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Two behavioural types in sociohydrology of floods have been described in more detail than others: the levee effect and the learning effect. However, additional types of behaviour operate and need further research. Taking the case of frequent flash floods occurring in an ephemeral stream, a tributary of the Vilama River, located in San Pedro de Atacama, Chile, at the interphase between the Atacama desert and the Andean Altiplano, we explore the existence of further behavioural types in the sociohydrology of floods at four neighbourhoods along the tributary. Fieldwork to reconstruct a recent flood event, hydrological analysis and semistructured interviews with key informants to characterize people's responses to floods were conducted. Results show evidence that the so-called status quo effect is present at the riparian community scale, i.e. when communities do not learn and adapt to prevent damage even when exposed to frequent floods. Possible causes of the status quo effect are discussed. [ABSTRACT FROM AUTHOR]

Jeddah is the most important port city in the Kingdom of Saudi Arabia. The climate of Jeddah is mostly dry; however, events of intensive rainfall followed by urban flooding have been reported in recent history. These floods interrupt the logistics and lifelines of the city. In this study runoff analysis and Flash Flood management by the green roof low impact development (LID) control system, was performed using United States Environmental Protection Agency (EPA)'s Stormwater Management Model (SWMM). Six middle districts of Jeddah city had been selected and modeled in five sub-catchment areas. The local soil and geographic conditions were incorporated into the model. The effect of the soil-based, rooftop, LID system has been emphasized in the analysis. In flood simulations, 30–70% of the impervious area of each sub-catchment has been treated with LID. It has been observed that the LID application can significantly reduce the overall flood flow. Thus, simple but well-planned LID control systems can be effectively utilized for the flood management of heavily urbanized regions. [ABSTRACT FROM AUTHOR]

Human lives and property are threatened by Flash floods (FF) worldwide and as a result of the unprecedented conditions of the climate change effects the losses are predicted to increase in the future. As it seems difficult to avoid and prevent them, real-time flash flood detections could be an appropriate solution for damage reduction and better management. Currently, the development of computer vision applications such as deep learning and AI has been advanced. Although AI models have been developed for applications in many fields, their implementations for geosciences are limited based on large amounts of training data and the highly required computational infrastructure. Hence, this work aims to train the latest YOLOv8 model and apply it to real-time flash flood detection for regions of Korea and possibly for other nations. To overcome the shortage of training data, we created small on-site flash flood models and took pictures and footage of them. More than 1500 photos of FF were used for model trains and validations gaining a model mean average precision of above 60% of all training depths (25, 50, 75, and 100 epochs). Despite some model false positives and missed false positive detections using the Korean FF test dataset, the YOLOv8 best model generated bounding boxes (BB) with high confidence values in most FF events. Furthermore, the robustness of the model is highlighted by its ability to smoothly detect the precise positions of the FF areas with high confidence values (best 0.86) when applied for input footage and webcam streams. It is highly encouraged to establish a real-time FF warning system to reduce their negative effects. Although YOLO is effective and fast, like other deep learning models, it requires large input data to ensure higher accuracy and confidence. Future works might explore this aspect, particularly the data acquired in light inefficiency to improve the model detections at night time. [ABSTRACT FROM AUTHOR]

Flash floods are one of the most devastating natural hazards in mountainous and hilly areas. In this study, a dynamic warning model was proposed to improve the warning accuracy by addressing the problem of ignoring the randomness and uncertainty of rainfall patterns in flash flood warning. A dynamic identification method for rainfall patterns was proposed based on the similarity theory and characteristic rainfall patterns database. The characteristic rainfall patterns were constructed by k-means clustering of historical rainfall data. Subsequently, the dynamic flood early warning model was proposed based on the real-time correction of rainfall patterns and flooding simulation by the HEC-HMS (Hydrologic Engineering Center's Hydrologic Modeling System) model. To verify the proposed model, three small watersheds in China were selected as case studies. The results show that the rainfall patterns identified by the proposed approach exhibit a high correlation with the observed rainfall. With the increase of measured rainfall information, the dynamic correction of the identified rainfall patterns results in corresponding flood forecasts with Nash-Sutcliffe efficiency (NSE) exceeding 0.8 at t = 4, t = 5, and t = 6, thereby improving the accuracy of flash flood warnings. Simultaneously, the proposed model extends the forecast lead time with high accuracy. For rainfall with a duration of six hours in the Xinxian watershed and eight hours in the Tengzhou watershed, the proposed model issues early warnings two hours and three hours before the end of the rainfall, respectively, with a warning accuracy of more than 0.90. The proposed model can provide technical support for flash flood management in mountainous and hilly watersheds. [ABSTRACT FROM AUTHOR]

Abstract Flash floods are one of the most devastating natural hazards in mountainous and hilly areas. In this study, a dynamic warning model was proposed to improve the warning accuracy by addressing the problem of ignoring the randomness and uncertainty of rainfall patterns in flash flood warning. A dynamic identification method for rainfall patterns was proposed based on the similarity theory and characteristic rainfall patterns database. The characteristic rainfall patterns were constructed by k-means clustering of historical rainfall data. Subsequently, the dynamic flood early warning model was proposed based on the real-time correction of rainfall patterns and flooding simulation by the HEC-HMS (Hydrologic Engineering Center’s Hydrologic Modeling System) model. To verify the proposed model, three small watersheds in China were selected as case studies. The results show that the rainfall patterns identified by the proposed approach exhibit a high correlation with the observed rainfall. With the increase of measured rainfall information, the dynamic correction of the identified rainfall patterns results in corresponding flood forecasts with Nash-Sutcliffe efficiency (NSE) exceeding 0.8 at t = 4, t = 5, and t = 6, thereby improving the accuracy of flash flood warnings. Simultaneously, the proposed model extends the forecast lead time with high accuracy. For rainfall with a duration of six hours in the Xinxian watershed and eight hours in the Tengzhou watershed, the proposed model issues early warnings two hours and three hours before the end of the rainfall, respectively, with a warning accuracy of more than 0.90. The proposed model can provide technical support for flash flood management in mountainous and hilly watersheds.

