Understanding the intra-annual variability of streamflow by incorporating terrestrial water storage from GRACE into the Budyko framework in the Qinba Mountains

• An extended monthly Budyko model was developed by incorporating TWS from GRACE. • The performance of the extended Budyko model was improved compared to the original Budyko model. • The contributors of intra-annual streamflow variability are mainly P (50%), TWSC (11%) and their combined action (-21%). • The regulation of intra-annual TWS on streamflow was analyzed. Streamflow from forested mountain watersheds is critical to aquatic ecosystems and social development in watersheds. However, understanding the intra-annual variability of streamflow is limited by the lack of observation of terrestrial water storage (TWS) in large-scale watersheds. This study developed a monthly Budyko framework incorporating TWS from the Gravity Recovery and Climate Experiment (GRACE). The extended Budyko framework was applied using four classic Budyko equations in the Qinba Mountains. The results showed that the extended Budyko framework could competently represent the relationship between monthly water supply and demand, with better performance than the original Budyko framework. Based on the extended Budyko framework, this study further quantified the contributors of streamflow variability using the variance decomposition method. The dominant contributor to intra-annual streamflow variability was precipitation (50%), followed by TWS (11%) and their covariance (-21%) in this region. Specifically, precipitation played a dominant role on streamflow variability in summer and autumn, while evapotranspiration and TWS significantly impacted streamflow in spring and winter, respectively. Furthermore, the hydrologic effects of rainfall intensity and vegetation were investigated to explain streamflow variability. As the rainfall intensity decreases, more precipitation is partitioned into evapotranspiration and TWS, while the increase of rainfall intensity leads to the partitioning of precipitation into streamflow. Similarly, monthly vegetation promotes the partitioning of precipitation into TWS, while inhibiting the partitioning of precipitation into streamflow. The opposite effect of vegetation on streamflow and TWS is weakened due to the neglect of TWS at an annual timescale, which may lead to an overestimation of the effect of annual vegetation on streamflow. The results have implications for improving the performance of the Budyko framework to reveal the relationship between monthly water supply and demand and understanding streamflow variability at an intra-annual timescale.