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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 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]

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]

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]

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]

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]

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.

With the advancement of society and the impact of various factors such as climate change, surface conditions, and human activities, there has been a significant increase in the frequency of extreme rainfall events, leading to substantial losses from flood disasters. The presence of numerous small and medium-sized water conservancy projects in the basin plays a crucial role in influencing runoff production and rainwater confluence. However, due to the lack of extensive historical hydrological data for simulation purposes, it is challenging to accurately predict floods in the basin. Therefore, there is a growing emphasis on flood simulation and forecasting that takes into account the influence of upstream water projects. Moushan Reservoir basin is located in a hilly area of an arid and semi-arid region in the north of China. Flooding has the characteristics of sudden strong, short confluence time, steep rise, and steep fall, especially floods caused by extreme weather events, which have a high frequency and a wide range of hazards, and has become one of the most threatening natural disasters to human life and property safety. There are many small and medium-sized reservoirs in this basin, which have a significant influence on the accuracy of flood prediction. Therefore, taking Moushan Reservoir as an example, this paper puts forward a flash flood simulation method for reservoirs in hilly areas, considering upstream reservoirs, which can better solve the problem of flood simulation accuracy. Using the virtual aggregation method, the 3 medium-sized reservoirs and 93 small upstream reservoirs are summarized into 7 aggregated reservoirs. Then, we construct the hydrological model combining two method sets with different runoff generation and confluence mechanisms. Finally, after model calibration and verification, the results of different methods are analyzed in terms of peak discharge error, runoff depth error, difference in peak time, and certainty coefficient. The results indicate that the flooding processes simulated by the proposed model are in line with the observed ones. The errors of flood peak and runoff depth are in the ranges of 2.3% to 15% and 0.1% to 19.6%, respectively, meeting the requirements of Class B accuracy of the "Water Forecast Code". Method set 1 demonstrates a better simulation of floods with an average flood peak error of 5.63%. All these findings illustrate that the developed model, utilizing aggregate reservoirs and dynamic parameters to reflect regulation and storage functions, can effectively capture the impact of small water conservancy projects on confluence. This approach addresses challenges in simulating floods caused by small and medium-sized reservoirs, facilitating basin-wide flood prediction. [ABSTRACT FROM AUTHOR]

Flash floods in mountainous regions like the Himalayas are considered to be common natural calamities. Their consequences often are more dangerous than any flood event in the plains. These hazards not only put human lives at threat but also cause economic deflation due to the loss of lands, properties, and agricultural production. Hence, assessing the impact of such hazards in the existing agricultural system is of utmost importance to understand the probable crop loss. In this paper, we studied the efficiency of the remotely sensed microwave data to map the croplands affected by the flash flood that occurred in July 2023 in Himachal Pradesh, a mountainous state in the Indian Himalayan Region. The Una, Hamirpur, Kangra, and Sirmaur districts were identified as the most affected areas, with about 9%, 6%, 5.74%, and 3.61% of the respective districts’ total geographical area under flood. Further, four machine learning algorithms (random forest, support vector regressor, k-nearest neighbor, and extreme gradient boosting) were evaluated to forecast maize and rice crop production and potential loss during the Kharif season in 2023. A regression algorithm with ten predictor variables consisting of the cropland area, two vegetation indices, and seven climatic parameters was applied to forecast the maize and rice production in the state. Amongst the four algorithms, random forest showed outstanding performance compared to others. The random forest regressor estimated the production of maize and rice with R2 more than 0.8 in most districts. The mean absolute error and the root mean squared error obtained from the random forest regressor were also minimal compared to the others. The maximum production loss of maize is estimated for Solan (54.13%), followed by Una (11.06%), and of rice in Kangra (19.1%), Una (18.8%) and Kinnaur (18.5%) districts. This indicated the utility of the proposed approach for a quick in-season forecast on crop production loss due to climatic hazards. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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Erosion is an ongoing environmental problem that leads to soil loss and damages ecosystems downstream of agriculture. Increasingly frequent heavy precipitation causes single erosion events with potentially high erosion rates owing to gully erosion. In this study, analyses of croplands affected by heavy precipitation and linear erosion indicate that erosion occurs only on sparsely vegetated fields with land cover ≤ 25% and that slope gradient and length are significant factors for the occurrence of linear erosion tracks. Existing erosion models are not calibrated to the conditions of heavy precipitation and linear erosion, namely high precipitation intensities and long and steep croplands. In this study, natural linear erosion was analyzed using an unmanned aerial vehicle and erosion volumes were determined for 32 rills and gullies of different sizes. Comparisons with the RUSLE2 and EROSION-3D model values showed an underestimation of linear erosion in both models. Therefore, calibration data for erosion models used for heavy precipitation conditions must be adapted. The data obtained in this study meet the required criteria. [ABSTRACT FROM AUTHOR]

