Combining Optical Remote Sensing, McFLI Discharge Estimation, Global Hydrologic Modeling, and Data Assimilation to Improve Daily Discharge Estimates Across an Entire Large Watershed
Combining Optical Remote Sensing, McFLI Discharge Estimation, Global Hydrologic Modeling, and Data Assimilation to Improve Daily Discharge Estimates Across an Entire Large Watershed
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DOI：
10.1029/2020wr027794
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发表时间：
2021-03-01
影响因子：
5.4
通讯作者：
Pavelsky, Tamlin M.
中科院分区：
文献类型：
--
作者：
Ishitsuka, Yuta;Gleason, Colin J.;Pavelsky, Tamlin M.
遥感作为估算河流流量的一种新的主要信息来源已引起人们的注意，质量守恒流量定律反演（McFLI）方法已成功地仅从光学卫星数据估算了无资料流域的河流流量。然而，McFLI目前有两个主要缺点：（1）现有的光学卫星导致时间和空间稀疏的流量估计和（2）由于所需的假设，McFLI不能保证下游流量的连续性。水文模拟没有缺点，但模型的精度往往是有限的流量观测的缺乏。因此，我们结合联合收割机McFLI和模型在全球适用的数据同化框架。我们建立了一个每日的“未测量”的基线模型，28,998达到密苏里州流域迫使最近公布的全球径流数据，我们不校准。我们使用从12，000个Landsat场景中获得的类似于100万个宽度测量值来估计McFLI的流量，并在403个USGS测量仪验证之前将McFLI同化到模型中。结果表明，同化排放并没有损害已经准确的基线流量，并实现了28%的标准化均方根误差，0.50纳什-萨克利夫效率（NSE），和0.23克林-古普塔效率的中位数改善基线性能差（定义为基线负NSE，225/403达到）。我们最终改善了92%的流量，这些原来不好建模的仪表，即使陆地卫星图像只提供McFLI排放在1.5%的河段和26%的模拟天。我们的研究结果表明，McFLI和国家的最先进的水文模型相结合，可以提高全球无资料流域的流量估计。
Remote sensing has gained attention as a novel source of primary information for estimating river discharge, and the Mass-conserved Flow Law Inversion (McFLI) approach has successfully estimated river discharge in ungauged basins solely from optical satellite data. However, McFLI currently suffers from two major drawbacks: (1) existing optical satellites lead to temporally and spatially sparse discharge estimates and (2) because of the assumptions required, McFLI cannot guarantee downstream flow continuity. Hydrological modeling has neither drawback, yet model accuracy is frequently limited by a lack of discharge observations. We therefore combine McFLI and models in a data assimilation framework applicable globally. We establish a daily "ungauged" baseline model for 28,998 reaches of the Missouri river basin forced by recently published global runoff data, which we do not calibrate. We estimate discharge via McFLI using similar to 1 million width measurements made from 12,000 Landsat scenes and assimilate McFLI into the model before validating at 403 USGS gauges. Results show that assimilated discharges did not impair already accurate baseline flows and achieved median improvements of 28% normalized root mean square error, 0.50 Nash-Sutcliffe efficiency (NSE), and 0.23 Kling-Gupta efficiency where baseline performance was poor (defined as baseline negative NSE, 225/403 reaches). We ultimately improved flows at 92% of these originally poorly modeled gauges, even though Landsat images only provide McFLI discharges at 1.5% of reaches and 26% of simulated days. Our results suggest that the combination of McFLI and state-of-the-art hydrology models can improve flow estimations in ungauged basins globally.