Journal of Mountain Science

, Volume 14, Issue 9, pp 1791–1800 | Cite as

Case study for investigating groundwater and the future of mountain spring discharges in Southern Italy

  • Nazzareno Diodato
  • Gianni BellocchiEmail author
  • Francesco Fiorillo
  • Gerardo Ventafridda


Groundwater extraction is used to alleviate drought in many habitats. However, widespread drought decreases spring discharge and there is a need to integrate climate change research into resource management and action. Accurate estimates of groundwater discharge may be valuable in improving decision support systems of hydrogeological resource exploitation. The present study performs a forecast for groundwater discharge in Aquifer’s Cervialto Mountains (southern Italy). A time series starting in 1883 was the basis for long-term predictions. An Ensemble Discharge Prediction (EDisP) was applied, and the progress of the discharge ensemble forecast was inferred with the aid of an Exponential Smoothing (ES) model initialized at different annual times. EDisP-ES hindcast model experiments were tested, and discharge plume-patterns forecast was assessed with horizon placed in the year 2044. A 46-year cycle pattern was identified by comparing simulations and observations, which is essential for the forecasting purpose. ED is P-ES performed an ensemble mean path for the coming decades that indicates a discharge regime within ± 1 standard deviation around the mean value of 4.1 m3 s−1. These fluctuations are comparable with those observed in the period 1961–1980 and further back, with change-points detectable around the years 2025 and 2035. Temporary drought conditions are expected after the year 2030.


Caposele (Italy) Ground water Drought Ensemble forecast Exponential smoothing Spring discharge 


