The countries of Central Asia are collectively known as the five "-stans": Uzbekistan, Kyrgyzstan, Turkmenistan, Tajikistan and Kazakhstan. In recent times, the Central Asian region has been affected by the ...The countries of Central Asia are collectively known as the five "-stans": Uzbekistan, Kyrgyzstan, Turkmenistan, Tajikistan and Kazakhstan. In recent times, the Central Asian region has been affected by the shrinkage of the Aral Sea, widespread desertification, soil salinization, biodiversity loss, frequent sand storms, and many other ecological disasters. This paper is a review article based upon the collection, identification and collation of previous studies of environmental changes and regional developments in Central Asia in the past 30 years. Most recent studies have reached a consensus that the temperature rise in Central Asia is occurring faster than the global average. This warming trend will not only result in a higher evaporation in the basin oases, but also to a significant retreat of glaciers in the mountainous areas. Water is the key to sustainable development in the arid and semi-arid regions in Central Asia. The uneven distribution, over consumption, and pollution of water resources in Central Asia have caused severe water supply problems, which have been affecting regional harmony and development for the past 30 years. The widespread and significant land use changes in the 1990 s could be used to improve our understanding of natural variability and human interaction in the region. There has been a positive trend of trans-border cooperation among the Central Asian countries in recent years. International attention has grown and research projects have been initiated to provide water and ecosystem protection in Central Asia. However, the agreements that have been reached might not be able to deliver practical action in time to prevent severe ecological disasters. Water management should be based on hydrographic borders and ministries should be able to make timely decisions without political intervention. Fully integrated management of water resources, land use and industrial development is essential in Central Asia. The ecological crisis should provide sufficient motivation to reach a consensus on unified water management throughout the region.展开更多
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 implementati...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.展开更多
A high degree of uncertainty with regard to soil parameterisation limits the significance of physically-based simulation of distributed flood control measures, which affect the runoff generation process, such as land-...A high degree of uncertainty with regard to soil parameterisation limits the significance of physically-based simulation of distributed flood control measures, which affect the runoff generation process, such as land-use changes or differing soil tillage practices. In this study, the soil measurement data from the hillslope scale at the Scheyern research farm were compared to demonstrate this uncertainty. To account for the spatial variability of soils in the investigation area of Scheyern, different approaches were applied to estimate soil hydraulic properties and saturated hydraulic conductivity, and were compared to field measurements展开更多
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 T...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.展开更多
The process-based water system models have been transitioning from single-functional to integrated multi-objective and multi-functional since the worldwide digital upgrade of urban water system management.The prolifer...The process-based water system models have been transitioning from single-functional to integrated multi-objective and multi-functional since the worldwide digital upgrade of urban water system management.The proliferation of model complexity results in more significant uncertainty and computational requirements.However,conventional model calibration methods are insufficient in dealing with extensive computational time and limited monitoring samples.Here we introduce a novel machine learning system designed to expedite parameter optimization with limited data and boost efficiency in parameter search.MLPS,termed the machine learning parallel system for fast parameter search of integrated process-based models,aims to enhance both the performance and efficiency of the integrated model by ensuring its comprehensiveness,accuracy,and stability.MLPS was constructed upon the concept of model surrogation t algorithm optimization using Ant Colony Optimization(ACO)coupled with Long Short-Term Memory(LSTM).The optimization results of the Integrated sewer network and urban river model demonstrate that the average relative percentage difference of the predicted river pollutant concentrations increases from 1.1 to 6.0,and the average absolute percent bias decreases from 124.3%to 8.8%.The model outputs closely align with the monitoring data,and parameter calibration time is reduced by 89.94%.MLPS enables the efficient optimization of integrated process-based models,facilitating the application of highly precise complex models in environmental management.The design of MLPS also presents valuable insights for optimizing complex models in other fields.展开更多
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences, Pan-Third Pole Environment Study for a Green Silk Road (XDA20060303)the Xinjiang Key Research and Development Program (2016B02017-4)+1 种基金the National Nature Science Foundation of China-United Nations Environment Programme (NSFC-UNEP, 41361140361)the ''High-level Talents Project'' (Y871171) of Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences
