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This study assesses the performance of the Weather Research and Forecasting-Hydrological modeling system (WRF-Hydro) in the simulation of street-scale flood inundation. The case study is the Hackensack River Watershed in New Jersey, US, which is part of the operational Stevens Flood Advisory System (SFAS), a one-way coupled hydrodynamic-hydrologic system that currently uses the Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) to simulate streamflow. The performance of the 50-m gridded WRF-Hydro model was assessed for potential integration into the operational SFAS system. The model was calibrated with the dynamically dimensioned search algorithm using streamflow observations. The model performance was assessed using (i) streamflow observations, (ii) USGS HWMs, and (iii) crowdsourced data on street inundation. Results show that WRF-Hydro outperformed the HEC-HMS model. WRF-Hydro over and underestimated flood inundation extent due to the inaccuracy of the synthetic rating curves and the modeling structure errors. An agreement was noticed between WRF-Hydro and crowdsourced data on flood extent.

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Climate change and intensified human activities are exacerbating the frequency and severity of extreme precipitation events, necessitating more precise and timely flood risk assessments. Traditional models often fail to dynamically and accurately assess flood risks due to their static nature and limited handling of spatiotemporal variations. This study confronts these challenges head-on by developing a novel coupled hydrological-hydrodynamic model integrated with a Block-wise use of the TOPMODEL (BTOP) and the Rainfall-Runoff-Inundation (RRI) model. This integrated approach enables the rapid acquisition of high-precision flood inundation simulation results across large-scale basins, addressing a significant gap in dynamic flood risk assessment and zoning. A critical original achievement of this research lies in developing and implementing a comprehensive vertical-horizontal combined weighting method that incorporates spatiotemporal information for dynamic evaluation indicators, significantly enhancing the accuracy and rationality of flood risk assessments. This innovative method successfully addresses the challenges posed by objective and subjective weighting methods, presenting a balanced and robust framework for flood risk evaluation. The findings from the Min River Basin in China, as a case study, demonstrate the effectiveness of the BTOP-RRI model in capturing the complex variations in runoff and the detailed simulations of flood processes. The model accurately identifies the timing of these peaks, offering insights into the dynamic evolution of flood risks and providing a more precise and timely assessment tool for policymakers and disaster management authorities. The flood risk assessment results demonstrate good consistency with the actual regional conditions. In particular, high-risk areas exhibit distinct characteristics along the river channel, with the distribution area significantly increasing with a sudden surge in runoff. Intense precipitation events expand areas classified as moderate and high risk, gradually shrinking as precipitation levels decrease. This study significantly advances flood risk assessment methodologies by integrating cutting-edge modeling techniques with comprehensive weighting strategies. This is essential for improving the scientific foundation and decision-making processes in regional flood control efforts.

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Quantifying flood risks through a cascade of hydraulic-cum-hydrodynamic modelling is data-intensive and computationally demanding- a major constraint for economically struggling and data-scarce low and middle-income nations. Under such circumstances, geomorphic flood descriptors (GFDs), that encompass the hidden characteristics of flood propensity may assist in developing a nuanced understanding of flood risk management. In line with this, the present study proposes a novel framework for estimating flood hazard and population exposure by leveraging GFDs and Machine Learning (ML) models over severely flood-prone Ganga basin. The study incorporates SHapley Additive exPlanations (SHAP) values in flood hazard modeling to justify the degree of influence of each GFD on the simulated floodplain maps. A set of 15 relevant GFDs derived from high-resolution CartoDEM are forced to five state-of-the-art ML models; AdaBoost, Random Forest, GBDT, XGBoost, and CatBoost, for predicting flood extents and depths. To enumerate the performance of ML models, a set of twelve statistical metrics are considered. Our result indicates a superior performance of XGBoost (κ = 0.72 and KGE = 82%) over other ML models in flood extent and flood depth prediction, resulting in about 47% of the population exposure to high-flood risks. The SHAP summary plots reveal a pre-dominance of Height Above Nearest Drainage during flood depth prediction. The study contributes significantly in comprehending our understanding of catchment characteristics and its influence in the process of sustainable disaster risk reduction. The results obtained from the study provide valuable recommendations for efficient flood management and mitigation strategies, especially over global data-scarce flood-prone basins.

