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.

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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