Recently, new numerical methods have been applied to weather data for the estimation of water budget, especially when the lack of measured data is considerable. Geostatistics is one of the most powerful approaches when it comes to studying spatially relevant natural phenomena, as it considers the spatial correlation among measurements over a specific study area and provides the associate uncertainty. In this study, we tested the feasibility of using a geostatistical method to provide a reliable estimation of the water budget of the Foro river basin (Central Italy) by comparing the obtained results with those of a traditional yet robust method. The results obtained with the geostatistical approach proved to be in line with the ones from the traditional method. Additionally, 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 distribution of river discharge obtained using both methods appeared to be dissimilar to the measured ones. The surface water uses, as well as the regulatory effect of the carbonate and alluvial aquifer regime, may affect the river discharge variability over the year and then can account for similar discrepancies between the inflow and outflow water volumes.@en

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.@en

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.@en

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.@en

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.@en

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.@en

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.@en

Rain gauge spatial sparsity and temporal discontinuity of data represent one of the major issues for reliable recharge estimations. In the past decades, the use of ground-based microwave weather RaDAR has dramatically improved quantitative rainfall estimation by providing spatially continuous estimates of rainfall over an area of more than 400 km2 every 10 minutes. Furthermore, weather RaDAR data have also proved relatively reliable in mountainous areas. These paramount features of RaDAR-derived precipitation data could improve the estimation of potential recharge of aquifers, which rely on geospatializations (e.g., Thiessen polygons) of rainfall data collected by a sparse rain gauge network which often shows lacking at high altitude (i.e., recharge areas), introducing additional uncertainty in the inflow volumes. Weather RaDAR rainfall estimation is also affected by various sources of error, which can be reduced by proper post-processing; however, uncertainties remain, especially for surface rain rate estimations. Despite the currently necessary complex numerical processing, the purpose of the study is to evaluate the use of the weather RaDAR data as an alternative or in addition to meteorological data. Based on the above considerations, the feasibility of using RaDAR-based precipitation data to estimate aquifer potential recharge and calculate a detailed water budget in the areas characterized by high elevations, such as the Majella massif in the central Apennines, has been evaluated. To address this objective, the water budget has been calculated in the 2017-2018 period using both RaDAR-based precipitation data and rain gauge data, as well as adopting different methods (i.e., Turc and Thornthwaite). Although intrinsically uncertain, the RaDAR-based precipitation data provided solid results, pointed out by comparing it with water budget obtained by rain gauge data, and especially with experimental literature data. This interdisciplinary work may pave the way for continuous monitoring of aquifer potential recharge at extremely high temporal and spatial resolution.@en

Rain gauge spatial sparsity and temporal discontinuity of data represent one of the major issues for reliable recharge estimations. In the past decades, the use of ground-based microwave weather RaDAR has dramatically improved quantitative rainfall estimation by providing spatially continuous estimates of rainfall over an area of more than 400 km2 every 10 minutes. Furthermore, weather RaDAR data have also proved relatively reliable in mountainous areas. These paramount features of RaDAR-derived precipitation data could improve the estimation of potential recharge of aquifers, which rely on geospatializations (e.g., Thiessen polygons) of rainfall data collected by a sparse rain gauge network which often shows lacking at high altitude (i.e., recharge areas), introducing additional uncertainty in the inflow volumes. Weather RaDAR rainfall estimation is also affected by various sources of error, which can be reduced by proper post-processing; however, uncertainties remain, especially for surface rain rate estimations. Despite the currently necessary complex numerical processing, the purpose of the study is to evaluate the use of the weather RaDAR data as an alternative or in addition to meteorological data. Based on the above considerations, the feasibility of using RaDAR-based precipitation data to estimate aquifer potential recharge and calculate a detailed water budget in the areas characterized by high elevations, such as the Majella massif in the central Apennines, has been evaluated. To address this objective, the water budget has been calculated in the 2017-2018 period using both RaDAR-based precipitation data and rain gauge data, as well as adopting different methods (i.e., Turc and Thornthwaite). Although intrinsically uncertain, the RaDAR-based precipitation data provided solid results, pointed out by comparing it with water budget obtained by rain gauge data, and especially with experimental literature data. This interdisciplinary work may pave the way for continuous monitoring of aquifer potential recharge at extremely high temporal and spatial resolution.@en