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Hydrodynamic models can accurately simulate flood inundation but are limited by their high computational demand that scales non-linearly with model complexity, resolution, and domain size. Therefore, it is often not feasible to use high-resolution hydrodynamic models for real-time flood predictions or when a large number of predictions are needed for probabilistic flood design. Computationally efficient surrogate models have been developed to address this issue. The recently developed Low-fidelity, Spatial analysis, and Gaussian Process Learning (LSG) model has shown strong performance in both computational efficiency and simulation accuracy. The LSG model is a physics-guided surrogate model that simulates flood inundation by first using an extremely coarse and simplified (i.e. low-fidelity) hydrodynamic model to provide an initial estimate of flood inundation. Then, the low-fidelity estimate is upskilled via Empirical Orthogonal Functions (EOF) analysis and Sparse Gaussian Process models to provide accurate high-resolution predictions. Despite the promising results achieved thus far, the LSG model has not been benchmarked against other surrogate models. Such a comparison is needed to fully understand the value of the LSG model and to provide guidance for future research efforts in flood inundation simulation. This study compares the LSG model to four state-of-the-art surrogate flood inundation models. The surrogate models are assessed for their ability to simulate the temporal and spatial evolution of flood inundation for events both within and beyond the range used for model training. The models are evaluated for three distinct case studies in Australia and the United Kingdom. The LSG model is found to be superior in accuracy for both flood extent and water depth, including when applied to flood events outside the range of training data used, while achieving high computational efficiency. In addition, the low-fidelity model is found to play a crucial role in achieving the overall superior performance of the LSG model.

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Due to rising land development, mitigating the negative effects of land use change is becoming a problem. Understanding how land development affects flood inundation is critical for long-term water resource management. This study evaluates the land use change in the Konkoure River Basin and its impact on flood inundation. The land use changes were assessed using Landsat image (level 1) in August 2006 and August 2021. In addition, we used GIS and remote sensing applications to assess the degree of changes that took place in the Konkoure watershed. According to the findings, 32.16% of the total area became built-up areas, and 35.51% was converted to other land uses in Konkoure watershed. Konkoure's most significant change is that 29.50% of forest area transformed into built-up areas and other land uses. The rainfall-runoff-inundation model (RRI) based inundation of the Konkoure River Basin was compared to the MODIS extent between 31 August 2006 and 30 August 2021 flood events. Flood inundation variations in the Konkoure watershed were studied in terms of inundation area, peak inundation depth, runoff volume, and the infiltration rate. As a result, the flood inundation area increased from 139.98 to 198.72 km2 and the infiltration rate decrease from 7 to 5 mm/h. Moreover, we used flow duration curves (FDCs) to fully comprehend the streamflow processes. The result indicates that the Konkoure watershed has experienced flooding partly due to land use change.

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The rapid development of the city leads to the continuous updating of the land use allocation ratio, particularly during the flood season, which will exacerbate the significant changes in the spatial and temporal patterns of urban flooding, increasing the difficulty of urban flood forecasting and early warning. In this study, the spatial and temporal evolution of flooding in a high-density urban area was analyzed based on the Mike Flood model, and the influence mechanisms of different rainfall peak locations and infiltration rate scenarios on the spatial and temporal characteristics of urban waterlogging were explored. The results revealed that under the same return period, the larger the rainfall peak coefficient, the larger the peak value of inundation volume and inundation area. When the rainfall peak coefficient is small, the higher the return period is, and the larger the peak lag time of the inundation volume is, in which P = 50a, r = 0.2, the peak lag time of the inundation volume reached 32 min and 45 min for the inundation depths H > 0.03 m and H > 0.15 m, respectively. There are also significant differences in the peak lag time of waterlogging inundation volume for different inundation depths. The greater the inundation depth, the longer the peak lag time of the inundation volume, and the higher the return period, the more significant the effect of lag time prolongation. It is worth noting that the increase in infiltration rate may lead to an advance in the peak time of inundation volume and inundation area, and the peak time of the inundation area is overall more obvious than that of inundation volume. The effect of infiltration rate on the peak time of inundation volume for larger inundation depths was relatively large; the peak times of inundation volume and inundation area were advanced by 4-6 min and 4-8 min for H > 0.03 m and H > 0.15 m, respectively, after the increase in infiltration rate, and the higher the rainfall return period, the longer the advance time. The spatial and temporal characteristics of waterlogging under different peak rainfall locations and infiltration capacities obtained in this study can help provide a new perspective for temporal forecasting and warning of urban waterlogging.

