Luis Cea

Distributed hydrological models based on shallow water equations have gained popularity in recent years for the simulation of storm events, due to their robust and physically based routing of surface runoff through the whole catchment, including hill slopes and water streams. However, significant challenges arise in their calibration due to their relatively high computational cost and the extensive parameter space. This study presents a surrogate-assisted evolutionary algorithm (SA-EA) for the calibration of a distributed hydrological model based on 2D shallow water equations. A surrogate model is used to reduce the computational cost of the calibration process by creating a simulation of the solution space, while an evolutionary algorithm guides the search for suitable parameter sets within the simulated space. The proposed methodology is evaluated in four rainfall events located in the northwest of Spain: one synthetic storm and three real storms in the Mandeo River basin. The results show that the SA-EA accelerates convergence and obtains superior fit values when compared to a conventional global calibration technique, reducing the execution time by up to six times and achieving between 98% and 100% accuracy in identifying behavioral parameter sets after four generations of the SA-EA. The proposed methodology offers an efficient solution for the calibration of complex hydrological models, delivering improved computational efficiency and robust performance.

Roofs represent a high percentage of the impervious surfaces in urban areas, and hence their implementation in urban drainage models is essential for accurate results to be achieved. Current modelling approaches are based on parameters such as a roof's slope and width, its roughness coefficient, and the initial abstraction. In this study, an experimental campaign was performed in order to assess the sensitivity of the roof runoff hydrographs to these parameters. The experimental tests were carried out in a new large‐scale urban drainage facility equipped with a rainfall simulator. The experimental tests were replicated numerically using three different levels of model resolution, from a high fidelity representation with a spatial resolution of 5 mm (which can be considered a digital twin) to a lumped representation. Our experimental results show that, for practical purposes, the sensitivity of the outlet runoff hydrograph to the roof slope tested is negligible. The numerical upscaling analysis carried out showed that flat roofs present a slightly different hydrograph behaviour with greater times of concentration than sloped roofs. No significant sensitivity of the outlet hydrograph to the surface roughness coefficient was found. In terms of numerical modelling, the use of a very high spatial resolution for the roof, which implies a high computational cost, does not affect the results significantly compared to the far simpler lumped approach. The current research involves the first thorough experimental and computational analysis of the runoff over roofs to date.