Flood Risk in Urban Areas: Modelling, Management and Adaptation to Climate Change. A Review

Hydrology, Journal Year: 2022, Volume and Issue: 9(3), P. 50 - 50

Published: March 18, 2022

The modelling and management of flood risk in urban areas are increasingly recognized as global challenges. complexity these issues is a consequence the existence several distinct sources risk, including not only fluvial, tidal coastal flooding, but also exposure to runoff local drainage failure, various strategies that can be proposed. high degree vulnerability characterizes such expected increase future due effects climate change, growth population living cities, densification. An increasing awareness socio-economic losses environmental impact flooding clearly reflected recent expansion number studies related sometimes within framework adaptation change. goal current paper provide general review advances flood-risk management, while exploring perspectives fields research.

Language: Английский

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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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Flash floods and landslides in the city of Recife, Northeast Brazil after heavy rain on May 25–28, 2022: Causes, impacts, and disaster preparedness

Weather and Climate Extremes, Journal Year: 2023, Volume and Issue: 39, P. 100545 - 100545

Published: Jan. 2, 2023

From late May to early June 2022, 130 people died in catastrophic landslides and flash flood events triggered by exceptionally heavy rains the states of Pernambuco, Alagoas, Paraíba, along coast Northeast Brazil. Total rainfall city Recife on 25–30 was 551 mm, 140 mm higher than average month May. Rain heaviest 25 28, with 100–200 151–250 respectively. This coincided easterly wave disturbances. 28 saw most rain, due a significant cold front. Fourteen municipalities metropolitan region declared state emergency. According Civil Defense Pernambuco state, rain impacted 130,000 there. Most precipitation fell over areas medium very high geological vulnerability extreme hydrological events. The loss life substantial economic impacts caused 2022 disasters induced it show that this city, like many others around world, has limited capacity cope climate extremes. Urbanization increased population density occupying hills slopes contributing problem. To reduce impact such disasters, residents must be made aware risks climate-related events, they encouraged heed alerts warning natural issued federal institutions. Efficient monitoring risk is also needed. Risk management will viable only when everyone participates, which requires education cultural change.

Language: Английский

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Flash Flood Water Depth Estimation Using SAR Images, Digital Elevation Models, and Machine Learning Algorithms

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(3), P. 440 - 440

Published: Jan. 18, 2022

In this article, the local spatial correlation of multiple remote sensing datasets, such as those from Sentinel-1, Sentinel-2, and digital surface models (DSMs), are linked to machine learning (ML) regression algorithms for flash floodwater depth retrieval. Edge detection filters applied images extract features that used independent by ML estimate flood depths. Data dependent variables were obtained Hydrologic Engineering Center’s River Analysis System (HEC-RAS 2D) simulation model, New Cairo, Egypt, post-flash event 24–26 April 2018. Gradient boosting (GBR), random forest (RFR), linear (LR), extreme gradient (XGBR), multilayer perceptron neural network (MLPR), k-nearest neighbors (KNR), support vector (SVR) depths; their outputs compared evaluated accuracy using root-mean-square error (RMSE). The RMSE all was 0.18–0.22 m depths less than 1 (96% test data), indicating relatively portable capable computing data an input.

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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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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LISFLOOD-FP 8.1: new GPU-accelerated solvers for faster fluvial/pluvial flood simulations

Geoscientific model development, Journal Year: 2023, Volume and Issue: 16(9), P. 2391 - 2413

Published: May 5, 2023

Abstract. The local inertial two-dimensional (2D) flow model on LISFLOOD-FP, the so-called ACCeleration (ACC) uniform grid solver, has been widely used to support fast, computationally efficient fluvial/pluvial flood simulations. This paper describes new releases, LISFLOOD-FP 8.1, for parallelised simulations graphical processing units (GPUs) boost efficiency of existing ACC solver central (CPUs) and enhance it further by enabling a non-uniform version. generates its using multiresolution analysis (MRA) multiwavelets (MWs) Galerkin polynomial projection digital elevation (DEM). sensibly coarsens resolutions where topographic details are below an error threshold ε allows classes land use be properly adapted. Both generator adapted implemented in GPU codebase, indexing Z-order curves alongside parallel tree traversal approach. performance solvers is assessed five case studies, accuracy latter explored ε=10-4 10−3 terms how close can reproduce prediction former. On GPU, found 2–28 times faster than CPU predecessor with increased number elements grid, increase speed up 320 reduction grid's decreased variability resolution. therefore, inundation modelling performed at both urban catchment scales. It openly available under GPL v3 license, additional documentation https://www.seamlesswave.com/LISFLOOD8.0 (last access: 12 March 2023).

Language: Английский

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A complete methodology to assess hydraulic risk in small ungauged catchments based on HEC-RAS 2D Rain-On-Grid simulations

Natural Hazards, Journal Year: 2024, Volume and Issue: 120(8), P. 7381 - 7409

Published: March 13, 2024

Abstract This paper explores the use of rain-on-grid (or direct rainfall) method for flood risk assessment at a basin scale. The is particularly useful rural catchments with small vertical variations and complex interactions man-made obstacles structures, which may be oversimplified by traditional hydrologically based estimations. hydrodynamic model solving mass momentum conservation equations allows simulation runoff over watershed As drawback, more detailed spatially distributed data are needed, computational time extended. On other hand, smaller number parameters needed compared to hydrological model. Roughness rainfall loss coefficients need calibrated only. methodology was here implemented within two-dimensional HEC-RAS low-land rural, ungauged, Terdoppio River, Northern Italy. resulting hydrographs closing section were synthetic design evaluated through pure modelling, showing agreement on peak discharge values low-probability scenarios, but not total volumes. results in terms water depth flow velocity maps used create hazard using Australian Institute Disaster Resilience methodology. Index Proportional Risk then adopted generate basin-scale map, combining maps, damage functions different building-use classes, value reconstruction content per unit area.

Language: Английский

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Interpretable flash flood susceptibility mapping in Yarlung Tsangpo River Basin using H2O Auto-ML

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 11, 2025

Flash flood susceptibility mapping is essential for identifying areas prone to flooding events and aiding decision-makers in formulating effective prevention measures. This study aims evaluate the flash Yarlung Tsangpo River Basin (YTRB) using multiple machine learning (ML) models facilitated by H2O automated ML platform. The best-performing model was used generate a map, its interpretability analyzed Shapley Additive Explanations (SHAP) tree interpretation method. results revealed that top four models, including both single ensemble demonstrated high accuracy tests. map generated eXtreme Randomized Trees (XRT) showed 8.92%, 12.95%, 15.42%, 31.34%, 31.37% of area exhibited very high, moderate, low, low susceptibility, respectively, with approximately 74.9% historical floods occurring classified as moderate susceptibility. SHAP plot identified topographic factors primary drivers floods, importance analysis ranking most influential such descending order DEM, wetness index, position normalized difference vegetation average multi-year precipitation. demonstrates benefits interpretable learning, which can provide guidance mitigation.

Language: Английский

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2D hydrodynamic approach supporting evaluations of hydrological response in small watersheds: Implications for lag time estimation

Journal of Hydrology, Journal Year: 2022, Volume and Issue: 610, P. 127870 - 127870

Published: April 26, 2022

Language: Английский

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