Advertisement

Environmental Monitoring and Assessment

, Volume 180, Issue 1–4, pp 537–556 | Cite as

WEPP and ANN models for simulating soil loss and runoff in a semi-arid Mediterranean region

  • Issa Albaradeyia
  • Azzedine Hani
  • Isam Shahrour
Article

Abstract

This paper presents the use of both the Water Erosion Prediction Project (WEPP) and the artificial neural network (ANN) for the prediction of runoff and soil loss in the central highland mountainous of the Palestinian territories. Analyses show that the soil erosion is highly dependent on both the rainfall depth and the rainfall event duration rather than on the rainfall intensity as mostly mentioned in the literature. The results obtained from the WEPP model for the soil loss and runoff disagree with the field data. The WEPP underestimates both the runoff and soil loss. Analyses conducted with the ANN agree well with the observation. In addition, the global network models developed using the data of all the land use type show a relatively unbiased estimation for both runoff and soil loss. The study showed that the ANN model could be used as a management tool for predicting runoff and soil loss.

Keywords

Land use Soil properties Runoff Water erosion Erodibility Erosivity WEPP Artificial neural network Palestine 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Supplementary material

10661_2010_1804_MOESM1_ESM.docx (103 kb)
(Docx 103 KB)

References

  1. Abu Hammad, A. (2004). Soil erosion and soil moisture conservation under old terracing system in the Palestinian Central Mountains. Ph.D. thesis, Agricultural University of Norway. Norway. Web link: http://www.umb.no/ipm/artikkel/soil-erosion-and-soil-moisture-conservation-under-old-terracing-systems-in-the-palestinian-central-mountains.
  2. Agassi, M. (Ed.) (1995). Soil erosion, conservation, and rehabilitation (402 pp.). New York: Marcel Dekker. Web link: http://www.amazon.fr/Erosion-Conservation-Rehabilitation-Menachem-Agassi/dp/0824789849#reader_0824789849.
  3. Albaradeyia, I. (2007). Modélisation de l’érosion en zone montagneuse semi-aride (144 pp.). Thèse de Doct., de l’USTL, France. Web link: https://iris.univ-lille1.fr/dspace/bitstream/1908/1080/1/50376-2007-Albaradeyia.pdf.
  4. American Society of Civil Engineers (ASCE), Task Committee on Application of Artificial Neural Networks in Hydrology (2000). Artificial neural networks in hydrology. I: Preliminary concepts. Journal of Hydrologic Engineering, 5(2), 115–123. Web link: http://scitation.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=JHYEFF000005000002000124000001&idtype=cvips&gifs=yes&ref=no.CrossRefGoogle Scholar
  5. Bhattacharya, B., & Solomatine, D. P. (2000). Application of artificial neural network in stage–discharge relationship. In Proceedings of the 4th international conference on hydro-informatics, Iowa City, IA (pp. 1–7). Web link: http://www.unesco-ihe.org/hi/sol/papers/HI2000-Stage-Discharge.pdf.
  6. Bhuyan, S. J., Kalita, P. K., Janssen, K. A., & Barnes, P. L. (2002). Soil loss predictions with three erosion simulation models. Environmental Modelling & Software, 17, 137–146. Web link: http://www.ingentaconnect.com/content/els/13648152/2002/00000017/00000002/art00046.
  7. Bowen, W., Baigorria, G., Barrera, V., Cordova, J., Muck, P., & Pastor, R. (1998). A processbased model (WEPP) for simulating soil erosion in the Andes. Natural Resource Management in the Andes. CIP Program Report, pp. 403–408. Web link: portal.acm.org/citation.cfm?id=1235886.1235986.
  8. Brazier, R. E., Beven, K. J., Freer, J., & Rowan, J. S. (2000). Equifinality and uncertainty in physically-based soil erosion models: Application of the GLUE methodology to WEPP, the Water Erosion Prediction Project—For sites in the U.K. and U.S.A. Earth Surface Processes and Landforms, 25, 825–845. Web link: http://onlinelibrary.wiley.com/doi/10.1002/1096-9837(200008)25:8%3C825::AID-ESP101%3E3.0.CO;2-3/abstract.CrossRefGoogle Scholar
  9. Bujan, A., Santanatoglia, O. J., Chagas, C., Massobrio, M., Castiglioni, M., Yanez, M. S., et al. (2000). Preliminary study on the use of the 137Cs method for soil erosion investigation in the Pampean region of Argentina. Acta Geologica Hispanica, 35(3–4), 271–277. Web link: http://www.iaea.org/programmes/.nafa/d1/crp/Bujan.pdf.Google Scholar
