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Urbanization increases regional impervious surface area, which generally reduces hydrologic response time and therefore increases flood risk. The objective of this work is to investigate the sensitivities of urban flooding to urban land growth through simulation of flood flows under different urbanization conditions and during different flooding stages. A sub-watershed in Toronto, Canada, with urban land conversion was selected as a test site for this study. In order to investigate the effects of urbanization on changes in urban flood risk, land use maps from six different years (1966, 1971, 1976, 1981, 1986, and 2000) and of six simulated land use scenarios (0%, 20%, 40%, 60, 80%, and 100% impervious surface area percentages) were input into coupled hydrologic and hydraulic models. The results show that urbanization creates higher surface runoff and river discharge rates and shortened times to achieve the peak runoff and discharge. Areas influenced by flash flood and floodplain increases due to urbanization are related not only to overall impervious surface area percentage but also to the spatial distribution of impervious surface coverage. With similar average impervious surface area percentage, land use with spatial variation may aggravate flash flood conditions more intensely compared to spatially uniform land use distribution.

This paper summarizes the main flash flood early-warning systems of America, Europe, Japan, and Taiwan China and discusses their advantages and disadvantages. The latest development in flash flood prevention is also presented. China’s flash flood prevention system involves three stages. Herein, the warning methods and achievements in the first two stages are introduced in detail. Based on the worldwide experience of flash flood early-warning systems, the general research idea of the third stage is proposed from the viewpoint of requirements for flash flood prevention and construction progress of the next stage in China. Real-time dynamic warning systems can be applied to the early-warning platform at four levels (central level, provincial level, municipal level, and county level) . Through this, soil moisture, peak flow, and water level can be calculated in real-time using distributed hydrological models, and then flash flood warning indexes can be computed based on defined thresholds of runoff and water level. A compound warning index (CWI) can be applied to regions where rainfall and water level are measured by simple equipment. In this manner, flash-flood-related factors such as rainfall intensity and antecedent and cumulative rainfall depths can be determined using the CWI method. The proposed methodology for the third stage could support flash flood prevention measures in the 13th 5-Year Plan for Economic and Social Development of the People’s Republic of China (2016–2020). The research achievements will serve as a guidance for flash flood monitoring and warning as well as flood warning in medium and small rivers.

Spatiotemporal heterogeneities in climatic, physiographic, and socio‐economic environments cause complex and varied formation mechanisms in flash flood disasters. However, previous studies were usually conducted at event or catchment scale in specific environments. Investigation on disaster formation mechanisms in climatic, physiographic, and socio‐economic environments with different combinations and quantities at large scale is not available, which further affects the decision‐making of mitigation measures. Our study develops a type‐based analytical framework of flash flood disasters and their causes from disaster‐prone environments using ten‐fold multivariate analysis including cluster analysis, analysis of similarities, and ordination analysis. Application of this framework to environment factors and losses of 37,332 disaster events across China revealed three disaster‐prone environment types, contributing 55.5% ± 0.3%, 55.9% ± 0.3%, and 50.9% ± 0.2% to variations in disaster attributes, respectively. The events with low disaster intensities (24.6%) in undeveloped northwestern China were governed by short rainfall, low retention capacity, and low prevention investments, and their mitigation focused on afforestation and construction of rainfall and flash flood monitoring systems. Those with high disaster intensities (38.5%) in developed and disturbed central and southeastern China were interpreted by frequent intense rainfall and good flood prevention infrastructures, and their mitigation prioritized development of flash flood forecasting warning models, and grain for green, etc. Those with intermediate disaster intensities (36.9%) in undeveloped southwestern and central China were shaped by frequent short intense rainfall and steep rivers, and their mitigation required satellites or radars in alpine regions, multi‐disaster prevention technology development, and dam construction. Plain Language Summary Flash flood disasters are one of the most dangerous natural disasters, and their formation mechanisms are influenced by the spatio‐temporal heterogeneities of climatic, physiographic, and socio‐economic environments. We develop a type‐based analytical framework of flash flood disasters and their causes from disaster‐prone environments. The framework and its robustness are applied and examined across China using massive flash flood disaster events and their environment factors. We discern three flash flood disaster‐prone environment types where 24.6%, 38.5%, and 36.9% of total flash flood disaster events have occurred during 1949–2019. We determine main causal factors and their contributions to shaping the attribute variability of flash flood disaster events for individual environment types, and further propose type‐specific measures to mitigate the occurrence and damage of flash flood disasters. This study provides a new insight for understanding flash flood disaster formation mechanisms, and provides supports for developing effective disaster management strategies. Key Points A type‐based analytical framework of flash flood disasters and their causes is proposed from disaster‐prone environments Three disaster formation types are derived through mining disaster‐prone environment factors and losses of 37,332 disaster events in China The variabilities of 50%∼56% in flash flood disasters are explained by the combinations of climate, physiography, and socio‐economy

