The Use of Erosion Proxies for the Spatial Assessment of Erosion in a Watershed and Modelling the Erosion Risk in a GIS

  • P.F. Okoth
  • P.A. Oketch
  • P.K. Kimani
Conference paper

Abstract

This paper presents a new approach to soil erosion risk assessment and modelling. The approach recognizes that erosion risk is linked to biophysical landscape features, societal activities and spatio-temporal attributes of the landscape processes. The assessment involves making measurements of soil erosion in the field and linking the erosion features to some selected landscape elements that act as either drivers or disruptors of erosion. These drivers or barriers of erosion are referred to in this paper as erosion proxies. Examples of the erosion proxies in a watershed include drainage ditches, field boundaries, footpaths, animal tracks and other man made elongate features that cause water concentration and flow in the watershed. These are referred to in this paper as erosion drivers. Barrier proxies or disruptors of erosion include: hedges, closed fences, grassed field boundaries, trashed field boundaries, bunded field boundaries, barrier ditches, constructed dykes or built earth dams. Erosion risk is on the other hand defined as the potential for the occurrence of soil erosion due to the presence or absence of the proxies. The watershed has a high or low risk of erosion depending on the intensity of occurrence of erosion proxies. The approach views the watershed more from the principles of energy and matter flows in them rather than by assessing the individual factors in a deterministic erosion model such as the Universal Soil Loss Equation (USLE) (Wischmeir and Smith, 1978). The net risk of erosion in the watershed, or sub-watershed is modelled by summing up the total length of the erosion drivers and the total length of the disruptors in a GIS database. The ratio between the sum of the drivers, and the total sum of the drivers and the disruptors provides an indication of whether the watershed is at high or low risk.

The method uses the spatial characteristics of the erosion proxies to extract them from the broader landscape mosaic through visual interpretation of aerial photographs or satellite images. The results of the interpretations are afterwards digitized into a geographic information system (GIS) database and processed to paper maps. The produced maps are then carried to the field to characterize and link the proxies to the presence or absence of soil erosion features. In the field, the occurrence of different soil erosion features on each erosion proxy is measured and recorded.

The influence of the individual erosion proxy is obtained by using the analysis of variance (ANOVA) and the F test. The statistical analysis forms the basis for ranking erosion proxies into different risk categories and for selecting the best mitigation options.

The method is tested on an agricultural test area at the National Agricultural Research Laboratories (NARL) in Nairobi. The results indicate that grassed field boundaries (an example of an erosion proxy) form good management practice for conserving the NARL sub-watershed. Other proxies for use against the risk of erosion include constructed earth and stone bunds along the field boundaries, trash and stover cover along the field boundaries or any other method that disrupts water flow in channels on the watershed or sub-watershed. Due to this method of assessment, field plots and large portions of the test area at the National Agricultural Research Laboratories compound is now well conserved and suffers a minimum risk of soil erosion by water.

