Snow depth is a general input variable in many models of agriculture,hydrology,climate and ecology.This study makes use of observational data of snow depth and explanatory variables to compare the accuracy and effect of geographically weighted regression kriging(GWRK)and regression kriging(RK)in a spatial interpolation of regional snow depth.The auxiliary variables are analyzed using correlation coefficients and the variance inflation factor(VIF).Three variables,Height,topographic ruggedness index(TRI),and land surface temperature(LST),are used as explanatory variables to establish a regression model for snow depth.The estimated spatial distribution of snow depth in the Bayanbulak Basin of the Tianshan Mountains in China with a spatial resolution of 1 km is obtained.The results indicate that 1)the result of GWRK's accuracy is slightly higher than that of RK(R^2=0.55 vs.R^2=0.50,RMSE(root mean square error)=0.102 m vs.RMSE=0.077 m);2)for the subareas,GWRK and RK exhibit similar estimation results of snow depth.Areas in the Bayanbulak Basin with a snow depth greater than 0.15m are mainly distributed in an elevation range of 2632.00–3269.00 m and the snow in this area comprises 45.00–46.00% of the total amount of snow in this basin.However,the GWRK resulted in more detailed information on snow depth distribution than the RK.The final conclusion is that GWRK is better suited for estimating regional snow depth distribution.
LIU YangLI Lan-haiCHEN XiYANG Jin-MingHAO Jian-Sheng
Hydrological models are often linked with other models in cognate sciences to understand the interactions among climate, earth, water, ecosystem, and human society. This paper presents the development and implementation of a decision support system（DSS） that links the outputs of hydrological models with real-time decision making on social-economic assessments and land use management. Discharge and glacier geometry changes were simulated with hydrological model, water availability in semiarid environments. Irrigation and ecological water were simulated by a new commercial software MIKE HYDRO. Groundwater was simulated by MODFLOW. All the outputs of theses hydrological models were taken as inputs into the DSS in three types of links： regression equations, stationary data inputs, or dynamic data inputs as the models running parallel in the simulation periods. The DSS integrates the hydrological data, geographic data, social and economic statistical data, and establishes the relationships with equations, conditional statements and fuzzy logics. The programming is realized in C＋＋. The DSS has four remarkable features：（1） editable land use maps to assist decision-making;（2） conjunctive use of surface and groundwater resources;（3） interactions among water, earth, ecosystem, and humans; and（4） links with hydrological models. The overall goal of the DSS is to combine the outputs of scientific models, knowledge of experts, and perspectives of stakeholders, into a computer-based system, which allows sustainability impact assessment within regional planning; and to understand ecosystem services and integrate them into land and water management.
YU YangCHEN XiPhilipp HUTTNERMarie HINNENTHALAndreas BRIEDENSUN LingxiaoMarkus DISSE
Grazing is a main human activity in the grasslands of Xinjiang, China. It is vital to identify the effects of grazing on the sustainable utilization of local grasslands. However, the effects of grazing on net primary productivity （NPP）, evapotranspiration （ET） and water use efficiency （WUE） in this region remain unclear. Using the spatial Biome-BGC grazing model, we explored the effects of grazing on NPP, ET and WUE across the different regions and grassland types in Xinjiang during 1979-2012. NPP, ET and WUE under the grazed scenario were generally lower than those under the ungrazed scenario, and the differences showed increasing trends over time. The decreases in NPP, ET and WUE varied significantly among the regions and grassland types. NPP decreased as follows： among the regions, Northern Xinjiang （16.60 g C/（m2·a））, Tianshan Mountains （15.94 g C/（m2·a）） and Southern Xinjiang （-3.54 g C/（m2·a））; and among the grassland types, typical grasslands （25.70 g C/（m2·a））, swamp meadows （25.26 g C/（m2·a））, mid-mountain meadows （23.39 g C/（m2·a））, alpine meadows （6.33 g C/（m2·a））, desert grasslands （5.82 g C/（m2·a）） and saline meadows （2.90 g C/（me.a））. ET decreased as follows： among the regions, Tianshan Mountains （28.95 mm/a）, Northern Xinjiang （8.11 mm/a） and Southern Xinjiang （7.57 mm/a）; and among the grassland types, mid-mountain meadows （29.30 mm/a）, swamp meadows （25.07 mm·a）, typical grasslands （24.56 mm/a）, alpine meadows （20.69 mm/a）, desert grasslands （11.06 mm/a） and saline meadows （3.44 mm/a）. WUE decreased as follows： among the regions, Northern Xinjiang （0.053 g C/kg H2O）, Tianshan Mountains （0.034 g C/kg H2O） and Southern Xinjiang （0.012 g C/kg H2O）; and among the grassland types, typical grasslands （0.0609 g C/kg H2O）, swamp meadows （0.0548 g C/kg H2O）, mid-mountain meadows （0.0501 g C/kg H2O）, desert grasslands （0.0172 g C/kg H2O）, alpine
