Evaluation of Groundwater Electrical Conductivity Regarding Rice Cultivation in Guilan Province, Iran

Document Type : Research Paper


1 MSc. Student, Department of Water Engineering, Faculty of Agricultural Sciences, University of Guilan; P.O.BOX 41635-3756, Rasht, Iran.

2 ssociate Professor, Department of Water Engineering, Faculty of Agricultural Sciences, University of Guilan; P.O.BOX 41635-3756, Rasht, Iran, and Department of Water Engineering and Environment, Caspian Sea Basin Research Center.,

3 Associate Professor, Department of Water Engineering, Faculty of Agricultural Sciences, University of Guilan; P.O.BOX 41635-3756, Rasht, Iran.

4 Agricultural Engineering Research Department, Guilan Agricultural and Natural Resources Research and Education Center, AREEO, Rasht, Iran.


Considering Sefidrud River discharge decrease in the last decade in Guilan province in the north of Iran, groundwater and surface water resources can meet the water demand of rice cultivation in this area. It is evident that irrigation water quality should be considered in rice cultivation. Electrical conductivity (EC) is one of the essential parameters of assessing the quality of groundwater. The purpose of this research is to identify areas at risk of groundwater EC decrease for rice cultivation. For this purpose, zoning and probabilistic maps were prepared by ArcGIS software. The models were evaluated using ME, RMSE, MSE, RMSSE, and ASE statistical indices. The accuracy of the models was very good; the RMSE values for ordinary kriging were between 0.2674 and 0.4172 dS/m, and for indicator kriging, they ranged from 0.2841 and 0.4087 dS/m. The zoning and probabilistic maps showed an increase in EC of more than 1 dS/m from 2002 to 2015. In addition, the highest EC in Guilan province was in the central and eastern parts of the province, including Rasht, Astaneh, and Lahijan cities. More than 30% of groundwater resources were exposed to excessive salinity exceeding rice's tolerance level. Therefore, to prevent the quality mitigation of groundwater resources in the province and prevent yield penalty related to irrigation water salinity, the regional water companies should take appropriate management measures such as a ban on digging new wells or reducing groundwater extraction in hazardous areas.


Main Subjects

  • Amiri-Bourkhani, M., Khaledian, M., Ashrafzadeh, A., Shahnazari, A. 2017. The temporal and spatial variations in groundwater salinity in Mazandaran Plain, Iran, during a long-term period of 26 years. Geofizika, 34(1): 119-139.


  • Ashrafzadeh, A., Roshandel, F., Khaledian, M., Vazifedoust, M. Assessment of groundwater salinity risk using kriging methods: a case study in northern Iran. Agricultural Water Management, 178: 215-224.


  • Adhikary, P.P., Dash, C.J., Chandrasekharan, H., Rajput, T.B.S., Dubey, S.K. 2012. Evaluation of groundwater quality for irrigation and drinking using GIS and geostatistics in a peri-urban area of Delhi, India. Arabian Journal of Geosciences, 5(6): 1423-1434.


  • Ahmadi, S., Sedghamiz, A. 2008. Application and evaluation of kriging and cokriging methods on groundwater depth mapping. Environmental Monitoring and Assessment, 138(1-3): 357-368.


  • Ahmadpour, H., Khaledian, M., Ashrafzadeh, A. 2015. Examine the relationship between the salinity of groundwater with the salinity of Sefidrud River and the Caspian Sea (case study: Guilan province). Third International Symposium on Environmental and Water Resources Engineering, 2-3 June, Tehran, Iran.


  • Coakes, S.J., Steed, L. 2007. SPSS Version 14.0 for windows: Analysis without anguish. Milton: John Wiley & Sons.


  • Dash, J.P., Sarangi, A., Singh, D.K. 2010. Spatial variability of groundwater depth and quality parameters in the National Capital Territory of Delhi. Environmental Management, 45: 640–651.


  • Delgado, C., Pacheco, J., Cabrera, A., Batllori, E., Orellana, .R, Bautista, F. 2010. Quality of groundwater for irrigation in tropical karst environment: The case of Yucatán, Mexico. Agricultural Water Management, 97(10): 1423-1433.


  • Goovaerts, P., AvRuskin, G., Meliker, J., Slotnick, M., Jacquez, G., Nriagu, J. 2005. Geostatistical modeling of the spatial variability of arsenic in groundwater of southeast Michigan. Water Resources Research, doi:1029/2004WR003705.


