The city of São Carlos, state of São Paulo, Brazil, has a historical coexistence between society and floods. Unplanned urbanization in this area is a representative feature of how Brazilian cities have developed, undermining the impact of natural hazards. The Gregório Creek catchment is an enigma of complex dynamics concerning the relationship between humans and water in Brazilian cities. Our hypothesis is that social memory of floods can improve future resilience. In this paper we analyse flood risk dynamics in a small urban catchment, identify the impacts of social memory on building resilience and propose measures to reduce the risk of floods. We applied a socio-hydrological model using data collected from newspapers from 1940 to 2018. The model was able to elucidate human–water processes in the catchment and the historical source data proved to be a useful tool to fill gaps in the data in small urban basins.

Poorly monitored catchments could pose a challenge in the provision of accurate flood predictions by hydrological models, especially in urbanized areas subject to heavy rainfall events. Data assimilation techniques have been widely used in hydraulic and hydrological models for model updating (typically updating model states) to provide a more reliable prediction. However, in the case of nonlinear systems, such procedures are quite complex and time-consuming, making them unsuitable for real-time forecasting. In this study, we present a data assimilation procedure, which corrects the uncertain inputs (rainfall), rather than states, of an urban catchment model by assimilating water-level data. Five rainfall correction methods are proposed and their effectiveness is explored under different scenarios for assimilating data from one or multiple sensors. The methodology is adopted in the city of São Carlos, Brazil. The results show a significant improvement in the simulation accuracy.

Stochastic modeling to forecast hydrological variables under changing climatic conditions is essential for water resource management and adaptation planning. This study explores the applicability of stochastic models, specifically SARIMA and SARIMAX, to forecast monthly average river discharge in a sub-basin of the Paranaíba River near Patos de Minas, MG, Brazil. The Paranaíba River is a vital water source for the Alto Paranaíba region, serving industrial supply, drinking water effluent dilution for urban communities, agriculture, fishing, and tourism. The study evaluates the performance of SARIMA and SARIMAX models in long-term discharge modeling and forecasting, demonstrating the SARIMAX model’s superior performance in various metrics, including the Nash–Sutcliffe coefficient (NSE), the root mean square error (RMSE), and the mean absolute percentage error (MAPE). The inclusion of precipitation as a regressor variable considerably improves the forecasting accuracy, and can be attributed to the multivariate structure of the SARIMAX model. While stochastic models like SARIMAX offer valuable decision-making tools for water resource management, the study underscores the significance of employing long-term time series encompassing flood and drought periods and including model uncertainty analysis to enhance the robustness of forecasts. In this study, the SARIMAX model provides a better fit for extreme values, overestimating peaks by around 11.6% and troughs by about 5.0%, compared with the SARIMA model, which tends to underestimate peaks by an average of 6.5% and overestimate troughs by approximately 76.0%. The findings contribute to the literature on water management strategies and mitigating risks associated with extreme hydrological events.

Cities must develop actions that reduce flood risk in the face of extreme rainfall events. In this study, the dynamic resilience of the Gregorio catchment (São Carlos, Brazil) was assessed. The catchment lacks environmental monitoring and suffers from recurrent floods. The resilience curves were made considering the water depth in the drainage system as the performance index, obtained by simulations with SWMM and HEC-RAS. The calibration of the flood extension was performed using citizen science data. The contribution to increasing the dynamic resilience by implementing decentralized low impact development (LID) practices was also evaluated. For this purpose, bioretention cells were added to the SWMM simulations. The resilience curves were then calculated for the current and future climate scenario, with and without LID, for return periods of 5, 10, 50, and 100 years and duration of 30, 60, and 120 min. Intensity–duration–frequency curves (IDFs) updated by the regional climate model MIROC5 for 2050 and 2100 were used. The results showed a significant improvement in the system’s resilience for light storms and the current period due to LID practice interventions. Efficiencies were reduced for moderate and heavy storms with no significant drops in floodwater depth and resilience regardless of the scenario.