## Abstract The generation of flow forecasts using rainfall inputs to hydrological models has been developed over many years. Unfortunately, errors in input data to models may vary considerably depending upon the different sources of data such as raingauges, radar and high resolution Numerical Weat
Hydrological model sensitivity to parameter and radar rainfall estimation uncertainty
β Scribed by Faisal Hossain; Emmanouil N. Anagnostou; Tufa Dinku; Marco Borga
- Publisher
- John Wiley and Sons
- Year
- 2004
- Tongue
- English
- Weight
- 675 KB
- Volume
- 18
- Category
- Article
- ISSN
- 0885-6087
- DOI
- 10.1002/hyp.5659
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β¦ Synopsis
Abstract
Radar estimates of rainfall are being increasingly applied to flood forecasting applications. Errors are inherent both in the process of estimating rainfall from radar and in the modelling of the rainfallβrunoff transformation. The study aims at building a framework for the assessment of uncertainty that is consistent with the limitations of the model and data available and that allows a direct quantitative comparison between model predictions obtained by using radar and raingauge rainfall inputs. The study uses radar data from a mountainous region in northern Italy where complex topography amplifies radar errors due to radar beam occlusion and variability of precipitation with height. These errors, together with other error sources, are adjusted by applying a radar rainfall estimation algorithm. Radar rainfall estimates, adjusted and not, are used as an input to TOPMODEL for flood simulation over the Posina catchment (116 km^2^). Hydrological model parameter uncertainty is explicitly accounted for by use of the GLUE (Generalized Likelihood Uncertainty Estimation). Statistics are proposed to evaluate both the wideness of the uncertainty limits and the percentage of observations which fall within the uncertainty bounds. Results show the critical importance of proper adjustment of radar estimates and the use of radar estimates as close to ground as possible. Uncertainties affecting runoff predictions from adjusted radar data are close to those obtained by using a dense raingauge network, at least for the lowest radar observations available. Copyright Β© 2004 John Wiley & Sons, Ltd.
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