  • Guimer, J. 1988. Anomalously high nitrate concentrations in ground water. Ground Water, 36(2): 275-282.


  • Jang, C.S., Chen, S.K. 2015. Integrating indicator based geostatistical estimation and aquifer vulnerability of nitrate-N for establishing groundwater protection zones. Journal of Hydrology, 523: 441-451.


  • Johnston, K., Ver Hoef, J.M., Krivoruchko, K., Lucas, N. 2001. Using ArcGIS geostatistical analyst (Vol. 380): Esri Redlands.


  • Juang, K.W., Lee, D.Y. 2000. Comparison of three nonparametric kriging methods for delineating heavy metal contaminated soils. Journal of Environmental Quality, 29: 197-205.


  • Kendall, M.G. 1975. Rank correlation methods: Griffin, London, UK.


  • Kisi, O., Ay, M. 2014. Comparison of Mann–Kendall and innovative trend method for water quality parameters of the Kizilirmak River, Turkey. Journal of Hydrology, 513: 362-375.


  • Kuisi, M.A, Al-Qinna, M., Margani, A., Aljazzar, T. 2009. Spatial assessment of salinity and nitrate pollution in Amman Zarqa Basin: a case study. Journal of Environmental Earth Sciences, 59: 117-129.


  • Kumar, S., Merwade, V., Kam, J., Thurner, K. 2009. Stream flow trends in Indiana: Effects of long term persistence, precipitation and subsurface drains. Journal of Hydrology, 374(1–2): 171-183.


  • Mann, H.B. 1945. Nonparametric tests against trend. Econometrica: Journal of the Econometric Society, 13: 245-259.


  • Obiefuna, G. and S. Eslamian. 2019. An Evaluation of Groundwater Storage Potentials in a Semiarid Climate, Nova Science Publishers, Inc., USA, 153 Pages.


  • Partha, P.A., Dash, C.J., Chandrasekharan, H., Rajput, T., Dubey, S. 2012. Evaluation of groundwater quality for irrigation and drinking using GIS and geostatistics in a Peri-Urban area of Delhi, India. Arabian Journal of Geosciences, 5: 1423-1434.


  • Remesan, R., Panda, R.K. 2007.Groundwater quality mapping using GIS: A study from India's Kapgari Journal of Environmental Quality and Management, 16: 41-60.


  • Rezaei, M., Davatgar, N., Khaledian, M., Pirmoradian, N. 2013. Using intermittent irrigation to use saline water in rice production. Acta Agriculturae Slovenica, 101(1): 49-57.


  • Salas, J.D. 1992. Analysis and Modeling of Hyolrologic Time Series. In: D.R. Maidment (Ed.), Handbook of Hydrology. McGraw Hill Book Company, U.S.A.


  • Samin, M. Soltani, J., Zeraatcar, Z., Moasheri, S.A., Sarani, N. 2012. Spatial estimation of groundwater quality parameters based on water salinity data using kriging and cokriging methods. International Conference on Transport, Environment and Civil Engineering. 25-26 August, Kuala Limpur, Malaysia.


  • Sen, P.K. 1968. Estimates of the regression coefficient based on Kendall's Tau. Journal of the American Statistical Association, 63(324): 1379-1389.


  • Theil, H. 1992. A rank-invariant method of linear and polynomial regression analysis. In B. Raj & J. Koerts (Eds.), Henri Theil's Contributions to Economics and Econometrics (Vol. 23, pp. 345-381): Springer Netherlands.


  • Wahlin, K., Grimvall, A. 2010. Roadmap for assessing regional trends in groundwater quality. Environmental Monitoring and Assessment, 165(1-4): 217-231.


  • Zaiming, Z., Guanghui, Z., Mingjiang, Y., Jinzhe, W. 2012. Spatial variability of the shallow groundwater level and its chemistry characteristics in the low plain around the Bohai Sea, North China. Environmental Monitoring and Assessment, 184(6): 3697-3710.


  • Zare, N., Khaledian, M., Pirmoradian, N., Rezaei, M. 2014. Simulation of rice yield under different irrigation and nitrogen application managements by CropSyst model. Acta Agriculturae Slovenica, 103(2): 181-190.
Volume 44, Issue 2
September 2021
Pages 113-127
  • Receive Date: 13 May 2021
  • Revise Date: 09 November 2021
  • Accept Date: 15 November 2021
  • Publish Date: 22 June 2021