ارزیابی و توسعه توابع انتقالی برای برآورد هدایت هیدرولیکی اشباع خاک در مقیاس زمین‌نما در زاگرس مرکزی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری گروه علوم خاک دانشکده کشاورزی، دانشگاه صنعتی اصفهان

2 دانشیار گروه علوم خاک، دانشکده کشاورزی، دانشگاه صنعتی اصفهان

3 استاد گروه علوم خاک، دانشکده کشاورزی، دانشگاه صنعتی اصفهان

چکیده

هدایت هیدرولیکی اشباع (Ks) خاک یکی از ویژگی­های ضروری برای مدیریت پروژه­های آبیاری، مدل­سازی حرکت آب و املاح و پیش­بینی توابع هدایت هیدرولیکی غیراشباع خاک می­باشد. اندازه­گیری Ks، به ویژه در مقیاس بزرگ در آزمایشگاه و مزرعه زمان­بر و پرهزینه است. از طرفی Ks تحت تأثیر عوامل مختلف دارای تغییرات مکانی و زمانی زیادی می­باشد. بنابراین پیش­بینی و مدل­سازی آن­ توسط توابع انتقالی خاک برای بسیاری از پژوهش­ها می­تواند مفید باشد. این پژوهش در بخشی از شهرستان­های کوهرنگ و فارسان واقع در استان چهارمحال و بختیاری با هدف ارزیابی کارایی برخی توابع انتقالی موجود در منابع و استخراج توابع انتقالی جدید (PTFs) برای برآورد Ks انجام گرفت. نمونه­های دست­نخورده به حجم cm3100 از خاک سطحی در 100 نقطه که توزیع مناسب و یکنواختی در منطقه و در کاربری‌های مختلف داشتند، برداشت شد. ضریب Ks به روش بار ثابت برای این نمونه­های دست­نخورده در آزمایشگاه اندازه­گیری شد. برخی ویژگی­های زودیافت خاک نیز اندازه­گیری شده و PTFs برای برآورد Ks به روش رگرسیون چندمتغیره گام به گام استخراج شد. هم­چنین مقادیر Ks اندازه­گیری­شده با برآورد PTFs موجود در منابع مقایسه شد. نتایج نشان داد که PTFs موجود در منابع قادر به برآورد مناسب Ks در منطقه نیستند. در بین توابع انتقالی بررسی‌شده موجود در منابع، مدل توانمند شبکه عصبی Rosetta (SSC+BD) بهترین بود. ناکارآمدترین PTFs برای برآورد Ks در خاک­های مورد بررسی مربوط به مدل­های رگرسیونی براکینز و همکاران (1984) بود. بـهر حـال PTFs استخـراج­شـده بـرای منطقـه بهتر از PTFs موجـود در منابــع توانسـت Ks را بـرآورد کنند (52%R2=،‌‌mmh-15/0RMSE =). یافته­ها نشان داد که لگاریتم Ks با چگالی ظاهری نسبی (تراکم نسبی)، چگالی ظاهری مؤثر، نسبت درصد رس به کربنات کلسیم (Clay/CaCO3)، نسبت درصد کربنات کلسیم به ماده آلی (CaCO3/OM) و لگاریتم برهم­کنش چگالی ظاهری و درصد رس (ln(BD×Clay)) هم­بستگی منفی دارد و با OM، لگاریتم نسبت OM/Clay، درصد شن خیلی ریز و تخلخل خاک هم­بستگی مثبت دارد. چون خاک­های مورد بررسی دارای ساختمان نسبتاً خوبی بودند و در استخراج PTFs ویژگی­های ساختمانی نقش بیش­تری داشتند، پیشنهاد می­شود در آینده برای پیش­بینی بهتر Ks خاک­ها در این منطقه برخی ویژگی­های ساختمانی دیگری مانند میانگین قطر خاک­دانه­ها به عنوان تخمین­گر مد نظر قرار گیرد.

کلیدواژه‌ها


عنوان مقاله [English]

Evaluating and Developing Pedotransfer Functions to Predict Soil Saturated Hydraulic Conductivity at Landscape Scale in Central Zagros

نویسندگان [English]

  • Hamid Kelishadi 1
  • Mohammad Reza Mosaddeghi 2
  • Mohammad Ali Hajabbasi 3
  • Shamsollah Ayoubi 2
1 PhD Student, Department of Soil Science, College of Agriculture, Isfahan University of Technology, Isfahan, Iran
2 Department of Soil Science, College of Agriculture, Isfahan University of Technology, Isfahan, Iran
3 Department of Soil Science, College of Agriculture, Isfahan University of Technology, Isfahan, Iran
چکیده [English]

Soil saturated hydraulic conductivity (Ks) is an essential property in managing irrigation projects, water and solute transport modeling and for predicting unsaturated hydraulic conductivity functions. Laboratory and field measurements of Ks are time-consuming and costly especially in the large scales. Moreover, Ks is affected by different factors and thus its predicting and modeling by derived pedotrasfer functions (PTFs) or those in the literature can be useful for many purposes. This study was performedto evaluate the already existed in literature and derived PTFs for predicting Ks in Farsan and Koohrang regions, Chaharmahal-va-Bakhtiari province in the central Zagros. Undisturbed samples (volume of 100 cm3) were collected from the surface soil at 100 locations which were well distributed in the region and in different land uses. The Ks of the undisturbed samples was measured using the constant-head method in laboratory. Easily-available soil properties were also determined and PTFs for Ks prediction were derived by multiple stepwise regression. The measured Ks values were also compared with the predictions of several PTFs in the literature. Results showed that PTFs in the literature could not accurately predict the Ks in the region. Among the literature PTFs, powerful neural network model of Rosetta (SCC+BD) was the best. The worse PTFs for Ks prediction in the studied structured soils were Brakensiek et al. (1984) PTFs. However, the derived PTFs predicted the Ks in the region better (R2=52%, RMSE=0.5 mm/h) than the literature PTFs. The findings revealed that logarithm of Ks negatively correlates with relative bulk density (relative compaction), effective bulk density, clay to carbonate content (Clay/CaCO3) and carbonate to organic matter content (CaCO3/OM) ratios and bulk density and clay interaction [ln (BD×Clay)] and has positive correlations with OM, ln(OM/Clay), very fine sand content and soil porosity. The studied soils had good structure and the structural properties had significant role in the derived PTFs. Therefore, it is suggested to include additional structural properties such as aggregates' mean diameter as a predictor for better Ks prediction in future.

کلیدواژه‌ها [English]

