Utility of SCaMPR satellite versus ground-based quantitative precipitation estimates in operational flood forecasting: The effects of TRMM data ingest

This study examines the utility of satellite-based quantitative precipitation estimates (QPEs) from the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm for hydrologic prediction. In this work, two sets of SCaMPR QPEs, one without and the other with Tropical Rainfall Measurement Mission (TRMM) version 6 data integrated, were used as input forcing to the lumped National Weather Service hydrologic model to retrospectively generate flow simulations for 10 Texas catchments over 2000–07. The year 2000 was used for the model spinup, 2001-04 for calibration, and 2005–07 for validation. The results were validated using observed streamflow alongside similar simulations obtained using interpolated gauge QPEs with varying gauge network densities, and still others using the operational radar–gauge multisensor product (MAPX). The focus of the evaluation was on the high-flow events. A number of factors that could impact the relative utility of SCaMPR satellite QPE and gauge-only analysis (GMOSAIC) for flood prediction were examined, namely, 1) the incremental impacts of TRMM version 6 data ingest, 2) gauge density, 3) effects of calibration approaches, and 4) basin properties. Results indicate that ground-sensor-based QPEs in a broad sense outperform SCaMPR QPEs, while SCaMPR QPEs are competitive in a minority of catchments. TRMM ingest helped substantially improve the SCaMPR QPE-based simulation results. Change in calibration forcing, that is, calibrating the model using individual QPEs rather than the MAPX (the most accurate QPE), yielded overall improvements to the simulation accuracy but did not change the relative performance of the QPEs.