Abstract
Arid and semi-arid regions are vulnerable to natural disturbances and particularly, to anthropogenic climate change. In these regions, water resources are scarce, and groundwater is commonly the main source of water for municipal supply, agricultural, and industrial purposes. Groundwater overexploitation is a common issue for these regions, where overextraction, climate variability, and recharge are responsible for groundwater storage evolution. This study aims to find teleconnections through wavelet analysis between inter-annual rates of change in storage, obtained from geostatistical groundwater modeling, and climate variability. Continuous wavelet transform, cross-wavelet analysis, and wavelet coherence were used to analyze and characterize non-stationary patterns of variability of precipitation (PP), standardized precipitation index (SPI), minimum (Min_Temp) and maximum temperature (Max_Temp), Enhanced Vegetation Index (EVI), the Oceanic Niño Index (ONI), the Pacific-North American pattern (PNA), the Caribbean Index (CAR), and groundwater change in storage (ΔGS) semestral time series. The results suggest significant influences of the indices on ΔGS, PP, Min_Temp, and EVI. PNA manifested an important influence in short periods (2–4 years) in all aquifers, ONI showed influence mainly over long periods (8–16 years), and CAR had influence in periods of high hurricane activities. In addition, under extreme climatic conditions (El Niño or La Niña) the higher/lower water demand increase their influence on groundwater level response creating an indirect link between climate indices and ΔGS. This research can serve as a good indicator of the climate variations behavior and can provide information to understand the regional climate effects of the ENSO phenomenon in overexploited aquifers.
Graphical abstract
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Adamowski J, Chan HF (2011) A wavelet neural network conjunction model for groundwater level forecasting. J Hydrol 407(1–4):28–40. https://doi.org/10.1016/j.jhydrol.2011.06.013
Anderson Jr WP, Emanuel RE (2008) Effect of interannual and interdecadal climate oscillations on groundwater in North Carolina. Geophysical Research Letters 35(23):1–4. https://doi.org/10.1029/2008GL036054
Barrett BS, Longoria ME (2013) Variability of precipitation and temperature in Guanajuato. Mexico. Atmósfera 26(4):521–536. https://doi.org/10.1016/S0187-6236(13)71093-2
Beguería S, Vicente-Serrano SM (2017) SPEI: Calculation of the Standardised Precipitation-Evapotranspiration Index. https://CRAN.R-project.org/package=SPEI. Accessed 20 Feb 2020
Bravo Cabrera JL, Azpra Romero E, Zarraluqui Such V, Gay García C, Estrada Porrúa F (2010) Significance tests for the relationship between “El Niño” phenomenon and precipitation in Mexico. Geofísica Int 49(4):245–261. https://doi.org/10.22201/igeof.00167169p.2010.49.4.132
Bravo Cabrera JL, Azpra Romero E, Zarraluqui Such V, Gay García C (2017) Effects of El Niño in Mexico during rainy and dry seasons: an extended treatment. Atmósfera 30(3):221–232. https://doi.org/10.20937/ATM.2017.30.03.03
Cavazos T, Rivas D (2004) Variability of extreme precipitation events in Tijuana, Mexico. Climate Res 25(3):229–243
Charlier J-B, Ladouche B, Maréchal J-C (2015) Identifying the impact of climate and anthropic pressures on karst aquifers using wavelet analysis. J Hydrol 523:610–623. https://doi.org/10.1016/j.jhydrol.2015.02.003
Coulibaly P (2006) Spatial and temporal variability of Canadian seasonal precipitation (1900–2000). Adv Water Resour 29(12):1846–1865. https://doi.org/10.1016/j.advwatres.2005.12.013
Coulibaly P, Anctil F, Rasmussen P, Bobée B (2000) A recurrent neural networks approach using indices of low-frequency climatic variability to forecast regional annual runoff. Hydrol Process 14(15):2755–2777. https://doi.org/10.1002/1099-1085(20001030)14:15%3c2755::AID-HYP90%3e3.0.CO;2-9
Coulibaly P, Burn DH (2004) Wavelet analysis of variability in annual Canadian streamflows. Water Resour Res 40(3):1–14. https://doi.org/10.1029/2003WR002667
Cruz-Rico J, Rivas D, Tejeda-Martínez A (2015) Variability of surface air temperature in Tampico, northeastern Mexico. Int J Climatol 35(11):3220–3228. https://doi.org/10.1002/joc.4200
