Analysis of fragmented piezometric levels records: the ARTE (Antecedent Recharge Temporal Effectiveness) approach


Submitted: 31 March 2022
Accepted: 21 June 2022
Published: 30 June 2022
Abstract Views: 537
PDF: 339
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Authors

  • Matteo Nigro Department of Earth Sciences, University of Pisa, Italy.
  • Michele Ambrosio Af Geoscience And Technology Consulting S.r.l., San Giuliano Terme (PI), Italy.
  • Maria-Teresa Fagioli Af Geoscience And Technology Consulting S.r.l., San Giuliano Terme (PI), Italy.
  • Chiara Curcio Department of Earth Sciences, University of Pisa, Italy.
  • Roberto Giannecchini Department of Earth Sciences, University of Pisa; Institute of Geosciences and Earth Resources, IGG-CNR, Pisa; Centre for Climatic Change Impact, CIRSEC, University of Pisa, Italy.

In contrast to climatic data, piezometric records are often fragmented both in time and space continuity, despite their crucial importance in groundwater studies. This work presents a new method for analysis of groundwater level vs. recharge processes relation from fragmented piezometric data, called Antecedent Recharge Temporal Effectiveness (ARTE). The ARTE method was tested on 5 year-long (2016-2020) water table level datasets measured by three automatic piezometers located in the Lucca plain (Tuscany, Italy). For each piezometric level time series, measurements were extracted every 30, 60, and 120 days, and randomly, obtaining fragmented records inlcuding less than 3% of the complete time series. As for recharge processes of the monitored aquifer, rainfall and riverbed infiltration were selected. Hence, daily rainfall and daily mean river stage time series were acquired from different automatic raingauges and hydrometers respectively. The relationship between these recharge processes and the variation of the piezometric level from the artificially fragmented datasets were evaluated with the ARTE method. The ARTE method was potentially able to identify maximum correlation time intervals, for which the recharge processes are most likely to influence the groundwater level. Based on the analysis conducted on the fragmented piezometric datasets, the reconstruction of each piezometric time series was attempted for the study period. The simulated daily groundwater level records have RMSE values between 0.21 m and 0.73 m and NRMSE values between 0.08 and 0.16, which are satisfactory results when compared with other more complex simulation procedures, in which the training datasets are increasingly larger.


Abhijit M, Huaming G, Simon L, Andrew M, Bridget S, Alice A (2020) Global Groundwater: Source, Scarcity, Sustainability, Security, and

Solutions. Elsevier, Amsterdam.

Borsi I, Mazzanti G, Barbagli A, Rossetto R (2014) L’impianto di ricarica riverbank filtration di S. Alessio (Lucca): attività di monitoraggio e modellistica nel progetto EU FP7 MARSOL. “The rirerbank filtration recharge pant of S. Alessio (lucca): monitoring activities

and modelling in the EU FP7 MARSOL project”. Acque Sotterranee - Italian Journal of Groundwater (2014) - AS10046: 067 – 070. DOI 10.7343/AS-085-14-0112

Cai Z, Ofterdinger U (2016) Analysis of groundwater-level response to rainfall and estimation of annual recharge in fractured hard rock aquifers, NW Ireland. Journal of Hydrology, 535, 71–84. https://doi.org/10.1016/j.jhydrol.2016.01.066 DOI: https://doi.org/10.1016/j.jhydrol.2016.01.066

Caren M, Pavlić K (2021) Autocorrelation and cross-correlation flow analysis along the confluence of the Kupa and Sava rivers. Rudarsko Geolosko Naftni Zbornik, 36 (5), 67–77. https://doi.org/10.17794/rgn.2021.5.7 DOI: https://doi.org/10.17794/rgn.2021.5.7

Chiaudani A, di Curzio D, Palmucci W, Pasculli A, Polemio M, Rusi S (2017) Statistical and fractal approaches on long time-series to surface-water/groundwater relationship assessment: A central Italy alluvial plain case study. Water (Switzerland), 9(11). https://doi.org/10.3390/w9110850 DOI: https://doi.org/10.3390/w9110850

Culibaly P, Anctil F, Aravena R, Bobde B (2001) Artificial neural network modeling of water table depth fluctuations. Water Resources Research, Vol. 37 (4), 885-896. DOI: https://doi.org/10.1029/2000WR900368

Denić-Jukić V, Lozić A, Jukić D (2020) An application of correlation and spectral analysis in hydrological study of neighboring karst

springs. Water (Switzerland), 12 (12). https://doi.org/10.3390/w12123570 DOI: https://doi.org/10.3390/w12123570

Duvert C, Jourde H, Raiber M, Cox ME (2015) Correlation and spectral analyses to assess the response of a shallow aquifer to low and high DOI: https://doi.org/10.1016/j.jhydrol.2015.05.054

frequency rainfall fluctuations. Journal of Hydrology, 527, 894–907. https://doi.org/10.1016/j.jhydrol.2015.05.054 Ferguson G, George SS (2003) Historical and estimated groundwater levels near Winnipeg, Canada, and their sensitivity to climatic

variability. Journal of the American Water Resources Association.

Fiorillo F, Doglioni A (2010) The relation between karst spring discharge and rainfall by cross-correlation analysis (Campania, Southern Italy). Hydrogeology Journal, 18 (8), 1881–1895. https://doi.org/10.1007/s10040-010-0666-1 DOI: https://doi.org/10.1007/s10040-010-0666-1

Ghose D, Das U, Roy P (2018) Modeling response of runoff and evapotranspiration for predicting water table depth in arid region using dynamic recurrent neural network. Groundwater for Sustainable Development, 6, 263-269. DOI: https://doi.org/10.1016/j.gsd.2018.01.007

Giannecchini R, Ambrosio M, Del Sordo A, Fagioli M-T, Sartelli A, Galanti Y (2019) Hydrogeological numerical modeling of the southeastern portion of the Lucca plain (Tuscany, Italy), stressed by groundwater exploitation. Atti Soc. Tosc. Sci. Nat., Mem., Serie A, 126, 95-110.

Huang X, Gao L, Crosbie RS, Zhang N, Fu G, Doble R (2019) Groundwater Recharge Prediction Using Linear Regression, Multi-Layer Perception Network, and Deep Learning. Water, 11, 1879. DOI: https://doi.org/10.3390/w11091879

Nigro, M., Ambrosio, M., Fagioli, M.-T., Curcio, C., & Giannecchini, R. (2022). Analysis of fragmented piezometric levels records: the ARTE (Antecedent Recharge Temporal Effectiveness) approach. Acque Sotterranee - Italian Journal of Groundwater, 11(4), 21–32. https://doi.org/10.7343/as-2022-566

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