Research papersA comprehensive numerical analysis of the hydraulic behavior of a permeable pavement

- Among Low Impact Development techniques permeable pavement are those that most lacks reliable and accurate modeling tools.
- A mechanistic model is used for the analysis of the hydraulic behavior of a permeable pavement.
- A Global Sensitivity Analysis is used to identify the main effect and interactions of soil hydraulic parameters.
- The model is calibrated by using the metaheuristic Particle Swarm Optimization algorithm.
- The calibrated model is validated on an independent set of measurements with optimal results.

The increasing frequency of flooding events in urban catchments related to an increase in impervious surfaces highlights the inadequacy of traditional urban drainage systems. Low Impact Development (LID) techniques have proven to be a viable and effective alternative by reducing stormwater runoff and increasing the infiltration and evapotranspiration capacity of urban areas. However, the lack of adequate modeling tools represents a barrier in designing and constructing such systems. This paper investigates the suitability of a mechanistic model, HYDRUS-1D, to correctly describe the hydraulic behavior of permeable pavement installed at the University of Calabria. Two different scenarios of describing the hydraulic behavior of the permeable pavement system were analyzed: the first one uses a single-porosity model for all layers of the permeable pavement; the second one uses a dual-porosity model for the base and sub-base layers. Measured and modeled month-long hydrographs were compared using the Nash-Sutcliffe efficiency (NSE) index. A Global Sensitivity Analysis (GSA) followed by a Monte Carlo filtering highlighted the influence of the wear layer on the hydraulic behavior of the pavement and identified the ranges of parameters generating behavioral solutions. Reduced ranges were then used in the calibration procedure conducted with the metaheuristic Particle swarm optimization (PSO) algorithm for the estimation of hydraulic parameters. The best fit value for the first scenario was NSE = 0.43; for the second scenario, it was NSE = 0.81, indicating that the dual-porosity approach is more appropriate for describing the variably-saturated flow in the base and sub-base layers. Estimated parameters were validated using an independent, month-long set of measurements, resulting in NSE values of 0.43 and 0.86 for the first and second scenarios, respectively. The improvement in correspondence between measured and modeled hydrographs confirmed the reliability of the combination of GSA and PSO in dealing with highly dimensional optimization problems. Obtained results have demonstrated that PSO, due to its easiness of implementation and effectiveness, can represent a new and viable alternative to traditional optimization algorithms for the inverse estimation of unsaturated hydraulic properties. Finally, the results confirmed the suitability and the accuracy of HYDRUS-1D in correctly describing the hydraulic behavior of permeable pavements.