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This study provides a comprehensive analysis of the hydrological effects and flood risks of the Hirakud Reservoir, considering different CMIP6 climate change scenarios. Using the HEC-HMS and HEC-RAS models, the study evaluates future flow patterns and the potential repercussions of dam breaches. The following summary of the work: firstly, the HEC-HMS model is calibrated and validated using daily stage-discharge observations from the Basantpur station. With coefficient of determination (R2) values of 0.764 and 0.858 for calibration and validation, respectively, the model demonstrates satisfactory performance. Secondly, The HEC-HMS model predicts future flow for the Hirakud Reservoir under three climate change scenarios (SSP2-4.5, SSP3-7.0 and SSP5-8.5) and for three future periods (near future, mid future and far future). Thirdly, by analyzing time-series hydrographs, the study identifies peak flooding events. In addition, the HEC-RAS model is used to assess the effects of dam breaches. Downstream of the Hirakud Dam, the analysis highlights potential inundation areas and depth variations. The study determines the following inundation areas for the worst flood scenarios: 3651.52 km2, 2931.46 km2 and 4207.6 km2 for the near-future, mid-future and far-future periods, respectively. In addition, the utmost flood depths for these scenarios are determined to be 31 m, 29 m and 39 m for the respective future periods. The study area identifies 105 vulnerable villages and several towns. This study emphasizes the importance of contemplating climate change scenarios and implementing proactive measures to mitigate the peak flooding events in the Hirakud reservoir region.

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As one of the most devastating tropical storms, 2017 Hurricane Harvey caused severe flooding and damage in Houston, Texas. Besides enormous rainfall amount, land subsidence might be another contributing factor to the Harvey flood. However, few studies have numerically quantified the evolvement of land subsidence over decades, largely due to the lack of reliable methods to realistically estimate land subsidence both continuously and at high spatial resolution. Therefore, this study aims to investigate retrospective changes of regional topology due to 117 years (1900 to 2017) of land subsidence and the consequent impacts on flood inundation. Based on continuous land subsidence, we conduct a series of simulations on the 2017 Hurricane Harvey in Brays Bayou, Texas using a hydrodynamic/hydraulic model. The results indicate that the overall change of flood depth caused by land subsidence is relatively minor with the flood water deepened by six centimeters per one meter of subsided land at the worst impacted location. The impact from land subsidence on flood depth exhibits strong nonlinearity in time, where effects from previous land subsidence hotspots could be altered by later continuing land subsidence. Spatially, changes in flood depth due to the land subsidence are not only heterogeneous but mixed with coexisting increased and reduced flood depths. The results of this study improve the understanding of the dynamic evolvement of flood inundation due to continuous land subsidence so that better planning can be initiated for sustainable urban development for coastal communities, which is imperative under ongoing climate change and sea level rise.

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This study maps flood inundation and estimates the damage caused by super cyclone Yaas in Purba Medinipur, India. We used Google Earth Engine (GEE) to create a flood inundation map of the research area using pre and post-cyclone Sentinel-1 SAR data. Using ESRI 2020 land cover data, flood damage was analysed. The flood affected 5% (239.69 km2) of the land of Purba Medinipur. The northern and southern regions were affected the most. 95% and 3% of the total flooded area are comprised of agricultural and vegetation, respectively. Kolaghat (24 km2) and Nandigram-II (1 km2) sustained the greatest damage to both agriculture and vegetation. The areas below 18 m were impacted by flooding, with the worst damage occurring below 5 m. The GEE platform was cost-effective, efficient, and faster at calculating with enhanced precision. The outcomes of this study will aid in the management of cyclone-induced hazards. We advocate planting native and salt-tolerant crops to reduce flood damage.