Flash flooding is considered one of the most dynamic natural disasters for which measures need to be taken to minimize economic damages, adverse effects, and consequences by mapping flood susceptibility. Identifying areas prone to flash flooding is a crucial step in flash flood hazard management. In the present study, the Kalvan watershed in Markazi Province, Iran, was chosen to evaluate the flash flood susceptibility modeling. Thus, to detect flash flood-prone zones in this study area, five machine learning (ML) algorithms were tested. These included boosted regression tree (BRT), random forest (RF), parallel random forest (PRF), regularized random forest (RRF), and extremely randomized trees (ERT). Fifteen climatic and geo-environmental variables were used as inputs of the flash flood susceptibility models. The results showed that ERT was the most optimal model with an area under curve (AUC) value of 0.82. The rest of the models’ AUC values, i.e., RRF, PRF, RF, and BRT, were 0.80, 0.79, 0.78, and 0.75, respectively. In the ERT model, the areal coverage for very high to moderate flash flood susceptible area was 582.56 km2 (28.33%), and the rest of the portion was associated with very low to low susceptibility zones. It is concluded that topographical and hydrological parameters, e.g., altitude, slope, rainfall, and the river’s distance, were the most effective parameters. The results of this study will play a vital role in the planning and implementation of flood mitigation strategies in the region.

The exponential growth in the number of flash flood events is a global threat, and detecting a flood-prone area has also become a top priority. The flash flood-susceptibility mapping can help to mitigate the worst effects of this type of risk phenomenon. However, there is an urgent need to construct precise models for predicting flash flood-susceptibility mapping, which can be useful in developing more effective flood management strategies. In this present research, support vector regression (SVR) was coupled with two meta-heuristic algorithms such as particle swarm optimization (PSO) and grasshopper optimization algorithm (GOA), to construct new GIS-based ensemble models (SVR–PSO and SVR–GOA) for flash flood-susceptibility mapping (FFSM) in the Gandheswari River basin, West Bengal, India. In this regard, 16 topographical and environmental flood causative factors have been identified to run the models using the multicollinearity (MC) test. The entire dataset was divided into 70:30 for training and validating purposes. Statistical measures including specificity, sensitivity, PPV, NPV, AUC–ROC, kappa and Taylor diagram have been employed to validate adopted models. The SVR-based factor importance analysis was employed to choose and prioritize significant factors for the spatial analysis. Among the three modeling approaches used here, the ensemble method of SVR–GOA is the most optimal (specificity 0.97 and 0.87, sensitivity 0.99 and 0.91, PPV 0.97 and 0.86, NPV 0.99 and 0.91, AUC 0.951 and 0.938 in training and validation, respectively), followed by the SVR–PSO (specificity 0.84 and 0.84, sensitivity 0.87 and 0.86, PPV 0.85 and 0.82, NPV 0.87 and 0.87, AUC 0.951 and 0.938 in training and validation, respectively) and SVR (specificity 0.80 and 0.77, sensitivity 0.93 and 0.89, PPV 0.82 and 0.77, NPV 0.91 and 0.89, AUC 0.951 and 0.938 in training and validation, respectively) model. The result shown that 40.10 km2 (10.99%) and 25.94 km2 (7.11%) areas are under very high and high flood-prone regions, respectively. This produced reliable results that can help policymakers at the local and national levels to implement a concrete strategy with an early warning system to reduce the occurrence of floods in a region.

