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A new automated method for improved flood defense representation in large-scale hydraulic models

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A new automated method for improved flood defense representation in large-scale hydraulic models. / Wing, Oliver E J; Bates, Paul D; Neal, Jeffrey C; Sampson, Christopher C; Smith, Andrew M; Quinn, Niall; Shustikova, Iuliia; Domeneghetti, Alessio; Gilles, Daniel W; Goska, Radoslaw; Krajewski, Witold F.

In: Water Resources Research, 11.11.2019.

Research output: Contribution to journalArticle

Harvard

Wing, OEJ, Bates, PD, Neal, JC, Sampson, CC, Smith, AM, Quinn, N, Shustikova, I, Domeneghetti, A, Gilles, DW, Goska, R & Krajewski, WF 2019, 'A new automated method for improved flood defense representation in large-scale hydraulic models', Water Resources Research. https://doi.org/10.1029/2019WR025957

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Wing, Oliver E J ; Bates, Paul D ; Neal, Jeffrey C ; Sampson, Christopher C ; Smith, Andrew M ; Quinn, Niall ; Shustikova, Iuliia ; Domeneghetti, Alessio ; Gilles, Daniel W ; Goska, Radoslaw ; Krajewski, Witold F. / A new automated method for improved flood defense representation in large-scale hydraulic models. In: Water Resources Research. 2019.

Bibtex

@article{59abbbc6d62f4f12b05b735902175086,
title = "A new automated method for improved flood defense representation in large-scale hydraulic models",
abstract = "The execution of hydraulic models at large spatial scales has yielded a step-change in our understanding of flood risk. Yet, their necessary simplification through the use of coarsened terrain data results in an artificially smooth Digital Elevation Model (DEM) with diminished representation of flood defense structures. Current approaches to dealing with this, if anything is done at all, involve either employing incomplete inventories of flood defense information or making largely unsubstantiated assumptions about defense locations and standards based on socio-economic data. Here, we introduce a novel solution for application at-scale. The geomorphometric characteristics of defense structures are sampled and these are fed into a probabilistic algorithm to identify hydraulically relevant features in the source DEM. The elevation of these features is then preserved during the grid coarsening process. The method was shown to compare favorably to surveyed US levee crest heights. When incorporated into a continental-scale hydrodynamic model based on LISFLOOD-FP and compared to local flood models in Iowa (US), median correspondence was 69{\%} for high frequency floods and 80{\%} for low frequency floods, approaching the error inherent in quantifying extreme flows. However, improvements versus a model with no defenses were muted and risk-based deviations between the local and continental models were large. When simulating an event on the Po River (Italy), built and tested with higher quality data, the method outperformed both undefended and even engineering-grade models. As such, particularly when employed alongside model components of commensurate quality, the method here generates improved-accuracy simulations of flood inundation.",
author = "Wing, {Oliver E J} and Bates, {Paul D} and Neal, {Jeffrey C} and Sampson, {Christopher C} and Smith, {Andrew M} and Niall Quinn and Iuliia Shustikova and Alessio Domeneghetti and Gilles, {Daniel W} and Radoslaw Goska and Krajewski, {Witold F}",
year = "2019",
month = "11",
day = "11",
doi = "10.1029/2019WR025957",
language = "English",
journal = "Water Resources Research",
issn = "0043-1397",
publisher = "American Geophysical Union",

}

RIS - suitable for import to EndNote

TY - JOUR

T1 - A new automated method for improved flood defense representation in large-scale hydraulic models

AU - Wing, Oliver E J

AU - Bates, Paul D

AU - Neal, Jeffrey C

AU - Sampson, Christopher C

AU - Smith, Andrew M

AU - Quinn, Niall

AU - Shustikova, Iuliia

AU - Domeneghetti, Alessio

AU - Gilles, Daniel W

AU - Goska, Radoslaw

AU - Krajewski, Witold F

PY - 2019/11/11

Y1 - 2019/11/11

N2 - The execution of hydraulic models at large spatial scales has yielded a step-change in our understanding of flood risk. Yet, their necessary simplification through the use of coarsened terrain data results in an artificially smooth Digital Elevation Model (DEM) with diminished representation of flood defense structures. Current approaches to dealing with this, if anything is done at all, involve either employing incomplete inventories of flood defense information or making largely unsubstantiated assumptions about defense locations and standards based on socio-economic data. Here, we introduce a novel solution for application at-scale. The geomorphometric characteristics of defense structures are sampled and these are fed into a probabilistic algorithm to identify hydraulically relevant features in the source DEM. The elevation of these features is then preserved during the grid coarsening process. The method was shown to compare favorably to surveyed US levee crest heights. When incorporated into a continental-scale hydrodynamic model based on LISFLOOD-FP and compared to local flood models in Iowa (US), median correspondence was 69% for high frequency floods and 80% for low frequency floods, approaching the error inherent in quantifying extreme flows. However, improvements versus a model with no defenses were muted and risk-based deviations between the local and continental models were large. When simulating an event on the Po River (Italy), built and tested with higher quality data, the method outperformed both undefended and even engineering-grade models. As such, particularly when employed alongside model components of commensurate quality, the method here generates improved-accuracy simulations of flood inundation.

AB - The execution of hydraulic models at large spatial scales has yielded a step-change in our understanding of flood risk. Yet, their necessary simplification through the use of coarsened terrain data results in an artificially smooth Digital Elevation Model (DEM) with diminished representation of flood defense structures. Current approaches to dealing with this, if anything is done at all, involve either employing incomplete inventories of flood defense information or making largely unsubstantiated assumptions about defense locations and standards based on socio-economic data. Here, we introduce a novel solution for application at-scale. The geomorphometric characteristics of defense structures are sampled and these are fed into a probabilistic algorithm to identify hydraulically relevant features in the source DEM. The elevation of these features is then preserved during the grid coarsening process. The method was shown to compare favorably to surveyed US levee crest heights. When incorporated into a continental-scale hydrodynamic model based on LISFLOOD-FP and compared to local flood models in Iowa (US), median correspondence was 69% for high frequency floods and 80% for low frequency floods, approaching the error inherent in quantifying extreme flows. However, improvements versus a model with no defenses were muted and risk-based deviations between the local and continental models were large. When simulating an event on the Po River (Italy), built and tested with higher quality data, the method outperformed both undefended and even engineering-grade models. As such, particularly when employed alongside model components of commensurate quality, the method here generates improved-accuracy simulations of flood inundation.

U2 - 10.1029/2019WR025957

DO - 10.1029/2019WR025957

M3 - Article

JO - Water Resources Research

JF - Water Resources Research

SN - 0043-1397

ER -