Distributed Hydrologic Forecast Reliability Using Next-Generation Radar: Fid-Bau Portal

Distributed Hydrologic Forecast Reliability Using Next-Generation Radar

Looper, Jonathan P. / Vieux, Baxter E.

Storm water runoff can significantly affect flooding in urban areas. Flood prediction depends on model structure uncertainties and the accurate determination of rainfall. Three aspects of hydrologic forecasting in real time and hydrologic predictions in off-line modes include the following: (1) distributed model reliability, (2) accuracy of radar-derived rainfall, and (3) scaling of basin input and response. The existing flood alert system (FAS) that is operational for Brays Bayou in Houston, Texas, forms the basis for testing the relative magnitudes of these effects on prediction accuracy. The importance of gauge-corrected radar input was demonstrated through a probabilistic approach and by comparison to events with streamflow observations. The difference in discharge, called dispersion, obtained from corrected and uncorrected radar input scales with drainage area, but at a nonlinear rate, and it differs from storm to storm. An additional comparison was made between the existing flood alert system’s kinematic wave model, Vflo, and the full dynamic wave model, Hydrologic Engineering Center’s River Analysis System (HEC-RAS). Both models showed similar scaling with radar bias correction. Considering that random errors in rainfall rates measured by radar should cancel out over large areas, the decline in forecast skill measured by the critical success index (CSI) was not intuitive. Both empirical observations and the perturbation experiment confirm that predictability decreased with increased drainage area. This article shows the benefit of accurate radar rainfall, but that predictability does not scale linearly with drainage area.