Climate and Ecosystem of Arid and Semi-arid Regions

Climate and Ecosystem of Arid and Semi-arid Regions

Comparison of the Accuracy of Geostatistical Different Methods in Estimating the Salinity of Alluvial Aquifers

Document Type : Original Article

Authors
Alireza Shasavaripoor 1
Alijan Abkar 2
Hamzeh Saeediyan 3
1 Senior expert in watershed management in agricultural jihad management of Sirjan city
2 Assistant Professor, Department of Soil Conservation and Watershed Management Research, Kerman Agricultural and Natural Resource Research Center, Agricultural Research, Education and Extension Organization, Kerman, Iran.
3 Assistant Professor, Department of Soil Conservation and Watershed Management Research, khuzestan Agricultural and Natural Resource Research Center, Agricultural Research, Education and Extension Organization, Ahvaz, Iran. hamzah.4900@yahoo.com,
10.22075/ceasr.2026.41944.1070
Abstract
Background and Objectives: The health of alluvial aquifers is one of the most important issues that have been considered by researchers and policymakers in the watersheds around the world; among them, water resources salinity is very important. Population growth and agricultural demand have increased the extraction of groundwater resources in arid and semi-arid regions. The quality of these resources is particularly affected by pollution from fertilizers and agricultural chemicals, the intensity of which has a direct relationship with the amount of these substances. Meanwhile, electrical conductivity of water is considered a key indicator for monitoring and determining qualitative changes in groundwater.

