تغییرات مکانی هدایت هیدرولیکی اشباع و مقاومت فروروی خاک در اراضی متأثر از نمک اطراف دریاچه ارومیه

نوع مقاله : مقالات پژوهشی

نویسندگان

1 دانشگاه محقق اردبیلی

2 دانشگاه تبریز

چکیده

هدف از این پژوهش مطالعه تغییرات مکانی هدایت هیدرولیکی اشباع (Ks) و مقاومت فروروی (PR) در خاک­های متأثر از نمک اطراف دریاچه ارومیه بود. نمونه­های خاک از دو کاربری کشاورزی (49 نمونه) و بایر (51 نمونه) به­هم چسبیده (ha 80) برای تعیین برخی متغیرهای فیزیکی و شیمیایی به­صورت شبکه­های منظم m 100×100 در بخش شندآباد منطقه شبستر برداشته ­شدند. متغیر Ks به روش بار ثابت یا افتان در آزمایشگاه و PR به­صورت درجا در صحرا اندازه­گیری­شد. از روش­های درون­یابی کریجینگ معمولی (OK) و وزن­دهی عکس فاصله (IDW) برای تحلیل تغییرات مکانی متغیرهای خاک استفاده گردید. ضریب همبستگی منفی و معنی­دار بین Ks با رس، جرم مخصوص ظاهری (BD)، SAR و EC و مثبت و معنی­دار بین Ks با شن، پایداری خاکدانه و کربن آلی یافت شد. این ضریب بین PR با BD مثبت و با رطوبت خاک مزرعه، منفی به­دست آمد. متغیر Ks دارای بالاترین ضریب تغییرات (6/155 درصد) در کاربری بایر و PR دارای کمترین دامنه تأثیر (m 335) در بین متغیرهای خاک بود. مدل نیم­تغییرنمای کروی و نمایی با وابستگی­ مکانی قوی به­ترتیب برای Ks و PR به­دست آمد. براساس آماره­های ریشه میانگین مربعات خطا (RMSE) و ضریب تطابق (CCC)، بین دو روش درون­یابی در برآورد Ks تفاوتی مشاهده نشد ولی در تخمین PR، روش OK به­علت داشتن RMSE کمتر و CCC بیشتر در مقایسه با روش IDW (توان 1 و 2) دارای دقت بالاتری بود. نقشه تغییرات مکانی نشان داد از کاربری کشاورزی به سمت کاربری بایر بر میزان BD و PR افزوده و از میزان Ks کاسته شد.

کلیدواژه‌ها


عنوان مقاله [English]

Spatial Variability of Soil Saturated Hydraulic Conductivity and Penetration Resistance in Salt-Affected Lands around Lake Urmia

نویسندگان [English]

  • shokrollah asghari 1
  • Mahmood Shahabi 2
1 University of Mohaghegh Ardabili
2 University of Tabriz
چکیده [English]

