WE-ACT Water Efficient Allocation in a Central Asian Transboundary River Basin

中亚山区容易受到气候变化后果的影响。由于经济和人口增长以及气候引起的中亚主要跨界河流流域水文系统的变化，就社区、环境以及农业和能源等关键经济部门的水分配作出适当决定越来越具有挑战性。我们行动计划建议在中亚跨界河流流域的两个分流域，即Syr Darya河流域(覆盖吉尔吉斯斯坦和乌兹别克斯坦部分地区)的Naryn和Kara Darya流域建立一个对气候敏感的水资源分配决策支持系统。基于一个创新的水信息系统，全面了解冰川补给的河流流域的水资源供应、需求、足迹和分配情况，WE-ACT将使水管理者能够与可访问和直观的决策支持系统互动，从而在短期和长期缓解社区、经济和环境的水压力。我们的行动将使它们能够使水资源的分配适应气候变化的已知和预期影响，同时鼓励改进政策，以正确设定水价，减少水足迹，提高农业和能源部门的用水效率。该项目的支柱是一个可靠的数据供应链，其基础是实时监测、综合用水需求--和使用建模方法、机器学习和跨界环境下的数据存储。这将与对水政策和优先事项的深入了解相匹配，这些政策和优先事项面临着气候变化、不断增长的需求和对水的依赖的越来越大的压力。将对项目的最终用户(水文气象站、综合模型、水资源分配决策支持系统)进行仔细的测绘、邀请、参与和培训，以便从项目一开始就建立和使用有意义的成果。

Mountainous regions in Central Asia are vulnerable to consequences of climate change. Taking appropriate decisions for allocation of water over communities, the environment and key economic sectors such as agriculture and energy, is increasingly challenging due to economic and population growth as well as climate-induced changes in hydrological regimes in Central Asia’s main transboundary river basins. WE-ACT proposes to establish a climate sensitive Decision Support System for water allocation in two sub-catchments of a transboundary river basin in Central Asia, namely the Naryn and Kara Darya catchments of the Syr Darya river basin (covering parts of Kyrgyzstan and Uzbekistan). Based on an innovative water information system that captures a thorough understanding of water availability, demand, footprint and allocation in a glacier-fed river basin, WE-ACT will enable water managers to interact with an accessible and intuitive DSS to alleviate water stress for communities, the economy and the environment on the short- and longterm. WE-ACT will enable them to adapt the allocation of water resources to the known and expected effects of climate change, while encouraging the improvement of policies to correctly set water tariffs, reduce water footprints and increase water use efficiency in agriculture and energy sectors. The backbone of the project is a reliable data supply chain based on real-time monitoring, integrated water demand-, availability- and use modelling approach, machine-learning, and data storage in a transboundary context. This will be matched with an in-depth understanding of water policies and priorities that face increasing pressures of climate change, growing demand and water dependency. End-users of the project (hydrometeorological stations, integrated models, DSS for water allocation) will be carefully mapped, invited, involved and trained to establish and use meaningful results from the outset of the project.