Flash flood disaster is a prominent issue threatening public safety and social development throughout the world, especially in mountainous regions. Rainfall threshold is a widely accepted alternative to hydrological forecasting for flash flood warning due to the short response time and limited observations of flash flood events. However, determination of rainfall threshold is still very complicated due to multiple impact factors, particular for antecedent soil moisture and rainfall patterns. In this study, hydrological simulation approach (i.e., China Flash Flood-Hydrological Modeling System: CNFF-HMS) was adopted to capture the flash flood processes. Multiple scenarios were further designed with consideration of antecedent soil moisture and rainfall temporal patterns to determine the possible assemble of rainfall thresholds by driving the CNFF-HMS. Moreover, their effects on rainfall thresholds were investigated. Three mountainous catchments (Zhong, Balisi and Yu villages) in southern China were selected for case study. Results showed that the model performance of CNFF-HMS was very satisfactory for flash flood simulations in all these catchments, especially for multimodal flood events. Specifically, the relative errors of runoff and peak flow were within ± 20%, the error of time to peak flow was within ± 2 h and the Nash–Sutcliffe efficiency was greater than 0.90 for over 90% of the flash flood events. The rainfall thresholds varied between 93 and 334 mm at Zhong village, between 77 and 246 mm at Balisi village and between 111 and 420 mm at Yu village. Both antecedent soil moistures and rainfall temporal pattern significantly affected the variations of rainfall threshold. Rainfall threshold decreased by 8–38 and 0–42% as soil saturation increased from 0.20 to 0.50 and from 0.20 to 0.80, respectively. The effect of rainfall threshold was the minimum for the decreasing hyetograph (advanced pattern) and the maximum for the increasing hyetograph (delayed pattern), while it was similar for the design hyetograph and triangular hyetograph (intermediate patterns). Moreover, rainfall thresholds with short time spans were more suitable for early flood warning, especially in small rural catchments with humid climatic characteristics. This study was expected to provide insights into flash flood disaster forecasting and early warning in mountainous regions, and scientific references for the implementation of flash flood disaster prevention in China.

The occurrence of heavy rainfall in the south-eastern hilly region of Bangladesh makes this area highly susceptible to recurrent flash flooding. As the region is the commercial capital of Bangladesh, these flash floods pose a significant threat to the national economy. Predicting this type of flooding is a complex task which requires a detailed understanding of the river basin characteristics. This study evaluated the susceptibility of the region to flash floods emanating from within the Karnaphuli and Sangu river basins. Twenty-two morphometric parameters were used. The occurrence and impact of flash floods within these basins are mainly associated with the volume of runoff, runoff velocity, and the surface infiltration capacity of the various watersheds. Analysis showed that major parts of the basin were susceptible to flash flooding events of a ‘moderate’-to-‘very high’ level of severity. The degree of susceptibility of ten of the watersheds was rated as ‘high’, and one was ‘very high’. The flash flood susceptibility map drawn from the analysis was used at the sub-district level to identify populated areas at risk. More than 80% of the total area of the 16 sub-districts were determined to have a ‘high’-to-‘very-high’-level flood susceptibility. The analysis noted that around 3.4 million people reside in flash flood-prone areas, therefore indicating the potential for loss of life and property. The study identified significant flash flood potential zones within a region of national importance, and exposure of the population to these events. Detailed analysis and display of flash flood susceptibility data at the sub-district level can enable the relevant organizations to improve watershed management practices and, as a consequence, alleviate future flood risk.

