In this article, a regional-scale hydrogeological budget is presented for the upper and middle plain of the Veneto and Friuli-Venezia Giulia regions in northeastern Italy. This work provides a comprehensive conceptual model of groundwater processes in the study area, including quantitative estimates of inflows and outflows due to both natural processes (precipitation, river interactions, groundwater flows) and anthropogenic activities (irrigation, well pumping). These estimates represent the basis for discussions on the sustainability of water uses and offer a reliable dataset to constrain numerical simulation calibration. Depending on data availability and quality, different methods were applied, including GIS-based analyses, water-use assessments, hydraulic scheme reconstructions, upscaling of available datasets, estimation of missing values in time series through a machine-learning approach, and statistical trend analyses (modified Mann– Kendall test and Sen's slope estimation). This combination allowed the integration of heterogeneous datasets, often characterized by spatial and temporal gaps, to estimate the water budget terms.

Hydrogeological water budget in the Venetian alluvial plain for the sustainable management of groundwater (northeastern Italy)

Cappellari, Davide;Tonucci, Roberto;Piccinini, Leonardo;Fabbri, Paolo
2026

Abstract

In this article, a regional-scale hydrogeological budget is presented for the upper and middle plain of the Veneto and Friuli-Venezia Giulia regions in northeastern Italy. This work provides a comprehensive conceptual model of groundwater processes in the study area, including quantitative estimates of inflows and outflows due to both natural processes (precipitation, river interactions, groundwater flows) and anthropogenic activities (irrigation, well pumping). These estimates represent the basis for discussions on the sustainability of water uses and offer a reliable dataset to constrain numerical simulation calibration. Depending on data availability and quality, different methods were applied, including GIS-based analyses, water-use assessments, hydraulic scheme reconstructions, upscaling of available datasets, estimation of missing values in time series through a machine-learning approach, and statistical trend analyses (modified Mann– Kendall test and Sen's slope estimation). This combination allowed the integration of heterogeneous datasets, often characterized by spatial and temporal gaps, to estimate the water budget terms.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3598878
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