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Floods are one of the main natural disaster threats to the safety of people’s lives and property. Flood hazards intensify as the global risk of flooding increases. The control of flood disasters on the basin scale has always been an urgent problem to be solved that is firmly associated with the sustainable development of water resources. As important nonengineering measures for flood simulation and flood control, the hydrological and hydraulic models have been widely applied in recent decades. In our study, on the basis of sufficient remote-sensing and hydrological data, a hydrological (Xin’anjiang (XAJ)) and a two-dimensional hydraulic (2D) model were constructed to simulate flood events and provide support for basin flood management. In the Chengcun basin, the two models were applied, and the model parameters were calibrated by the parameter estimation (PEST) automatic calibration algorithm in combination with the measured data of 10 typical flood events from 1990 to 1996. Results show that the two models performed well in the Chengcun basin. The average Nash–Sutcliffe efficiency (NSE), percentage error of peak discharge (PE), and percentage error of flood volume (RE) were 0.79, 16.55%, and 18.27%, respectively, for the XAJ model, and those values were 0.76, 12.83%, and 11.03% for 2D model. These results indicate that the models had high accuracy, and hydrological and hydraulic models both had good application performance in the Chengcun basin. The study can a provide decision-making basis and theoretical support for flood simulation, and the formulation of flood control and disaster mitigation measures in the basin.

A warmer climate is expected to accelerate global hydrological cycle, causing more intense precipitation and floods. Despite recent progress in global flood risk assessment, the accuracy and improvement of global hydrological models (GHMs)-based flood simulation is insufficient for most applications. Here we compared flood simulations from five GHMs under the Inter-Sectoral Impact Model Intercomparison Project 2a (ISIMIP2a) protocol, against those calculated from 1032 gauging stations in the Global Streamflow Indices and Metadata Archive for the historical period 1971-2010. A machine learning approach, namely the long short-term memory units (LSTM) was adopted to improve the GHMs-based flood simulations within a hybrid physics- machine learning approach (using basin-averaged daily mean air temperature, precipitation, wind speed and the simulated daily discharge from GHMs-CaMa-Flood model chain as the inputs of LSTM, and observed daily discharge as the output value). We found that the GHMs perform reasonably well in terms of amplitude of peak discharge but are relatively poor in terms of their timing. The performance indicated great discrepancy under different climate zones. The large difference in performance between GHMs and observations reflected that those simulations require improvements. The LSTM used in combination with those GHMs was then shown to drastically improve the performance of global flood simulations (especially in terms of amplitude of peak discharge), suggesting that the combination of classical flood simulation and machine learning techniques might be a way forward for more robust and confident flood risk assessment.

Flooding is a major threat that presents a significant risk to human survival and development worldwide. Regarding flood risk management, flood modeling enables understanding, assessing, and forecasting flood conditions and their impact. This paper gives an overview of prevailing flood simulation models given their potentials and limitations for reflecting pluvial floods in watershed settings. The existing models are categorized into hydrologic, hydrodynamic, and coupled hydrologic-hydrodynamic models. The coupled hydrologic-hydrodynamic model can be further classified into full, external, and internal coupling models. The definitions, advantages, and limitations of each coupling model are discussed. It is found that the existing coupling types cannot accurately reflect the flood evolution processes. A dynamic bidirectional coupled hydrologic-hydrodynamic model is then detailed, where the watershed is spatially divided into inundation and non-inundation regions. These two regions are connected by a coupling moving interface. Only 2D hydrodynamic models are applied to the local inundation regions to ensure numerical accuracy, whereas the fully distributed hydrologic model is applied to non-inundation regions to improve computational efficiency. Future investigation should focus on the development of a dynamic bidirectional coupling procedure that can accurately represent the complex physical interactions between upstream rainfall-runoff and the local inundation process. This paper would help flood managers and potential users undertake effective flood modeling tasks, balancing their needs, model complexity, and requirements of input data and time.

