نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مرتع و آبخیزداری، دانشکده منابع طبیعی، دانشگاه صنعتی بهبهان، بهبهان ایران

2 گروه مهندسی علوم خاک، دانشکده کشاورزی، دانشگاه لرستان، خرم آباد، ایران

3 گروه مرتع و آبخیزداری، دانشکده محیط زیست و منابع طبیعی، دانشگاه صنعتی خاتم الانبیاء بهبهان، بهبهان، ایران

چکیده

برای کنترل فرسایش خاک اقدامات مختلفی انجام می­گیرد که اطلاع از تأثیر این گونه اقدامات اهمیت زیادی در مدیریت فرسایش دارد. به‌همین دلیل این مطالعه با هدف بررسی شاخص فرسایش­پذیری خاک (K) و تغییرات مکانی آن در یک منطقه­ تحت عملیات درخت‌کاری و کنتور­فارو و یک منطقه مشابه بدون این عملیات در منطقه چاه­ماری بهبهان (استان خوزستان) انجام شد. تعداد 150 نمونه خاک از پنج سانتی­متر سطحی برداشته شد و مقدار K به روش‌های ویشمایر و اسمیت (روش A) و واعظی و همکاران (روش B) تعیین گردید. مدل­سازی K با استفاده از تکنیک­های نقشه­برداری رقومی خاک (DSM) بر اساس متغیرهای محیطی استخراج شده از تصویر لندست 8 و مدل رقومی ارتفاع (DEM) و توسط مدل­های جنگل تصادفی (RF) و شبکه­های عصبی مصنوعی (ANN) انجام شد. میانگین K روش­های A و B به ترتیب برابر با 067/0 و 006/0 تن بر هکتار ساعت بر هکتار مگاژول میلی­متر بود. نتایج بیانگر همبستگی بالا بین K به‌دست آمده از هر دو روش و داده­های سنجش از دور بود. بین K روش B با برخی متغیرهای استخراج شده از DEM همبستگی معنی­داری وجود داشت، ولی بین این متغیرها با K روش A همبستگی معنی‌داری وجود نداشت. نتایج مقایسه میانگین­ها نیز نشان داد بین میانگین K به‌دست آمده از روش A در منطقه شاهد با منطقه اجرای عملیات کنترل فرسایش تفاوت معنی­داری وجود داشت؛ ولی این تفاوت برای K روش B معنی­دار نبود. ارزیابی کارآیی مدل­ها نشان داد که هر دو مدل RF و ANN کارآیی نسبتاً بالایی در تخمین K از طریق دو روش A و B داشتند و هر دو روش منجر به تخمین­های نااریب گردیدند. به‌طورکلی، نتایج نشان داد که هر چند کارآیی روش­های DSM در مدل­سازی K بالا بود، ولی نتایج کارایی مدل­ها و مقایسه تیمارهای مختلف حفاظت خاک به روش تعیین K همبستگی داشت.

کلیدواژه‌ها

عنوان مقاله [English]

Soil Erodibility and its Spatial Variation in Areas under Erosion Control Measures in Behbahan Region

نویسندگان [English]

  • Maliheh Jahandideh 1
  • Alireza Amirian-Chakan 2
  • Mohammad Faraji 3
  • Masoud Jafarizadeh 3

1 Department of Rangeland and Watershed Management, Faculty of Natural Resources, Behbahan Khatam Alanbia University of Technology, Behbahan, Iran

2 Department of Soil Science, Faculty of Agriculture, Lorestan University, Khorramabad, Iran

3 Department of Rangeland and Watershed Management, Faculty of Natural Resources, Behbahan Khatam Alanbia University of Technology, Behbahan, Iran

چکیده [English]

