Prediction of streamflow based on the long-term response of streamflow to climatic factors in the source region of the Yellow River
Ruirui Xu, Dexun Qiu, Peng Gao, Changxue Wu, Xingmin Mu, Muhammad Ismail
Abstract
The source region of the Yellow River (SRYE) is located in the eastern part of the Tibetan Plateau is a major water production and water conservation area for the Yellow River. This study aims to investigate the correlations between streamflow and meteorological factors/ocean in the SRYE from 1960 to 2018 using wavelet analysis. The effects of meteorological factors/ocean signals on streamflow were calculated using the Partial Least Squares-Structural Equation Model (PLS-SEM). Furthermore, climate factors with strong correlation with streamflow were used as inputs to random forest (RF) and multiple linear regression (MLR) models to predict monthly streamflow. Meteorological factors showed stronger correlation with streamflow compared to ocean signals, explaining 79.3% of the streamflow variation and much higher than ocean signals (0.1%). Among the meteorological factors, precipitation had the largest direct effect on streamflow (P < 0.01), followed by potential evapotranspiration (P < 0.01), and snow depth (P > 0.05), which together explained 78% of the streamflow variability. Temperature and relative humidity are two important factors that indirectly influenced streamflow through potential evapotranspiration (P < 0.01). Finally, precipitation, relative humidity, and minimum temperature were chosen as streamflow predictors in the SRYE. The RF showed a better performance in predicting long-term monthly streamflow than the MLR under complex climate-hydrological system.