UsingStage Sampling Technique to Determine Saturated Hydraulic Conductivity Measurement Points on the Farm

Document Type : Original Article

Authors

1 Ph.D. Candidate, of Irrigation & Drainage, Sari Agricultural Sciences and Natural Resources University

2 Associate Professor, Department of Water Engineering Sari Agricultural Sciences and Natural Resources University

Abstract

Saturated hydraulic conductivity is important physical parameters of water movement in the soil, especially in determining the distance between the drain lines is the drainage projects. High cost and Time-consuming the field measurement and high variability of this parameter, a correct understanding of the changes in the amounts of saturated hydraulic conductivity is difficult in projects. So selecting the location and number of measuring is the first step to determining the spatial variability of Hydraulic conductivity which is corresponds to reality. Therefore, it is necessary to examine the impact the new sampling methods on the accuracy of geostatistics estimates. Optimal sampling design, is the stage sampling. In this method, sampling of a parameter will done in two or more stages and data from each step, will form the basis of the next step designation. This study is done in 40 thousand hectares, within Siahrud to Talar River of Mazandaran province to compare the stage sampling method based on the standard error of geostatistical methods, with conventional sampling methods (single-stage) on improving the estimation of spatial variability of saturated hydraulic conductivity. In this study, four different proposed scenarios were examined. One scenario based on common sampling method and three scenarios based on the stage sampling. The results indicate that the use of all scenarios, including stage sampling, resulting in fewer errors, better estimation and stronger Spatial structure in determination the saturated hydraulic conductivity values. Among the stage methods, the spatially balanced design approach was less accurate relative to the standard error of prediction index. The RMSE, MAE and ASE values ​​in the scenario designed by kriging standard error of prediction than conventional sampling methods reduced respectively from 5.56, 4.7 and 17.3 to 3.79, 2.84 and 3.86 m.d-1 and so improving 39.6, 31.8 and 77.7 percent these three indicators error.

Keywords

References

بی­نام. 1392. مطالعات مرحله اول زهکشی اراضی شالیزاری محدوده تالار تا سیاه­رود استان مازندران، گزارش مطالعات پایه. مهندسین مشاور آب و خاک پارس، 185 ص.
بی­نام. 1388. پروژه بانک جهانی سد البرز، مطالعات پایه. مهندسین مشاور مهاب قدس، 126ص.
حسین مرشدی،ا و معماریان،ح. 1389. طراحی مرحله­ای شبکه نمونه­برداری، بر اساس پارامترهای ژئوتکنیکی و خصوصیات کیفی ساختگاه سد سمیلان، با استفاده از کریجینگ و شبکه عصبی. نشریه علمی و پژوهشی مهندسی معدن.5 . 10: 1-20.
دلبری،م و افراسیاب،پ.1394. شبیه­سازی تصادفی زمین­آماری هدایت هیدرولیکی اشباع خاک. تحقیقات آب و خاک ایران. 46. 2: 231-244.
علیزاده،ا. 1387. زهکشی جدید (برنامه­ریزی، طراحی و مدیریت سیستم­های زهکشی). چاپ سوم. انتشارات دانشگاه امام رضا(ع).
Bhat,S., Motz,L., Pathak,C and Kuebler,L. 2015. Geostatistics-based groundwater-level monitoring network design and its application to the Upper Floridan aquifer, USA. Environmental monitoring and assessment. 187:4183.
Barca,E., Calzolari,M.C., Passarella,G and Ungaro,F. 2013. Predicting shallow water table depth at regional scale: optimizing monitoring network in space and time. Water Resources Management. 27: 5171-5190.
Brus,D.J. 2010. Design-based and model-based sampling strategies for soil monitoring. 19th World Congress of Soil Science, Soil Solutions for a Changing World, 1– 6 August 2010, Brisbane, Australia. Published on DVD.
Brus,D.J and Heuvelink,G.B.M. 2007. Optimization of sample patterns for universal kriging of environmental variables. Geoderma. 138: 86-95.
Brus,D.J., Gruijter,J and Van Groenigen,J.W. 2006. Designing spatial coverage samples using the k-means clustering algorithm. Box,G.E.P. and Cox,D.R. 1964. An analysis of transformations. Journal of the Royal  Statistical Society. Series B (Methodological). 2: 211-252.
Delmelle,E.M and Goovaerts,P. 2009. Second-phase sampling designs for non-stationary spatial variables. Geoderma 153:205-216.
ESRI. 2015. An introduction to sampling monitoring networks- Help. ArcGIS Desktop. ESRI Incorporated.
Geoff,B. 2005. Introduction to Geostatistics and Variogram Analysis.
Hassler,S.K., Lark,R.M., Zimmermann,B and Elsenbeer,H. 2014. Which sampling design to monitor saturated hydraulic conductivity? European Journal of Soil Science.  65.6: 792-802.
Knotters,M and Brus,D.J. 2010. Model-based vs. design-based sampling strategies for monitoring, with a case study on testing surface water quality against standards. M3 Workshop, June 16-17, Luxembourg.
Mohanty,B.P., KanwarK,R.S and Everts,C. J. 1994. Comparison of saturated hydraulic conductivity measurement methods for a glacial-till soil. Soil Science Society of America Journal. 58: 672-677.
Moustafa,M.M. 2000. A geostatistical approach to optimize the determination of saturated hydraulic conductivity for large-scale subsurface drainage design in Egypt. Agricultural Water Management. 42: 291-312.
Pereira,G.T., De Souza,Z.M., Teixeira,D.D.B., Montanari, R and Jnior,J.M. 2013. Optimization of the sampling scheme for maps of physical and chemical properties estimated by kriging. Revista Brasileira de Cincia do Solo. 37:1128-1135.
 Szatmri,G., Barta,K and Pásztor,L. 2016. Multivariate Sampling Design Optimization for Digital Soil Mapping. Digital Soil Mapping Across Paradigms, Scales and Boundaries, Springer Environmental Science and Engineering:77-87
Triki,I., Zairi,M and Dhia,H.B. 2013. A geostatistical approach for groundwater head monitoring network optimisation: case of the Sfax superficial aquifer (Tunisia). Water and Environment Journal, 27: 362-372.
Theobald,D.M., Stevens,D.L., White,D., Urquhart,N.S., Olsen,A.R and Norman,J.B. 2007.Using GIS to Generate Spatially Balanced Random Survey Designs for Natural Resource Applications. Environmental Management, 40: 134-146.
Van Groenigen,J.W., Siderius,W and Stein,A. 1999. Constrained optimisation of soil sampling for minimisation of the kriging variance. Geoderma 87: 239-259.
Verstraete,S and Van Meirvenne,M. 2008. A multi-stage sampling strategy for the delineation of soil pollution in a contaminated browneld. Environmental Pollution. 154: 184-191.
Yangsha,G., Wanga,J.F and Yinb,X.L. 2011. Optimizing the groundwater monitoring network using MSN theory. Procedia - Social and Behavioral Sciences, 21: 240-245.
Zahid,E., Hussain,I., Spöck,G., Faisal,M., Shabbir,J.M., AbdEl-Salam,N and Hussain,T. 2016. Spatial Prediction and Optimized Sampling Design for Sodium Concentration in Groundwater.
Iranian Journal of Irrigation & Drainage
Volume 11, Issue 3
September and October 2017
Pages 367-376

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