26 Feb 2021

26 Feb 2021

Review status: a revised version of this preprint is currently under review for the journal HESS.

Aquifer recharge in the Piedmont Alpine zone: Historical trends and future scenarios

Elisa Brussolo1, Elisa Palazzi2, Jost von Hardenberg3,2, Giulio Masetti4, Gianna Vivaldo4, Maurizio Previati5, Davide Canone5, Davide Gisolo5, Ivan Bevilacqua5, Antonello Provenzale4, and Stefano Ferraris5 Elisa Brussolo et al.
  • 1Research Center, Società Metropolitana Acque Torino S.p.A., Torino, Italy
  • 2Institute of Atmospheric Sciences and Climate, National Research Council of Italy (CNR), Torino, Italy
  • 3Department of Environment, Land and Infrastructure Engineering (DIATI), Politecnico di Torino, Torino, Italy
  • 4Institute of Geosciences and Earth Resources, National Research Council of Italy (CNR), Pisa, Italy
  • 5Interuniversity Department of Regional and Urban Studies and Planning (DIST), Politecnico di Torino and Università di Torino, Torino, Italy

Abstract. The spatial and temporal variability of temperature, precipitation, actual evapotranspiration and of the related water balance components, as well as their responses to anthropogenic climate change, provide fundamental information for an effective management of water resources.

In this study we evaluated the past, present and future quantity of groundwater resources available for drinking supply in the water catchments feeding the about 2.3 million inhabitants of the Greater Turin metropolitan area (North-Western Italy), considering climatologies at the quarterly and yearly timescales. Observed temperature and precipitation data from 1959 to 2017 were analyzed to assess historical trends, their significance and the possible cross-correlations between the water balance components. Regional climate model (RCM) simulations from a small ensemble were analysed to provide mid-century projections of aquifer recharge for the area of interest under two emission scenarios.

The analysis over the historical period indicated that the driest areas of the study region displayed negative annual (and spring quarter) trends of the difference between precipitation and actual evapotranspiration. Even close-by watersheds exhibit different behaviors, given the large spatial variability of this area at the edge between the Alps and the Mediterranean region. The analysis of future projections suggested almost stationary conditions for annual recharge, with a slight decrease in the second half of the year. The future trends of drainage are very different across the considered RCM ensemble.

The large interannual variability of precipitation was identified as the most relevant risk factor for water management, expected to play a major role also in future decades.

Elisa Brussolo et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2020-501', Anonymous Referee #1, 10 Jun 2021
  • RC2: 'Comment on hess-2020-501', Anonymous Referee #2, 14 Jun 2021

Elisa Brussolo et al.

Elisa Brussolo et al.


Total article views: 658 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
436 209 13 658 1 3
  • HTML: 436
  • PDF: 209
  • XML: 13
  • Total: 658
  • BibTeX: 1
  • EndNote: 3
Views and downloads (calculated since 26 Feb 2021)
Cumulative views and downloads (calculated since 26 Feb 2021)

Viewed (geographical distribution)

Total article views: 534 (including HTML, PDF, and XML) Thereof 534 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 01 Dec 2021
Short summary
In this study we evaluate the past, present and future quantity of groundwater potentially available for drinking purposes in the metropolitan area of Turin, northwestern Italy. In order to effectively manage water resources, a knowledge of the water cycle components is necessary, including precipitation, evapotranspiration and subsurface reservoirs. All these components have been carefully evaluated in this paper, using observational datasets and modelling approaches.