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This aricle discusses the reliability of flood inundation information that is obtained from participatory mapping. The commonly applied method to map flood inundation requires both direct and interpretive measurement data based on remote sensing images. Such assessments have limited availability of data; as a result, participatory mapping has become the solution. A number of studies have conducted participatory mapping to obtain flood hazard information in areas with limited sources of data, however, there has been little discussion about its reliability. This research conducted participatory flood inundation mapping by involving local leaders as respondents. The mental map drawn by the local leaders was digitised to obtain a shapefile format map. The information obtained from the semistructured interview was then included in the geographic information system (GIS) data as attributes. The obtained information was compared with the field data to determine its quality. A literature study was then conducted to discuss how the participatory mapping could support managing a disaster. Information obtained through participatory mapping can be effectively applied to disaster management because of its precise location information, lower cost and less time-consuming nature. The reliability of the information has weak accuracy of quantitative data; however, it has advantages in terms of qualitative data, especially in the detailed descriptions of flood information. In the future, participatory mapping should rely on integrating the perspectives of cross-disciplinary researchers, a comprehensive study of multidisciplinary knowledge and level of understanding of the stakeholders.

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Fast and accurate modelling of flood inundation has gained increasing attention in recent years. One approach gaining popularity recently is the development of emulation models using data driven methods, such as artificial neural networks. These emulation models are often developed to model flood depth for each grid cell in the modelling domain in order to maintain accurate spatial representation of the flood inundation surface. This leads to redundancy in modelling, as well as difficulties in achieving good model performance across floodplains where there are limited data available. In this paper, a spatial reduction and reconstruction (SRR) method is developed to (1) identify representative locations within the model domain where water levels can be used to represent flood inundation surface using deep learning models; and (2) reconstruct the flood inundation surface based on water levels simulated at these representative locations. The SRR method is part of the SRR-Deep-Learning framework for flood inundation modelling and therefore, it needs to be used together with data driven models. The SRR method is programmed using the Python programming language and is freely available from https://github.com/yuerongz/SRR-method.•The SRR method identifies locations which are representative of flood inundation behavior in surrounding areas.•The representative locations selected following the SRR method have sufficient flood data for developing emulation models.•Flood inundation surfaces can be reconstructed using the SRR method with a detection rate of above 99%.

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Flood modeling provides useful information to support flood risk assessment and management and reduce flood impacts in urbanized area. The accuracy of urban flood simulation results is highly dependent on the quality of input data for which the appropriate values are generally difficult to determine for complex urbanized environment and from which various uncertainties are induced into the modeling procedure. In this study, variance-based global sensitivity analysis is applied for the hydrodynamic modeling of urban flood to explore the relative importance of the factors of interest as model inputs and their contributions to the final results of the numerical model for different outputs. The factors include the spatial resolution, the forcing condition and the characteristics of the underlying urbanized surface. The global sensitivity analysis results are examined in both spatially lumped and distributed perspective. Findings indicate that importance of the input factors varies with regard to different model output and the influence of the spatial resolution is more tightly related to the flood flow movements whereas that of the rainfall inputs is more relevant to the flood water volume. Spatial variability in the influence of the input factors is revealed to be hidden by the spatially lumped results and the importance of the factors describing the underlying urban surface is found to be largely dependent on the location of the analyzed model output associated with the land-use type. Improved understanding of sensitivity of hydrodynamic modeling of urban floods may help the modelers to decide which input factors to prioritize on according to which model outputs are assessed and where they are assessed.

