87th AMS Annual Meeting

Wednesday, 17 January 2007
Assessing the capability of a regional-scale weather model to simulate extreme precipitation patterns and flooding: Central Texas, USA
Exhibit Hall C (Henry B. Gonzalez Convention Center)
Marla R. Knebl, University of Texas at Austin, Austin, TX; and Z. L. Yang
A regional-scale weather model is used to determine the potential for flood forecasting based on model-predicted rainfall. Extreme precipitation and flooding events are a significant concern in Central Texas, due to both the high occurrence and severity of flooding in the area. However, many current regional prediction models do not provide sufficient accuracy at the watershed scale necessary for flood mitigation efforts. The Weather Research and Forecasting (WRF) Model, created with the purpose of improving upon the current Pennsylvania State University / National Center for Atmospheric Research Fifth-Generation Mesoscale Model (PSU/NCAR MM5), is specifically designed for regional resolutions of 1-10 km. Previous research by the authors resulted in the development of a regional-scale prediction system over the San Antonio River Basin, using a GIS database, a hydrologic model, and a hydraulic model. Observed precipitation drives the prediction system; the authors hypothesize that the WRF model has the potential to predict flooding, at a lead time of several days, with an accuracy near that of observed precipitation. Causes of model bias are also investigated, to determine the relative errors caused by model physics, initialization interval, buffer zone and domain size, and small-amplitude random errors. Finally, the WRF model is used to study error propagation through hydrologic and hydraulic modeling. Specifically, the effects of errors in rainfall magnitude, location, and timing on flood forecast outcomes are studied. Results from this study show that the most important causes of error are intensity and location error. Location error types are most strongly propagated through to streamflow output, while simple timing error types have little effect at the watershed scale.

Supplementary URL: