The Chaobai River basin in northern China consists of two major tributaries, the Chao River and Bai River. Monthly observations of precipitation, streamflow, and panevaporation data are available for 35 years (1961–1966 and 1973–2001). Using the annual time series of the observed streamflow, one break point at 1979 is detected and is adopted to divide the data set into two study periods, the “before” and “after” periods marking the onset of significant anthropogenic alteration of the flow (reservoirs and silt retention dams, five times increase in population) and significant changes in land use (conversion to terraced fields versus sloping fields). The distributed time‐variant gain model (DTVGM) was used to evaluate the water resources of the area. Furthermore, the Bayesian method used by Engeland et al. (2005) was used in this paper to evaluate two uncertainty sources (i.e., the model parameter and model structure) and for assessing the DTVGM's performance over the Chaobai River basin. Comparing the annual precipitation means over 13 years (1961–1966 and 1973–1979), the means of the second period (1980–2001) decreased by 5.4% and 4.9% in the Chao River and Bai River basins, respectively. However, the related annual runoff decreased by 40.3% and 52.8%, respectively, a much greater decline than exhibited by precipitation. Through the monthly model simulation and the fixing‐changing method, it is determined that decreases in runoff between the two periods can be attributed to 35% (31%) from climate variations and 68% (70%) from human activities in the Chao River (Bai River). Thus, human impact exerts a dominant influence upon runoff decline in the Chaobai River basin compared to climate. This study enhances our understanding of the relative roles of climate variations and human activities on runoff.

In order to make the prediction of land surface heat fluxes more robust, two improvements were made to an operational two-layer model proposed previously by Zhang. These improvements are: 1) a surface energy balance method is used to determine the theoretical boundary lines (namely ‘true wet/cool edge’ and ‘true dry/warm edge’ in the trapezoid) in the scatter plot for the surface temperature versus the fractional vegetation cover in mixed pixels; 2) a new assumption that the slope of the T_m – f curves is mainly controlled by soil water content is introduced. The variables required by the improved method include near surface vapor pressure, air temperature, surface resistance, aerodynamic resistance, fractional vegetation cover, surface temperature and net radiation. The model predictions from the improved model were assessed in this study by in situ measurements, which show that the total latent heat flux from the soil and vegetation are in close agreement with the in situ measurement with an RMSE (Root Mean Square Error) ranging from 30 w/m²~50 w/m²,which is consistent with the site scale measurement of latent heat flux. Because soil evaporation and vegetation transpiration are not measured separately from the field site, in situ measured CO₂ flux is used to examine the modeled λE_veg. Similar trends of seasonal variations of vegetation were found for the canopy transpiration retrievals and in situ CO₂ flux measurements. The above differences are mainly caused by 1) the scale disparity between the field measurement and the MODIS observation; 2) the non-closure problem of the surface energy balance from the surface fluxes observations themselves. The improved method was successfully used to predict the component surface heat fluxes from the soil and vegetation and it provides a promising approach to study the canopy transpiration and the soil evaporation quantitatively during the rapid growing season of winter wheat in northern China.