Preprints
https://doi.org/10.5194/hess-2021-551
https://doi.org/10.5194/hess-2021-551
03 Dec 2021
 | 03 Dec 2021
Status: this preprint was under review for the journal HESS. A final paper is not foreseen.

Extraction of roughness parameters from remotely-sensed products for hydrology applications

Charlotte Marie Emery, Kevin Larnier, Maxime Liquet, João Hemptinne, Arthur Vincent, and Santiago Peña Luque

Abstract. Along rivers, where local insitu gauges are unavailable, estimation of river discharge are undirectly derived from the Manning formula that relate discharge to geomorphological characteristics of the rivers and flow conditions. Most components of the Manning formula can currently be derived from spaceborne products except for two features: the unobserved always-wet bathymetry and the roughness coefficient. Global-scale applications use simplified equivalent riverbed shapes and empirical parameters while local-scale applications rely on finer model dynamics, field survey and expert knowledge. Within the framework of the incoming Surface Water and Ocean Topography (SWOT) mission, scheduled for a launch in 2022, and more particularly, the development of the SWOT-based discharge product, fine-resolution but global discharge estimates should be produced. Currently implemented SWOT-based discharge algorithms require prior information on bathymetry and roughness and their performances highly depend on the quality of such priors. Here we introduce an automatic and spaceborne-data-based-only methodology to derive physically-based roughness coefficients to use in one-dimensional hydrological models. The evaluation of those friction coefficients showed that they allow model performances comparable to calibrated models. Finally, we illutrate two cases of application where our roughness coefficients are used as-is to initiate the experiment: a data assimilation experiment designed to correct the roughness parameters and an application around the HiVDI SWOT-based discharge algorithm. In both cases, the roughness coefficients showed promising perspectives by reproducing, for the data assimilation application, and sometimes improving, in the SWOT discharge algorithm case, the calibrated-parameter-based performances.

This preprint has been withdrawn.

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Charlotte Marie Emery, Kevin Larnier, Maxime Liquet, João Hemptinne, Arthur Vincent, and Santiago Peña Luque

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-551', Anonymous Referee #1, 03 Jan 2022
  • RC2: 'Comment on hess-2021-551', Anonymous Referee #2, 06 Jan 2022
  • EC1: 'Editor Comment on hess-2021-551', Hubert H.G. Savenije, 06 Jan 2022

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-551', Anonymous Referee #1, 03 Jan 2022
  • RC2: 'Comment on hess-2021-551', Anonymous Referee #2, 06 Jan 2022
  • EC1: 'Editor Comment on hess-2021-551', Hubert H.G. Savenije, 06 Jan 2022
Charlotte Marie Emery, Kevin Larnier, Maxime Liquet, João Hemptinne, Arthur Vincent, and Santiago Peña Luque
Charlotte Marie Emery, Kevin Larnier, Maxime Liquet, João Hemptinne, Arthur Vincent, and Santiago Peña Luque

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Latest update: 18 May 2024
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This preprint has been withdrawn.

Short summary
Nowadays, products from satellite offer an alternative to field products to monitor river globally. River discharge depends, among others, on the shape and smoothness of the riverbed but also on the material along with vegetation and rock at the river bottom and surroundings. The roughness of the riverbed quantifies the channel resistance to the water flow, which impacts the discharge. Here, we show an automatic method to derive roughness parameters along rivers using only data from satellites.