Curtis S (2008) The Atlantic multidecadal oscillation and extreme daily precipitation over the US and Mexico during the hurricane season. Climate Dynamics 30(4):343–351. https://doi.org/10.1007/s00382-007-0295-0
Dahlman L (2009) Climate variability: oceanic Niño index. https://www.climate.gov/news-features/understanding-climate/climate-variability-oceanic-niño-index. Accessed 15 January 2020
Deng C, Pisani B, Hernández H, Li Y (2020) Assessing the impact of climate change on water resources in a semi-arid area in central Mexico using a SWAT model. Boletín de la Sociedad Geológica Mexicana 72(2):1–19. https://doi.org/10.18268/BSGM2020v72n2a150819
Dominguez C, Magaña V (2018) The role of tropical cyclones in precipitation over the tropical and subtropical North America. Front Earth Sci 6:19. https://doi.org/10.3389/feart.2018.00019
Elbeltagi A, Salam R, Pal SC, Zerouali B, Shahid S, Mallick J, Islam MS, Islam ARMT (2022) Groundwater level estimation in northern region of Bangladesh using hybrid locally weighted linear regression and Gaussian process regression modeling. Theor App Climatol 149(1–2):131–151. https://doi.org/10.1007/s00704-022-04037-0
Elshall AS, Arik AD, El-Kadi AI, Pierce S, Ye M, Burnett KM, Wada CA, Bremer LL, Chun G (2020) Groundwater sustainability: a review of the interactions between science and policy. Environ Res Lett 15(9):093004. https://doi.org/10.1088/1748-9326/ab8e8c
Fleming SW, Quilty EJ (2006) Aquifer responses to El Niño-Southern oscillation, southwest British Columbia. Groundwater 44(4):595–599. https://doi.org/10.1111/j.1745-6584.2006.00187.x
Gallegati M (2018) A systematic wavelet-based exploratory analysis of climatic variables. Clim Chang 148(1):325–338. https://doi.org/10.1007/s10584-018-2172-8
Grinsted A, Moore JC, Jevrejeva S (2004) Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Process Geophys 11(5/6):561–566. https://doi.org/10.5194/npg-11-561-2004
Gurdak JJ, Hanson RT, McMahon PB, Bruce BW, McCray JE, Thyne GD, Reedy RC (2007) Climate variability controls on unsaturated water and chemical movement, high plains aquifer, USA. Vadose Zone J 6(3):533–547. https://doi.org/10.2136/vzj2006.0087
Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ 83(1–2):195–213. https://doi.org/10.1016/S0034-4257(02)00096-2
Jimenez Quiroz MDC (2011) INDICADORES CLIMATICOS. Una manera para identificar la variabilidad climática a escala global. https://www.inapesca.gob.mx/portal/documentos/publicaciones/BOLETINES/hidroclimatico/INDICES-CLIMATICOS.pdf. Accessed 15 January 2020
** X, Liu J, Wang S, **a W (2016) Vegetation dynamics and their response to groundwater and climate variables in Qaidam Basin, China. Int J Remote Sens 37(3):710–728. https://doi.org/10.1080/01431161.2015.1137648
Joo TW, Kim SB (2015) Time series forecasting based on wavelet filtering. Exp Syst Appl 42(8):3868–3874. https://doi.org/10.1016/j.eswa.2015.01.026
Jury MR, Melice JL (2000) Analysis of Durban rainfall and Nile river flow 1871–1999. Theor App Climatol 67:161–169. https://doi.org/10.1007/s007040070005
Keener V, Feyereisen G, Lall U, Jones J, Bosch D, Lowrance R (2010) El-Niño/Southern Oscillation (ENSO) influences on monthly NO3 load and concentration, stream flow and precipitation in the Little River Watershed, Tifton, Georgia (GA). J Hydrol 381(3–4):352–363. https://doi.org/10.1016/j.jhydrol.2009.12.008
Kløve B, Ala-Aho P, Bertrand G, Gurdak JJ, Kupfersberger H, Kværner J, Muotka T, Mykrä H, Preda E, Rossi P (2014) Climate change impacts on groundwater and dependent ecosystems. J Hydrol 518:250–266. https://doi.org/10.1016/j.jhydrol.2013.06.037
Knappett PS, Li Y, Hernandez H, Rodriguez R, Aviles M, Deng C, Piña V, Giardino JR, Mahlknecht J, Datta S (2018) Changing recharge pathways within an intensively pumped aquifer with high fluoride concentrations in Central Mexico. Sci Total Environ 622–623:1029–1045. https://doi.org/10.1016/j.scitotenv.2017.12.031
Knappett PS, Li Y, Loza I, Hernandez H, Avilés M, Haaf D, Majumder S, Huang Y, Lynch B, Piña V (2020) Rising arsenic concentrations from dewatering a geothermally influenced aquifer in central Mexico. Water Res 185:116257. https://doi.org/10.1016/j.watres.2020.116257