Background: Floods, especially flash floods, are the major transporting agent for fluvial sediments, whose pollution is a global concern. As floods result in the dispersion of and exposure to these sediments, a profound understanding of sedimentary dynamics during flood events and the related pollutant dispersion is of relevance. However, the characteristics of extreme flood events concerning pollutant dynamics are insufficiently known so far. Results: In a Central European catchment impacted by intense industrial activities and former mining, over the course of five years, we surveyed six high-discharge events, five of them approx. bankfull discharge and one major flash flood event, supplemented by sampling of bank sediments. Fluvial sediments were analyzed for elemental composition by X-Ray fluorescence and for grain size distribution of the fine faction by laser diffraction. By applying a local enrichment factor, trace metal(loid) signatures in these sample sets were compared. Furthermore, Positive Matrix Factorization was used to investigate the trace metal(loid)s' sources. The sediments deposited by minor flooding had continual trace metal(loid) signatures. However, for the extreme event, significant divergencies arose and persisted for the following years: The enrichment of anthropogenically influenced elements increased, with a slowly decreasing trend in the subsequent two years. Naturally dominated metal(oid)s decrease in enrichment without indicating a return to original levels. In contrast, other elements were insensitive to the extreme event. Positive Matrix Factorization identified anthropogenic influences in elements originating from copper and lead processing and mining activities. Furthermore, bed sediments and a natural background factor were found to dominate the non-anthropogenically influenced metal(loid)s. Conclusions: In between extreme events, winnowing processes slowly alter the elemental composition of bed sediments. The depletion of such sediments due to the flash flood proves catchment-wide flushing, which induces a natural resetting of the geochemical signals. This ability to renew is an integral part of resilience in fluvial systems. This mechanism is disturbed by industrial activities in floodplains. The exceptional flooding reaches infrastructure that is assumed to be safe and, therefore, unprotected. These additional sources can shift flood sediments' trace metal(loid) signature, which has a long-lasting impact on the catchment sediments. However, the modifications depend on the flooding extent, possible emitters, and protection measures. [ABSTRACT FROM AUTHOR]

Purpose: Golestan province in the northern part of Iran has been affected by devastating floods. There has been a significant change in the pattern of rainfall in Golestan province based on an analysis of the seven heaviest rainfall events in recent decades. Climate change appears to be a significant contributing factor to destructive floods. Thus, this paper aims to assess the susceptibility of this area to flash floods in case of heavy downpours. Design/methodology/approach: This paper uses a variety of computational approaches. Following the collection of data, spatial analyses have been conducted and validated. The layers of information are then weighted, and a final risk map is created. Fuzzy analytical hierarchy process, geographic information system and frequency ratio have been used for data analysis. In the final step, a flood risk map is prepared and discussed. Findings: Due to the complex interaction between thermal fluctuations and precipitation, the situation in the area is further complicated by climate change and the variations in its patterns and intensities. According to the study results, coastal areas of the Caspian Sea, the Gorganrood Basin and the southern regions of the province are predicted to experience flash floods in the future. The research criteria are generalizable and can be used for decision-making in areas exposed to flash flood risk. Originality/value: The unique feature of this paper is that it evaluates flash flood risks and predicts flood-prone areas in the northern part of Iran. Furthermore, some interventions (e.g. remapping land use and urban zoning) are provided based on the socioeconomic characteristics of the region to reduce flood risk. Based on the generated risk map, a practical suggestion would be to install and operate an integrated rapid flood warning system in high-risk zones. [ABSTRACT FROM AUTHOR]