Rain gauge spatial sparsity and temporal discontinuity of data represent one of the major issues for reliable recharge estimations. In the past decades, the use of ground-based microwave weather RaDAR has dramatically improved quantitative rainfall estimation by providing spatially continuous estimates of rainfall over an area of more than 400 km2 every 10 minutes. Furthermore, weather RaDAR data have also proved relatively reliable in mountainous areas. These paramount features of RaDAR-derived precipitation data could improve the estimation of potential recharge of aquifers, which rely on geospatializations (e.g., Thiessen polygons) of rainfall data collected by a sparse rain gauge network which often shows lacking at high altitude (i.e., recharge areas), introducing additional uncertainty in the inflow volumes. Weather RaDAR rainfall estimation is also affected by various sources of error, which can be reduced by proper post-processing; however, uncertainties remain, especially for surface rain rate estimations. Despite the currently necessary complex numerical processing, the purpose of the study is to evaluate the use of the weather RaDAR data as an alternative or in addition to meteorological data. Based on the above considerations, the feasibility of using RaDAR-based precipitation data to estimate aquifer potential recharge and calculate a detailed water budget in the areas characterized by high elevations, such as the Majella massif in the central Apennines, has been evaluated. To address this objective, the water budget has been calculated in the 2017-2018 period using both RaDAR-based precipitation data and rain gauge data, as well as adopting different methods (i.e., Turc and Thornthwaite). Although intrinsically uncertain, the RaDAR-based precipitation data provided solid results, pointed out by comparing it with water budget obtained by rain gauge data, and especially with experimental literature data. This interdisciplinary work may pave the way for continuous monitoring of aquifer potential recharge at extremely high temporal and spatial resolution.@en

Rain gauge spatial sparsity and temporal discontinuity of data represent one of the major issues for reliable recharge estimations. In the past decades, the use of ground-based microwave weather RaDAR has dramatically improved quantitative rainfall estimation by providing spatially continuous estimates of rainfall over an area of more than 400 km2 every 10 minutes. Furthermore, weather RaDAR data have also proved relatively reliable in mountainous areas. These paramount features of RaDAR-derived precipitation data could improve the estimation of potential recharge of aquifers, which rely on geospatializations (e.g., Thiessen polygons) of rainfall data collected by a sparse rain gauge network which often shows lacking at high altitude (i.e., recharge areas), introducing additional uncertainty in the inflow volumes. Weather RaDAR rainfall estimation is also affected by various sources of error, which can be reduced by proper post-processing; however, uncertainties remain, especially for surface rain rate estimations. Despite the currently necessary complex numerical processing, the purpose of the study is to evaluate the use of the weather RaDAR data as an alternative or in addition to meteorological data. Based on the above considerations, the feasibility of using RaDAR-based precipitation data to estimate aquifer potential recharge and calculate a detailed water budget in the areas characterized by high elevations, such as the Majella massif in the central Apennines, has been evaluated. To address this objective, the water budget has been calculated in the 2017-2018 period using both RaDAR-based precipitation data and rain gauge data, as well as adopting different methods (i.e., Turc and Thornthwaite). Although intrinsically uncertain, the RaDAR-based precipitation data provided solid results, pointed out by comparing it with water budget obtained by rain gauge data, and especially with experimental literature data. This interdisciplinary work may pave the way for continuous monitoring of aquifer potential recharge at extremely high temporal and spatial resolution.@en

Rain gauge spatial sparsity and temporal discontinuity of data represent one of the major issues for reliable recharge estimations. In the past decades, the use of ground-based microwave weather RaDAR has dramatically improved quantitative rainfall estimation by providing spatially continuous estimates of rainfall over an area of more than 400 km2 every 10 minutes. Furthermore, weather RaDAR data have also proved relatively reliable in mountainous areas. These paramount features of RaDAR-derived precipitation data could improve the estimation of potential recharge of aquifers, which rely on geospatializations (e.g., Thiessen polygons) of rainfall data collected by a sparse rain gauge network which often shows lacking at high altitude (i.e., recharge areas), introducing additional uncertainty in the inflow volumes. Weather RaDAR rainfall estimation is also affected by various sources of error, which can be reduced by proper post-processing; however, uncertainties remain, especially for surface rain rate estimations. Despite the currently necessary complex numerical processing, the purpose of the study is to evaluate the use of the weather RaDAR data as an alternative or in addition to meteorological data. Based on the above considerations, the feasibility of using RaDAR-based precipitation data to estimate aquifer potential recharge and calculate a detailed water budget in the areas characterized by high elevations, such as the Majella massif in the central Apennines, has been evaluated. To address this objective, the water budget has been calculated in the 2017-2018 period using both RaDAR-based precipitation data and rain gauge data, as well as adopting different methods (i.e., Turc and Thornthwaite). Although intrinsically uncertain, the RaDAR-based precipitation data provided solid results, pointed out by comparing it with water budget obtained by rain gauge data, and especially with experimental literature data. This interdisciplinary work may pave the way for continuous monitoring of aquifer potential recharge at extremely high temporal and spatial resolution.@en

The present work aims to assess the origin of the boron contamination of groundwater that affect some areas of central-Adriatic Italy, through the use of boron and strontium isotopic analysis. The research results indicate that boron contamination is mainly related with salinization of groundwater attributable to the uprising of connate groundwater and mixing processes between more shallow fresh groundwater and deeper saline groundwater. As a result, the main boron contamination of groundwater is not related to anthropogenic inputs but rather to natural processes.@en

The present work aims to assess the origin of the boron contamination of groundwater that affect some areas of central-Adriatic Italy, through the use of boron and strontium isotopic analysis. The research results indicate that boron contamination is mainly related with salinization of groundwater attributable to the uprising of connate groundwater and mixing processes between more shallow fresh groundwater and deeper saline groundwater. As a result, the main boron contamination of groundwater is not related to anthropogenic inputs but rather to natural processes.@en

