Improving flood forecasting using real-time data to update urban models in poorly gauged areas

Abstract

Flood forecasting techniques have been widely studied as a tool to mitigate damage from extreme events. However, their nature in urban areas developed without properly drainage planning, coupled with the scarcity of hydrological monitoring data, becomes a significant challenge for real-time flood forecasting. This doctoral thesis proposes deterministic methods of data assimilation for real-time hydrological forecasting. The methodology is developed using the semi-distributed hydrodynamic Storm Water Management Model (SWMM). It also aims to evaluate the impact of using traditional monitoring data together with citizen science data for model updating. The first and the second chapter present the general introduction and methodology of the thesis. The third chapter presents an automatic calibration tool-SWMM calibrator-developed to allow the adjustment of SWMM model parameters with data from multiple sources and to use observed level data as a priori knowledge. The fourth chapter deals with the use of citizen science data for urban model updating through a real-time estimator. The fifth chapter presents a data assimilation method by updating hydrological model inputs based on water level observations and evaluates the effectiveness of the technique in a distributed manner in the catchment. The proposed methodologies are validated in a case study at the Monjolinho urban catchment. The sixth chapter discusses general conclusions and recommendations. In conclusion, SWMM calibrator tool provides flexibility in calibration, allowing shaping the process according to the real-world limitations, and achieved satisfactory calibration results at Monjolinho catchment. The deterministic data assimilation methods proposed in the fourth and fifth chapters have shown effective results in a significant improvement in simulations accuracy.