Jeddah is the most important port city in the Kingdom of Saudi Arabia. The climate of Jeddah is mostly dry; however, events of intensive rainfall followed by urban flooding have been reported in recent history. These floods interrupt the logistics and lifelines of the city. In this study runoff analysis and Flash Flood management by the green roof low impact development (LID) control system, was performed using United States Environmental Protection Agency (EPA)’s Stormwater Management Model (SWMM). Six middle districts of Jeddah city had been selected and modeled in five sub-catchment areas. The local soil and geographic conditions were incorporated into the model. The effect of the soil-based, rooftop, LID system has been emphasized in the analysis. In flood simulations, 30–70% of the impervious area of each sub-catchment has been treated with LID. It has been observed that the LID application can significantly reduce the overall flood flow. Thus, simple but well-planned LID control systems can be effectively utilized for the flood management of heavily urbanized regions.

We study the interrelations between salt karst and landscape evolution at the Ze'elim and Hever alluvial fans, Dead Sea (DS), Israel, in an attempt to characterize the ongoing surface and subsurface processes and identify future trends. Using light detection and ranging, interferometric synthetic aperture radar, drone photography, time-lapse cameras, and direct measurements of floodwater levels, we document floodwater recharge through riverbed sinkholes, subsurface salt dissolution, groundwater flow, and brine discharge at shoreline sinkholes during the years 2011–2023. At the Ze'elim fan, most of the surface floodwater drains into streambed sinkholes and discharges at shoreline sinkholes, whereas at the Hever fan, only a small fraction of the floodwater drains into sinkholes, while the majority flows downstream to the DS. This difference is attributed to the low-gradient stream profiles in Ze'elim, which enable water accumulation and recharge in sinkholes and their surrounding depressions, in contrast with the higher-gradient Hever profiles, which yield high-energy floods capable of carrying coarse gravel that eventually fill the sinkholes. The rapid drainage of floodwater into sinkholes also involves slope failure due to pore-pressure drop and cohesion loss within hours after each drainage event. Surface subsidence lineaments detected by InSAR indicate the presence of subsurface dissolution channels between recharge and discharge sites in the two fans and in the nearby Lynch straits. Subsidence and streambed sinkholes occur in most other fans and streams that flow to the DS; however, with the exception of Ze'elim, all other streams show only minor or no recharge along their course. This is due to either the high-gradient profiles, the gravelly sediments, the limited floods, or the lack of conditions for sinkhole development in the other streambeds. Thus, understanding the factors that govern the flood-related karst formation is of great importance for predicting landscape evolution in the DS region and elsewhere and for sinkhole hazard assessment. [ABSTRACT FROM AUTHOR]

Recent flood disasters caused by extreme meteorological events highlight the need of fast and reliable tools for flooding forecast. For our purposes, the danger associated with floods is embodied in a single risk-level flag which considers both local water depth and velocity. The methodology here derived is applied and validated for the case study of the St. Lucia island in the eastern Caribbean Sea that experiences flash flooding as a result of combined intense rainfall and steep slopes, difficult to predict with traditional early-warning systems. A multi-layer perceptron neural network is trained on a high-fidelity dataset generated through full two-dimensional shallow water simulations of real and synthetic events. The dataset is validated against social markers obtained from real events. The predictive capabilities of the neural network model are tested on the out-of-box case of the Dean and Tomas hurricanes and compared with the solutions of the shallow water solver. The surrogate solver allows a significant speed-up in the prediction time with respect to traditional CFD (seconds vs hours), showing a high precision and accuracy, with accuracy, precision and F1-score above 0.99. [ABSTRACT FROM AUTHOR]

Global warming and climate change cause large temperature oscillations and uneven annual rainfall patterns. The rainy cycles characterized by frequent high-intensity rainfall in the area of the Stubo–Rovni water reservoir, which in 2014 peaked at 129 mm of water in 24 h (the City of Valjevo, the Republic of Serbia), caused major floods in the wider area. Such extremes negatively affect erosion processes, sediment production, and the occurrence of flash floods. The erosion coefficient before the construction of the water reservoir was Zm = 0.40, while the specific sediment production was about 916.49 m3∙km−2∙year−1. A hydrological study at the profile near the confluence of the Jadar and Obnica rivers, i.e., the beginning of the Kolubara river, the right tributary of the Sava (in the Danube river basin), indicates that the natural riverbed can accommodate flows with a 20% to 50% probability of occurrence (about 94 m3/s), while centennial flows of about 218 m3/s exceed the capacities of the natural riverbed of the Jadar river, causing flooding of the terrain and increasing risks to the safety of the population and property. The paper presents the impacts of the man-made Stubo–Rovni water reservoir on the catchment area and land use as the primary condition for preventing erosion processes (specific sediment production has decreased by about 20%, the forest cover increased by about 25%, and barren land decreased by 90%). Moreover, planned and controlled management of the Stubo–Rovni reservoir has significantly influenced the downstream flow, reducing the risks of flash floods. [ABSTRACT FROM AUTHOR]

Flash floods stand as a substantial peril linked to climate change, imposing a severe menace to both human existence and built structures. This study aims to assess and compare the effectiveness of four distinct machine learning (ML) methodologies in the production of flood susceptibility maps (FSMs) in Ibaraki prefecture, Japan. Additionally, the investigation aims to examine the influence of excluding plan and profile curvature factors on the accuracy of the resulting maps. The dataset comprised 224 spots, consisting of 112 flooded and 112 non-flooded locations, and 11 environmental factors. The models were trained using 70% of the dataset, while the remaining 30% was utilized for model evaluation using the ROC curve method. The results indicated that both the ANN-MLP and SVR models achieved notable accuracy, with area under curve values of 95.23% and 95.83% respectively. An intriguing observation was made when the plan and profile curvature factors were excluded, as it led to an improvement in the accuracy of the ANN-MLP model, resulting in an accuracy of 96.7%. Furthermore, the generated FSMs were classified into five distinct hazard levels. The northern region of the maps predominantly exhibited very low and low hazard levels, while areas located in the southern region, closer to main streams, demonstrated considerably higher hazard levels categorized as very high and high. Ultimately, this study marks novel endeavor to investigate the impact of the curvature factor on the precision of machine learning algorithms in the creation of FSMs, which serve as fundamental tools for subsequent investigations. [ABSTRACT FROM AUTHOR]

