Relevance of spatio-temporal rainfall variability regarding groundwater management challenges under global change: case study in Doñana (SW Spain)

Abstract

Rainfall is the major contribution for groundwater recharge in arid and semiarid climates, therefore a key factor in water resources estimation. This work presents the results of an in-depth study in Doñana National Park concerning groundwater recharge behavior over a long period (1975–2016). The spatio-temporal kriging algorithm was used as a supportive tool to improve the reconstruction of the spatio-temporal rainfall variability. One of the main findings was that monthly recharge estimations range between 21 and 91% of the maximum rainfall, being overestimated in areas that also demonstrate spatial heterogeneity in rainfall distribution. In the light of these results, for water management purposes in the Mediterranean area, rainfall spatio-temporal scale is a critical aspect and it must be taken into account in groundwater reservoir allocation. Moreover, it is highlighted that local studies of rainfall and recharge, in an area of high ecological fragility, are essential to developing management strategies that prevent climate change effects and guarantee optimal conditions for groundwater resources in the future.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

References

  1. Álvarez-Rodríguez J, Sanchez A, Quintas L (2005) SIMPA, a GRASS based too for hydrological studies. Int J Geoinform 1:1–14

    Google Scholar 

  2. Armstrong M (1998) Basic linear geostatistics. Springer, Berlin. https://doi.org/10.1007/978-3-642-58727-6

    Book  Google Scholar 

  3. Arun M, Sananda K, Anirban M (2012) Rainfall trend analysis by Mann-Kendell test: a case study of North-Eastern Part of Cuttack District, Orissa. Int J Geol Earth Environ Sci 2:70–78

    Google Scholar 

  4. Bennett B, Thyer M, Leonard M, Lambert M, Bates B (2018) A comprehensive and systematic evaluation framework for a parsimonious daily rainfall field model. J Hydrol 556:1123–1138

    Article  Google Scholar 

  5. Burgueño A, Martínez MD, Lana X, Serra de Larrocha C (2005) Statistical distributions of the daily rainfall regime in Catalonia (Northeastern Spain) for the years 1950–2000. Int J Climatol 25(10):1381–1403. https://doi.org/10.1002/joc.1197

    Article  Google Scholar 

  6. Buytaert W, Celleri R, Willems P, De Bievre B, Wyseure G (2006) Spatial and temporal rainfall variability in mountainous areas: a case study from the south Ecuadorian Andes. J Hydrol 329:413–421. https://doi.org/10.1016/j.jhydrol.2006.02.031

    Article  Google Scholar 

  7. Carrera-Hernández JJ, Gaskin SJ (2007) Spatio temporal analysis of daily precipitation and temperature in the Basin of Mexico. J Hydrol 336(3–4):231–249

    Article  Google Scholar 

  8. Chappell A, Renzullo LJ, Raupach TH, Haylock M (2013) Evaluating geostatistical methods of blending satellite and gauge data to estimate near real-time daily rainfall for Australia. J Hydrol 493:105–114

    Article  Google Scholar 

  9. Chen L, Xu J, Wang G, Shen Z (2019) Comparison of the multiple imputation approaches for imputing rainfall data series and their applications to watershed models. J Hydrol 572:449–460

    Article  Google Scholar 

  10. CHG Confederación Hidrográfica del Guadalquivir (2015) Plan Hidrológico de la Demarcación Hidrográfica del Guadalquivir. Segundo ciclo de planificación 2016–2021

  11. CHG Confederación hidrográfica del Guadalquivir (2017) Plan especial de sequía. Demarcación hidrográfica del Guadalquivir. Borrador para consulta pública. Diciembre de 2017. Memoria

  12. Christakos G (2000) Modern Spatiotemporal Geostatistics. Oxford University Press, IAMG #6, Oxford. ISBN 0-19-513895-3

    Google Scholar 

  13. Custodio E (2001) Aguas subterráneas y humedales. Papeles del Proyecto Aguas Subterráneas, Serie C, Aguas Subterráneas y Medio Ambiente. Fundación Marcelino Botín. Madrid. pp 33–72