This study investigates the best approach to calibrate an event-based conceptual Hydrologiska Byråns Vattenbalansavdelning (HBV) model, comparing different trials of single-objective, single-event multi-objective (SEMO), and multi-event-multi-objective (MEMO) model calibrations using root mean squared error (RMSE), Nash-Sutcliffe model efficiency coefficient (NSE), and Bias as objective functions. Model performance was validated for several peak events via 90% confidence interval (CI)-based output uncertainty quantification of relative error of discharges. Multi-objective optimization yielded more accurate and robust solutions compared to single-objective calibrations. Ensembles of Pareto solutions from the multi-objective calibrations better characterized the flood peaks within the uncertainty intervals. MEMO calibration exhibited lower uncertainties and better prediction of peak events versus SEMO calibration. Moreover, the MEMO_6D (six-dimensional) approach outperformed the SEMO_3D and MEMO_3D in capturing the larger peak events. This study suggests that the MEMO_6D is the best approach for predicting large flood events with lower model output uncertainties when the calibration is performed with a better combination of peak events. [ABSTRACT FROM AUTHOR]

Extreme precipitation‐related hazards like flash floods pose a widespread risk to humans and infrastructure around the world. In the current study, the Fisher information was applied to understand the nonstationarity of the extreme precipitation regimes, whereas copula was used to quantify the likelihood of joint occurrence of two extreme precipitation indices and associated risk assessment in the upper Midwestern United States (UMUS). The trend analysis revealed an increasing trend in 37% of the stations in heavy precipitation amount in the UMUS. The regime shift analysis showed the non‐stationary nature of extreme precipitation in about half of the total stations in UMUS. Further, the bivariate analysis using copula demonstrated the risk of the joint occurrence of extreme precipitation indices potentially causing flash floods. The risk index analysis indicated about 28.8% of stations under moderate, 10.6% of stations under high and 0.4% of stations under very high risk of flash flooding. The results from the study can provide important insights for the (re)design of resilient and sustainable water infrastructure in the changing climate condition and can also inform managers and planners for better response and preparedness toward extreme precipitation‐related hazards in this region. The results from this study can also help in a more accurate risk assessment, especially in the socio‐economically vulnerable community. [ABSTRACT FROM AUTHOR]

Using hydrodynamic models to carry out early warning and flash floods forecasting is an essential measure for loss reduction. Nevertheless, many current hydrodynamic models lack the necessary forecasting timeliness. To address this limitation, a method combining a hydrodynamic model with the K nearest neighbours (KNN) algorithm is proposed to facilitate the rapid prediction of flash flood processes. With the rainfall sequence as the input data and the simulation results of the hydrodynamic model as the target data, the rapid forecast of water depth, water velocity and discharge are achieved. Then the Baogai Temple basin is utilized as a case study, and the rapid forecast model (RFM) is established and subjected to verification for reliability and timeliness. The results demonstrate that the established model exhibits remarkable accuracy, with 99% of the test data effectively limiting the error of accumulated inundation extent within 20%. Furthermore, the Nash-Sutcliffe efficiency (NSE) for cross-sectional discharge achieves a value of 0.98. In 75% of rainfall scenarios, both the maximum average water depth and velocity errors for the cross-sections are effectively confined to 7.5% and 10%, respectively. The model also boasts a substantial improvement in computational efficiency, enabling it to complete the prediction of the flooding process for the next 10 h within 25s. This enhancement offers valuable lead time for emergency decision-making and highlights its extensive application potential in managing flash floods. [ABSTRACT FROM AUTHOR]

Abstract 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 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.

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.