There has been growing interest in the application of smart technologies for hazard management. However, very limited studies have reviewed the trends of such technologies in the context of flash floods. This study reviews innovative technologies such as artificial intelligence (AI)/machine learning (ML), the Internet of Things (IoT), cloud computing, and robotics used for flash flood early warnings and susceptibility predictions. Articles published between 2010 and 2023 were manually collected from scientific databases such as Google Scholar, Scopus, and Web of Science. Based on the review, AI/ML has been applied to flash flood susceptibility and early warning prediction in 64% of the published papers, followed by the IoT (19%), cloud computing (6%), and robotics (2%). Among the most common AI/ML methods used in susceptibility and early warning predictions are random forests and support vector machines. However, further optimization and emerging technologies, such as computer vision, are required to improve these technologies. AI/ML algorithms have demonstrated very accurate prediction performance, with receiver operating characteristics (ROC) and areas under the curve (AUC) greater than 0.90. However, there is a need to improve on these current models with large test datasets. Through AI/ML, IoT, and cloud computing technologies, early warnings can be disseminated to targeted communities in real time via electronic media, such as SMS and social media platforms. In spite of this, these systems have issues with internet connectivity, as well as data loss. Additionally, Al/ML used a number of topographical variables (such as slope), geological variables (such as lithology), and hydrological variables (such as stream density) to predict susceptibility, but the selection of these variables lacks a clear theoretical basis and has inconsistencies. To generate more reliable flood risk assessment maps, future studies should also consider sociodemographic, health, and housing data. Considering future climate change impacts, susceptibility or early warning studies may be projected under different climate change scenarios to help design long-term adaptation strategies. [ABSTRACT FROM AUTHOR]

Flash flooding is a significant societal problem, but related precipitation forecasts are often poor. To address, one can try to use output from convection‐parametrising (global) ensembles, post‐processed to forecast at point‐scale, or convection‐resolving limited area ensembles. The new methodology described here combines both. We apply "ecPoint‐rainfall" post‐processing to the ECMWF global ensemble. Alongside we use 2.2 km COSMO LAM ensemble output (centred on Italy), and also post‐process that, using a scale‐selective neighbourhood approach to compensate for insufficient members and to preserve consistently forecast local details. The two resulting scale‐compatible components then undergo lead‐time‐weighted blending, to create the final probabilistic 6 h rainfall forecasts. Product creation for forecasters, in this way, constituted the "Italy Flash Flood use case" within the EU‐funded MISTRAL project; real‐time delivery of open access products is ongoing. One year of verification shows that, of the five components (2 ×$$ \times $$ raw, 2 ×$$ \times $$ post‐processed and blended), ecPoint is the most skilful. The post‐processed COSMO ensemble adds most value to summer convective events in the evening, when the global model has an underprediction bias. In two typical heavy rainfall case studies we observed underestimation of the largest point totals in the raw ECMWF ensemble, and overestimation in the raw COSMO ensemble. However, ecPoint elevated the ECMWF maxima and highlighted best the most affected areas and merged products seemed to be the most skilful of all. Even though our LAM post‐processing does not include (or arguably need) bias‐correction, this study still provides a unique blueprint for successfully combining ensemble rainfall forecasts from global and LAM systems around the world. It also has important implications for forecast products as global ensembles move ever closer to having convection‐permitting resolution. [ABSTRACT FROM AUTHOR]

On 5.9.2023, flash floods occurred in Igneada town of Demirkoy district of Kırklareli province, which caused serious loss of life and property. Since this unfortunate event was a natural hazard with a hydro-meteorological characteristic, it occurred at the river basin scale. Therefore, the present study aims to systematically reveal the chain of events leading to the flash floods in the Degirmen River basin, where the natural hazard occurred. The study data were processed and interpreted in the light of the relevant literature within the framework of the findings obtained from office and field studies. At this stage, the Root Cause Analysis (RCA) method was used to determine how and why the event occurred and how to prevent similar problems from occurring again. As a result of the study, it was determined that flash floods caused by sudden torrential rains led to high-energy water flows on the slope lands depending on topographic features. Consequently, they forced the transportation of materials of various sizes. The materials coming from the river's upper basin with strong flash floods with irregularly changing flow rates triggered flooding in places where the slope of the river bed decreased. However, this natural event has become a natural hazard in the lower basin due to the wrong land use caused by the negative anthropogenic effects of human beings on the natural environment.

Railway infrastructure plays a critical role in transportation networks, and ensuring its integrity and resilience is of utmost importance. Culverts are vital components of railway tracks, providing drainage and water management to prevent structural damage and disruptions. Identifying suitable locations for culverts requires careful evaluation and consideration of various factors. A study using GIS techniques was conducted on an existing commercial railway track in the Eastern Desert of Upper Egypt to assess the effectiveness of existing culverts in preventing flash floods. The culvert suitability index map revealed that areas with high slopes and large drainage areas were more susceptible to water accumulation, indicating the need for culverts. The study also highlighted areas where culverts could be installed without significantly impacting existing infrastructure. It was recommended to install culverts in 27 locations along the track where they intersect with watercourses. Existing culverts covered just 93 watercourses, while 5 specialized culverts needed to be relocated. The findings have significant implications for railway engineering, as using GIS techniques streamlines the process of culvert location evaluation, saving time and resources. The systematic approach ensures culverts are installed in the most appropriate locations, minimizing flooding risks and ensuring the safety and efficiency of railway operations. HIGHLIGHT The research aims to develop a GIS-based methodology for assessing culvert locations on Egyptian railways, specifically the Qena–Safaga Track. The methodology aims to improve decision-making and infrastructure development by enhancing efficiency and accuracy. It addresses traditional methods and uses GIS techniques for railway engineering.;

Abstract Climate change in desert areas and semi-arid watersheds may offer a promising solution for the water scarcity problem that Bedouins and local inhabitants face. This study investigated the integrated water resources management in arid and semi-arid regions using rainwater harvesting in combination with the managed aquifer recharge (RWH-MAR) technique. The study also used recharge wells and storage dams to achieve the sustainability of groundwater supplies in the context of climate change and management of the flow to the Gulf of Suez. Therefore, different return periods of 10, 25, 50, and 100 years were considered for the annual flood volume resulting from those watersheds. Moreover, hydrologic modeling was carried out for the El Qaa plain area, South Sinai, Egypt, using the Watershed Modeling System (WMS) and the groundwater modeling of SEAWAT code. Our findings show that for different scenarios of climate change based on return periods of 10, 25, 50, and 100 years, the aquifer potentiality reached 24.3 MCM (million cubic meters) per year, 28.8 MCM, 36.7 MCM, and 49.4 MCM compared to 21.7 MCM at 2014 with storage of groundwater ranges 11.8%, 32.1%, 69%, and 127.4%, respectively. These findings have significant implications for the system of RWH-MAR and groundwater sustainability in El Qaa Plain, South Sinai. The RWH-MAR proved to be an effective approach that can be applied in different water-stressed and arid regions to support freshwater resources for sustainable future development and food security, as well as protect communities from extreme flash flood events.

