No abstract

The increase in floods and flash floods over the last decades has motivated researchers to develop improved methodologies for flood risk prevention and warning. Flood forecasting models available today have evolved technologically but are subject to limitations due to the lack of data and limited community participation. This paper presents the Hydrological Alert Model with Participatory Basis (HAMPB) model, an approach for integrating water level data reported by citizens, which has the advantage of being inexpensive and potentially highly available, with traditional data to improve flood forecasting. The model assimilates spatiotemporal water levels measured in the field when they are available through a real‐time estimator. We added random perturbations of up to |10| and |15| cm to those data using the Monte Carlo Method to mimic the uncertainty in citizen science data collection. Applying the HAMPB model for urban nested‐scale catchments (0.11 km2 ≤ Area ≤ 21.84 km2) in Brazil shows: (a) significant improvements in flood simulations when field data was assimilated even considering the volunteered data uncertainty; (b) capability to update simulations in more than one point in the semi‐distributed hydrological model by a regionalization method; and (c) flood hazard indexes and their uncertainties show better estimations using field data for updating.

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.