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

Abstract

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.

Assessing the influence of climate change on sediment dynamics in the proposed makhool dam reservoir, Iraq

“…Due to the scarcity of data necessary to identify the primary values of Manning's 'n' for river overbanks, this study has assigned 'n' values to overbank regions based on the study by Arcement and Schneider [20]. This approach, as developed by Emery et al [21], employed satellite imagery, land use/land cover (LULC), and sediment data. The satellite images for the study area are available from the United States Geological Survey (USGS) with a spatial resolution of 30 m. accessible at the following website: https://earthexplorer.usgs.gov/.…”

Modeling the Territorial Structure Dynamics of the Northern Part of the Volga-Akhtuba Floodplain

“…which is determined by the effective coefficient of turbulent viscosity ν turb and the interaction of the flow with the underlying surface, for which various models are used, for example, the Manning model in the form [4,6,[39][40][41]:…”

“…is often used to describe a wide variety of physical factors that affect the hydraulic resistance of the flow in the channel in addition to the actual bottom roughness n 0 , for example, meandering/branching of the channel (n 1 ), changes in the channel cross section (n 2 ), the presence of vegetation (n 3 ), drag due to sediment transport (n 4 ), inhomogeneity of the channel shape (n 5 ), presence of bottom ridges (n 6 ), unsteady flow (n 7 ) and other similar factors [4,6,[39][40][41][42].…”