Occurrence Of Flash Floods Research Articles

Влияние ливневых паводков на строительство сооружений по сбору дождевой воды

Introduction. The lack of fresh water has become a serious problem. The main causes of the water crisis are population growth and climate change. Egypt is a very arid country which is facing water scarcity due to increasing demand and limited supply. Applying new approaches is required in this situation to deal with water scarcity. Wadi Watir in the Sinai Peninsula, chosen as the research area, is an important tourist and commercial watershed. It experiences water scarcity and the threat of flash floods. The residents are losing their crops and sheep. Roads are deserted, and their lives are in danger due to the periodic occurrence of flash floods. Materials and methods. For the first time, the current research investigates the impact of flash floods on the implementation of rainwater harvesting (RWH) systems. The flash flood risk and RWH structure maps were overlaid using GIS. Results. The results showed that five percolation tanks, five check dams, 11.15 km2 of area for on-ground percolation tanks, and 0.48 km2 of space for farm ponds are optimal for water harvesting during flash floods. Conclusions. The development of Wadi Watir was prioritized and divided into three phases. Therefore, the occurrence of flash floods clearly affects the development plans. The impact of flash floods should be considered during the calculation of the design capacity of the RWH structures.

Flash Flood Hazard Mapping Using Landsat-8 Imagery, Ahp, And Gis In The Ngan Sau And Ngan Pho River Basins, North-Central Vietnam

Flash floods have been blamed for significant losses and destruction all around the world are widely, including Vietnam, a developing nation that has been particularly hard hit by climate change. Therefore, flash flood hazards are essential for reducing flood risks. The topographic wetness index (TWI), altitude, slope, aspect, rainfall, land cover, normalized difference vegetation index (NDVI), distances to rivers and roads, and flow length were used in this study to create a spatial database of ten exploratory factors influencing the occurrence of flash floods in the Ngan Sau and Ngan Pho river basins (North-Central Vietnam). Subsequently, the analytic hierarchy process (AHP) was applied to calculate the weights of these influencing factors. The flood threat was then mapped using GIS techniques. The validation of the flash flood hazards involved 151 flood inventory sites in total. The findings demonstrate that (i) distance from rivers (0.14) and TWI (0.14) factors have the greatest influence on flash flooding, whereas distance from roads (0.06) and NDVI (0.06) factors were found to have the least influence; (ii) a good conformity of 84.8 percent between flood inventory sites and moderate to very high levels of flash flood hazard areas was also discovered; (iii) high and very high flood hazard levels covering areas of 275 and 621.1 km2 were mainly detected along and close to the main rivers and streams, respectively. These results demonstrated the effectiveness of GIS techniques, AHP, and Landsat-8 remote sensing data for flash flood hazard mapping.

Enhancing community resilience in arid regions: A smart framework for flash flood risk assessment

This paper presents a novel framework for smart integrated risk management in arid regions. The framework combines flash flood modelling, statistical methods, artificial intelligence (AI), geographic evaluations, risk analysis, and decision-making modules to enhance community resilience. Flash flood is simulated by using Watershed Modelling System (WMS). Statistical methods are also used to trim outlier data from physical systems and climatic data. Furthermore, three AI methods, including Support Vector Machine (SVM), Artificial Neural Network (ANN), and Nearest Neighbours Classification (NNC), are used to predict and classify flash flood occurrences. Geographic Information System (GIS) is also utilised to assess potential risks in vulnerable regions, together with Failure Mode and Effects Analysis (FMEA) and Hazard and Operability Study (HAZOP) methods. The decision-making module employs the Classic Delphi technique to classify the appropriate solutions for flood risk control. The methodology is demonstrated by its application to the real case study of the Khosf region in Iran, which suffers from both drought and severe floods simultaneously, exacerbated by recent climate changes. The results show high Coefficient of determination (R2) scores for the three AI methods, with SVM at 0.88, ANN at 0.79, and NNC at 0.89. FMEA results indicate that over 50% of scenarios are at high flood risk, while HAZOP indicates 30% of scenarios with the same risk rate. Additionally, peak flows of over 24 m3/s are considered flood occurrences that can cause financial damage in all scenarios and risk techniques of the case study. Finally, our research findings indicate a practical decision support system that is compatible with sustainable development concepts and can enhance community resilience in arid regions.

