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Hydrological cycle changes that occur due to a changing environment is a hot topic in the field of hydrological science. It is of great practical significance to study the response mechanism of hydrological process change for future water resources planning and management. In this study, the effects of climate and watershed characteristic change on the streamflow in a typical tributary of the Yellow River (the Fen River watershed) are studied based on the Budyko hypothesis. The results show that: the sensitivity coefficients of streamflow to precipitation, potential evapotranspiration, and the watershed characteristic coefficient were 0.1809, −0.0551, and −27.0882, respectively. This meant that a 1 mm decrease in the precipitation would induce a 0.1809 mm decrease in the streamflow. Additionally, a 1 mm decrease in the potential evapotranspiration would induce a 0.0551 mm increase in the streamflow, and an increase of 1 in the watershed characteristic coefficient would induce a 27.0882 mm decrease in the streamflow. The streamflow of the Fen River watershed showed a significant decreasing trend during the reference period (1951–1977). In addition, the streamflow of the change period (1978–2010) decreased 26.87 mm; and this was primarily caused by watershed characteristic change which accounted for 92.27%, while climate change only accounted for 6.50%.

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In this study, variation characteristics of hydrometeorological factors were explored based on observed time-series data between 1957 and 2010 in four subregions of the Yellow River Basin. For each region, precipitation-streamflow models at annual and flood-season scales were developed to quantify the impact of annual precipitation, temperature, percentage of flood-season precipitation, and anthropogenic interference. The sensitivities of annual streamflow to these three climatic factors were then calculated using a modified elasticity coefficient model. The results presented the following:(1) Annual streamflow exhibited a negative trend in all regions; (2) The reduction of annual streamflow was mainly caused by a precipitation decrease and temperature increase for all regions before 2000, whereas the contribution of anthropogenic interference increased significantly-more than 45%, except for Tang-Tou region after 2000. The percentage of flood-season precipitation variation can also be responsible for annual streamflow reduction with a range of 7.36% (Tang-Tou) to 21.88% (Source); (3) Annual streamflow was more sensitive to annual precipitation than temperature in the humid region, and the opposite situation was observed in the arid region. The sensitivities to intra-annual climate variation increased after 2000 for all regions, and the increase was more significant in Tou-Long and Long-Hua regions.importance for a better understanding of the hydrologic mechanisms, which is beneficial for planning suitable adaptation strategies and water management.There are various methods to separate the impacts of climate change and anthropogenic interference on streamflow, mainly including catchment experiments, hydrological models, and statistical methods [9]. Catchment experiments are the most rigorous empirical research design for estimating the effects of land use on aquatic systems [10], but they can be influenced by the variation in experimental conditions and the presentation of results [11]. Most relevant studies indicate that catchment streamflow decreased significantly after afforestation and increased after deforestation [10,12,13]. Hydrological models, both distributed and lumped, have been widely used [7,[14][15][16]. Hu et al. applied the water and energy budget-based distributed hydrological model (WEB-DHM) to diagnose and quantify climate and human impacts on streamflow change [17]. Hundecha et al. applied a conceptual rainfall-runoff model to 95 catchments in the Rhine basin to model the effect of land use change on runoff [18]. Statistical methods such as streamflow elasticity have also been used in regions specifically with available long-term climate and hydrologic data [9,19,20]. Tian et al. used regression analysis to illustrate runoff decline via comparison of precipitation-runoff correlation for the period prior to and after sharp runoff decline [21].The semiarid and arid Yellow River Basin (YRB) is the main source of surface water in the northwest and northern part of China. The annual streamflow is a...

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Exploring the variations in the water use efficiency (WUE) is helpful in gaining an in-depth understanding of the regional carbon and water cycles on the Chinese Loess Plateau (CLP). Here, we employed the spatial variations in the WUE and the quantitative contributions of the influencing factors, including the precipitation (P), temperature (Temp), vapor pressure deficit (VPD), sunshine duration (SD), and leaf area index (LAI), with the drought index varying over the last two decades. Results showed that the multiyear average WUE decreased significantly as the drought index increased for all of the vegetation types. Per-pixel interannual variability of WUE trend was 0.024 gC·m−2·mm−1·yr−1. As the drought index increased, the WUE initially increased and then decreased for the forests, grassland, and shrubland, and their peaks occurred at drought index values of 2.60–3.10. Among the influencing factors, the WUE was predominantly controlled by the LAI, with an impact and relative contribution of 0.014 gC·m−2·mm−1·yr−1 and 58.3%, respectively. The P and SD contributed the least to the trend in WUE, and impact and relative contribution of both were 0.001 gC·m−2·mm−1·yr−1 and 4.17%. Our study also demonstrated that the LAI was the dominant factor affecting the WUE trends for grassland and the Yan River due to the structural parameters and geographical location. In addition, the impact and relative contribution of the residual factors on the WUE trend were 0.004 gC·m−2·mm−1·yr−1 and 16.7%. Our findings suggested that comprehensive effects such as micro-geomorphic changes and nitrogen deposition could not be ignored except for vegetation and climate change. This study will clarify the spatial and temporal evolution of WUE and its influence mechanism.