Using river hypsometry to improve remote sensing of river discharge

IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES Remote Sensing of Environment Pub Date : 2024-10-07 DOI:10.1016/j.rse.2024.114455
Michael Durand , Chunli Dai , Joachim Moortgat , Bidhyananda Yadav , Renato Prata de Moraes Frasson , Ziwei Li , Kylie Wadkwoski , Ian Howat , Tamlin M. Pavelsky
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Abstract

Remote sensing has the potential to dramatically advance river discharge monitoring globally, but precision of primary data (water surface elevation (WSE) and river width) remains a limiting factor. WSE can be measured from altimeters, and river width from imagers, but the measurements historically have not been made concurrently from space. This is changing with the advent of the Surface Water and Ocean Topography (SWOT) mission and is anticipated by the combination of high-resolution commercial imagery and DEMs from ArcticDEM. WSE and width respond to changing flow conditions as modulated by the three-dimensional structure of the river channel bed and banks. The relationship between WSE and width thus increases monotonically and is essentially the hypsometric curve of the river. In this study, we explore how simultaneous measurements of WSE and width, combined with the monotonic nature of the river hypsometric curve, can be used to improve measurements of river discharge. First, we present an algorithm to compute the river hypsometric curve from noisy measurements of WSE and width. Second, we demonstrate a method to compute estimates of WSE and width constrained to the river hypsometric curve, and we analyze the probability distribution function of the hypsometrically constrained WSE and width estimates. Specifically, we show that the variance of width and WSE is reduced by invoking a hypsometric constraint, at the cost of an induced correlation between the WSE and width errors. Third, we show that river discharge estimated with the hypsometrically constrained WSE and width is more precise than that without hypsometric constraint, and we predict the expected reduction in discharge error. Fourth, we look at six example river reaches measured by ArcticDEM. The WSE root mean square error had a median across the six reaches of 39.3 cm, which was improved to 33.4 cm across the six reaches using the hypsometric constraint. The discharge predictions were similarly improved: the constrained height and width produce more accurate discharge estimates for five of the six reaches and show reduced variation among flow laws. With the launch of SWOT, river hypsometry constraints applied to simultaneous measurement of WSE and width will support new discharge estimates globally.
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利用河流湿度测量改进河流排泄量的遥感测量
遥感技术有可能在全球范围内极大地推动河流排放监测工作,但主要数据(水面高程(WSE)和河宽)的精确度仍然是一个限制因素。水面高程可以通过测高仪测量,河宽可以通过成像仪测量,但这些测量历来无法同时从太空进行。随着地表水和海洋地形图(SWOT)任务的到来,这种情况正在发生变化,预计高分辨率商业图像和 ArcticDEM 的 DEMs 将结合使用。在河床和河岸三维结构的调节下,WSE 和宽度会随着水流条件的变化而变化。因此,WSE 和宽度之间的关系是单调递增的,基本上就是河流的吸水性曲线。在本研究中,我们探讨了如何利用同时测量 WSE 和宽度以及河流吸水性曲线的单调性来改进河流排水量的测量。首先,我们提出了一种根据 WSE 和宽度的噪声测量值计算河流测湿曲线的算法。其次,我们演示了一种计算受河流吸水曲线约束的 WSE 和宽度估计值的方法,并分析了受吸水曲线约束的 WSE 和宽度估计值的概率分布函数。具体而言,我们表明,通过引用吸水性约束条件,宽度和 WSE 的方差减小了,但代价是 WSE 和宽度误差之间存在诱导相关性。第三,我们证明了使用受湿度约束的 WSE 和宽度估算的河流排放量比不使用湿度约束的更精确,并预测了排放量误差的预期减小。第四,我们以 ArcticDEM 测量的六条河流为例。六条河段的 WSE 均方根误差中位数为 39.3 厘米,使用湿度测量约束后,六条河段的 WSE 均方根误差中位数提高到 33.4 厘米。排泄量预测也得到了类似的改善:在六个河段中,有五个河段的高度和宽度约束条件下得出的排泄量估计值更为准确,并且流量规律之间的差异也有所减小。随着 SWOT 的推出,应用于同时测量 WSE 和宽度的河流湿度测量约束将支持全球范围内新的排放量估算。
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来源期刊
Remote Sensing of Environment
Remote Sensing of Environment 环境科学-成像科学与照相技术
CiteScore
25.10
自引率
8.90%
发文量
455
审稿时长
53 days
期刊介绍: Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.
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