A First Look at River Discharge Estimation From SWOT Satellite Observations

IF 4.6 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Konstantinos M. Andreadis, Steve P. Coss, Michael Durand, Colin J. Gleason, Travis T. Simmons, Nikki Tebaldi, David M. Bjerklie, Craig Brinkerhoff, Robert W. Dudley, Igor Gejadze, Kevin Larnier, Pierre-Olivier Malaterre, Hind Oubanas, George H. Allen, Paul D. Bates, Cédric H. David, Alessio Domeneghetti, Omid Elmi, Luciana Fenoglio Marc, Renato Prata de Moraes Frasson, Elisa Friedmann, Pierre-André Garambois, Jaclyn Gehring, Augusto Getirana, Marissa Hughes, Jonghyun Lee, Pascal Matte, J. Toby Minear, Jérôme Monnier, Aggrey Muhebwa, Mohammad J. Tourian, Tamlin M. Pavelsky, Ryan M. Riggs, Ernesto Rodríguez, Md Safat Sikder, Laurence C. Smith, Cassie Stuurman, Jay Taneja, Angelica Tarpanelli, Jida Wang, Brent A. Williams, Bidhyananda Yadav
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引用次数: 0

Abstract

The Surface Water and Ocean Topography (SWOT) satellite has the potential to transform global hydrologic science by offering simultaneous and synoptic estimates of river discharge and other hydraulic variables. Discharge is estimated from SWOT observations of water surface elevation, width, and slope. A first assessment using just the highest quality SWOT measurements, over the first 15 months (March 2023–July 2024) of the mission evaluated at 65 gauged reaches shows results consistent with pre-launch expectations. SWOT estimates track discharge dynamics without relying on any gauge information: median correlation is 0.73, with a correlation interquartile range of 0.51–0.89. SWOT estimates capture discharge magnitude correctly in some cases but are biased (median bias is 50%) in others. There are already a total of 11,274 ungauged global locations with highest quality SWOT measurements where SWOT discharge is expected to accurately track discharge variations: this value will increase as SWOT data record length grows, algorithms are refined and SWOT measurements are reprocessed. This first look indicates that SWOT discharge is performing as expected for SWOT data that achieve performance requirements, providing observed information on discharge variations in ungauged basins globally.

Abstract Image

从SWOT卫星观测初步估计河流流量
地表水和海洋地形(SWOT)卫星通过提供河流流量和其他水力变量的同步和天气估计,有可能改变全球水文学科学。流量是通过对水面高程、宽度和坡度的SWOT观察来估计的。第一次评估仅使用最高质量的SWOT测量,在任务的前15个月(2023年3月至2024年7月)对65个测量到达进行评估,结果与发射前的预期一致。SWOT在不依赖任何计量信息的情况下估计轨道流量动态:中位数相关系数为0.73,相关四分位数区间为0.51-0.89。SWOT估计在某些情况下正确地捕获了放电幅度,但在其他情况下存在偏差(中位数偏差为50%)。目前,全球共有11,274个未测量的地点拥有最高质量的SWOT测量,预计SWOT排放将准确地跟踪排放变化:随着SWOT数据记录长度的增长,算法的改进和SWOT测量的重新处理,这个值将会增加。第一次观察表明,SWOT数据达到了预期的性能要求,提供了全球未测量盆地的排放变化的观察信息。
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来源期刊
Geophysical Research Letters
Geophysical Research Letters 地学-地球科学综合
CiteScore
9.00
自引率
9.60%
发文量
1588
审稿时长
2.2 months
期刊介绍: Geophysical Research Letters (GRL) publishes high-impact, innovative, and timely research on major scientific advances in all the major geoscience disciplines. Papers are communications-length articles and should have broad and immediate implications in their discipline or across the geosciences. GRLmaintains the fastest turn-around of all high-impact publications in the geosciences and works closely with authors to ensure broad visibility of top papers.
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