To control soil erosion, several measures can be conducted which information on their effects is very important in managing soil erosion. Therefore, this study was conducted to assess and model soil erodibility in two adjacent sites in Behbahan region (Khuzestan province). At one site afforestation and contour furrowing were conducted to control soil erosion and the other site without any controlling measures was considered as control. Totally 150 soil samples were collected from the surface layer (0-5 cm) and K was estimated using the methods introduced by Wischmeier and Smith (method A) and Vaezi et al. (method B). For spatial modelling of K, based on digital soil mapping (DSM) techniques, several environmental covariates were derived from a Landsat 8 image and a digital elevation model (DEM) and two models including random forests (RF) and artificial neural networks (ANN) were employed. The values of K for methods A and B varied from 0.025 to 0.087 and 0.002 to 0.008 t.ha.h/ha.Mj.mm with means of 0.067 and 0.006 t.ha.h/ha.Mj.mm, respectively. Results revealed good correlation between K and remotely sensed covariates. Although K (method B) had significant correlation with some of the covariates derived from DEM, but there was no significant correlation between K (method A) with all covariates derived from DEM. Results indicated a significant difference between two sites it terms of K estimated by method A, while there was no significant difference in case of K estimated by method B. Model validation showed that both RF and ANN models resulted in good and unbiased estimates of K (methods A and B). In general, the findings indicated, although the performance of DSM techniques in modeling K were high, performances of the models and the results of means compassion may significantly differ in terms of the method through which K is estimated.

کلیدواژه‌ها [English]

