Keywords
swat, uncertainty, catchment properties, modeling scale, soil and water degradation
Start Date
1-7-2012 12:00 AM
Abstract
Distributed physically-based models require large amount of data,including detailed spatial information (e.g. geology, soil, vegetation). The relevanceof spatial information highly depends on the modeling scale and may control themodeling issue, mainly model parameters, which already depend on modelassumptions and target processes. In this study, scale dependent catchmentproperties were used to derive SWAT model parameters (for ungauged basins)using uncertainty thresholds and statistical approaches. Six individual sub-basinsof the Ouémé River (Benin) ranging from 586 to 10,072 km2 in size wereinvestigated, leading to the multi-scale modeling of discharge, sediment andnutrient dynamic. The Sequential Uncertainty Fitting approach was applied forcalibration and an uncertainty analysis. Calibrated parameters set were consideredonly when more than 50% of the measurements were captured by the 95%prediction uncertainty, and when the ratio of the average distance between 2.5 and97.5 percentiles of the cumulative distribution of the simulated variable and thestandard deviation of the corresponding measured variable was less than 0.5.Regression models between the calibrated model parameter sets and linearlyindependent catchment property sets were established. Following a confidencethreshold of 5%, nine predicted model parameters (e.g. soil depth) may fall withinthe confidence interval with 95 to 99% of chance, and six model parameters (e.g.Curve Number) may be predicted with 83 to 93% of chance. Globally, geologyappeared to be a major driver of the regional hydrological response, correlatingsignificantly with eleven out of fifteen model parameters. Validation was performedby applying the derived model parameters at different scales (1,200 and 25,000km²) with goodness-of-fit (to daily measurements) around 0.7 for Nash-Sutcliffemodel efficiency and R2. This study revealed that runoff-sediment-nutrient dynamic(soil and water degradation) may be simulated for ungauged large scalecatchments in Benin with reasonable degree of accuracy.
Estimating scale effects of catchment properties on modeling soil and water degradation in Benin (West Africa)
Distributed physically-based models require large amount of data,including detailed spatial information (e.g. geology, soil, vegetation). The relevanceof spatial information highly depends on the modeling scale and may control themodeling issue, mainly model parameters, which already depend on modelassumptions and target processes. In this study, scale dependent catchmentproperties were used to derive SWAT model parameters (for ungauged basins)using uncertainty thresholds and statistical approaches. Six individual sub-basinsof the Ouémé River (Benin) ranging from 586 to 10,072 km2 in size wereinvestigated, leading to the multi-scale modeling of discharge, sediment andnutrient dynamic. The Sequential Uncertainty Fitting approach was applied forcalibration and an uncertainty analysis. Calibrated parameters set were consideredonly when more than 50% of the measurements were captured by the 95%prediction uncertainty, and when the ratio of the average distance between 2.5 and97.5 percentiles of the cumulative distribution of the simulated variable and thestandard deviation of the corresponding measured variable was less than 0.5.Regression models between the calibrated model parameter sets and linearlyindependent catchment property sets were established. Following a confidencethreshold of 5%, nine predicted model parameters (e.g. soil depth) may fall withinthe confidence interval with 95 to 99% of chance, and six model parameters (e.g.Curve Number) may be predicted with 83 to 93% of chance. Globally, geologyappeared to be a major driver of the regional hydrological response, correlatingsignificantly with eleven out of fifteen model parameters. Validation was performedby applying the derived model parameters at different scales (1,200 and 25,000km²) with goodness-of-fit (to daily measurements) around 0.7 for Nash-Sutcliffemodel efficiency and R2. This study revealed that runoff-sediment-nutrient dynamic(soil and water degradation) may be simulated for ungauged large scalecatchments in Benin with reasonable degree of accuracy.
