Comparison of Some Geostatistical and Deterministic Interpolation Methods for Estimating Depth to the Water Table (Case study: The Iranshahr- Bampour Plain)
Subject Areas : Article frome a thesisOmlbanin Podineh 1 , Masoomeh Delbari 2
1 - دانش آموخته کارشناسی ارشد مهندسی منابع آب، دانشکده آب و خاک، دانشگاه زابل
2 - scientific staff/University of Zabol
Keywords: ordinary Kriging, Trend, ArcGis, Groundwater depth, estimation, universal kriging, deterministic interpolator,
Abstract :
In this study, some interpolation methods were evaluated for estimating groundwater depth in the Iranshahr- Bampour Plain during 2003, 2007 and 2012 in the months of May and October. Data used belonged to 42- 48 wells scattered across the study area. The methods used contained geostatistical approaches of Ordinary Kriging (OK) and Universal Kriging (UK), and deterministic approaches of Inverse Distance Weighting (IDW), Radial Basis Function (RBF) and Local Polynomial Interpolation (LPI). The performance of the prediction methods was evaluated through cross-validation with comparison criteria of determination coefficient (R2), root mean square error (RMSE) and mean bias error (MBE). The statistical analysis showed a high variance and coefficient of variation of groundwater depth and an increase in the average depth to groundwater during bygone years especially during 2003-2007 period. Directional semivariograms were calculated to find out the drift direction. UK method, with the first and second-order polynomials as drift, and different semivariogram models was examined. According to cross-validation results, the best geostatistical method for estimating groundwater depth was OK (with spherical semivariogram) for 1382 and 1391, and UK with J-bessel semivariogram model and second and first drift orders, respectively, for May and October 2007. Moreover, the cross-validation results indicated that LPI, with RMSE equal to 6.94, 5.87 and 8.65 m, respectively, for May 2003, 2007 and 2012, and 6.86, 6.54 and 8.68 m, respectively, for October 2003, 2007 and 2012 is the best method of interpolation among others. The generated maps of groundwater depth revealed a drop in depth to groundwater; therefore, an occurrence of water crisis over the study region during the recent years. Therefore, it is necessary to consider some management scenarios including exploitation control and alteration of crop pattern and irrigation systems for an optimum use of water resources and achieving a sustainable agriculture across the region.
1) ثقفیان، ب، 1391. راهنمای روشهای توزیع مکانی عوامل اقلیمی با استفاده از دادههای نقطهای (نشریه شماره 585). معاونت نظارت راهبردی وزارت نیرو.
2) حسینعلیزاده، م. و ع، یعقوبی. 1389. بررسی تغییرات زمانی و مکانی سطح سفرهی آب زیرزمینی گناباد با استفاده از زمینآمار. مجله علمی- پژوهشی علوم و مهندسی آبخیزداری ایران. 4: 63- 67.
3) دلبری، م.، پ. افراسیاب و س.ر.، میرعمادی. 1389. تجزیه و تحلیل تغییرات مکانی- زمانی شوری و عمق آب زیرزمینی استان مازندران. نشریه آبیاری و زهکشی ایران. 4: 374-359.
4) دلبری، م. و س.، جهانی. 1391. ارزیابی اثر استفاده از مدل رقومی ارتفاع (DEM) در تخمین بارش ماهانه و سالانه در استان گلستان. مجله آبیاری و زهکشی ایران. 2: 118-132.
5) کالیراد، ز.، آ.، ملکیان و ب.، معتمدوزیری. 1392. تعیین الگوی توزیع منابع آب زیرزمینی (مطالعه موردی: حوزه آبخیز الشتر، استان لرستان). پژوهشنامه مدیریت حوزه آبخیز. 4: 57- 69.
6) کمالی، م. و ف.، شمس. 1390. ارائه شیوهای برای یافتن بهترین روش میانیابی سطح آب زیرزمینی (مطالعه موردی). مجموعه مقالات سیامین گردهمایی علوم زمین. 1-7.
7) گزارش شرکت مدیریت منابع آب ایران، دفتر پژوهشهای کاربردی، وزارت نیرو، 1391. سیستان و بلوچستان.
8) مشعل، م.، ا.، درویشی و ح.ا.، قلیچ ثابت. 1386. ارزیابی شبکه چاههای مشاهدهای عمق آب زیرزمینی با استفاده از روشهای زمینآماری در دشت اراک. سومین کنفرانس سراسری آبخیزدرای و مدیریت منابع آب و خاک کرمان. 2: 884-888.
