Model based decision support system for land use changes and socio-economic assessments

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.

The study of artificial intelligence for predicting land use changes in an arid ecosystem

During the 21 st century,artificial intelligence methods have been broadly applied in geosciences to simulate complex dynamic ecosystems,but the use of artificial intelligence(AI)methods to reproduce land-use/cover change(LUCC)in arid ecosystems remains rare.This paper presents a hybrid modeling approach to understand the complexity in LUCC.Fuzzy logic,equation-based systems,and expert systems are combined to predict LUCC as determined by water resources and other factors.The driving factors of LUCC in this study include climate change,ecological flooding,groundwater conditions,and human activities.The increase of natural flooding was found to be effective in preventing vegetation degradation.LUCCs are sensitive under different climate projections of RCP2.6,RCP4.5,and RCP8.5.Simulation results indicate that the increase of precipitation is not able to compensate for the additional evaporation losses resulting from temperature increases.The results indicate that grassland,shrub,and riparian forest regions will shrink in this study area.The change in grasslands has a strong negative correlation with the change in groundwater salinity,whereas forest change had a strong positive correlation with ecological flooding.The application of artificial intelligence to study LUCC can guide land management policies and make predictions regarding land degradation.

中亚气候变化:中德合作研究成果

Central Asia(CA)occupies the hinterland of the Eurasian continent,containing the countries of Uzbekistan,Kyrgyzstan,Turkmenistan,Tajikistan,and Kazakhstan[1,2].Being isolated by the Pamir Mountains in Tajikistan,the Tibetan Plateau and the Tian Shan Mountains on the border between China and Kyrgyzstan.