Yunnan Province, located in Southwest China, suffers from massive flash flood hazards due to its complex mountainous hydrometeorology. However, traditional flash flood forecasting approaches can hardly provide an effective and comprehensive guide. Aiming to build a multilevel guidance system of flash flood warning for Yunnan, this study develops a Cascading Flash Flood Guidance (CFFG) system, progressively from the Flash Flood Potential Index (FFPI), the Flash Flood Hazard Index (FFHI) to the Flash Flood Risk Index (FFRI). First, land cover and vegetation cover data from MODIS products, the Harmonized World Soil Database soil map, and SRTM slope data are used in generating a composite FFPI map. In this process, an integrated approach of the analytic hierarchy process and the information entropy theory is used as a weighting method. Then, three standardized rainfall amounts (average daily amount in flood seasons, maximum 6 h and maximum 24 h amount) are added to derive FFHI. Further inclusion of GDP, population and flood prevention measures as vulnerability factors for the FFRI enabled prediction of the flash flood risk. The spatial patterns of the CFFG indices indicate that counties in east Yunnan are most susceptible to flash floods, which agrees with the distribution of observed flash flood events. Compared to other approaches, the CFFG system could be a useful prototype in mapping characteristics of China’s flash floods in a cascading manner (i.e., potential, hazard and risk) for users at different administrative levels (e.g., town, county, province and even nation).

Flash flood is one of the most dangerous natural phenomena because of its high magnitudes and sudden occurrence, resulting in huge damages for people and properties. Our work aims to propose a state-of-the-art model for susceptibility mapping of the flash flood using the decision tree random subspace ensemble optimized by hybrid firefly–particle swarm optimization (HFPS), namely the HFPS-RSTree model. In this work, we used data from a flood inventory map consisting of 1866 polygons derived from Sentinel-1 C-band synthetic aperture radar (SAR) data and a field survey conducted in the northwest mountainous area of the Van Ban district, Lao Cai Province in Vietnam. A total of eleven flooding conditioning factors (soil type, geology, rainfall, river density, elevation, slope, aspect, topographic wetness index (TWI), normalized difference vegetation index (NDVI), plant curvature, and profile curvature) were used as explanatory variables. These indicators were compiled from a geological and mineral resources map, soil type map, and topographic map, ALOS PALSAR DEM 30 m, and Landsat-8 imagery. The HFPS-RSTree model was trained and verified using the inventory map and the eleven conditioning variables and then compared with four machine learning algorithms, i.e., the support vector machine (SVM), the random forests (RF), the C4.5 decision trees (C4.5 DT), and the logistic model trees (LMT) models. We employed a range of statistical standard metrics to assess the predictive performance of the proposed model. The results show that the HFPS-RSTree model had the best predictive performance and achieved better results than those of other benchmarks with the ability to predict flash flood, reaching an overall accuracy of over 90%. It can be concluded that the proposed approach provides new insights into flash flood prediction in mountainous regions.

On 26 August 2020, a devastating flash flood struck Charikar city, Parwan province, Afghanistan, causing building damage and killing hundreds of people. Rapid identification and frequent mapping of the flood-affected area are essential for post-disaster support and rapid response. In this study, we used Google Earth Engine to evaluate the performance of automatic detection of flood-inundated areas by using the spectral index technique based on the relative difference in the Normalized Difference Vegetation Index (rdNDVI) between pre- and post-event Sentinel-2 images. We found that rdNDVI was effective in detecting the land cover change from a flash flood event in a semi-arid region in Afghanistan and in providing a reasonable inundation map. The result of the rdNDVI-based flood detection was compared and assessed by visual interpretation of changes in the satellite images. The overall accuracy obtained from the confusion matrix was 88%, and the kappa coefficient was 0.75, indicating that the methodology is recommendable for rapid assessment and mapping of future flash flood events. We also evaluated the NDVIs’ changes over the course of two years after the event to monitor the recovery process of the affected area. Finally, we performed a digital elevation model-based flow simulation to discuss the applicability of the simulation in identifying hazardous areas for future flood events.