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.
Highlights
Riparian wetland ecosystems are flooded on multiple occasions every year, as floods are generated from excess rainfall in the catchment
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 as calibrated models
Swampy areas are areas dominated by frequently flooded grasslands, so during the dry season these areas characterized by healthy grass, which could be mapped with the lowest accuracies among the natural wetland vegetation classes
Summary
Riparian wetland ecosystems are flooded on multiple occasions every year, as floods are generated from excess rainfall in the catchment. The alteration of hydraulic regime in the river affects several ecological processes in the wetland floodplains, e.g., ecosystem productivity, species distribution and occurrence, nutrients and sediment dynamics [1,2,3,4]. To predict the impacts of flood wave on the floodplain, the hydraulic processes of the river have to be assessed at optimal temporal and spatial scales using hydraulic models. Several efforts have been made to develop hydraulic models that can simulate flow patterns and predict extreme flood levels in rivers and floodplains [5,6,7,8,9,10]. The development of an accurate and reliable hydraulic model that well describes surface water flow across a large wetland floodplain depends on topographic data and hydraulic roughness, viz. Manning’s roughness of the floodplain [11,12]. The uncertainty of roughness parameterization leads to errors in water level estimation and affects the hydrograph characteristics
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