximum Entropy, Support Vector Machine, Extreme Gradient Boosting (XGB), Random Forest (RF), multi-layer perceptron, and Naive Bayes, were utilized to evaluate urban flood hazard in Zaio, NE Morocco, and estimate the flood presence extent. Nine flood conditioning factors were used as input variables. Historical flood presence and absence data were employed for models training and testing, incorporating 663 flood presence and absence locations dating back to past flood events. Performance evaluation metrics such as Kappa statistic, accuracy, sensitivity, specificity, and area under the curve (AUC) were calculated for each model. RF (AUC = 0.92) and XGB (AUC = 0.9) models showed excellent classification capabilities, surpassing the performance of the other models, while the other models exhibited lower but recognizable performances. Additionally, the hazard presence extent maps generated by the ML models exhibited a decent alignment with a historical flood event maps created by the hydrodynamic and the cellular automata models. The results imply that ML models offer effective solutions for mapping urban flood hazards. The innovative integration of various ensemble and single ML models demonstrates their potential in urban flood hazard susceptibility and extent mapping, effectively surpassing the limitations associated with limited availability of hydrologic/hydraulic data and computational burden. These mapped results can be instrumental for local authorities in shaping mitigation strategies in the city of Zaio. [ABSTRACT FROM AUTHOR]

Flash floods are recognized as some of the most devastating natural disasters globally, causing significant damage to socio-economic infrastructures, ecosystems, and human lives, thus highlighting the critical need for accurately identifying areas at risk. In order to address this challenge, our study introduces a novel approach by integrating Harris Hawks Optimization (HHO) with the TensorFlow Deep Neural Network (TFDNN), termed HHO-TFDNN, for assessing flash flood susceptibility. The innovation of HHO-TFDNN resides in its dual structure: TFDNN is employed to develop flash flood prediction models, while HHO is utilized to optimize their parameters. This methodology was applied to a region in northern Vietnam, frequently impacted by flash floods. A detailed flash flood database was assembled using various geospatial data sources for the model's training and validation. The results underscore the model's exceptional predictive accuracy, demonstrated by a high F-score of 0.913, a Kappa statistic of 0.825, and an overall accuracy of 91.2%. These findings establish HHO-TFDNN as a highly effective tool for predictive modeling in flash flood management. [ABSTRACT FROM AUTHOR]

Peak discharge is an essential element of hydrological forecasting. Due to rapid outbreaks of flash floods in hilly areas and the lack of measured data, the fast and accurate estimation of peak discharge is crucial for flash flood hazard management. Three machine learning algorithms were applied to estimate peak discharge; this estimation was compared with the results of hydrological--hydraulic models, and the results were verified with measured watershed data. In this paper, 10 hydrological and geomorphological parameters were selected to predict the flood peak discharge in 103 watersheds in Taiyi Mountain North District. The results show that the particle swarm optimization backpropagation (PSO-BP) neural network model outperforms the BP neural network and random forest regression in prediction performance. PSO-BP has a lower mean absolute error (2.51%), root mean square error (3.74%), and mean absolute percentage error (2.74%) than the other models, which indicates that PSO-BP has high prediction accuracy. Importance analysis revealed that rainfall, early impact rainfall, catchment area, and rain intensity are the key input parameters of PSO-BP. The proposed method was confirmed to be a fast and relatively accurate algorithm for estimating the peak discharge of flash floods in ungauged basins. [ABSTRACT FROM AUTHOR]

Short-calendar-time flash flood is one of the serious disasters threatening the safety of transportation of villages and towns in Longnan mountainous area. In order to improve the disaster prevention and mitigation capability of the area, it is of great scientific significance to calculate the degree of flood inundation loss through different rainfall intensities, and to guide the local people in the prevention of flash floods by using the flood risk zoning map. In this paper, based on the HEC-RAS hydrological analysis method, combined with GIS to simulate the flood inundation process of the watershed, and taking Pujiagou in Longnan mountainous area as the research object, the traditional flood risk zoning maps were optimized under the conditions of one-in-5-year, one-in-10-year, one-in-50-year, and one-in-100-year design rainfall, taking into account the multiple factors such as the slope, the land type, the loss rate, the water level, the flow rate, and so on. The results show that compared with the risk zoning map drawn by the traditional method, the optimized risk zoning map by the preferential map method pays more attention to the affected degree of the disaster-bearing body, and solves the shortcomings of the traditional risk zoning map, which is difficult to classify the risk level because of the large span of the risk level of the small areas. In the optimized risk zoning map, the risk level of the upstream and middle reaches of the uninhabited area is reduced, and the risk level of the downstream area of Maquan Village is more clear. Taking Wangjiazui in Maquan Village as an example, under the design rainfall of one-in-50-year, the risk zoning map drawn by the traditional method covers five risk levels, and the area difference of each zone is not significant, which makes it difficult to determine the final risk level. The optimized risk zoning map of the preferred method is more concentrated, and the area of Wangjiazui high-risk area is less than 4% of the medium-risk area, and the risk area of Wangjiazui can be clearly located in the medium-risk area. The optimized risk zoning map of this paper is more advantageous in practicality and adaptability, which can provide help for the early warning and the prediction of flash floods in small watersheds, and are also helpful to disaster prevention and mitigation work. [ABSTRACT FROM AUTHOR]

