Due to the ever-increasing degree of urbanization, blue and green infrastructures are becoming important tools for achieving stormwater management sustainability in urban areas. Concerning green roofs, although scientists have investigated their behaviors under different climates and building practices, their hydrological performance is still a thought-provoking field of research. An event scale analysis based on thirty-five rainfall–runoff events recorded at a new set of experimental green roofs located in Southern Italy has been performed with the aim of identifying the relative roles of climate, substrate moisture conditions, and building practices on retention properties. The retention coefficient showed a wide range of variability, which could not be captured by neither simple nor multiple linear regression analysis, relating the latter to rainfall characteristics and substrate soil water content. Significant improvements in the prediction of the retention coefficient were obtained by a preliminary identification of groups of rainfall–runoff events, based on substrate soil water content thresholds. Within each group, a primary role is played by rainfall. At the experimental site, building practices, particularly those concerning the drainage layer properties, appeared to affect the retention properties only for specific event types.

Evapotranspiration (ET) is a key variable in the hydrological cycle and it directly impacts the surface balance and its accurate assessment is essential for a correct water management. ET is difficult to measure, since the existing methods for its direct estimate, such as the weighing lysimeter or the eddy-covariance system, are often expensive and require well-trained research personnel. To overcome this limit, different authors developed experimental models for indirect estimation of ET. However, since the accuracy of ET prediction is crucial from different points of view, the continuous search for more and more precise modeling approaches is encouraged. In light of this, the aim of the present work is to test the efficiency in predicting ET fluxes in a newly introduced physical-based model, named Prospero, which is based on the ability to compute the ET using a multi-layer canopy model, solving the energy balance both for the sunlight and shadow vegetation, extending the recently developed Schymanski and Or method to canopy level. Additionally, Prospero is able to compute the actual ET using a Jarvis-like model. The model is integrated as a component in the hydrological modelling system GEOframe. Its estimates were validated against observed data from five Eddy covariance (EC) sites with different climatic conditions and the same vegetation cover. Then, its performances were compared with those of two already consolidated models, the Priestley–Taylor model and Penman FAO model, using four goodness-of-fit indices. Subsequently a calibration of the three methods has been carried out using LUCA calibration within GEOframe, with the purpose of prediction errors. The results showed that Prospero is more accurate and precise with respect to the other two models, even if no calibrations were performed, with better performances in dry climatic conditions. In addition, Prospero model turned to be the least affected by the calibration procedure and, therefore, it can be effectively also used in a context of data scarcity.

Evapotranspiration is the major component of the water cycle, so a correct estimate of this variable is fundamental. The purpose of the present research is to assess the monthly scale accuracy of six meteorological data-based models in the prediction of evapotranspiration (ET) losses by comparing the modelled fluxes with the observed ones from eight sites equipped with eddy covariance stations which differ in terms of vegetation and climate type. Three potential ET methods (Penman-Monteith, Priestley-Taylor, and Blaney-Criddle models) and three actual ET models (the Advection-Aridity, the Granger and Gray, and the Antecedent Precipitation Index method) have been proposed. The findings show that the models performances differ from site to site and they depend on the vegetation and climate characteristics. Indeed, they show a wide range of error values ranging from 0.18 to 2.78. It has been not possible to identify a single model able to outperform the others in each biome, but in general, the Advection-Aridity approach seems to be the most accurate, especially when the model calibration in not carried out. It returns very low error values close to 0.38. When the calibration procedure is performed, the most accurate model is the Granger and Gray approach with minimum error of 0.13 but, at the same time, it is the most impacted by this process, and therefore, in a context of data scarcity, it results the less recommended for ET prediction. The performances of the investigated ET approaches have been furthermore tested in case of lack of measured data of soil heat fluxes and net radiation considering using empirical relationships based on meteorological data to derive these variables. Results show that, the use of empirical formulas to derive ET estimates increases the errors up to 200% with the consequent loss of model accuracy.

Green roofs (GR) are effective tools for the mitigation of the negative hydrological impact linked to uncontrolled urbanization. Models for runoff response of vegetated covers support planning decisions about the use of this technology in ever-expanding areas, but there is still large uncertainty in this research area. The goal of the present study was to define the accuracy of three selected models for the simulation of the hydrological behavior of a GR, with a particular focus on the precipitation pattern characteristics. The Nash cascade model, Storm Water Management Model (SWMM), and HYDRUS-1D models were selected. Each model was calibrated over 24 rainfall-runoff events collected at the experimental site located in Southern Italy. Rainfall events were characterized using several criteria and were divided into convective, stratiform, and tropical precipitation types according to the shape of the rainfall profile identified by the rainfall binary shape code. The models returned overall satisfactory performances with average Nash–Sutcliffe Efficiency index of 0.65 for the SWMM and HYDRUS and 0.73 for the Nash model. In general, the models were better performing for stratiform and tropical events. SWMM and HYDRUS predicted with higher accuracy the convective events while the Nash model appeared more suitable for stratiform events.

In the last decades, there has been a widespread implementation of Green Infrastructures worldwide. Among these, green roofs appear to be particularly flexible sustainable drainage facilities. To predict their effectiveness for planning purposes, a tool is required that provides information as a function of local meteorological variables. Thus, a relatively simple daily scale, one-dimensional water balance approach has been proposed. The crucial evapotranspiration process, usually considered as a water balance dependent variable, is replaced here by empirical relationships providing an a-priori assessment of soil water losses through actual evapotranspiration. The modelling scheme, which under some simplification can be used without a calibration process, has been applied to experimental runoff data monitored at a green roof located near Bernkastel (Germany), between April 2005 and December 2006. Two different empirical relationships have been used to model actual evapotranspiration, considering a water availability limited and an energy limited scheme. Model errors quantification, ranging from 2% to 40% on the long-term scale and from 1% to 36% at the event scale, appear strongly related to the particularly considered relationship.

In accordance with the latest german regulations, the basic idea of the project “sustainable urban storm Water management”, funded by...