Autori: Vivaldo G, Raco B, Menichini M, Masetti G, Fibbi L, Gozzini B, et al. (2026)

Rivista: PLoS One 21(8): e0351623.

DOI: https://doi.org/10.1371/journal.pone.0351623

Abstract:
Karst aquifers are highly sensitive to climate change due to their complex internal
structure, which makes them both reactive and difficult to model. This study pro-
poses a transdisciplinary decision-support analytical framework to support long-term
groundwater management in a dynamic karst system in northwestern Tuscany (Italy).
The approach combines time- and frequency-domains techniques – multivariate
regression models and singular spectrum analysis, respectively – to character-
ize both short-term system memory and the low-frequency variability of the spring
discharge. The methodology was applied to the Cartaro spring (Apuan Alps) using a
18-year dataset of discharge and meteorological variables (precipitation and tem-
perature). Time-domain analysis showed that meteorological variables alone cannot
fully explain the long-term variability of spring discharge, and that accounting for the
short memory of the karst system (about five days) significantly improved the per-
formance of the classical models. Furthermore, spectral decomposition allowed us
to extract the significant annual and semi-annual oscillatory components with modu-
lated amplitude, highlighting the role of low-frequency climatic variability in controlling
spring behavior. By combining the results from the time and frequency domains, we
fine-tuned a forecasting procedure that enables statistically reliable annual predic-
tions of long-term average discharge trends, while limiting noise propagation and
without relying on physical governing equations. The research is transdisciplinary, as
it was developed from its preliminary phases in collaboration with stakeholders and
local managers. The methodological workflow can be applied to other karst aquifers,
provided that it is recalibrated using the site-specific hydro-climatic and discharrge
data.