Employing the Flash Flood Potential Index (FFPI) with Physical Environmental Factors in Baling, Kedah through GIS Analysis

On July 4, 2022, Sungai Kupang, Baling, Kedah experienced a devastating flood that caused 3 fatalities, destroyed or damaged 17 houses, affected 3,546 residents, and resulted in losses estimated at RM25.91 million. The flood was triggered by heavy rainfall in the highland area, which caused multiple landslides to occur simultaneously. The landslides led to a debris flow phenomenon in four main river branches, ultimately resulting in a tragic debris and mud flood in the lowlands and downstream villages. The aim of this study is to analyze the location of flash flood occurrences in Baling and to estimate the likelihood of flash floods based on the identified land physical factors. This study also identifies the critical area of Baling basin that have high potential for a flash flood and evaluates the effectiveness and applicability of the FFPI model compared with historical flood events and remote sensing imagery which have occurred in the few watersheds area. The FFPI model, which was created for the first time in 2003, is used in this study to analyze the flash flood that occurred in Baling by considering slope, land cover, soil data, and vegetation. The FFPI technique is applied in five scenarios to determine the flash flood potential, and the value used is also based on the references. A value of 1 on the index denotes a minimal probability of flash floods, while a value of 10 indicates the highest probability. Based on the findings, the study area had a high possibility of having a flash flood at an index value of 7. The danger level of a severe flash flood is present throughout the research region in all scenarios when the value is more than 50%. The outcome is then utilized to do comparisons using historical information on flash floods and their hotspots area, as well as utilizing satellite imagery to determine the true scale of the flood. This is also important to reduce the impact of floods occurrence in the same place as well managing risk and to plan for disaster-mitigation operations.

Mitigation of urban waterlogging from flash floods hazards in vulnerable watersheds

Study regionThe Aswan region is a vast plateau in the South of Egypt located 150 m above mean sea level (AMSL). Within this region, the tourist city of Aswan is Egypt’s southernmost city, located on the east bank of the Nile River. Study focusThe occurrence of flash floods can severely impact low-lying and densely populated areas. Therefore, highly vulnerable areas require effective mitigation measures to guarantee public safety and preserve archeological sites of great importance. This study investigates the interaction between the soil, surface water, and groundwater in the Aswan region of Egypt. Based on the rainfall analysis and the watershed hydrology, six different scenarios were run using the MODFLOW and Watershed Modeling System (WMS) software, which simulated rainfall recurrence intervals of 2, 5, 10, 25, 50 and 100 years. New hydrological insights for the regionThe model's results indicated that an increase in the recurrence interval produced a rise in the groundwater level (GWL) up to 8.82 m (AMSL). Therefore, constructing three dams was proposed as a solution at the three basins of Al- Haytah, Al-Kimab, and Umm-Buwayrat. The proposed solution allows the storage of large volumes of water upstream. It mitigates GWL's rise within and near Aswan City. The presented study can be applied to vulnerable watersheds in arid and semi-arid regions. It can help policymakers to integrate additional sustainable solutions into construction dams and their implementation in development plans.

