Vegetation plays an important role in regulating terrestrial water and energy fluxes. Recent research reported that China is the largest contributor to the global net increase in green foliage. Such large‐scale vegetation greenness change would inevitably affect the water budget across China. In this study, we evaluated the effects of vegetation greenness change on evapotranspiration (ET) and water yield in China from 1982 to 2014 with a process‐based conceptual hydrologic model and a detrending experiment of vegetation index. On the national scale, the change in vegetation greenness was dominated by an increasing trend (greening), which has led to an increase in ET and a decline in water yield from 1982 to 2014. The contribution of vegetation greenness change to the trend of annual ET and water yield is 0.25 mm/yr and −0.16 mm/yr, respectively. Spatially, the relative change in ET and runoff caused by vegetation greenness change is significantly greater in water‐limited areas than in energy‐limited areas. Vegetation greening exacerbated water scarcity in water‐limited areas. Therefore, the ecological restoration policy should fully consider the negative effect of vegetation greening on water yield in water‐limited regions and balance the conflicting water demands between the ecosystem and human beings.

Potential evapotranspiration (PET), which determines the upper limit of actual evapotranspiration (AET), is a necessary input in monthly hydrological models. In this study, the sensitivities of monthly hydrological models to different PET inputs are investigated in 37 catchments under different climatic conditions. Four types of PET estimation methods (i.e., Penman–Monteith, Hargreaves–Samani, Jensen–Haise, and Hamon) give significantly different PET values in the 37 catchments. However, similar runoff simulations are produced based on different PET inputs in both nonhumid and humid regions. It is found that parameter calibration of the hydrological model can eliminate the influences of different PET inputs on runoff simulations in both nonhumid and humid regions. However, the influences of parameter calibration on the simulated water balance components, including AET and water storage change (WSC), are different in nonhumid and humid regions. In nonhumid regions, simulated runoff, AET, and WSC are similar using different PET inputs. In humid regions, simulated AET and WSC using different PET inputs are significantly different, and simulated runoff and the sums of AET and WSC are similar to each other. It is suggested that the choice of PET inputs for monthly hydrological models be based on the selected region and the relevant hydrological practices. In runoff modeling, different PET inputs can produce similar runoff simulations in both nonhumid and humid regions. However, when estimating AET and WSC in humid regions, different PET inputs will result in significantly different AET and WSC simulations, which should be noted by model users.

The sparse rain gauge networks over the Tibetan Plateau (TP) cause challenges for hydrological studies and applications. Satellite-based precipitation datasets have the potential to overcome the issues of data scarcity caused by sparse rain gauges. However, large uncertainties usually exist in these precipitation datasets, particularly in complex orographic areas, such as the TP. The accuracy of these precipitation products needs to be evaluated before being practically applied. In this study, five (quasi-)global satellite precipitation products were evaluated in two gauge-sparse river basins on the TP during the period 1998–2012; the evaluated products are CHIRPS, CMORPH, PERSIANN-CDR, TMPA 3B42, and MSWEP. The five precipitation products were first intercompared with each other to identify their consistency in depicting the spatial–temporal distribution of precipitation. Then, the accuracy of these products was validated against precipitation observations from 21 rain gauges using a point-to-pixel method. We also investigated the streamflow simulation capacity of these products via a distributed hydrological model. The results indicated that these precipitation products have similar spatial patterns but significantly different precipitation estimates. A point-to-pixel validation indicated that all products cannot efficiently reproduce the daily precipitation observations, with the median Kling–Gupta efficiency (KGE) in the range of 0.10–0.26. Among the five products, MSWEP has the best consistency with the gauge observations (with a median KGE = 0.26), which is thus recommended as the preferred choice for applications among the five satellite precipitation products. However, as model forcing data, all the precipitation products showed a comparable capacity of streamflow simulations and were all able to accurately reproduce the observed streamflow records. The values of the KGE obtained from these precipitation products exceed 0.83 in the upper Yangtze River (UYA) basin and 0.84 in the upper Yellow River (UYE) basin. Thus, evaluation of precipitation products only focusing on the accuracy of streamflow simulations is less meaningful, which will mask the differences between these products. A further attribution analysis indicated that the influences of the different precipitation inputs on the streamflow simulations were largely offset by the parameter calibration, leading to significantly different evaporation and water storage estimates. Therefore, an efficient hydrological evaluation for precipitation products should focus on both streamflow simulations and the simulations of other hydrological variables, such as evaporation and soil moisture.