Monitoring and predicting the groundwater-level fluctuations for better management of groundwater resource in Lowlands Using Geographic Information System (GIS)


1 Lecturer, Faculty Member of Soil Engineering Department, Faculty of Water and Soil, University of Zabol, Iran

2 Ph.D. Graduated, Water Engineering Department, Faculty of Water and Soil, University of Zabol, Iran


 In order to be aware of groundwater-level fluctuations in arid and semi-arid regions, it is necessary to make an accurate forecast of the groundwater depth situation. The drying of Hamoon lake, severe water shortages and significant reduction in groundwater levels have led to critical environmental conditions in the Sistan plain. Spatial understanding of groundwater depth changes in the region and awareness of the severity of groundwater depletion are important for the development of water resources management strategies. Therefore, this study was conducted with the aim of zoning groundwater depth using geostatistics and GIS techniques in the agricultural lands of Sistan plain located in the east of Hamoon Lake, with an area of about 201000 ha. For this purpose, groundwater depth data were collected from 846 wells by field survey using piezometric wells in the study area.  In this research, various geostatistical methods including deterministic interpolation method and geostatistical methods were evaluated to compare the prediction ability of groundwater depth spatial variations. The results showed that the intensity of groundwater depth changes in the study area with a coefficient of variation of 19.87% is moderate. The spherical model could better explain the spatial variation of the experimental variogram of the studied parameter in the region. Finally, the results related to the deterministic method of inverse distance weighted with power 2 estimates a better prediction for groundwater depth zoning than kriging and cokriging geostatistical methods.


Alcamo. J., Herichs. T. & Rosch. T. (2000). World water in 2025: Global modeling and scenario analysis for the world commission on water for the century. Center for Environmental Systems Research, Report A0002, University of Kassel, Germany.
Bameri, A., Khormali, F., Kiani, F., Dehghani, A.A. (2015). Spatial variability of soil organic carbon in different hillslope positions in Toshan area, Golestan Province, Iran: Geostatistical approaches. J. Mountain Science. (2015) 12: 1422.
Bazzi. H., Ebrahimi. H. & Aminnejad. B. (2021). A comprehensive statistical analysis of evaporation rates under climate change in Southern Iran using WEAP (Case study: Chahnimeh Reservoirs of Sistan Plain). Ain Shams Engineering Journal, 12 (2), 1339-1352.
Kambhammettu, B., Praveena. A. & King. J. (2011). Application of evaluation of universal kriging for optimal contouring of ground water levels. J. Syst. Sci. 3: 413-422.
Kelin, H., Yuangfang, H., Hong, L., Baoguo, L., Deli, C. & Robert. E.W. (2005). Spatial variability of shallow groundwater level, electrical conductivity and nitrate concentration, and risk assessment of nitrate contamination in North China Plain. Environment International 31: 896 – 903.
Li, X.Y., Song, D.M. & Xiao, D.N. (2005). The variability of groundwater mineralization in Minqin oasis. Acta Geographica Sinica 60 (2), 319–327.
Mohammadi, J. (2006). Pedometry: Spatial statistics. Pelk Publications.454 pp. (In Persian)
Podineh, O. & Delbari, M. (2017). Comparison of Some Geostatistical and Deterministic Interpolation Methods for Estimating Depth to the Water Table (Case study: The Iranshahr- Bampour Plain). Water Engineering, 10, 83-100. (In Persian)
Shahzad, H., Farid, H., Mahmood Khan, Z., Anjum, M., Ahmad, I., Chen, X., Sakindar, P., Mubeen, M., Ahmad, M. & Gulakhmadov, A. (2020). An Integrated Use of GIS, Geostatistical and MapOverlay Techniques for Spatio-Temporal Variability Analysis of Groundwater Quality and Level in the Punjab Province of Pakistan, South Asia. Water, 12, 3555.
Smakthin. V., Revenga. C. & Doll. P. (2004). Taking into account environmental water requirements in global scale water resources assessments. Comprehensive assessment of Water Management in agriculture research report 2, IWMI, Colombo, Srilanka.
Sun,Y., Kang, Sh., Li, F. & Zhang, L. (2009). Comparison of interpolation methods for depth to groundwater and its temporal and spatial variations in the Minqin oasis of northwest China. Environmental Modelling & Software 24 (2009) 1163–1170.
Theodossiou, N. & Latinopoulos, P. (2007). Evaluation and optimisation of groundwater observation networks using the Kriging methodology. Environmental Modelling and Software 22 (3), 414.
Wilding, L.P., Smeck, N. E. & Hall, G.F. (1983). Pedogenesis and soil taxonomy. I. Concepts and interactions. Elsevier Publishing Company, 303p.
Yang. H., Reichert. P., Abbaspour. K. & Zehnder. AJB. (2003). A Water resources threshold and its implication for food security. Environ Sci and Technol 37: 3048-3054.
Zoraghi. G. R., Shabani Goraji. K., Noura. M. R., Rashki. A. R & Bumby, A. (2019). Identification of sand dune sources in the east Sistan, Iran by using mineralogical and morphoscopic characterization of sediments. Iranian Journal of Earth Sciences, 11, 3, 2019, 183-195.