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Improvement of the SWAT Model for Snowmelt Runoff Simulation in Seasonal Snowmelt Area Using Remote Sensing Data

Hongling Zhao, Hongyan Li, Yunqing Xuan Orcid Logo, Changhai Li, Heshan Ni

Remote Sensing, Volume: 14, Issue: 22, Start page: 5823

Swansea University Author: Yunqing Xuan Orcid Logo

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DOI (Published version): 10.3390/rs14225823

Abstract

The SWAT model has been widely used to simulate snowmelt runoff in cold regions thanks to its ability of representing the effects of snowmelt and permafrost on runoff generation and confluence. However, a core method used in the SWAT model, the temperature index method, assumes both the dates for ma...

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Published in: Remote Sensing
ISSN: 2072-4292
Published: MDPI AG 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa61952
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spelling 2022-11-22T11:12:42.3554397 v2 61952 2022-11-18 Improvement of the SWAT Model for Snowmelt Runoff Simulation in Seasonal Snowmelt Area Using Remote Sensing Data 3ece84458da360ff84fa95aa1c0c912b 0000-0003-2736-8625 Yunqing Xuan Yunqing Xuan true false 2022-11-18 CIVL The SWAT model has been widely used to simulate snowmelt runoff in cold regions thanks to its ability of representing the effects of snowmelt and permafrost on runoff generation and confluence. However, a core method used in the SWAT model, the temperature index method, assumes both the dates for maximum and minimum snowmelt factors and the snowmelt temperature threshold, which leads to inaccuracies in simulating snowmelt runoff in seasonal snowmelt regions. In this paper, we present the development and application of an improved temperature index method for SWAT (SWAT+) in simulating the daily snowmelt runoff in a seasonal snowmelt area of Northeast China. The improvements include the introduction of total radiation to the temperature index method, modification of the snowmelt factor seasonal variation formula, and changing the snowmelt temperature threshold according to the snow depth derived from passive microwave remote sensing data and temperature in the seasonal snowmelt area. Further, the SWAT+ model is applied to study climate change impact on future snowmelt runoff (2025–2054) under the climate change scenarios including SSP2.6, SSP4.5, and SSP8.5. Much improved snowmelt runoff simulation is obtained as a result, supported by several metrics, such as MAE, RE, RMSE, R2, and NSE for both the calibration and validation. Compared with the baseline period (1980–2019), the March–April ensemble average snowmelt runoff is shown to decrease under the SSP2.6, SSP4.5, and SSP8.5 scenario during 2025–2054. This study provides a valuable insight into the efficient development and utilization of spring water resources in seasonal snowmelt areas. Journal Article Remote Sensing 14 22 5823 MDPI AG 2072-4292 snowmelt runoff; climate change; snowmelt flood; SWAT model; remote sensing; seasonal snowmelt area 17 11 2022 2022-11-17 10.3390/rs14225823 COLLEGE NANME Civil Engineering COLLEGE CODE CIVL Swansea University External research funder(s) paid the OA fee (includes OA grants disbursed by the Library) This study was supported by Key R&D project funding from Jilin Province Science and Technology Department, China (20200403070SF) and Key Program of National Natural Science Foundation of China (42230204). 2022-11-22T11:12:42.3554397 2022-11-18T16:15:51.6745792 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Hongling Zhao 1 Hongyan Li 2 Yunqing Xuan 0000-0003-2736-8625 3 Changhai Li 4 Heshan Ni 5 61952__25832__d4bbe234b98946af8492d8b13357db42.pdf remotesensing-14-05823.pdf 2022-11-18T16:18:05.1042190 Output 3935404 application/pdf Version of Record true © 2022 by the authors.This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license true eng https://creativecommons.org/license
title Improvement of the SWAT Model for Snowmelt Runoff Simulation in Seasonal Snowmelt Area Using Remote Sensing Data
spellingShingle Improvement of the SWAT Model for Snowmelt Runoff Simulation in Seasonal Snowmelt Area Using Remote Sensing Data
Yunqing Xuan
title_short Improvement of the SWAT Model for Snowmelt Runoff Simulation in Seasonal Snowmelt Area Using Remote Sensing Data
title_full Improvement of the SWAT Model for Snowmelt Runoff Simulation in Seasonal Snowmelt Area Using Remote Sensing Data
title_fullStr Improvement of the SWAT Model for Snowmelt Runoff Simulation in Seasonal Snowmelt Area Using Remote Sensing Data
title_full_unstemmed Improvement of the SWAT Model for Snowmelt Runoff Simulation in Seasonal Snowmelt Area Using Remote Sensing Data
title_sort Improvement of the SWAT Model for Snowmelt Runoff Simulation in Seasonal Snowmelt Area Using Remote Sensing Data
author_id_str_mv 3ece84458da360ff84fa95aa1c0c912b
author_id_fullname_str_mv 3ece84458da360ff84fa95aa1c0c912b_***_Yunqing Xuan
author Yunqing Xuan
author2 Hongling Zhao
Hongyan Li
Yunqing Xuan
Changhai Li
Heshan Ni
format Journal article
container_title Remote Sensing
container_volume 14
container_issue 22
container_start_page 5823
publishDate 2022
institution Swansea University
issn 2072-4292
doi_str_mv 10.3390/rs14225823
publisher MDPI AG
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
document_store_str 1
active_str 0
description The SWAT model has been widely used to simulate snowmelt runoff in cold regions thanks to its ability of representing the effects of snowmelt and permafrost on runoff generation and confluence. However, a core method used in the SWAT model, the temperature index method, assumes both the dates for maximum and minimum snowmelt factors and the snowmelt temperature threshold, which leads to inaccuracies in simulating snowmelt runoff in seasonal snowmelt regions. In this paper, we present the development and application of an improved temperature index method for SWAT (SWAT+) in simulating the daily snowmelt runoff in a seasonal snowmelt area of Northeast China. The improvements include the introduction of total radiation to the temperature index method, modification of the snowmelt factor seasonal variation formula, and changing the snowmelt temperature threshold according to the snow depth derived from passive microwave remote sensing data and temperature in the seasonal snowmelt area. Further, the SWAT+ model is applied to study climate change impact on future snowmelt runoff (2025–2054) under the climate change scenarios including SSP2.6, SSP4.5, and SSP8.5. Much improved snowmelt runoff simulation is obtained as a result, supported by several metrics, such as MAE, RE, RMSE, R2, and NSE for both the calibration and validation. Compared with the baseline period (1980–2019), the March–April ensemble average snowmelt runoff is shown to decrease under the SSP2.6, SSP4.5, and SSP8.5 scenario during 2025–2054. This study provides a valuable insight into the efficient development and utilization of spring water resources in seasonal snowmelt areas.
published_date 2022-11-17T04:21:11Z
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score 11.037275

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