With the increasing frequency of extreme convective weather, the spatial–temporal variability of rainfall becomes more diversified. As a result of the insufficient quality of rainfall monitoring data in mountainous areas, the flash flood simulation usually does not consider the effect of the rainfall center location. In this work, the GPU Accelerated Surface Water Flow and Associated Transport hydrodynamic model is used to simulate the flash flood discharge process. The effect of the rainfall center location and the basin scale on the discharge process were analyzed based on simulated data. The results show that when the rainfall center is in the upstream and midstream basins, because of gravitational potential energy conversion, the total flood volume and the flood peak discharge increase to 2–10 times, and the peak time of flash flood caused by 100 mm rainfall amount can be advanced by up to 3,000 s compared to the 20 mm rainfall amount condition. The peak discharge and the delay of peak time increase with the increase of rain peak coefficient. In addition, the increase of the basin area enhances the effect of the rainfall center location. This work is helpful to quantify the effect of the rainfall center location, which can clarify the uncertainty of flash flood simulation caused by not considering the rainfall center factor. HIGHLIGHTS When the rainfall center is in the upstream and midstream basins, the total flood volume and the peak discharge increase to about 2–10 times, the flood peak time can be advanced by 3,000 s.; The flood peak discharge increases with the increase of the rain peak coefficient, while the delay of the flood peak time is longer.; The increase of the basin area enhances the effect of the rainfall center location on flash flood.;

Flash flood causes severe damage to the environment and human life across the world, no exception is Bangladesh. Severe flash floods affect the northeastern portion of Bangladesh in the early monsoon and pose a serious threat to every aspect of socioeconomic development and environmental sustainability. To manage the threat and reduce flood loss, the map of flash flood susceptible zones plays a key role. Thus, the aim of this research is to map the flash flood-susceptible areas of the northeastern haor areas of Bangladesh utilizing GIS-based bivariate statistical models. The models utilized are frequency ratio (FR), weights of evidence (WoE), certainty factor (CF), Shanon’s entropy (SE) and information value (IV). Among the 250 identified flash flood locations, 80 % data was used for training purposes and 20 % data for testing purposes. Eleven selected conditioning factors of flash flood include elevation, slope, aspect, curvature, TWI, TRI, SPI, distance to stream, stream density, rainfall and physiography. The calculated weights are assigned to the conditioning factors using ArcGIS environment to prepare the final flash flood maps. Results of AUC of ROC indicate WoE (success rate = 0.833 and prediction rate = 0.925) is the best model for flash flood susceptibility mapping followed by FR (success rate = 0.828 and prediction rate = 0.928) and SE (success rate = 0.827 and prediction rate = 0.923). According to the models, topographic (flat area) and hydrologic factors significantly control flash flood occurrence in the study area. The prepared flash flood susceptibility maps will be helpful for disaster managers and haor master planners of the study area.

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.

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.

Flash floods are a rapid hydrological response that occurs within a short time with rapidly rising water levels and could lead to massive structural, social and economic damages. Therefore, generating flood inundation maps becomes necessary to distinguish areas exposed to floods. Hydrodynamic models are commonly used to generate inundation maps; however, they require high computational power and time, depending on the complexity of the model. For that, researchers developed effective, fast and simplified models. Among the simplified models, the Geomorphic Flood Index (GFI) is one of the most useful classifiers to generate inundation maps. Three main objectives are addressed in this study: (1) extend the GFI classifier to predict flood extent maps for uncalibrated rainfall depths, which will enhance early warning models for better risk assessments of extreme events; (2) enhance the accuracy of the simulated inundation maps using different calibration methods; and (3) investigate the performance of the GFI in various terrains with different resolutions. Three case studies in arid regions in Saudi Arabia were examined with different topographies, using terrains of high resolutions of 1 m and resampled low resolutions, as well as various rainfall depths corresponding to 5–100-yr return periods. The HEC-RAS 2D model was used to generate reference flood inundation maps. The obtained flood extent maps show high similarity compared to the reference maps with accuracy above 80%. Strong relationships between rainfall depths and the threshold GFI parameter were developed which allow producing inundation maps for any rainfall event. [ABSTRACT FROM AUTHOR]

