Abstract
Soil depth is an important input parameter in hydrological and ecological modeling. Presently, the soil depth data available in national soil databases (STATSGO, SSURGO) is provided as averages within generalized map units. Spatial uncertainty within these units limits their applicability for spatially distributed modeling. This work reports a statistical model for prediction of soil depth in a semi-arid mountainous watershed that is based upon topographic and other landscape attributes. Soil depth was surveyed by driving a rod into the ground until refusal at geo-referenced locations selected to represent the range of topographic and land cover variations in Dry Creek Experimental Watershed, Boise, Idaho, USA. The soil depth survey consisted of a model calibration set, measured at 819 locations over 8 sub-watersheds, and a model testing set, measured at 130 locations randomly distributed over the remainder of the watershed. Topographic attributes were derived from a Digital Elevation Model. Land cover attributes were derived from Landsat TM remote sensing images and high resolution aerial photographs. A Generalized Additive Model was developed to predict soil depth over the watershed from these attributes. This model explained about 50% of the soil depth spatial variation and is an important improvement towards solving the need in distributed modeling for distributed soil depth input data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Breiman, L., 2001. Random forests. Machine Learning, 45:5–32.
Dietrich, W. E., Reiss, R., Hsu, M.-L., and Montgomery, D. R., 1995. A process-based model for colluvial soil depth and shallow landsliding using digital elevation data. Hydrological Processes, 9:383–400.
Freer, J., McDonnell, J. J., Beven, K. J., Peters, N. E., Burns, D. A., Hooper, R. P., Aulenbach, B., and Kendall, C., 2002. The role of bedrock topography on subsurface storm flow. Water Resources Research, 38(12):1269–1285.
Hastie, T., and Tibshirani, R., 1990. Generalized Additive Models. Chapman and Hall, London.
Hastie, T., Tibshirani, R., and Friedman, J., 2001. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 533 pp., Springer, New York.
Jensen, J. R., 1996. Introductory to Digital Image Processing: A Remote Sensing Perspective, 2 ed., Prentice-Hall, Englewood Cliffs, NJ.
Johnson, K.M., Lewis, R.S., Bennet, E.H., and Kiilsgaard, T.H., 1988. Cretaceous and tertiary intrusive rocks of south-central Idaho, pp. 55–86. In: Link, P.K., and Hackett, W.R. (eds.), Guidebook to the Geology of Central and Southern Idaho: Idaho Geological Survey, Bulletin 27, University of Idaho, Moscow, Idaho.
Lewis, R.S., Kiilsgaard, T.H., Bennet, E.H., and Hall, W.H., 1987. Lithologic and chemical characteristics of the central and southeastern part of the southern lobe of the Idaho Batholith, pp. 171–196. In: Vallier, T.L., Brooks, H.C. (eds), Geology of the Blue Mountains region of Oregon, Idaho, and Washington – the Idaho Batholith and Its Border Zone: U.S. Geological Survey Professional Paper 1436.
McNamara, J.P., Chandler, D., Seyfried, M., and Achet, S., 2005. Soil moisture states, lateral flow, and streamflow generation in a semi-arid, snowmelt-driven catchment. Hydrological Processes 19(20):4023–4038.
Moore, I.D., Gessler, P.E., Nielsen, G.A., and Peterson, G.A., 1993. Soil attribute prediction using terrain analysis. Soil Science Society of America Journal 57(2):443–452.
Moore, I.D., Grayson, R.B., and Ladson, A.R., 1991. Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrological Processes 5(1):3–30.
R Development Core Team, 2007. R: A Language and Environment for Statistical Computing, edited, R Foundation for Statistical Computing, Vienna, Austria.
Stieglitz, M., Shaman, J., McNamara, J., Engel, V., Shanley, J., and Kling, G.W., 2003. An approach to understanding hydrologic connectivity on the hillslope and the implications for nutrient transport. Global Biogeochemical Cycles 17(4):1105–1120.
Tarboton, D.G., and Baker, M.E., 2008. Towards an algebra for terrain-based flow analysis, pp. 167–194. In: Mount, N.J., Harvey, G.L., Aplin, P., and Priestnall, G., (eds.), Representing, Modeling and Visualizing the Natural Environment: Innovations in GIS 13. CRC Press, Boca Raton, FL.
Tesfa, T.K., Tarboton, D.G., Chandler, D.G., and McNamara, J.P., 2009. Modeling soil depth from topographic and land cover attributes, Water Resources Research 45:W10438, doi: 10.1029/2008WR007474.
USDA, 1997. Soil survey of the Boise Front Project, Idaho: Interim and supplemental report, Boise, Idaho.
Williams, C.J., 2005, Characterization of the spatial and temporal controls on soil moisture and streamflow generation in a semi-arid headwater catchment, Masters Thesis, Boise State University.
Williams, C.J., McNamara, J.P., and Chandler, D.G., 2008. Controls on the temporal and spatial variability of soil moisture in a mountainous landscape: the signatures of snow and complex terrain. Hydrology and Earth System Sciences 5(4):1927–1966.
Zhu, A.X., 1997. A similarity model for representing soil spatial information. Geoderma 77(2–4):217–242.
Zhu, A.X., and Band, L.E., 1994. A knowledge-based approach to data integration for soil mapping. Canadian Journal of Remote Sensing 20:408–418.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer Science+Business Media B.V.
About this chapter
Cite this chapter
Tesfa, T., Tarboton, D., Chandler, D., McNamara, J. (2010). A Generalized Additive Soil Depth Model for a Mountainous Semi-Arid Watershed Based Upon Topographic and Land Cover Attributes. In: Boettinger, J.L., Howell, D.W., Moore, A.C., Hartemink, A.E., Kienast-Brown, S. (eds) Digital Soil Mapping. Progress in Soil Science, vol 2. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-8863-5_3
Download citation
DOI: https://doi.org/10.1007/978-90-481-8863-5_3
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-8862-8
Online ISBN: 978-90-481-8863-5
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)