Zoning Electrical Conductivity and Acidity of Groundwater through Using Geo-statistical Method: A Case Study in Semirom Plain, Esfahan Province

Subject Areas : Irrigation and Drainage

سیاوش طائی سمیرمی ¹ , حمیدرضا مرادی ² , مرتضی خداقلی ³ , وحید کریمیان ⁴

1 - گروه مهندسی آبخیزداری، دانشگاه تربیت مدرس، تهران، ایران.
2 - گروه مهندسی آبخیزداری، دانشگاه تربیت مدرس، تهران، ایران.
3 - مرکز تحقیقات کشاورزی و منابع طبیعی اصفهان، ایران.
4 - گروه مرتع داری، دانشگاه گرگان، ایران.

Received: 2012-09-04 Accepted : 2012-11-17 Published : 2012-12-21

Keywords: Ground water, هدایت الکتریکی, اسیدیته, pH, زمین آمار, Electrical Conductivity, Geo-statistic, Semiromplian, آب‌های زیرزمینی, دشت سمیرم,

Abstract :

The groundwater quality research is one of the important and its pollution control was included insome research literatures. Ground water quality has spatial and temporal variation so classical statisticscould not account these variations at the regional scale researches. This study usedgeo-statisticalmethodsto optimize an interpolation method in order to estimate the spatial distribution of pH andelectrical conductivity in ground water. The geo-statistical methods which used in this procedure,includedkriging, ordinary kriging, simple kriging, disjunctive kriging, inverse distance weighting andradial. Cross validation was used to evaluate fault detection, root mean square error for statisticalcomparisons,and the geo-statistical analysis was performed in ArcGIS9x software environment. Thecase study was Semirom plain, Esfahan and historical data was collected from 386 springs in yearsfrom 2006 to 2007.The resultsof model validations showed that the variogram spherical model has thebest fit to the spatial data structure of the electrical conductivity and pH. The analysis of RMSE andMAE showed that inverse distance method (with raised to the power of one); RMSE = 0.065; MAE=0.041) and radial function (with RMSE = 3.57; MAE=2.27) were more statistically accurate or havelower RMSE and MAE in comparison to the other methods. Both of these methods have optimumspatial distribution of the pH and electrical conductivity of ground water inSemirom plain. Finally,spatial distributions maps of pH and electrical conductivity was created using ArcGIS9.x (orgeographical information systems). The resultsof maps showed reduction of both variables from theeast to west, in Semirom plain.

References:

اربطانی، و.، احمدی، ع.و فاتحی، م،م. (1388). مدل سازی تغییرات مکانی برخی از ویژگی های شیمیایی آب های زیرزمینی به کمک روش های زمین آماری. مجله علوم و مهندسی آبخیزداری ایران،3(7)،ص23-34.

اوسطی، خ.، سلاجقه، ع.و آرخی، ص. (1390). تغییرات مکانی میزان نیترات در آب زیرزمینی با استفاده از زمین آمار (مطالعه موردی: دشت کردان). مرتع و آبخیزداری، 65(4)،ص461-472.‎

 حسنی پاک، ع.ا. (1389). زمین آمار (ژئواستاتیک)، چاپ اول، انتشارات دانشگاه تهران، ص314.

شعبانی، م. (1387). ارسنجان تعیین مناسب ترین روش زمین آمار در تهیه نقشه تغییراتph  و TDS آبهای زیرزمینی (مطالعه موردی: دشت ارسنجان). مجله مهندسی آب.1 (3): ص47-57.

شیخ گودرزی، م.، موسوی، س،م.، خراسانی، ن.( 1391).شبیه سازی تغییرات مکانی در ویژگی های کیفی آب های زیرزمینی با روش های زمین آمار (مطالعه موردی :دشت تهران – کرج). 65 (1) ، ص.83-93.

دلبری،م.، خیاط خلقی، م.و مهدیان. م،ح.( 1382). ارزیابی روشهای زمین آمار در برآورد هدایت الکتریکی خاک در مناطق شیب آب و پشت آب پایین دشت سیستان. مجله علوم کشاورزی ایران. 35(1)،ص 1-12.