9) نگارش، ح. و م.، کریمی. 1390. تحلیل خشکسالی اخیر منطقه ایرانشهر به روش SPI. محیط شناسی. 37 :31-58.
10) Akima, H. 1970. A new method of interpolation and smooth curve fitting based on local procedures. J. of Assoc. for Comput. Mach. 17: 589–602.
11) Abedian, H., K. Mohammadi, and R., Rafiee. 2013. Optimizing monitoring network of water table by geostatistical methods. J. of Geol. and Min. Res. 5: 223-231.
12) Agoubi, B., A. Kharroubi, S. Bouri, and H. Abida. 2010. Contribution of geostatistical modelling to mapping groundwater level and aquifer geometry, case study of Sfax’s deep aquifer, Tunisia. Middle-East J Sci Res, 6: 305-316.
13) Ahmadi, S.H., and A. Sedghamiz. 2007. Geostatistical analysis of spatial and temporal variations of groundwater level. Environ. Mon. Assess. 129: 277-294
14) Araghinejad, S., and D.H. Burn. 2005. Probabilistic forecasting of hydrological events using geostatistical analysis. Hydrol. Sci. J. 50: 837- 856.
15) Brus, D.J. and G. Heuvelink. 2007. Optimization of sample patterns for universal kriging of environmental variables. Geoderma. 138: 86-95.
16) Delbari, M., M. Bahraini Motlagh, and M. Amiri. 2013. Spatio-temporal variability of groundwater depth in the Eghlid aquifer in southern Iran. Earth Sci. Res. J. 17: 105-114.
17) Dick, J.B. and B.M.H. Gerard. 2006. Optimization of sample patterns for universal kriging of environmental variables. Geoderma. 138: 86-95.
18) Dirks, K.N., J.E. Hay, C.D. Stow, and D. Harris. 1998. High-resolution studies of rainfall on Norfolk Island, Part II: Interpolation of rainfall data. J. of Hydrol. 208: 187-193.
19) Goovaerts, P. 1997. Geostatistics for natural resources evaluation. Oxford University Press, New York.
20) Gundogdu, K.S., and I. Guney. 2007. Spatial analyses of groundwater levels using universal kriging. J. of Earth Syst. Sci. 116: 49-55.
21) Hu, K., Y. Huang, H. Li, B. Li, D. Chen, and R.E. White. 2005. Spatial variability of shallow groundwater level, electrical conductivity and nitrate concentration, and risk assessment of nitrate contamination in North China Plain. Environ. Int. 31: 896- 903
22) Isaaks, E.H., and R.M. Srivastava. 1989. An introduction to applied geostatistics. Oxford University Press. New York.
23) Johnston, K., J.M. Ver Hoef, K. Krivoruchko, and N. Lucas. 2001. Using ArcGIS geostatistical analyst. Esri Redlands. USA.
24) Kambhammettu, B.V.N.P., P. Allena, and J.P. King. 2011. Application and evaluation of universal kriging for optimal contouring of groundwater levels. J. Earth Syst. Sci., 120: 413-422.
25) Kitanidis, P.K. 1996. On the geostatistical approach to the inverse problem. Adv. Water Res. 19: 333-342.
26) Kumar, V. 2007. Optimal contour mapping of groundwater levels using universal kriging- A Case Study Hydrological Sciences Journal. 52: 1038-1050.
27) Kumar, V., and H. Remadevi. 2006. Kriging of groundwater levels- A case study. J. of Spat. Hydrol. 6: 81-94.
28) Sahoo, S. and M.K. Jha. 2014. Analysis of spatial variation of groundwater depths using geostatistical modeling. Int. J. of Appl. Eng. Res. 9: 317-322.
29) Sun, Y., Sh. Kang, F. Li and L. Zhang. 2009. Comparison of interpolation methods for depth to groundwater and its temporal and spatial variations in the Minqin Oasis of northwest China. Environ. Model. Soft. 24: 1163-1170.
30) Zedek, R.A.A. 2014. Geostatistical analysis of the Gorran water protection area in Nynäshamn Municipality. Master’s thesis in physical geography and quaternary geology at the Department of Physical Geography and Quaternary Geology, Stockholm University.
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