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Impact of Precipitation Pre-Processing Methods on Hydrological Model Performance using High-Resolution Gridded Dataset

Salam A. A. Abbas Orcid Logo, Yunqing Xuan Orcid Logo

Water, Volume: 12, Issue: 3, Start page: 840

Swansea University Authors: Salam A. A. Abbas Orcid Logo, Yunqing Xuan Orcid Logo

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DOI (Published version): 10.3390/w12030840

Abstract

Effective representation of precipitation inputs is one of the essential components in hydrological model structures, especially when gauge measurements for the modelled catchment are sparse. Assessment of the impact of precipitation pre-processing is often nontrivial as precipitation data are very...

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Published in: Water
ISSN: 2073-4441
Published: MDPI AG 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa53902
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spelling 2021-08-18T14:32:44.8860429 v2 53902 2020-04-06 Impact of Precipitation Pre-Processing Methods on Hydrological Model Performance using High-Resolution Gridded Dataset 2aa12c228ee22b06b6c07b8f857ea4f3 0000-0001-5782-5319 Salam A. A. Abbas Salam A. A. Abbas true true 3ece84458da360ff84fa95aa1c0c912b 0000-0003-2736-8625 Yunqing Xuan Yunqing Xuan true false 2020-04-06 Effective representation of precipitation inputs is one of the essential components in hydrological model structures, especially when gauge measurements for the modelled catchment are sparse. Assessment of the impact of precipitation pre-processing is often nontrivial as precipitation data are very limited in the first place. In this paper, we demonstrate a study using a semi-distributed hydrological model, the Soil and Water Assessment Tool (SWAT) to examine the impact of different precipitation pre-processing methods on model calibration and the overall model performance with regards to the operational use. A river catchment in the UK is modelled to test against the three pre-processing methods: the Centroid Point Estimation Method (CPEM), the Grid Area Method (GAM) and the Grid Point Method (GPM). Cross-calibration and validation are then carried out by using the high-resolution Centre for Ecology & Hydrology–Gridded Estimate Areal Rainfall (CEH-GEAR) dataset. The results show that the proposed methods GAM and GPM can improve the model calibration significantly against the one calibrated with the existing CPEM method used by the model; the performance differences in the validation among the calibrated models, however, remain small and become irrelevant. The findings indicate that it is preferable to always make use of high-quality rainfall data, when available, with a better pre-processing method, even with models that are previously calibrated with low-quality rainfall inputs. It is also shown that such improvements are affected by the size of catchment and become less significant for smaller catchments. Journal Article Water 12 3 840 MDPI AG 2073-4441 Hydrological modelling; Precipitation pre-processing; Calibration; Cross-validation; SWAT; Gridded Rainfall Dataset 17 3 2020 2020-03-17 10.3390/w12030840 COLLEGE NANME Engineering COLLEGE CODE Swansea University 2021-08-18T14:32:44.8860429 2020-04-06T10:47:45.7636622 College of Engineering Engineering Salam A. A. Abbas 0000-0001-5782-5319 1 Yunqing Xuan 0000-0003-2736-8625 2 53902__17014__e111b769018a493a88aec8a0bea61380.pdf 53902.pdf 2020-04-06T10:49:20.9136525 Output 4667809 application/pdf Version of Record true Released under the terms of a Creative Commons Attribution License (CC-BY). true eng http://creativecommons.org/licenses/by/4.0/
title Impact of Precipitation Pre-Processing Methods on Hydrological Model Performance using High-Resolution Gridded Dataset
spellingShingle Impact of Precipitation Pre-Processing Methods on Hydrological Model Performance using High-Resolution Gridded Dataset
Salam A. A. Abbas
Yunqing Xuan
title_short Impact of Precipitation Pre-Processing Methods on Hydrological Model Performance using High-Resolution Gridded Dataset
title_full Impact of Precipitation Pre-Processing Methods on Hydrological Model Performance using High-Resolution Gridded Dataset
title_fullStr Impact of Precipitation Pre-Processing Methods on Hydrological Model Performance using High-Resolution Gridded Dataset
title_full_unstemmed Impact of Precipitation Pre-Processing Methods on Hydrological Model Performance using High-Resolution Gridded Dataset
title_sort Impact of Precipitation Pre-Processing Methods on Hydrological Model Performance using High-Resolution Gridded Dataset
author_id_str_mv 2aa12c228ee22b06b6c07b8f857ea4f3
3ece84458da360ff84fa95aa1c0c912b
author_id_fullname_str_mv 2aa12c228ee22b06b6c07b8f857ea4f3_***_Salam A. A. Abbas
3ece84458da360ff84fa95aa1c0c912b_***_Yunqing Xuan
author Salam A. A. Abbas
Yunqing Xuan
author2 Salam A. A. Abbas
Yunqing Xuan
format Journal article
container_title Water
container_volume 12
container_issue 3
container_start_page 840
publishDate 2020
institution Swansea University
issn 2073-4441
doi_str_mv 10.3390/w12030840
publisher MDPI AG
college_str College of Engineering
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description Effective representation of precipitation inputs is one of the essential components in hydrological model structures, especially when gauge measurements for the modelled catchment are sparse. Assessment of the impact of precipitation pre-processing is often nontrivial as precipitation data are very limited in the first place. In this paper, we demonstrate a study using a semi-distributed hydrological model, the Soil and Water Assessment Tool (SWAT) to examine the impact of different precipitation pre-processing methods on model calibration and the overall model performance with regards to the operational use. A river catchment in the UK is modelled to test against the three pre-processing methods: the Centroid Point Estimation Method (CPEM), the Grid Area Method (GAM) and the Grid Point Method (GPM). Cross-calibration and validation are then carried out by using the high-resolution Centre for Ecology & Hydrology–Gridded Estimate Areal Rainfall (CEH-GEAR) dataset. The results show that the proposed methods GAM and GPM can improve the model calibration significantly against the one calibrated with the existing CPEM method used by the model; the performance differences in the validation among the calibrated models, however, remain small and become irrelevant. The findings indicate that it is preferable to always make use of high-quality rainfall data, when available, with a better pre-processing method, even with models that are previously calibrated with low-quality rainfall inputs. It is also shown that such improvements are affected by the size of catchment and become less significant for smaller catchments.
published_date 2020-03-17T04:08:20Z
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