An improved RUSLE/SDR model for the evaluation of soil erosion

Abstract

The accurate assessment of soil erosion is key to assess environmental parameters, such as the reduction in soil fertility, the increase in flood risk, the loss of nutrients, and degradation of water quality. In this study, we developed a methodology using the Revised Universal Soil Loss Equation (RUSLE) with the sediment delivery ratio (SDR) to estimate the annual amount of soil erosion and sediment yield in the Nozhian watershed (western Iran). The weighted total least-squares (WTLS) algorithm was applied to generate the rainfall–runoff erosivity surface using rainfall data and a digital elevation model (DEM) instead of traditional interpolation methods. The results demonstrated that the obtained sediment yield by the RUSLE/SDR model was approximately 802,000 tons per year. More than half of the watershed (61.6%) belonged to the high and severe erosion classes (20–100 t/ha year), and the mean soil erosion rate in the study area was 89.32 t/ha year. Several landslides extracted using a Google Earth map by expert interpretation were exactly consistent with areas that had high erosion rates based on the RUSLE results. This compatibility implies the compatibility between the results and reality. According to the statistical analysis, topographic features, especially slope steepness, had the greatest effect on the rate of soil erosion in the region. The results of our RUSLE/SDR analysis were also compared with the reported results from the Modify Pacific Southwest Interagency Committee (MPSIAC), Erosion Potential method (EPM), and Hydrophysical model. In situ data from the measured annual sediment yield during a 40-year interval from a hydrometric station were used for the accuracy analysis. The comparison indicated improvement in the accuracy of our approach by up to 65% in comparison to other reported results. These results can surely aid in the implementation of soil management and conservation practices to reduce soil erosion in the Nozhian watershed.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

References

  1. Amsalu T, Mengaw A (2014) GIS based soil loss estimation using rusle model: the case of jabi tehinan woreda, ANRS, Ethiopia. Nat Resour 5(11):616

    Google Scholar 

  2. Arnold JG, Srinivasan R, Muttiah RS, Williams JR (1998) Large area hydrologic modeling and assessment. Part I. Model development. J Am Water Resour Assoc 34:73–89

    Article  Google Scholar 

  3. Beasley DB, Huggins LF, Monke EJ (1980) ANSWERS: a model for watershed planning. Trans ASAE 23:938–944

    Article  Google Scholar 

  4. Bhandari KP, Aryal J, Darnsawasdi R (2015) A geospatial approach to assessing soil erosion in a watershed by integrating socio-economic determinants and the RUSLE model. Nat Hazards 75(1):321–342

    Article  Google Scholar 

  5. Bizuwerk A, Taddese G, Getahun Y (2008) Application of GIS for Modeling Soil Loss Rate in Awash Basin, Ethiopia, International Livestock Research Institute (ILRI)

  6. Cooper K (2011) Evaluation of the relationship between the RUSLE R-Factor and mean annual precipitation. Colo State 1:37

    Google Scholar 

  7. Davari M, Bahrami HA, Ghoddoosi J, Tahmasbipoor N (2005) A comparison between MPSIAC, hydrophysical and EPM models in estimating erosion and sediment load using GIS, (Nozhian Watershed, A Case Study). Iran J Soil Water Sci 19(1):61–76

    Google Scholar 

  8. De Rosa P, Cencetti C, Fredduzzi A (2016) A GRASS tool for the Sediment delivery ratio mapping. PeerJ Preprints 4:e2227v1

    Google Scholar 

  9. Emadodin I, Narita D, Bork HR (2012) Soil degradation and agricultural sustainability: an overview from Iran. Environ Dev Sustain 14(5):611–625

    Article  Google Scholar 

  10. Ganasri BP, Ramesh H (2016) Assessment of soil erosion by RUSLE model using remote sensing and GIS—a case study of Nethravathi Basin. Geosci Front 7(6):953–961

    Article  Google Scholar 

  11. Gelagay HS (2016) RUSLE and SDR Model Based Sediment Yield Assessment in a GIS and Remote Sensing Environment; A Case Study of Koga Watershed, Upper Blue Nile Basin, Ethiopia. Hydrol Current Res 7(239):2

    Google Scholar 

  12. Gołąb J, Urban K (2017) Potential erosion of the areas deforested for ski slopes-an example of mount Jaworzyna Krynicka. Infrastruktura i Ekologia Terenów Wiejskich

  13. Goovaerts P (1999) Using elevation to aid the geostatistical mapping of rainfall erosivity. Catena 34(3–4):227–242

    Article  Google Scholar 

  14. Grauso S, Verrubbi V, Zini A, Peloso A, Crovato C, Sciortino M (2015) Soil Erosion Estimate in Southern Latium (Central Italy) Using RUSLE and Geostatistical Techniques

  15. Hernando D, Romana MG (2015) Estimating the rainfall erosivity factor from monthly precipitation data in the Madrid Region (Spain). J Hydrol Hydromech 63(1):55–62