The mesoscale dynamics of a record‐breaking Atmospheric River (AR) that impacted the Middle East in mid‐April 2023 and caused property damage and loss of life are investigated using model, reanalysis and observational data. The high‐resolution (2.5 km) simulations revealed the presence of AR rapids, narrow and long convective structures embedded within the AR that generated heavy precipitation (>4 mm hr−1) as they moved at high speeds (>30 m s−1) from northeastern Africa into western Iran. Gravity waves triggered by the complex terrain in Saudi Arabia further intensified their effects. Given the rising frequency of ARs in this region, AR rapids may be even more impactful in a warming climate, and need to be accounted for in reanalysis and numerical models. Plain Language Summary: Atmospheric Rivers (ARs) are narrow and long bands of high water vapor content, which largely originate in the tropics or subtropics and propagate into mid‐ and high‐latitudes. They can bring beneficial rain and snow but, in particular the most intense, can lead to catastrophic flooding and loss of life. One of such occurrences in the Middle East in mid‐April 2023 is investigated using model and observational data. The high‐resolution (2.5 km) simulation put in evidence narrow (5–15 km) and long (100–200 km) convective structures within the AR, known as AR rapids, which produced heavy precipitation (>4 mm hr−1), further enhanced by gravity waves that developed over the high terrain in western Saudi Arabia, and propagated at high speeds (>30 m s−1). ARs are occurring more frequently in the Middle East as they are globally, and with increased atmospheric water vapor in a warming climate, AR rapids may be even more destructive. Key Points: The exceptional and impactful Atmospheric River of mid‐April 2023 in the Middle East is investigated with model and observational dataA 2.5 km simulation reveals the presence of "Atmospheric River rapids," narrow along‐flow structures that generated rain rates >4 mm hr−1Model simulations and ground‐based observations underlined the effects of the Atmospheric River and associated trailing cold front [ABSTRACT FROM AUTHOR]

On 5.9.2023, flash floods occurred in Igneada town of Demirkoy district of Kırklareli province, which caused serious loss of life and property. Since this unfortunate event was a natural hazard with a hydro-meteorological characteristic, it occurred at the river basin scale. Therefore, the present study aims to systematically reveal the chain of events leading to the flash floods in the Degirmen River basin, where the natural hazard occurred. The study data were processed and interpreted in the light of the relevant literature within the framework of the findings obtained from office and field studies. At this stage, the Root Cause Analysis (RCA) method was used to determine how and why the event occurred and how to prevent similar problems from occurring again. As a result of the study, it was determined that flash floods caused by sudden torrential rains led to high-energy water flows on the slope lands depending on topographic features. Consequently, they forced the transportation of materials of various sizes. The materials coming from the river's upper basin with strong flash floods with irregularly changing flow rates triggered flooding in places where the slope of the river bed decreased. However, this natural event has become a natural hazard in the lower basin due to the wrong land use caused by the negative anthropogenic effects of human beings on the natural environment. [ABSTRACT FROM AUTHOR]

The knowledge of the expected amounts and location of precipitation is crucial to avoid disasters, especially in arid countries—like the Sultanate of Oman—which is subjected to flash floods and tropical storms. Oman has experienced two flash floods that caused significant losses of lives and severe damage. According to recent literature, trying to collect precipitation data using ground means only is an almost impossible task. The GPM-based near-real-time satellite precipitation estimates are specifically designed to set a new standard for the measurements of precipitation using advanced radar technology in which a radar pulse for electromagnetic energy is used to determine the reflection of the hydrometeors in the atmosphere. However, the evaluation of the accuracy of these technologies is important before using them in any application. This study aims to compare precipitation estimates obtained from the Global Precipitation Mission (GPM) and global satellite mapping of precipitation GSMaP with the ground data obtained from the rain gauges during the two most recent flash floods in Oman, Shaheen cyclone and Al Azm trough, using several representative statistic metrics—qualitative and quantitative. Results show that GSMaP_NRT gave slight errors in estimations that varied between overestimations and underestimations but gave an excellent performance when it comes to the detection capability. Such study investigates the appropriation of using these satellite means and flood mitigation and warning systems as well as the recommendations found to improve their algorithm. [ABSTRACT FROM AUTHOR]

It is increasingly acknowledged that the acceleration of the global water cycle, largely driven by anthropogenic climate change, has a disproportionate impact on sub‐daily and small‐scale hydrological extreme events such as flash floods. These events occur thereby at local scales within minutes to hours, typically in response to high‐intensity rainfall events associated with convective storms. In the present work, we show that by employing physically based representative hillslope models that resolve the main gradients controlling overland flow hydrology and hydraulics, we can get reliable simulations of flash flood response in small data‐scarce catchments. To this end, we use climate reanalysis products and transfer soil parameters previously obtained for hydrological predictions in an experimental catchment in the same landscape. The inverted mass balance of flood reservoirs downstream is employed for model evaluation in these nearly ungauged basins. We show that our approach using representative hillslopes and climate data sets can provide reasonable uncalibrated estimates of the overland runoff response (flood magnitude, storm volume, and event runoff coefficients) in three of the four catchments considered. Given that flash floods typically occur at scales of a few km2 and in ungauged places, our results have implications for operational flash flood forecasting and open new avenues for using gradient resolving physically based models for the design of small and medium flood retention basins around the world. Plain Language Summary: Flash floods have become increasingly common worldwide, with catastrophic damages to both human life and the economy. While the extent of global warming and climate change impacting these events is still under much debate, it is almost certain now that we need to be better equipped to understand and model these extremes to prevent and mitigate the possible risk to human life and infrastructure in a warming climate. To test, if we can use first principles derived from thermodynamic conservation laws and process based hydrological models for the same, we modeled flash flood response in four headwater catchments over Southern Germany using the concept of "representative hillslope." Since the regions considered in our work are poorly gauged, we made use of global climate reanalysis products and parameter transfer from past experiments. The encouraging results obtained in predicting the flood magnitude and volume speak to the overall applicability of our approach. We are able to get decent uncalibrated predictions in three out of the four catchments considered with minimum computational effort. Understanding and managing the adverse impacts of such extreme hydroclimatic events remains one of the crucial hurdles facing humanity toward the sustainable development goals (SDG17) in this decade. Key Points: Physically based representative hillslope models can be used for flash flood predictions in small data‐scarce and rural catchmentsClimate reanalysis data enable the initialization of a process‐based model, helping to reduce the uncertainties in estimating antecedent soil conditionsTransfer of model parameters within the same hydrological landscape is feasible [ABSTRACT FROM AUTHOR]

