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Remote Sens. 2015, 7(1), 836-864; doi:10.3390/rs70100836

Use of Radarsat-2 and Landsat TM Images for Spatial Parameterization of Manning’s Roughness Coefficient in Hydraulic Modeling

1
Department of Water Resources Engineering, University of Dar es Salaam, P.O. Box 35131, 14115 Dar es Salaam, Tanzania
2
Department of Water Resources, Faculty of Geo-Information and Earth Observation (ITC), University of Twente, P.O. Box 6, AA Enschede7500, The Netherlands
3
Department of Water and Waste Management, Szent István University, Gödöllő, 2100 Páter Károly utca 1., Hungary
*
Author to whom correspondence should be addressed.
Academic Editors: Nicolas Baghdadi and Prasad S. Thenkabail
Received: 21 March 2014 / Accepted: 23 December 2014 / Published: 14 January 2015
(This article belongs to the Special Issue Earth Observation for Water Resource Management in Africa)
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Abstract

Vegetation resistance influences water flow in floodplains. Characterization of vegetation for hydraulic modeling includes the description of the spatial variability of vegetation type, height and density. In this research, we explored the use of dual polarized Radarsat-2 wide swath mode backscatter coefficients (σ°) and Landsat 5 TM to derive spatial hydraulic roughness. The spatial roughness parameterization included four steps: (i) land use classification from Landsat 5 TM; (ii) establishing a relationship between σ° statistics and vegetation parameters; (iii) relative surface roughness (Ks) determination from Synthetic Aperture Radar (SAR) backscatter temporal variability; (iv) derivation of the spatial distribution of the spatial hydraulic roughness both from Manning’s roughness coefficient look up table (LUT) and relative surface roughness. Hydraulic simulations were performed using the FLO-2D hydrodynamic model to evaluate model performance under three different hydraulic modeling simulations results with different Manning’s coefficient parameterizations, which includes SWL1, SWL2 and SWL3. SWL1 is simulated water levels with optimum floodplain roughness (np) with channel roughness nc = 0.03 m−1/3/s; SWL2 is simulated water levels with calibrated values for both floodplain roughness np = 0.65 m−1/3/s and channel roughness nc = 0.021 m−1/3/s; and SWL3 is simulated water levels with calibrated channel roughness nc and spatial Manning’s coefficients as derived with aid of relative surface roughness. The model performance was evaluated using Nash-Sutcliffe model efficiency coefficient (E) and coefficient of determination (R2), based on water levels measured at a gauging station in the wetland. The overall performance of scenario SWL1 was characterized with E = 0.75 and R2 = 0.95, which was improved in SWL2 to E = 0.95 and R2 = 0.99. When spatially distributed Manning values derived from SAR relative surface values were parameterized in the model, the model also performed well and yielding E = 0.97 and R2 = 0.98. Improved model performance using spatial roughness shows that spatial roughness parameterization can support flood modeling and provide better flood wave simulation over the inundated riparian areas equally as calibrated models. View Full-Text
Keywords: synthetic aperture radar; hydraulic roughness; relative surface roughness; vegetation mapping; hydraulic modeling synthetic aperture radar; hydraulic roughness; relative surface roughness; vegetation mapping; hydraulic modeling
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Mtamba, J.; van der Velde, R.; Ndomba, P.; Zoltán, V.; Mtalo, F. Use of Radarsat-2 and Landsat TM Images for Spatial Parameterization of Manning’s Roughness Coefficient in Hydraulic Modeling. Remote Sens. 2015, 7, 836-864.

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