Water Resources Management

, Volume 32, Issue 8, pp 2717–2734 | Cite as

North Atlantic Oscillation as a Cause of the Hydrological Changes in the Mediterranean (Júcar River, Spain)

  • Gabriel Gómez-Martínez
  • Miguel A. Pérez-Martín
  • Teodoro Estrela-Monreal
  • Patricia del-Amo


Significant changes in the Júcar River Basin District’s hydrology in the Mediterranean side of Spain, have been observed during last decades. A statistical change-point in the year 1980 was detected in the basins’ hydrological series in the main upper river, Júcar and Túria basins. In the study scope are, the North Atlantic Oscillation (NAO) is linked with the winter precipitations in the Upper Basins, which are here responsible for the major part of streamflow. So changes in the rainfall has an important effect in the natural river flows. The statistical analysis detected a change at NAO’s seasonal pattern, what means a considerable reduction of winter rainfalls in the Upper River basins located in the inland zone which is simultaneously the water collection and reservoirs area (a − 40% of water resources availability since 1980). Hydro-meteorological data and a Water Balance Model, Patrical, have been used to assess these water resources’ reduction. Results points out to the change in the Basin’s precipitation pattern in the inland areas (upper basins), associated to Atlantic weather patterns, as the main cause, while it has not been detected in the coastal areas. All these changes implies water stress for water resources planning, management and allocation, where more than 5.2 million people and irrigation of 390,000 ha are served, joint to the time variability, an important territorial imbalance exists between resources and demands. Thus, in the main upper basins, with the biggest streamflow’s reductions, locate the largest reservoirs in terms of water resources collection and reserves.


Hydrological regime changes Water balance model Mediterranean Climate Patterns Change Point Detection 


