Prediction of Soil Solum Depth Using Topographic Attributes in Some Hilly Land of Koohrang in Central Zagros

آب و خاک

Article 10, Volume 29, Issue 4, September 2015, Pages 980-990   

Document Type: Research Article

DOI: 10.22067/jsw.v0i0.36214

Authors

A. Mehnatkesh^{* 1}; S. Ayoubi²; A. Jalalian³

¹Chaharmahal-o-Bakhtiari Agricultural and Natural Resources Research and Education Center

²Isfahan University of Technology

³College of Agriculture, Islamic Azad University, Khorasgan Branch

Abstract

Introduction: Soil depth is defined as the depth from the surface to more-or-less consolidated material and can be considered as the most crucial soil indicator, affecting desertification and degradation in disturbed ecosystems. Soil depth varies as a function of many different factors, including slope, land use, curvature, parent material, weathering rate, climate, vegetation cover, upslope contributing area, and lithology. Topography, one of the major soil forming factors, controls various soil properties. Thus, quantitative information on the topographic attributes has been applied in the form of digital terrain models (DTMs). The prediction of soil depth by topographic attributes depends mainly on: i) the spatial scale of topographic variation in the area, ii) the nature of the processes that are responsible for spatial variation in soil depth, and iii) the degree to which terrain-soil relationships have been disturbed by human activities. This study was conducted to explore the relationships of soil depth with topographic attributes in a hilly region of western Iran.
Materials and Methods: The study area is located at Koohrang district between 32°20′ to 32°30′ N latitudes and 50°14′ to 50°24′ E longitudes, in Charmahal and Bakhtiari province, western Iran. The field sites with an area of 30,000 ha are located on the hillslopes at about 20% transversal slope. The soils at the site are classified as Typic Calcixerepts, Typic Xerorthents and Calcic Haploxerepts for the representative excavated profiles in summit, shoulder and backslope, respectively. The soils located at footslope and toeslope were classified as Chromic Calcixererts. Measurements were made in twenty representative hillslopes of the studied area. At the selected site, one hundred points were selected using randomly stratified methodology, considering all geomorphic surfaces including summit, shoulder, backslope, footslope and toeslope during sampling. Overall, 100 profiles were dug and described; and the solum thickness was measured for each profile. DEM data were created by using a 1:2,5000 topographic map. Topographical indices were generated from the DEM using TAS software. Terrain attributes in two categories, primary and secondary (compound) attributes; primary attributes are included elevation, slope, aspect, catchment area, dispersal area, plan curvature, profile curvature, tangential curvature, shaded relief. Secondary or compound attributes such as soil water content or the potential for sheet erosion, stream power index, wetness index, and sediment transport index. Correlation coefficients to define relationships between soil depth and terrain attributes, and analysis of variance by Duncan test were done using the SPSS software. The statistical software SPSS was used for developing multiple linear regression models. Terrain attributes were selected as the independent variables and soil depth was employed as dependent variable in the model. Thirty sampling sites were used to validate the developed soil-landscape model. In testing soil-landscape model, we calculated two indices from the observed and predicted values included mean error (ME) and root mean square error (RMSE).
Results and Discussion: The soil depth in the studied profiles varied from 30 cm to 150 cm with an average of 108.6 cm. Relatively high variability (CV = 76%) was obtained for soil depth in the study area. The linear correlation analysis of the 12 topographic attributes and one soil property (soil depth), showed that there was a significant correlation among 36 of the 77 attribute pairs. Soil depth showed high positive significant correlations with catchment area, plan curvature, and wetness index, and showed high negative correlation with sediment transport index, sediment power index and slope. Low positive significant correlations of soil depth were identified with tangential curvature, and profile curvature. Moreover, soil depth was negatively correlated with elevation. The rest of the topographic attributes including aspect, shaded relief, and dispersal area were not significantly correlated with soil depth. Many of these relationships are similar to those found in other landscapes. The results of analysis of variance showed that there are significant differences for soil depth among the selected slope positions in the studied area. The highest values of soil depth were observed in the downslope positions including footslope and toeslope. The lowest soil depth was observed in shoulder position with the highest rate of soil erosion.
Conclusions: It seems that the high variability for soil depth depends on topography of the field, and the landscape position, causing differential accumulation of water at different positions on the landscape; and moreover the soil erosion and deposition processes, resulting in high variability in the soil depth. We found relatively high correlation coefficients of soil depth with two groups of topographic attributes (erosional processes and water accumulation). Empirical model (MLR) using selected terrain attributes explains 76% of the variation of soil depth in the studied area. The terrain attributes that best predicted soil depth variability in the selected site were mainly the attributes that had significant relationships with soil depth. The dominant attributes in the MLR model included slope, wetness index, catchment area and sediment transport index.

