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Open AccessArticle

Characterization of SWOT Water Level Errors on Seine Reservoirs and La Bassée Gravel Pits: Impacts on Water Surface Energy Budget Modeling

1
Laboratoire des Sciences du Climat et de l’Environnement (LSCE), CNRS-CEA-UVSQ, F-91190 Gif-sur-Yvette, France
2
Laboratoire d’Etudes en Géophysique et Océanographie Spatiales (LEGOS), UPS (OMP-PCA), F-31400 Toulouse, France
3
CNRS, EPHE, METIS, Sorbonne Université, F-75005 Paris, France
4
Centre National d’Etudes Spatiales, F-31400 Toulouse, France
5
INRAE, UR HYCAR, Université Paris Saclay, F-92160 Antony, France
6
Centre de Géosciences, MINES Paris-Tech, F-77305 Fontainebleau, France
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(18), 2911; https://doi.org/10.3390/rs12182911
Received: 27 July 2020 / Revised: 3 September 2020 / Accepted: 4 September 2020 / Published: 8 September 2020
(This article belongs to the Special Issue Remote Sensing of Lake Properties and Dynamics)
The Surface Water and Ocean Topography (SWOT) space mission will map surface area and water level changes in lakes at the global scale. Such new data are of great interest to better understand and model lake dynamics as well as to improve water management. In this study, we used the large-scale SWOT simulator developed at the French Space National Center (CNES) to estimate the expected measurement errors of the water level of different water bodies in France. These water bodies include five large reservoirs of the Seine River and numerous small gravel pits located in the Seine alluvial plain of La Bassée upstream of the city of Paris. The results show that the SWOT mission will allow to observe water levels with a precision of a few tens of centimeters (10 cm for the largest water reservoir (Orient), 23 km2), even for the small gravel pits of size of a few hectares (standard deviation error lower than 0.25 m for water bodies larger than 6 ha). The benefit of the temporal sampling for water level monitoring is also highlighted on time series of pseudo-observations based on real measurements perturbed with the simulated noise errors. Then, the added value of these future data for the simulation of lake energy budgets is shown using the FLake lake model through sensitivity experiments. Results show that the SWOT data will help to model the surface temperature of the studied water bodies with a precision better than 0.5 K and the evaporation with an accuracy better than 0.2 mm/day. These large improvements compared to the errors obtained when a constant water level is prescribed (1.2 K and 0.6 mm/day) demonstrate the potential of SWOT for monitoring the lake energy budgets at global scale in addition to the other foreseen applications in operational reservoir management. View Full-Text
Keywords: SWOT errors; Seine reservoirs; La Bassée gravel pits; FLake model; radar altimetry SWOT errors; Seine reservoirs; La Bassée gravel pits; FLake model; radar altimetry
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MDPI and ACS Style

Ottlé, C.; Bernus, A.; Verbeke, T.; Pétrus, K.; Yin, Z.; Biancamaria, S.; Jost, A.; Desroches, D.; Pottier, C.; Perrin, C.; de Lavenne, A.; Flipo, N.; Rivière, A. Characterization of SWOT Water Level Errors on Seine Reservoirs and La Bassée Gravel Pits: Impacts on Water Surface Energy Budget Modeling. Remote Sens. 2020, 12, 2911. https://doi.org/10.3390/rs12182911

AMA Style

Ottlé C, Bernus A, Verbeke T, Pétrus K, Yin Z, Biancamaria S, Jost A, Desroches D, Pottier C, Perrin C, de Lavenne A, Flipo N, Rivière A. Characterization of SWOT Water Level Errors on Seine Reservoirs and La Bassée Gravel Pits: Impacts on Water Surface Energy Budget Modeling. Remote Sensing. 2020; 12(18):2911. https://doi.org/10.3390/rs12182911

Chicago/Turabian Style

Ottlé, Catherine; Bernus, Anthony; Verbeke, Thomas; Pétrus, Karine; Yin, Zun; Biancamaria, Sylvain; Jost, Anne; Desroches, Damien; Pottier, Claire; Perrin, Charles; de Lavenne, Alban; Flipo, Nicolas; Rivière, Agnès. 2020. "Characterization of SWOT Water Level Errors on Seine Reservoirs and La Bassée Gravel Pits: Impacts on Water Surface Energy Budget Modeling" Remote Sens. 12, no. 18: 2911. https://doi.org/10.3390/rs12182911

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