Statistical and fractal approaches on long time-series to surface-water/groundwater relationship assessment: A central Italy alluvial plain case study

Alessandro Chiaudani*, Diego Di Curzio, William Palmucci, Antonio Pasculli, Maurizio Polemio, Sergio Rusi

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

39 Citations (Scopus)

Abstract

In this research, univariate and bivariate statistical methods were applied to rainfall, river and piezometric level datasets belonging to 24-year time series (1986-2009). These methods, which often are used to understand the effects of precipitation on rivers and karstic springs discharge, have been used to assess piezometric level response to rainfall and river level fluctuations in a porous aquifer. A rain gauge, a river level gauge and three wells, located in Central Italy along the lower Pescara River valley in correspondence of its important alluvial aquifer, provided the data. Statistical analysis has been used within a known hydrogeological framework, which has been refined by mean of a photo-interpretation and a GPS survey. Water-groundwater relationships were identified following the autocorrelation and cross-correlation analyses. Spectral analysis and mono-fractal features of time series were assessed to provide information on multi-year variability, data distributions, their fractal dimension and the distribution return time within the historical time series. The statistical-mathematical results were interpreted through fieldwork that identified distinct groundwater flowpaths within the aquifer and enabled the implementation of a conceptual model, improving the knowledge on water resources management tools.

Original languageEnglish
Article number850
Number of pages28
JournalWater (Switzerland)
Volume9
Issue number11
DOIs
Publication statusPublished - 2017
Externally publishedYes

Keywords

  • Alluvial aquifer
  • Autocorrelation
  • Central Italy
  • Cross-correlation
  • GPS survey
  • Hydrological time series
  • Mono-fractal analysis
  • Spectral analysis

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