Introduction: Over the last few years, due to the depletion of Lake Urmia located in the northwest of Iran, the proportion of surrounding saline agricultural lands increased at a fast pace. Digital mapping of regional soils affected by salt is essential when monitoring the dynamics of soil salts and planning land development and reclamation schemes. The soil hydraulic and mechanical parameters are very important factors that affect water and chemical transport in soil pores. In the salt-affected soils, saturated hydraulic conductivity (Ks) is very low due to the high contents of sodium and weak aggregate stability. Penetration resistance (PR) indicates soil mechanical strength to penetration of a cone or flat penetrometer; it is important in seedling, root growth and tillage operations. Generally, PR values exceed 2.5 MPa, while root elongation is significantly restricted. The analysis of spatial variability of Ks and PR is essential to implement a site-specific soil management especially in the salt-affected lands. The objective of this study was to evaluate the influence of two different bare and agricultural land uses on the spatial variability of Ks and PR in the salt-affected soils around Lake Urmia.  
Materials and Methods: This study was conducted in the agricultural and bare lands of Shend Abad region located at the 15 km of Shabestar city, northwest of Iran (45° 36ʹ 34ʺ to 45° 36ʹ 38ʺ E and 38° 6ʹ 37ʺ to 38° 7ʹ 42ʺ N). Totally, 100 geo-referenced samples were taken from 0-10 cm soil depth with 100×100 m intervals (80 ha) in agricultural (n=49) and bare (n=51) land uses. Sand, silt, clay, organic carbon (OC), mean weight diameter of aggregates (MWD), sodium adsorption ratio (SAR) and electrical conductivity (EC), were measured in the collected soil samples. The EC and SAR were measured in 1:2.5 (soil: distilled water) extract. Ks was measured using constant or falling head method. Bulk density (BD) and field water content (FWC) were measured in the undisturbed soil samples taken by steal cylinders with 5 cm diameter and height. Total porosity calculated from BD and particle density (PD). PR was directly measured at the field using a cone penetrometer. The best fit semivariograms model (Gaussian, spherical and exponential) was chosen by considering the minimum residual sum of square (RSS) and maximum coefficient of determination (R2). Ordinary Kriging (OK) and inverse distance weighting (IDW) interpolation methods were used to analyze the spatial variability of Ks and PR. Spatial distribution maps of soil variables were provided by Arc GIS software. The accuracy of OK and IDW methods in estimating Ks and PR was evaluated by mean error (ME), mean absolute error (MAE), root mean square error (RMSE) and concordance correlation coefficient (CCC) criteria.The CCC indicates the degree to which pairs of the measured and estimated parameter value fall on the 45° line through the origin.    
Results and Discussion: According to coefficient of variation (CV) from the study area, the most variable soil indicator was Ks (CV=155.6%), whereas the least variable was PD (CV= 3.05%) both in bare land use. The Lognormal distribution was found for Ks data in the studied region. The Pearson correlation coefficients (r values) indicated that there are significant correlations between Ks and OC (r=0.36), sand (r=0.60), SAR (r=-0.35), EC (r=-0.22), BD (r=-0.52), TP (r= 0.31), silt (r=-0.60), and clay (r=-0.43). Also, significant correlations were obtained between PR and FWC (r=-0.32), BD (r=0.21), and TP (r=-0.21). The spatial dependency classes of soil variables were determined according to the ratio of nugget variance to sill expressed in percentages: If the ratio was >25% and <75%, the variable was considered moderately spatially dependent; if the ratio was >75%, variable was considered weakly spatially dependent; and if the ratio was <25%, the variable was considered strongly spatially dependent. The strong spatial dependences with the effective ranges of 2443m were found for Ks. The PR and PD variables had the least (335 m) and the highest (2844 m) effective range, respectively. The range of influence indicates the limit distance at which a sample point has influence over another points, that is, the maximum distance for correlation between two sampling point. The models of fitted semivariograms were spherical for Ks and exponential for PR. According to RMSE and CCC criteria, there was not found significant difference between Ks estimates by OK and IDW interpolation methods. The high CCC and low RMSE values for OK compared with IDW indicated the more precision and accuracy of OK in estimating PR in the studied area. Generally, the spatial maps showed that from agricultural to bare land use by nearing to Lake Urmia, the BD and PR increased and consequently TP and Ks decreased.
Conclusion: The results showed that Ks negatively related to the SAR, EC, BD, silt and clay and positively related to the OC, sand, MWD and TP in the study area. Also, PR negatively related to the FWC and TP and positively related to the BD and silt. The spatial dependency was found strong for Ks. The PR revealed the smallest effective range (335 m) among the studied variables. As a suggestion, for subsequent study, soil sampling distance could be taken as 335 m instead of 100 m in order to save time and minimize cost.

کلیدواژه‌ها [English]