  • Digital soil mapping
  • Machine learning
  • Soil Erosion
  • Spatial modeling
Addis H.K., and Klik A. 2015. Predicting the spatial distribution of soil erodibility factor using USLE nomograph in an agricultural watershed, Ethiopia. International Soil and Water Conservation Research, 3: 282–290.
Amini M., Abbaspour K.C., Khademi H., Fathianpour N., Afyuni M. and Schulin R. 2005. Neural network models to predict cation exchange capacity in arid regions of Iran. European Journal of Soil Science, 56(4): 551-559.
Amirian Chakan, A. Taghizadeh-Mehrjardi R., Kerry R., Kumar S., Khordehbin S., and Yusefi Khanghah S. 2017. Spatial 3D distribution of soil organic carbon under different land use types. Environmental Monitoring and Assessment, 189: 131-148.
Amirian-Chakan A., Minasny B., Taghizadeh-Mehrjardi R., Akbarifazli R., Zahra Darvishpasand Z., and Khordehbin S. 2019. Some practical aspects of predicting texture data in digital soil mapping. Soil and Tillage Research, 149, 104289.
Andronikov V.L., and Dorbrolv'skiy G.V. 1991. Theory and methods for the use of remote sensing in the study of soils. Mapping Sciences and Remote Sensing, 28(1): 92-101.
Arnau-Rosalén E., Calvo-Cases A., Boix-Fayos C., Lavee H., and Sarah P. 2008. Analysis of soil surface component patterns affecting runoff generation. An example of methods applied to Mediterranean hillslopes in Alicante (Spain). Geomorphology, 101(4): 595-606.
Bonilla C.A., and Johnson O.I. 2012. Soil erodibility mapping and its correlation with soil properties in Central Chile. Geoderma, 189-190: 116-123.
Breiman L. 2001. Random forests. Machine learning, 45(1): 5-32.
Dietterich T.G. 2000. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning, 40(2): 139-157.
Emadi M., Baghernejad M., and Memarian H.M. 2009. Effect of land use change on soil fertility characteristics within water-stable aggregates of two cultivated soils in northern Iran. Land Use Policy, 26(2): 452-457.
Ghasemi A. and Mohammadi J. 2003. Study of spatial variability of soil erodibility (case study of Chaghakhor watershed in Chaharmahal va Bakhtiari Province. Proceedings of the Eighth Congress of Soil Science of Iran, pp. 9-12. (In Persian)
Goodwin L.D., and Leech N.L. 2006. Understanding correlation: Factors that affect the size of r. The Journal of Experimental Education, 74: 249-266.
Goh T.B., and Mermut A.R. 2007. Carbonates. In: Carter M.R., and Gregorich E.G. (Eds.), Soil Sampling and Methods and Analysis. Canadian Society of Soil Science. CRC Press, Boca Raton, pp. 215-223.
Golmohamadi F., Nabiollahi K., Taghizadeh-Mehrjardi R., and Davari M. 2018. Digital mapping of soil erodibility (Case study: Dehgolan, Kurdistan province). Journal of Water and Soil Conservation, 24(6): 87-103. (In Persian)
Gupta S., and Kumar S. 2017. Simulating climate change impact on soil erosion using RUSLE model - A case study in a watershed of mid-Himalayan landscape. Journal of Earth System Science, 126: 43.
Hengl T., Toomanian N., Reuter H.I. and Malakouti M.J. 2007. Methods to interpolate soil categorical variables from profile observations: Lessons from Iran. Geoderma, 140, 417 – 427.
Hengl T., and Reuter, H.I. 2009. Geomorphometry: Concepts, Software, Applications. 1st Ed. Elsevier, Amsterdam, 765p. 
Hussein M.H., Kariem T.H., and Othman A.K. 2007. Predicting soil erodibility in northern Iraq using natural runoff plot data. Soil and Tillage Research, 94(1): 220-228. ‏
Jafari Honar A., Kiani F., and Khormali F. 2015. Effect of climate difference on variation of loess soil erodibility indices in Golestan province. Journal of Water and Soil Conservation, 22(1): 48-70. (In Persian)
Jeffrey J.Y. 2005. Effect of grazing exclusion on rangeland vegetation and soils. East Central Idaho. Western North American Naturalist, 65: 91-102.
Kamali K., Jafari A., and Eslami M. 2015. The relationship between measured soil erodibility using simulator and Wischmeier nomograph and Bisal method. Watershed Management Research, 28(2): 66-72. (In Persian)
Kouli M., Soupios P., and Vallianatos F. 2009. Soil erosion prediction using the revised universal soil loss equation (RUSLE) in a GIS framework, Chania, Northwestern Crete, Greece. Environmental Geology, 57(3): 483-497.
 Kulikov M., Schickhoff U., Grongroft A., and Borchardt P. 2020. Modelling soil erodibility in mountain rangelands of south-western Kyrgyzstan. Pedosphere, 30(4): 443-456.
 Li X.Y., Zhao W.W., Song Y.X., Wang W. and Zhang X.Y. 2008. Rainfall harvesting on slopes using contour furrows with plastic-covered transverse ridges for growing Caragana korshinskii in the semiarid region of China. Agricultural Water Management, 95(5): 539-544.