  14. Custodio E, Manzano M, Montes C (2009) Las aguas subterráneas en Doñana: aspectos ecológicos y sociales. Agencia Andaluza del Agua, Consejería de Medio Ambiente de la Junta de Andalucía, p 243

    Google Scholar 

  15. De Iaco S, Posa D (2012) Predicting spatio-temporal random fields: some computational aspects. Comput Geosci 41:12–24. https://doi.org/10.1016/j.cageo.2011.11.014

    Article  Google Scholar 

  16. De Iaco S, Maggio M, Palma M, Posa D (2012) Towards an automatic procedure for modeling multivariate space-time data. Comput Geosci 41(4):1–11

    Article  Google Scholar 

  17. De Silva RD (2010) The effect of using different time steps in a soil water balance model to estimate groundwater recharge in the dry zone of Sri Lanka. J Environ Hydrol 18:1–12

    Google Scholar 

  18. De Silva CS, Rushton KR (1996) Interpretation of the behavior of agro-well systems in Sri Lanka using radial flow models. J Hydrol Sci 41(6):825–835

    Article  Google Scholar 

  19. Deng S, Chen T, Yang N, Qu L, Li Mand Chen D (2018) Spatial and temporal distribution of rainfall and drought characteristics across the Pearl River basin. Sci Total Environ 619:28–41. https://doi.org/10.1016/j.scitotenv.2017.10.339

    CAS  Article  Google Scholar 

  20. Dimitriou E, Moussoulis E, Díaz-Paniagua C, Serrano L (2017) Hydrodynamic numerical modelling of the water level decline in four temporary ponds of the Doñana National Park (SW Spain). J Arid Environ 147:90–102. https://doi.org/10.1016/j.jaridenv.2017.09.004

    Article  Google Scholar 

  21. Gräler E, Pebesma E, Heuvelink G (2016) Spatio-temporal interpolation using gstat. RFID J 8(1):204–2018

    Google Scholar 

  22. Green AJ, Alcorlo P, Peeters ET, Morris EP, Espinar JL, Bravo-Utrera MA, Bustamante J, Díaz-Delgado R, Koelmans A, Mateo R, Mooij W, Rodríguez-Rodríguez M, Van Nes E, Scheffer M (2017) Creating a safe operating space for wetlands in a changing climate. Front Ecol Environ 15:99–107

    Article  Google Scholar 

  23. Guardiola-Albert C, Jackson CR (2011) Potential impacts of climate change on groundwater supplies to the Doñana wetland, Spain. Wetlands 31(5):907–920

    Article  Google Scholar 

  24. Guardiola-Albert C, Mediavilla-Laso C, Aguilera H, Fernández-Naranjo N, Ruiz-Bermudo F, García-Bravo N (2016) Recurso natural o recarga en la gestión del sistema acuífero Almonte-Marismas (Doñana) según la revisión del Plan Hidrológico (2016–2021) de la Demarcación Hidrográfica del Guadalquivir. Las aguas subterráneas y la planificación hidrológica. Congreso hispano-luso. AIH-GE, Madrid, pp 193–199. ISBN 978-84-938046-5-7

    Google Scholar 

  25. Harbaugh AW (2005) MODFLOW-2005, The US geological survey modular ground-water model: the ground-water flow process; Department of the Interior, Techniques and Methods 6–A16. U.S. Geological Survey

  26. Hargreaves GH (1989) Accuracy of estimated reference evapotranspiration. J Irrig Drain Eng 115(6):1000–1007

    Article  Google Scholar 

  27. Healy RW (2010) Estimating groundwater recharge. Cambridge University Press, Cambridge. https://doi.org/10.1017/CBO9780511780745

    Book  Google Scholar 

  28. Herrera S, Gutiérrez JM, Ancell R, Pons MR, Frías MD, Fernández J (2012) Development and analysis of a 50-year high-resolution daily gridded precipitation dataset over Spain (Spain02). Int J Climatol 32:74–85

    Article  Google Scholar 

  29. International Panel on Climate Change (IPCC) (2007) Climate Change 2007: Synthesis Report. Summary for Policymakers, An Assessment of the Intergovernmental Panel on Climate Change