Assessment of the Feasibility of Implementing a Flash Flood Early Warning System in a Small Catchment Area

The occurrence of flash floods is an increasingly common phenomenon. In many parts of the world, it is associated with an increase in the intensity of rainfall. Reducing the financial and social losses caused by the occurrence of local urban floods is possible through the use of hydrodynamic modeling and real-time flood forecasting. The purpose of this study is to assess the ability of the modeling technique to simulate the flow in a small catchment area and to determine the time remaining to reach the set warning and danger levels. SWMM 5.2 and QuantumGIS software were used in the study. The analysis showed that for the considered catchment area with a short length of the main stream (1612 m), the time possible for implementing countermeasures and evacuating the population is 70 to 120 min. The study also confirmed that short-term rainfall requires less depth to reach high stormwater elevations than long-term rainfall. In addition, a relationship was noted between the preceding rainfall and the height of stormwater and the forecast time. There was an unfavorable reduction in forecasting time as the depth of rainfall increased and its duration shortened. In the case of the analyzed catchment, the maximum elevation of stormwater (Esw,max) is generated by rainfall that is characterized by the highest intensity in the final phase of their duration. Similarly, the longest forecast time (tf) for the maximum stormwater elevation is caused by rainfall, which is characterized by the highest intensity in its final phase. The results of the study can significantly assist local governments when developing a catchment management plan and when trying to implement practices to minimize the negative effects of flash floods.

Mitigation of Flash Floods in Kokrajhar of the State of Assam in North East India

Assam, one of the northeastern state of India witnesses heavy rainfall almost every year because of its geographical location. Kokrajhar, a district of Assam witnesses flash floods at certain parts of it during rainy season. Detailed study of the reasons for occurrence of flash floods have made and presented in this paper. Incorrect estimation of maximum flood or providing undersized storm water drains are the major reasons for occurrence of flash floods. Improper construction and maintenance of storm water drains are also the reasons for flash flood occurrence. Kokrajhar, being the headquarter of Bodoland Territorial Region is coming up with large number of infrastructural development projects. However, due to flood situations, these construction projects are halted for a considerable time resulting in slower rate of completion of these projects. The present case study aims at understanding the reasons behind occurrence of flash floods at central part of Kokrajhar town and finding the appropriate preventive and remedial measures to prevent such floods. These measures will thus helps in avoiding difficulties caused to the general public during rainy season and also helps in the faster rate of development of the region.

A Review of Managed Aquifer Recharge Potential in the Middle East and North Africa Region with Examples from the Kingdom of Saudi Arabia and the United Arab Emirates

Groundwater extraction in most Middle East and North Africa (MENA) countries far exceeds its renewability, which ranges from 6% to 100%. Freshwater resources to support food production are very limited in this region. Future climate predictions include more consistent and longer wet periods with increasing surplus rainfall, which will enhance flood and flash flood occurrences in the MENA. Demand management of groundwater resources and managed aquifer recharge (MAR, also called groundwater replenishment, water banking, and artificial recharge, is the purposeful recharge of water to aquifers for subsequent recovery or environmental benefits) represent essential strategies to overcome the challenges associated with groundwater depletion and climate change impacts. Such strategies would enable the development of groundwater resources in the MENA region by minimizing the stress placed on these resources, as well as reducing deterioration in groundwater quality. Groundwater augmentation through recharge dams is a common practice in different countries around the globe. Most dams in the MENA region were built to enhance groundwater recharge, and even the few protection dams also act as recharge dams in one way or another. However, the operating systems of these dams are mostly dependent on the natural infiltration of the accumulated water in the reservoir area, with limited application of MAR. This review presents analyses of groundwater renewability and the effectiveness of recharge dams on groundwater recharge, as well as the potential of MAR technology. This study indicates that the recharge efficiency of dam’s ranges between 15 to 47% and is clustered more around the lower limit. Efficiency is reduced by the clogging of the reservoir bed with fine materials. Therefore, there is a need to improve the operation of dams using MAR technology.

