Identification of climate variables dominating streamflow generation and quantification of streamflow decline in the Loess Plateau, China

Precipitation-extremes-driven floods, which compose an important proportion of streamflow but cause severe adverse impacts in the Loess Plateau of China, urged the progressive implementation of ecological restoration (ER) strategies in the Loess Plateau (LP) of China. Knowledge of the linkage between climate variables (especially precipitation extremes) and streamflow generation become more essential for advanced catchment management as ER and climate variability have resulted in reduced streamflow and freshwater stress. Here, a partial least squares regression (PLSR) approach was used to investigate this issue at 16 main catchments of the LP over a reference period (1961–1979). Then, we quantified streamflow decline during the “Integrated Soil and Water Conservation” (1980–1999) and the “Grain for Grain” (2000–2015) strategies by PLSR modeling. We found that the dominant climatic variables controlling annual streamflow include heavy precipitation amount and heavy precipitation days, maximum precipitation event amount, number of consecutive wet days, annual total precipitation (daily precipitation ≥1 mm), and effective precipitation amount (daily precipitation ≥5 mm). Further, the effect of precipitation extremes on streamflow generation is stronger in drier catchments. The impacts of precipitation extremes on streamflow generation can be strengthened by agricultural cultivation and weakened by revegetation (especially reforestation). Overall, we found that climate-driven annual streamflow decreased by 7.5 mm during 1980–1999 and by 5.6 mm during 2000–2015, in comparison to 1961–1979. The dominant cause of streamflow reduction was ER, with the contribution increasing from 59% in 1980–1999 to 82% in 2000–2015. The PLSR approach enables the identification of linkages between climate variables and streamflow generation, and the prediction of climate-driven streamflow. This study yields a greater understanding of the influences of climate variability and ER on streamflow change, and is helpful to identify hydroclimatological trends and projections.