Event-based hydrological modeling for real-time flood forecasting under data-scarce conditions: insights from a subtropical watershed in Brazil

Flood forecasting and early-warning systems have become a central non-structural strategy to mitigate the impacts of increasingly frequent hydrometeorological extremes. These challenges have become particularly critical in southern Brazil, where unprecedented flood events in 2023 and especially in 2024 resulted in record river and lagoon water levels, widespread inundation, and severe social and economic impacts. In response to these events, the state of Rio Grande do Sul has fostered scientific and technological initiatives focused on real-time hydrological forecasting systems. Within this region, the Piratini River watershed has experienced recurrent and severe urban flooding along its main urban reach, particularly affecting the municipalities of Pedro Osório and Cerrito, with historical events and more recent episodes in 2023–2024 that resulted in extensive urban inundation and persistent social and economic impacts, reinforcing its strategic relevance for hydrometeorological monitoring and early-warning actions. This watershed drains approximately 4,700 km² upstream of the main urbanized river reach, representing an intermediate-scale watershed that remains underrepresented in event-based hydrological modeling studies, particularly under conditions of limited real-time hydrometeorological monitoring, which predominantly focus on smaller catchments. Since 2023, this context has marked the beginning of structured hydrological forecasting activities, developed in direct collaboration with municipal governments and Civil Defense agencies to support decision-making during flood emergencies. Within the real-time, hourly hydrological forecasting framework being developed for this watershed, event-based hydrological modeling using the Hydrologic Engineering Center – Hydrologic Modeling System (HEC-HMS) has been adopted to represent rainfall–runoff processes. The objective of this study is to assess the robustness and structural sensitivity of different event-based conceptual hydrological model configurations implemented in HEC-HMS, addressing key operational questions related to the suitability of different combinations of loss, rainfall–runoff transformation, and routing methods, the impact of spatial discretization on model robustness under limited rainfall and streamflow monitoring, and the role of antecedent hydrological conditions and rainfall temporal concentration in controlling flood generation. Five recent extreme rainfall–runoff events were analyzed using multiple combinations of loss methods, rainfall–runoff transformation methods, baseflow representation, and channel routing schemes, as well as two spatial discretization thresholds, based on rainfall inputs from automatic rain gauges with poor spatial coverage across the watershed and on water level and streamflow data from the Pedro Osório non-automatic gauging station. These data were used for model calibration and validation, and model behavior was subsequently assessed using standard goodness-of-fit and error metrics. Results indicate that model robustness is strongly influenced by the combination of hydrological methods adopted, with configurations including explicit channel routing providing a more realistic representation of flood wave routing. Coarser spatial discretization produced more stable and robust simulations, suggesting reduced parameter uncertainty under limited rainfall station density. Finally, antecedent hydrological conditions and rainfall temporal concentration were identified as critical constraints on flood generation and forecasting reliability. The findings enhance the understanding of flood response mechanisms in subtropical lowland watersheds and provide technical guidance for the design of parsimonious and reliable event-based hydrological models to support operational flood forecasting, highlighting their relevance for climate risk adaptation in developing countries.