Risk assessment is an effective means to alleviate urban flood disasters and has attracted the attention of many studies. However, most previous studies about urban flood risk assessment often focused more on urban inundation area and depth, less on the inter-relationship of the components of risk. In this study, an urban flood risk assessment approach that characterizes the relationship among the three components of risk “hazard-exposure-vulnerability” (H-E-V) is developed. Firstly, eleven flood risk indicators are selected based on the flood simulation results of urban flood model and statistical data to establish the urban flood risk assessment index system. Then, the combination of analytic hierarchy process (AHP) and entropy weight method is employed to determine the weight of each indicator and the comprehensive urban flood risk is assessed. Most importantly, the coupling coordination degree model (CCDM) is used to reveal the relationship among H-E-V. After applying this method to Haikou city, China, the results show that the comprehensive effect and the coupling coordination degrees among H-E-V have a multidimensional impact on urban flood risk. For example, some sub-catchments, although at high risk of flooding, may experience a potential waste of resources. Urban flood assessment can be made more detailed and three-dimensional by comparing hazard, exposure, and vulnerability horizontally. Understanding and grasping the internal relationships among these three risk components can help implement flood prevention measures, optimize the allocation of flood prevention resources, and effectively reduce urban flood risks.

Flooding is a natural disaster with extensive impacts. Desert regions face altered flooding patterns owing to climate change, water scarcity, regulations, and rising water demands. This study assessed and predicted flash flood hazards by calculating discharge volume, peak flow, flood depth, and velocity using the Hydrologic Engineering Centre-River Analysis System and Hydrologic Modelling System (HEC-HMS and HEC-RAS) software. We employed meteorological and morphological data analyses, incorporating the soil conservation service (SCS) curve number method for precipitation losses and the SCS-Hydrograph for runoff transformation. The model was applied to two drainage basins (An-Nawayah and Al-Rashrash) in southeastern Cairo, Egypt, which recently encountered several destructive floods. The applied model revealed that 25-, 50-, and 100-year storms produced runoff volumes of 2461.8 × 103, 4299.6 × 103, and 5204.5 × 103 m3 for An-Nawayah and 6212 × 103, 8129.4 × 103, and 10,330.6 × 103 m3 for Al-Rashrash, respectively. Flood risk levels, categorised as high (35.6%), extreme (21.9%), and medium (21.12%) were assessed in low- and very-low-hazard areas. The study highlighted that the areas closer to the Nile River mouth faced greater flood impacts from torrential rain. Our findings demonstrate the effectiveness of these methods in assessing and predicting flood risk. As a mitigation measure, this study recommends the construction of five 10 m high dams to create storage lakes. This integrated approach can be applied to flood risk assessment and mitigation in comparable regions.

The lack of discharge observations and reliable drainage information is a pervasive problem in urban catchments, resulting in difficulties in parameterizing urban hydrological models. Current parameterization methods for ungauged urban catchments mostly rely on subjective experiences or simplified models, resulting in inadequate accuracy for urban flood prediction. Parameter regionalization has been widely used to tackle model parameterization issues, but has rarely been employed for urban hydrological models. How to conduct effective parameter regionalization for urban hydrological models remains to be investigated. Here we propose a parameter regionalization framework (PRF) that integrates donor catchment clustering and the optimal regression‐based methods in each cluster. The PRF is applied to an urban hydrological model, the Time Variant Gain Model in urban areas (TVGM_Urban), in 37 urban catchments in Shenzhen City, China. We first show satisfactory flood simulation performance of TVGM_Urban for all urban catchments. Subsequently, we employ the PRF for parameter regionalization of TVGM_Urban. PRF classifies 37 urban catchments into three groups, and the partial least‐squares regression is identified as optimal regression‐based method for Groups 1 and 2, while the random forest model is found to be best for Group 3. To evaluate the simulation performance of PRF, we compare it with eight single regionalization methods. The results indicate better simulation performance and lower uncertainty of PRF, and donor catchment clustering can effectively enhance the simulation performance of linear regression‐based methods. Lastly, we identify curve number, land cover area ratios, and slope as critical factors for most TVGM_Urban parameters based on PRF results. Plain Language Summary Frequent urban flooding and waterlogging pose significant threats to human life and property. Accurate modeling urban floods is crucial for minimizing damages caused by urban flooding. However, the delayed initiation of urban observations has resulted in prevalent data scarcity in urban areas, leading to difficulties and low accuracy in estimating model parameters for ungauged urban catchments. This study proposes a parameter regionalization framework for an urban hydrological model to improve the accuracy of flood simulations in ungauged urban catchments. This framework involves classifying gauged catchments into multiple groups and identifying the optimal regression‐based methods in each group for parameter regionalization. The flood simulation performance and stability of the parameter regionalization framework is evaluated by comparing it with eight widely‐used regionalization methods in 37 urban catchments located in Shenzhen City, China. Our results show that the classification of gauged catchments can significantly improve the simulation performance of linear regression methods. Additionally, the parameter regionalization framework outperforms the other regionalization methods for both urban flood simulation in the study area and urban flood prediction in the ungauged urban catchments. This study provides a novel and efficient parameterization method for accurate flood prediction in ungauged urban catchments to reduce potential losses. Key Points TVGM_Urban presents satisfying performance in urban flood modeling The PRF integrating donor catchment clustering and optimal regression methods in each cluster outperforms single regionalization methods Curve number, land cover area ratio, and slope are critical factors for runoff generation parameters of TVGM_Urban

