Browsing by Author "Mtamba, Joseph"
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Item Use Of Earth Observation Data For Hydrodynamic Modelling In The Mara Wetlands(ESA Living Planet Symposium, 2013-09-13) Mtamba, Joseph; Van der Velde, Rogier; Ndomba, Preksedis M.; Zoltan, Verkedy; Mtalo, Felix W.; Crosato, AlessandraVegetation characteristics can not only be used to derive spatial hydrodynamic roughness parameters but also to correct vegetation artefacts in freely downloadable Digital Elevation Models for hydrodynamic modelling. An exponential backscattering model for vegetation canopy height model was developed using standard deviation of cross polarization backscatter coefficient of Radarsat-2 SAR wide swath mode and in situ vegetation height data. The retrieved spatial vegetation height was used to correct vegetation artefacts in freely downloadable Advanced Spaceborne Thermal Emission and Reflectance Radiometer Global Digital Elevation Model (ASTERDEM) released in 2011. The relative spatial hydrodynamic roughness within a vegetation class was derived from cross polarization ratio. Preliminary results show that the accuracy of ASTERDEM improved the elevation estimates by root mean square error from 5.1 m to 3.0m. Simulation results using Earth Observation (EO) data for calibration and validation using an internal gauging station yielded promising Nash - Sutcliffe efficiency criterion of 0.38 and 0.45. The results shows that if high resolution DEM is available, spatial roughness parametrization using cross-polarization ratio of Synthetic Aperture Radar (SAR) imagery may be useful in modelling extensive floodplains where optimization of roughness parameter is not necessary due to computational limitations.Item Use of Radarsat-2 and Landsat TM Images for Spatial Parameterization of Manning’s Roughness Coefficient in Hydraulic Modeling(remote sensing, 2015-01-14) Mtamba, Joseph; Van der Velde, Rogier; Ndomba, Preksedis M.; Zoltán, Vekerdy; Mtalo, Felix W.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.