How Do Different Sources of Gridded Precipitation Affect Streamflow Prediction? A Case Study Using TC Beryl (2012)

Abstract Coastal and inland communities are increasingly vulnerable to tropical cyclones (TCs), yet accurately assessing flood risk remains challenging due to uncertainties in precipitation inputs. This study evaluates how three gridded precipitation products, gauge based [Gridded Cooperative Observer Program (GCOOP)], radar based (Stage IV), and satellite based [Integrated Multi-satellitE Retrievals for GPM (IMERG)], influence surface hydrology, specifically total runoff, streamflow volume, and timing of peak flow through a case study of TC Beryl 2012 in the South Atlantic–Gulf region. Using the Variable Infiltration Capacity (VIC) model and a GIS routing model, we evaluate how biases in daily precipitation estimates propagate into streamflow outputs. While precipitation differences of up to 186 mm in storm totals among datasets had minimal effect on peak streamflow timing, however, the peak discharge magnitudes varied with relative errors of 24% and 12% for IMERG compared to GCOOP and Stage IV, respectively. Results indicate that surface runoff showed high sensitivity to precipitation variability, while base flow and evapotranspiration exhibited buffering effects. The watershed-scale integration dampened local precipitation variability, resulting in a 2.4 cm day −1 streamflow difference between GCOOP and IMERG, which is acceptable for regional flood assessment. These findings show that, for this drought-preconditioned watershed and TC event, satellite precipitation products provide reasonable streamflow estimates, particularly when peak timing is more critical than absolute magnitude. Further evaluation across diverse TC events and watershed conditions is needed before broader operational conclusions can be drawn. Significance Statement Reliable and timely flood forecasts are vital during hurricanes, yet many vulnerable regions lack dense ground-based rainfall networks. Our previous research found striking differences in rainfall estimates for the same hurricane among three products using different algorithms. This raised an intriguing question—does this variability in rainfall measurements propagate to the flooding estimation during extreme events like hurricanes? Using Tropical Cyclone (TC) Beryl (2012) as a case study, we find that despite large rainfall differences, all three products predicted flood arrival times within hours of each other. Satellite data underestimated flood peaks by only 12%–24%. Natural watershed processes smooth out local rainfall variations, making satellites more reliable than expected. These findings support using satellite data for flood prediction in vulnerable regions lacking ground observations.