The present work aims to assess the origin of the boron contamination of groundwater that affect some areas of central-Adriatic Italy, through the use of boron and strontium isotopic analysis. The research results indicate that boron contamination is mainly related with salinization of groundwater attributable to the uprising of connate groundwater and mixing processes between more shallow fresh groundwater and deeper saline groundwater. As a result, the main boron contamination of groundwater is not related to anthropogenic inputs but rather to natural processes.@en

The present work aims to assess the origin of the boron contamination of groundwater that affect some areas of central-Adriatic Italy, through the use of boron and strontium isotopic analysis. The research results indicate that boron contamination is mainly related with salinization of groundwater attributable to the uprising of connate groundwater and mixing processes between more shallow fresh groundwater and deeper saline groundwater. As a result, the main boron contamination of groundwater is not related to anthropogenic inputs but rather to natural processes.@en

In this research, a multi-scenario numerical modeling was implemented to assess the effects of changes to abstraction patterns in the Sant'Angelo well-field (central Italy) and their implications on the aquifer hydrodynamic and the advective transport of contaminants. Once implemented and calibrated the steady-state numerical model by means of MODFLOW-2005, the well-field turning off scenario was modelled. In addition, the numerical results were analyzed by means of the post-processors ZONEBUDGET and MODPATH, to assess respectively the contribution of each hydrogeological feature to the total budget and the advective transport of contaminant particles. Comparing the two steady-state numerical models and the relative particle tracking analyses, the well-field turning off, although no longer acting as a hydraulic barrier, increased the residence time of contaminant particles and limited their mobility in the aquifer. Furthermore, the general decrease in groundwater abstractions also caused a higher increase in river flow, favoring contaminants' dilution in surface water.@en

In this research, a multi-scenario numerical modeling was implemented to assess the effects of changes to abstraction patterns in the Sant'Angelo well-field (central Italy) and their implications on the aquifer hydrodynamic and the advective transport of contaminants. Once implemented and calibrated the steady-state numerical model by means of MODFLOW-2005, the well-field turning off scenario was modelled. In addition, the numerical results were analyzed by means of the post-processors ZONEBUDGET and MODPATH, to assess respectively the contribution of each hydrogeological feature to the total budget and the advective transport of contaminant particles. Comparing the two steady-state numerical models and the relative particle tracking analyses, the well-field turning off, although no longer acting as a hydraulic barrier, increased the residence time of contaminant particles and limited their mobility in the aquifer. Furthermore, the general decrease in groundwater abstractions also caused a higher increase in river flow, favoring contaminants' dilution in surface water.@en

In this research, a multi-scenario numerical modeling was implemented to assess the effects of changes to abstraction patterns in the Sant'Angelo well-field (central Italy) and their implications on the aquifer hydrodynamic and the advective transport of contaminants. Once implemented and calibrated the steady-state numerical model by means of MODFLOW-2005, the well-field turning off scenario was modelled. In addition, the numerical results were analyzed by means of the post-processors ZONEBUDGET and MODPATH, to assess respectively the contribution of each hydrogeological feature to the total budget and the advective transport of contaminant particles. Comparing the two steady-state numerical models and the relative particle tracking analyses, the well-field turning off, although no longer acting as a hydraulic barrier, increased the residence time of contaminant particles and limited their mobility in the aquifer. Furthermore, the general decrease in groundwater abstractions also caused a higher increase in river flow, favoring contaminants' dilution in surface water.@en

Accurate knowledge of the rain amount is a crucial driver in several hydrometeorological applications. This is especially true in complex orography territories, which are typically impervious, thus, leaving most mountain areas ungauged. Due 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, and 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 seaside to Apennine ridge. Consequently, the Abruzzo region has an inhomogeneous distribution of the rain gauges, with station density decreasing with the altitude reaching approximately 1500 m a.s.l. Notwithstanding, pluviometric inflow spatial distribution shows a subregional 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 the importance of an accurate selection of the climatic and altimetric subregional areas where the radar vs. rain gauge comparison is undertaken. This aspect is not only of great importance to correct biases in radar retrieval in a more selective way, but it also paves the way for more accurate hydrometeorological applications (e.g., hydrological model initialization and quantification of aquifer recharge), 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 (OK) was applied on a regional scale to obtain 2D maps of rainfall data, which were cumulated on a monthly and yearly basis. Weather radar data from the Italian mosaic were also considered, in terms of rain rate retrievals and cumulations performed on the same time frame used for rain gauges. The period considered for the analysis was two continuous years: 2017 and 2018. The output of the elaborations included raster maps for both radar and interpolated rain gauges, where each pixel contained a rainfall quantity. Although the results showed a general underestimation of the weather radar data, especially in mountain and Marsica areas, they were within the 95% confidence interval of the OK estimation. Our analysis highlighted that the average bias between radar and rain gauges, in terms of precipitation amounts, was a function of altitude and was almost constant in each of the selected areas. This achievement suggests that after a proper selection of homogeneous target areas, radar retrieval can be corrected using the denser network of rain gauges typically distributed at lower altitudes, and such correction can be extended at higher altitudes without loss of generality.@en