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ERA5-Land Data for Understanding Spring Dynamics in Complex Hydro-Meteorological Settings and for Sustainable Water Management

Author

Listed:
  • Lucio Di Matteo

    (Department of Physics and Geology, University of Perugia, Via Pascoli snc, 06123 Perugia, Italy)

  • Costanza Cambi

    (Department of Physics and Geology, University of Perugia, Via Pascoli snc, 06123 Perugia, Italy)

  • Sofia Ortenzi

    (National Research Council, Research Institute for Geo-Hydrological Protection, Via Madonna Alta 126, 06126 Perugia, Italy)

  • Alex Manucci

    (Department of Physics and Geology, University of Perugia, Via Pascoli snc, 06123 Perugia, Italy)

  • Sara Venturi

    (Department of Civil and Environmental Engineering, University of Perugia, Via Duranti 93, 06125 Perugia, Italy)

  • Davide Fronzi

    (Department of Science and Matter Engineering, Environment, and Urban Planning, Marche Polytechnic University, Via Brecce Bianche 12, 60131 Ancona, Italy)

  • Daniela Valigi

    (Department of Physics and Geology, University of Perugia, Via Pascoli snc, 06123 Perugia, Italy)

Abstract

Springs fed by carbonate-fractured/karst aquifers support spring-dependent ecosystems and provide drinking water in the Italian Apennines, where complex hydro-meteorological environments are increasingly affected by prolonged droughts. The aim of this study was to investigate the hydrogeological behavior of two springs (Alzabove and Lupa) on the mountain ridge of Central Italy, using monthly reanalysis datasets to support sustainable water management. The Master Recession Curves based on the 1998–2023 recession periods highlighted a slightly higher average recession coefficient for Lupa (α = −0.0053 days −1 ) than for Alzabove (α = −0.0020 days −1 ). The hydrogeological settings of the Lupa recharge area led to a less resilient response to prolonged, extreme droughts as detected via the Standardized Precipitation-Evapotranspiration Index (SPEI) computed at different time scales using ERA-5 Land datasets. The SPEI computed at a 6-month scale (SPEI 6 ) showed the best correlation with monthly spring discharge, with a 1-month delay time. A parsimonious linear regression model was built using the antecedent monthly spring discharge values and SPEI 6 as independent variables. The best modeling performance was achieved for the Alzabove spring, with some overestimation of spring discharge during extremely dry conditions (e.g., 2002–2003 and 2012), especially for the Lupa spring. The findings are encouraging as they reflect the use of a simple tool developed to support decisions on the sustainable management of springs in mountain environments, although issues related to evapotranspiration underestimation during extreme droughts remain.

Suggested Citation

  • Lucio Di Matteo & Costanza Cambi & Sofia Ortenzi & Alex Manucci & Sara Venturi & Davide Fronzi & Daniela Valigi, 2026. "ERA5-Land Data for Understanding Spring Dynamics in Complex Hydro-Meteorological Settings and for Sustainable Water Management," Sustainability, MDPI, vol. 18(2), pages 1-22, January.
  • Handle: RePEc:gam:jsusta:v:18:y:2026:i:2:p:970-:d:1842968
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