To evaluate the performances of regression models applied in the urban flash flood risk assessment, the historical urban flash flood occurrences points were used to build the Voronoi polygon networks for calculating Ripley's K values which can be adopted to be the risk value and the predictands in regression. The first level risk indicators of hazard, vulnerability, sensitivity and exposure risk factors in the risk assessment, as well as the sensitivity subordinate indicators of imperviousness and terrain factor, were listed to be the predictors in the regression model. Subsequently, methods of the linear regression equation (LRE), nonlinear regression power-form function (PF) and a simplified power-form function (SPF), as well as support vector machine (SVM) model and random forests (RF) model, were all nominated for the performance evaluation and comparison of the fitness of their regression relationships between the predictors and the predictands. With the support of samples, the benchmarking firstly demonstrated the SPF is the best of the regression equation; but the full PF equation cannot be figured out on account of the sample data deficiency. The SVM model behaves better than the regression equations of SPE and LRE, while the SVM of nonlinear polynomial kernel function is slightly better than that of the nonlinear Gaussian kernel function. Above all, the RF model performed perfectly in the regression fitting, which the relative bias index is − 0.009 and the relative mean squared error is 0.0773. Meanwhile, it mostly resolves the problems of overfitting, outliers and noise in regression. The variable importance (VI) evaluated by the RF model indicated that the top four important risk factors are the imperviousness, terrain factor, vulnerability, and exposure factor, which the VI index value is 0.38, 0.16, 0.11 and 0.1, respectively. Unexpectedly, the hazard factor appears to be the least important factor with a VI value of 0.04. The homogeneity of invariable hazard being preserved in regional climate background makes the hazard a minor role in risk contribution. The model performance evaluation demonstrated the artificial intelligence RF model should be recommended to be the common-use model for aftermath meteorology-related risk assessment. On the other hand, the VI analysis tools of RF were also recognized to be a welcome toolbox items for the risk analysis. [ABSTRACT FROM AUTHOR]

Intense rainfall in the Meghalaya and Assam regions leads to flash floods in the northeastern Haor region of Bangladesh, as a substantial volume of water enters the Surma River via the downstream river network, resulting in extensive damage. Our objective was to construct a comprehensive geospatial multi-index model, incorporating four key indices (hazard, exposure, sensitivity, and resilience) to assess flood risk in the Sunamganj district. This model considers a range of flood risk indicators related to the district's economic, social, and physical environment. Risk calculations were carried out using modeling techniques, and principal component analysis (PCA) was applied for composite analysis. The results reveal that approximately 40% of the district's area (~ 1452.51 km2) falls within high- and very high–risk zones, while around 45% (~ 1554.66 km2) is categorized as very low- and very low–risk zones within the Sunamganj District. Regarding the risk rankings, Dharampasha Upazila stands out with the highest percentage, at around 60%, surpassing neighboring Upazilas like Shalla, Derai, Jamalganj, Dakshin Sunamganj, and Tahirpur. This information offers valuable insights for prioritizing the region's risk reduction and management efforts. We also note that the Upazilas in the northwestern region of Sunamganj district are situated in a particularly high-risk zone for flash flooding. This heightened risk is primarily due to their low elevation, a notable concentration of deep depressions, and a high drainage density. The recurring flash floods in these areas have significant repercussions, impacting the literacy rate and the socioeconomic conditions of these Upazilas. The current study, employing geospatial and statistical techniques, facilitates the identification of the root causes of flash floods. The findings from this research are expected to provide valuable insights for policymakers and developers, enabling them to formulate strategies to reduce flash flood risks in the region. [ABSTRACT FROM AUTHOR]

More frequent high-intensity, short-duration rainfall events increase the risk of flash floods on steeply sloped watersheds. Where measured data are unavailable, numerical models emerge as valuable tools for predicting flash floods. Recent applications of various hydrological and hydrodynamic models to predict overland flow have highlighted the need for improved representations of the complex flow processes that are inherent in flash floods. This study aimed to identify an optimal modeling approach for characterizing leaf litter losses during flash floods. At a gauged watershed in the Hidegvíz Valley in Hungary, a physical-based model was calibrated using two distinct rainfall–runoff events. Two modeling methodologies were implemented, integrating canopy interception and leaf litter storage, to understand their contributions during flash flood events. The results from the model's calibration demonstrated this approach's effectiveness in determining the impact of leaf litter on steep-sloped watersheds. Soil parameters can estimate the behavior of leaf litter during flash flood events. In this study, hydraulic conductivity and initial water content emerged as critical factors for effective parametrization. The findings underscore the potential of a hydrodynamic model to explore the relationship between leaf litter and flash flood events, providing a framework for future studies in watershed management and risk-mitigation strategies. [ABSTRACT FROM AUTHOR]

