The Evaluation of Groundwater Suitability for Irrigation and Changes in Agricultural Land of Garmsar basin

Document Type : Research Article


1 ُSemnan University

2 0

3 Semnan University


Introduction: In recent years, due to the reduction in surface water, utilization of groundwater has been increased to meet the growing demand of irrigation water. The quality of these water resources is continually changing, due to the geological formations, the amount of utilization, and climatic parameters. In many developing countries, the irrigation water is obtained from poor quality groundwater resources, which in turn, creates unfavorable circumstances for plant growth and reduces the agricultural yield. Providing adequate water resources for agricultural utilization is one of the most important steps needed to achieve the developmental targets of sustainable agriculture. Thus, this necessitates the assessment and evaluation of the quality of irrigation water. There are many proposed methods to determine the suitability of water for different applications, such as Piper, Wilcox, and Schoeller diagrams. Zoning of quality and suitability of irrigation water could represent the prone and critical areas to groundwater exploitation. Garmsar alluvial fan is one of the most sensitive areas in the country where traditional agriculture practices had turned into modern techniques and excessive exploitation of groundwater has caused an intensepressure on aquifers and increased water salinity. The aim of this study is to evaluate the suitability of groundwater for irrigation in a 10-year period (2002-2012) and its changes in this basin.
Materials and Methods: Garmsar alluvial fan is located in the North-West of Semnan Province. Semnan is situated in the Southern hillside of the Alborz Mountains, in North of Iran. The study area includes the agricultural land on this alluvial fan and covers over 3750 hectares of this basin. In order to evaluate the quality of groundwater in this area, the electrical conductivity and sodium absorption ratio of 42 sample wells were calculated. The raster maps of these indicators were obtained using Geo-statistical techniques. The suitability of irrigation water was determined by Wilcox diagram. Upon evaluating the data distribution and testing the data from Klomogrov-Smirnov normality test, normalization of the data was performed in SPSS software. Spatial correlation and spatial structure of variables were analyzed by drawing their semi-variograms in GS+ software. The most accurate variogram model was selected according to the lowest Residual Sums of Squares (RSS) and the highest correlation coefficient (R2). Interpolation and zoning of the indicators were performed in ArcGIS software and the Quality classes were determined.
Results and Discussion: According to the results of Kolmogorov-Smirnov test, none of the data series had normal distribution. Therefore, they were normalized through calculating the logarithm of variables. Fitting and the selection of variograms were performed in GS+ software and after the calculation of errors, kriging method with Guassian model was determined as the best fitting model. The correlation coefficient was 0.896 for electrical conductivity and 0.99 for sodium absorption ratio. Interpolation of indicators in ArcGIS implied fewer measurements of these indicators in north of the study area (Hableh-Rood inlet). The maximum measurement of indicators was observed on the western edge of the alluvial fan. In total, the values of both electrical conductivity and a sodium absorption ratio indicators in the western half of the area, in the vicinity of the third period domes, were more than the eastern half. The result of the water classification using Wilcox diagram represented the unsuitability of groundwater for irrigation in all of the study area. The area with unusable groundwater for irrigation has increased over the 2005 – 2009 period.
Conclusion: In this study, relying on the use of GIS and Geo-statistical methods, the quality of Garmsar basin groundwater has been evaluated. The electrical conductivity was applied to monitor water salinity, and Sodium absorption ratio was used to monitor alkalinity. The interpolation of these indicators was performed by Kriging method and Guassian fitting model. Likewise, in other studies, the Kriging method was introduced as an appropriate method for the interpolation of chemical parameters of the groundwater. The accuracy of various fitting models in the prediction of interpolated values differed according to the number and the distribution of sample points. In the current study, the Guassian fitting model was determined as the best model to interpolate both of the indicators. According to the maps, it seems that the third period domes in the western margin of the study area have a great influence on the quality of Garmsar’s surface water and groundwater. In total, the groundwater of Garmsar basin didn’t poss high suitability for irrigation, and was classified into two unsuitable and unusable classes. Moreover, according to the maps, the maximum area of unusable groundwater for irrigation in the area was observed in 2008.


