Comprehensive Overview of Flood Modeling Approaches: A Review of Recent Advances

Hydrology, Journal Year: 2023, Volume and Issue: 10(7), P. 141 - 141

Published: June 30, 2023

As one of nature’s most destructive calamities, floods cause fatalities, property destruction, and infrastructure damage, affecting millions people worldwide. Due to its ability accurately anticipate successfully mitigate the effects floods, flood modeling is an important approach in control. This study provides a thorough summary modeling’s current condition, problems, probable future directions. The includes models based on hydrologic, hydraulic, numerical, rainfall–runoff, remote sensing GIS, artificial intelligence machine learning, multiple-criteria decision analysis. Additionally, it covers heuristic metaheuristic techniques employed evaluation examines advantages disadvantages various models, evaluates how well they are able predict course impacts floods. constraints data, unpredictable nature model, complexity model some difficulties that must overcome. In study’s conclusion, prospects for development advancement field discussed, including use advanced technologies integrated models. To improve risk management lessen society, report emphasizes necessity ongoing research modeling.

Language: Английский

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Fast simulation and prediction of urban pluvial floods using a deep convolutional neural network model

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 624, P. 129945 - 129945

Published: July 18, 2023

Language: Английский

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Toward Street‐Level Nowcasting of Flash Floods Impacts Based on HPC Hydrodynamic Modeling at the Watershed Scale and High‐Resolution Weather Radar Data

Water Resources Research, Journal Year: 2023, Volume and Issue: 59(10)

Published: Oct. 1, 2023

Abstract In our era, the rapid increase of parallel programming coupled with high‐performance computing (HPC) facilities allows for use two‐dimensional shallow water equation (2D‐SWE) algorithms simulating floods at “hydrological” catchment scale, rather than just “hydraulic” fluvial scale. This approach paves way development new operational systems focused on impact‐based flash‐floods nowcasting, wherein hydrodynamic simulations directly model spatial and temporal variability measured or predicted rainfall impacts even a street Specifically, main goal this research is to make step move toward implementation an effective flash flood nowcasting system in which timely accurate impact warnings are provided by including weather radar products HPC 2D‐SWEs modelling framework able integrate watershed hydrology, flow hydrodynamics, river urban flooding one model. The timing, location, intensity street‐level evolution some key elements risk (people, vehicles, infrastructures) also discussed considering both calibration issues role played resolution. All these analyzed having as starting point event hit Mandra town (Athens, Greece) 15 November 2017, highlighting feasibility accuracy overall providing insights field.

Language: Английский

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Assessment of surrogate models for flood inundation: The physics-guided LSG model vs. state-of-the-art machine learning models

Water Research, Journal Year: 2024, Volume and Issue: 252, P. 121202 - 121202

Published: Jan. 24, 2024

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 for real-time predictions or when a large number of needed probabilistic design. Computationally efficient surrogate have been developed address this issue. The recently Low-fidelity, Spatial analysis, Gaussian Process Learning (LSG) has shown strong performance in both efficiency simulation accuracy. LSG physics-guided simulates first using an extremely coarse simplified (i.e. low-fidelity) provide initial estimate inundation. Then, the low-fidelity upskilled via Empirical Orthogonal Functions (EOF) analysis Sparse accurate predictions. Despite promising results achieved thus far, benchmarked against other models. Such comparison fully understand value guidance future research efforts simulation. This study compares four state-of-the-art assessed ability temporal spatial evolution events within beyond range used training. evaluated three distinct case studies Australia United Kingdom. found be superior accuracy extent water depth, including applied outside training data used, while achieving efficiency. In addition, play crucial role overall model.

Language: Английский

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SERGHEI (SERGHEI-SWE) v1.0: a performance-portable high-performance parallel-computing shallow-water solver for hydrology and environmental hydraulics

Geoscientific model development, Journal Year: 2023, Volume and Issue: 16(3), P. 977 - 1008

Published: Feb. 8, 2023

Abstract. The Simulation EnviRonment for Geomorphology, Hydrodynamics, and Ecohydrology in Integrated form (SERGHEI) is a multi-dimensional, multi-domain, multi-physics model framework environmental landscape simulation, designed with an outlook towards Earth system modelling. At the core of SERGHEI's innovation its performance-portable high-performance parallel-computing (HPC) implementation, built from scratch on Kokkos portability layer, allowing SERGHEI to be deployed, fashion, graphics processing unit (GPU)-based heterogeneous systems. In this work, we explore combinations MPI using OpenMP CUDA backends. contribution, introduce present detail first operational module solving shallow-water equations (SERGHEI-SWE) HPC implementation. This applicable hydrological problems including flooding runoff generation, Its applicability demonstrated by testing several well-known benchmarks large-scale problems, which SERGHEI-SWE achieves excellent results different types problems. Finally, scalability performance evaluated TOP500 systems, very good scaling range over 20 000 CPUs up 256 state-of-the art GPUs.