The Occurrence of Catastrophic Multiple-Fatality Flash Floods in the Eastern Mediterranean Region

Despite recent technological advances, many parts of the world continue to experience flood disasters accompanied by significant loss of human lives. Understanding how frequent these deadly catastrophes are creates many uncertainties, especially in areas where disaster records are scarce or have short timeframes. It is, however, very important from a preparedness and civil protection standpoint to assess the frequency of such high-mortality events, especially considering the threat of climate change. This work develops a high-mortality flood event database using multiple international sources, covering a relatively long time window (1882–2021), exploring the deadliest floods in the Eastern Mediterranean region, and examining their seasonal distribution, their temporal evolution, and their basic spatial patterns. The study identifies 132 flash flood events (causing ≥10 fatalities) with a return period of only 1.56 years. Additionally, higher-magnitude events (>85th percentile) were found to be less common but still not very rare (return period = 9.1 years). The number of events shows an increase in recent decades, while seasonal and spatial patterns were identified as well. Overall, the findings provide a foundation for understanding how common catastrophic flood events are in the region, are beneficial for policymakers and relevant professionals, and are an important stepping stone towards a complete understanding of how extreme floods have changed in the last century or will change in the near future.

Atmospheric conditions favouring extreme precipitation and flash floods in temperate regions of Europe

Abstract. In recent years, flash floods have repeatedly occurred in temperate regions of central western Europe. Unlike in Mediterranean catchments, this flooding behaviour is unusual. In the past (especially in the 1990s), floods have been characterized by predictable, slowly rising water levels during winter and driven by westerly atmospheric fluxes. Here, we explore potential links and causes between the recent occurrence of flash floods in central western Europe to extreme precipitation and specific atmospheric conditions. We hypothesize that a change in atmospheric conditions has led to more frequent extreme precipitation events that have subsequently triggered flash flood events in central western Europe. To test this hypothesis, we compiled data on flash floods in central western Europe and selected precipitation events above 40 mm h−1 from radar data (the RADOLAN “Radar-Online-Adjustment” dataset from the German Weather Service). Moreover, we identified proxy parameters representative of extreme precipitation favouring atmospheric conditions from the ERA5 reanalysis dataset. High specific humidity (q) in the lower troposphere (q≥0.004 kg kg−1), sufficient latent instability (convective available potential energy (CAPE) ≥ 327 J kg−1), and weak wind speeds between 10 m a.g.l. and 500 hPa (WS10m-500hPa≤6 m s−1) proved to be characteristic of intense rainfall that can potentially trigger flash floods. We relied on linear models to analyse 40 years worth (1981–2020) of atmospheric parameters as well as related precipitation events. We found significant increases in the atmospheric moisture content and increases in atmospheric instability. Parameters representing the motion and organization of convective systems remained largely unchanged in the considered period (1981–2020); however, the number of precipitation events, their maximum 5 min intensities, and their hourly sums were characterized by large interannual variations, and no trends could be identified between 2002 and 2020. Our study shows that there is no single mechanistic path leading from atmospheric conditions to extreme precipitation and subsequently to flash floods. The interactions between the processes involved are so intricate that more analyses which consider other potentially relevant factors, such as intra-annual precipitation patterns or catchment-specific parameters, are required.

Precursory Characteristics of Flash Flood Occurrence in Small Catchment of Upper Brantas River

Indonesia’s flash floods have increased significantly since the flash flood in Bohorok, North Sumatra, in 2003 and Jember, East Java, in 2006. Such flash floods have caused negative impacts, loss of human life and damage to vital infrastructures such as houses, roads, bridges, or other public facilities. Understanding the factors driving flash floods and their mechanism at the outset is essential in mitigating the negative impacts mentioned above. This paper presents the results of the analysis of the flash flood characteristics of several hydraulic parameters under conditions just before the flash flood incident, including the intensity of the rainfall, the time of concentration, and the time lag of the occurrence. As the object of the study, a flash flood event was selected in Batu City, Upper Brantas River, which occurred on November 4, 2021. The results show that the lag time of the flash flood occurrence at 40 minutes magnitude at Bulukerto Village of Batu City, the application of Kirpich for the time of concentration equation performed better than the Ventura and Watt & Chow equations. Further assessment of precursory characteristics for other similar small catchment and hydro-meteorological conditions is still the subject of beneficial study or research.