  • Geostatistics
  • Physical properties
  • Saline and sodic soils
  • Shabestar plain
1- Afzali Moghadam E., Boroomand N., Jalali V.R., and Sanjari S. 2017. Evaluation the effect of different land use and soil characteristics on saturated hydraulic conductivity. Journal of Water and Soil 31, (5): 1302-1312. (In Persian with English abstract)
2- Asghari Sh., and Shahabi M. 2018. Geostatistical assessment of aggregates stability and sodium adsorption ratio in salt-affected soils around Urmia Lake. Journal of Water and Soil 32, (4): 795-807. (In Persian with English abstract)
3- Asghari Sh., Sheykhzadeh G.R., and Shahabi M. 2017. Geostatistical analysis of soil mechanical properties in Ardabil plain of Iran. Archives of Agronomy and Soil Science 63, (12): 1631-1643.
4- Barik K., Aksakal E.L., Islam K.R., Sari S., and Angin I. 2014. Spatial variability of soil compaction properties associated with field traffic operations. Catena 120: 122–133.
5- Blake G.R., and Hartge K.H. 1986a. Bulk density. p. 363-375. In: Klute A (ed). Methods of Soil Analysis Part 1, Physical and Mineralogical Methods. 2nd ed. American Society of Agronomy, Madison, WI.
6- Blake G.R., and Hartge K.H. 1986b. Particle density. p. 377-381. In: Klute A (ed). Methods of Soil Analysis Part 1, Physical and Mineralogical Methods. 2nd ed. ASA and SSSA, Madison, WI.
7- Cambardella C., Moorman T., Novak J., Parkin T., Karlen D., Turco R., and Konopka A. 1994. Field-scale variability of soil properties in central Iowa soils. Soil Science Society of America Journal 58: 1501-1510.
8- Dai F., Zhou Q., Lv Z., Wang X., and Liu G. 2014. Spatial prediction of soil organic matter content integrating artificial neural network and ordinary kriging in Tibetan Plateau. Ecological Indicator 45:184-194.
9- Danielson R,E., and Sutherland P.L. 1986. Porosity. p. 443-461. In׃ Klute A (ed). Methods of Soil Analysis. Part 1, 2 nd ed. Agronomy Monograph. 9. ASA and SSSA, Madison, WI.
10- Douaik A., Van Meirvenne M., and Toth T. 2005. Soil salinity mapping using spatio-temporal kriging and Bayesian Maximum Entropy with interval soft data. Geoderma, 128: 234-248.
11- Gardner W.H. 1986. Water content. p. 493-544. In: Klute A. (ed). Methods of Soil Analysis. Part 1. 2nd ed. Agronomy. Monograph. 9. ASA, Madison, WI.
12- Gee G.W., and Or D. 2002. Particle-size analysis. p. 255–293. In: Dane J. H., and Topp G. C. (eds.). Methods of Soil Analysis. Part 4. SSSA Book Series No. 5. Soil Science Society of America, Madison, WI.
13- Goovaerts P. 1997. Geostatistics for Natural Resources Evaluation. Oxford University Press. Oxford.
14- GS+5.1. 2001. Gamma Design software. Plainwell, MI, USA.
15- Hamzehpoura N., and Bogaert P. 2017. Improved spatiotemporal monitoring of soil salinity using filtered kriging with measurement errors: An application to the West Urmia Lake, Iran. Geoderma 295: 22–33.
16- Hillel D. 2004. Environmental soil physics. New York, USA: Academic Press.
17- Isaaks H.E., and Srivastava R.M. 1989. An Introduction to Applied Geostatistics. Oxford University Press, NY.
18- Iqbal J., Thomasson A., Jenkins JN., Owens PR., and Whisler FD. 2005. Spatial variability analysis of soil physical properties of alluvial soils. Soil Science Society of America Journal 69: 1338-1350.
19- Kelishadi H., Mossaddeghi M.R., Hajabbasi M.A., and Ayoubi S. 2014. Near-saturated soil hydraulic properties as influenced by land use management systems in Koohrang region of central Zagros, Iran. Geoderma, 213: 426-434.
20- Kilic K., Ozgoz E., and Akbas F. 2004. Assessment of spatial variability in penetration resistance as related to some soil physical properties of two fluvents in Turkey. Soil and Tillage Research 76:1–11.
21- Klute A., and Dirksen C. 1986. Hydraulic conductivity and diffusivity: Laboratory methods. p. 687-734. In: Klute A(ed). Methods of Soil Analysis. Part 1, Physical and Mineralogical Methods, 2nd ed. ASA and SSSA, Madison, WI.
22- Lin L.I. 1989. A concordance correlation coefficient to evaluate reproducibility. Bio-metrics, 45: 255–268.
23- Lowery B., and Morrison JE. 2002. Soil penetrometer and penetrability. In: Dane J.H., and Topp GC (eds.). Methods of soil analysis, part 4. Physical methods. Madison (WI): Soil Science Society of America; pp. 363–388.
24- Motaghian M.H., Karimi A., and Mohammadi J. 2008. Analysis of spatial variability of specific physical and hydraulic properties of soil on a catchment scale. Journal of Water and Soil 22, (2): 433-446. (In Persian with English abstract)
25- Mohammadi J., and Motaghian M.H. 2011. Spatial estimation of saturated hydraulic conductivity from terrain attributes using regression, kriging, and artificial neural networks. Pedosphere, 21(2): 170–175.
26- Nelson D.W., and Sommers L.E. 1982. Total carbon, organic carbon, and organic matter. p. 539–579. In A.L. Page et al. (ed.) Methods of Soil Analysis. Part 2. 2nd ed. Agron. Monogr. 9. ASA and SSSA, Madison, WI.
27- Richards L.A. 1954. Diagnosis and improvement of saline and alkali soils. Agricultural Handbook No. 60. U.S. Salinity Laboratory Riverside, California.
28- Sheng J., Maa L., Jiang P., Li B., Huang F., and Wu, H. 2010. Digital soil mapping to enable classification of the salt-affected soils in desert agro-ecological zones. Agricultural Water Management 97: 1944–1951.
29- Tajik F. 2004. Evaluation of aggregates stability in some regions of Iran. Journal of Sciences and Technology of Agriculture and Natural Resources 8(1): 107-122. (In Persian with English abstract)
30- Warrick AW. 2002. Soil Physics Companion. CRC Press. New York.
31- Wilding L.P, and Dress L.R. 1983. Spatial variability and pedology. p: 83-116. In: Wilding L.P, Smeckand N.E, and Hall GF, (EDs). Pedogenesis and Soil Taxonomy. I. Concepts and Interactions. Elsevier Science Pub.
32- Yazdani A., Mosaddeghi M.R., Khademi H., Ayoubi S., and Khayamim F. 2014. Relationship between surface aggregate stability and some soil and climate properties in Isfahan province. Soil Management 3(2): 23-31. (In Persian with English abstract)
33- Yoder R.E. 1936. A direct method of aggregate analysis of soils and a study of the physical nature of erosion losses. Journal of American Society Agronomy 28: 337-35.
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دوره 33، شماره 1 - شماره پیاپی 63
فروردین و اردیبهشت 1398
صفحه 103-116
  • تاریخ دریافت: 06 مرداد 1397
  • تاریخ بازنگری: 26 آذر 1397
  • تاریخ پذیرش: 31 اردیبهشت 1398
  • تاریخ اولین انتشار: 31 اردیبهشت 1398