Language: Английский

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Supercharging hydrodynamic inundation models for instant flood insight

Nature Water, Journal Year: 2023, Volume and Issue: 1(10), P. 835 - 843

Published: Sept. 11, 2023

Language: Английский

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Mapping Compound Flooding Risks for Urban Resilience in Coastal Zones: A Comprehensive Methodological Review

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(2), P. 350 - 350

Published: Jan. 16, 2024

Coastal regions, increasingly threatened by floods due to climate-change-driven extreme weather, lack a comprehensive study that integrates coastal and riverine flood dynamics. In response this research gap, we conducted bibliometric analysis thorough visualization mapping of studies compound flooding risk in cities over the period 2014–2022, using VOSviewer CiteSpace analyze 407 publications Web Science Core Collection database. The analytical results reveal two persistent topics: way explore return periods or joint probabilities drivers statistical modeling, quantification with different through numerical simulation. This article examines critical causes flooding, outlines principal methodologies, details each method’s features, compares their strengths, limitations, uncertainties. paper advocates for an integrated approach encompassing climate change, ocean–land systems, topography, human activity, land use, hazard chains enhance our understanding mechanisms. includes adopting Earth system modeling framework holistic coupling components, merging process-based data-driven models, enhancing model grid resolution, refining dynamical frameworks, comparing complex physical models more straightforward methods, exploring advanced data assimilation, machine learning, quasi-real-time forecasting researchers emergency responders.

Language: Английский

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Unravelling spatial heterogeneity of inundation pattern domains for 2D analysis of fluvial landscapes and drainage networks

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 632, P. 130728 - 130728

Published: Jan. 24, 2024

Fluvial landscape analysis represents an essential component in geomorphology, hydrology, ecology and cartography. It is traditionally focused on the transition between hillslopes channel domain, which network drainage represented by static flow lines. However, natural fluctuations of processes occurring watershed induce lateral longitudinal expansions contractions patterns variations stream surface area. These can be better understood introducing a two-dimensional (2D) view catchment hydrography, river width floodplain are included analysis. The novelty introduced this work development hydrodynamic hierarchical framework (HHF) to analyse transitions among geomorphic hydrographic features fluvial landscape, distinguishing hillslope, unchanneled valleys, floodplains, single/multithreads channels. HHF based estimation nested inundation pattern domains (IPDs) from digital elevation models 2D modeling. IPDs defined scaling laws that characterize log–log relations density unit discharge thresholds extracted direct rainfall method (DRM) approach under steady state solutions. physical significance analysed within context both physiographic rates employed as input for modeling approach. Initially, spatial heterogeneity initially used derive metrics function rate. Then, index, representative IPDs' heterogeneity, measure susceptibility area expand/contract. Finally, consistency results assessed comparison another hydrodynamic-based recently proposed literature. using challenging mountain low-relief environments, characterized multithread channels, meander cut-offs, oxbow lakes, extreme landscapes feature glacial outwash, permafrost, peatlands.

Language: Английский

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Evaluation of 2D hydrodynamic-based rainfall/runoff modelling for soil erosion assessment at a seasonal scale

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 632, P. 130778 - 130778

Published: Jan. 26, 2024

Badlands are often the source of a significant fraction sediment reaching river network due to exposure bare soil impact rain drops and bed shear stress generated by surface runoff. Hence, correct understanding erosion transport processes inside badlands can help better characterisation suspended production at catchment scale. In this work we study suitability two-dimensional (2D) physically-based event-scale model as tool represent in seasonal The solves 2D shallow water equations, including infiltration rainfall, order compute generation routing runoff within badland. Coupled hydrodynamic equation with terms that account for rainfall- runoff-driven deposition. Based on model, an overall procedure was developed tested considering, case study, badland located El Soto (central Pyrenees, Iberian Peninsula). For analysed badland, several high-resolution topography surveys were available, which allowed estimation loss spatial distribution patterns periods 3-4 months over two years. These data sets used calibrate validate proposed modelling approach, analyse its capabilities limitations assessment

Language: Английский

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Research on Urban Storm Flood Simulation by Coupling K-means Machine Learning Algorithm and GIS Spatial Analysis Technology into SWMM Model

Water Resources Management, Journal Year: 2024, Volume and Issue: 38(6), P. 2059 - 2078

Published: Feb. 14, 2024

Language: Английский

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