Comparative analysis of decision tree, time series, and semi-distributed hydrological models for runoff prediction in snowmelt-influenced and human-activity-intensive basins: a case study of the Shiribeshi-Toshibetsu basin, Japan

ABSTRACT This study evaluates the performance of decision tree model (XGBoost), time series model (LSTM), and semi-distributed hydrological model (HBV) in simulating daily runoff in a watershed with significant snowmelt contributions and intensive human activity. Daily runoff data from the Shiribeshi-Toshibetsu basin in Japan, spanning the period from 1998 to 2015, were used for model training, with data from 2016 to 2019 for validation. Comparative analysis reveals that the XGBoost model outperforms the LSTM and HBV models,achieving a Nash–Sutcliffe Efficiency (NSE) of 0.94, compared to 0.85 and 0.84 for the LSTM and HBV models, respectively. Additionally, Partial Dependence Plots (PDPs) were utilized to investigate the nonlinear impacts of climatic factors on runoff, identifying precipitation, average temperature, and wind speed as the most significant drivers. Specifically, the relationship between temperature and runoff follows a unique pattern: runoff initially increases with rising temperatures, then decreases, and subsequently increases again. These findings underscore the robustness of decision tree models in handling complex hydrological data and highlight the importance of understanding the intricate interactions between climatic variables and runoff. This study provides scientific support for water resource management in similar watersheds under changing climatic conditions.