    Article  Google Scholar 

  16. Jensen JR (2000) Remote Sensing of the Environment: An Earth Resource Perspective‖. Prent ice Hall, New Jersey

    Google Scholar 

  17. Jobin T, Sabu J, Thrivikramji KP (2018) Assessment of soil erosion in a monsoon-dominated mountain river basin in India using RUSLE-SDR and AHP. Hydrological Sciences Journal (just-accepted)

  18. Kamuju N (2016) Soil erosion and sediment yield analysis using prototype and enhanced SATEEC GIS System Models. Int J Adv Remote Sens GIS 1471

  19. Karaburun A (2010) Estimation of C factor for soil erosion modeling using NDVI in Buyukcekmece watershed. Ozean J Appl Sci 3(1):77–85

    Google Scholar 

  20. Knisel WG (1980) CREAMS: a fieldscale model for chemical, runoff, and erosion from agricultural management systems. USDA, Science and Education Administration, Conservation Report No. 26, Washington, DC

    Google Scholar 

  21. Koloa C, Samanta S (2013) Development impact assessment along Merkham River through remote sensing and GIS technology. Int J Asian Acad Res Assoc 5(1):26–41

    Google Scholar 

  22. Kouli M, Soupios P, Vallianatos F (2009) Soil erosion prediction using the revised universal soil loss equation (RUSLE) in a GIS framework, Chania, Northwestern Crete, Greece. Environ Geol 57(3):483–497

    Article  Google Scholar 

  23. Kushwaha NL, Yousuf A (2017) Soil erosion risk mapping of watersheds using RUSLE, remote sensing and GIS: A review. Res J Agric Sci 8(2):269–277

    Google Scholar 

  24. Laflen JM, Lane LJ, Foster GR (1991) WEPP: A new generation of erosion prediction technology. J Soil Water Conserv 46:34–38

    Google Scholar 

  25. Ma L, Chi X, Zuo C (2012) Evaluation of interpolation models for rainfall erosivity on a large scale. In: IEEE, 2012 First International Conference on Agro-Geoinformatics (Agro-Geoinformatics), pp. 1–5

  26. Mahboub V (2012) On weighted total least-squares for geodetic transformations. J Geodesy 86(5):359–367

    Article  Google Scholar 

  27. Mahboub V, Sharifi MA (2013a) On weighted total least-squares with linear and quadratic constraints. J Geodesy 87(3):279–286

    Article  Google Scholar 

  28. Mahboub V, Sharifi MA (2013b) Erratum to: On weighted total least squares with linear and quadratic constraints. J Geodesy 87:607–608

    Article  Google Scholar 

  29. Mahboub V, Ardalan AA, Ebrahimzadeh S (2015) Adjustment of non-typical errors-in-variables models. Acta Geod Geoph 50(2):207–218

    Article  Google Scholar 

  30. Maner SB (1962) Factors influencing sediment delivery ratios in the Blackland Prairie land resource area. US Dept. of Agriculture, Soil Conservation Service, Fort Worth, Texas

    Google Scholar 

  31. Markose VJ, Jayappa KS (2016) Soil loss estimation and prioritization of sub-watersheds of Kali River basin. Karnataka, India, using RUSLE and GIS. Environ Monit Assess 188(4):225

    Article  Google Scholar 

  32. Morgan RPC, Quinton JN, Smith RE, Govers G, Poesen JWA, Auerswald K, Chisci G, Torri D, Styczen ME (1998) The European Soil Erosion Model (EUROSEM): a dynamic approach for predicting sediment transport from fields and small catchments. Earth Surf Process Landf 23:527–544

    Article  Google Scholar 

  33. Mullingan M, Wainwright J (2004) Modelling and model building. In: Wainwright J, Mullingan M. Environmental modeling finding simplicity in complexity. Environmental Monitoring and Modeling Research Group Department of Geography. John Wiley and Sons, Ltd., London pp 7–68

    Google Scholar 

  34. Nearing MA, Yin SQ, Borrelli P, Polyakov VO (2017) Rainfall erosivity: An historical review. Catena 157:357–362

    Article  Google Scholar 

  35. Ochoa-Cueva P, Fries A, Montesinos P, Rodríguez-Díaz JA, Boll J (2015) Spatial estimation of soil erosion risk by land-cover change in the Andes of southern Ecuador. Land Degr Dev 26(6):565–573

    Article  Google Scholar 

  36. Ouyang D, Bartholic J (1997) Predicting sediment delivery ratio in Saginaw Bay watershed. In: Proceedings of the 22nd National Association of Environmental Professionals Conference, pp 659–671

  37. Poirier C, Poitevin C, Chaumillon E (2016) Comparison of estuarine sediment record with modelled rates of sediment supply from a western European catchment since 1500. CR Geosci 348(7):479–488

    Article  Google Scholar 

  38. Prasannakumar V, Vijith H, Geetha N, Shiny R (2011) Regional scale erosion assessment of a sub-tropical highland segment in the Western Ghats of Kerala, South India. Water Resour Manag 25(14):3715

