Preprint Article Version 1 This version is not peer-reviewed

Statistical and Fractal Approaches on Long Time-Series to Surface-Water/Groundwater Relationship Assessment: A Central Italy Alluvial Plain Case Study

Version 1 : Received: 11 October 2017 / Approved: 11 October 2017 / Online: 11 October 2017 (11:54:07 CEST)

A peer-reviewed article of this Preprint also exists.

Chiaudani, A.; Di Curzio, D.; Palmucci, W.; Pasculli, A.; Polemio, M.; Rusi, S. Statistical and Fractal Approaches on Long Time-Series to Surface-Water/Groundwater Relationship Assessment: A Central Italy Alluvial Plain Case Study. Water 2017, 9, 850. Chiaudani, A.; Di Curzio, D.; Palmucci, W.; Pasculli, A.; Polemio, M.; Rusi, S. Statistical and Fractal Approaches on Long Time-Series to Surface-Water/Groundwater Relationship Assessment: A Central Italy Alluvial Plain Case Study. Water 2017, 9, 850.

Journal reference: Water 2017, 9, 850
DOI: 10.3390/w9110850

Abstract

In this research, univariate and bivariate statistical methods were applied to rainfall, river and piezometric level datasets belonging to 24 years long time series (1986-2009). These methods, that 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. The 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; the spectral analysis and mono-fractal features of time series were assessed, in order to provide information on multy-year variability, data distributions, their fractal dimension and the distribution return time within the historical time series. The statistical-mathematical results were interpreted through field work that identified distinct groundwater flowpaths within the aquifer and enabled the implementation of a conceptual model, improving the knowledge on water resources management tools.

Subject Areas

alluvial aquifer; GPS survey; hydrological time-series; autocorrelation; cross-correlation; spectral analysis; mono-fractal analysis; central Italy

Readers' Comments and Ratings (0)

Leave a public comment
Send a private comment to the author(s)
Rate this article
Views 0
Downloads 0
Comments 0
Metrics 0
Leave a public comment

×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.