Mirka Mobilia

Introduction: The changes in land use associated with urban development cause an increase in urban flooding. Low Impact Development (LID) systems help to mitigate this hazardous phenomenon. Methods: Among LIDs, Permeable Pavement (PP) proved to be a very effective technology in reducing surface runoff. In light of this, the present research analyzes the Retention Capacities (RC) of three different PP samples, which differ in terms of composition and percentage of bitumen and aggregates and have been realized according to Italian national regulations and technical specifications. Hydraulic laboratory tests are conducted using a rainfall simulator to quantify the Retention Capacity (RC) of the three samples in response to rainfall events with different intensities (5, 10, 20, 30 mm/h). Results: The values of RC range between 85% and 20%, depending on the rainfall and sample properties, confirming the high potential of PPs in reducing surface stormwater production. The accuracy of HYDRUS-1D model in simulating the surface runoff from the PP samples has been investigated. HYDRUS-1D has been calibrated using measured data of runoff from the laboratory tests and adopting NSE as an optimization criterion. Conclusion: The parameters sets obtained by the calibration procedure give back Nash–Sutcliffe Efficiency (NSE) values close to 1 for each PP configuration, which means a very high accuracy in model prediction. Finally, a sensitivity analysis has allowed to identify, by means of a global sensitivity index S, the most and the less influential parameters within the model, which respectively are the saturated hydraulic conductivity Ks (S=0.57) and the tortuosity coefficient L (S=0.015).

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 performance is strongly impacted by climatic conditions, design parameters and aging. In particular, the evolution in time of physical and chemical properties may lead to substantial changes in their retention and detention capacity. The growth of the roots, above all, seems to affect the interpretation of the soil water content, a key parameter for GRs retention performance. Generally, Frequency Domain Reflectometry sensors are used in the assessment of the volumetric water content of the soil but they require a calibration procedure in order to obtain reliable measurements. In this study, changes in Frequency Domain Reflectometry sensors calibration caused by the presence of root system were investigated. For this purpose, two substrate soil samples have been collected from an experimental Green Roofs located within the University of Salerno: the first, mainly consisting of peat, during the construction phase and the second, consisting of peat with a developed root system, two years later. Frequency Domain Reflectometry sensor measurements were plotted against observed volumetric water content to obtain calibration curves. Results show that the sensors seem not to be able to predict the water adsorbed by the root system, confirming the hypothesis that Green Roofs evolution can have an important impact on substrate volumetric water content observation.

Evapotranspiration is a key process within the hydrological cycle, so it requires an accurate assessment. This work aims at assessing monthly scale performances of six meteorological data-based methods to predict evapotranspiration by comparing model estimates with observations from six flux tower sites differing for land cover and climate. Three of the proposed methodologies use a potential evapotranspiration approach (Penman, Priestley-Taylor and Blaney-Criddle models) while the additional three an actual evapotranspiration approach (the Advection-Aridity, the Granger and Gray and the Antecedent Precipation Index method). The results show that models efficiency varies from site to site, even though land cover and climate features appear to have some influence. It is difficult to comment on a general accuracy, but an overall moderate better performance of the Advection-Aridity model can be reported within a context where model calibration is not accounted for. If model calibration is further taken into consideration, the Granger and Gray model appears the best performing method but, at the same time, it is also the approach which is mostly affected by the calibration process, and therefore less suited to evapotranspiration prediction tools dealing with a data scarcity context. Keywords Actual evapotranspiration Potential evapotranspiration Ameriflux Fluxnet Tereno earth observation

This study aims at illustrating a methodology for predicting monthly scale actual evapotranspiration losses only based on meteorological data, which mimics the evapotranspiration intra-annual dynamic. For this purpose, micrometeorological data at the Rollesbroich and Bondone mountain sites, which are energy-limited systems, and the Sister site, a water-limited system, have been analyzed. Based on an observed intra-annual transition between dry and wet states governed by a threshold value of net radiation at each site, an approach that couples meteorological data-based potential evapotranspiration and actual evapotranspiration relationships has been proposed and validated against eddy covariance measurements, and further compared to two well-known actual evapotranspiration prediction models, namely the advection-aridity and the antecedent precipitation index models. The threshold approach improves the intra-annual actual evapotranspiration variability prediction, particularly during the wet state periods, and especially concerning the Sister site, where errors are almost four times smaller compared to the basic models. To further improve the prediction within the dry state periods, a calibration of the Priestley-Taylor advection coefficient was necessary. This led to an error reduction of about 80% in the case of the Sister site, of about 30% in the case of Rollesbroich, and close to 60% in the case of Bondone Mountain. For cases with a lack of measured data of net radiation and soil heat fluxes, which are essential for the implementation of the models, an application derived from empirical relationships is discussed. In addition, the study assessed whether this variation from meteorological data worsened the prediction performances of the models.

In time, several models with different complexity have been proposed to predict the retention performances of a green roof. In the current study three conceptual models of increasing complexity in descriptive details, are calibrated and compared to experimental data. The proposed approaches consist of daily scale hydrological models, based on water balance equations, where the main processes and variables accounted for are the precipitation input, the evapotranspiration losses, and the maximum water storage capacity. Model detail increase is achieved moving from an approach using potential evapotranspiration and constant storage threshold to an approach using actual evapotranspiration and a variable storage threshold. The main findings confirm on one side the role played by evapotranspiration modeling and, on the other side, the good accuracy achieved, in a minimal calibration requirement approach, through the modeling of basic and elemental processes.

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.

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.