یگانه، ح.، خواجه الدین،س.ج.و سفیانیان، ع. (1387). بررسی قابلیت شاخص های طیفی سنجنده (MODIS) در برآورد تولید گیاهی مراتع سمیرم. مجله علمی پژوهشی مرتع. 2(1)،ص 36-77.

Abdideh, M. and Ghasemi, A. (2014). A Comparison of Various Statistical and Geostatistical Methods in Estimating the Geomechanical Properties of Reservoir Rocks. Petroleum Science and Technology, 32(9),pp: 1058-1064.

Aronoff, S. (1989). Geographic Information System, Management Perspective, WDL publication, ttawa. Canada.

Barcae, E., Passarella, G. (2008). Spatial evaluation of the risk of groundwater quality degradation: A comparison between disjunctive kriging and geostatistical simulation, Journal of Environmental monitoring and Assessment,133, pp:261-273.

Berndt, C., Rabiei, E. and Haberlandt, U. (2014). Geostatistical merging of rain gauge and radar data for high temporal resolutions and various station density scenarios. Journal of Hydrology, 508, pp: 88-101

Fetouani, S., Sbaa, M., Vanclooster, M. and Bendra, B. (2008). Assessing Groundwater Quality in the irrigated plain of triffa (north-east Morocco). Journal of Agricultural Water Management, 95, pp: 133-142.

Flipo, N., Jeanee, N., Poulin, M., Even, S. and Ledoux, E. (2007). Assessment of nitrate pollution in the Grand Morin aquifers (France): combined use of geostatistical and physically based modeling. Enviromental Pollution, 146, pp: 241-256.

Franke, R. (1982). Scattered data interpolation: test of some methods. Mathematic of Computations, 33, pp: 181-200.

Habibi arbatani, V. Ahmadi, A.and Fatahi, M. (2009). spatial changes modeling of groundwater chemical traits using geostatistical methods. Iran-Watershed Management Science & Engineering, Vol.3(7), pp: 23-34. (In Persian)

Haji hashemi, M. Atashgahi, M.and Hmiddian, A. (2011). Spatial estimation of groundwater quality factors usinggeostatistical methods(case study: Golpayegan plain), Journal of Natural Environmental, Iranian Journal of Natural Resources, Vol. 63,  pp: 347-357. (In Persian)

Hooshmand. A., Delghandi, M., Izadi, A. and Ahmad, K .(2011). Application Kriging and cokriging in spatial estimation of groundwater quality parameters, African Journal of  Agricultural Research. 6 (14), pp: 3402 – 3408.

Jager, N. (1990). Hydrogeology and Groundwater Simulation. Lewis Publishers.

Laaha, G., Skøien, J. O. and  Blöschl, G. (2014). Spatial prediction on river networks: comparison of top‐kriging with regional regression. Hydrological Processes, 28(2), 315-324.

Lu, G., David, Y. and Wong, W. (2008). An adaptive inverse-distance weighting spatial interpolation technique Computers and Geosciences, 34, pp: 1044-1055.

Maroufi, S., toranjian, A. and Zare abiyane, H. (2009). Evaluation of geostatistical methods for estimating electrical conductivity and pH of stream drained water in Hamedan-Bahar Plain, Journal of Water and Soil Conservation, Vol. 16(2), pp: 169-187.

Mohamadi, J. (2005). pedometery (spatial statistics), Vol. 2, pelk pub.pp:453-449.

Sahebjalal, E. (2012). Application of Geostatistical Analysis for Evaluating Variation in GroundwaterCharacteristics. Word applied Sciences, 18(1), pp: 135-141.

Sun, Y., Shaozhong, F. and Zhang, L (2009). Comparison of interpolation methods for depth to Groundwater and its temporal and spatial variation in the Minqin oasis of northwest China. Enviromental Modelling and Software, 24, pp: 1163-1170.

Taghizadeh mehrjardi, R., Zarein Jahromi, M., Mahmodi, Sh. and Heidari, A. (2008). Spatial distribution of groundwater quality with Geostatistics (Case Study: Yazd-Ardakan Plain), World Applied Sciences Journal 4(1), pp: 9-17.

Wang, S., Huang, G. H., Lin, Q. G., Zhang, H.and Fan, Y. R. (2014). Comparison of interpolation methods for estimating spatial distribution of precipitation in Ontario, Canada. International Journal of Climatology.pp:654-659.