Inversion of river discharge from remotely sensed river widths: A critical assessment at three-thousand global river gauges
Inversion of river discharge from remotely sensed river widths: A critical assessment at three-thousand global river gauges
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DOI：
10.1016/j.rse.2023.113489
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发表时间：
2023-03
影响因子：
13.5
通讯作者：
P. Lin;D. Feng;C. Gleason;M. Pan;C. Brinkerhoff;X. Yang;H. Beck;Renato Prata de Moraes Frasson
中科院分区：
文献类型：
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作者：
P. Lin;D. Feng;C. Gleason;M. Pan;C. Brinkerhoff;X. Yang;H. Beck;Renato Prata de Moraes Frasson
从卫星获取的河流水力变量（例如，宽度、高度和坡度）是排放遥感（RSQ）团体的首要目标。许多过去的研究已经开发和相互比较了不同的RSQ算法，以证明其可行性，但相对较少的集中在评估RSQ算法是如何适应全球范围内的河流。随着计算能力、传感器的进步以及地表水和海洋地形（SWOT）卫星使命的发射，该社区现在准备扩展到全球范围，因此应优先考虑更广泛的RSQ准确性地理视图，以实现“更好的普遍性”，而不是“在有限的地方实现更高的准确性”。为了缩小这一差距，我们从全球>3 K河流河段的>350 K陆地卫星场景中提取了多时相河流宽度，并使用贝叶斯AMHG-Manning（BAM）算法和地貌增强变体（geoBAM）来估计流量。我们使用这个框架来演示如何应用“现成的”RSQ算法，并在没有方法干预的情况下在全球范围内对其进行测试，并回答：它是否符合其承诺？我们的每日流量反演（1984-2019）显示，BAM的27%和geoBAM的39%的Kling-Gupta效率（KGE）为正，在提供更丰富的流量季节性和月变化先验后，这一百分比增加到46-65%，共计1400-2000次成功反演。探索性分析表明，反演是最敏感的通道地貌参数波段气候干旱，最佳条件是高b，潮湿的环境，以及中等宽度的变化，叶面积指数（LAI），和河流宽度。虽然特定于BAM/geoBAM，但将这些因素限制在其最佳范围内，导致>600个量规的中位数KGE为0.33，这突出了全球RSQ的前景。通过分析不同先验信息量下的结果，进一步讨论了RS/先验的最优配置。总的来说，我们的BAM/geoBAM的关键评估揭示了一个成功的全球实施现有的算法，SWOT将改善。我们建议优先考虑对其他RSQ进行类似的大规模评估，以确定新出现的挑战，因为我们进入了一个具有从太空监测全球河流能力的新时代。
Accurately estimating river discharge from satellite-derived river hydraulic variables (e.g., width, height, and slope) is the overarching goal of the remote sensing of discharge (RSQ) community. Numerous past studies have developed and intercompared different RSQ algorithms to demonstrate their feasibility, yet relatively few have focused on assessing how the RSQ algorithms are adapted to a wide range of rivers globally. As the community is now ready to expand to the global scale given advances in computing power, sensors, and the launch of the Surface Water and Ocean Topography (SWOT) satellite mission, a much broader geographic view of RSQ accuracy should be prioritized toward “better generalizability” instead of “higher accuracy at limited places”. To help close this gap, we extracted multi-temporal river widths from >350 K Landsat scenes at >3 K river reaches globally, and used them to estimate discharge using the Bayesian AMHG-Manning (BAM) algorithm and the geomorphologically-enhanced variant (geoBAM). We use this framework to demonstrate how to apply an ‘off the shelf’ RSQ algorithm and test it globally without methodological intervention and answer: does it live up to its promise? Our daily discharge inversions (1984–2019) showed positive Kling-Gupta Efficiency (KGE) at 27% of the gauges for BAM and 39% for geoBAM, and this percentage increased to 46–65% after feeding richer priors on flow seasonality and monthly variability, amounting to 1400–2000 successful inversions. Exploratory analyses showed that the inversion is the most sensitive to a channel geomorphological parameterband climate aridity, where the optimal conditions are high-b, humid environments, as well as moderate width variability, leaf area index (LAI), and river width. Although specific to BAM/geoBAM, constraining the factors to their optimal ranges led to a median KGE of 0.33 for >600 gauges, which highlights the promising potential for global RSQ. We further discussed the optimal configuration of the RS/priors by analyzing results derived from different information content in priors. Overall, our critical assessment of BAM/geoBAM reveals a successful global implementation of an existing algorithm that SWOT will improve. We suggest similar large-scale assessments for other RSQs be prioritized to identify the emerging challenges as we move into a new era with global river monitoring capability from space.