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  1. Alexakis D, Tsakiris G (2010) Drought impacts on karstic spring annual water potential. Application on Almyros (Crete) brackish spring. Desalination and Water Treatment 16: 229–237. Scholar
  2. Alley WM, Healy RH, La Baugh JW, Reilly TE (2002) Flow and storage in groundwater systems. Science 296: 1985–1990. Scholar
  3. Armstrong JS (2001) Combining forecasts. In: Armstrong JS (eds.), Principles of forecasting: a handbook for researchers and practitioners. Kluwer Academic Publishers, Norwell, USA. pp 1–19.CrossRefGoogle Scholar
  4. Bierkens MFP (1998) Modeling water table fluctuations by means of a stochastic differential equation. Water Resources Research 34: 2485–2499. Scholar
  5. Box GEP, Jenkins GM (1976) Time series analysis: forecasting and control. San Francisco: Holden Day. p 575.Google Scholar
  6. Caloiero T, Coscarelli R, Ferrari E, Mancini M (2011) Precipitation change in Southern Italy linked to global scale oscillation indexes. Natural Hazards and Earth System Sciences 11: 1683–1694. Scholar
  7. Celico P, Civita M (1976) Sulla tettonica del massiccio del Cervialto (Campania) e le implicazioni idrogeologiche ad essa connesse (About the tectonics of the Cervialto massif (Campania) and its hydrogeological implications). Bollettino della Società dei Naturalisti di Napoli 85: 555–580. (In Italian)Google Scholar
  8. Dai Z, Keating E, Bacon D, et al. (2014) Probabilistic evaluation of shallow groundwater resources at a hypothetical carbon sequestration site. Scientific Reports 4: 4006. Scholar
  9. Dai Z, Keating E, Gable CW, et al. (2010) Stepwise inversion of a groundwater flow model with multi-scale observation data. Hydrogeology Journal 18: 607–624. Scholar
  10. Diodato N, Bellocchi G (2011) Historical perspective of drought response in Mediterranean Italy. Climate Research 49: 189–200. Scholar
  11. Diodato N, Bellocchi G (2014) Long-term winter temperatures in central Mediterranean: forecast skill of an ensemble statistical model. Theoretical and Applied Climatology 116: 131–146. Scholar
  12. Diodato N, Fiorillo F (2013) Complexity-reduced in the hydroclimatological modeling of aquifer’s discharge. Water and Environment Journal 27: 170–176. Scholar
  13. Fiorillo F (2011) The role of the evapotranspiration in the aquifer recharge processes of Mediterranean areas. In: Gerosa G (ed.), Evapotranspiration–from measurements to agricultural and environmental applications. Published by InTech, Rijeka, Croatia. pp 373–388.Google Scholar
  14. Fiorillo F, Guadagno FM (2010) Karst spring discharges analysis in relation to drought periods, using the SPI. Water Resources Management 24: 1867–1884. Scholar
  15. Fiorillo F, Guadagno FM (2012) Long karst spring discharge time series and droughts occurrence in Southern Italy. Environmental Earth Sciences 65: 2273–2283. Scholar
  16. Fiorillo F, Pagnozzi M, Ventafridda G (2015a) A model to simulate recharge processes of karst massifs. Hydrological Processes 29: 2301–2314. Scholar
  17. Fiorillo F, Petitta M, Preziosi E, et al. (2015b) Long-term trend and fluctuations of karst spring discharge in a Mediterranean area (central-southern Italy). Environmental Earth Sciences 74: 153–172. Scholar
  18. ENSEMBLES (2009) Climate change and its impacts at seasonal, decadal and centennial timescales. Final report. p164. Available online:, (Accessed on 13 May 2017)Google Scholar
  19. Foley AM (2010) Uncertainty in regional climate modeling: a review. Progress in Physical Geography 34: 647–670. Scholar
  20. Gardner Jr ES (2006) Exponential smoothing: the state of the art-part II. International Journal of Forecasting 22: 637–666. Scholar
  21. Ghadampour Z, Rakhshandehroo G (2010) Using artificial neural network to forecast groundwater depth in Union County well. World Academy of Science, Engineering and Technology 62: 957–960.Google Scholar
  22. Hao Y, Yeh T-CJ, Wang Y, Zhao Y (2007) Analysis of karst aquifer spring flows with a gray system decomposition model. Groundwater 45: 46–52. Scholar
  23. Holt CC (2004) Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting 20: 5–10. Scholar
  24. Huang S, Hattermann FF, Krysanova V, Bronstert A (2012) Projections of impact of climate change on river flood conditions in Germany by combining three different RCMs with a regional hydrological model. Climatic Change 116: 631–663. Scholar
  25. Hyndman RJ, Koehler AB, Ord JK, Snyder RD (2008) Forecasting with exponential smoothing: the state space approach. Berlin: Springer. p 362.CrossRefGoogle Scholar
  26. Iglesias A, Garrote L, Diz A, et al. (2011) Re-thinking water policy priorities in the Mediterranean region in view of climate change. Environmental Science & Policy 14: 744–757. Scholar
  27. Jian WB, Yao H, Wen XH, Chen BR (1998) A nonlinear time series model for spring flow: An example from Shanxi Province, China. Groundwater 36: 147–150. Scholar
  28. Knotters M, Bierkens MFP (2000) Physical basis of time series models for water table depths. Water Resources Research 36: 181–188. Scholar
  29. Krakauer NY, Fekete BM (2014) Are climate model simulations useful for forecasting precipitation trends? Hindcast and synthetic-data experiments. Environmental Research Letters 9: 024009. Scholar
  30. López-Moreno JI, Beniston M, García-Ruiz JM (2008) Environmental change and water management in the Pyrenees: Facts and future perspectives for Mediterranean mountains. Global and Planetary Change 61: 300–312. Scholar
