Utility of Quantitative Precipitation Estimates for high resolution hydrologic forecasts in mountain watersheds of the Colorado Front Range

SummaryQuantitative Precipitation Estimates (QPEs) can serve as input to distributed hydrologic models to issue flood and flash flood forecasts in mountain watersheds. Improvements in flood predictions are expected as the quality of radar, multisensor and satellite observations improves. In this study, we assess the value of ten different high resolution QPEs (hourly, 4-km) in four study basins of the Colorado Front Range using a calibrated distributed hydrologic model as a verification tool. To evaluate QPE skill, we compared the precipitation properties at the site (i.e., rain gauge location), basin-average and regional scales and evaluated their influence on the simulated basin response, including the outlet discharge, runoff mechanisms and seasonal water balance. We also analyzed the value of gridded QPEs with respect to uniform forcing derived from rain gauges. Results reveal that radar and multisensor QPEs lead to improved hydrologic model performance compared to simulations driven with rain gauge data only with respect to the observed streamflow. Satellite QPEs exhibit lower overall streamflow simulation skill compared with estimates derived from radar-based QPEs, but are preferable to assuming uniform forcing from nearby rain gauges in the mountain settings studied here. However, satellite QPEs preserve the fundamental properties of the basin response, including a simple scaling relation between the relative spatial variability of runoff and its magnitude. As a result, satellite QPE products open new avenues for forecasting in regions with limited access and sparse observations.

► Hydrologic models can be used to verify quantitative precipitation estimates in mountain areas. ► Radar, multisensor and satellite QPEs have varying skill in capturing convective storm events. ► Satellite QPEs have improved flood forecast skill as compared to uniform rain gauge forcing. ► A scaling relation exists between the relative spatial variability of runoff and its magnitude.