  • Pedotransfer functions
  • saturated hydraulic conductivity
  • relative compaction
  • effective bulk density
  • Central Zagros
References
Abu-Hashim MSD. 2011. Impact of land-use and land management on water infiltration capacity of soils on a catchment scale. PhD Thesis. Fakultät Architektur, Bauingenieurwesen und Umweltwissenschaften der Technischen Universität Carolo-Wilhelmina zu Braunschweig, Germany.
Asgarzadeh, H. 2012. New quantities for soil water availability to plants: Consistency between lab and field measurements. PhD Thesis, Department of Soil Science, Bu-AliSinaUniversity. Iran, Hamadan (in Farsi with English Summary).
Asgarzadeh H, Mosaddeghi MR, Mahboubi AA, Nosrati A and Dexter AR. 2010. Soil water availability for plants as quantified by conventional available water, least limiting   water range and integral water capacity. Plant Soil, 335(1-2): 229–244.
Asgarzadeh H, Mosaddeghi MR, Mahboubi AA, Nosrati A and Dexter AR. 2011. Integral energy of conventional available water, least limiting water range and integral water capacity for better characterization of water availability and soil physical quality. Geoderma, 166: 34–42.
Bouma J. 1989. Using soil survey data for quantitative land evaluation. Adv. Soil Sci. 9:177–213.
Brakensiek DL, Rawls WJ and Stephenson GR. 1984. Modifying SCS hydrologic soil groups and curve numbers for rangeland soils. ASAE Paper No. PNR-84-203, St. Joseph, MI.
Cosby BJ, Hornberger GM, Clapp RB and Ginn TR. 1984. A statistical exploration of the relationship of soil moisture characteristics to the physical properties of soils. Water Resour. Res. 20(6): 682–690.
Cronican AE and Gribb MM. 2004. Hydraulic conductivity prediction for sandy soil. Ground Water, 42(3): 459–464.
Dexter AR. 2004. Soil physical quality. Part I: theory, effects of soil texture, density, and organic matter, and effects on root growth. Geoderma, 120: 201–214.
Dexter, AR., EA. Czyż, and OP. Gaţe. 2004. Soil stucture and saturated hydraulic conductivity of subsoil. Soil Till. Res. 79: 185–189.
Hu, W., MA. Shao, QJ Wang, J. Fan, and K. Reichardt. 2008. Spatial variability of soil hydrualic propertice on a steep slope in the loess plateau of China. Sci. Agric. (Piracicaba, Braz.), 65(3): 268–276.
Gee GW and Bauder JW. 1986. Particle size analysis. PP. 383–411. In: Klute A (ed.). Method of Soil Analysis, Part 1. Physical and Mineralogical Methods, Agronomy Handbook No 9. ASA and SSSA, Madison, WI.
Ghorbani Dashtaki SH, Dehghani Baniani S, Khodaverdiloo H, Mohammad J. and Khalilmoghaddam B. 2012. Estimation of saturated hydraulic conductivity and inverse of macroscopic capillary length using PTFs. J. Sci. & Technol. Agric. & Natur. Resour., Water and Soil Sci. 16(60): 145–157 (in Farsi with English Summary).
Jones CA. 1983. Effect of soil texture on critical bulk densities for root growth. Soil Sci. Soc. Am. J. 47: 1208–1211.
Khalilmoghaddam B. 2009. Estimating shear strength, saturated hydraulic conductivity and infiltration rate using pedotransfer function and artificial neural networks. PhD Thesis, Department of Soil Science, College of Agriculture, IsfahanUniversity of Technology (in Farsi with English Summary).
Klute A and Dirksen C. 1986. Hydraulic conductivity and diffusivity: Laboratory methods. PP. 687–732. In: Klute  A (ed.). Methods of Soil Analysis, Part 1: Physical and Mineralogical Methods, .Agron. Monogr. 9. ASA/SSSA, Madison. WI.
Lee, D.H. 2005. Comparing the inverse parameter estimation approach with pedo-transfer function method for estimating soil hydraulic conductivity. Geosci J, 9(3): 269–276.
Loague, K. 1992. Using soil texture to estimate saturated hydraulic conductivity and the impact on rainfall-runoff simulation. Water Resour. Res. 28: 678–693.
Mehnatkesh A, Ayoubi S, Jalalian A and Sahrawat KL. 2013. Relasioships between soil depth and terrain attributes in a semi arid hilly region in western Iran. J. Mount. Sci. 10: 163‒172.
Merdun H, Çınar Ö, 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 Till. Res. 90: 108–116.
