Sustainable Urban Drainage Systems (SUDS) include strategies and solutions for distributed stormwater management and control. They are strongly encouraged, especially in highly urbanized areas that suffer the combined effect of impervious surfaces and the increase in extreme rainfall events due to urbanization growth and climate change. Their integration into traditional urban drainage systems can mitigate flood risk and pollution of receiving water bodies. The main goal of SUDS is to restore the natural water balance by increasing infiltration and evapotranspiration processes and promoting rainwater harvesting and reuse. This paper proposes an analytical-probabilistic approach for SUDS modeling applicable to different systems. Developed equations allow estimating the runoff and residual storage probability for evaluating the efficiency of the storage volume both in terms of flood control and, depending on SUDS type, in terms of emptying time or water needs supply. The modeling considers the possibility of consecutive chained rainfalls; this feature is relevant for SUDS, often characterized by low outflow rates. Relating characteristic parameters to a probabilistic level (the Average Return Interval, ARI) makes the formulas interesting to be used in the design practice. An application to two case studies confirmed the goodness of the proposed method.

Storage tanks from rainwater harvesting systems (RWHs) are designed to provide flow equalization between rainfall and water demand. The minimum storage capacity required to take into account the maximum variations of stored water volumes, i.e., the active storage, depends basically on the magnitude and the variability of rainfall profiles and the size of the demand. Given the random nature of the variables involved in the hydrological process, probability theory is a suitable technique for active storage estimation. This research proposes a probabilistic approach to determine an analytical expression for the cumulative distribution function (CDF) of the active storage as a function of rainfall moments, water demand and the mean number of consecutive storm events in a deficit sub-period. The equation can be used by developers to decide on the storage capacity required at a desired non-exceedance probability and under a preset water demand. The model is validated through a continuous simulation of the tank behavior using rainfall time series from Milan (Northern Italy).

With growing water scarcity and increasing variability in rainfall patterns due to climate change, along with the pressures of urbanization and population growth, the adoption of non-conventional water resources such as rainwater harvesting and water reuse has become a crucial sustainable strategy. Traditional approaches to the design of rainwater tanks, based on the demand volume in a predefined dry period, don’t take into account properly the risk of failure. Two probabilistic methods are then proposed, to address this issue. The first method is a modification of the conventional “demand-side” approach, by the use of a probabilistic estimation of the inter-event time. The second one, more reliable and more complex, takes into account in a parametric way the full stochastic rainfall process, allowing to consider also the pre-filling possibility due to consecutive storm events. Complexity in this second method is managed in order to develop direct relationships for practical applications. Although enough simple, these methods improve the design of Rainwater Harvesting Systems (RWHs), allowing to apply cost-benefit analysis procedures. Methods are compared and evaluated through the application to a case study in Milan, Italy.

Traditional urban drainage systems face increasing pressures due to growing urbanization and climate change, which amplify stormwater volumes beyond the design capacity of sewer networks. These conditions often result in combined sewer overflows (CSOs) and wastewater treatment plant (WWTP) bypasses, discharging untreated, nutrient-laden effluents into receiving water bodies. Elevated nitrogen (N) and phosphorus (P) loads from these events exacerbate eutrophication and harmful algal blooms in lakes. The ENVISION project (Lake Pollution: Integrating Nature-Based Solutions Into Environmental Urban Planning for Risk Mitigation) addresses the impact of urban discharges on lakes and evaluates the potential of Nature-Based Solutions (NBSs) to mitigate N and P releases from CSOs. A coupled hydrological–hydraulic and probabilistic modeling framework is applied to the Lake Maggiore (Northern Italy) catchment as a case study, to assess the potential of NBSs in reducing CSO events and, consequently, the N and P loads discharged into the lake. Preliminary results from the hydrological–hydraulic approach are presented.