Sediment precipitation in riverbeds influences the effectiveness of structural and non-structural measures for flash flood mitigation and increases the potential for flooding. This study aimed to disclose the effectiveness of the implemented measures for flood risk mitigation in Kassala town, eastern Sudan. We employed remote sensing (RS) and GIS techniques to determine the change in the Gash River riverbed, the morphology, and the leveling of both the eastern and western sides of the river. Flood model simulation and a 3D path profile were generated using the digital elevation model (DEM) with a data resolution of 12.5 m from the ALOS BILSAR satellite. The main purpose of this study is to extract the layer of elevation of the riverbed on both the western and eastern banks and to determine the variations and their relationship to flood occurrence and mitigation. The construction of dikes and spurs near Kassala town has led to sediment precipitation, causing the riverbed to rise. The results show that it is now 1.5 m above the eastern Kassala town level, with a steep slope of 2 m/km, and the cross-section area at Kassala bridge has shrunk, which indicates that the bridge body will partially impede the river's high discharge and increase the potential for flood risk in the study area. The eastern part of Kassala town has a higher likelihood of flooding than the western side. This study suggests redesigning structural measures like widening the Gash River, extending Kassala bridge for normal water flow, strengthening early warning systems, and implementing soil conservation activities for normal water flow. [ABSTRACT FROM AUTHOR]

The development of human society over the past century has led to an explosion in population numbers and a migration of settlements to river valleys, which have become increasingly exposed to the risk of flooding. In this context, the scientific community has begun to work on identifying mathematical and spatial models that can help to identify areas at risk as quickly as possible. The present article is one that follows this objective, proposing an automatic model that can be implemented in ArcGIS and that aims to identify only areas at risk of flooding using a single file, the DEM. The novelty of this article and the usefulness of the method are given precisely by the fact that it is possible to quickly find out which areas may be exposed to flooding, i.e., water accumulations, only based on relief, which is extremely useful for local authorities. The analysis was conducted on all hierarchy orders, according to the Horton–Strahler classification system, for the entire Romanian territory. The results consist of a polygonal vector layer in shapefile format, containing an attribute table with all the initial, intermediary, and final calculations in separate numeric fields. Each parameter was normalized in order to obtain the final morphometric flood vulnerability score. Postprocessing these results involved applying a Principal Component Analysis to identify weights for the components that encompass all morphometric parameters. Each drainage basin reveals a dimensionless morphometric flood vulnerability score value that is comparable with all other basins in Romania. [ABSTRACT FROM AUTHOR]

Climate change has intensified the risk of extreme precipitation, while mountainous areas are constrained by complex disaster mechanisms and difficulties in data acquisition, making it challenging for existing critical rainfall threshold accuracy to meet practical needs. Therefore, this study focuses on Yunnan Province as the research area. Based on historical flash flood events, and combining remote sensing data and measured data, 12 causative factors are selected from four aspects: terrain and landforms, land use, meteorology and hydrology, and population and economy. A combined qualitative and quantitative method is employed to analyze the relationship between flash floods and triggering factors, and to calibrate the parameters of the RTI (Rainfall Threshold Index) model. Meanwhile, machine learning is introduced to quantify the contribution of different causative factors and identify key causative factors of flash floods. Based on this, a parameter η coupling the causative mechanism is proposed to optimize the RTI method, and develop a framework for calculating county-level critical rainfall thresholds. The results show that: (1) Extreme rainfall, elevation, slope, and other factors are direct triggers of flash floods, and the high-risk areas for flash floods are mainly concentrated in the northeast and southeast of Yunnan Province. (2) The intraday rainfall has the highest correlation with the accumulated rainfall of the previous ten days; the critical cumulative rainfall ranges from 50 mm to 400 mm. (3) The county-level critical rainfall threshold for Yunnan Province is relatively accurate. These findings will provide theoretical references for improving flash flood early warning methods. [ABSTRACT FROM AUTHOR]

