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    Rights statement: This is the author’s version of a work that was accepted for publication in Journal of Hydrology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Hydrology, 598, 2021 DOI: 10.1016/j.jhydrol.2021.126442

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Elucidating the performance of hybrid models for predicting extreme water flow events through variography and wavelet analyses

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Elucidating the performance of hybrid models for predicting extreme water flow events through variography and wavelet analyses. / Curceac, S.; Milne, A.; Atkinson, P.M. et al.

In: Journal of Hydrology, Vol. 598, 126442, 31.07.2021.

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Curceac S, Milne A, Atkinson PM, Wu L, Harris P. Elucidating the performance of hybrid models for predicting extreme water flow events through variography and wavelet analyses. Journal of Hydrology. 2021 Jul 31;598:126442. Epub 2021 May 8. doi: 10.1016/j.jhydrol.2021.126442

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@article{14f9e266f69540f3998e9198641e01fb,
title = "Elucidating the performance of hybrid models for predicting extreme water flow events through variography and wavelet analyses",
abstract = "Accurate prediction of extreme flow events is important for mitigating natural disasters such as flooding. We explore and refine two modelling approaches (both separately and in combination) that have been demonstrated to improve the prediction of daily peak flow events. These two approaches are firstly, models that aggregate fine resolution (sub-daily) simulated flow from a process-based model (PBM) to daily, and secondly, hybrid models that combine PBMs with statistical and machine learning methods. We propose the use of variography and wavelet analyses to evaluate these models across temporal scales. These exploratory methods are applied to both measured and modelled data in order to assess the performance of the latter in capturing variation, at different scales, of the former. We compare change points detected by the wavelet analysis (measured and modelled) with the extreme flow events identified in the measured data. We found that combining the two modelling approaches improves prediction at finer scales, but at coarser scales advantages are less pronounced. Although aggregating fine-scale model outputs improved the partition of wavelet variation across scales, the autocorrelation in the signal is less well represented as demonstrated by variography. We demonstrate that exploratory time-series analyses, using variograms and wavelets, provides a useful assessment of existing and newly proposed models, with respect to how they capture changes in flow variance at different scales and also how this correlates with measured flow data – all in the context of extreme flow events. {\textcopyright} 2021 Elsevier B.V.",
keywords = "Hydrology, Peak flows, Process scale, Variogram analysis, Wavelet analysis",
author = "S. Curceac and A. Milne and P.M. Atkinson and L. Wu and P. Harris",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Journal of Hydrology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Hydrology, 598, 2021 DOI: 10.1016/j.jhydrol.2021.126442",
year = "2021",
month = jul,
day = "31",
doi = "10.1016/j.jhydrol.2021.126442",
language = "English",
volume = "598",
journal = "Journal of Hydrology",
issn = "0022-1694",
publisher = "Elsevier Science B.V.",

}

RIS

TY - JOUR

T1 - Elucidating the performance of hybrid models for predicting extreme water flow events through variography and wavelet analyses

AU - Curceac, S.

AU - Milne, A.

AU - Atkinson, P.M.

AU - Wu, L.

AU - Harris, P.

N1 - This is the author’s version of a work that was accepted for publication in Journal of Hydrology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Hydrology, 598, 2021 DOI: 10.1016/j.jhydrol.2021.126442

PY - 2021/7/31

Y1 - 2021/7/31

N2 - Accurate prediction of extreme flow events is important for mitigating natural disasters such as flooding. We explore and refine two modelling approaches (both separately and in combination) that have been demonstrated to improve the prediction of daily peak flow events. These two approaches are firstly, models that aggregate fine resolution (sub-daily) simulated flow from a process-based model (PBM) to daily, and secondly, hybrid models that combine PBMs with statistical and machine learning methods. We propose the use of variography and wavelet analyses to evaluate these models across temporal scales. These exploratory methods are applied to both measured and modelled data in order to assess the performance of the latter in capturing variation, at different scales, of the former. We compare change points detected by the wavelet analysis (measured and modelled) with the extreme flow events identified in the measured data. We found that combining the two modelling approaches improves prediction at finer scales, but at coarser scales advantages are less pronounced. Although aggregating fine-scale model outputs improved the partition of wavelet variation across scales, the autocorrelation in the signal is less well represented as demonstrated by variography. We demonstrate that exploratory time-series analyses, using variograms and wavelets, provides a useful assessment of existing and newly proposed models, with respect to how they capture changes in flow variance at different scales and also how this correlates with measured flow data – all in the context of extreme flow events. © 2021 Elsevier B.V.

AB - Accurate prediction of extreme flow events is important for mitigating natural disasters such as flooding. We explore and refine two modelling approaches (both separately and in combination) that have been demonstrated to improve the prediction of daily peak flow events. These two approaches are firstly, models that aggregate fine resolution (sub-daily) simulated flow from a process-based model (PBM) to daily, and secondly, hybrid models that combine PBMs with statistical and machine learning methods. We propose the use of variography and wavelet analyses to evaluate these models across temporal scales. These exploratory methods are applied to both measured and modelled data in order to assess the performance of the latter in capturing variation, at different scales, of the former. We compare change points detected by the wavelet analysis (measured and modelled) with the extreme flow events identified in the measured data. We found that combining the two modelling approaches improves prediction at finer scales, but at coarser scales advantages are less pronounced. Although aggregating fine-scale model outputs improved the partition of wavelet variation across scales, the autocorrelation in the signal is less well represented as demonstrated by variography. We demonstrate that exploratory time-series analyses, using variograms and wavelets, provides a useful assessment of existing and newly proposed models, with respect to how they capture changes in flow variance at different scales and also how this correlates with measured flow data – all in the context of extreme flow events. © 2021 Elsevier B.V.

KW - Hydrology

KW - Peak flows

KW - Process scale

KW - Variogram analysis

KW - Wavelet analysis

U2 - 10.1016/j.jhydrol.2021.126442

DO - 10.1016/j.jhydrol.2021.126442

M3 - Journal article

VL - 598

JO - Journal of Hydrology

JF - Journal of Hydrology

SN - 0022-1694

M1 - 126442

ER -