Post-Event Evaluation of Rainfall Estimates and Forecasts for Major Floods in Southern Brazil

Antecedent precipitation estimates and precipitation forecasts are critical inputs for flood forecasting systems, particularly in basins where flood response is strongly controlled by short-term rainfall variability rather than by slowly evolving catchment states. In regions with sparse rain gauge networks, satellite-based precipitation products and numerical weather prediction (NWP) models are therefore frequently relied upon, despite their known uncertainties.

This study presents a post-event evaluation of rainfall estimates and forecasts during four recent major flood events in the state of Rio Grande do Sul, southern Brazil — a region that has experienced recurrent and increasingly severe flooding in recent years. The analysis considers both simulation and operational forecasting contexts, assessing the performance of near–real-time satellite precipitation products (IMERG Early Run and GSMaP Near Real-Time) and short-range precipitation forecasts from two widely used NWP systems: ECMWF and NCEP.

Satellite products were evaluated against telemetric rain gauge observations for historical flood events (2023–2025) as well as for a longer reference period (2018–2025), using standard performance metrics. Results indicate that IMERG Early Run outperformed GSMaP Near Real-Time in terms of bias and overall representativeness, particularly under data-scarce conditions.

The analysis of NWP forecasts during the extreme April–May 2024 flood revealed substantial limitations even at a 24-hour lead time. Rainfall underestimations of up to 60 mm (basin-average) were identified in the Guaíba basin during peak impact periods, while spatial displacement of rainfall maxima further reduced forecast usability.

These results highlight that improvements in hydrological modeling alone are insufficient to enhance flood forecasting reliability. Advancing rainfall estimation and predictability — through improved satellite products, enhanced data merging strategies, and more accurate meteorological forecasts — remains a critical challenge for flood early warning systems in Southern Brazil and similar hydroclimatic regions.