Nguyen Thu Thuy, Ngo Huu Hao, Guo Wenshan, Nguyen Hong Quan, Luu Chinh, Dang Kinh Bac, Liu Yiwen, Zhang Xinbo
Center for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia.
Center for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia; NTT Institute of Hi-Technology, Nguyen Tat Thanh University, Ho Chi Minh City, Viet Nam; Joint Research Centre for Protective Infrastructure Technology and Environmental Green Bioprocess, School of Environmental and Municipal Engineering, Tianjin Chengjian University, Tianjin 300384, China.
Sci Total Environ. 2020 Oct 1;737:139784. doi: 10.1016/j.scitotenv.2020.139784. Epub 2020 May 29.
Water deficiency due to climate change and the world's population growth increases the demand for the water industry to carry out vulnerability assessments. Although many studies have been done on climate change vulnerability assessment, a specific framework with sufficient indicators for water vulnerability assessment is still lacking. This highlights the urgent need to devise an effective model framework in order to provide water managers and authorities with the level of water exposure, sensitivity, adaptive capacity and water vulnerability to formulate their responses in implementing water management strategies. The present study proposes a new approach for water quantity vulnerability assessment based on remote sensing satellite data and GIS ModelBuilder. The developed approach has three layers: (1) data acquisition mainly from remote sensing datasets and statistical sources; (2) calculation layer based on the integration of GIS-based model and the Intergovernmental Panel on Climate Change's vulnerability assessment framework; and (3) output layer including the indices of exposure, sensitivity, adaptive capacity and water vulnerability and spatial distribution of remote sensing indicators and these indices in provincial and regional scale. In total 27 indicators were incorporated for the case study in Vietnam based on their availability and reliability. Results show that the most water vulnerable is the South Central Coast of the country, followed by the Northwest area. The novel approach is based on reliable and updated spatial-temporal datasets (soil water stress, aridity index, water use efficiency, rain use efficiency and leaf area index), and the incorporation of the GIS-based model. This framework can then be applied effectively for water vulnerability assessment of other regions and countries.
气候变化和世界人口增长导致的水资源短缺增加了水行业进行脆弱性评估的需求。尽管已经开展了许多关于气候变化脆弱性评估的研究，但仍缺乏一个具有足够指标的水脆弱性评估特定框架。这凸显了迫切需要设计一个有效的模型框架，以便为水资源管理者和当局提供水暴露程度、敏感性、适应能力和水脆弱性水平，从而在实施水资源管理战略时制定应对措施。本研究提出了一种基于遥感卫星数据和GIS ModelBuilder的水量脆弱性评估新方法。所开发的方法有三个层次：（1）数据获取主要来自遥感数据集和统计来源；（2）基于GIS模型与政府间气候变化专门委员会脆弱性评估框架相结合的计算层；（3）输出层包括暴露、敏感性、适应能力和水脆弱性指数以及遥感指标和这些指数在省级和区域尺度上的空间分布。基于其可用性和可靠性，越南案例研究总共纳入了27个指标。结果表明，该国水最脆弱的地区是中南部海岸，其次是西北地区。这种新方法基于可靠且更新的时空数据集（土壤水分胁迫、干旱指数、水分利用效率、雨水利用效率和叶面积指数）以及基于GIS的模型。该框架随后可有效应用于其他地区和国家的水脆弱性评估。