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Extreme flood events are disastrous and can cause serious damages to society. Flood frequency obtained based on historical flow records may also be changing under future climate conditions. The associated flood inundation and environmental transport processes will also be affected. In this study, an integrated numerical modeling framework is proposed to investigate the inundation and sedimentation during multiple flood events (2,5,10, 20, 50, 100, 200-year) under future climate change scenarios in a watershed system in northern California, USA. The proposed modeling framework couples physical models of various spatial resolution: kilometers to several hundred kilometers climatic processes, hillslope scale hydrological processes in a watershed, and centimeters to meters scale hydrodynamic and sediment transport processes in a riverine system. The modeling results show that compared to the flows during historical periods, extreme events become more extreme in the 21st century and higher flows tend to be larger and smaller flows tend to be smaller in the system. Flood inundation in the study area, especially during 200-year events, is projected to increase in the future. More sediment will be trapped as the flow increases and the deposition will also increase in the settling basin. Sediment trap efficiency values are within 37.5-65.4% for the historical conditions, within 32.4-68.8% in the first half of the 21st century, and within 34.9-69.3% in the second half of the 21st century. The results highlight the impact of climate change on extreme flood events, the resulting sedimentation, and reflected the importance of incorporating the coupling of physical models into the adaptive watershed and river system management.

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It is well known that rainwater harvesting (RWH) can augment water supply and reduce stormwater pollutant discharges. Due to the lack of continuous 2D modelling of urban flood coverage and its associated damage, the ability of RWH to reduce urban flood risks has not been fully evaluated. Literature suggests that small distributed storage spaces using RWH tanks will reduce flood damage only during small to medium flooding events and therefore cumulative assessment of their benefits is needed. In this study we developed a new integrated modelling framework that implements a semi-continuous simulation approach to investigate flood prevention and water supply benefits of RWH tanks. The framework includes a continuous mass balance simulation model that considers antecedent rainfall conditions and water demand/usage of tanks and predicts the available storage prior to each storm event. To do so, this model couples a rainfall-runoff tank storage model with a detailed stochastic end-use water demand model. The available storage capacity of tanks is then used as a boundary condition for the novel rapid flood simulation model. This flood model was developed by coupling the U.S. EPA Storm Water Management Model (SWMM) to the Cellular-Automata Fast Flood Evaluation (CA-ffé) model to predict the inundation depth caused by surcharges over the capacity of the drainage network. The stage-depth damage curves method was used to calculate time series of flood damage, which are then directly used for flood risk and cost-benefit analysis. The model was tested through a case study in Melbourne, using a recorded rainfall time series of 85 years (after validating the flood model against 1D-2D MIKE-FLOOD). Results showed that extensive implementation of RWH tanks in the study area is economically feasible and can reduce expected annual damage in the catchment by up to approximately 30 percent. Availability of storage space and temporal distribution of rainfall within an event were important factors affecting tank performance for flood reduction.

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RESUMO: O objetivo da pesquisa foi analisar a influência da distribuição temporal das chuvas em eventos hidrológicos extremos na bacia do Córrego do Gregório (São Carlos, São Paulo). Foram aplicadas duas metodologias de distribuição temporal das chuvas e adotados períodos de retorno de 25, 50 e 100 anos: o método de Huff 1º quartil e o método dos blocos alternados; e simularam-se as manchas de inundação com o software HEC-GeoRAS. A alteração do método de distribuição temporal das chuvas resultou em hidrogramas com diferenças de até 46% na vazão de pico, 57% nas áreas da mancha de inundação da região e 1,5 m na altura de inundação.

ABSTRACT: The research objective was to analyze the time distribution of rainfall caused by flash floods in Gregorio watershed (São Carlos, São Paulo, Brazil). Two methodologies of temporal distribution of rainfall were applied for adopted return periods of 25, 50 and 100 years: the Huff 1st quartile method and the alternating blocks method; wherein the flood inundation areas were simulated with HEC-GeoRAS software. The time distribution of both rainfall methods exhibit 46% discrepancy in peak flow, 57% in flood inundation area and 1.5 m in water depth.