Details zur Publikation

Kategorie Textpublikation
Referenztyp Zeitschriften
DOI 10.1007/s12205-010-0413-0
Titel (primär) Streamflow Prediction with Uncertainty Analysis, Weida Catchment, Germany
Autor Lee, H.; Balin, D.; Shrestha, R.R.; Rode, M.
Quelle KSCE Journal of Civil Engineering
Erscheinungsjahr 2010
Department ASAM
Band/Volume 14
Heft 3
Seite von 413
Seite bis 420
Sprache englisch
Keywords prediction of streamflow, rainfall runoff modelling, uncertainty analysis, PDM, GLUE
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.
dauerhafte UFZ-Verlinkung
Lee, H., Balin, D., Shrestha, R.R., Rode, M. (2010):
Streamflow Prediction with Uncertainty Analysis, Weida Catchment, Germany
KSCE J. Civ. Eng. 14 (3), 413 - 420 10.1007/s12205-010-0413-0