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Streamflow prediction with uncertainty analysis, Weida catchment, Germany
Abstract This study investigated the effects of rainfall input uncertainty on parameter estimation and predictions of stream flow in Weida catchment, Germany. Based on rainfall systematic and non-systematic errors, the uncertainty in the rainfall input data was implemented using the rainfall data of five gauging stations. 100 rainfall time series were generated based on precipitations for each gauging station. These randomly generated rainfall time series were employed with a type of Probability Distribution Model (PDM). Using the Monte Carlo method, the posterior distributions of the model parameters were computed, and the effects of the input uncertainty were assessed. This was done by following the concept of the extended GLUE. The hydrographs were simulated using all combinations of feasible rainfall data (i.e., 100 series) and model parameters (i.e., behavioural model parameter sets), the aim of which was to include the uncertainty sources of input data and model parameter. The 90% confidence interval of these hydrographs covers 31% of observed flows. Although the results in this study provide no clear evidence of the effects of rainfall uncertainty on parameter estimation, it does indicate that the suggested method in this study has the potential to cover major uncertainty in input data and model parameter.
Streamflow prediction with uncertainty analysis, Weida catchment, Germany
Abstract This study investigated the effects of rainfall input uncertainty on parameter estimation and predictions of stream flow in Weida catchment, Germany. Based on rainfall systematic and non-systematic errors, the uncertainty in the rainfall input data was implemented using the rainfall data of five gauging stations. 100 rainfall time series were generated based on precipitations for each gauging station. These randomly generated rainfall time series were employed with a type of Probability Distribution Model (PDM). Using the Monte Carlo method, the posterior distributions of the model parameters were computed, and the effects of the input uncertainty were assessed. This was done by following the concept of the extended GLUE. The hydrographs were simulated using all combinations of feasible rainfall data (i.e., 100 series) and model parameters (i.e., behavioural model parameter sets), the aim of which was to include the uncertainty sources of input data and model parameter. The 90% confidence interval of these hydrographs covers 31% of observed flows. Although the results in this study provide no clear evidence of the effects of rainfall uncertainty on parameter estimation, it does indicate that the suggested method in this study has the potential to cover major uncertainty in input data and model parameter.
Streamflow prediction with uncertainty analysis, Weida catchment, Germany
Lee, Hyosang (author) / Balin, Daniela (author) / Shrestha, Rajesh Raj (author) / Rode, Michael (author)
KSCE Journal of Civil Engineering ; 14 ; 413-420
2010-05-01
8 pages
Article (Journal)
Electronic Resource
English
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