Flood modelling using synthesised citizen science urban streamflow observations

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 km(2) <= Area <= 21.84 km(2)) 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.