Kolokytha E, Oishi S, Teegavarapu RS (2017) Sustainable water resources planning and management under climate change. Springer
Kuss AJM, Gurdak JJ (2014) Groundwater level response in US principal aquifers to ENSO, NAO, PDO, and AMO. J Hydrol 519:1939–1952. https://doi.org/10.1016/j.jhydrol.2014.09.069
Li Y, Hernandez JH, Aviles M, Knappett PS, Giardino JR, Miranda R, Puy MJ, Padilla F, Morales J (2020) Empirical Bayesian kriging method to evaluate inter-annual water-table evolution in the Cuenca Alta del Río Laja aquifer, Guanajuato, México. Hydrol 582:124517. https://doi.org/10.1016/j.jhydrol.2019.124517
Lyerly SB (1952) The average Spearman rank correlation coefficient. Psychometrika 17(4):421–428. https://doi.org/10.1007/BF02288917
Magaña VO, Vázquez JL, Pérez JL, Pérez JB (2003) Impact of El Niño on precipitation in Mexico. Geofís Int 42(3):313–330. https://doi.org/10.22201/igeof.00167169p.2003.42.3
Manatsa D, Mukwada G, Siziba E, Chinyanganya T (2010) Analysis of multidimensional aspects of agricultural droughts in Zimbabwe using the standardized precipitation index (SPI). Theor App Climatol 102:287–305. https://doi.org/10.1007/s00704-010-0262-2
McKee TB, Doesken NJ, Kleist J (1993) The relationship of drought frequency and duration to time scales. https://climate.colostate.edu/pdfs/relationshipofdroughtfrequency.pdf. Accessed 20 Feb 2020
Melgarejo AE, Ordoñez P, Nieto R, Gimeno L, Ribera P (2017) Moisture transport related to the ENSO effects in the Mexican precipitation. First International Electronic Conference on the Hydrological Cycle 2017(1):1–8. https://doi.org/10.3390/CHyCle-2017-04884
Mirzavand M, Ghazavi R (2015) A stochastic modelling technique for groundwater level forecasting in an arid environment using time series methods. Water Resour Manag 29(4):1315–1328. https://doi.org/10.1007/s11269-014-0875-9
Morales JL, Horta Rangel FA, Segovia Domínguez I, Morua AR, Hernández Anguiano JH (2019) Analysis of a new spatial interpolation weighting method to estimate missing data applied to rainfall records. Atmósfera 32(3):237–259. https://doi.org/10.20937/ATM.2019.32.03.06
Muñoz Aguayo P (2013) Apuntes de teledetección: índices de vegetación. https://bibliotecadigital.ciren.cl/handle/20.500.13082/26389. Accessed 20 Feb 2020
Nalley D, Adamowski J, Khalil B, Biswas A (2016) Inter-annual to inter-decadal streamflow variability in Quebec and Ontario in relation to dominant large-scale climate indices. J Hydrol 536:426–446. https://doi.org/10.1016/j.jhydrol.2016.02.049
Navarro Céspedes JM, Hernández Anguiano JH, Alcántara Concepcion PC, Morales Martínez JL, Carreño Aguilera G, Padilla Benítez F (2023) A comparison of missing value imputation methods applied to daily precipitation in a semi-arid and a humid region of México. Atmósfera 37:33–52. https://doi.org/10.20937/ATM.53095
Navarro de León I, Gárfias-Soliz J, Mahlknecht J (2005) Groundwater flow regime under natural conditions as inferred from past evidence and contemporary field observations in a semi-arid basin: Cuenca de la Independencia, Guanajuato. México. Journal of arid environments 63(4):756–771. https://doi.org/10.1016/j.jaridenv.2005.04.003
Null J (2020) El Niño and La Niña years and intensities. https://ggweather.com/enso/oni.htm. Accessed 15 January 2020
Partal T (2018) Wavelet based periodical analysis of the precipitation data of the Mediterranean region and its relation to atmospheric indices. Model Earth Syst Environ 4(4):1309–1318. https://doi.org/10.1007/s40808-018-0505-2
Pavia EG, Graef F, Reyes J (2006) PDO–ENSO effects in the climate of Mexico. Journal of climate 19(24):6433–6438. https://doi.org/10.1175/JCLI4045.1
Perez‐Valdivia C, Sauchyn D, Vanstone J (2012) Groundwater levels and teleconnection patterns in the Canadian Prairies. Water Resources Research 48(7):1–13. https://doi.org/10.1029/2011WR010930
R Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. https://www.R-project.org/. Accessed 01 September 2019
Redmond KT, Koch RW (1991) Surface climate and streamflow variability in the western United States and their relationship to large-scale circulation indices. Water Resour Res 27(9):2381–2399. https://doi.org/10.1029/91WR00690