Mosaddeghi MR. 2007. Fitting well-known models of soil water characteristic curve and hydraulic conductivity and deriving pedo-transfer functions for soils in HamadanProvince. Research Report, Department of Soil Science, College of Agriculture, Bu-AliSinaUniversity ((in Farsi with English Summary).
Mosaddeghi MR, Morshedizad M, Mahboubi AA, Dexter AR and Schulin R. 2009. Laboratory evaluation of a model for soil crumbling for prediction of the optimum soil water content for tillage. Soil Till. Res. 105: 242–250.
Nemes, A., W.J. Rawls, and Ya.A. Pachepsky. 2005. Influence of organic matter on the estimation of saturated hydraulic conductivity. Soil Sci. Soc. Am. J. 69: 1330–1337.
Nelson RE. 1982. Carbonate and gypsum. PP. 181–197. In: Buxton DR (ed.). Method of Soil Analysis, Part 2. Chemical Methods, Agronomy Handbook No 9. ASA and SSSA, Madison, WI..
Nelson DW and Sommers LP. 1986. Total carbon, organic carbon and organic matter. PP. 539–579. In: Buxton DR (ed.). Method of Soil Analysis, Part 2. Chemical Methods, Agronomy Handbook No 9. ASA and SSSA, Madison, WI.
Pachepsky YA Rawls WJ (eds.) 2004. Development of pedotransfer functions in soil hydrology. Elsevier, Amsterdam, The Netherlands.
Parasuraman K, Elshorbagy A and SiBC. 2006. Estimating saturated hydraulic conductivity in spatially variable fields using neural network ensembles. SSS. Am. J. 70: 1851–1859.
Rawls W., Gish TJ and Brakensiek DL. 1991. Estimating soil water retention from soil physical properties and characteristics. Adv. Soil Sci. 16: 213–234.
Rezae Arshad R, Sayyad GH, Mazloom M, Shorafa M and Jafarnejady A. 2012. Comparison of artificial neural networks and regression pedotransfer functions for predicting saturated hydraulic conductivity in soils of Khuzestan province. J. Sci. & Technol. Agric. & Natur. Resour., Water and Soil Sci. 16(60): 107–118 (in Farsi with English Summary).
SAS Institute, 1996. SAS Institute SAS/STATTM Software: Changes and enhancements through release 6.11. SAS Institute, Cary, NC.
Saxton KE and  Rawls WJ. 2006. Soil water characteristic estimates by texture and organic matter for hydrologic solutions. Soil Sci. Soc. Am. J. 70: 1569–1578.
Schaap MG, LeijFL and van Genuchten MTh. 1998. Neural network analysis for hierarchical prediction of soil hydraulic properties. Soil Sci, Soc, Am, J. 62: 847–855.
Sharifi J. 2011. Some physicochemical, micromorphological and mineralogical properties of soils on three slope positions in the chelgerd region, Chaharmahal and Bakhtiari Province, Iran. MSc Thesis, GuilanUniversity, 100 pp. (in Farsi with English Summary).
Shirazi MA and Boersma L. 1984. A unifying quantitative analysis of soil texture. Soil Sci. Soc. Am. J. 48: 142–147.
Tietje O and Hennings V. 1996. Accuracy of the saturated hydraulic conductivity prediction by pedo-transfer functions compared to the variability within FAO textural classes. Geoderma, 69: 71–84.
van Genuchten MTh and Nielsen DR. 1985. On describing and predicting the hydraulic properties of unsaturated soils. Ann. Geophys. 3: 625–628.
Wagner B, Tarnawski VR, Hennings V, Müller U, Wessolek G and Plagge R. 2001. Evaluation of pedo-transfer functions for unsaturated soil hydraulic conductivity using an independent data set. Geoderma, 102: 275–297.
Wösten JHM. 1997. Pedotransfer functions to evaluate soil quality. PP. 221–245. In: Gregorich EG and Carter MR (eds.). Soil Quality for Crop Production and Ecosystem Health. Developments in Soils Science, Vol. 25, Elsevier, Amsterdam.
Wösten  JHM and. vanGenuchten MTh. 1988. Using texture and other soil properties to predict the unsaturated soil hydraulic function. Geoderma, 52: 1762–1770.
Wösten JHM, Lilly A, Nemes A and Le Bas C. 1998. Using existing soil data to derive hydraulic parameters for simulation models in environmental studies and in land use planning. Report 156, Winand Staring Centre, SC–DLO,Wageningen, Netherlands. 106pp.
 Wösten JHM, Pachepsky YA and Rawls WJ. 2001. Pedotransfer functions: bridging the gap between available basic soil data and missing soil hydraulic characteristics. J. Hydrol. 251: 123–150.