Keywords

Erosion proxies Spatial assessment Spatial modelling Water watershed Soil water erosion GIS Environmental conservation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bergsma E. 1970. Aerial photo-interpretation for soil erosion and conservation surveys. Part I: Soil Erosion features. ITC Lecture Notes SOL 13 Revision April 1980.Google Scholar
  2. Clark R. 1980. Revision condition classification system. US Department of the Interior Bureau of Land Management. Denver Service Center, Denver, Colorado.Google Scholar
  3. Edwards K.A. 1979. Regio-l contrasts in rates of Soil erosion and their significance with respect to agricultural development in Kenya. In: Lal, R and Greenland, D.J. (eds). Soil physical and crop production in the willey and Sons, Cluchester.Google Scholar
  4. FAO–UNESCO 1997. Soil map of the world. Revised Legends with corrections and updates. Reprint of World soil Resources Report 60. Food and Agriculture Organization of the United Nations, Rome, 1988. Reprinted with corrections 1990.Google Scholar
  5. Foster G.R. and Meyer L.D. 1975. Mathematical Simulation of upland erosion by fundamental erosion mechanics. In ANONYMOUS (1977), 190–207.Google Scholar
  6. Foster G.R., Jane L.J., Nowlin J.D., Laflen J.M. and Young R.A. 1980. A model to estimate sediment yield from field-size areas: Development of a model. International Institute of applied systems analysis, A-2361 Laxenburg, Austria. P40.Google Scholar
  7. Foster G.R. 1988. Modelling soil erosion and sediment yield. In Soil Erosion Research Methods LalR. (ed) Published by Soil and Water Conservation Society, Ankeny, Iowa, USA.Google Scholar
  8. Martínez-Casasnovas J.A. and Stuiver H.J. 1998. Automated delineation of drainage networks and elementary watersheds from digital elevation models. ITC Journal, Volume 3/4 pp 198–208.Google Scholar
  9. Molenaar M. 1989. Single valued vector maps. A concept in GIS. Geo-information Systems, 2(1), pp. 18–26.Google Scholar
  10. Morgan R.P.C., Rickson R.J., McIntyne K., Brewer T.T. and Altshul H. J. 1997. Soil erosion survey of the central part of the Swaziland. Soil Technology Jur-l 11: 263–289.CrossRefGoogle Scholar
  11. Musgrave G.W. 1947. The quantative evaluation of factors in water erosion, a first approximation. J. Soil Water Cons. 2, 3: 133-138.Google Scholar
  12. Nill D., Schwertmann U., Sabel-Koschella U., Ber-rd M. and Brewer J. 1996. Soil erosion by water in Africa. Principles, predication and Protection. GTZ GmbH Number, 257. 292pp.Google Scholar
  13. Okoth et al. 1999. Consequences of field management and soil erosion on the sustainability of large-scale coffee farming in Kiambu. Soil Science Society of East Africa Proceedings, Uganda. 17^th Conference, pp. 331–339.Google Scholar
  14. Ongwenyi G.S.O 1978. Erosion and sediment transport in the upper Ta- watershed with special reference to the Thiba Basin. Ph.D. thesis, University of Nairobi, 1978.Google Scholar
  15. Renard K.G., Foster G.R., Weesies G.A. and. Porter J.P. 1991. RUSLE, Revised Universal soil Loss Equation. Jour-l of soil and water conservation, 41(1): 30–33.Google Scholar
  16. Robinson A.R. 1979. Sediment yield as a function of upstream erosion. In: Peterson A.E. and Swan J.B. (ed.). Universal soil loss equation: Past, present and future. SSSA special publication no. 8, Soil Science Society of America, Madison Wisconsin. 53 p.Google Scholar
  17. Saggerson E.P. 1971. Geological map of the Nairobi area. (To accompany Geological Report No. 98). Geological survey of Kenya.Google Scholar
  18. Siderius W. 1976. Environment and the characteristics of the Nitisols at (Kabete, Nairobi). Miscellaneous Soil Paper No. 10. Ministry of Agriculture – National Agricultural Laboratories-Kenya Soil Survey.Google Scholar
  19. Smith D.D. 1941. Interpretation of soil conservation data for field use. Agr. Eng., 22: 173–175.Google Scholar
  20. Stocking M. 1987. Measuring land degradation. In: Blaikie P. and Brookfield H. (Eds). Land degradation and society. Published by Methuen & Company Ltd., London.Google Scholar
  21. USDA –Soil Conservation Service 1975. Soil Taxonomy. A system for soil classification. Soil survey staff. United States Department of Agriculture. SMSS Technical Monograph No. 19.Google Scholar
  22. Williams J.R. 1975. Sediment Routing for small scale agricultural watersheds. Water Research Bulletin 11 (5).Google Scholar
  23. Wischmeier W.H. and Smith D.D. 1978. Predicting rainfall erosion losses. Agricultural handbook No 537, USDA-science and education administration, pp.58.Google Scholar
  24. Zingg A.W. 1940. Degree and length of land slope as it affects soil loss in runoff. Agric. Eng., 21, 2: 59–64.Google Scholar

Copyright information

© Springer 2007

Authors and Affiliations

  • P.F. Okoth
    • 1
  • P.A. Oketch
    • 2
  • P.K. Kimani
    • 2
  1. 1.Tropical Soil Biology and Fertility (TSBF)Institute of CIAT, c/o The World Agroforestry Centre (ICRAF)NairobiKenya
  2. 2.Kenya Soil Survey, National Agricultural Research Laboratories, Kenya Agricultural Research InstituteNairobiKenya

Personalised recommendations