Climate change in desert areas and semi-arid watersheds may offer a promising solution for the water scarcity problem that Bedouins and local inhabitants face. This study investigated the integrated water resources management in arid and semi-arid regions using rainwater harvesting in combination with the managed aquifer recharge (RWH-MAR) technique. The study also used recharge wells and storage dams to achieve the sustainability of groundwater supplies in the context of climate change and management of the flow to the Gulf of Suez. Therefore, different return periods of 10, 25, 50, and 100 years were considered for the annual flood volume resulting from those watersheds. Moreover, hydrologic modeling was carried out for the El Qaa plain area, South Sinai, Egypt, using the Watershed Modeling System (WMS) and the groundwater modeling of SEAWAT code. Our findings show that for different scenarios of climate change based on return periods of 10, 25, 50, and 100 years, the aquifer potentiality reached 24.3 MCM (million cubic meters) per year, 28.8 MCM, 36.7 MCM, and 49.4 MCM compared to 21.7 MCM at 2014 with storage of groundwater ranges 11.8%, 32.1%, 69%, and 127.4%, respectively. These findings have significant implications for the system of RWH-MAR and groundwater sustainability in El Qaa Plain, South Sinai. The RWH-MAR proved to be an effective approach that can be applied in different water-stressed and arid regions to support freshwater resources for sustainable future development and food security, as well as protect communities from extreme flash flood events. [ABSTRACT FROM AUTHOR]

Recently, the earth's climate has changed considerably, leading to several hazards, including flash floods (FFs). This study aims to introduce an innovative approach to mapping and identifying FF exposure in the city of Tetouan, Morocco. To address this problem, the study uses different machine learning methods applied to remote sensing imagery within the Google Earth Engine (GEE) platform. To achieve this, the first phase of this study was to map land use and land cover (LULC) using Random Forest (RF), a Support Vector Machine (SVM), and Classification and Regression Trees (CART). By comparing the results of five composite methods (mode, maximum, minimum, mean, and median) based on Sentinel images, LULC was generated for each method. In the second phase, the precise LULC was used as a related factor to others (Stream Power Index (SPI), Topographic Position Index (TPI), Slope, Profile Curvature, Plan Curvature, Aspect, Elevation, and Topographic Wetness Index (TWI)). In addition to 2024 non-flood and flood points to predict and detect FF susceptibility, 70% of the dataset was used to train the model by comparing different algorithms (RF, SVM, Logistic Regression (LR), Multilayer Perceptron (MLP), and Naive Bayes (NB)); the rest of the dataset (30%) was used for evaluation. Model performance was evaluated by five-fold cross-validation to assess the model's ability on new data using metrics such as precision, score, kappa index, recall, and the receiver operating characteristic (ROC) curve. In the third phase, the high FF susceptibility areas were analyzed for two-way validation with inundated areas generated from Sentinel-1 SAR imagery with coherent change detection (CDD). Finally, the validated inundation map was intersected with the LULC areas and population density for FF exposure and assessment. The initial results of this study in terms of LULC mapping showed that the most appropriate method in this research region is the use of an SVM trained on a mean composite. Similarly, the results of the FF susceptibility assessment showed that the RF algorithm performed best with an accuracy of 96%. In the final analysis, the FF exposure map showed that 2465 hectares were affected and 198,913 inhabitants were at risk. In conclusion, the proposed approach not only allows us to assess the impact of FF in this study area but also provides a versatile approach that can be applied in different regions around the world and can help decision-makers plan FF mitigation strategies. [ABSTRACT FROM AUTHOR]

Flash floods are common in the town of Kokrajhar in north-eastern Indian state of Assam. Many areas in the town of Kokrajhar are submerged in water almost every year due to floods during rainy season. These floods cause heavy damage to roads, agricultural production and halt the progress of major infrastructural development projects. Hence, prevention of floods has become an important concern in the town of Kokrajhar. This study investigated the causes of floods and offered a solution for their elimination. The investigation showed that inadequate and inefficient network of storm drains was the reason for the flash floods. A new drainage network for the town of Kokrajhar has resulted from studying the shortcomings of the existing network of storm drains. The new network is designed in accordance with the recommendations stated in the “Manual on storm water drainage systems” prepared by the Central Public Health and Environmental Engineering Organization in 2019. [ABSTRACT FROM AUTHOR]

The rainfall characteristics of flash floods are highly sensitive to the joint impacts of different rainfall event features. In this study, we propose an objective framework for identifying rainfall characteristics for flash floods and assessing the risks while considering the combined impacts of multiple rainfall characteristics. The flash flood events are first classified into different types in terms of flood intensity and process using fuzzy C-means clustering with a subtractive clustering algorithm. Strong association rules between rainfall indices and flood types are subsequently identified based on an association rule mining method. The rainfall indices that strongly affect flash flood processes are obtained based on these association rules. Based on the results of the association analysis, the risks of different types of flash floods under different combinations of key rainfall indices are evaluated based on the Bayesian formula and copula function. The association rule analysis with single and multiple rainfall indices demonstrates that the maximum 12-h rainfall intensity and total antecedent cumulative rainfall are the major rainfall characteristics that affect flash flood processes in the study area. We also examine the risk of flash floods under different combinations of maximum 12-h rainfall intensity and total cumulative rainfall. This study provides an effective and quantitative approach to improve the risk analysis of flash floods and advances its application in the risk management of future flash floods. [ABSTRACT FROM AUTHOR]

