Development of high-resolution multi-scale modelling system for simulation of coastal-fluvial urban flooding

Comer, Joanne; Olbert, Agnieszka Indiana; Hartnett, Michael

doi:10.5194/nhess-17-205-2017

Cited by 15 publications

(9 citation statements)

References 43 publications

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“…Nevertheless, they are of interest in terms of taking advantage of the high-resolution LIDAR data that are available nowadays and which, in general, cannot be explicitly included in a 2D model since it would require numerical grids with hundreds of millions of computational cells. The use of nested grids has also been proposed in some studies that include a large computational domain [76,77]. Nested grids provide very high spatial resolution in selected areas of interest without incurring the high computational expense of fine grid resolution over the entire model domain.…”

Section: Modelling Flood Hazardmentioning

confidence: 99%

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

Cea

Costabile

2022

Hydrology

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

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Section: Modelling Flood Hazardmentioning

confidence: 99%

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

Cea

Costabile

2022

Hydrology

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“…A widely used example of the dynamic approach is the LISFLOOD-LP model, which includes only the physical processes that are relevant for predicting inundation extents for riverine floods (Bates and de Roo 2000). Another model, MSN_Flood, has been nested within a storm surge model to provide higher-resolution flooding predictions (Hartnett and Nash 2017;Comer et al 2017;.…”

Section: Introductionmentioning

confidence: 99%

Downscaling of real-time coastal flooding predictions for decision support

et al. 2021

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During coastal storms, forecasters and researchers use numerical models to predict the magnitude and extent of coastal flooding. These models must represent the large regions that may be affected by a storm, and thus, they can be computationally costly and may not use the highest geospatial resolution. However, predicted flood extents can be downscaled (by increasing resolution) as a post-processing step. Existing downscaling methods use either a static extrapolation of the flooding as a flat surface, or rely on subsequent simulations with nested, full-physics models at higher resolution. This research explores a middle way, in which the downscaling includes simplified physics to improve accuracy. Using results from a state-of-the-art model, we downscale its flood predictions with three methods: (1) static, in which the water surface elevations are extrapolated horizontally until they intersect the ground surface; (2) slopes, in which the gradient of the water surface is used; and (3) head loss, which accounts for energy losses due to land cover characteristics. The downscaling methods are then evaluated for forecasts and hindcasts of Hurricane Florence (2018), which caused widespread flooding in North Carolina. The static and slopes methods tend to overestimate the flood extents. However, the head loss method generates a downscaled flooding extent that is a close match to the predictions from a higher-resolution, full-physics model. These results are encouraging for the use of these downscaling methods to support decision-making during coastal storms.

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“…In the context of urban flood modelling, this typically involves the use of coarser grids and the development of sub‐grid models to account for topographic variability that is too small to be resolved with the computational mesh (Bruwier, Archambeau, Erpicum, Pirotton, & Dewals, ; Cea & Vázquez‐Cendón, ; Chen et al, ; Guinot, ; Schubert & Sanders, ). The use of nested grids has also been proposed in some studies that require a large computational domain (Bermúdez et al, ; Comer, Olbert, Nash, & Hartnett, ). Nested grids provide very high spatial resolution in selected areas of interest without incurring into the computational expense of fine grid resolution over the entire model domain.…”

Section: Introductionmentioning

confidence: 99%

A rapid flood inundation model for hazard mapping based on least squares support vector machine regression

Bermúdez

Cea

Puertas

2019

J Flood Risk Management

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Two-dimensional shallow water models are widely used tools for flood inundation mapping. However, even if High Performance Computing techniques have greatly decreased the computational time needed to run a 2D inundation model, this approach remains unsuitable for applications that require results in a very short time or a large number of model runs. In this paper we test a non-parametric regression model based on least squares support vector machines as a computationally efficient surrogate of the 2D shallow water equations for flood inundation mapping. The methodology is initially applied to a synthetic case study consisting of a straight river reach flowing towards the sea. A coastal urban area is then used as a real test case. Discharge in three streams and tide levels are used as predictor variables to estimate the spatial distribution of maximum water depth and velocity in the study area. The suitability of this regression model for the spatial prediction of flood hazard is evaluated. The results show the potential of the proposed regression technique for fast and accurate computation of flood extent and hazard maps. K E Y W O R D S flood hazard, flood inundation, Iber model, shallow water equations, support vector machine 1 | INTRODUCTIONPhysically-based flood inundation models are widely used tools for simulating river hydraulics and floodplain inundation processes. In these models, the flood propagation is generally described by the two-dimensional shallow water equations (2D-SWEs), which must be solved using an appropriate numerical technique. Models solve either the full or simplified forms of these equations, as the local inertial approximation or the diffusive wave approximation (Neal et al., 2012). In practice, an inevitable compromise between accuracy and efficiency must be found when defining the spatial resolution of the numerical mesh and the time discretization (Chen et al., 2012). Higher accuracy can be obtained by increasing the physical complexity and the spatial and temporal resolution of a model, but at the cost of higher computation time.This conventional physically-based approach is hence generally impractical for applications that require results in a very short time or a large number of model runs. A typical example is a probabilistic analysis such as the propagation of uncertainty in a Monte Carlo context, which involves the evaluation of thousands model runs. A few Monte Carlo probabilistic analyses using 2D-SWE models have been presented in the literature but the number of runs is generally limited to a few hundreds (Cea, Bermúdez, & Puertas, 2011;Fraga et al., 2016), which limits the number of parameters that can be analysed and the statistical significance of the results. Real-time forecasting systems are another example where fast computation is imperative to issue flood warnings with sufficient lead-time. In these kinds of applications, a possible way to reduce significantly the computation time is the development of computationally more efficient surrogates of these models. A fi...

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Development of high-resolution multi-scale modelling system for simulation of coastal-fluvial urban flooding

Cited by 15 publications

References 43 publications

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

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

Downscaling of real-time coastal flooding predictions for decision support

A rapid flood inundation model for hazard mapping based on least squares support vector machine regression

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