  10. Chitrakar, S. (2004). Testing of the WEPP soil erosion model in the west Usambara mountains of Tanzania. MSc. thesis, Erosion and Soil & Water Conservation Group, Wageningen Universiteit, The Netherlands. Web link: edepot.wur.nl/121650.
  11. De la Rosa, D., Moreno, J. A., Mayol, F., & Bonsón, T. (2000). Assessment of soil erosion vulnerability in Western Europe and potential impact on crop productivity due to loss of soil depth using the ImpelERO model. Africulture, Ecosystems & Environment, 81, 179–190. Web link: http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6T3Y-417WD2G-3&_user=10&_coverDate=11%2F30%2F2000&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_searchStrId=1535932189&_rerunOrigin=google&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=fb63d1d67cb98e2ba70e7c8046b88b24&searchtype=a.CrossRefGoogle Scholar
  12. Dudeen, B. (2001). Land degradation in Palestine. Jerusalem: Land Research Center.Google Scholar
  13. Eila, S. M. (2005). Sustainability: An effective approach for land use management-application to Gaza City. Ph.D. thesis, Lille Laboratory of Mechanics, University of Lille for Science and Technology.Google Scholar
  14. Elliot, W. J., Hall, D. E., & Scheele, D. L. (2000). WEPP interface for disturbed forest and range runoff, erosion, and sediment delivery: Technical documentation. USDA Forest Service Rocky Mountain Research Station and San Dimas Technology and Development Center.Google Scholar
  15. Flanagan, D. C., & Frankenberger, J. R. (2002). Water Erosion Prediction Project (WEPP) Windows Interface Tutorial USDA-Agricultural Research Service & Purdue University National Soil Erosion Research Laboratory, West Lafayette, Indiana, USA.Google Scholar
  16. Gérard-Marchant, P., Walter, M. T., & Steenhuis, T. S. (2005). Simple models for phosphorus loss from manure in runoff. Journal of Environmental Quality, 34(3), 872–876.CrossRefGoogle Scholar
  17. Grønsten, H. A., & Lundekvam, H. (2006). Prediction of surface runoff and soil loss in southeastern Norway using the WEPP Hillslope model. Soil & Tillage Research, 85, 186–199.CrossRefGoogle Scholar
  18. Harris, T. M., & Boardman, J. (1998). Alternative approaches to soil erosion prediction and conservation using expert systems and artificial neural networks. In J. Boardman, & D. T. Favis-Mortlock (Eds.), Modelling soil erosion by water, NATO-ASI Series I-55 (pp. 461–478). Berlin: Springer.Google Scholar
  19. Hassan, A. E. (2001). Prediction of plume migration in heterogeneous media using artificial neural networks. Water Resources Research, 37(3), 605–623. Web link: http://www.agu.org/journals/ABS/2001/2000WR900279.shtml.CrossRefGoogle Scholar
  20. Hudson, N. (1995). Soil conservation. Fully revised and updated (3rd Edn., pp. 93–153). London: BT Bastford Ltd.Google Scholar
  21. Kothyari, U. C., Tiwari, A. K., & Singh, R. (1993). Prediction of sediment yield. Journal of Irrigation and Drainage Engineering, 120(6), 1122–1131.CrossRefGoogle Scholar
  22. Laflen, J. M., & Roose, E. J. (1997). Methodologies for assessment of soil degradation due to water erosion. In R. Lal, W. H. Blum, C. Valentine, & B. A. Stewart (Eds.), Methods for assessment of soil degradation. New York: CRC. Web link: books.google.fr/books?isbn=9048126657....Google Scholar
  23. Licznar, P., & Nearing, M. A. (2003). Artificial neural networks of soil erosion and runoff prediction at the plot scale. Catena, 51, 89–114. Web link: http://www.ejpau.media.pl/articles/volume8/issue1/art-04.html.CrossRefGoogle Scholar
  24. Maier, H. R., & Dandy, G. C. (2000). Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications. Environmental Modelling and Software, 15, 101–123. Web link: www.gpa.etsmtl.ca/cours/sys843/pdf/Maier2000.pdf.CrossRefGoogle Scholar
  25. Mainguet, M. (1994). Desertification: Natural background and human mismanagement. Berlin: Springer. Web link: www.futura-sciences.com/.../mainguet_120/.Google Scholar
  26. Merrit, W. S., Letcher, R. A., & Jakeman, A. J. (2003). A review of erosion and sediment transport models. Environmental Modelling & Software, 18, 761–799. Web link: http://www.ingentaconnect.com/content/els/13648152/2003/00000018/00000008/art00078;jsessionid=w0mf2inqawb1.alexandra.CrossRefGoogle Scholar