    Book  Google Scholar 

  30. Ly S, Charles C, Degré A (2011) Geostatistical interpolation of daily rainfall at catchment scale: the use of several variogram models in the Ourthe and Ambleve catchments, Belgium. Hydrol Earth Syst Sci 15:2259–2274. https://doi.org/10.5194/hess-15-2259-2011

    Article  Google Scholar 

  31. Ly S, Charles C, Degré A (2013) Different methods for spatial interpolation of rainfall data for operational hydrology and hydrological modeling at watershed scale. A review. Biotechnol Agron Soc Environ 17(2):392–406

    Google Scholar 

  32. Manzano M (2001) Clasificación de los humedales de Doñana atendiendo a su funcionamiento hidrológico. Hidrogeología y Recursos Hidráulicos 24:57–75

    Google Scholar 

  33. Manzano M, Custodio E, Higueras H, Puig R, Soler A (2009) Influencia de la gestión del acuífero sobre los humedales del manto eólico de Doñana. Boletín Geológico y Minero 120(3):377–392 (ISSN: 0366-0176)

    Google Scholar 

  34. Mazione RL, Ehdm T, De Iaco S, Monteiro da Rocha M, Capello C (2019) Spatio-temporal kriging to predict water table depths from monitoring data in a conservation area at São Paulo State, Brazil

  35. Militino AF, Ugarte MD, Goicoa T, Genton M (2015) Interpolation of daily rainfall using spatiotemporal models and clustering. Int J climatol 35(7):1453–1464

    Article  Google Scholar 

  36. Monjo R, Martin-Vide J (2016) Daily precipitation concentration around the world according to several indices. Int J Climatol 36(11):3828–3838. https://doi.org/10.1002/joc.4596

    Article  Google Scholar 

  37. Moschou EC, Batelis SC, Dimakos Y, Fountoulakis I, Markonis Y, Papalexiou SM, Mamassis N, Koutsoyiannis D (2013) Spatial and temporal rainfall variability over Greece. In: 5th EGU Leonardo conference, Hydrofractals 2013. STAHY ‘13, 2013

  38. Mumtaz R, Baig S, Kazmi SSA, Ahmad F, Fatima I, Ghauri B (2018) Delineation of groundwater prospective resources by exploiting geospatial decision-making techniques for the Kingdom of Saudi Arabia. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3370-z

    Article  Google Scholar 

  39. Muthusamy M, Schellart A, Tait S, Heuvelink GBM (2016) Geostatistical upscaling of rain gauge data to support uncertainty analysis of lumped urban hydrological models. Hydrol Earth Syst Sci 21:1077–1091. https://doi.org/10.5194/hess-2016-279

    Article  Google Scholar 

  40. Nandargi S, Mulye SS (2012) Relationships between rainy days, mean daily intensity, and seasonal rainfall over the Koyna catchment during 1961–2005. Sci World J. https://doi.org/10.1100/2012/894313

    Article  Google Scholar 

  41. Narjary B, Kumar S, Kamra SK, Bundela DS, Sharma DK (2014) Impact of rainfall variability on groundwater resources and opportunities of artificial recharge structure to reduce its exploitation in fresh groundwater zones of Haryana. Curr Sci 107(8):1305–1312

    Google Scholar 

  42. Negreiros J, Painho M, Aguilar F, Aguilar M (2010) Geographical information systems principles of ordinary kriging interpolator. J Appl Sci 10(11):852–867. https://doi.org/10.3923/jas.2010.852.867

    Article  Google Scholar 

  43. Padilla A, Delgado J (2013) Tratamiento y gestión de series temporales hidrológicas: manual del usuario: aplicación Trasero 2.0. Departamento de Ciclo Hídrico, Diputación de Alicante

  44. Pebesma E (2012) Spacetime: spatio-temporal data in R. J Stat Softw 51(7):1–30

    Article  Google Scholar 

  45. Rodríguez-Rodríguez M, Fernández-Ayuso A, Moral F (2017) Cuantificación de los aportes hídricos subterráneos a la laguna de Santa Olalla a partir de balances hídricos diarios (Parque Nacional de Doñana, Huelva). Geogaceta 61:47–50