Materials and Methods: In this study, the salinity of 55 water sources in Sirjan plain aquifer was investigated. Then, geostatistical methods such as ordinary kriging (OK), inverse distance weight (IDW) and normal distance weight (NDW) were used for zoning of water salinity in Sirjan plain. To evaluate the performance of prediction and estimation models, performance indicators of coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), correlation coefficient (R) and Nash-Sutcliff (NS) coefficient are used. After selecting the best method of interpolation, spatial zoning maps related to water salinity were prepared in Arc GIS and GS+ softwares.
Results: Results showed that minimum salinity of water is 440 µ mhos/cm that is related to the water source of Deh Ghazi aqueduct and maximum salinity level is 19700 µ mhos/cm that is related to the Mollahaji water source. In general, average salinity of studied water sources in Sirjan plain alluvial aquifer is 2323.7273 µ mhos/cm. The ordinary kriging (OK) method showed the most appropriate amount in terms of MSE (5797901), RMSE of 2407.883, R2 of 0.856806, R of 1 and NS of 0.580792, compared to IDW and NDW.
Conclusion: Water salinity is an important factor in determining the chemical properties of natural waters and its biological processes. So, it is highly regarded by researchers as well as water policymakers. Obtaining an appropriate database in relation to salinity of water resources in different regions, especially in desert provinces, is of particular importance. Results of this research showed that in the west and north-west of this region, because of proximity of Sirjan salt crust, water salinity is higher and in the southeastern part due to the proximity to the Chahghale and accumulation of sediments and high evaporation, the salinity is increasing compared to other regions. It is recommended that water resources of different aquifers of Iran be studied in a highly specialized and scientific manner along with different methods of determining water salinity in order to obtain a suitable database of water resources of different aquifers throughout the country and be used in hydro-soil projects.
Keywords
Keywords: Geostatistics
Salinity
Zoning
Sirjan Plain
Subjects
1. Ayoubi, Sh. (2007). Estimation of soil nitrogen content using organic matter using Kriging, Cokriging and Kriging-Regression methods in a part of Sorkhankalate lands of Golestan province. Journal of Agricultural Sciences and Natural Resources, 14, 33-43. [In Persian]
2. Balali, H., & Kasbian Lal, F. (2022). Economic valuation of groundwater in agriculture sector (Case study: Hamedan-Bahar Plain). Journal of Agricultural Economics and Development, 36(1), 37-48. [In Persian]
3. Chen, Y., Chen, J., Xevi, E., Mobin-ud-Din, A., & Walker, G. (2010). GIS-based spatial hydrological zoning for sustainable water management of irrigation areas. International Congress on Environmental Modelling and Software Modelling for Environment’s Sake, International Environmental Modelling and Software Society (IEMSS), Fifth Biennial Meeting, Ottawa, Canada.
4. Dent, D. (2007). Environment grophysics mapping salinity and water resources. International Journal of Applied Earth Observation and Geoinformation, 9, 130-136.
5. Dorgham, M, M., Abdel-Aziz, N., El-Deeb, K., & Okbah, M. (2004). Eutrophication problems in the western harbour of Alexandria, Egypt. Oceanologia, 46(1), 25-44.
6. Faryabi, M. (2023). Delineating the source and mechanism of groundwater salinization in a semi-arid region of southeastern Iran using geophysical and hydrochemical approaches. Water and Soil Management and Modeling, 3(2), 93-111.
7. Fashaee, M., & Ghoochanian, M. (2022). Spatial evaluation of qualitative parameters of groundwater resources with the aim of its application in agriculture and drinking (Case study: Mehvalat-Feyzabad plain), Water and Soil Management and Modelling, 2(2), 28-44. [In Persian]
8.  Gheisari, M., Hoodaji, M., Marzban, M., Najafi, P., & Abdollahi, A. (2007). Investigation of nitrate contamination in groundwater in southeast of Isfahan city. Journal of Environmentalism, 33(42), 43-50. [In Persian]
9.  Hossaini, F., Hill, A. J. & Bagtzoglou, A. C. )2007(. Geostatistically based management of arsenic contaminated ground water in shallow wells of Bangladesh. Water Resources Management, 21, 1245-1261.
10. Isaak, E. H., & Srivastava, R. M. )1989(. An introduction to applied geostatistic. New York, Oxford University Press, pp. 278-322.
11. Jang, C. S., Chen, S. K., & Kuo, Y. M. (2013). Applying indicator-based geostatistical approaches to determine potential zones of groundwater recharge based on borehole data. Catena, 101, 178-187.
12. Johnston, A. (2001). Using geostatistical analyst. Environmental System Research Institute, Inc., 226, 340-351.
13. Johnston, K., Ver Hoef, J. M., Krivoruchko, K., & Lucas, N. (2001). Using ArcGIS geostatistical analyst. Esri Redlands, CA, New York, USA, 306 p.
14. Madani, H. N. (1994). Basics of geostatistics. First Edition, Amirkabir University of Technology Press, Tehran. [In Persian]
15. Marengo, E., Gennaro, M. C., Robotti, E., Maiocchi, A., Pavese, G., Indaco, A., & Rainero, A. (2008). Statistical analysis of groundwater distribution in Alessandria Province (Piedmont-Italy). Microchemical Journal, 88(2), 167-177.
16. Salarifar, M. (2022). Comparison of the performance of algebraic methods and statistical context in determining spatial changes in groundwater quality in Tabriz plain. Hydrogeology, 7(2), 160-171. [In Persian]
17. Shi, J., Wang, H., Xu, J., Wu, J., Liu, X., Zhu, H., & Yu, C. (2007). Spatial distribution of heavy metals in soils: A case study of Changxing, China. Environmental Geology, 52(1), 1-10.
18. Taghizadeh, M., Halabian, H., Alipour, M., & Kiumarsi, H. (2017). Identification and zoning of groundwater salinity using GIS (Case study: Namdan plain of Eghlid township). Journal of Geography and Environmental Planning, 28(3), 132-146. [In Persian]
19. Valizadeh Kamran, Kh., Roustaei, Sh., Rahimpour, T., & Nakhostinrouhi, M. (2016). Determination of the most appropriate geostatistical method in preparing a map of the changes in groundwater salinity (Case study: Shiramin plain of East Azarbaijan province). Hydrogeomorphology, 2(6), 17-32. [In Persian]
20. Yaouti, F., Mandour, A., Khattach, D., Benavente, J., & Kaufman, O. (2009). Salinization processes in the unconfined aquifer of Bou-Areg (NE Morocco), a geostastistical, geochemical and tomographic study. Applied Geochemistry, 24, 16-31.
21. Yari, R. & Koochakzadeh M. (2008). Comparison of geostatistical methods for prediction of groundwater salinity distribution. 3rd Iranian Conference on Water Resources Management. [In Persian]
22. Yazdani Moghadam, Y., Vali, A., & Ghazavi, R. (2014). Geostatistical methods in qualitative zoning of groundwater resources in Kashan Plain. Geography and Environmental Planning, 25(3), 171-184. [In Persian]
Climate and Ecosystem of Arid and Semi-arid Regions
Volume 3, Issue 1
August 2026
Pages 104-119

  • Receive Date 12 July 2026
  • Revise Date 11 August 2026
  • Accept Date 06 September 2026