Development of geo-environmental factors controlled flash flood hazard map for emergency relief operation in complex hydro-geomorphic environment of tropical river, India.

The occurrences of flash floods in sub-tropical climatic regions like India are ubiquitous phenomena, particularly during the monsoon season. This type of flood occurs within a short period of time and makes it distinctive from all-natural hazards, which causes huge loss of economy and causalities of life. Therefore, its prediction is crucial and one of the challenging tasks for researchers to mitigate this sustainably. Furthermore, identifying flash flood susceptible regions is the foremost responsibility in managing flood events, which helps the local administration take emergency relief operations in flood-prone regions. In September 2021, the flood in the Gandheswari river basin was the most severe compared to the past decade. The occurrences of flash floods in the lower course of the Gandheswari river has been affected riparian habitats rigorously. Thus, in this study, we proposed the bivariate logistic regression (LR) method to delineate this river basin's flash flood hazard (FFH) map. Here, sixteen flood conditioning factors were selected for modeling purposes with the help of a multicollinearity test, and a total of 71 flood points were identified from the historical dataset. The produced result was validated by six distinctive validating techniques, including receiver operating characteristics (ROC) analysis, specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), and F-score. These techniques have shown that present modeling has high predictive performance in both training and testing dataset with the values of ROC (training-0.928, validating-0.892), specificity (training-0.911, validating-0.882), sensitivity (training-0.915, validating-0.885), PPV (training-0.912, validating-0.874), NPV (training-0.91, validating-0.875), and F-score (training-0.92, validating-0.89). Therefore, the proposed method in this and the outcome result will help the disaster manager make proper decisions to mitigate the hazardous situation and take sustainable emergency relief operations.

Flash Flood Susceptibility Assessment and Zonation by Integrating Analytic Hierarchy Process and Frequency Ratio Model with Diverse Spatial Data

Flash floods are the most dangerous kinds of floods because they combine the destructive power of a flood with incredible speed. They occur when heavy rainfall exceeds the ability of the ground to absorb it. The main aim of this study is to generate flash flood maps using Analytical Hierarchy Process (AHP) and Frequency Ratio (FR) models in the river’s floodplain between the Jhelum River and Chenab rivers. A total of eight flash flood-causative physical parameters are considered for this study. Six parameters are based on remote sensing images of the Advanced Land Observation Satellite (ALOS), Digital Elevation Model (DEM), and Sentinel-2 Satellite, which include slope, elevation, distance from the stream, drainage density, flow accumulation, and land use/land cover (LULC), respectively. The other two parameters are soil and geology, which consist of different rock and soil formations, respectively. In the case of AHP, each of the criteria is allotted an estimated weight according to its significant importance in the occurrence of flash floods. In the end, all the parameters were integrated using weighted overlay analysis in which the influence value of drainage density was given the highest weight. The analysis shows that a distance of 2500 m from the river has values of FR ranging from 0.54, 0.56, 1.21, 1.26, and 0.48, respectively. The output zones were categorized into very low, low, moderate, high, and very high risk, covering 7354, 5147, 3665, 2592, and 1343 km2, respectively. Finally, the results show that the very high flood areas cover 1343 km2, or 6.68% of the total area. The Mangla, Marala, and Trimmu valleys were identified as high-risk zones of the study area, which have been damaged drastically many times by flash floods. It provides policy guidelines for risk managers, emergency and disaster response services, urban and infrastructure planners, hydrologists, and climate scientists.