文摘The countries of Central Asia are collectively known as the five "-stans": Uzbekistan, Kyrgyzstan, Turkmenistan, Tajikistan and Kazakhstan. In recent times, the Central Asian region has been affected by the shrinkage of the Aral Sea, widespread desertification, soil salinization, biodiversity loss, frequent sand storms, and many other ecological disasters. This paper is a review article based upon the collection, identification and collation of previous studies of environmental changes and regional developments in Central Asia in the past 30 years. Most recent studies have reached a consensus that the temperature rise in Central Asia is occurring faster than the global average. This warming trend will not only result in a higher evaporation in the basin oases, but also to a significant retreat of glaciers in the mountainous areas. Water is the key to sustainable development in the arid and semi-arid regions in Central Asia. The uneven distribution, over consumption, and pollution of water resources in Central Asia have caused severe water supply problems, which have been affecting regional harmony and development for the past 30 years. The widespread and significant land use changes in the 1990 s could be used to improve our understanding of natural variability and human interaction in the region. There has been a positive trend of trans-border cooperation among the Central Asian countries in recent years. International attention has grown and research projects have been initiated to provide water and ecosystem protection in Central Asia. However, the agreements that have been reached might not be able to deliver practical action in time to prevent severe ecological disasters. Water management should be based on hydrographic borders and ministries should be able to make timely decisions without political intervention. Fully integrated management of water resources, land use and industrial development is essential in Central Asia. The ecological crisis should provide sufficient motivation to reach a consensus on unified water management throughout the region.
基金supported by German-Sino bilateral collaboration research project SuMaRiO funded by the German Federal Ministry of Education and Researchthe support of NSFC-UNEP Project (41361140361): Ecological Responses to Climatic Change and Land-cover Change in Arid and Semiarid Central Asia during the Past 500 Years
文摘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.
基金supported by the German Research Foundation (DFG)
文摘A high degree of uncertainty with regard to soil parameterisation limits the significance of physically-based simulation of distributed flood control measures, which affect the runoff generation process, such as land-use changes or differing soil tillage practices. In this study, the soil measurement data from the hillslope scale at the Scheyern research farm were compared to demonstrate this uncertainty. To account for the spatial variability of soils in the investigation area of Scheyern, different approaches were applied to estimate soil hydraulic properties and saturated hydraulic conductivity, and were compared to field measurements
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA20060303)the Fund“Light of West China”Program of Chinese Academy of Sciences(2018-XBQNXZ-B-017)+1 种基金the High-level Talents Project in Xinjiang(Y942171)“One Hundred Person Project of Chinese Academy of Sciences”(Y931201)。
文摘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.
基金supported by the National Key R&D Program of China(2019YFD1100300)the Fellowship of China Postdoctoral Science Foundation(2020M681105)the State Key Laboratory of Urban Water Resource and Environment,Harbin Institute of Technology(No.2021TS23).
文摘The process-based water system models have been transitioning from single-functional to integrated multi-objective and multi-functional since the worldwide digital upgrade of urban water system management.The proliferation of model complexity results in more significant uncertainty and computational requirements.However,conventional model calibration methods are insufficient in dealing with extensive computational time and limited monitoring samples.Here we introduce a novel machine learning system designed to expedite parameter optimization with limited data and boost efficiency in parameter search.MLPS,termed the machine learning parallel system for fast parameter search of integrated process-based models,aims to enhance both the performance and efficiency of the integrated model by ensuring its comprehensiveness,accuracy,and stability.MLPS was constructed upon the concept of model surrogation t algorithm optimization using Ant Colony Optimization(ACO)coupled with Long Short-Term Memory(LSTM).The optimization results of the Integrated sewer network and urban river model demonstrate that the average relative percentage difference of the predicted river pollutant concentrations increases from 1.1 to 6.0,and the average absolute percent bias decreases from 124.3%to 8.8%.The model outputs closely align with the monitoring data,and parameter calibration time is reduced by 89.94%.MLPS enables the efficient optimization of integrated process-based models,facilitating the application of highly precise complex models in environmental management.The design of MLPS also presents valuable insights for optimizing complex models in other fields.