    Article  Google Scholar 

  39. Qin W, Guo Q, Zuo C, Shan Z, Ma L, Sun G (2016) Spatial distribution and temporal trends of rainfall erosivity in mainland China for 1951–2010. Catena 147:177–186

    Article  Google Scholar 

  40. Rawat KS, Mishra AK, Bhattacharyya R (2016) Soil erosion risk assessment and spatial mapping using LANDSAT-7 ETM+, RUSLE, and GIS—a case study. Arab J Geosci 9(4):288

    Article  Google Scholar 

  41. Renard KG (1997) Predicting soil erosion by water: a guide to conservation planning with the revised universal soil loss equation (RUSLE)

  42. Renard KG, Foster GR, Weesies GA, McCool DK, Yoder DC (1997) P)redicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE). Handbook #703. US Department of Agriculture, Agriculture Handbook 703:404

  43. Renfro GW (1975) Use of erosion equations and sediment delivery ratios for predicting sediment yield. Present and Prospective Technology for Predicting Sediment Yields and Sources, US Department of Agriculture Publication ARS-S-40:33–45

  44. Shoshany M, Goldshleger N, Chudnovsky A (2013) Monitoring of agricultural soil degradation by remote-sensing methods: a review. Int J Remote Sens 34(17):6152–6181

    Article  Google Scholar 

  45. Subarna D, Purwanto MYJ, Murtilaksono K (2014) The relationship between monthly rainfall and Elevation in the Cisangkuy watershed Bandung Regency. Int J Latest Res Sci Technol 3(2):55–60

    Google Scholar 

  46. Tamene L, Le QB (2015) Estimating soil erosion in sub-Saharan Africa based on landscape similarity mapping and using the revised universal soil loss equation (RUSLE). Nutr Cycl Agroecosyst 102(1):17–31

  47. Tang Q, Xu Y, Bennett SJ, Li Y (2015) Assessment of soil erosion using RUSLE and GIS: a case study of the Yangou watershed in the Loess Plateau, China. Environ Earth Sci 73(4):1715–1724

    Article  Google Scholar 

  48. Tiwari AK, Risse LM, Nearing MA (2000) Evaluation of WEPP and its comparison with USLE and RUSLE. Trans ASAE 43(5):1129

    Article  Google Scholar 

  49. USDA (1972) National Engineering Handbook. Soil Conservation Service. US Department of Agriculture, Washington, DC, Sect. 3

    Google Scholar 

  50. USDA (2002) NRCS: State Oce of Michigan, Technical Guide to RUSLE Use in Michigan

  51. USDA SCS (1979) United States Department of Agriculture - Soil Conservation Service. National Engineering Handbook, Sec. 4. Hydrology

  52. Van der Knijff JM, Jones RJA, Montanarella L (2000) Soil erosion risk assessment in Europe. EUR 19044 EN. Office for Official Publications of the European Communities, Luxembourg, p 34

    Google Scholar 

  53. Vanoni VA (1975) Sediment deposition engineering. ASCE Manuals and Reports on Engineering Practices p 54

  54. Vrieling A (2006) Satellite remote sensing for water erosion assessment: a review. Catena 65(1):2–18

    Article  Google Scholar 

  55. Wischmeier WH, Smith DD (1978) Predicting rainfall erosion losses, a guide to conservation planning. USDA Handb. 537. U.S. Gov. Print. Off., Washington DC

    Google Scholar 

  56. Yan R, Zhang X, Yan S, Chen H (2018) Estimating soil erosion response to land use/cover change in a catchment of the Loess Plateau, China. International Soil and Water Conservation Research

  57. Yin SQ, Xie Y, Liu B, Nearing MA (2015) Rainfall erosivity estimation based on rainfall data collected over a range of temporal resolutions. Hydrol Earth Syst Sci 19(10):4113

    Article  Google Scholar 

  58. Young RA, Onstad CA, Bosch DD (1989) AGNPS: a nonpoint-source pollution model for evaluating agricultural watersheds. J Soil Water Conserv 44:168–173

    Google Scholar 

  59. ZHU H, JIA S (2004) Uncertainty in the spatial interpolation of rainfall data [J]. Prog Geogr 2:004

    Google Scholar 

Download references

Acknowledgements

We would like to thank Dr. Sanaz from Leibniz University Hannover (LUH) for her valuable guidance and assistance in this research. In addition, we would like to thank Mr. Abdolreza Nooryazdan, the expert from the Watershed Management Department of Lorestan Province, for providing some of the data used in this work and his constructive comments.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Somayeh Ebrahimzadeh.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ebrahimzadeh, S., Motagh, M., Mahboub, V. et al. An improved RUSLE/SDR model for the evaluation of soil erosion. Environ Earth Sci 77, 454 (2018). https://doi.org/10.1007/s12665-018-7635-8

Download citation

Keywords

  • Soil erosion
  • Sediment yield
  • RUSLE
  • Sediment delivery ratio (SDR)
  • Nozhian watershed
  • Remote sensing
  • WTLS