Improving flash flood forecasting using a frequentist approach to identify rainfall thresholds for flash flood occurrence

Improving flash flood forecasting using a frequentist approach to identify rainfall thresholds for flash flood occurrence

Water retention and infiltration affected by conventional and conservational tillage on a maize plot; rainfall simulator and infiltrometer comparison study

Tillage practices have a profound influence on the propensity for overland flow formation. The hydrological behavior of nine experimental plots was investigated by means of saturated hydraulic conductivity (Ks) and water retention analysis provided by single-ring infiltrometer (INF) and rainfall simulator (RS) infiltration measurements. The applied management practices encompassed no-tillage, reduced tillage and several forms of conventional tillage methods. Specifically, the influence of wide and narrow sowing rows and the use of cover crops were examined. Finally, the applicability of INF and RS for overland flow formation evaluation on cropped plots was compared. The reduced tillage practices resulted in the Ks and water retention increase, implying a lower propensity for overland flow formation. The Ks in the reduced tillage plots were higher by 30–50% compared to conventionally tilled and by 20% compared to no-tilled plots, respectively. The differences in water retention were less straightforward as the reduced tilled plots exhibited higher retention than conventionally tilled plots (by >20%) but lower values compared to no-tilled plots in June (by 15%). The utilization of narrow sowing rows generally resulted in higher Ks and enhanced water retention compared to wide rows. The use of cover crops in the conventionally tilled plots led to a 40% increase in Ks and a 90% increase in water retention. Hence, the use of reduced tillage, narrow sowing rows or cover crops in the conventionally tilled plots were found desirable for the mitigation of soil erosion, flash flood occurrence and sufficient groundwater recharge. Eight times higher variance of Ks using a single-ring infiltrometer compared to RS indicated the incomparability of these two methods. Therefore, the RS should be considered as a benchmark for evaluating the propensity to overland flow formation on cropped plots.

Multi-hazard susceptibility mapping based on Convolutional Neural Networks

Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions. However, with the rapid development of artificial intelligence technology, multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge bottleneck. In order to effectively solve this problem, this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks (CNN). First, we use historical flash flood, debris flow and landslide locations based on Google Earth images, extensive field surveys, topography, hydrology, and environmental data sets to train and validate the proposed CNN method. Next, the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria, i.e., coefficient of determination, overall accuracy, mean absolute error and the root mean square error. Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods, debris flows and landslides. Finally, the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map. It can be observed from the map that 62.43% of the study area are prone to hazards, while 37.57% of the study area are harmless. In hazard-prone areas, 16.14%, 4.94% and 30.66% of the study area are susceptible to flash floods, debris flows and landslides, respectively. In terms of concurrent hazards, 0.28%, 7.11% and 3.13% of the study area are susceptible to the joint occurrence of flash floods and debris flow, debris flow and landslides, and flash floods and landslides, respectively, whereas, 0.18% of the study area is subject to all the three hazards. The results of this study can benefit engineers, disaster managers and local government officials involved in sustainable land management and disaster risk mitigation.

A Case Study of the 12 July 2021 Bhagsunath (McLeod Ganj) Flash Flood in Dharamshala, Himachal Pradesh: A Warning Against Constricting Natural Drainage

Abstract Extreme rainfall events leading to the occurrence of flash floods and cloudbursts are common in the Himalaya. Bhagsu nala in McLeod Ganj area of Dharamshala township, Himachal Pradesh has witnessed the worst flash flood in the morning of 12th July 2021 causing damage to property and infrastructure in the region. Though the catchment area of the Bhagsu nala is very small of the order of ~ 2.8 km2, the geomorphological and hydrological characteristics of the catchment are susceptible to peak flow for a shorter duration. Further, the anthropogenic intervention of slopes in the form of constricting natural drainage has reduced the carrying capacity of the nala, exaggerating the consequences of the flash flood.

