The Empirical Rainfall Thresholds (ERTs) for shallow landslide initiation are commonly devised worldwide mostly to be implemented within landslide early warning systems. Nonetheless, since the pioneering works on ERTs in the 1980s, only meteorological variables - that are cumulated E or intensity I and duration D values of rainfalls that are likely to trigger landslides - have been used to predict landslide occurrence, even though they are characterized by a large uncertainty. Over time, many efforts have been devoted to constrain ERT to geo-morphological characters of the landslide locations but, since nowadays, they did not get to a sound new method to derive ERT and strengthen its ability to forecast future rainfall-induced landslide. In this study, local geo-morphological characters have been taken into account by means of the co-kriging technique to constrain the E and D mean values of a regional ERT and their confidence intervals. The study area, where the proposed method was trained, is the hilly side of the Abruzzo region (Italy). Here, 62 shallow landslides have been analyzed in the time span of 2013–2017 by collecting 62 (D,E) pairs related to the rainfalls that were likely to trigger them. The relevant geo-morphological features for the considered territory have been selected through the principal component analysis. Then, the Multi-Collocated Co-Kriging technique, through ISATIS Geovariances software, has been applied to derive the spatial variability structures of E and D values conditioned by the selected geo-morphological parameters. Therefore, threshold values of E and D and their confidence intervals have been calculated generating a new shape of regional ERT, consisting of maps of continuous estimated threshold values of (D,E) and confidence interval values suitable for being used in early warning systems for shallow landslide initiation.

The wide regional aquifer system springs are one of the most important sources of freshwater, especially because of the ever-increasing global water demand. Thus, these springs must be protected and managed in a sustainable manner. To this purpose, a detailed knowledge about the corresponding aquifer systems hydrodynamic is vital. In this general framework, the Majella carbonate aquifer is one of the most important as well as heterogenous aquifer systems in Central Italy. It is characterized by high elevation, a heterogeneous hydraulic conductivity distribution and groundwater flow, and its recharge is mainly due to both rainfall and snowmelt. Its most important spring is the Verde spring, located in the eastern side, exploited for drinking and hydroelectric purposes. The main objectives of this research are (1) to get a deeper insight into the hydrogeological conceptual model of the Verde spring, combining multiparameter input and output time-series, and (2) to quantify each inflow effect on the Verde spring parameters by a methodological approach that is quite uncommon in literature: comparing univariate and bivariate statistical analyses of both raw and residual (non-systematic component) time-series. The results obtained highlighted that the snowmelt is the most important inflow of the Majella aquifer system. The snow cover melting leaks slowly large amount of water into the aquifer and creates smoothly significative modifications to the spring discharge, electrical conductivity and temperature, in terms of groundwater volume increase and dilution. Contrariwise, although the rainfall inflow volume and the corresponding spring parameters changes are very limited, the transient behavior of the rainfall inflow allowed identifying different recharge modes. These recharge modes depend on several flow paths in the unsaturated zone, characterized by different water volumes brought toward the saturated zone, sizes, hydraulic conductivities, and distances from the Verde spring.

In this research, univariate and bivariate statistical methods were applied to rainfall, river and piezometric level datasets belonging to 24-year time series (1986-2009). These methods, which often are used to understand the effects of precipitation on rivers and karstic springs discharge, have been used to assess piezometric level response to rainfall and river level fluctuations in a porous aquifer. A rain gauge, a river level gauge and three wells, located in Central Italy along the lower Pescara River valley in correspondence of its important alluvial aquifer, provided the data. Statistical analysis has been used within a known hydrogeological framework, which has been refined by mean of a photo-interpretation and a GPS survey. Water-groundwater relationships were identified following the autocorrelation and cross-correlation analyses. Spectral analysis and mono-fractal features of time series were assessed to provide information on multi-year variability, data distributions, their fractal dimension and the distribution return time within the historical time series. The statistical-mathematical results were interpreted through fieldwork that identified distinct groundwater flowpaths within the aquifer and enabled the implementation of a conceptual model, improving the knowledge on water resources management tools.