Precipitation trend analysis, on different spatial and temporal scales, has been of great concern during the past century because of the attention given to global climate change by the scientific community. According to some recent studies, the Italian territory has been suffering a precipitation decrease, especially in the last 50 years, and the southern areas seem to be more affected. The aim of the present study is to analyse rainfall time series over a wide time interval and a wide area, detecting potential trends and assessing their significance. For this purpose, 211 gauged stations, mainly located within the Campania region, southern Italy, have been analysed for the period 1918–1999. An accurate database has been set up through a data quality and time series homogeneity process. Statistical analysis of the database highlight that (1) the trend appears predominantly negative, both at the annual and seasonal scale, except for the summer period when it appears to be positive; (2) over the whole reference period, positive and negative trends are significant respectively for 9 and 27% of total stations and (3) over the last 30 years, a negative trend is instead significant for 97% of the total stations. Copyright © 2009 Royal Meteorological Society

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.

Baseflow index (BFI) prediction in ungauged basins has largely been based on the use of catchment physiographic attributes as dominant variables. In a context where changes in climate are increasingly evident, it is also important to study how the slow component of flow is potentially affected by climate. The aim of this study was to illustrate the impact of climate variability on the baseflow process based on analysis of daily rainfall characteristics and hydrological modelling simulation exercises validated with observed data. Ten catchments were analysed that span southern to northern Europe and range from arid Mediterranean to maritime temperate climate conditions. Additionally, more than 2,000 virtual catchments were modelled that cover an extended gradient of physiographic and climate properties. The relative amounts of baseflow were summarized by the BFI. The catchment slow response delay time (Ks) was assumed to be a measure of catchment effects, and the impact of climate properties was investigated with the dry spell length (d). Well‐drained and poorly‐drained groups were identified based on Ks and d, and their response to an increase or decrease in dry spell length was analysed. Overall, for either well‐ or poorly‐drained groups, an extension in dry spell length appeared to have minor effects on the baseflow compared with a decrease in dry spell length. Under the same dry spell variation, the BFI vulnerability appeared higher for catchments characterized by large initial d values in combination with poorly‐drained systems, but attributing an equal weight to the variations in d both in the case of dry and wet initial conditions, it is in the end concluded that the BFI vulnerability appears higher for systems laying in the transition zone between well‐ and poorly‐drained systems.