Global warming has accelerated the frequency and intensity of extreme rainfall events in mountainous areas. Coupled with their vulnerable environment and the impact of intensive human activities, along with the complex and variable causes of flash floods, this exacerbates casualties and property losses. Therefore, this article investigates the triggering mechanisms and potential disaster-causing factors of the extreme "720"flood in the WZD-HGZ basin of Henan. The research results indicate that the flash floods in the WZD-HGZ basin were primarily caused by prolonged heavy rainfall, combined with the complex terrain, obstructive backwater, and human activities. The amplification of the flood mainly occurred in three stages: concentrated runoff from multiple channels, water obstruction caused by the successive collapse of roadbeds and bridges, and the generation of backwater. Besides, due to the lack of basic flood prevention awareness, unclear warnings, and inadequate guidance, the transition chain from issuing warnings to taking action was disrupted. The aforementioned research findings provide references for current flash flood disaster prevention efforts. [ABSTRACT FROM AUTHOR]

Extreme rainfall events are frequent, particularly in economically underdeveloped hilly areas, where conventional hydrological models struggle to accurately simulate the formation of flash floods. Therefore, this study focuses on the Daxi River Basin in Guangdong Province. First, CMIP6 precipitation data is utilized to analyze the future precipitation variations on interannual and monthly scales. Compared to the baseline period, the annual precipitation increases under all three scenarios. Next, design storms with a return period greater than 2 years are allocated into rainfall patterns. By combining the accumulated precipitation with the soil moisture content, different distributed hydrological models are applied to calculate the corresponding flood discharges for different rainfall events. The results indicate that: 1) Precipitation under the SSP5-8.5 scenario is generally higher than under the SSP1-2.6 and SSP2-4.5 scenarios, with the SSP1-2.6 scenario showing the mildest increase. 2) The peak flood simulated by the CREST model are relatively low, at 235.4 m³/s, with fewer precipitation events covered, which is significantly lower than the simulation accuracy of the CNFF model. 3) The Daxi River Basin has a low probability of experiencing flash flood disasters exceeding the 10-year return period in the period from 2026 to 2070. The above research results will provide important references for flash flood disaster prevention in similar basins.

The development of a detailed strategy to mitigate the negative consequences of any natural calamities depends on accurately identifying sensitive zones where natural hazards frequently happen. In the present investigations, three widely utilized probabilistic approaches viz., frequency ratio (FR), statistical index (SI), and weighting factor (WF) have been utilized for prediction of flsh flood susceptibility zones in the Coochbehar urban and peri-urban area (CUPUA) (area = 26.22 km2). Ten flash flood conditioning factors have been used in this assessment based on previous literatures and experts' opinions. In the FR model, 29.40 % area is observed in the high and very high flood zones, whereas 36.27 % and 31.16 % area is identified in SI and WF model, respectively. The FR model demonstrates that five conditioning factors, viz., topographic position index (TPI), land use and land cover (LULC), normalized difference vegetation index (NDVI), distance to drainage (DtD) and rainfall were highly impacted in flash flood prediction (FFP) analysis; in SI model, LULC is the major influencing parameter, and in WF model LULC, rainfall, NDVI, and distance to road (DtR) are the effective parameters. The success rate curve of the FR, SI and WF models manifest SI model has highest training (AUC=0.903) and prediction (AUC=0.925) accuracy, and FR and WF also have very good accuracy as their AUC values are 0.899 and 0.877 (in success rate curve) and 0.900 and 0.881 (in prediction rate curve). Therefore, the application of probabilistic approaches in this active flash flood-prone region is excellently performed, and the results of this study will help hydrologists, engineers, and water management administrators to control the areas that are extremely susceptible to flash floods and reduce possible damages.

