Search

The generality of the Christiansen model is verified using potential evaporation data as reference for the unit model developed in stella. The spatial model in SME is calibrated with data from six hydrological and meteorological stations around the study area. The results show that the observed and simulated data have a good fit, in which the revised Nash-Sutcliffe efficiency (NSE), balance error ( B), and explained variance (EV) are 0.56, −0.2, and 0.55, respectively. The best fit is seen in autumn and winter; for spring, the actual values are generally higher than in simulated observations. The spatial model can simulate the majority of summer peaks, but some are not simulated well. Running the model under different spatial resolutions shows a certain degree of stability, with the revised NSE ranging from 0.29 to 0.50. As spatial resolution increases, the simulation NSE values behave non-linearly. With the other parameters unchanged the impact of human activities, which is represented by climate change scenarios on the hydrological process, is analyzed. Assuming constant temperature, precipitation has a greater influence on runoff than on evaporation; while when precipitation is constant, evaporation is influenced more by temperature. Although the model can validate the effect of climate change on runoff and evapotranspiration, it is not enough to describe the complex relationship between runoff, evaporation, temperature, and precipitation accurately, because the description of the water cycle process is simplified compared with a highly specialized hydrological model. The water and heat coupling process is not included in the soil freezing and thawing process; the source code of SME needs to be deeply understood to improve the algorithms of interpolation and conflux. More importantly, the current work does not consider the impact of human activities endogenously. The main purpose of this paper is to build a better basis for developing a meaningful integrated ecological economic model in the Heihe River Basin to solve its ecological and economic problems. [ABSTRACT FROM AUTHOR]

Summary: Miyun Reservoir is the main raw water source for Beijing’s domestic water supply. Flow discharge to Miyun Reservoir decreased drastically over a 50-year period, from 1956 to 2005, and had seriously affected Beijing’s water supply. Climate variability and human activity had been identified as the two main reasons for the decrease in flow. Here, climate variability refers to changes in precipitation and temperature. Impact of human activity includes direct withdrawal of water (referred to as “direct abstraction” in this paper) from the river (primary) or groundwater and indirect impact due to man-made changes in land use and vegetation in the upstream of the reservoir. According to the historical record, “direct abstraction” from the upstream of the reservoir has increased significantly since 1984. The study period was split into two sub-periods, from 1956 to 1983 and from 1984 to 2005. Based on the historical record, annual runoff in the upstream catchment of Miyun Reservoir (i.e., inflow into the reservoir) had decreased from 90.3mm to 41.8mm for the two sub-periods, a decrease of 48.5mm. Over the same period, average annual “direct abstraction” increased from 2.2mm to 13.4mm, an increase of 11.2mm. The latter accounted for 23% (11.2mm/48.5mm) of the decrease in inflow into the reservoir. This study utilised: (1) a distributed hydrological model (geomorphology-based hydrological model, or GBHM) and (2) a climate elasticity model to conduct a quantitative assessment of the impact of climate variability and the indirect impact of human activity on the inflow into the reservoir. Simulation results of GBHM and the climate elasticity model showed that climate impact was accountable for about 55% and 51% of the decrease in reservoir inflow, respectively. The indirect impact of human activity (mainly man-made land use and vegetation changes) accounted for 18% of the decrease in reservoir inflow. [Copyright &y& Elsevier]

As the two largest rivers in China, the Yangtze and Yellow Rivers are of great importance for water cycle and hydrological processes over the country, which have been significantly affected by climate change. In this study, by assessing the suitability of multiple GCMs (General Circulation Models) recommended by IPCC (Intergovernmental Panel on Climate Change), SDSM (Statistical Downscaling Model) and ASD (Automated Statistical Downscaling) were used to generate future climate change scenarios, which were used to drive VIC (Variable Infiltration Capacity) and SWAT (Soil and Water Assessment Tool) models to quantify climate change impacts in the Yangtze River Delta region and upper reaches of the Yellow River basin, respectively. Results showed that suitability assessment method adopted in this study coupled with statistical downscaling could effectively reduce uncertainties of GCMs. Compared with the baseline period (1961-1990), projected annual runoff in future periods (2046-2065 and 2081-2100) would decreased by 9.5% and 3% in the Yangtze River Delta region. While annual runoff in the upper Yellow River basin during future two periods would decrease by 16.9% and 22.2%. The results are of great significance to future water resources management and sustainable development under climate change in major river basins of China. [ABSTRACT FROM AUTHOR]