1- Akbarzadeh M., and Ghahreman B. 2013. A combined strategy of entropy and spatio-temporal kriging in determining optimal network for groundwater quality monitoring of Mashhad basin. Journal of Water and Soil, 27(3):613-629. (in Persian with English abstract).
2- Alizadeh A. 1997. Principal of applied hydrology. Ferdowsi University of Mashhad, Mashhad.
3- Amiraslani F., and Dragovich D. 2011. Combating desertification in Iran over the last 50 years: An overview of changing approaches. Journal of Environmental Management, 92:1-13.
4- Arsalan H. 2012. Spatial and temporal mapping of groundwater salinity using ordinaru kriging and indicator kriging: The case of Bafra plain, Turkey. Agriculture Water Management, 113:57-63.
5- Barikani E., Ahmadian M., and Khalilian S. 2011. Sustainable optimal utilization of grounwater resources in agriculture: A case study in agricultural zone of Qazvin basin Journal of Agricultural Economics and development, 25(2):253-262. (in persian).
6- Bihamta M.R., and Zare Chahouki M.A. 2011. Principles of statistics in natural resources. University of Tehran Press, Tehran, Iran.
7- Cinti D., Poncia P.P., Procesi M., Galli G., and Quattrocchi F. 2013. Geostatiscal techniques application to dissolved radon hazard mapping: An example from the western sector of Sabatini Volcanic District and the Tolfa Mountains ( central Italy). Applied Geochemistry, 35:312-324.
8- Hajihashemi jazi M.R., Atashgahi M., and Hamidian A.H. 2011. Spatial estimation of groundwater quality factors using geostatistical methods (case study: Golpayegan Plain). Journal of Natural Environment, Irainian Journal of Natural resources, 63(4):347-357. (in Persian with English abstract).
9- Heravi S.A., Nazari H., Shahidi A., and Talebian M. 2013. Geometry and Kinematic of the Garmsar fault since Neogene. Scientific Quarterly Journal, Geoscience, 22(88):175-186.
10- Hooshmand A., Delghandi M., Izadi A., and Aali K.A. 2011. Application of kriging an cokriging in spatial estimation of groundwater quality parameters. African Journal of Agricultural Research, 6(14):3402-3408.
11- Khodakarami L., Soffianian A., Mirghafari N., Afyuni M., and Golshani A. 2012. Concentration zoning of chromium, cobalt and nickel in the soils of three sub-sbasin of the Hamadan province using GIS technology and the geostatistics. Journal of Science and Technology of Agriculture and Natural Resources, Water and Soil Science, 15(58):243-254. (In Persian).
12- Piper A.M. 1944. A graphic procedure in the geochemical interpretation of water analyses. Transactions. American Geophysical Union, 25:914-923.
13- Rezaei M., Davatgar N., Tajdari K., and Abolpour B. 2010. Investigation the spatial variability of some important groundwater quality factors in Guilan, Iran. Journal of Water and Soil, 24(5):932-941. (in Persian with English abstract).
14- Samson M., Swaminathan G., and Venkat Kumar N. 2010. Assessing groundwater quality for potability using a fuzzy logic and GIS- A case study for Tiruchirappalli City- India. Computer Modeling and New Technologies, 14(2):58-68.
15- Sarukkalige R. 2012. Geostatistical analysis of groundwater quality in western Australia. . Engineering science and technology: An international journal, 2(4):790-794.
16- Schoeller H. 1961. Groundwater. Masson, Paris.
17- Shaabani M. 2008. Determination of most suitable geostatistics approach in mapping the change of the groundwater pH and TDS: Case study: Arsenjan basin. Journal of Water Engineering, 1(1):47-57. (In Persian).
18- Shayan S., Sharifikia M., and Zare G.R. 2011. Spatial analysis and salinity geomorphologic hazard assessment in Garmsar alluvial fan. Journal of Arid Regions Geographics Studies, 2(5):47-58. (In Persian with English abstract).
19- Solaimanisardo M., Vali A.A., Ghazavi R., and Saidi Gorghani H.R. 2013. Trend analysis of Chemical Water quality parameters: Case study Cham Anjir river Irrigation and water engineering, 12:95-106. (In persian with English abstract).
20- Tabatabaei S.H., and Ghazali M. 2011. Accuracy of Interpolation Methods in Estimating the Groundwater Level (Case Study: Farsan- Jooneghan and SefidDasht Aquifers). Journal of Science and Technology of Agriculture and Natural Resources, Water and Soil Science, 15(57):11-22. (In Persian with English abstract).
21- Tabatabaeifar M., Zehtabian G., Rahimi M., Khosravi H., and Nikoo S. 2013. The Analysis of Groundwater Quality and Quantity Changes and Climate Abnormalities Influencing on Desertification Trend in Garmsar Plain. Arid Regions Geographic Studies, 4(13):55-68. (In Persian with English abstract).
22- Taghizadeh Mehrjerdi R., Zareian Jahromi M., Mahmodi S., and Heidari A. 2008. Spatial distribution of groundeaterr quality with geostatistics (Case Study: Yazd-Ardakan Plain). World Applied Sciences Journal, 4(1):9-17.
23- Vaziri S.H., and Majidifard M.R. 2011. Stratigraphic situation of the Salt deposits in Garmsar area. Journal of Salt, 1(1):17-27.
24- Venkateswaran S., and Vediappan S. 2013. Assessment of groundwater quality for irrigation use and evaluate the feasibility zones through geospatial technology in Lower Bhavani subbasin, Cauver river, Tamil Nadu,India. International Journal of Innovative technology and Explorind Engineering, 3(2):180-187.
25- Wanda E.M.M., Gulula L.C., and Phiri A. 2013. Hydrochemical assessment of groundwater used for irrigation in Rumphi and Karonga districts, Northern Malawi. Physics and Chemistry of the Earth, 66:51-59.
26- Wilcox L.V. 1955. Classification and uses of irrigation waters. US Departement of Agriculture Circular, Washington, DC.