Mahmoudabadi E., Karimi A., Haghnia G.H., and Sepehr A. 2017. Assessing performance of multivariate linear regression (MLR), artificial neural network (ANN) and gene expression programming (GEP) in estimating soil properties. Journal of Water and Soil Conservation, 24(2): 23-44. (In Persian)
McBratney A.B., Odeh I.O., Bishop T.F., Dunbar M.S., and Shatar T.M. 2000. An overview of pedometric techniques for use in soil survey. Geoderma, 97(3-4): 293-327.
 McBratney A.B., Santos M.M., and Minasny B. 2003. On digital soil mapping. Geoderma, 117(1-2): 3-52.
 Metternicht G., and Zinck J.A. 1997. Spatial discrimination of salt-and sodium-affected soil surfaces. International Journal of Remote Sensing, 18(12): 2571-2586.
Nield S.J., Boettinger J.L. and Ramsey R.D. 2007. Digitally mapping gypsic and natric soil areas using Landsat ETM data. Soil Science Society of America Journal, 71(1): 245–252.
Minasny B., McBratney A.B., and Hartemink A.E. 2010. Global pedodiversity, taxonomic distance, and the World Reference Base. Geoderma, 155(3-4): 132-139.
Mohaghegh P., Naderi M. and Mohammadi J. 2016. Determination of minimum data set for assessment of soil quality: A case study in Choghakhur Lake Basin. Journal of Water and Soil, 30(4): 1232-1243. (In Persian)
Nabiollahi K., Haidari A., and Taghizadeh-Mehrjerdi R. 2014. Digital mapping of soil texture using regression tree and artificial neural network in Bijar, Kurdistan. Journal of Water and Soil, 28(5): 1025-1036. (In Persian)
Nield S.J., Boettinger J.L. and Ramsey R.D. 2007. Digitally mapping gypsic and natric soil areas using Landsat ETM data. Soil Science Society of America Journal, 71(1): 245-252.
Ostovari Y., Ghorbani-Dashtaki S., Bahrami H.A., Abbasi M., Dematte A.M., Arthur E., and Panagos P. 2018. Towards prediction of soil erodibility, SOM and CaCO3 using laboratory Vis- NIR spectra: A case study in a semi-arid region of Iran. Geoderma, 314: 102-112.
Panagos P., Meusburger K., Ballabio C., Borrelli P., and Alewell C. 2014. Soil erodibility in Europe: A high-resolution dataset based on LUCAS. Science of the Total Environment, 479: 189-200.
Panomtaranichagul M., and Nareuba S. 2005. Improvement of water harvesting and anti-erosive cultural practices for sustainable rainfed multiple crop production on sloping land. Conference on International Agricultural Research for Development, Stuttgart-Hohenheim.
Rumpel C., Chabbi A., Nunan N., and Dignac M.F. 2009. Impact of land use change on the molecular composition of soil organic matter. Journal of Analytical and Applied Pyrolysis, 85(1-2): 431-434.
Schwab G.O., Frevert R.K., Edminster T.W. and Barnes K.K. 1981. Soil and Water Conservation Engineering. 3rd Ed, John Willey and Sons, New York, USA.
Shabani F., Kumar L., and Esmaeili A. 2014. Improvement to the prediction of the USLE K-factor. Geomorphology, 204: 229-234.
Soofi M.B., and Emami H. 2017. Evaluation soil erodibility in catchment of Torogh dam of Mashhad. Journal of Environmental Erosion Research, 7(3): 25-38. (In Persian)
Vaezi A.R., Sadeghi S.H.R., Bahrami H.A., and Mahdian M.H. 2008. Modeling the USLE K-factor for calcareous soils in northwestern Iran. Geomorphology, 97(3): 414-423.
Vaezi A.R., Hasanzadeh H., and Cerdà A. 2016. Developing an erodibility triangle for soil texture in semi-arid regions, NW Iran. Catena, 142: 221-232.
Veihe A., 2002. The spatial variability of erodibility and its relation to soil types: a study from northern Ghana. Geoderma, 106(1-2): 101-120.
Walkley A., and Black I.A. 1934. An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Science, 37(1): 29-38.
Wang G., Fang Q., Wu B., Yang H., and Xu Z. 2015. Relationship between soil erodibility and modeled infiltration rate in different soils. Journal of Hydrology, 528: 408-418.
Wang H., Zhang G.H., Li N.N., Zhang B.J., and Yang H.Y. 2018. Soil erodibility influenced by natural restoration time of abandoned farmland on the Loess Plateau of China. Geoderma, 325(1): 18-27.
Wischmeier W.H., and Smith D.D. 1978. Prediction Rainfall Erosion Losses. A Guide for Conservation Planning Agriculture. USDA, Agriculture Handbook No. 537.
Yang X., Gray J., Chapman G., Zhu O., Tulau M., and McInnes-Clarke S. 2018. Digital mapping of soil erodibility for water erosion in New South Wales, Australia. Soil Research, 56: 158-170.
Yousefifard M., Jalaliyan A., and Khademi H. 2007. Estimation of soil and nutrients loss due to land use change using a rainfall simulator. Journal of Agriculture and Natural Resources, 40(1): 93-106. (In Persian)
Zhang K.L., Shu A. P., Xu X.L., Yang Q.K., and Yu B. 2008. Soil erodibility and its estimation for agricultural soils in China. Journal of Arid Environments, 72(6): 1002-1011. ‏
Zhou Z.C., Gan Z.T. Shangguan Z.P., and Dong Z.B. 2010. Effects of grazing on soil physical properties and soil erodibility in semiarid grassland of the Northern Loess Plateau (China). Catena, 82(2): 87-91.