Keywords

Multiple linear regression model; Slope position; Soil depth; Soil-landscape model

References

1- Afshar F.A, Ayoubi S., and 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.

2- Boer M., Del Barrio G., and Puigdefabregas J. 1996. Mapping of soil depth in dry Mediterranean area using terrain attributes derived from a digital elevation model. Geoderma, 72:99-118.

3- Florinsky I.V., Eilers R.G., and Manning G.R. 2002. Prediction of soil properties by digital terrain modeling. Environmental Modeling and Software, 17:295-311.

4- Gessler P.E., Chadwick O.A., and Charman F. 2000. Modeling soil-landscape and ecosystem properties using terrain attributes. Soil Science Society of America Journal, 64:2046-2056.

5- Guisan A., Edwards T.C., and Hastie T. 2002. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological Modelling, 157:89-100.

6- Kalivas D.P., Triantakonstantis D.P., and Kollias V.J. 2002. Spatial prediction of two soil properties using topographic information. Global Nest Journal, 4:41-49.

7- Khormali F., and Ajami M. 2011. Pedogenetic investigation of soil degradation on a deforested loess hillslope of Golestan Province, Northern Iran. Geoderma, 274- 283.

8- Kuriakose S.L., Devkota S., and Rossiter D.G. 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.

9- Lindsay, J. 2005. TAS Software. Manchester, UK.

10- McBratney A.B., Mendonca Santos M.L., and Minasny B. 2003. On digital soil mapping. Geoderma, 117:3-52.

11- Meyer M.D., North M.P., and Gray A.N. 2007. Influence of soil thickness on stand characteristics in a Sierra Nevada mixed-conifer forest. Plant and Soil, 294:113-123.

12- Minasny B., and McBratney A.B. 1999. A rudimentary mechanistic model for soil production and landscape development. Geoderma, 90:3-21.

13- Moore I.D., and Hutchinson M.F. 1991. Spatial extension of hydrologic process modeling. Proc. Int. Hydrology and Water Resources Symposium. Institution of Engineers-Australia, 91/22, pp. 803-808.

14- Mueller T.G., and Pierce F.J. 2003. Soil carbon maps: En‌hancing spatial estimates with simple terrain attributes at multiple scales. Soil Science Society of America Journal, 67:258-267.

15- Norouzi M., Ayoubi S., Jalalian A., Khademi H., and Dehghani A. A. 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.

16- Odeh I.O.A., Chittleborough D.J., and McBratney A.B. 1991. Elucidation of soil–landform interrelationships by canonical ordination analysis. Geoderma, 49:1-32.

17- Odeh I.O.A., McBratney A.B., and Chittleborough D.J. 1994. Spatial prediction of soil properties from landform attributes derived from a digital elevation model. Geoderma, 63:197-214.

18- Penizek V., and Boruka L. 2006. Soil depth prediction supported by primary terrain attributes: a comparison of methods. Plant Soil Environment, 52:424- 430.

19- Saulnier G.M., Beven K., and Obled C. 1997. Including spatially variable effective soil depths in TOPMODEL. Journal of Hydrology, 202:158-172.

20- Thompson J.A., Bell J.C., and Butler C.A. 1997. Quantitative soil- land-scape modeling for estimating the areal extent of hydromorphic soils. Soil Science Society of America Journal, 61:971-980.

21- Thompson J.A., Pena-Yewtukhiw E.M., and Grove J.H. 2006. Soil–landscape modeling across a physiographic region: Topographic patterns and model transportability. Geoderma, 133: 57-70.

22- Tsui C.C., Chen Z.S., Duh C.T. 2004. Prediction of soil depth using a soillandscape 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.

23- Vanwalleghem T., Poesen J., McBratney A. 2010. Spatial variability of soil horizon depth in natural loess-derived soils. Geoderma, 157:37-45.

24- Wilson J.P., and Gallant J.C. 2000. Secondary Topographic Attributes. Terrain Analysis: Principles and Applications. J. P. Wilson, Gallant, J. C. New York, John Wiley and Sons, 87-131.

25- Zhu A.X., Hudson B., and Burt J. 2001. Soil mapping using GIS, expert knowledge, and fuzzy logic. Soil Science Society of America Journal, 65:1463-1472.

26- Ziadat F.M. 2005. Analyzing digital terrain attributes to predict soil attributes for a relatively large area. Soil Science Society of America Journal, 69:1590-1598.