Geostatistical based framework for spatial modeling of groundwater level during dry and wet seasons in an arid region: a case study at Hadat Ash-Sham experimental station, Saudi Arabia


Saudi Arabia (SA) lies in an arid region where groundwater is the main natural resource; therefore, it is essential to understand the groundwater dynamics for the best groundwater management practice in SA. In Hadat Ash-Sham Farm Experimental Station, SA, water table data from 11 wells and rainfall data were monitored for 16 months. The water table (WT) data is analyzed using the geostatistical method with the ordinary Kriging technique to generate the best WT spatial distribution map for each month and the expected flow direction. The cross-validation technique is used to evaluate the goodness of the developed WT maps. The Kriging maps show two regimes: weak spatial dependence (WSD, the ratio of the nugget to sill > 75%) and strong spatial dependence (SSD, the ratio of the nugget to sill < 25%). The WSD regime happens during dry seasons, while the SSD happens during wet seasons. The SSD gives better results and accuracy when compared to WSD. The root-mean-square error (RMSE) of WT varies between 0.26 and 3.4 m in the case of SSD, while it varies between 0.51 and 4.8 m in the case of WSD. WT maps show that the groundwater flow direction is from south-east to north-west during the wet season (SSD). This direction is in the orientation of surface stream with higher elevation (in the south) to the surface stream with lower elevation (in the north), where the study area is between these surface streams. While during the dry season (WSD), there is no preferred direction since there is almost no flow.

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

Data availability statement

We will make all data available upon request.


  1. Ahmadi SH, Sedghamiz A (2007) Geostatistical analysis of spatial and temporal variations of groundwater level. Environ Monit Assess 127:277–294.

    Article  Google Scholar 

  2. Ahmed S, Devi K (2008) Kriging for estimating hydrogeological parameters. In: Ahmed S, Jayakumar R, Salih A (eds) Groundwater dynamics in hard rock aquifers. Springer, Dordrecht.

    Google Scholar 

  3. Al-Omran AM, Al-Wabel MI, El-Maghraby SE, Nadeem ME, Al-Sharani S (2013) Spatial variability for some properties of the wastewater irrigated soils. J Saudi Soc Agric Sci 12(2):167–175.

    Article  Google Scholar 

  4. Cambardella CA, Moorman TB, Novak JF, Parkin TB, Karlen DL, Turco RF, Konopka AE (1994) Field-scale variability of soil properties in Central Iowa soils. Soil Sci Soc Am J 58(5):1501–1510.

    Article  Google Scholar 

  5. Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature. Geosci Model Dev 7:1247–1250.

    Article  Google Scholar 

  6. Delhomme JP (1978) Kriging in the hydrosciences. Adv Water Resour 1(5):251–266.

    Article  Google Scholar 

  7. El-Hames AS (2005) Determination of groundwater availability in shallow arid region aquifers utilizing GIS technology: a case study in Hada Al-Sham, Western Saudi Arabia. Hydrogeol J 13:640–648.

    CAS  Article  Google Scholar 

  8. ESRI (2020) Understanding a semivariogram: the range, sill, and nugget. Retrieved 18 Dec 2020, from ArcGIS Pro help:

  9. Fitts CR (2012) Groundwater science. Academic Press, London

    Google Scholar 

  10. Hohn ME (1999) The semivariogram. In: Geostatistics and petroleum geology, pp 15–80. Springer, Dordrecht.

  11. Johnston K, Ver Hoef J, Krivoruchko K, Lucas N (2003) Using ArcGIS geostatistical analyst. Retrieved from ArcGIS 9:

  12. Kis IM (2016) Comparison of ordinary and universal Kriging interpolation techniques on a depth variable (a case of linear spatial trend), case study of the Sandrovac field. Min Geol Pet Eng Bull.

    Article  Google Scholar 

  13. Kumar S, Sondhi SK, Phogat V (2005) Network design for groundwater level monitoring in upper Bari Doab canal tract. Irrig Drain 54(4):431–442.

    Article  Google Scholar 

  14. Kuswantoro M, Al-Amri NS, Elfeki AM (2014) Geostatistical analysis using GIS for mapping groundwater quality: case study in the recharge area of Wadi Usfan, western Saudi Arabia. Arab J Geosci.

    Article  Google Scholar 

  15. Lloyd CD (2010) Model for spatial analysis, 2nd edn. Boca Raton, CRC Press

    Google Scholar 

  16. McLean MI, Evers L, Bowman AW, Bonte M, Jones WR (2018) Statistical modelling of groundwater contamination monitoring data: a comparison of spatial and spatiotemporal methods. Sci Total Environ 652:1339–1346.

    CAS  Article  Google Scholar 

  17. Mehrjardi RT, Jahromi MZ, Mahmodi S, Heidari A (2008) Spatial distribution of groundwater quality with geostatistics (case study: Yazd-Ardakan Plan). World Appl Sci J 4(1):9–17

    Google Scholar 

  18. Okello C, Tomasello B, Greggio N, Wambiji N, Antonellini M (2015) Impact of population growth and climate change on the freshwater resources of Lamu Island, Kenya. Water 7:1264–1290.

    Article  Google Scholar 

  19. Oliver MA, Webster R (2015) Basic steps in geostatistics: the variogram and Kriging. Springer, Berlin.

    Google Scholar 

  20. Rouhani S (1989) Geostatistics in water resources. In: Proceedings of the 1989 Georgia water reosources conference. The University of Georgia, Georgia

  21. Rouhani S, Hall TJ (1989) Space-time Kriging of groundwater data. In: Amstrong M (ed) In quantitative geology and geostatistics, vol 4, 4th edn. Springer, Dordrecht, pp 639–650.

    Google Scholar 

  22. Rubin A (2012) Statistics for evidence-based practice and evaluation, 3rd edn. University of Texas at Austin, Austin

    Google Scholar 

  23. Wackernagel H (1995) Ordinary Kriging. Springer, Berlin, Heidelberg.

    Google Scholar 

  24. World Bank Group (2019) World Bank open data. Retrieved 05 Mar 2020, from

Download references


The authors would like to express gratitude to the technical staff of the Department of Hydrology and Water Resources Management, Faculty of Meteorology, Environment, and Arid Land Agriculture, King Abdulaziz University, who measured and collected the data at Hadat Ash-Sham Farm Experimental Station in 16 months.


Not available.

Author information



Corresponding author

Correspondence to Jaka S. Budiman.

Ethics declarations

Conflict of interest

Authors declare that no conflict of interest could be perceived as prejudicing the impartiality of the research reported.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Budiman, J.S., Al-Amri, N.S., Chaabani, A. et al. Geostatistical based framework for spatial modeling of groundwater level during dry and wet seasons in an arid region: a case study at Hadat Ash-Sham experimental station, Saudi Arabia. Stoch Environ Res Risk Assess (2021).

Download citation


  • Statistical modeling
  • Kriging technique
  • Groundwater level
  • Spatial variability
  • Arid regions
  • Saudi Arabia