KOLABORASI UGM, UNIVERSITAS PETRA SURABAYA, UNCEN DAN UOGP SERTA PARTISIPASI MAHASISWA KKN DALAM PEMBUATAN MCK RAMAH LINGKUNGAN AKIBAT BENCANA ALAM KABUPATEN JAYAPURA

The occurrence of flash floods in Sentani claimed many victims, some suffered minor and serious injuries that could not be counted, as well as many people's houses that were washed away and damaged so that the community was placed in refugee camps prepared by the government. To deal with the people affected by the flash floods that occurred in Sentani, the government could not reach out to the entirety to evacuate the evacuation sites that had been prepared. Some of the refugee campsprepared by the government do not have facilities for the community's necessities of life, namely MCK. Through this service, we are moving to conduct a field survey to see which locations can be helped to cope with life in refugee camps. The purpose of this service is to help small asey communities living in refugee camps who do not have MCK, so we encourage final semester Planology students to do KKN in that place to build environmentally friendly MCK. The materials used to build MCK houses are light steel, the process is easy to do. simple and practical. In this service, we provide training to students and the community how to cut canal C or mild steel using a hand grinder. Likewise in the assembly of bolts to the frame using a hand drill for both installation and removal if there is an error. This training is easily accepted and carried out by KKN students and the community, so that the making of MCK can be completed properly and used by the community of Kampung Asei Kecil Telaga Maya. In this service, together with KKN students, it really helps the government to overcome the lives of the people in refugee campus. Keywords: Mild steel; environmentally friendly MCK; Sentani

Numerical Simulation of Alpine Flash Flood Flow and Sedimentation in Gullies With Large Gradient Variations

Compared with floods occurring over plains, alpine flash floods are formed over scattered locations with complex terrain and data is often lacking regarding the land topography, flow and sedimentation, causing difficulties when developing mathematical models to predict flash floods. The existing flash flood models mainly focus on the influence of water flow, such as the sharp increase in flow discharge caused by convergence at steep slopes, disregarding the sediment load carried by the water flow. However, under the effect of high-intensity sediment transport, the sedimentation in gullies may lead to surges in water level, causing the phenomenon of “great disasters resulting from minor flooding”. In this study, an efficient and accurate water-sediment coupling model was established with a Godunov-type finite volume method based 2D flow model, and a sediment module and OPENMP parallel computing module were added as well. Firstly, a common gully in mountainous area with large gradient variations was used as a generalized model to explore the impact of sedimentation on the flow field of the gully and compared with the physical model. Then, the alpine flash flood incident in Gengdi Village was simulated with the computer model. The calculation results show that the high-intensity sedimentation significantly increased the magnitude of alpine flash floods. Calculated by this mathematical model, the research results verified that this mathematic model can efficiently, accurately and concisely predict the occurrence of flash floods in gullies with large gradient variations. The model also provides a flow and sediment modeling method that incorporates the effect of high-intensity sediment transport into the traditional flash flood flow model. Thus, this model can be a powerful tool for detecting flash floods.

Mapping homogeneous regions for flash floods using machine learning: A case study in Jiangxi province, China

Regionalization of flash floods aims to partition a geographical space into homogeneous regions in which flash floods have similar generation mechanism. In this paper, we present a flash flood regionalization approach using machine learning algorithms to generate the flash flood regionalization map. First, the random forest algorithm is used to identify thirteen key factors of flash floods from a series of rainfall and topographic factors that have great potential to drive the occurrence of flash floods. Second, the two-stage hybrid self-organizing-map-based clustering algorithm is built to delineate the homogeneous regions of flash floods according to the identified thirteen key factors. Third, the best clustering result is selected by the clustering validity indices and it is regarded as the initial regionalization map. Last, the post-processing is implemented to obtain the final regionalization map of flash floods. A case study in the Jiangxi province of China was conducted to generate the flash flood regionalization map with eighteen homogeneous regions. The regionalization map clearly divides the historical flash flood events with different densities into different regions and it is visually satisfied. The quantitative evaluation method further confirmed that the flash flood regionalization map can provide the determinant power of 77.31% for the spatial distribution of historical flash flood events. Hence, it is a valuable option to perform the flash flood regionalization of watersheds and it is beneficial for local government in the Jiangxi province of China in future flash flood mitigation and prevention.