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Partitioning climate and human contributions to changes in mean annual streamflow based on the Budyko complementary relationship in the Loess Plateau, China

Wang, Feiyu, Duan, Keqin, Fu, Shuyi, Gou, Fen, Liang, Wei, Yan, Jianwu, Zhang, Weibin
The Science of the total environment 2019 v.665 pp. 579-590
anthropogenic activities, climate, climate change, decision making, evapotranspiration, hydrologic cycle, spatial variation, stream flow, temporal variation, water resources, watersheds, China
Reliable attribution of changes in streamflow is fundamental to our understanding of the hydrological cycle and is needed to enable decision makers to manage water resources in a sustainable way. Here, we used a new attribution method based on the Budyko framework (complementary method) to quantify the contributions of climate change and human activities to the changes in annual streamflow in 22 catchments on China's Loess Plateau during the past three decades. Our results showed that after the Grain-for-Green (GFG) project, the annual streamflow decreased by 36% on average (3–72%), with reductions being more intense in northern catchments. The sensitivity of streamflow to precipitation and potential evapotranspiration also decreased, with a mean rate of −0.7 mm yr−1/mm yr−1 and −0.2 mm yr−1/mm yr−1, respectively. Using the upper and lower bounds of the human effects on streamflow from the complementary method as a reference, we found that these effects at half of the stations were under- or over-estimated by the total differential method. The contribution analysis from the complementary method showed that although human activities decreased streamflow by 26% (or 54% as a relative value) on average, the contribution of potential evapotranspiration alone to the decrease in streamflow was 9% (42%), highlighting the important role of increasing atmospheric moisture demand in the water cycle. In addition, the 5-year incremental analysis showed that the impacts of climate and human activities on streamflow had strong spatiotemporal variability.