The risk related to embankment dam breaches needs to be evaluated in order to prepare emergency action plans. The physical and hydrodynamic parameters of the flood wave generated from the dam failure event correspond to various breach parameters, such as width, slope, and formation time. This study aimed to simulate the dam breach failure scenario of the Yabous dam (northeast Algeria) and analyze its influence on the related areas (urban and natural environments) downstream of the dam. The simulation was completed using the sensitivity analysis method to assess the impact of breach parameters and flooding on the dam break scenario. The flood wave propagation associated with the dam break was simulated using the one-dimensional HEC-RAS hydraulic model. This study applied a sensitivity analysis of three breach parameters (slope, width, and formation time) on five sites selected downstream of the embankment dam. The simulation showed that the maximum flow of the flood wave recorded at the level of the breach was 8768 m3/s, which gradually attenuated along the river course to reach 1972.7 m3/s at about 8.5 km downstream the dam. This study established the map of flood risk areas that illustrated zones threatened by the flooding wave triggered by the dam failure due to extreme rainfall events. The sensitivity analysis showed that flood wave flow, height, and width revealed positive and similar changes for the increases in adjustments (±25% and ±50%) of breach width and slope in the five sites. However, flood wave parameters of breach formation time showed significant trends that changed in the opposite direction compared to breach slope and width. Meanwhile, the adjustments (±25% and ±50%) of the flood hydrograph did not significantly influence the flood parameters downstream of the dam. In the present study, the HEC-RAS 1-D modeling demonstrated effectiveness in simulating the propagation of flood waves downstream of the dam in the event of dam failure and highlighted the impact of the breach parameters and the flood hydrographical pattern on flood wave parameters.

Flooding is now becoming one of the most frequent and widely distributed natural hazards, with significant losses to human lives and property around the world. Evacuation of pedestrians during flooding events is a crucial factor in flood risk management, in addition to saving people’s lives and increasing time for rescue. The key objective of this work is to propose a shortest evacuation path planning algorithm by considering the evacuable areas and human instability during floods. A shortest route optimization algorithm based on cellular automata is established while using diagonal distance calculation methods in heuristic search algorithms. The Morpeth flood event that occurred in 2008 in the UK is used as a case study, and a highly accurate and efficient 2D hydrodynamic model is adopted to discuss the flood characteristics in flood plains. Two flood hazard assessment approaches [i.e., empirical and mechanics-based and experimental calibrated (M&E)] are chosen to study human instability. A comprehensive analysis shows that extreme events are better identified with mechanics-based and experimental calibration methods than with an empirical method. The result of M&E is used as the initial condition for the Morpeth evacuation scenario. Evacuation path planning in Morpeth shows that this algorithm can realize shortest route planning with multiple starting points and ending points at the microscale. These findings are of significance for flood risk management and emergency evacuation research.

Malaysia, being a tropical country located near the equatorial doldrums, experiences the annual occurrence of flood hazards due to monsoon rainfalls and urban development. In recent years, environmental policies in the country have shifted towards sustainable flood risk management. As part of the development of flood forecasting and warning systems, this study presented the urban flood simulation using InfoWorks ICM hydrological−hydraulic modeling of the Damansara catchment as a case study. The response of catchments to the rainfall was modeled using the Probability Distributed Moisture (PDM) model due to its capability for large catchments with long-term runoff prediction. The interferometric synthetic aperture radar (IFSAR) technique was used to obtain high-resolution digital terrain model (DTM) data. The calibrated and validated model was first applied to investigate the effectiveness of the existing regional ponds on flood mitigation. For a 100-year flood, the extent of flooded areas decreased from 12.41 km2 to 3.61 km2 as a result of 64-ha ponds in the catchment, which is equivalent to a 71% reduction. The flood hazard maps were then generated based on several average recurrence intervals (ARIs) and uniform rainfall depths, and the results showed that both parameters had significant influences on the magnitude of flooding in terms of flood depth and extent. These findings are important for understanding urban flood vulnerability and resilience, which could help in sustainable management planning to deal with urban flooding issues.