HR: 1330h
AN: H23A-06    [Abstracts]
TI: Analysis of Radar-Rainfall Error and its Effect on Runoff Predictions
AU: * Habib, E
EM: habib@louisiana.edu
AF: University of Louisiana at Lafayette, P.O. Box 42291, Lafayette, LA 70504 United States
AU: Meselhe, E A
EM: meselhe@louisiana.edu
AF: University of Louisiana at Lafayette, P.O. Box 42291, Lafayette, LA 70504 United States
AU: Aduvala, A V
EM: anandvishnu@louisiana.edu
AF: University of Louisiana at Lafayette, P.O. Box 42291, Lafayette, LA 70504 United States
AB: Recent years have witnessed significant advances in the development of operational radar-rainfall products. These products are desirable for several hydrologic applications such as flood forecasting and rainfall-runoff modeling. It is recognized that radar-rainfall estimates are associated with unknown uncertainties. The nature of these uncertainties and their impact on the prediction accuracy of hydrologic models is not fully understood. The complexity of the spatial and temporal structure of radar-rainfall error has lead most of the previous studies have to approach this problem using simulation-based analyses where the effects of model-related errors can be separated from those of the radar-rainfall input. The present study presents a preliminary analysis of the uncertainties of operational radar-rainfall products and how they propagate into rainfall-runoff models. The study uses the NWS Multi-sensor Precipitation Estimator (MPE) radar-rainfall products over the Goodwin Creek experimental watershed. The products have hourly temporal resolution and are available over the HRAP grid (4x4 km2 approximately). Surface rainfall observations from a dense rain gauge network in the watershed are used to analyze the error characteristics of the radar products. The MPE radar data are used as input to a semi-distributed hydrologic model to simulate runoff response during 12 storms recorded in 2001. The study focuses on the effect of three different radar error sources: systematic error (bias), random error, and temporal and spatial resolution effects. Initial results indicate that, for the study watershed, the bias and random components of the radar error have the most significant impact on prediction accuracy of the hydrologic model.
DE: 1854 Precipitation (3354)
SC: Hydrology [H]
MN: 2005 Joint Assembly