  1. Alexandersson H (1986) A homogeneity test applied to precipitation data. J Climatol 6:661–675CrossRefGoogle Scholar
  2. Bayazit M (2015) Nonstationarity of Hydrological Records and Recent Trends in Trend Analysis: A State-of-the-art Review. Environ Process 2:527–542. CrossRefGoogle Scholar
  3. Bindoff NL, Stott PA, Allen MR, Gillett N, Gutzler D, Hansingo K, Hegerl G, Hu Y, Jain S, Overland J, Perlwitz J, Sebbari R, Zhang X (2013) Detection and attribution of climate change: From global to regional. In: Mokhov II, Stocker TF, Qin D, et al. (eds), Climate change 2013: The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge; New York, pp. 867–952Google Scholar
  4. CEDEX (2009) Mapa de caudales máximos. Centro de Estudios y Experimentaciones CEDEX. Ministerio de Medio Ambiente y Medio Rural y Marino, MadridGoogle Scholar
  5. Chiew FHS, McMahon TA (1993) Detection of trend or change in annual flow of Australian rivers. Int J Climatol 13:643–653. CrossRefGoogle Scholar
  6. CLC1990 Corine Land Cover (1990) Directorate-General for Environment. European Environment Agency (EEA)Google Scholar
  7. CLC2000 Corine Land Cover (2000) Directorate-General for Environment. European Environment Agency (EEA). Published 1 January 2002Google Scholar
  8. Chirivella V, Capilla JE, Pérez MA (2015) Modelling Regional Impacts of Climate Change on Water Resources: the Júcar Basin, Spain. Hydrol Sci J.
  9. CHJ 2005 Provisional Art. 5 Report Pursuant to the Water Framework Directive. Júcar River Basin Authority (Confederación Hidrográfica del Júcar), Ministry of Environment, SpainGoogle Scholar
  10. CHJ (2015) Júcar RB Management Plan 2015_2021. Júcar RBA (Demarcación hidrográfica del Júcar). Confederación Hidrográfica del Júcar. Ministry of Environment, MadridGoogle Scholar
  11. Du T, Xiong L, Xu C-Y, Gippel CJ, Guo S, Liu P (2015) Return period and risk analysis of nonstationary low-flow series under climate change. J Hydrol 527:234–250. CrossRefGoogle Scholar
  12. EEA (European Environment Agency), 2003. Indicator Factsheet WQ01c. Available online, URL:
  13. El Adlouni S, Ouarda TBMJ, Zhang X, Roy R, Bobee B (2007) Generalized maximum likelihood estimators for the nonstationary generalized extreme value model. Water Resour Res 43:W03410. CrossRefGoogle Scholar
  14. Estrela T, Pérez-Martín MA, Vargas E (2012) Impacts of Climate Change on Water Resources in Spain. Hydrol Sci J 57(6):1154–1167 CrossRefGoogle Scholar
  15. Ferrer J, Pérez-Martín MA, Jiménez S, Estrela T, Andreu J (2012) GIS based models for water quantity and quality assessment in the Júcar River Basin, Spain, including climate change effects. Sci Total Environ 440:42–59.
  16. García-Ruiz JM, López-Moreno JI, Vicente-Serrano SM, Lasanta-Martínez T, Beguería S (2011) Mediterranean water resources in a global change scenario. Earth-Sci Rev 105(2011):121–139CrossRefGoogle Scholar
  17. Hegerl G, Zwiers F (2011) Use of models in detection and attribution of climate change. Wiley Interdiscip Rev Clim Chang 2(4):570–591. CrossRefGoogle Scholar
  18. HURRELL (2016) North Atlantic Oscillation (NAO) INDEX (STATION-BASED)
  19. Hurrell JW, Deser C (2009) North Atlantic climate variability: The role of the North Atlantic Oscillation. J Mar Syst 78(1):28–41CrossRefGoogle Scholar
  20. Kendall MG (1975) Rank Correlation Measures. Charles Griffin, LondonGoogle Scholar
  21. Livingston EH (2004) Who Was Student and Why Do We Care So Much about His t-Test? J Surg Res 118:58–65. CrossRefGoogle Scholar
  22. López-Bustins JA, Martín-Vide J, Sánchez-Lorenzo A (2008) Iberia winter rainfall trends based upon changes in teleconnection and circulation patterns. Glob Planet Chang 63(2008):171–176CrossRefGoogle Scholar
  23. Lorenzo-Lacruz J, Vicente-Serrano SM, López-Moreno JI, Morán-Tejeda E, Zabalza J (2012) Recent trends in Iberian streamflows (1945–2005). J Hydrol 414–415(2012):463–475CrossRefGoogle Scholar
  24. Martín-Vide J, Lopez Bustins JA (2006) The Western Mediterranean Oscillation and Rainfall in the Iberian Peninsula. Int J Climatol 26(11):1455–1475CrossRefGoogle Scholar
  25. Merz B, Vorogushyn S, Uhlemann S, Delgado J, Hundecha Y (2012) HESS Opinions More efforts and scientific rigour are needed to attribute trends in flood time series. Hydrol Earth Syst Sci 16(5):1379–1387. CrossRefGoogle Scholar
  26. Miao W, Chiou P (2008) Confidence intervals for the difference between two means. Comput Stat Data Anal 52(2008):2238–2248. CrossRefGoogle Scholar
  27. Milly PC, Betancourt J, Falkenmark M, Hirsch RM, Kundzewicz ZW, Lettenmaier DP (2008) Stationary is dead: Whither water management? J Sci 318:573–574CrossRefGoogle Scholar
  28. Morán-Tejeda E, López-Moreno JI, Ceballos-Barbancho A, Vicente-Serrano SM (2011) River regimes and recent hydrological changes in the Duero basin (Spain). J Hydrol 404(2011):241–258CrossRefGoogle Scholar
  29. Morán-Tejeda E, Ceballos-Barbancho A, Llorente-Pinto JM, López-Moreno JI (2012) Land-cover changes and recent hydrological evolution in the Duero Basin (Spain). Reg Environ Chang 12:17–33. CrossRefGoogle Scholar
  30. Moraes JM, Pellegrino HQ, Ballester MV, Martinelli LA, Victoria R, Krusche AV (1998) Trends in hydrological parameters of southern Brazilian watershed and its relation to human induced changes. Water Resour Manag 12:295–311. CrossRefGoogle Scholar
  31. Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans Am Soc Agric Biol Eng 50(3):885–900. Google Scholar
  32. Nash JE, Sutcliffe, JV (1970) River flow forecasting through conceptual models part I — A discussion of principles. 10(3):282-290.
  33. National Academies of Sciences, Engineering, and Medicine (2016) Attribution of Extreme Weather Events in the Context of Climate Change. National Academies Press, Washington, D.C. Google Scholar
