Applying fundamental geostatistical approaches to determine potential zones of groundwater recharge of Tehran-Karaj Plain Aquifer

Subject Areas : Environment Pullotion (water and wastewater)

Behzad Rayegani ¹

1 - Assistant Professor, Faculty of Environment, Department of Environment, Karaj, Iran. *(Corresponding Author)

Received: 2016-12-05 Accepted : 2017-01-18 Published : 2018-12-22

Keywords: geostatistics, Interpolation, Gstat Software, Geostatistical Analyst, Groundwater, Groundwater Direction Flow, Aquifer Recharge Area,

Abstract :

Groundwater, especially in arid and semi-arid is one of the main sources of drinking water. Contrary to public perception in these areas the water quality is more important than quantify. Therefore, monitoring of water quality and identification of pollution sources is one of the main concerns of researchers in this area. In this study, in order to identify sources of groundwater contamination, areas are likely aquifer recharge sources of Tehran-Karaj plain detected. So, after extraction of groundwater level data different interpolation and geostatistics methods are used to create surface images. The Gstat software and Geostatistical Analyst were used for this study then performance and ability of each one to produce surface images are evaluated. According to the results, the Geostatistical Analyst software has better flexibility to do the special analysis. Based on the training and test data interpolation methods were very similar, but the surface images regarding groundwater direction flow and histogram look very different. According to the results, universal kriging showed better performance. Accordingly, surface images of different time created by an appropriate method to simulate groundwater flow direction and eventually areas were identified which are more likely ground water recharge source of Tehran-Karaj aquifer.

References:

1. Nwankwoala HO, Eludoyin OS, Obafemi AA, 2012. Groundwater Quality Assessment And Monitoring Using Geographic Information Sysytems (Gis) In Port Harcourt, Nigeria. Ethiopian Journal of Environmental Studies and Management,  vol. 5,  pp. 583-96.


2. Machiwal D, Jha MK, 2015. Identifying sources of groundwater contamination in a hard-rock aquifer system using multivariate statistical analyses and GIS-based geostatistical modeling techniques. Journal of Hydrology: Regional Studies,


3. Ahmed SS, Okour YH, Said EH, 2015. A Methodology Based on Advanced Modeling Techniques for Groundwater Monitoring and Management-Part A. International Journal For Research & Development In Technology,  vol. 3,  pp. 6-12.


4. Liu L, Yi L, Cheng X, 2014. Stochastic simulation of shallow aquifer heterogeneity and it’s using in contaminant transport modeling in Tianjin plains. Journal of Water Resources and Ocean Science,  vol. 3,  pp. 80-8.


5. Huang PM, Li Y, Sumner ME. Handbook of soil sciences : properties and processes. 2nd ed. Boca Raton, FL: CRC Press; 2011.


6. Gong G, Mattevada S, O’Bryant SE, 2014. Comparison of the accuracy of kriging and IDW interpolations in estimating groundwater arsenic concentrations in Texas. Environmental Research,  vol. 130,  pp. 59-69.


7. Karimi HA. Handbook of research on geoinformatics. Hershey: Information Science Reference; 2009. xxxiv, 481 p. p.


8. Di Piazza A, Conti FL, Noto LV, Viola F, La Loggia G, 2011. Comparative analysis of different techniques for spatial interpolation of rainfall data to create a serially complete monthly time series of precipitation for Sicily, Italy. International Journal of Applied Earth Observation and Geoinformation,  vol. 13,  pp. 396-408.


9. Johnston K, Ver Hoef JM, Krivoruchko K, Lucas N. Using ArcGIS geostatistical analyst: Esri Redlands; 2001.


10. Kebaili Bargaoui Z, Chebbi A, 2009. Comparison of two kriging interpolation methods applied to spatiotemporal rainfall. Journal of Hydrology,  vol. 365,  pp. 56-73.


11. Zhang K, Li H, Achari G, 2009. Fuzzy-stochastic characterization of site uncertainty and variability in groundwater flow and contaminant transport through a heterogeneous aquifer. Journal of Contaminant Hydrology,  vol. 106,  pp. 73-82.


12. Tapoglou E, Karatzas GP, Trichakis IC, Varouchakis EA, 2014. A spatio-temporal hybrid neural network-Kriging model for groundwater level simulation. Journal of Hydrology,  vol. 519,  pp. 3193-203.


13. Oliver MA, Webster R, 2014. A tutorial guide to geostatistics: Computing and modelling variograms and kriging. Catena,  vol. 113,  pp. 56-69.


14. Özdemir Y. Determination of surface water reseources changes by Multi Temporal Satellite Imagery and Investigations of groundwater by using of geoistatistical method.  30th EARSeL Symposium: Remote Sensing for Science, Education and Culture; Paris, France2010.


15. Theodossiou N, Latinopoulos P, 2006. Evaluation and optimisation of groundwater observation networks using the Kriging methodology. Environmental Modelling & Software,  vol. 21,  pp. 991-1000.


16. Hassan J, 2014. A Geostatistical approach for mapping groundwater quality (Case Study: Tehsil Sheikhupura). International Journal,


17. Candela L, Olea RA, Custodio E, 1988. Lognormal Kriging For The Assessment Of Reliability In Groundwater Quality Control Observation Networks. Journal of Hydrology,  vol. 103,  pp. 67-84.


18. Anderson F, 2014. Multivariate Geostatistical Model for Groundwater Constituents in Texas. International Journal of Geosciences,  vol. 5,  pp. 1609-17.


19. Ghadermazi J, Sayyad G, Mohammadi J, Moezzi A, Ahmadi F, Schulin R, 2011. Spatial Prediction of Nitrate Concentration in Drinking Water Using pH as Auxiliary Co-kriging Variable. Procedia Environmental Sciences,  vol. 3,  pp. 130-5.


20. Jensen JR. Introductory digital image processing : a remote sensing perspective. 3rd ed. Upper Saddle River, N.J.: Prentice Hall; 2005. xv, 526 p. p.


21. Negreiros J, Painho M, Costa A, Santos J, Lopes I, editors. Geostatistical Analysis: Software Flashpoint. Geocomputation Conference, Dublin, Ireland; 2007.


22. Pebesma EJ, Gstat user’s manual.


23. Pebesma EJ, Wesseling CG, 1998. Gstat: a program for geostatistical modelling, prediction and simulation. Computers & Geosciences,  vol. 24,  pp. 17-31.


24. Pebesma EJ, editor Gstat: multivariable geostatistics for S. Proceedings of DSC; 2003.


25. Eastman J. IDRISI Selva manual: Clark Labs; 2012. 324 p.


26. Eastman J. IDRISI Selva Tutorial: Clark Labs; 2012.


27. 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,  vol. 101,  pp. 178-87.