Liu Zheng, Chao Nengfang, Chen Gang, Zhang Guoqing, Wang Zhengtao, Li Fupeng, Ouyang Guichong
College of Marine Science and Technology, Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, China.
College of Marine Science and Technology, Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, China.
Sci Total Environ. 2024 Sep 20;944:173840. doi: 10.1016/j.scitotenv.2024.173840. Epub 2024 Jun 10.
Long-term, high spatiotemporal resolution of surface water area, water level, and storage changes in the Yangtze River Basin (YRB) has great scientific and practical importance for improving the management of water resources. Here, three distinct area estimations were first derived using the water classification enhancement method, automated water extraction method based on random forest, and the modified normalized difference water index. The optimized area data was determined by comparing against Sentinel-2 with the minimum root mean square error. A new area data was constructed with the optimized area as the primary data, while the remaining datasets were employed to fill in gaps. The elevation-area relationship was used to derive monthly water level. Changes in water storage were calculated by applying the pyramidal frustum formula from surface water area and water level data. Finally, a new comprehensive dataset of the monthly area, level, and storage changes in the 119 lakes and 75 reservoirs across the YRB with area larger than 10 km from 1990 to 2021 were first reconstructed. The spatiotemporal trends of surface water area/level/storage in lakes and reservoirs over 11 sub-basins of the YRB were quantified from 1990 to 2021, as well as before (1990-2003) and after (2003-2021) the construction of the Three Gorges Dam (TGD). During 1990-2021, there was a marked decrease in surface water area/level/storage in most of the YRB sub-basins, which contain 79 % of the lakes and 30 % of the reservoirs. After TGD was constructed, the surface water in lakes decreased by 10 %, while that of reservoirs remained consistent with the pre-construction. The surface water area/level/storage in the lower sub-basins of YRB exhibited a decline to an upward trend before and after the construction of TGD. This study provides a new comprehensive dataset for understanding the dynamic changes of water resource and climate change.
长期以来，长江流域（YRB）地表水面积、水位和蓄水量变化的高时空分辨率对于改善水资源管理具有重要的科学和实践意义。在此，首先使用水分类增强方法、基于随机森林的自动水提取方法和改进的归一化差异水指数得出三种不同的面积估计值。通过与哨兵 - 2 数据进行比较，以最小均方根误差确定优化后的面积数据。以优化后的面积数据为主要数据构建新的面积数据，同时使用其余数据集填补空白。利用高程 - 面积关系得出月水位。通过应用基于地表水面积和水位数据的棱台公式计算蓄水量变化。最后，首次重建了1990年至2021年长江流域119个湖泊和75座面积大于10平方千米的水库的月面积、水位和蓄水量变化的新综合数据集。对长江流域11个子流域内湖泊和水库1990年至2021年以及三峡大坝（TGD）建设前（1990 - 2003年）和建设后（2003 - 2021年）的地表水面积/水位/蓄水量的时空趋势进行了量化。在1990 - 2021年期间，长江流域大部分子流域的地表水面积/水位/蓄水量显著下降，这些子流域包含79%的湖泊和30%的水库。三峡大坝建成后，湖泊地表水减少了10%，而水库地表水与建设前保持一致。长江流域下游子流域的地表水面积/水位/蓄水量在三峡大坝建设前后呈现出先下降后上升的趋势。本研究为理解水资源动态变化和气候变化提供了一个新的综合数据集。