  31. López-Moreno JI, Vicente-Serrano SM, Gimeno L, Nieto R (2009) Stability of the seasonal distribution of precipitation in the Mediterranean region: Observations since 1950 and projections for the 21st century. Geophysical Research Letters 36: L10703. Scholar
  32. Lorenz EN (1963) Deterministic nonperiodic flow. Journal of Atmospheric Sciences 20: 130–141.<0130:DNF>2.0.CO;2CrossRefGoogle Scholar
  33. Madden RA (1976) Estimates of the natural variability of timeaveraged sea-level pressure. Monthly Weather Review 104: 942–952.<0942: EOTNVO>2.0.CO;2CrossRefGoogle Scholar
  34. Maxwell RM, Kollet SJ (2008) Interdependence of groundwater dynamics and land-energy feedbacks under climate change. Nature Geoscience 1: 665–669. Scholar
  35. Martin E, Etchevers P (2005) Impact of climatic changes on snow cover and snow hydrology in the French Alps. In: Huber UM, Bugmann HKM, Reasoner MA (eds.), Global change and mountain regions: an overview of current knowledge. Springer, Dordrecth, The Netherlands. pp 235–242.CrossRefGoogle Scholar
  36. McClain JO (1974) Dynamics of exponential smoothing with trend and seasonal terms. Management Science 20: 1300–1304. Scholar
  37. MED-EUWI (2017) Mediterranean Groundwater Report-Technical report on groundwater management in the Mediterranean and the Water Framework Directive. European Union Water Initiative-Mediterranean region (Working Group on groundwater). p 120. Available online: inal_150207_clear.pdf (Accessed on 13 May 2017)Google Scholar
  38. Nicholls N (2010) Why do we care about past climates? An editorial essay. Wiley Interdisciplinary Reviews: Climate Change 1: 155–157. Scholar
  39. Panagopoulos G, Lambrakis N (2006) The contribution of time series analysis to the study of the hydrodynamic characteristics of the karst systems: Application on two typical karst aquifers of Greece (Trifilia, Almyros Crete). Journal of Hydrology 329: 368–376. Scholar
  40. Polemio M, Casarano D (2008) Climate change, drought and groundwater availability in southern Italy. In: Dragoni W (ed.), Climate change and groundwater. Geological Society, Special Publications 288, London, United Kingdom. pp 39–51.Google Scholar
  41. Preziosi E, Del Bon A, Romano E, et al. (2013) Vulnerability to drought of a complex water supply system. The upper Tiber Basin case study (Central Italy). Water Resources Management 27: 4655–4678. Scholar
  42. Quian B, Rasheed K (2004) Hurst exponent and financial market predictability. 2nd IASTED International Conference on Financial Engineering and Applications, Cambridge, USA. pp 203–209. Available online: (Accessed on 13 May 2017)Google Scholar
  43. Qin SQ, Wang SJ, Jiao JJ (2001) The predictable time scale of landslides. Bulletin of Engineering Geology and the Environment 59: 307–312. Scholar
  44. Rodríguez-Iturbe I (2000) Ecohydrology: a hydrologic perspective on climate-soil-vegetation dynamics. Water Resources Research 36: 3–9.CrossRefGoogle Scholar
  45. Scibek J, Allen DM (2006) Modeled impacts of predicted climate change on recharge and groundwater levels. Water Resources Research 42: W11405. Scholar
  46. Sheng H, Chen YQ (2009) Robustness analysis of the estimators for noisy long-range dependent time series. ASME 2009 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, San Diego, USA. Available online: l_design_engineering_technical_conferences_and_computer s_and_information_in_engineering_conference.html, (Accessed on 13 May 2017)Google Scholar
  47. Semenov V, Latif M, Dommenget D, et al. (2010) The impact of North Atlantic-Arctic multidecadal variability on northern hemisphere surface air temperature. Journal of Climate 23: 5668–5677. Scholar
  48. Sophocleous M (2002) Interactions between groundwater and surface water: the state of the science. Hydrogeology Journal 10: 52–67. Scholar
  49. Sutton RT, Dong B (2012) Atlantic Ocean influence on a shift in European climate in the 1990s. Nature Geoscience 5: 788–792. Scholar
  50. Taylor JW (2003) Exponential smoothing with a damped multiplicative trend. International Journal of Forecasting 19: 715–725. Scholar
  51. Valipour M (2012) Critical areas of Iran for agriculture water management according to the annual rainfall. European Journal of Scientific Research 84: 600–608.Google Scholar
  52. Valipour M (2013) Use of surface water supply index to assessing of water resources management in Colorado and Oregon. Advances in Agriculture 3: 631–640. Scholar
  53. Valipour M (2015) Long-term runoff study using SARIMA and ARIMA models in the United States. Meteorological Applications 22: 592–598. Scholar
  54. Valipour M (2016) Optimization of neural networks for precipitation analysis in a humid region to detect drought and wet year alarms. Meteorological Applications 13: 91–100. Scholar
  55. Valipour M, Reza Behbahani SM, Banihabib ME (2013) Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir. Journal of Hydrology 476: 433–441. Scholar
  56. Wada Y, van Beek LPH, van Kempen CM, Reckman JWTM (2012) Global depletion of groundwater resources. Geophysical Research Letters 37: L20402. Scholar

Copyright information

© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Science and Technology DepartmentUniversity of SannioBeneventoItaly
  2. 2.Met European Research ObservatoryBeneventoItaly
  3. 3.UMR Ecosystème Prairial, INRA, Vet Agro SupClermont-FerrandFrance
  4. 4.Acquedotto Pugliese S.p.A.BariItaly

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