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Accurate and prompt flood forecasting is essential for effective decision making in flood control to help minimize or prevent flood damage. We propose a new custom deep learning model, IF-CNN-GRU, for multi-step-ahead flood forecasting that incorporates the flood index (IF) to improve the prediction accuracy. The model integrates convolutional neural networks (CNNs) and gated recurrent neural networks (GRUs) to analyze the spatiotemporal characteristics of hydrological data, while using a custom recursive neural network that adjusts the neural unit output at each moment based on the flood index. The IF-CNN-GRU model was applied to forecast floods with a lead time of 1–5 d at the Baihe hydrological station in the middle reaches of the Han River, China, accompanied by an in-depth investigation of model uncertainty. The results showed that incorporating the flood index IF improved the forecast precision by up to 20%. The analysis of uncertainty revealed that the contributions of modeling factors, such as the datasets, model structures, and their interactions, varied across the forecast periods. The interaction factors contributed 17–36% of the uncertainty, while the contribution of the datasets increased with the forecast period (32–53%) and that of the model structure decreased (32–28%). The experiment also demonstrated that data samples play a critical role in improving the flood forecasting accuracy, offering actionable insights to reduce the predictive uncertainty and providing a scientific basis for flood early warning systems and water resource management.

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).

This study aims at providing expertise for preparing public-based flood mapping and estimating flood risks in growing urban areas. To model and predict the magnitude of flood risk areas, an integrated Analytical Hierarchy Process (AHP) and Geographic Information System (GIS) analysis techniques are used for the case of Eldoret Municipality in Kenya. The flood risk vulnerability mapping follows a multi-parametric approach and integrates some of the flooding causative factors such as rainfall distribution, elevation and slope, drainage network and density, land-use/land-cover and soil type. From the vulnerability mapping, urban flood risk index (UFRI) for the case study area, which is determined by the degree of vulnerability and exposure is also derived. The results are validated using flood depth measurements, with a minimum average difference of 0.01 m and a maximum average difference of 0.37 m in depth of observed flooding in the different flood prone areas. Similarly with respect to area extents, a maximum error of not more than 8% was observed in the highly vulnerable flood zones. In addition, the Consistency Ratio which shows an acceptable level of 0.09 was calculated and further validated the strength of the proposed approach.

The importance of identifying the areas vulnerable for both floods and flash-floods is an important component of risk management. The assessment of vulnerable areas is a major challenge in the scientific world. The aim of this study is to provide a methodology-oriented study of how to identify the areas vulnerable to floods and flash-floods in the Buzău river catchment by computing two indices: the Flash-Flood Potential Index (FFPI) for the mountainous and the Sub-Carpathian areas, and the Flood Potential Index (FPI) for the low-altitude areas, using the frequency ratio (FR), a bivariate statistical model, the Multilayer Perceptron Neural Networks (MLP), and the ensemble model MLP–FR. A database containing historical flood locations (168 flood locations) and the areas with torrentiality (172 locations with torrentiality) was created and used to train and test the models. The resulting models were computed using GIS techniques, thus resulting the flood and flash-flood vulnerability maps. The results show that the MLP–FR hybrid model had the most performance. The use of the two indices represents a preliminary step in creating flood vulnerability maps, which could represent an important tool for local authorities and a support for flood risk management policies.

Settlements have been exposed to various disasters depending on their location and characteristics. One of these disasters is flooding, which affects the most widespread areas globally and causes significant economic loss. Numerous studies have been conducted at various scales, and different methods have been used to understand and mitigate the effects of floods. None of these studies conducted flood susceptibility analyses for settlement basins on a provincial scale using parameters derived from inventory basins. This study aims to determine the flood susceptibility of the settlements and their basins located along riversides and valley floors at the provincial scale based on the predominant morphometric characteristics of settlement basins that have previously experienced floods. For this purpose, a flood susceptibility analysis was conducted on 483 basins of 344 settlements located along riversides and valley floor morphologies out of 735 settlements in the province of Bursa (Türkiye). Among the morphometric and surface parameters frequently used in the literature, the nine parameters identified as having the highest impact for the 28 settlement basins in the training set—the bifurcation ratio ( R b ), drainage density ( D d ), time of concentration ( T c ), slope, topographic wetness index (TWI), stream power index (SPI), precipitation, hydrological soil groups (HSG), and lithology were applied to the other settlement basins. The results were validated with data from 22 settlement basins where flooding occurs, and maximum consistency was found. In the flood susceptibility analysis, the Normalised Morphometric Flood Index (NMFI) was used to evaluate each and overall parameters collectively. As a result, 51% of the settlements in Bursa province were identified as having high flood potential. This finding also reveals the prioritising of flood studies in three districts of Bursa province. This study identifies which settlements within the provincial border are susceptible to flooding and provides critical insights into which districts should be prioritised for flood risk management.

