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Investigating the critical influencing factors of snowmelt runoff and development of a mid-long term snowmelt runoff forecasting

Hongling Zhao, Hongyan Li, Yunqing Xuan Orcid Logo, Shanshan Bao, Yangzong Cidan, Yingying Liu, Changhai Li, Meichu Yao

Journal of Geographical Sciences, Volume: 33, Issue: 6, Pages: 1313 - 1333

Swansea University Author: Yunqing Xuan Orcid Logo

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DOI (Published version): 10.1007/s11442-023-2131-9

Abstract

Snowmelt runoff is a vital source of fresh water in cold regions. Accurate snowmelt runoff forecasting is crucial in supporting the integrated management of water resources in these regions. However, the performances of such forecasts are often very low as they involve many meteorological factors an...

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Published in: Journal of Geographical Sciences
ISSN: 1009-637X 1861-9568
Published: Springer Science and Business Media LLC 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa63775
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first_indexed 2023-07-26T09:51:27Z
last_indexed 2023-07-26T09:51:27Z
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spelling v2 63775 2023-07-04 Investigating the critical influencing factors of snowmelt runoff and development of a mid-long term snowmelt runoff forecasting 3ece84458da360ff84fa95aa1c0c912b 0000-0003-2736-8625 Yunqing Xuan Yunqing Xuan true false 2023-07-04 CIVL Snowmelt runoff is a vital source of fresh water in cold regions. Accurate snowmelt runoff forecasting is crucial in supporting the integrated management of water resources in these regions. However, the performances of such forecasts are often very low as they involve many meteorological factors and complex physical processes. Aiming to improve the understanding of these influencing factors on snowmelt runoff forecast, this study investigated the time lag of various meteorological factors before identifying the key factor in snowmelt processes. The results show that solar radiation, followed by temperature, are the two critical influencing factors with time lags being 0 and 2 days, respectively. This study further quantifies the effect of the two factors in terms of their contribution rate using a set of empirical equations developed. Their contribution rates as to yearly snowmelt runoff are found to be 56% and 44%, respectively. A mid-long term snowmelt forecasting model is developed using machine learning techniques and the identified most critical influencing factor with the biggest contribution rate. It is shown that forecasting based on Supporting Vector Regression (SVR) method can meet the requirements of forecast standards. Journal Article Journal of Geographical Sciences 33 6 1313 1333 Springer Science and Business Media LLC 1009-637X 1861-9568 Snowmelt runoff, mid-long term forecast, SVR, cold regions 30 6 2023 2023-06-30 10.1007/s11442-023-2131-9 http://dx.doi.org/10.1007/s11442-023-2131-9 COLLEGE NANME Civil Engineering COLLEGE CODE CIVL Swansea University 2023-10-03T12:11:56.8779098 2023-07-04T12:54:31.9972194 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 Shanshan Bao 4 Yangzong Cidan 5 Yingying Liu 6 Changhai Li 7 Meichu Yao 8
title Investigating the critical influencing factors of snowmelt runoff and development of a mid-long term snowmelt runoff forecasting
spellingShingle Investigating the critical influencing factors of snowmelt runoff and development of a mid-long term snowmelt runoff forecasting
Yunqing Xuan
title_short Investigating the critical influencing factors of snowmelt runoff and development of a mid-long term snowmelt runoff forecasting
title_full Investigating the critical influencing factors of snowmelt runoff and development of a mid-long term snowmelt runoff forecasting
title_fullStr Investigating the critical influencing factors of snowmelt runoff and development of a mid-long term snowmelt runoff forecasting
title_full_unstemmed Investigating the critical influencing factors of snowmelt runoff and development of a mid-long term snowmelt runoff forecasting
title_sort Investigating the critical influencing factors of snowmelt runoff and development of a mid-long term snowmelt runoff forecasting
author_id_str_mv 3ece84458da360ff84fa95aa1c0c912b
author_id_fullname_str_mv 3ece84458da360ff84fa95aa1c0c912b_***_Yunqing Xuan
author Yunqing Xuan
author2 Hongling Zhao
Hongyan Li
Yunqing Xuan
Shanshan Bao
Yangzong Cidan
Yingying Liu
Changhai Li
Meichu Yao
format Journal article
container_title Journal of Geographical Sciences
container_volume 33
container_issue 6
container_start_page 1313
publishDate 2023
institution Swansea University
issn 1009-637X
1861-9568
doi_str_mv 10.1007/s11442-023-2131-9
publisher Springer Science and Business Media LLC
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
url http://dx.doi.org/10.1007/s11442-023-2131-9
document_store_str 0
active_str 0
description Snowmelt runoff is a vital source of fresh water in cold regions. Accurate snowmelt runoff forecasting is crucial in supporting the integrated management of water resources in these regions. However, the performances of such forecasts are often very low as they involve many meteorological factors and complex physical processes. Aiming to improve the understanding of these influencing factors on snowmelt runoff forecast, this study investigated the time lag of various meteorological factors before identifying the key factor in snowmelt processes. The results show that solar radiation, followed by temperature, are the two critical influencing factors with time lags being 0 and 2 days, respectively. This study further quantifies the effect of the two factors in terms of their contribution rate using a set of empirical equations developed. Their contribution rates as to yearly snowmelt runoff are found to be 56% and 44%, respectively. A mid-long term snowmelt forecasting model is developed using machine learning techniques and the identified most critical influencing factor with the biggest contribution rate. It is shown that forecasting based on Supporting Vector Regression (SVR) method can meet the requirements of forecast standards.
published_date 2023-06-30T12:11:58Z
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score 11.017797

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