  34. Pajares-Candela A. 2002. Modelación cuasidistribuida de los recursos hídricos y establecimiento de zonas hidroclimáticamente afines en el ámbito de la Confederación Hidrográfica del Júcar. Escuela de Caminos Canales y Puertos, Universidad Politécnica de ValenciaGoogle Scholar
  35. Pérez-Martín MA, Estrela T, Andreu J, Ferrer J (2014) Modeling Water Resources and River-Aquifer Interaction in the Júcar River Basin, Spain. Water Resour Manag 28:4337–4358. CrossRefGoogle Scholar
  36. Perreault L, Hache M, Slivitsky M, Bobee B (1999) Detection of changes in precipitation and runoff over eastern Canada and US using a Bayesian approach. Stoch Env Res Risk A 13:201–216. CrossRefGoogle Scholar
  37. Pettit AN (1979) A non-parametric approach to the change-point problem. Appl Stat 28:126–135CrossRefGoogle Scholar
  38. Pinto JG, Raible CC (2012) Past and recent changes in the North Atlantic oscillation. WIREs Clim Change 2012(3):79–90. CrossRefGoogle Scholar
  39. Rasmussen P (2001) Bayesian estimation of change points using the general linear model. Water Resour Res 37:2723–2731. CrossRefGoogle Scholar
  40. Reeves J, Chen J, Wang XL, Lund R, Lu Q (2007) A Review and Comparison of Changepoint Detection Techniques for Climate Data. J Appl Meteorol Climatol.
  41. Satterthwaite FE (1946) An approximate distribution of estimates of variance components. Biom Bull 2:110–114CrossRefGoogle Scholar
  42. Senatore A et al 2011 Regional climate change projections and hydrological impact analysis for a Mediterranean basin in Southern Italy. Alfonso Senatore, Giuseppe Mendicino, Gerhard Smiatekb, Harald Kunstmann. a Dipartimento di Difesa del Suolo, Università della Calabria, P.te P. Bucci 41b, 87036 Rende (CS), Italy. b Institute for Meteorology and Climate Research (IMK-IFU), Karlsruhe Institute of Technology, Kreuzeckbahnstrasse 19, 82467 Garmisch-Partenkirchen, Germany. J Hydrol 399 (1–2):70–92Google Scholar
  43. Huang S, Liu D Huang Q, Chen Y (2016) Contributions of climate variability and human activities to the variation of runoff in the Wei River Basin, China. ISSN: 0262–6667 (Print) 2150–3435 (Online) JournalGoogle Scholar
  44. Stott PA, Stone DA, Allen MR (2004) Human contribution to the European heatwave of 2003. Nature 432:610–614CrossRefGoogle Scholar
  45. Stott PA, Christidis N, Otto FEL, Sun Y, Vanderlinden J-P, van Oldenborgh GJ, Vautard R, von Storch H, Walton P, Yiou P, Zwiers FW (2016) Attribution of extreme weather and climate-related events. Wiley Interdiscip Rev Clim Chang 7(1):23–41. CrossRefGoogle Scholar
  46. Strupczewski WG, Kaczmarek Z (2001) Non-stationary approach to at-site flood frequency modeling II. Weighted least squares estimation. J Hydrol 248:143–151. CrossRefGoogle Scholar
  47. Strupczewski WG, Singh VP, Feluch W (2001a) Non-stationary approach to at-site flood frequency modeling I. Maximum likelihood estimation. J Hydrol 248:123–142. CrossRefGoogle Scholar
  48. Strupczewski WG, Singh VP, Mitosek HT (2001b) Nonstationary approach to at-site flood frequency modeling III. Flood analysis of Polish rivers. J Hydrol 248:152–167. CrossRefGoogle Scholar
  49. Sridhar V, Nayak A (2010) Implications of climate-driven variability and trends for the hydrologic assessment of the Reynolds Creek Experimental Watershed, Idaho. J Hydrol 385(2010):183–202CrossRefGoogle Scholar
  50. Tao H, Gemmer M, Bai Y, Su B, Mao W (2011) Trends of streamflow in the Tarim River Basin during the past 50 years: Human impact or climate change? J Hydrol 400(2011):1–9CrossRefGoogle Scholar
  51. Trenberth KE, Fasullo JT, Shepherd TG (2015) Attribution of climate extreme events. Nat Clim Chang 5(8):725–730. CrossRefGoogle Scholar
  52. Valero Villarroya M 2007 Evaluación de los efectos del cambio en los usos del suelo mediante el uso de un modelo de simulación del ciclo hidrológico aplicado en la cuenca del Júcar. Ejercicio final de carrera. Escuela de Caminos Canales y Puertos, Universidad Politécnica de ValenciaGoogle Scholar
  53. Villarini G, Serinaldi F, Smith JA, Krajewski WF (2009) On the stationarity of annual flood peaks in the Continental United States during the 20th Century. Water Resour Res 45:W08417. Google Scholar
  54. Villarini G, Smith JA, Napolitano F (2010) Nonstationary modeling of a long record of rainfall and temperature over Rome. Adv Water Resour 33:1256–1267. CrossRefGoogle Scholar
  55. Welch BL (1938) The significance of the difference between two means when the population variances are unequal. Biometrika 29:350–362CrossRefGoogle Scholar
  56. Wong H, Hu BQ, Ip WC, Xia J (2006) Change-point analysis of hydrological time series using grey relational method. J Hydrol 324:323–338. CrossRefGoogle Scholar
  57. Xie H, Li D, Xiong L (2014) Exploring the ability of the Pettit method for detecting change point by Monte Carlo simulation. Stoch Env Res Risk A 28(7):1643–1655. CrossRefGoogle Scholar
  58. Xiong L, Jiang C, Xu C-Y, Yu K-x, Guo S (2015) A framework of change-point detection for multivariate hydrological series Water Resour Res 51. doi:
  59. Yue S, Pilon P, Cavadias G (2002) Power of the Mann–Kendall and Sperman’s rho tests for detecting monotonic trends in hydrological series. J Hydrol 259:254–271. CrossRefGoogle Scholar
  60. Zhang Q, Singh VP, Sun P, Chen X, Zhang Z, Li J (2011) Precipitation and streamflow changes in China: Changing patterns, causes and implications. J Hydrol 410(2011):204–216CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Universitat Politècnica de ValenciaValenciaSpain
  2. 2.Research Institute of Water and Environmental Engineering (IIAMA)Universitat Politècnica de ValenciaValenciaSpain
  3. 3.Confederación Hidrográfica del Júcar (CHJ) Júcar River Basin AuthorityValenciaSpain

Personalised recommendations