检索结果-南通市图书馆

Journal of Geographical Sciences 2023年第6期33卷 1313-1333页

作者：赵红玲李鸿雁轩云卿鲍珊珊次旦央宗刘莹莹李昌海姚美初 College of New Energy and Environment Jilin UniversityChangchun 130021China Key Laboratory of Groundwater Resources and Environment Ministry of EducationJilin UniversityChangchun 130021China Jilin Provincial Key Laboratory of Water Resources and Environment Jilin UniversityChangchun 130021China Department of Civil Engineering Swansea University Bay CampusFabian WaySwansea SA18ENUK Yellow River Engineering Consulting Co.Ltd. Zhengzhou 450003China

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.

关键词： snowmelt runoff mid-long term forecast SVR cold regions

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