High-temporal-resolution monitoring of reservoir water storage of the Lancang-Mekong River

遥感 航天飞机雷达地形任务 湄公河 合成孔径雷达 数字高程模型 环境科学 蓄水 水文学(农业) 地表水 水资源 流域 地质学 地貌学 地理 地图学 构造盆地 环境工程 生物 岩土工程 入口 生态学
作者
Yiming Wang,Di Long,Xingdong Li
出处
期刊:Remote Sensing of Environment [Elsevier BV]
卷期号:292: 113575-113575 被引量:43
标识
DOI:10.1016/j.rse.2023.113575
摘要

The Lancang-Mekong River (LMR) is an important transboundary river in Southeast Asia shared by China, Myanmar, Laos, Thailand, Cambodia, and Vietnam from upstream to downstream. Construction and operation of dams in the LMR basin has profoundly affected its natural streamflow regime. It is therefore important to monitor changes in reservoir water storage and to quantify the impact of reservoir operation on the redistribution of surface water resources over this basin. Given the difficulty in obtaining in-situ measurements of reservoirs on the LMR, we integrated multisource remote sensing, including satellite altimetry and optical and synthetic aperture radar (SAR) images, to generate weekly water levels and water storages of nine largest reservoirs on the main stem of the LMR from 2017 to 2021. Specifically, partial surface water extent (SWE) of reservoirs was extracted from Sentinel-1 SAR images and digital elevation models (DEMs), using Random Forest algorithms trained by partial SWE derived from Sentinel-2 optical images, showing an overall accuracy higher than 95%. Based on the partial SWE and water level estimates from ICESat-2 and Global Ecosystem Dynamics Investigation (GEDI, International Space Station-based) data, the relationships between water levels and partial SWE were derived to convert partial SWE into water level time series. Furthermore, water storage time series of the nine reservoirs were obtained from water level time series and hypsometric functions derived from SRTM DEMs that were corrected by ICESat-2 data to remove systematic errors. For the Xiaowan Reservoir on the Lancang River, there is close agreement between remote sensing-derived water levels and in-situ water levels in terms of a normalized RMSE lower than 5%. Results indicate that multisource remote sensing has large potential for high-temporal-resolution monitoring of reservoir water levels and water storage. This could more precisely evaluate impacts of cascade reservoirs on the streamflow of the LMR and facilitate drought and flood mitigation for riverine countries.
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