While research on quantitative measures of vulnerability is increasing, current studies may be criticized for failing to consider the spatiotemporal dynamics of vulnerability, ignoring uncertainty in the assessment process, and lacking validation. Using the index-based method, this study quantitatively assesses the spatiotemporal dynamics of flash flood vulnerability in the Hengduan Mountain Areas (HMA) of the southeastern Tibetan Plateau. First, a vulnerability index system is constructed based on existing vulnerability assessment studies, and the uncertainty is controlled by introducing statistical and machine learning methods. Then, we explore the spatiotemporal change trend of vulnerability and the characteristics of spatiotemporal hotspots, and validate the vulnerability results with a spatial matching method based on the actual flash flood frequency and damage records. The results show that our method is effective in capturing the spatial and temporal patterns of flash flood vulnerability, and the validation shows that the assessment results can reflect the relationship between flash flood vulnerability and observed flash flood impacts. Our study highlights the importance of spatiotemporal dynamics, uncertainty control, and validation for quantitative vulnerability assessment, and proposes methods to achieve these goals. The spatiotemporal dynamics assessment of flash floods vulnerability in the HMA is an empirical application of our methods, showing the process and results of spatiotemporal vulnerability assessment. The study of spatiotemporal dynamics of flash flood vulnerability is of great significance for predicting the impacts of future flash floods, which is important for policy makers to plan flash flood prevention and response measures, reduce flash flood impacts, and improve resilience.

The research study was carried out to assess the impact and coping measures of flash floods in Quetta City during Monsoon 2022. A combination of both qualitative and quantitative research method was used as part of “After-Only” research design. Primary data sources included survey of the affected areas in Quetta City and interviews of officials from various departments. Secondary data sources involved records from relevant departments, newspapers, journals and internet. Random sampling technique was adopted during the survey and thematic analysis of total sample size of 390 was carried out. .The findings have revealed that unchecked urbanization and construction in waterways amplified the damaged caused by flash floods in Quetta in 2022. The empirical evidences and survey conducted of affected areas pointed out lack of implementation of available regulatory measures. In order to mitigate the effect of any future flash flood, existing regulatory mechanism in required to be implemented in true letter and spirit which should include reclaiming of encroached waterways, planning for rehabilitation of affected households to suitable location and detailed urban planning to cater for expansion of Quetta City.

Flash floods, rapid and devastating inundations of water, are increasingly linked to the intensifying effects of climate change, posing significant challenges for both vulnerable communities and sustainable environmental management. The primary goal of this research is to investigate and predict a Flood Susceptibility Map (FSM) for the Ibaraki prefecture in Japan. This research utilizes a Random Forest (RF) regression model and GIS, incorporating 11 environmental variables (involving elevation, slope, aspect, distance to stream, distance to river, distance to road, land cover, topographic wetness index, stream power index, and plan and profile curvature), alongside a dataset comprising 224 instances of flooded and non-flooded locations. The data was randomly classified into a 70 % training set for model development, with the remaining 30 % used for model validation through Receiver Operating Characteristics (ROC) curve analysis. The resulting map indicated that approximately two-thirds of the prefecture as exhibiting low to very low flood susceptibility, while approximately one-fifth of the region is categorized as high to very high flood susceptibility. Furthermore, the RF model achieved a noteworthy validation with an area under the ROC curve of 99.56 %. Ultimately, this FSM serves as a crucial tool for policymakers in guiding appropriate spatial planning and flood mitigation strategies.

Rainwater harvesting (RWH) is principally based on collecting, storing, and using rainfall which would otherwise be lost as surface runoff. Runoff threatens in several ways: accelerating erosion, intensifying flooding, and reducing groundwater recharge. Therefore, purposely retaining rainfall in the urban water cycle rather than draining has several positive impacts on designing sustainable cities. This work presents a proposal on how to avoid flooding in cities by systematically harvesting, storing rainwater, and using it for multiple purposes. The concept of RWH presented here has the potential to be a radical innovation to solve the social, economic, and environmental challenges associated with flash flooding. Each residence is regarded as a water production unit. Depending on the climatic conditions, people can meet their water needs on a local household basis, or alternatively use piped water as a complement. By infiltrating rainwater, groundwater is locally recharged and downstream wells are more productive. The implementation of this idea involves entrepreneurial agency that challenges existing structures, rather than adapting to them. Clearly, social entrepreneurship and social innovation are expected to catalyse the realization of this social innovation, also in rural areas. It is about mobilizing ideas, capacities, and resources to create a sustainable social transformation. HIGHLIGHTS Rainwater harvesting is a well-established tool to retain water within the city.; Harvested rainwater is conventionally used for non-potable used (clean and green city).; The potential of rainwater for safe drinking water provision has been largely overlooked.; A new concept for urban water management, encompassing flash flood mitigation is presented.; Harnessing active engagement of all citizens facilitates the efficiency of this concept.;

This article introduces a groundwater vulnerability assessment model that utilises the fuzzy analytic hierarchy process (FAHP) in the Wadi AlHasa catchment, Jordan. The assessment takes into account both geomorphological and hydrogeological variables, employing a comprehensive methodology that integrates various parameters. To evaluate the catchment, the study employs remote sensing and Geographic Information System (GIS) techniques. The analysis of the digital elevation model enables the creation of a map illustrating the diverse geomorphology of the catchment. This geomorphology significantly influences drainage density, direction, and the spatial distribution and intensity of flash flood events. Moreover, the study develops and maps a fuzzy FAHP DRASTIC vulnerability index, which proves to be a valuable tool for assessing the susceptibility of groundwater resources to contamination. The unique feature of the index is its ability to incorporate uncertain or subjective data, providing a means to evaluate the significance of various influencing factors. This information serves as critical support for decision-making and management efforts geared towards safeguarding and enhancing groundwater resources. Within the study area, the DRASTIC vulnerability index values span from 0.08325 to 0.28409, with 18% of the site exhibiting a high vulnerability rate. Additionally, the article implements a managed aquifer recharge model (MAR), with 31% of the area falling into MAR classes. Among these, 22.1% are classified as a high MAR class, while 0.7% belong to a very high MAR class. These findings underscore the feasibility of MAR projects in regions with limited water resources.

