Seasonality and Impact Factor Analysis of Streamflow Sensitivity to Climate Change Across China

Streamflow sensitivity to climate change is an important indicator for evaluating the effects of climate change on terrestrial water. This study analyzed the spatial pattern and seasonality of streamflow sensitivity to climate change for 425 catchments across China. The results indicate that precipitation is consistently a more important contributor than temperature to streamflow variability (mean streamflow sensitivity to annual precipitation [epsilon(P,a)] = 0.92; absolute mean sensitivity to annual temperature [epsilon(T,a)] = 0.05). Meanwhile, the seasonal sensitivity evaluation found that streamflow response to precipitation change in warm seasons (mean epsilon(P,w) = 0.90) is significantly greater than in cool seasons (mean epsilon(P,c) = 0.46), but the sensitivity of streamflow to changes in temperature was stronger in cool seasons than in warm seasons. The magnitude of streamflow sensitivity to temperature in cool seasons is significantly affected by elevation (Pearson's r = 0.31, P < 0.01), which is possibly due to the increased snow and glacial melt volume of the high-elevation catchments in cool seasons. We also found that catchments with higher elevation, drier climate, less vegetation cover, and lower infiltration rates are more likely to present seasonally asymmetric precipitation sensitivity. A possible reason for this spatial pattern is that the highland and continental climate regimes generally exhibit strong precipitation seasonality, thus enhancing the asymmetry of streamflow response to seasonal precipitation change. These findings are essential to improve understanding of the impact of climate change on hydrological processes and thus for formulating adaptive water management strategies for future climate change in China. Plain Language Summary Streamflow sensitivity to climate change is an important indicator for evaluating the effects of climate change on terrestrial water. However, the response of streamflow to climate change across China is unclear, particularly the seasonality of streamflow sensitivity, and the impact factors across different hydroclimate regions remain poorly understood. This study developed a multivariate linear regression model to analyze the spatial pattern and seasonality of streamflow sensitivity to climate change for 425 catchments across China. The results indicate that precipitation is consistently a more important contributor than temperature to streamflow variability. Meanwhile, those catchments with higher elevation, dryer climate, less vegetation cover, and lower infiltration rates are more likely to present seasonally asymmetric precipitation sensitivity.