Against the backdrop of global climate warming, the issue of flash flood disasters in small watersheds triggered by heavy rainfall is gradually becoming more prominent. Selecting an appropriate hydrological model is crucial for flash flood disaster defense. This article focuses on the Daxi Water Basin in Lianping County, Guangdong Province, as the research area. Firstly, organize the data and subject it to standardization processing. Subsequently, establish the topological relationships within the basin, construct a hydrological model for simulating flood processes in Chinese mountainous regions, and obtain a set of model parameters applicable to the specific basin. The results indicated that: ① the relative errors of flood runoff depth were all less than 7%, with an average of 4.5%; ② the relative errors of peak flow for all events were less than 6%, with an average of 4.2%; ③ peak time errors were all within ±2 h, either earlier or later than the actual peak by 1 h; ④ the Nash-Sutcliffe efficiency coefficient for floods were all greater than 0.8, with an average of 0.86. The research results above will serve as a reference and guidance for flood defense management in the Daxi Water Basin. [ABSTRACT FROM AUTHOR]

The features of precipitation and its spatial distribution over the territory of the Primorsky krai during the passage of Typhoon Hinnamnor (September 4–7, 2022), as well as the characteristics of the flood caused by the typhoon, are studied based on ground observational data and the ERA5-Land reanalysis. A list of the settlements affected by the flood and an assessment of the impact of the disaster on transport infrastructure are given. The relationship between the precipitation maxima and the movement of storm cells is shown. Deformations were recorded for 30% of the length of the Primorsky krai river channels as a result of the typhoon passage. The relationship among the changes in the channels, spatial characteristics of precipitation (including their intensity) and the slope of the catchment were determined. Areas of extreme channel deformations (including mudflows) and their magnitudes on rivers of different scales are described. [ABSTRACT FROM AUTHOR]

Flash floods have recently become a recurrent phenomenon with devastating impacts on different cities, particularly vulnerable communities in Iran. Community resilience is a relatively recent approach to resilience, increasingly used in the natural hazards and climate change literature. This study aims to assess the community resilience of a flood-prone district, the Saadi neighborhood, in Shiraz, Iran. Based on an extensive literature review, an indicator-based framework was outlined to measure community resilience to flash floods using five dimensions: social, community capital, economic, institutional, and infrastructural and housing resilience. The primary data on community flood resilience assessment was collected through a survey using questionnaires. Using simple random sampling, 374 individuals from the residents of the study area were selected. The data were ranked and analyzed through qualification methods, descriptive statistics and expert panel weighting system. The overall composite community resilience and the community resilience indices' scores were.56 out of 1 for the selected community, indicating a moderate level of resilience. The findings showed that institutional and infrastructure/housing conditions had a limited impact on community resilience. However, social trust and community capital were crucial for aiding the community's rapid recovery from a flood disaster and preparing for future floods. Policymakers and resilience planners, thus, should focus on the lessons that can be learnt from the past floods, particularly in terms of infrastructure and institutional resilience, as these have a significant impact on the overall resilience of local communities. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing interest in flood susceptibility modeling. In this study, we conducted a bibliometric analysis followed by a meta-data analysis to capture the nature and evolution of literature, intellectual structure networks, emerging themes, and knowledge gaps in flood susceptibility modeling. Relevant publications were retrieved from the Web of Science database to identify the leading authors, influential journals, and trending articles. The results of the meta-data analysis indicated that hybrid models were the most frequently used prediction models. Results of bibliometric analysis show that GIS, machine learning, statistical models, and the analytical hierarchy process were the central focuses of this research area. The analysis also revealed that slope, elevation, and distance from the river are the most commonly used factors in flood susceptibility modeling. The present study discussed the importance of the resolution of input data, the size and representation of the training sample, other lessons learned, and future research directions in this field. [ABSTRACT FROM AUTHOR]