Flash floods are common in the town of Kokrajhar in north-eastern Indian state of Assam. Many areas in the town of Kokrajhar are submerged in water almost every year due to floods during rainy season. These floods cause heavy damage to roads, agricultural production and halt the progress of major infrastructural development projects. Hence, prevention of floods has become an important concern in the town of Kokrajhar. This study investigated the causes of floods and offered a solution for their elimination. The investigation showed that inadequate and inefficient network of storm drains was the reason for the flash floods. A new drainage network for the town of Kokrajhar has resulted from studying the shortcomings of the existing network of storm drains. The new network is designed in accordance with the recommendations stated in the “Manual on storm water drainage systems” prepared by the Central Public Health and Environmental Engineering Organization in 2019.

Abstract A flash flood is one of the hazardous phenomena, especially in dry regions. In Saudi Arabia, Jazan Province experiences data scarcity especially historical hydrological data and a lack of studies related to flash flood analysis and the rainfall and runoff interrelationship. Thus, this research aims to study the rainfall-runoff inter-relation, predict flash floods, and to map the risk areas in Jazan Province by the geological, geomorphological, and hydrogeological characteristics along with digital elevation model (DEM), watershed modeling system (WMS) and HEC-HMS models. Jazan Province encompasses 25 drainage basins, receiving a considerable amount of rainfall (ranging from 100 to 500 mm) (August, October-November, March) which intermittently cause strong and destructive flash floods. The DEM was used for delineating the catchment (drainage basins) parameters. Physiographic parameters of the catchments have been analyzed for mapping the hazard degree of the flash flood strength. Further, basins with high hazard degrees of flash floods were selected to assess rainfall-runoff inter-relation using the HEC-HMS models, GIS, and morphometric parameters. Forty %, 8%, 52% of the study area are high, medium, and low hazard degrees of flash floods, respectively. The groundwater recharge in the study area was calculated through the integration of satellite image analysis, SWAT and GIS techniques and it ranges from 0.002 mm/km2/year to 8 mm/km2/year with an average of 2.5 mm/km2/year. Rainfall-runoff inter-relation of study basins, assessed based on the integration of WMS and HEC-HMS models, indicates that the resulting runoff volume ranges from 18.5 × 106 m3 to 473.1 × 106 m3 at a recurrence period of 5 and 100 years at rainfall events of 65 mm and 116.8 mm, respectively.

The analysis of the influencing factors of flash floods, one of the most destructive natural disasters, is the basis of scientific disaster prevention and mitigation. There is little research considering the influence of tropical cyclones (TCs) and water conservancy projects on flash floods, which cannot be ignored in the island areas where flash floods often occur due to the complex influence of various factors. In this study, under the pressure-state-response framework (PSR framework), the factors affecting the distribution of flash floods on Hainan Island, China, from 1970 to 2010 were quantitatively analyzed by using the geographical detector method. By dividing the time period, give full play to the advantages of the PSR framework and show the evolution process of various factors. Different from inland areas, extreme precipitation and tropical cyclones play a major role in the spatial distribution of flash floods on Hainan Island, China, and the driving force of tropical cyclones is 1.1 times that of extreme precipitation on average. Medium-sized reservoirs play the greatest role in the prevention of flash floods on Hainan Island, and their driving forces reach 0.38 times of extreme precipitation on average, followed by large-sized reservoirs and small-sized reservoirs. Large-sized reservoirs are limited in quantity and have limited effectiveness in preventing flash floods on Hainan Island. Therefore, in the forecasting and risk management of flash flood in the island area, more attention should be paid to the impact of extreme precipitation and TCs, and the role of medium-sized reservoir should be fully exerted. [ABSTRACT FROM AUTHOR]

A chronological study was conducted on the Neolithic depositional layers widely known as ‘Rubble layers’ at the Neolithic site ‘Ain Ghazal, Jordan. The aim of this study was to provide new knowledge about the layers, and their deposition, using luminescence dating. Luminescence ages suggests that the rubble layers were deposited in two distinct episodes, the first around 8.2–8.5 ka ago during the Yarmoukian period, and the second during Chalcolithic period around 6.6–7.4 ka ago. The earlier episode is contemporaneous with records of episodic torrential rainfall during an arid period between 8.6 and 8.0 ka ago, supporting the hypothesis of deposition by natural causes such as flash floods. [ABSTRACT FROM AUTHOR]

A flash flood is one of the hazardous phenomena, especially in dry regions. In Saudi Arabia, Jazan Province experiences data scarcity especially historical hydrological data and a lack of studies related to flash flood analysis and the rainfall and runoff interrelationship. Thus, this research aims to study the rainfall-runoff inter-relation, predict flash floods, and to map the risk areas in Jazan Province by the geological, geomorphological, and hydrogeological characteristics along with digital elevation model (DEM), watershed modeling system (WMS) and HEC-HMS models. Jazan Province encompasses 25 drainage basins, receiving a considerable amount of rainfall (ranging from 100 to 500 mm) (August, October-November, March) which intermittently cause strong and destructive flash floods. The DEM was used for delineating the catchment (drainage basins) parameters. Physiographic parameters of the catchments have been analyzed for mapping the hazard degree of the flash flood strength. Further, basins with high hazard degrees of flash floods were selected to assess rainfall-runoff inter-relation using the HEC-HMS models, GIS, and morphometric parameters. Forty %, 8%, 52% of the study area are high, medium, and low hazard degrees of flash floods, respectively. The groundwater recharge in the study area was calculated through the integration of satellite image analysis, SWAT and GIS techniques and it ranges from 0.002 mm/km2/year to 8 mm/km2/year with an average of 2.5 mm/km2/year. Rainfall-runoff inter-relation of study basins, assessed based on the integration of WMS and HEC-HMS models, indicates that the resulting runoff volume ranges from 18.5 × 106 m3 to 473.1 × 106 m3 at a recurrence period of 5 and 100 years at rainfall events of 65 mm and 116.8 mm, respectively. [ABSTRACT FROM AUTHOR]