The main purpose of this study is to detect flash-flood potential areas based on the pre-event characteristics in the study area. Five physical factors including slope, land use, soil texture, tree canopy density, and drainage density were used to create index maps in the Geographic Information System (GIS) environment. Each index was classified from 1 to 10 by identifying their influence levels with the presence or absence of flash floods based on the Flash Flood Potential Index (FFPI) model. As the result, the most susceptible areas were given a value of 10 and the least susceptible areas were given a value of 1. These indices were then mapped and integrated into a weighted linear model. Analytic Hierarchy Process (AHP) was used to determine the weighted correlation among elements based on their importance to this phenomenon. Weighted Flash Flood Potential Index (WFFPI) map was classified into four levels, including very high, high, moderate, and low flash flood potential. The result shows that more than 10% of the total area has the greater possibility to be affected by a high-intensity flood, and distributed at the north and northeast mountain ranges while most of the area is at a medium value with 84.35%. Finally, the similarity between results in this study and reference results from HEC-RAS model showed the reliability of the applied method.

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

Flood is one of the worst natural disasters, which causes the damage of billions of dollars each year globally. To reduce the flood damage, we need to estimate design floods accurately, which are used in the design and operation of water infrastructure. For gauged catchments, flood frequency analysis can be used to estimate design floods; however, for ungauged catchments, regional flood frequency analysis (RFFA) is used. This paper compares two popular RFFA techniques, namely the quantile regression technique (QRT) and the index flood method (IFM). A total of 181 catchments are selected for this study from south-east Australia. Eight predictor variables are used to develop prediction equations. It has been found that IFM outperforms QRT in general. For higher annual exceedance probabilities (AEPs), IFM generally demonstrates a smaller estimation error than QRT; however, for smaller AEPs (e.g. 1 in 100), QRT provides more accurate quantile estimates. The IFM provides comparable design flood estimates with the Australian Rainfall and Runoff—the national guide for design flood estimation in Australia.

Developing an index that can assess the marked changes in water resources is necessary for drought/flood hazard prevention and water resource planning, particularly in the context of global climate change and intensified human activities. In this study, spatiotemporal variations of water resources variables, namely precipitation (P), evapotranspiration (ET), and runoff (R), regarded as water input, output, and storage, respectively, were systematically statistically analyzed. Following this, a comprehensive Copula-based Drought/Flood Index (CDFI) was constructed to identify the drought/flood hazards, and machine learning and game theory were then successively applied to quantify the influence of the Large-scale Climate Indices (LCIs) on drought/flood intensity. The results reveal that temporally, P, ET, and R present similar turning-point years in the 1990s, with values changing from significantly decreasing to insignificantly decreasing, significantly increasing, and insignificantly decreasing, respectively. Spatially, the water resources become drier in the arid area and wetter in the humid area. The CDFI results demonstrate an increased flood probability in the mountains and near-polar rivers, and drought in extremely arid areas. Moreover, the influencing factors of CDFI exhibited notable spatial heterogeneity. CDFI is controlled by the Arctic Oscillation in most regions of the world, and the influence of sea surface temperature on CDFI increases gradually with the decrease of latitude, and extreme events are generally linked to extreme LCIs. Notably, vegetation change (area, type, and growth) affects ET and R, thus enhancing the climate’s impact on drought/flood hazards. The conclusions provide an essential basis for the identification and policy planning of drought/flood hazards caused by the spatiotemporal variations of water resource variables.

The Himalayas experiences several cloudburst events due to its varied physiographical, geomorphological, and geological conditions and high rainfall. Uttarakhand is one of the Indian states circumscribed by the Himalayan ranges and has experienced a rise in the number of cloudburst catastrophes in the last few decades. These events cause substantial loss of life and property; however, very few studies have characterized these unpredictable cloudburst-induced flash floods in different regions of Uttarakhand. This study examines the geological and hydrological factors associated with the Raipur-Kumalda cloudburst event that occurred from 20 to 21 August 2022 in the Dehradun district of Uttarakhand. The resulting flash flood caused significant damage to roads, bridges, and settlements across the valley. The study aims to understand the geological and geomorphological controls of the event by analyzing the peak discharge and various flood parameters. The basin geomorphometry and rainfall intensity of the region reveal poorly developed drainage networks with low drainage density, steep slopes, rapid peak flows, a sharp peak hydrograph, and intense, concentrated rainfall, all of which worsen the impact of the flood. Various flood indices, including the rising curve gradient (K), flood magnitude ratio (M), and flood response time (TP), indicate a discharge 50 to 100 times higher during the event compared to the average monsoonal discharge. This study also discusses the role of mountain topography, climate, regional geology, and irreversible land use–land cover (LULC) changes associated with urbanization in intensifying the destruction.