Resende TC, Longuevergne L, Gurdak J, Leblanc M, Favreau G, Ansems N, Van der Gun J, Gaye C, Aureli A (2019) Assessment of the impacts of climate variability on total water storage across Africa: implications for groundwater resources management. Hydrogeol J 27(1):493–512. https://doi.org/10.1007/s10040-018-1864-5
Rhif M, Ben Abbes A, Farah IR, Martínez B, Sang Y (2019) Wavelet transform application for/in non-stationary time-series analysis: a review. App Sci 9(7):1345. https://doi.org/10.3390/app9071345
Roesch A, Schmidbauer H (2018) WaveletComp: Computational Wavelet Analysis. https://CRAN.R-project.org/package=WaveletComp. Accessed 20 Feb 2020
Rösch A, Schmidbauer H (2016) WaveletComp 1.1: A guided tour through the R package. http://www.hs-stat.com/projects/WaveletComp/WaveletComp_guided_tour.pdf. Accessed 20 Feb 2020
SMN (2020) Información Estadística Climatológica. https://smn.conagua.gob.mx/es/climatologia/informacion-climatologica/informacion-estadistica-climatologica. Accessed 20 Feb 2020
Szolgayova E, Parajka J, Blöschl G, Bucher C (2014) Long term variability of the Danube River flow and its relation to precipitation and air temperature. J Hydrol 519:871–880. https://doi.org/10.1016/j.jhydrol.2014.07.047
Tiwari AK, Mutascu M, Andries AM (2013) Decomposing time-frequency relationship between producer price and consumer price indices in Romania through wavelet analysis. Econ Model 31:151–159. https://doi.org/10.1016/j.econmod.2012.11.057
Torrence C, Compo GP (1998) A practical guide to wavelet analysis. Bullet Amer Meteorol Soc 79(1):61–78. https://doi.org/10.1175/1520-0477(1998)079%3c0061:APGTWA%3e2.0.CO;2
Tremblay L, Larocque M, Anctil F, Rivard C (2011) Teleconnections and interannual variability in Canadian groundwater levels. J Hydrol 410(3–4):178–188. https://doi.org/10.1016/j.jhydrol.2011.09.013
Valois R, MacDonell S, NúñezCobo JH, Maureira-Cortés H (2020) Groundwater level trends and recharge event characterization using historical observed data in semi-arid Chile. Hydrol Sci J 65(4):597–609. https://doi.org/10.1080/02626667.2020.1711912
Velasco EM, Gurdak JJ, Dickinson JE, Ferré TPA, Corona CR (2017) Interannual to multidecadal climate forcings on groundwater resources of the U.S. West Coast. J Hydrol: Reg Stud 11:250–265. https://doi.org/10.1016/j.ejrh.2015.11.018
Wang D, Du S-C, Jia W (2022) Multiscale variability of China’s historical flood/drought index and precipitation teleconnections with ENSO using wavelet analyses. Theor App Climatol 149(3–4):1583–1597. https://doi.org/10.1007/s00704-022-04125-1
Wolter K, Timlin MS (1998) Measuring the strength of ENSO events: how does 1997/98 rank? Weather 53(9):315–324. https://doi.org/10.1002/j.1477-8696.1998.tb06408.x
Acknowledgements
The authors thank two anonymous reviewers and an associate editor for their objective comments and constructive criticism, which helped to improve the quality of this paper. In addition, J.M.N.C. thanks the Consejo Nacional de Humanidades, Ciencias y Tecnologías (CONAHCYT) of Mexico for financial support throughout the Doctoral Program of Science and Technology of Water, Grant No. 1015533, University of Guanajuato and Universidad Central “Marta Abreu” de las Villas.
Funding
Authors J.M., M.A., V.P., and X.Z. were financially supported by the Consejo Nacional de Humanidades, Ciencias y Tecnologías (CONAHCYT) of Mexico, throughout the Doctoral Program of Science and Technology of Water, Grants No. 1015533, 330122, 786364, and 1155759, respectively.
Author information
Authors and Affiliations
Contributions
All the authors contributed to conceptualizing, designing, and discussing the study and result. The initial draft of the paper was prepared by J.M. and reviewed by J.H., and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Navarro Céspedes, J.M., Hernández Anguiano, J.H., Alcántara Concepción, P.C. et al. Influence of climate variability on change in storage of overexploited aquifers in a semi-arid region. Theor Appl Climatol 155, 2087–2103 (2024). https://doi.org/10.1007/s00704-023-04749-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00704-023-04749-x