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

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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Bibliographic Details
Published in:Journal of geographical sciences 2023-06, Vol.33 (6), p.1313-1333
Main Authors: Zhao, Hongling, Li, Hongyan, Xuan, Yunqing, Bao, Shanshan, Cidan, Yangzong, Liu, Yingying, Li, Changhai, Yao, Meichu
Format: Article
Language:English
Subjects:
Aquatic resources
Cold regions
Earth and Environmental Science
Fresh water
Geographical Information Systems/Cartography
Geography
Investigations
Machine learning
Management
Nature Conservation
Physical Geography
Radiation
Remote Sensing/Photogrammetry
Runoff
Runoff forecasting
Snowmelt
Solar radiation
Water
Water resources management
Weather
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Summary: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.
ISSN:1009-637X
1861-9568
DOI:10.1007/s11442-023-2131-9