Streamflow Prediction in Highly Regulated, Transboundary Watersheds Using Multi-Basin Modeling and Remote Sensing Imagery.
Streamflow Prediction in Highly Regulated, Transboundary Watersheds Using Multi-Basin Modeling and Remote Sensing Imagery.
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利用多流域模型和遥感图像预测高度调节的跨界流域的径流。
DOI：
10.1029/2021wr031191
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发表时间：
2022-03
影响因子：
5.4
通讯作者：
Hwang, Euiho
中科院分区：
文献类型：
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作者：
Du, Tien L. T.;Lee, Hyongki;Bui, Duong D.;Graham, L. Phil;Darby, Stephen D.;Pechlivanidis, Ilias G.;Leyland, Julian;Biswas, Nishan K.;Choi, Gyewoon;Batelaan, Okke;Bui, Thao T. P.;Do, Son K.;Tran, Tinh, V;Hoa Thi Nguyen;Hwang, Euiho
关键词：
尽管遥感在监测水库运行方面具有潜力，但很少有研究调查了在不同空间和时间尺度上可以推断水库释放的程度。通过对大湄公河地区的21座水库进行评估，发现遥感图像可用于估算年内和多年水库的日储水量（相关系数[CC]≥0.9，标准化均方根误差[NRMSE]≤31%），但不适用于河流水库（CC < 0.4, 40%≤NRMSE≤270%）。考虑到全球和当地数据库之间水库数量的巨大差距，所提出的框架可以改善全球水库数据库中现有水库的表示，从而改善人类对水文模型的影响。研究发现，在多流域模型中采用水库综合调度方案可以克服遥感的局限性，改善未测量级联水库系统的流量预测，而以前的建模方法是不成功的。因此，对所有类型水库（CC中值= 0.65,NRMSE = 8%， Kling - Gupta效率[KGE] = 0.55）和下游水水站（CC中值= 0.94,NRMSE = 8%, KGE = 0.81）的日调节流量进行了有效的预测。这些发现对于帮助理解水库和水坝对河流流量的影响以及在数据稀疏的河流流域制定更有用的适应极端事件的措施是有价值的。卫星图像可用于估算年内和多年水库的日库存量，但不适用于径流型水库。所提出的框架可改善全球水库数据库中当地水坝的代表性和水文模型中的人类影响，可通过多流域模型和卫星图像的综合水库运行方案合理地模拟每日调节的流量
Despite the potential of remote sensing for monitoring reservoir operation, few studies have investigated the extent to which reservoir releases can be inferred across different spatial and temporal scales. Through evaluating 21 reservoirs in the highly regulated Greater Mekong region, remote sensing imagery was found to be useful in estimating daily storage volumes for within‐year and over‐year reservoirs (correlation coefficients [CC] ≥ 0.9, normalized root mean squared error [NRMSE] ≤ 31%), but not for run‐of‐river reservoirs (CC < 0.4, 40% ≤ NRMSE ≤ 270%). Given a large gap in the number of reservoirs between global and local databases, the proposed framework can improve representation of existing reservoirs in the global reservoir database and thus human impacts in hydrological models. Adopting an Integrated Reservoir Operation Scheme within a multi‐basin model was found to overcome the limitations of remote sensing and improve streamflow prediction at ungauged cascade reservoir systems where previous modeling approaches were unsuccessful. As a result, daily regulated streamflow was predicted competently across all types of reservoirs (median values of CC = 0.65, NRMSE = 8%, and Kling‐Gupta efficiency [KGE] = 0.55) and downstream hydrological stations (median values of CC = 0.94, NRMSE = 8%, and KGE = 0.81). The findings are valuable for helping to understand the impacts of reservoirs and dams on streamflow and for developing more useful adaptation measures to extreme events in data sparse river basins. Satellite images are useful to estimate daily storage volumes for within‐year, over‐year reservoirs, but not for run‐of‐river reservoirs The proposed framework can improve representation of local dams in the global reservoir databases and human impacts in hydrological models Daily regulated streamflow can be reasonably modeled by an integrated reservoir operation scheme of a multi‐basin model and satellite images