Hydrological predictability of large rivers in southern Brazil

Código

I-EBHE0143

Autores

Fernando Mainardi Fan, Ingrid Petry

Tema

WG 1.12: Development & application of river basin simulators

Resumo

Unprecedented flood events occurred in the state of Rio Grande do Sul (RS), Brazil, in the years 2023 and 2024. These events caused extensive damage to property and the lives of inhabitants of the region. One of the measures that can be adopted to minimize the impacts of these types of events are flow forecasting systems, which as part of efficient warning systems can help populations to leave their homes or remove their goods. There are currently some systems of this type operating in RS, however the impacts of these events make it clear that there are also gaps to be filled in terms of improving these systems. One effect that is known and that was widely verified during the 2023/2024 events was the uncertainty of meteorological models? rainfall outputs, which were not able to consistently capture the occurrence of unprecedented rainfalls. This low confidence in meteorological models, already a well-known issue in hydrological forecasting, also means for the region that forecasting extreme events with the confidence necessary to issue warnings and evacuate people is very dependent on modeling approaches that represents the occurrence of floods as soon as the observed rainfall begins to be measured by the network of available rain gauges. In this case, the forecast horizons depend to some extent on the classic hydrological parameter known as concentration time. The concentration time is variable depending on the humidity conditions of the basin, previous rain, among others. Thus, flow forecasters commonly work with a predictability estimated by rainfall and flows observed or simulated in the basin, called "hydrological predictability" of the river. Hydrological predictability would be the extent to which a flow in a river is predictable within the forecast horizon only with information about the rain that has already fallen in the river basin. Along these lines, this research sought to detail what this hydrological predictability would be for the southern region of Brazil, aiming to guide the development of tools, technologies and disaster prevention policies in this region, which has been so affected recently. The results were obtained using hydrological modeling tools on a continental scale and through a simple model performance metric in hindcasting experiments. The predictability of the rivers varied from less than a day for rivers that respond faster to rainfall to up to around five days for rivers with a slower response.