Michele Turco

This study proposes an integrated predictive framework for analysis of substrates’ hydrological response in urban nature-based solutions (NBS), combining numerical modeling with HYDRUS-1D, probabilistic techniques, and advanced tools of statistical analysis and machine learning. The aim is to quantify the influence of Van Genuchten’s hydraulic parameters on flow generation and identify critical factors for optimized green infrastructures’ design. Monte Carlo simulations were carried out on eight parameterized families of NBS substrates, each represented by about 1,000 combinations of hydraulic parameters derived from laboratory analysis. The results show that the most influential parameters are saturated hydraulic conductivity ( Ksat ), parameter α (retention curve position), and, in some cases, parameter n (curve slope). The simulations returned average annual outflow values ranging from − 4,096    mm (less efficient substrate) to − 4,336    mm (more draining substrate), with standard deviations between 1.2 mm (high stability) and 188 mm (high instability). Among the analyzed substrates, through regression analysis, the coarse-textured T5, T6, and T7 (sand- and gravel-rich growing media with high hydraulic conductivity) showed the best overall performance, combining high drainage efficiency, robust model predictability, and a dominant influence of the α parameter. The parameters α and Ksat have emerged as dominant drivers in the most draining substrates, while θ s (saturated water content) and n are critical in substrates with intermediate retention capacity. The methodology also integrates correlation analysis (Pearson, Spearman), partial dependence plot, and nonparametric bootstrap, providing a robust and multiperspective diagnosis of hydraulic sensitivity. The results identify the most stable and predictable substrate configurations and provide practical guidance for NBS design and calibration in complex urban scenarios.

In submitting conference proceedings to Engineering Proceedings, the Volume Editors of the proceedings would like to certify to the publisher that all papers published in this volume have been subjected to peer review by the designated expert referees and were administered by the Volume Editors

Nature-based solutions (NbS) are sustainable tools to mitigate the impacts of climate change and urbanization. Thus, we present a specific research activity of the “Tech4You” Project, whose main objective is to contribute to the widespread implementation of NbS. In this regard, a specific area of the Cerisano urban catchment was selected for the implementation of a rain garden. A preliminary design and a predictive model were developed to assess its hydrological performance. The findings are promising and show how this green infrastructure can positively contribute to urban stormwater management.

The integration of remote sensors with nature-based solutions (NBS) offers new opportunities for suitable hydrological monitoring and risk reduction. This study implements 2D-1D numerical modelling tools to guide the allocation of both sensors and NBS interventions. Using the Cerisano catchment as a case study, the software IBER-SWMM (3.3.1–5.2 respectively) was employed to reproduce rainfall-runoff under different scenarios. Outputs were analysed to identify hydrologically sensitive zones where sensor deployment and NBS implementation would maximise monitoring efficiency and mitigation benefits. Results demonstrate how modelling supports effective decisions that are often limited by budget and site-specific constraints.

The CSDU-CSSI DAYS 25—II International Conference on “Challenges and Perspectives in Urban Water Management Systems”—was held on the 18th and 19th of November 2025 in Trieste (Italy), hosted by Università degli Studi di Trieste and jointly organized by Centro Studi Idraulica Urbana (CSDU) and Centro Studi Sistemi Idrici (CSSI), which are two Italian non-profit private societies engaged in urban water scientific research and dissemination

Urbanization and climate change have intensified the challenges of managing stormwater due to increased impervious surfaces and more frequent extreme weather events. Effective water management strategies are essential to mitigate flood risks, enhance water quality, and improve urban resilience. Green infrastructure (GI) represents a key nature-based solution to address these challenges by integrating natural processes into the built environment. Numerical modelling plays a crucial role in the widespread adoption of GIs at the urban scale. In this study, the hydrological models, HYDRUS-1D and SWMM, are coupled to assess the hydrological performance of GIs in managing urban runoff. The methodology involves characterizing GI components, defining representative hydrological scenarios, and applying stratified models. This integrated approach enables accurate simulation of water flow dynamics within GI systems and their interaction with the urban drainage network. The results of the coupled models highlight the effectiveness of GI in reducing runoff volumes and delaying peak flows, thereby alleviating hydraulic pressure on conventional drainage systems and enhancing urban resilience to extreme weather events. To define the soil hydraulic properties in the HYDRUS model, the evaporation method was used, and the van Genuchten-Mualem model was selected to characterize the Soil Water Retention Curve of the substrate. This model showed a saturated volumetric water content of 0.543 cm³ cm⁻³ and high permeability, ensuring efficient drainage and avoiding ponding. Building-scale modeling demonstrated a 36% reduction in runoff volume, while urban-scale simulations showed decreased surcharge of nodes and conduits, confirming improved stormwater management and flood risk mitigation. By combining the analytical strengths of both models, this study provides a robust tool for evaluating performance under varying environmental conditions. The findings support the broader adoption of nature-based solutions in sustainable urban planning, aligning with global initiatives such as the SDG 11 and SDG 13, which focus on fostering resilient and sustainable cities. This research underscores the importance of integrating advanced modeling tools into the design and management of green infrastructure to effectively address contemporary urban water management challenges.

Urban green spaces, including green roofs (GRs), are vital infrastructure for climate resilience, retaining water in city landscapes and supporting ecohydrological processes. Quantifying the hydrologic performance of GRs in the urban environment for future climate scenarios is the original contribution of this research developed within the URCA! project. For this purpose, a continuous modelling approach is undertaken to evaluate the hydrological performance of GRs expressed by means of the runoff volume and peak flow reduction at the event scale for long data series (at least 20 years). To investigate the prediction of GRs performance in future climates, a simple methodological approach is proposed, using monthly projection factors for the definition of future rainfall and temperature time series, and transferring the system parametrization of the current model to the future one. The proposed approach is tested for experimental GR sites in Genoa and Rende, located in Northern and Southern Italy, respectively. Referring to both the Genoa and Rende experimental sites, simulation results are analysed to demonstrate how the GR performance varies with respect to rainfall event characteristics, including total depth, maximum rainfall intensity and ADWP for current and future scenarios.

In this study, to improve the application and performance of conventional green roof systems, a novel multi-purpose green roof system was simulated numerically using computational fluid dynamics (CFD). The innovative multi-purpose green roof contains a soil layer and water filter, meaning the water retention time not only depends on the soil media but also depends on the filter’s pore size, improving the impact on runoff quality and quantity. In this regard, after mesh sensitivity analysis, the developed model was validated using experimental data, and the results show the accuracy of CFD in the simulation of porous media and filters. Comparisons between experimental and numerical results demonstrate the impact of proper porosity values in the simulation of a porous environment and reveal the source of errors in the numerical prediction of capillary flow in soil media, which can be minimized by adaptive consideration of the parameters, such as wall adhesion and appropriate wettability.