3B.6 Hydrological intercomparison of quantitative precipitation estimates in the Colorado Front Range

Monday, 24 January 2011: 5:15 PM
612 (Washington State Convention Center)
Hernan Moreno, Arizona State University, Tempe, AZ; and E. R. Vivoni and D. J. Gochis

Quantitative Precipitation Estimates (QPEs) from ground networks and satellite platforms can serve as input to distributed hydrologic models to issue flood forecasts in mountainous watersheds. Improvements in flood predictability are expected as the quality of radar, satellite and multi-sensor products is enhanced. This work compares the impact of ten different high-resolution (4-km and hourly) precipitation products on hydrologic forecast skill in a large region of the Colorado Front Range. These products range from operational to experimental radar fields (Level II, Stage III and IV) and satellite estimates (HydroEstimator, AutoEstimator, Blend, GMSRA, PERSIANN-CCS). To evaluate the QPE skill, analyses are carried out that describe their spatial, temporal and statistical properties in comparison to ground rain gauges. Results from this analysis allow detecting QPE error characteristics. We then quantify QPE skill by using the TIN-based Real time Integrated Basin Simulator (tRIBS) distributed hydrologic model as a verification tool in four mountain catchments. We identify the propagation of QPE errors into the lumped and distributed basin response provided by the model. The inherent characteristics of radar and satellite-based precipitation products determine the timing and magnitude of summer flow peaks in the study catchments. The spatial variability of total rainfall is found to be important for determining the flood magnitude as well as to the dominant runoff response mechanism responsible for peak events. Significant differences among QPEs are observed from both the precipitation and hydrologic analyses, reflecting the distinct origin of the precipitation products (satellite infrared observations or ground radar measurements or a combination). Radar and multi-sensor estimates minimize the precipitation and streamflow differences at the point rain gauges and outlet stream flows. Satellite-derived QPEs underestimate rainfall volumes, resulting in significant hydrologic uncertainties. Given the generally low rainfall estimates from satellite products, a mean field bias correction is applied and results are compared against non-corrected precipitation products in terms of the hydrologic response at the catchments. Analysis of spatiotemporal precipitation and streamflow patterns allow identifying the benefits and disadvantages of high-resolution QPEs for convective storms in mountainous areas.
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