    Google Scholar 

  46. Ruiz-Villanueva V, Bodoque JM, Díez-Herrero A, Eguibar MA, Pardo-Igúzquiza E (2013) Reconstruction of a flash flood with large wood transport and its influence on hazard patterns in an ungauged mountain basin. Hydrol Process 27:3424–3437. https://doi.org/10.1002/hyp.9433

    Article  Google Scholar 

  47. Ruybal CJ, Hogue TS, McCray JE (2019) Evaluation of groundwater levels in the Arapahoe aquifer using spatiotemporal regression kriging. Water Resour Res 55:2820–2837. https://doi.org/10.1029/2018wr023437

    Article  Google Scholar 

  48. Sacks LA, Herman JS, Konikow LF, Vela AL (1992) Seasonal dynamics of groundwater-lake interactions of Doñana National Park, Spain. J Hydrol 136:123–154. https://doi.org/10.1016/0022-1694(92)90008-J

    CAS  Article  Google Scholar 

  49. Salvany JM, Custodio E (1995) Características litoestratigráficas de los depósitos pliocuaternarios del bajo Guadalquivir en el área de Doñana; implicaciones hidrogeológicas. Revista de la Sociedad Geológica de España 8(1–2):21–31

    Google Scholar 

  50. Samdi MM, Zghoul A (2006) A sudden change in rainfall characteristics in Amman, Jordan during the mid. Am J Environ Sci 2:84–91

    Article  Google Scholar 

  51. Scanlon BR, Healy RW, Cook PG (2002) Choosing appropriate techniques for quantifying groundwater recharge. Hydrogeol J 10(1):18–39

    CAS  Article  Google Scholar 

  52. Scheffer M, Barrett S, Carpenter SR, Folke C, Green AJ, Holmgren M, Hughes TP, Kosten S, van de Leemput IA, Nepstad DC, van Nes EH, Peeters ET, Walker B (2015) Creating a safe operating space for iconic ecosystems. Science 347(6228):1317–1319. https://doi.org/10.1126/science.aaa3769

    CAS  Article  Google Scholar 

  53. SCS Soil Conservation Service (1975) Urban hydrology for small watersheds. Technical realize. No. 55 U.S. Department of Agriculture, January 1975

  54. Serrano L, Serrano L (1996) Influence of groundwater exploitation for urban water supply on temporary ponds from the Doñana National Park (SW Spain). J Environ Manag 46(3):229–238. https://doi.org/10.1006/jema.1996.0018

    Article  Google Scholar 

  55. Shi P, Quiao X, Chen X, Zhou M, Qu S, Ma X, Zhang Z (2014) Spatial distribution and temporal trends in daily and monthly precipitation concentration indices in the upper reaches of the Huai River, China. Stoch Environ Res Risk Assess 28(2):201–212. https://doi.org/10.1007/s00477-013-0740-z

    Article  Google Scholar 

  56. Stekhoven DJ (2013) Nonparametric missing value imputation using random forest. 1.4. version. CRAN repository. http://www.r-project.org, https://github.com/stekhoven/missForest

  57. Stekhoven DJ, Bühlmann P (2012) MissForest—nonparametric missing value imputation for mixed-type data. Bioinformatics 28(1):112–118. https://doi.org/10.1093/bioinformatics/btr597

    CAS  Article  Google Scholar 

  58. Tang F, Ishwaran H (2017) Random forest missing data algorithms. Stat Anal Data Min 10(6):363–377. https://doi.org/10.1002/sam.11348

    Article  Google Scholar 

  59. Thornthwaite CW (1948) An approach toward a rational classification of climate. Geogr Rev 38(1):55–94. https://doi.org/10.2307/210739

    Article  Google Scholar 

  60. USGS (2008) Estimates of ground-water recharge in Minnesota. https://www.eqo.state.mn.us/sites/default/files/documents/USGS_GW_Recharge_06_09_08.ppt. Accessed 20 Oct 2017

  61. Varouchakis EA, Hristopulos DT (2019) Comparison of spatiotemporal variogram functions based on a sparse dataset of groundwater level variations. Spat Stat 34:100245. https://doi.org/10.1016/j.spasta.2017.07.003

