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.

Low-impact developments (LIDs), such as green roofs, have proven to be valuable alternatives for stormwater management and hydrological restoration. Mechanistic models are reliable and accurate tools for analysis of the hydrologic behavior of LIDs, yet only a few studies provide a comprehensive numerical analysis of the hydrological processes involved and test their model predictions against field-scale data. Moreover, more research is needed to determine the unsaturated hydraulic properties of the substrates used in LIDs. For these reasons, the aim of this study was to provide a comprehensive description of the hydrological behavior of an extensive green roof installed at the University of Calabria. The soil hydraulic properties were determined by using the simplified evaporation method. Both unimodal and bimodal soil hydraulic functions were used in the analysis. The estimated parameters were then used in the HYDRUS-3D model to simulate a 2-molong period. Precipitation, irrigation, evaporation, and root water uptake processes were included in the numerical analysis. The values of 0.74 and 0.8 of the Nash–Sutcliffe efficiency index for the model predictions using unimodal and bimodal functions, respectively, confirmed the good agreement between the modeled and measured outflows. The bimodal model was able to both accurately reproduce the hydrographs in both dry and wet periods and account for daily fluctuations of soil moisture. Finally, the validated model was used to carry out a hydrological analysis of the green roof and its hydrological performance during the entire simulated period as well as during single precipitation events.

The aim of this study is to investigate the use of different global sensitivity analysis techniques in conjunction with a mechanistic model in the numerical analysis of a permeable pavement installed at the University of Calabria. The Morris method and the variance-based E-FAST procedure are applied to investigate the influence of soil hydraulic parameters on the pavement’s behavior. The analysis reveals that the Morris method represents a reliable computationally cheap alternative to variance-based procedures for screening important factors and provides the first inspection of the model. The study is completed by a combined GSA-GLUE uncertainty analysis used to evaluate the model accuracy.

Mechanistic models have proven to be accurate tools for the numerical analysis of the hydraulic behavior of Low Impact Development (LIDs) techniques. However, their widespread adoption has been limited by their computational cost. In this view, surrogate modeling is focused on developing and using a computationally inexpensive surrogate of the original model. While having been previously applied to various water-related and environmental modeling problems, no studies have used surrogate models for the analysis of LIDs. The aim of this research thus was to investigate the benefit of surrogate-based modeling in the numerical analysis of LIDs. The kriging technique was used to approximate the deterministic response of the widely used mechanistic model HYDRUS-2D, which was employed to simulate the variably-saturated hydraulic behavior of a contained stormwater filter. The Nash-Sutcliffe efficiency (NSE) index was used to compare the simulated and measured outflows and as the variable of interest for the construction of the response surface. The validated kriging model was first used to carry out a Global Sensitivity Analysis of the unknown soil hydraulic parameters of the filter layer, revealing that only the shape parameter α and the saturated hydraulic conductivity Ks significantly affected the model response. Next, the Particle Swarm Optimization algorithm was used to estimate their values. The NSE value of 0.85 indicated a good accuracy of estimated parameters. Finally, the calibrated model was validated against an independent set of measured outflows with a NSE value of 0.8, which again corroborated the reliability of the surrogate-based optimized parameters.