Simulations of Snowmelt Runoff in a High-Altitude Mountainous Area Based on Big Data and Machine Learning Models: Taking the Xiying River Basin as an Example

Abstract

As an essential data-driven model, machine learning can simulate runoff based on meteorological data at the watershed level. It has been widely used in the simulation of hydrological runoff. Considering the impact of snow cover on runoff in high-altitude mountainous areas, based on remote sensing data and atmospheric reanalysis data, in this paper we established a runoff simulation model with a random forest model and ANN (artificial neural network) model for the Xiying River Basin in the western Qilian region The verification of the measured data showed that the NSE (Nash–Sutcliffe efficiency), RMSE (root mean square error), and PBIAS (percent bias) values of the random forest model and ANN model were 0.701 and 0.748, 6.228 m3/s and 4.554 m3/s, and 4.903% and 8.329%, respectively. Considering the influence of ice and snow on runoff, the simulation accuracy of both the random forest model and ANN model was improved during the period of significant decreases in the annual snow and ice water equivalent in the Xiying River Basin from April to May, after the snow remote sensing data were introduced into the model. Specifically, for the random forest model, the NSE increased by 0.099, the RMSE decreased by 0.369 m3/s, and the PBIAS decreased by 1.689%. For the ANN model, the NSE increased by 0.207, the RMSE decreased by 0.700 m3/s, and the PBIAS decreased by 1.103%. In this study, based on remote sensing data and atmospheric reanalysis data, the random forest model and ANN model were used to effectively simulate hydrological runoff processes in high-altitude mountainous areas without observational data. In particular, the accuracy of the machine learning simulations of snowmelt runoff (especially during the snowmelt period) was effectively improved by introducing the snow remote sensing data, which can provide a methodological reference for the simulation and prediction of snowmelt runoff in alpine mountains.

Keywords

• daily runoff simulation
• snowmelt runoff
• machine learning
• random forest
• artificial neural network