    Article  Google Scholar 

  62. Varouchakis EA, Corzo GA, Karatzas GP, Kotsopoulou A (2018) Spatio-temporal analysis of annual rainfall in Crete, Greece. Acta Geophys 66(3):319–328. https://doi.org/10.1007/s11600-018-0128-z

    Article  Google Scholar 

  63. Wikle CK, Zammit-Mangion A, Cressie N (2019) Spatio-temporal Statistics with R. CRC Press, Taylor & Francis Group, Boca Raton

    Book  Google Scholar 

  64. WWF (2016) Salvemos Doñana del peligro a la prosperidad. http://awsassets.panda.org/downloads/wwf_dalberg_salvemos_donana_lr.pdf. Accessed 22 May 2018

  65. Yang X, Xie X, Liu DL, Ji F, Wang L (2015) Spatial interpolation of daily rainfall data for local climate impact assessment over Greater Sydney Region. Adv Meteorol. https://doi.org/10.1155/2015/563629

    Article  Google Scholar 

  66. Yue S, Wang CY (2002) Applicability of prewhitening to eliminate the influence of serial correlation on the Mann-Kendall test. Water Resour Res 38(6):41–47

    Article  Google Scholar 

  67. Zimmerman DW (1997) Teacher’s corner: a note on interpretation of the paired-samples t test. J Educ Behav Stat 22(3):349–360

    Google Scholar 

  68. Zurbieta R, Saavedra M, Silva Y, Giráldez L (2017) Spatial analysis and temporal trends of daily precipitation concentration in the Mantaro River basin: Central Andes of Peru. Stoch Environ Res Risk Assess 31(6):1305–1318. https://doi.org/10.1007/s00477-016-1235-5

    Article  Google Scholar 

Download references

Acknowledgements

We thank the reviewers and editors for their remarks that helped to improve the manuscript. This research has been funded by CLIGRO Project (MICINN, CGL2016-77473-C3-1-R) of the Spanish National Plan for Scientific and Technical Research and Innovation and it is also part of the activities subsidized within the National System of Youth Guarantee (MINECO activity with reference PEJ-2014-85121 and Ministry of Education, Youth and Sport of the Community of Madrid with ref. PEJ15/AMB/AI-0218)) co-financed under the Youth Employment Operational Program, with financial resources from the Youth Employment Initiative (YEI) and the European Social Fund (ESF).

Author information

Affiliations

Authors

Corresponding author

Correspondence to C. Guardiola-Albert.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix 1

See Table 2.

Table 2 Summary statistics of 112 rain gauges used for the spatio-temporal interpolation

Appendix 2

See Figs. 12 and 13.

Fig. 12
figure12

Observed versus predicted daily rainfall for the 10 rain gauge stations chosen to test the goodness of the interpolation. Figures a to e correspond to the five rain gauges with a high number of missing values (90%). Figures f to j represent five rain gauges with low degrees of missing information (up to 25%)

Fig. 13
figure13

Daily rainfall bias plots for the 10 rain gauge stations chosen to test the goodness of the interpolation. Figures a to e correspond to the five rain gauges with a high number of missing values (90%). Figures f to j represent five rain gauges with low degrees of missing information (up to 25%)

Appendix 3

See Figs. 14, 15 and 16.

Fig. 14
figure14

Median and variance of monthly rainfall, when 20 or more days are measured in each moth of the year. Values are computed over the studied period for every rain gauge laying inside each recharge zone defined in Fig. 4

Fig. 15
figure15

Mean and variance of daily values for every year of the studied period averaging the values of rain gauges lying inside each recharge zone defined in Fig. 4

Fig. 16
figure16

Median percentage rate of rain days in a month and the number of days with data in that month. This ratio was computed for all rain gauges laying inside each recharge zone defined in Fig. 4

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Naranjo-Fernández, N., Guardiola-Albert, C., Aguilera, H. et al. Relevance of spatio-temporal rainfall variability regarding groundwater management challenges under global change: case study in Doñana (SW Spain). Stoch Environ Res Risk Assess 34, 1289–1311 (2020). https://doi.org/10.1007/s00477-020-01771-7

Download citation

Keywords

  • Global change impacts
  • Rainfall variability
  • Spatio-temporal kriging
  • Water resource management