In the last years, new numerical methods have been applied to weather data for water budget es-timation. Geostatistics is one of the most powerful approaches, when it comes to studying spatially relevant natural phenomena, as it takes into account the spatial correlation among measurements over a specific study area and increases the reliability of the estimation by providing the associate uncertainty. In this study, we tested the feasibility of using a geostatistical method called Ordinary Kriging to provide a reliable estimation of the water budget of the Foro river basin (Periadriatic area, Central Italy), by comparing the obtained results with a more traditional yet robust approach based on the Thiessen polygon spatialization method, considered here as a reference. The spatial estimation with the selected geostatistical and traditional approaches was performed on the av-erage monthly rainfall and temperatures derived from 33-year-long time series measured at the weather station of the regional monitoring network. To transform the spatial variables quantities of both rainfall and air temperature into actual runoff estimates that ideally represent the river dis-charge, we used both the Turc and Thornthwaite methods, considering variable Potential Infiltra-tion Coefficients according to the hydrogeological complexes’ distribution. For the geostatistical method, the same procedure was applied to the 95% confidence interval limits, to quantitatively assess the possible impact of the spatial uncertainty on the estimation of the river discharge. The results obtained by both methods, subsequently converted into monthly discharges, were com-pared to the average monthly discharges related to 33-year-long time series measured at different locations along the Foro river. The results obtained with the geostatistical approach proved to be in line with the ones from the traditional method. Additionally, using the rainfall and temperature values obtained with the Ordinary Kriging and the corresponding confidence interval limits, it was possible to quantify the uncertainty associated with the discharge values, making the estimates more reliable than the ones obtained with the traditional approach. However, the yearly distribu-tion of river discharge obtained by both the geostatistical and traditional methods appeared to be dissimilar to the measured ones. In such anthropized and groundwater-dependent river basins, the surface water use as well as the regulatory effect of the alluvial aquifer regime may affect the river discharge variability over the year and then can account for similar discrepancies between the in-flow and outflow water volumes.

Accurate knowledge of the rain amount is an crucial driver in several hydro-meteorological applications. This is especially true in complex orography territories, which are typically impervious, thus leaving ungauged most of the mountain areas. Thanks to their spatial and temporal coverage, weather radars can potentially overcome such an issue. However, weather radar, if not accurately processed, can suffer from several limitations (e.g., beam blocking, altitude of the observation, path attenuation, indirectness of the measurement) that can hamper the reliability of the rain estimates performed. In this study, a comparison between rain gauge and weather radar retrievals is performed in the target area of the Abruzzo region in Italy, which is characterized by a heterogeneous orography ranging from the sea side to Apennine ridge. Consequently, the Abruzzo region has an inhomogeneous distribution of the rain gauges, with station density decreasing with the altitude reaching up to approximately 1500 m a.s.l. Notwithstanding, pluviometric inflow spatial distribution shows a sub-regional dependency as a function of four climatic and altimetric factors: coastal, hilly, mountain, and inner plain areas (i.e., Marsica). Such areas are used in this analysis to characterize the radar retrieval vs. rain gauge amounts in each of those zones. Compared to previous studies on the topic, the analysis presented an attention to the importance of an accurate selection of the climatic and altimetric sub-regional areas where undertake the radar vs. rain gauge comparison. This aspect is not only of great importance to correct biases in radar retrievals in a more selective way, but it also paves the way for more accurate hydro-meteorological applications (e.g., hydrological model initialization, quantify the aquifers recharge etc.) which, in general, require the accurate knowledge of rain amounts upstream of a basin. To fill the gap caused by the uneven rain gauge distribution, Ordinary Kriging has been applied on a regional scale to obtain 2D maps of rainfall data, which are cumulated on a monthly and yearly base. Weather radar data from the Italian mosaic are considered as well, in terms of rain rate retrievals and cumulations performed on the same time frame used for rain gauges. The period considered for the analysis is two continuous years: 2017 and 2018. The output of the elaborations are raster maps for both radar and interpolated rain gauges, where every pixel contains a rainfall quantity. Although the results show a general underestimation in the weather radar data especially in mountain and Marsica areas, even though within the 95% confidence interval of the OK estimation. Our analysis highlights that the average bias between radar and rain gauges, in terms of precipitation amounts, is a function of altitude and is almost constant in each of the selected areas. This achievement suggests that after a proper selection of homogeneous target areas, the radar retrievals can be corrected using the denser network of rain gauges typically distributed at lower altitudes and extend such correction at higher altitudes without loss of generality.

In this research, univariate and bivariate statistical methods were applied to rainfall, river and piezometric level datasets belonging to 24 years long time series (1986-2009). These methods, that 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. The 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; the spectral analysis and mono-fractal features of time series were assessed, in order to provide information on multy-year variability, data distributions, their fractal dimension and the distribution return time within the historical time series. The statistical-mathematical results were interpreted through field work that identified distinct groundwater flowpaths within the aquifer and enabled the implementation of a conceptual model, improving the knowledge on water resources management tools.