  27. Ministry of Agriculture (2004a). A strategy for sustainable agriculture in Palestine. Ramallah, Palestine.Google Scholar
  28. Ministry of Agriculture (2004b). Rainfall data base. Unpublished data.Google Scholar
  29. Najjar, Y. (1999). Quick manual for the use of ANN program TR-SEQ1. Department of Civil Engineering, Kansas State University, Manhattan, Kansas, USA. Web link: www.ib.pwr.wroc.pl/zmg/sgem/n12-05/art18_no12_2005.pdf.
  30. Najjar, Y., & Zhang, X. (2000). Characterizing the 3D stress-strain behavior of sandy soils: A neural–mechanistic approach. In Geotechnical special publication no. 96 (pp. 43–75). U.S.A.: American Society of Civil Engineers. Web link: article.pubs.nrc-cnrc.gc.ca/ppv/RPViewDoc?issn=1208–6010...6....Google Scholar
  31. Nearing, M. A., Foster, G. R., Lane, L. J., & Finkner, S. C. (1989). A process-based soil erosion model for USDA-water erosion prediction project technology. Transactions of the ASAE, 32, 1587–1593. Web link: www.tucson.ars.ag.gov/unit/publications/PDFfiles/846.pdf.Google Scholar
  32. Pachepsky, Y. A., Timlin, D., & Varallyay, G. (1996). Artificial neural networks to estimate soil water retention from easily measurable data. Soil Science Society of America Journal, 55, 938–943.Google Scholar
  33. Palestinian Environmental Authority (PEnA) (1999). National biodiversity strategy and action plan for Palestine. Hebron. Palestine. Web link: www.cicred.org/pripode/IMG/pdf_PL8-InitialProject.pdf.
  34. Risse, L. M., Nearing, M. A., & Savabi, M. R. (1994). Determining the Green–Ampt effective hydraulic conductivity from rainfall–runoff data for the WEPP model. Transactions of the ASAE, 37, 411–418. Web link: http://etmd.nal.usda.gov/bitstream/10113/6591/1/IND20412610.pdf.Google Scholar
  35. Romero León, C. C. (2005). A multi-scale approach for erosion assessment in the Andes. Doctoral thesis, Wageningen University. Web link: edepot.wur.nl/121650.
  36. Schneiderman, E. M., Pierson, D. C., Lounsbury, D. G., & Zion, M. S. (2002). Modeling the hydrochemistry of the Cannonsville watershed with generalized watershed loading functions (GWLF). Journal of the American Water Resources Association, 38(5), 1323–1347. Web link: www.ecs.umass.edu/.../Schneiderman%20etal%202002%20Cannonsville%20watershed.pdf.CrossRefGoogle Scholar
  37. Simonato, T., Bischetti, G. B., & Crosta, G. B. (2002). Evaluating soil erosion with RUSLE and WEPP in an alpine environment (Dorena Valley–Central Alps, Italy), Sustain. Land Management—Environmental Protection, 35, 481–494. Web link: www.hydrol-earth-syst-sci.net/.../hess-14–675–2010.xml..Google Scholar
  38. Soil and Water Conservation Society (1994). Soil erosion research methods. Ankeny, Iowa: St. Lucie Press. Web link: www.amazon.co.uk/Erosion-Research-Methods-Conservation-Society/.../1884015093.
  39. Starrett, S. K., Najjar, Y. M., & Hill, J. C. (1996). Neural networks predict pesticide leaching. In Proc., North American water and environmental conf. (pp. 1693–1698). New York: ASCE.Google Scholar
  40. Wischmeier, W. H., & Smith, D. D. (1978). Predicting rainfall erosion losses: A guide to conservation planning. United States Department of Agriculture, Agriculture Handbook 537, United States Government Printing Office, Washington DC. Web link: www.inrs-ete.uquebec.ca/activites/modeles/.../simulation.htm.
  41. Yu, B., & Rosewell, C. J. (2001). Evaluation of WEPP for runoff and soil loss prediction at Gunnedah, NSW, Australia. Australian Journal of Soil Research, 39, 1131–1145. Web link: http://www.accessmylibrary.com/article-1G1–79547399/evaluation-wepp-runoff-and.html.CrossRefGoogle Scholar
  42. Zhang, X. C., Nearing, M. A., Risse, L. M., & McGregor, K. C. (1996). Evaluation of WEPP runoff and soil loss predictions using natural runoff plot data. Transactions of the ASAE, 39(3), 855–863. Web link: http://etmd.nal.usda.gov/bitstream/10113/6600/1/IND20539635.pdf.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Issa Albaradeyia
    • 1
  • Azzedine Hani
    • 1
    • 2
  • Isam Shahrour
    • 1
  1. 1.Laboratoire de Génie Civil et géo-Environnement (LGCgE)Université des Sciences et Technologies de LilleVilleneuve d’AscqFrance
  2. 2.Laboratoire de GéologieUniversité Badji Mokhtar AnnabaAnnabaAlgérie

Personalised recommendations