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بررسی سه روش غیر مستقیم در برآورد منحنی مشخصه رطوبتی خاک | ||
تحقیقات آب و خاک ایران | ||
دوره 52، شماره 10، دی 1400، صفحه 2529-2538 اصل مقاله (1.57 M) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/ijswr.2021.326215.669006 | ||
نویسنده | ||
پریسا مشایخی* | ||
بخش تحقیقات خاک و آب، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان اصفهان، سازمان تحقیقات، آموزش و ترویج کشاورزی، اصفهان، ایران | ||
چکیده | ||
در پژوهش حاضر سه روش حل عددی معکوس، تابع انتقالی و شبکه عصبی مصنوعی در برآورد پارامترهای هیدرولیکی خاک مورد ارزیابی قرار گرفت. برای این منظور، آزمایش نفوذ آب به خاک از طریق استوانههای دوگانه در سه منطقه از استان اصفهان با بافتهای مختلف خاک انجام شد. در هر منطقه نمونههای دستخورده و دستنخورده خاک از سه عمق ) 10-0، 30-10 و 60-30 سانتیمتر برداشت شده و ویژگیهای مختلف فیزیکی و هیدرولیکی خاک در این نمونهها اندازهگیری شد. در این پژوهش، برای برآورد پارامترهای هیدرولیکی به روش معکوس از نرمافزار HYDRUS-2D/3D استفاده شد. برای ارزیابی روشهای مذکور از شاخصهای ضریب همبستگی پیرسون (r)، ریشه میانگین مربعات خطا (RMSD)، اختلاف میانگینها (MSD) و قدر مطلق خطای میانگینها (MD) استفاده شد. نتایج نشان داد که روش حل معکوس یک روش قابل اعتماد برای تعیین پارامترهای هیدرولیکی خاک در مقیاس میدانی است. بر اساس ارزیابیهای آماری صورت گرفته، منحنی مشخصه رطوبتی برآوردشده به روش حل معکوس با منحنی مشخصه رطوبتی به دست آمده از طریق برازش مدل ونگنوختن بر دادههای اندازهگیریشده، همخوانی بسیار خوبی داشت. بیشترین مقدار ضریب تبیین (R2) بین میزان رطوبت حجمی اندازهگیری و برآورد شده در روش حل عددی معکوس مشاهده شد (9363/0= R2) و بعد از آن بهترتیب رطوبت حجمی برآوردشده با نرم افزار Rosetta (8629/0= R2) و تابع انتقالی قربانی دشتکی و همایی (8401/0= R2) قرار گرفتند. | ||
کلیدواژهها | ||
پارامترهای هیدرولیکی خاک؛ توابع انتقالی؛ شبکه عصبی؛ مدلسازی معکوس | ||
مراجع | ||
Abbasi. F. (2009). Assessment of Indirect Methods to Estimate Soil Hydraulic Properties for Simulating Soil Moisture in a Sandy Loam Soil. Journal of Agricultural Engineering Research, 9 (4), 31-44. (In Persian). Abbasi, F., and Tajic, F. (2007). Simultaneous estimation of hydraulic parameters and solute transportation by inverse solution method at field scale. Journal of Science and Technology of Agriculture and Natural Resources, 11 (1): 111-122. Abd Rashid, N.S., Askari, M., Tanaka, T., Simunek, J., and van Genuchten, M.Th. (2015). Inverse estimation of soil hydraulic properties under oil palm trees. Geoderma, 241–242, 306–312. Alletto, L., Pot, V., Giuliano, S., Costes, M., Perdrieux, F., and Justes, E. (2015) Temporal variation in soil physical properties improves the water dynamics modeling in a conventionally-tilled soil. Geoderma, 243( 244), 18–28. Asgarzadeh, H., Mosaddeghi, M. R., Dexter, A. R., Mahboubi, A. A., and Neyshabouri, M. R. (2014). Determination of soil available water for plants: consistency between laboratory and field measurements. Geoderma, (226–227), 8–20. Babaeian, E., Homaee, M., and Noroozi, A.A. (2013). Assessing spectrotransfer functions and pedotransfer functions in predicting soil water retentions. Conservation of soil and water resources, 3(2), 25-43. (In Persian). Bahrami, A., and Aghamir, F. (2020). Simulation of vadose zone flow processes via inverse modeling of modified multistep outflow for fine-grained soils. Soil Science Society of America Journal, 84 (5), https://doi.org/10.1002/saj2. 20112. Baker, L., and Ellison, D. (2008). Optimisation of pedotransfer functions using an artificial neural network ensemble method. Geoderma, 144, 212–224. Blake, G.R., and Hartge, K.H. (1986) Bulk density. In: Klute, A., Ed., Methods of Soil Analysis, Part 1—Physical and Mineralogical Methods, 2nd Edition, Agronomy Monograph 9, American Society of Agronomy—Soil Science Society of America, Madison, 363-382. Charles, W., Oluwapelumi, O. (2021). Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression. SN Applied Sciences.3. https://doi.org/10.1007/s42452-020-03974-7. Dobarco, M.R., Isabelle Cousin, I., Bas, C.L., Martin, M. P. (2019). Pedotransfer functions for predicting available water capacity in French soils, their applicability domain and associated uncertainty. Geoderma, 336, 81–95. https://doi.org/10.1016/j.geoderma.2018.08.022. Da Silva Junior, J.J., Colombo, A., Oliveira, G.C., Silva, B., and Juliaci, J. (2020). Estimation of tropical soils’ hydraulic pro-perties with inverse method and tension infiltrometer field data. Ambiente & Água, 15(3), 1-15https://doi.org/10.4136/ambi-agua.2503. Ebrahimi, F., and Raoof, M. (2015). Effect of different Rosetta Predictive Model on Soil Hydraulic Properties. Estimation Using HYDRUS-2D and Effect of Land use changing on their. Iranian Journal of Irrigation and Drainage, 2(9), 303-313. (In Persian). Ethan, D.G., and Eric, E. S. (2007). A comparison of land surface model soil hydraulic properties estimated by inverse modeling and pedotransfer functions. Water Resourse Research, 43. W05418. Fooladmand, H.R. (2011). Pedotransfer functions for point estimation of soil moisture characteristic curve in some Iranian soils. African Journal of Agricultural Research, 6(6), 1586-1591. Ghorbani Dashtaki, S., Homaee, M. and Khodaverdiloo, H. (2010). Derivation and validation of pedotransfer functions for estimating soil water retention curve using a variety of soil data. Soil Use and Management, 26(1), 68–74. Gribb, M. M., Forkutsa, I., Hansen, A., Chandler, D. G. and McNamara, J. P. (2009). The Effect of Various Soil Hydraulic Property Estimates on Soil Moisture Simulations. Vadose Zone Journal, 8, 321–331. doi:10.2136/vzj2008.0088. Gunarathna, M.H., Sakaic, K., Nakandakaric, T., Momiid, K., Kumaria, M.K.N., and Amarasekaraa, M.G.T.S. (2019). Pedotransfer functions to estimate hydraulic properties of tropical Sri Lankan soils. Soil & Tillage Research 190, 109–119. https://doi.org/10.1016/j.still.2019.02.009 Jafari Gilandeh, S., Rasoulzadeh, A., and Khodaverdiloo, H. (2013). Evaluating some pedotransfer functions for simulation of transient water flow in soil. Conservation of soil and water resources, 2(4), 1-13. (In Persian). Kirkham, J.M., Smith, C.J., Doyle, R.B., and Brown, P.H. 2019. Inverse modelling for predicting both water and nitrate movement in a structured-clay soil (Red Ferrosol). Peer Journal, 6, e6002 https://doi.org/10.7717/peerj.6002 Klute, A. (1986). Methods of Soil Analysis. Part 1- Physical and Mineralogical Methods. 2nd ed., Agronomy No. 9. ASA/SSSA Inc., Madison, Wisconsin, USA. Lai, J., and Ren, L. (2016). Buffer index effects on hydraulic conductivity measurements using numerical simulations of double-ring infiltration. Soil Science Society of America Journal, 74, 1526–1536. Mashayekhi, P., Ghorbani Dashtaki, S., Mosaddeghi, M.R., Shirani , H. and Mohammadi Nodoushan, A.R . (2016). Different scenarios for inverse estimation of soil hydraulic parameters from double ring infiltrometer data using HYDRUS 2D/3D. International Agrophysics, 30(2), 203-210. Mashayekhi P., Ghorbani Dashtaki S., Mosaddeghi M.R., Shirani H., and Nouri M.R. (2017). Estimation of soil hydraulic parameters using double-ring infiltrometer data via inverse method. Iranian Journal of Water and Soil Research, 47(4): 829-838. (In Persian). Merdun, H., Cinar, O., Meral, R., and Apan, M. (2006). Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity. Soil and Tillage Research, 90,108–116. Mirzaee, S., Zolfaghari, A. A, Gorjib, M Miles Dyckc, M., and Ghorbani Dashtakia, S. (2013). Evaluation of infiltration models with different numbers of fitting parameters in different soil texture classes Archives of Agronomy and Soil Science, http://dx.doi.org/10.1080/03650340.2013.823477 Mualem, Y. (1976). A new model for predicting the hydraulic conductivity of unsaturated porous media. Water Resources Research, 12(3), 513–522. Naik, A. P., Ghosh, B., and Pekkat, S. (2018). Estimating soil hydraulic properties using mini disk infiltrometer. ISH Journal of Hydraulic Engineering, 25(1), 2164-3040. https://doi.org/10.1080/09715010.2018.1471363. Nakhaei, M. and Šimůnek, J. (2014). Parameter estimation of soil hydraulic and thermal property functions for unsaturated porous media using the HYDRUS-2D code. Journal of Hydrology and Hydromechanics, 62(1), 7–15. Nemes, A., Roberts, R.T., Rawls, W.J., Pachepsky, Ya. A., and van Genuchten, M.Th. (2008). Software to estimate –33 and –1500 kPa soil water retention using the non-parametric k-Nearest Neighbor technique. Environmental Modelling and Software, 23, 254–255. Pachepsky, Y., and Rawls, W.J. 2004. Development of Pedotransfer Functions in Soil Hydrology. 30, Pp, 512. Rastgou, M., Bayatb, H., Mansoorizadehc, M., Gregoryd, A.S. (2020). Estimating the soil water retention curve: Comparison of multiple nonlinear regression approach and random forest data mining technique. Computers and Electronics in Agriculture, 174. https://doi.org/10.1016/j.compag.2020.105502. Richards, L. A. (1931). Capillary conduction of liquids through porous mediums. Physics, 1,318–333. Scharnagl, B., Vrugt, J. A., Vereecken, H., and Herbst, M. (2011). Inverse modeling of in situ soil water dynamics: investigating the effect of different prior distributions of the soil hydraulic parameters. Hydrology and Earth System Sciences, 15, 3043– 059. Schaap, M.G., Leij, F.J., van Genuchten, M.Th. (1998). Neural network analysis for hierarchical prediction of soil hydraulic properties. Soil Science Society of America Journal, 62, 847–855. Schaap, M. G., and van Genuchten, M.Th. (2001). ROSETTA: a computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions. Journal of Hydrology, 251,163-176. Schelle, H. Iden S.C., Schlüter, S., Vogel, H.J. and Durner, W. (2012). Identification of effective flow processes and properties from virtual soils using inverse modeling. Geophysical Research Abstracts, 14. Šimůnek, J., van Genuchten, M.Th. Šejna, M. (2012). HYDRUS: model use, calibration, and validation. American Society of Agricultural and Biological Engineers, 55(4), 1261–1274. Trejo-Alonso J., Carlos Fuentes, C., Chávez, C., Quevedo, A., Gutierrez-Lopez, A., and Brandon González-Correa, B. (2021). Saturated Hydraulic Conductivity Estimation Using Artificial Neural Networks Water, 13, 705. https://doi.org/10.3390/w13050705. Van Genuchten M. Th. 1980. A closed–form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Science Society of America Journal, 44(5), 892–898. Vereecken, H., Weynants, M., Javaux, M., Pachepsky, Y., Schaap, M.G. and van Genuchten, M.Th. (2010). Using pedotransfer functions to estimate the van Genuchten–Mualem soil hydraulic properties: A review. Vadose Zone Journal, 9, 795–820. doi:10.2136/vzj2010.0045 Vrugt, J. A., Stauffer, P. H., Wöhling, Th., Robinson, B.A., and Vesselinov, V.V. (2008). Inverse modeling of subsurface flow and transport Properties: a review with new developments. Vadose Zone Journal, 7(2), 843–864.
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