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 ...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.展开更多
基金The Key Program of National Natural Science Foundation of China,No.42230204。
文摘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.