This study assesses and maps the vulnerability index of Bangladesh at the subdistrict level to a range of natural and climate-induced disasters. Four vulnerability index maps are created using principal component analysis and categorized into five risk levels: (1) no/very low risk, (2) low risk, (3) moderate risk, (4) high risk and (5) very high risk for each sub-district. The results reveal that the south east region is highly vulnerable to cyclones, Haor region stands out as the most vulnerable area for flash floods, with numerous subdistricts facing very high to high risk levels; northern and north-eastern regions are prone to cold waves, while the western part of Bangladesh is highly vulnerable to heat waves. This comprehensive spatial analysis provides critical information for disaster risk reduction and adaptation strategies, assisting decision-makers in identifying the most vulnerable areas and prioritizing interventions. The findings of this study might be useful for policymakers as well as planners.

This study addresses the challenges of flash flood susceptibility mapping in Yemen’s Qaa’Jahran Basin, characterized by complex terrain and limited hydro-meteorological data. To enhance predictive accuracy, we integrate metaheuristic feature selection with ensemble learning models. Initially, fifteen flash flood variables were retrieved using Geographic Information System (GIS) based remote sensing, setting the stage for a novel feature selection algorithm. Then, the Memo Search Algorithm (MSA), a metaheuristic approach is proposed to efficiently reduce feature space. Through comprehensive comparisons with established algorithms such as the Artificial Bee Colony (ABC) and Gray Wolf Optimizer (GWO), MSA refined the selection, identifying 'elevation’ and 'distance to streams’ as optimal factors. Statistical validations using the Friedman and Wilcoxon signed-rank tests confirmed the significant superiority of MSA over competing algorithms. Ensemble classifiers (bagging, boosting, stacking) were then applied to the reduced feature space. Comprehensive evaluation revealed the boosting ensemble with MSA outperformed traditional techniques reaching 98.75% accuracy, 0.9896 Area Under the Curve (AUC), and 98.95% the harmonic mean of the precision and recall (F1-score). Precision in identifying high-risk flash flood zones was underlined via spatial prediction, confirming the integrated framework’s ability to significantly improve forecast accuracy. The findings aid disaster management with powerful geographic mapping in data-poor regions. The proposed framework is adaptable globally for flash flood-prone areas with similar constraints. As climate change is expected to increase extreme rainfall events, communities globally will need robust data-driven methodologies for flash flood susceptibility mapping. The Key recommendations of the current study include investigating hybrid feature selection methods to better enhance predictive inputs and analyzing transferability across hydro-climatic zones.

The devastating 1968 flash flood in the River Chew, South-West of England, serves as a stark reminder of the unpredictable nature of such natural disasters and highlights the importance of natural hazard assessments. The uncertain and often incomplete historical data, and the limited field measurements at the time hindered our understanding of this event. By integrating historical evidence, including technical reports, newspapers, literature, and eyewitness accounts, with advanced hydraulic modelling (HEC-RAS 2D), this study reconstructs the 1968 flash flood. A sensitivity analysis of the computational methodologies in HEC-RAS, examining various governing equations and numerical methods, introduces an additional dimension to this research. The results verify a maximum flow rate of 165 m3/s at the Compton Dando hydrometric station, marking a 65% increase from the previous official estimate. This update aligns with over 90% of the historical flood marks observed. Findings suggest recalibrating hydrological models, revising risk assessments, and updating flood frequency analyses in the study area. This novel framework confronts the challenges of uncertain and incomplete historical records through a reverse engineering methodology to reconstruct missing peak discharges. The study also presents a new methodological blueprint that can be replicated for reconstructing historical flash flood events in various regions.

The Haors in Bangladesh are saucer-shaped, low-lying land depressions that form deep basins; they remain submerged for approximately half of the year, typically from June onwards. This fragile ecosystem spans over 2.0 million hectares in the northeastern region of the country, accounting for roughly 14% of the total areas, where approximately 19.4 million people reside. Factors including floods, flash floods, and low winter temperatures constrain agricultural productivity in the haor areas. It is a great challenge to change the haor areas from less productive to more productive land. This is a comprehensive analysis of the biophysical and socioeconomic characteristics of haors which also highlights the constraints and opportunities in agricultural production. It explores strategies for significantly increasing crop, livestock, and fish production within the haor ecosystem, in alignment with government policies. Some of the proposed agricultural development strategies for the haor areas include the development of short-duration, cold-tolerant crop varieties, such as Boro rice, utilizing relatively flood-free elevated lands and homesteads for vegetable production and promoting agricultural mechanization, livestock rearing, fisheries, and agribusiness development. The recommendations presented in this paper focus on enhancing crop yields, increasing cropping intensity, and boosting livestock and fish production; ultimately, they contribute to food security, poverty reduction, and improved livelihoods for the inhabitants of the haor areas.

