Abstract
Soil depth generally varies in mountainous regions in rather complex ways. Conventional soil survey methods for evaluating the soil depth in mountainous and hilly regions require a lot of time, effort and consequently relatively large budget to perform. This study was conducted to explore the relationships between soil depth and topographic attributes in a hilly region in western Iran. For this, one hundred sampling points were selected using randomly stratified methodology, and considering all geomorphic surfaces including summit, shoulder, backslope, footslope and toeslope; and soil depth was actually measured. Eleven primary and secondary topographic attributes were derived from the digital elevation model (DEM) at the study area. The result of multiple linear regression indicated that slope, wetness index, catchment area and sediment transport index, which were included in the model, could explain about 76 % of total variability in soil depth at the selected site. This proposed approach may be applicable to other hilly regions in the semi-arid areas at a larger scale.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Afshar FA, Ayoubi S, Jalalian A (2010) Soil redistribution rate and its relationship with soil organic carbon and total nitrogen using 137Cs technique in a cultivated complex hillslope in western Iran. Journal of Environmental Radioactivity 101: 606–614.
Basso B (2005) Digital terrain analysis: Data source, resolution and application for modeling physical processes in agroecosystems. Rivista Italiana di Agrometeorologia 2: 5–14.
Boer M, Del Barrio G, Puigdefabregas J (1996) Mapping of soil depth in dry Mediterranean area using terrain attributes derived from a digital elevation model. Geoderma 72: 99–118.
Campling P, Gobin A, Feyen J (2002) Logistic modeling to spatially predict the probability of soil drainage classes. Soil Science Society of America Journal 66: 1390–1401.
Debella-Gilo M, Etzelmuller B (2009) Spatial prediction of soil classes using digital terrain analysis and multinomial logistic regression modeling integrated in GIS: Examples from Vestfold County, Norway. Catena 77: 8–18.
Florinsky IV, Eilers RG, Manning GR, et al. (2002) Prediction of soil properties by digital terrain modeling. Environmental Modeling and Software 17: 295–311.
Gessler PE, Chadwick OA, Charman F, et al. (2000) Modeling soil-landscape and ecosystem properties using terrain attributes. Soil Science Society of America Journal 64: 2046–2056.
Hengl T, Toomanian N, Reuter H, et al. (2007) Methods to interpolate soil categorical variables from profile observations: Lessons from Iran. Geoderma 140: 417–427.
Iran Metrological Organization (2012). Meteorological data of Charmahal and Bakhtiari Province. Available at http://irimo.ir/tmax.pdf. (Accessed: 12 January 2012).
Kalivas DP, Triantakonstantis DP, Kollias VJ (2002) Spatial prediction of two soil properties using topographic information. The Global Nest: the International Journal 4(1): 41–49.
Khormali F, Ajami M (2011) Pedogenetic investigation of soil degradation on a deforested loess hillslope of Golestan Province, Northern Iran. Geoderma 274–283.
Kuriakose SL, Devkota S, Rossiter DG (2009) Prediction of soil depth using environmental variables in an anthropogenic landscape, a case study in the Western Ghats of Kerala, India. Catena 79: 27–38.
McBratney AB, Mendonca Santos ML, Minasny B (2003) On digital soil mapping. Geoderma 117: 3–52.
Meyer MD, North MP, Gray AN, et al. (2007) Influence of soil thickness on stand characteristics in a Sierra Nevada mixedconifer forest. Plant and Soil 294: 113–123.
Minasny B, McBratney AB (1999) A rudimentary mechanistic model for soil production and landscape development. Geoderma 90: 3–21.
Moore ID, Hutchinson MF (1991) Spatial extension of hydrologic process modeling. Proc. Int. Hydrology and Water Resources Symposium. Institution of Engineers-Australia 91/22. pp. 803–808.
Mueller TG, Pierce FJ (2003) Soil carbon maps: Enhancing spatial estimates with simple terrain attributes at multiple scales. Soil Science Society of America Journal 67: 258–267.
Norouzi M, Ayoubi S, Jalalian A, et al. (2009) Predicting rainfed wheat quality by artificial neural network using terrain and soil characteristics. Acta Agriculturae Scandinavia, Section B-Soil and Plant Science 60: 341–352.
Odeh IOA, Chittleborough DJ, McBratney AB (1991) Elucidation of soil-landform interrelationships by canonical ordination analysis. Geoderma 49: 1–32.
Odeh IOA, McBratney AB, Chittleborough DJ (1994) Spatial prediction of soil properties from landform attributes derived from a digital elevation model. Geoderma 63: 197–214.
Penizek V, Boruka L (2006) Soil depth prediction supported by primary terrain attributes: a comparison of methods. Plant Soil Environment 52: 424–430.
Saulnier GM, Beven K, Obled C (1997) Including spatially variable effective soil depths in TOPMODEL. Journal of Hydrology 202: 158–172.
Soil Survey Staff (2006) Keys to Soil Taxonomy, 10th ed USDANatural Resources Conservation Service, Washington, DC, USA.
Thompson JA, Bell JC, Butler CA (1997) Quantitative soil-landscape modeling for estimating the areal extent of hydromorphic soils. Soil Science Society of America journal 61: 971–980.
Thompson JA, Pena-Yewtukhiw EM, Grove JH (2006) Soil-landscape modeling across a physiographic region: Topographic patterns and model transportability. Geoderma 133: 57–70.
Tsai CC, Chen ZS, Duh CT, et al. (2001) Prediction of soil depth using a soil landscape regression model: a case study on forest soils in southern Taiwan. National Science Council of the Republic of China, Part B: Life Sciences 25: 34–39.
Vanwalleghem T, Poesen J, McBratney A, et al. (2010) Spatial variability of soil horizon depth in natural loess-derived soils. Geoderma 157: 37–45.
Wilson JP, Gallant JC (2000) Secondary Topographic Attributes. In: Wilson JP and Gallant JC (eds), Terrain Analysis: Principles and Applications. New York, John Wiley and Sons: 87–131.
Zhu AX, Hudson B, Burt J, et al. (2001) Soil mapping using GIS, expert knowledge, and fuzzy logic. Soil Science Society of America Journal 65: 1463–1472.
Ziadat FM (2005) Analyzing digital terrain attributes to predict soil attributes for a relatively large area. Soil Science Society of America Journal 69: 1590–1598.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Mehnatkesh, A., Ayoubi, S., Jalalian, A. et al. Relationships between soil depth and terrain attributes in a semi arid hilly region in western Iran. J. Mt. Sci. 10, 163–172 (2013). https://doi.org/10.1007/s11629-013-2427-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11629-013-2427-9