Estimating the UK Index Flood: an Improved spatial flooding analysis
Estimating the UK Index Flood: an Improved spatial flooding analysis
Flooding is one of the major natural hazards in the UK. Accurate flood estimation at ungauged catchment is an important component to understand and mitigate flood hazards, but still a difficult issue. This study therefore attempts to explore and improve an index flood estimation model, known as the FEH-QMED model, popular in the UK. It was developed under the assumption that the index flood of QMED, i.e., the median of the set of annual maximum (AMAX) flood data, standing for a flooding level of 2-year return period, can be explained by catchment descriptors. In this study, two fundamentals are empirically explored, including assessing reliability of the nonlinear functional impacts of the catchment descriptors on the logarithmic transformation of QMED, specified by the FEH-QMED model, and the potential to improve the model for more accurate index flood estimation, based on the flooding data of 586 gauged stations across the UK. Through a spatial additive regression analysis, we empirically find that the nonlinear impacts of the catchment descriptors in an updated FEH-QMED model appear reliable. However, spatial correlation tests including Moran’s I and Lagrange multiplier tests show that strong spatial dependence exists in the residuals of, but was not fully taken into account by, the QMED type models. We have therefore empirically established new spatial index flood estimation models by proposing spatial autoregressive models to model the impacts of the neighboring sites. Cross-validation assessments demonstrate that the suggested spatial error-based index flood model outperforms the updated FEH-QMED model with a significant improvement, which is robust in the sense of different error measures, say by a reduction of 13.8% of the mean squared error of prediction, for the UK index flood estimation.
Flood Estimation Handbook (FEH)-QMED Model, Flood catchment descriptors, Index flood estimation, Nonlinear effect of covariates, Spatial error model, Spatial neighboring effect
731–748
Muhammad, Marinah
d65579b0-bc87-4941-a5f2-6edea5ab63a4
Lu, Zudi
4aa7d988-ac2b-4150-a586-ca92b8adda95
1 October 2020
Muhammad, Marinah
d65579b0-bc87-4941-a5f2-6edea5ab63a4
Lu, Zudi
4aa7d988-ac2b-4150-a586-ca92b8adda95
Muhammad, Marinah and Lu, Zudi
(2020)
Estimating the UK Index Flood: an Improved spatial flooding analysis.
Environmental Modeling & Assessment, 25 (5), .
(doi:10.1007/s10666-020-09713-x).
Abstract
Flooding is one of the major natural hazards in the UK. Accurate flood estimation at ungauged catchment is an important component to understand and mitigate flood hazards, but still a difficult issue. This study therefore attempts to explore and improve an index flood estimation model, known as the FEH-QMED model, popular in the UK. It was developed under the assumption that the index flood of QMED, i.e., the median of the set of annual maximum (AMAX) flood data, standing for a flooding level of 2-year return period, can be explained by catchment descriptors. In this study, two fundamentals are empirically explored, including assessing reliability of the nonlinear functional impacts of the catchment descriptors on the logarithmic transformation of QMED, specified by the FEH-QMED model, and the potential to improve the model for more accurate index flood estimation, based on the flooding data of 586 gauged stations across the UK. Through a spatial additive regression analysis, we empirically find that the nonlinear impacts of the catchment descriptors in an updated FEH-QMED model appear reliable. However, spatial correlation tests including Moran’s I and Lagrange multiplier tests show that strong spatial dependence exists in the residuals of, but was not fully taken into account by, the QMED type models. We have therefore empirically established new spatial index flood estimation models by proposing spatial autoregressive models to model the impacts of the neighboring sites. Cross-validation assessments demonstrate that the suggested spatial error-based index flood model outperforms the updated FEH-QMED model with a significant improvement, which is robust in the sense of different error measures, say by a reduction of 13.8% of the mean squared error of prediction, for the UK index flood estimation.
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Accepted/In Press date: 5 May 2020
Published date: 1 October 2020
Additional Information:
Funding Information:
The authors are grateful to the editor, an associate editor and three referees for their insightful comments which have greatly improved the presentation of this paper. The first author would like to thank the Ministry of Higher Education of Malaysia and Universiti Malaysia Kelantan for the scholarship offered for this research. We also thank Dr. Thomas Kjeldsen from the Department of Architecture and Civil Engineering at University of Bath for his helps and comments with a lot of related information provided to the first author, which have improved the manuscript. This research was also partially supported by an European Commission’s Marie Curie Career Integration Grant, which is acknowledged.
Publisher Copyright:
© 2020, The Author(s).
Keywords:
Flood Estimation Handbook (FEH)-QMED Model, Flood catchment descriptors, Index flood estimation, Nonlinear effect of covariates, Spatial error model, Spatial neighboring effect
Identifiers
Local EPrints ID: 443798
URI: http://eprints.soton.ac.uk/id/eprint/443798
ISSN: 1420-2026
PURE UUID: 0db9dce9-ee2c-4cda-8a1b-f0adcbcaedf9
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Date deposited: 14 Sep 2020 16:30
Last modified: 17 Mar 2024 03:34
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Author:
Marinah Muhammad
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