Permeable pavements have been used widely across the world to manage urban stormwater. The hydrological behaviour of permeable surfaces is a complex process affected by many factors, such as rainfall intensity, rainfall duration, pavement geometrical conditions, and clogging level of the permeable surface, amongst others. This laboratory study was carried out to assess the influence of clogging level and rainfall intensity on the infiltration capacity of porous mixture surfaces used in Permeable Pavement Systems (PPS). Porous Concrete (PC) and Porous Asphalt (PA) mixtures with different air void contents (15%, 20%, and 25%) were subject to different clogging scenarios by using varying sediment loads (0, 500, and 1000 g/m²). Permeability experiments were carried out for each clogging scenario through a new rainfall simulator specially developed, tailored, and calibrated for the laboratory simulation of a wide range of rainfall events. Permeability measurements were taken under all different scenarios as a result of the combination of the different rainfall events (50, 100, and 150 mm/h) simulated over the specimens of porous mixtures and the sediment loads applied to them. The results showed that the PC mixtures tested perform better than the PA ones in terms of infiltration capacity, showing less potential for clogging and being more easily cleaned by the wash-off produced by the simulated rainfall events.

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).

The implementation of green roofs as sustainable urban drainage systems provides benefits for stormwater control and the environment and is more and more encouraged. A model for the estimation of the probability of vegetation survival without irrigation is proposed. The model, developed through a probabilistic analytical derivation procedure, can also consider the effects of chained rainfall events, without the need of continuous simulation of hydrological processes. The model equations can be useful in the design of green roofs, allowing to determine the growing medium thickness in terms of an assumed risk of vegetation withering in dry periods. The proposed model is also able to identify the optimal thickness of the growing medium, over which the survival performances can be increased only with irrigation. Model performances were tested by the application to two case studies in Italy. Comparison between the probabilities and the cumulative frequencies from a continuous simulation of water content in the growing medium shows a good agreement and provide a first confirmation of reliability.

Permeable asphalt (PA) is a composite material with an open graded mix design that provides a pore structure facilitating stormwater infiltration. PA is often constructed as a wearing course for permeable pavements and on impervious pavements to reduce aquaplaning and noise. The pore structure of PA functions as a filter promoting particulate matter (PM) separation. The infiltrating flow characteristics are predominately dependent on pore diameter and pore interconnectivity. X-Ray microTomography (XRT) has successfully estimated these parameters that are otherwise difficult to obtain through conventional gravimetric methods. Pore structure parameters allow modeling of hydraulic conductivity (k) and filtration mechanisms; required to examine the material behavior for infiltration and PM separation. In this study, pore structure parameters were determined through XTR for three PA mixture designs. Additionally, the Kozeny-Kovàv model was implemented to estimate k. PM separation was evaluated using a pore-to-PM diameter categorical model. This filtration mechanism model was validated with data from a rainfall simulator. The filtration model provided a good correlation between measured and modeled data. The identification of filtration mechanisms and k facilitate the design and evaluation of permeable pavement systems as a best management practice (BMP) for runoff volume and peak flow as well as PM and PM-partitioned chemical separation.

Permeable pavement (PP) can be a primary source-area component of a sustainable drainage system. PP facilitates runoff reduction and particulate matter (PM) separation. PM separated by the PP surface reduces hydraulic conductivity ($K$ ) and influences PP functionality and maintenance. Pragmatic and reproducible methods are necessary to identify $K$ reduction and maintenance. Because $K$ is the proportionality constant between specific discharge and hydraulic gradient (Darcy’s law), disparate test methods and conditions produce disparate $K$ results. Consequently, evaluations based solely on $K$ can lack guidance when the goal is runoff reduction. Herein, PP response is examined based on $K$ , outflow time (OT) through the PP across a fixed driving head range, and volumetric runoff coefficient ($C$ ). The PP test matrix parameters are (1) total porosity from 0.15 to 0.25, (2) rainfall loading from 0 to $150\mathrm{mm}/\mathrm{h}$ , (3) PM aerial loads from 0.5 to $2.0\mathrm{kg}/{\mathrm{m}}^2$ as a silty-sand schmutzdecke, (4) slopes from 2.5% to 7.0%, and (5) PP maintenance. Results demonstrate (1) higher PM aerial loads increase OT ($K$ decreases), (2) higher total porosity decreases OT ($K$ increases), and (3) higher rain rates inconsistently produce lower OT compared with the no-rainfall control at higher PM aerial loads. For all tests ($N=285$ ) with $C$ measurements, lumping all other parameters, the mean $C$ is 0.07, the standard deviation is 0.11, and the maximum is 0.67. These $C$ results are significantly lower than impervious pavements. As the PP slopes and PM aerial loads increased, $C$ increased and $K$ decreased but with no clear trend between $K$ and $C$ . For tests where $K<0.01\mathrm{mm}/\mathrm{s}$ and $\mathrm{OT}<300\mathrm{s}$ , $C$ was $<0.35$ . An OT limit of 300 s provides guidance for PP conditions that provide runoff reduction, with $C<0.20$ for 2.0% slopes. PP conditions in this limit provide discernable runoff reduction. OT and $C$ can be pragmatic adjuvants with disparate $K$ methods.