| Autor: |
Frasson, Renato Prata de Moraes, Durand, Michael T., Larnier, Kevin, Gleason, Colin, Andreadis, Konstantinos M., Hagemann, Mark, Dudley, Robert, Bjerklie, David, Oubanas, Hind, Garambois, Pierre‐André, Malaterre, Pierre‐Olivier, Lin, Peirong, Pavelsky, Tamlin M., Monnier, Jérôme, Brinkerhoff, Craig B., David, Cédric H. |
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| Zdroj: |
Water Resources Research; Jun2021, Vol. 57 Issue 6, p1-29, 29p |
| Abstrakt: |
The Surface Water and Ocean Topography (SWOT) satellite mission will measure river width, water surface elevation, and slope for rivers wider than 50–100 m. SWOT observations will enable estimation of river discharge by using simple flow laws such as the Manning‐Strickler equation, complementing in situ streamgages. Several discharge inversion algorithms designed to compute unobserved flow law parameters (e.g., friction coefficient and bathymetry) have been proposed, but to date, a systematic assessment of factors controlling algorithm performance has not been conducted. Here, we assess the performance of the five algorithms that are expected to be used in the construction of the SWOT product. To perform this assessment, we used synthetic SWOT observations created with hydraulic model output corrupted with SWOT‐like error. Prior information provided to the algorithms was purposefully limited to an estimate of mean annual flow (MAF), designed to produce a "worst case" benchmark. Prior MAF error was an important control on algorithm performance, but discharge estimates produced by the algorithms are less biased than the MAF; thus, the discharge algorithms improve on the prior. We show for the first time that accuracy and frequency of remote sensing observations are less important than prior bias, hydraulic variability among reaches, and flow law accuracy in governing discharge algorithm performance. The discharge errors and error sensitivities reported herein are a bounding benchmark, representing worst possible expected errors and error sensitivities. This study lays the groundwork to develop predictive power of algorithm performance, and thus map the global distribution of worst‐case SWOT discharge accuracy. Plain Language Summary: Measurements of river flow are essential for the allocation of water resources, flood and drought forecast and mitigation efforts, and others. Access to local measurements is not ubiquitous and is particularly difficult for rivers flowing in remote locations or across country borders. Measurements taken by satellites such as the upcoming Surface Water and Ocean Topography (SWOT) mission will offer freely available global data and methods to estimate discharge using such data have been in development. We conducted a comprehensive assessment of the accuracy and precision of five SWOT discharge inversion algorithms under three conditions: (a) ideal, that is if the measurements were available once a day and contained no error; (b) with no measurement error but changing how frequently the measurements were taken, and (c) under different levels of measurement error. We found that the methods consistently improved over the initial estimates of discharge and we identified river hydraulic properties that increase the chances of successful parameter estimation. We also found that despite the use of very similar discharge equations, the subtle differences in equations among the methods can be important. Finally, we found that at least two methods can work well with the expected amount of measurement error and frequency. Key Points: The ability of algorithms to produce improved discharge estimates is related to measurable hydraulic properties of the domainSampling frequency had little impact on algorithm performance. Algorithms based on unsteady continuity equation experienced bigger impactsThe algorithms were robust to Surface Water and Ocean Topography measurement errors. Best performance found among algorithms using the low Froude approximation [ABSTRACT FROM AUTHOR] |
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Complementary Index |
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