Saudi Arabia is threatened by recurrent flash floods caused by extreme precipitation events. To mitigate the risks associated with these natural disasters, we implemented an advanced nationwide flash flood forecast system, boosting disaster preparedness and response. A noteworthy feature of this system is its national-scale operational approach, providing comprehensive coverage across the entire country. Using cutting-edge technology, the setup incorporates a state-of-the-art, three-component system that couples an atmospheric model with hydrological and hydrodynamic models to enable the prediction of precipitation patterns and their potential impacts on local communities. This paper showcases the system's effectiveness during an extreme precipitation event that struck Jeddah on 24 November 2022. The event, recorded as the heaviest rainfall in the region's history, led to widespread flash floods, highlighting the critical need for accurate and timely forecasting. The flash flood forecast system proved to be an effective tool, enabling authorities to issue warnings well before the flooding, allowing residents to take precautionary measures, and allowing emergency responders to mobilize resources effectively. [ABSTRACT FROM AUTHOR]

Changes to the physicochemical parameters of water in alpine/mountain streams can provide evidence of ongoing natural and anthropogenic processes in their catchment. In this study, we analysed a mountain stream (Javorinka) on the north-eastern side of the Tatra Mountains (Western Carpathians), which is minimally influenced by human activity. The stream was monitored weekly for 5 years (2017–2021) and evaluated for its seasonal variations in physicochemical parameters. These seasonal variations were influenced by the large summer flash flood in July 2018. We hypothesise that floods are essential for the oligotrophic profile of alpine/mountain streams. To support this idea, our main objective was to compare the seasonal trends of the main physicochemical parameters in the stream before and after floods or periods of high flow. We found evidence to support our hypothesis. For example, there was a significant decrease in the chemical consumption of oxygen and ammonia, and, conversely, an increase in the ratio of saturated oxygen and nitrate concentrations. Stream bed erosion also resulted in increased phosphates (over the next 2 years) and high enrichment of the water by dissolved solids in the spring. Interestingly outside of the main objectives, we observed a significant decrease in sulphates, especially in the summer and autumn of 2020 and 2021, which may be related to suppressed emissions due to the restriction of the COVID-19 lockdown. The observed trends and their changes therefore support the idea that alpine/mountain streams are excellent indicators of ongoing environmental processes, and that occasional summer flash floods support the oligotrophic profile of the stream system. [ABSTRACT FROM AUTHOR]

Jordan experienced flash floods that resulted in numerous fatalities and injuries. This research focuses on identifying the Wadi Mujib's flash flood potential zones and evaluating their potential magnitude. In this work, hydrological models were developed by integrating GIS settings with HEC-HMS software (V. 4.11). The hydrological model for Wadi Mujib is simulated in this research by means of the Soil Conservation Service (curve number method) while using rainfall data from 1970 to 2022. The results show that the optimum curve number values (CN) were 78.5 at normal antecedent moisture content. Additionally, in order to aid in the decision-making process for flash flood warnings, a flash flood potential index (FFPI) was also introduced based on four main physiographic parameters (slope, land use, plant cover, and soil texture) ranging from 1 to 10. The accumulative chart's FFPI threshold, which indicates the areas with the highest potential for flash floods, was set at 95% or above. The FFPI threshold was chosen using the accumulative chart of FFPI, which shows that the FFPM threshold value is 7 and covers 13.39% of the study area. [ABSTRACT FROM AUTHOR]

In the current study, the Generalized Structure of the Group Method Of Data Handling (GSGMDH) is developed to overcome the main drawbacks of the classical GDMH. The performance of the GSGMDH was checked in two case studies for multi-step flood forecasting at the upstream station (i.e., Saint-Charles station) using the historical records of upstream stations (i.e., Nelson and Croche stations). The results revealed high accuracy in flood forecasting one to six hours ahead for all sample ranges and peak flows, with indices showing R: [0.993, 0.9995], NSE: [0.986, 0.999], RMSE: [0.416, 1.453], NRMSE: [0.0239, 0.152], MAE: [0.146, 0.761], MARE: [0.023, 0.156], and BIAS: [-0.058, 0.01]. Indeed, the descriptive performance of the developed model rates as Very Good for both R and NSE, and Good for NRMSE. The uncertainty analysis of the GSGMDH models demonstrates remarkable precision in flood forecasting, with relative differences between the minimum and maximum uncertainty ranges of less than 1% for both Nelson and Croche upstream stations. Specifically, U95 for Nelson is [0.148, 0.149], and for Croche, it is [0.166, 0.167]. Besides, The reliability analysis of the GSGMDH highlights its effective peak flow forecasting capabilities, with MARE values for various flow discharges remaining below 10% across different lead times, demonstrating the model's precision in predicting high-impact flood events. Moreover, a comparison between the developed GSGMDH and the traditional model reveals that the former surpasses the latter, achieving a maximum relative error of less than 7%, in contrast to the traditional GMDH's minimum MARE exceeding 12%. [ABSTRACT FROM AUTHOR]