In a mountainous basin, flash-flood-induced soil erosion and its assessment are of major concern to the researchers. Cloudburst-induced extreme rainfall, undulating terrain, and inadequate water and soil conservation practices aggravate the problem of soil erosion. Alaknanda, a major basin of the upper Ganges in India, has experienced several incidents of flash floods. These have resulted in heavy soil loss, water siltation, and pollution in the basin. This study uses the revised universal soil-loss equation (RUSLE) in the GIS framework to assess the soil loss rate in various sub-basins of the Alaknanda. Further, the study suggests sub-basin prioritisation based on high soil erosion rate and past vulnerability that need attention of the decision-makers. RUSLE, an empirical soil erosion model, is easy to comprehend physically, involves minimum resources, and works on readily available inputs. The parameters used in the assessment include R-Rainfall Erosivity, Cover Management-C, Topographic-LS, Conservation Practice-P, and Soil Erodibility-K. The average R, K, LS, C & P factors for the Alaknanda basis are estimated as 437 MJ mm/ha/yr., 0.07 t ha h MJ-1 ha-1 mm-1, 1.4, 0.7, and 0.62, respectively. The mean annual soil erosion rate in the sub-basins varies from 6 to 27.9 t /ha/year with a mean rate of 13.4 t/ha/year and total erosion of 13.7 Mt/year. The results show that out of the twelve sub-basins, two (WS7 and WS5) fall in the high erosional class (18–30 t/ha/year), nine fall in the moderate erosional class (7–18 t /ha /year) and one basin in the low erosional class (1.0–7.0 t /ha/year). It is hoped that the present study will be helpful to the decision-makers while prioritising the actions to be taken for the water and soil conservation purposes. [ABSTRACT FROM AUTHOR]

Low-impact development practices (LIDs) have been used to reduce the impacts of flash floods in Brazil. However, there is a lack of investigations on their construction costs concerning the effects on the mitigation of flash floods. This paper addresses the construction costs and the runoff reduction by using seven scenarios of LIDs for a 32-km2 basin in the Midwest Brazil region. The LIDs considered were permeable pavement (PP), rainwater harvesting (RWH), and infiltration trenches (IT). The first three LID scenarios considered PP, RWH, and IT techniques, respectively, and the other four used different combinations of the three LIDs. Scenario 3 showed a decrease of 15% in the runoff, achieved at a lower cost using only IT. This result corresponds to a 2.6% runoff reduction per $1 million spent. These analyses may help in the identification of the balance between benefit and cost by local stakeholders for the use of LIDs. [ABSTRACT FROM AUTHOR]

Climate change represents an overwhelming challenge that demands urgent intervention for effective resolution. Among the devastating consequences of climate change, flash floods stand out as one of the most catastrophic repercussions. This research focuses on two primary objectives. Firstly, it aims to evaluate the existing state of flash flood intensity (FFI) in a specific area of Hamamatsu city, Japan, which frequently experiences flash flood incidents. Secondly, it seeks to develop a mitigation plan to alleviate the adverse impacts of flooding on buildings within the area. To accomplish these objectives, four parameters related to FFI (namely, runoff depth, runoff velocity, runoff duration, and affected portion) were selected and estimated through the implementation of hydrological and hydrodynamic models. Additionally, a hydrological model was employed, utilizing a storm event with a return period of 100 years as input. During this simulated storm event, FFI values were calculated and categorized into four distinct levels. The results revealed that more than one-tenth of the examined buildings encountered the highest scale of FFI (category 4), while categories 3 and 4 combined accounted for nearly three-quarters of all buildings in the study area. Moreover, two mitigation strategies were adopted to prevent flooding within the buildings' vicinity. Finally, this study provides a valuable framework and guidance for decision-makers and insurance companies, enabling them to assess the flood hazard status of buildings and make informed decisions accordingly. [ABSTRACT FROM AUTHOR]

The Uttarakhand Himalaya is highly prone to geo-hydrological disasters—landslides, flash floods, cloudbursts, debris flows, rock falls, extreme weather, and land submergence. Cloudbursts and glacier bursts generally trigger these disasters, which are very active and frequent in the Uttarakhand Himalaya and occur mainly during the monsoon season. This study examines the geo-hydrological disasters in the Uttarakhand Himalaya, which occurred from 2020 to 2023 mainly during the three months of Monsoon season. Further, the study assesses the magnitude of the disaster and maps the disaster hotspot areas. Both qualitative and quantitative approaches were used to conduct this study. Data on geo-hydrological hazards that occurred from 2020 to 2023 in Uttarakhand were gathered from multiple sources such as the records of the State Disaster Management Department, Dehradun, various media reports—print and electronic, and personal observation of some affected areas through field visits. Further, an empirical study of the Bandal and Song River valleys was conducted. These valleys were severely hit by a cloudburst-triggered flash flood and debris flow. Household-level survey of the damage caused by huge flash floods and debris flows in three villages of Bandal Valley was carried out. The data were analyzed and mapped and the Uttarakhand Himalaya was divided into disaster hotspots and frequency zones. ArcGIS was used to construct maps and the Google Earth imagery and Sentinel 1-SAR were the major sources of maps. This study recommends that the construction of settlements, infrastructural facilities, and economic avenues should be banned in the most disaster-affected areas, along river valleys, and on fragile slopes. Eco-disaster risk reduction, such as conserving forests and wetlands, and plantation drives in the most degraded areas such as the river banks and fragile slopes, can help communities to prepare for disasters. [ABSTRACT FROM AUTHOR]

The objective of this study is to simulate the rainfall-runoff process of mountain torrents in the Shou River Basin located in the southern part of Wenchuan County, Sichuan Province. The basin encountered a significant flood disaster on August 20, 2019, resulting in substantial human and economic losses, as well as limited hydrological data availability. This research employs a unidirectional coupling approach by integrating the Weather Research and Forecasting Model (WRF) with the Storm Water Management Model (SWMM) to accurately simulate rainfall-runoff processes. To account for the non-uniform distribution of precipitation within mountainous watersheds, high-precision rainfall data generated by WRF are extracted and utilized as input for the SWMM model. The quantitative comparison between the evolution of spatial distribution of rainstorms and hourly precipitation processes demonstrates that the WRF model effectively captures dynamic changes in rainstorms. Analysis results indicate a trend of rainfall center movement from midstream to downstream, with sub-watersheds' superimposed effect on rainfall response leading to increased flood peak response, duration, and destructive power. This study provides an effective method and reference for predicting and assessing mountain flood disasters in data-scarce areas such as the Shou River Basin. [ABSTRACT FROM AUTHOR]