Assessment Soil Erosion and Deposition in the Menderjan Watershed Using USPED and RUSLE Models in the Environment of Geographical Information System (GIS)

Authors

10.22052/6.17.43

Abstract

Introduction
Water erosion is one of the most important factors in land degradation in large parts of Iran destroying fertile soils and agricultural land. The impact of soil erosion and related sediments decreases significantly water quality and reservoir capacity. Especially in semiarid areas like in the Menderjan watershed in the west Isfahan province sheet and rill erosion contributes to the sediment dynamics in a significant way. Particularly, sheet and rill erosion processes and related forms and features are very common in this region. Hence, this study is aimed at identifying and quantifying the major erosion process dynamics. Therefore, we applied an integrated approach combining the USPED and RUSLE models with data mining, remote sensing, and GIS methods.
Materials and methods
The study area is Menderjan watershed locating at east Isfahan and has an area of about 21,100 hectares. In this study, the USPED and RUSLE was used.  The USPED model is based on the assumption that soil erosion depends on the detachment capacity and the sediment transport capacity of surface runoff. However, the USPED models do not consider the sediment yields from gullies, stream banks, and stream bed erosion. In the USPED model erosion and deposition (ED) are computed as the change in sediment flow in the direction of flow.
ED=d×(T×cosa)/ dx +  d×(T×sina) /dy
Where a is the aspect of the terrain surface, dx, dy is the grid resolution, and T is the sediment flow at transport capacity. ED can be positive, indicating soil deposition, or negative, indicating soil erosion. Transport capacity (A) is expressed as:
A=R×K×C×P×L m×(sin S) n
Where A: Average yearly soil erosion (t ha-1 y-1), R: yearly rainfall erosivity factor (MJ mm ha-1 year-1h-1), K: the soil edibility factor, LS: Slope Length and Steepness factor, C: cover management factor and P:  Support Practice Factor, S is the slope, L is the upslope contributing area, and m and n are constants. For prevailing rill erosion m = 1.6, n = 1.3, while for prevailing sheet erosion, m = n = 1. The USPED and RUSLE model was applied using Arcmap10. In this study, the Landsat satellite images 8 (OLI) and rainfall data, soil properties and digital elevation model (DEM) were used, and GIS plays a major role in preparing thematic layers and estimating soil erosion.
Result
The range of obtained R factor values range was from 82 to 118 MJ mm/ ha h year. The average values for the Menderjan watershed amount to 265.2 MJ mm/ ha year-1. According to the soil laboratory analysis soil texture is dominated by silt clay loam and clay loam and thus is highly susceptible to soil erosion. The amount of organic matter in all samples was 2 %. Soil organic matter reduces the erodibility of soil. In many arid and semiarid areas soil organic matter is low due to scarce vegetation, and hence, soil is more susceptible to erosion. The annual average soil erodibility of this basin 0.04 (t h MJ-1 mm-1). The amount of topography for RUSLE varies from 0.001 to 16.7 and USPED varies 0.01 to 30 Support Practice Factor of the study area is variable from 0.1 to 1 and C factor value varies was 0.2 to 0.5. According to result more than 35 % of the area is affected by high to very high erosion and deposition process intensities. The stable areas and low erosion and deposition zones cover about 15 % of the area. However, some of the mapped and predicted sheet and rill processes are located in the stable and low intensity soil erosion classes. The extreme values are characterized by steep slopes in ridge positions in the northern and southern parts of the watershed.
Discussion and Conclusions
During recent years, the role of water erosion as one of the land degradation factors in arid and semi-arid areas of large parts of Iran has increased. In our study we applied a combined approach using the RUSLE and USPED models rill/inter-rill (sheet) erosion processes, and deposition processes. To the knowledge of the authors, this is the first attempt integrating different erosion processes and deposition dynamics in Iran. In the study area soil loss is concentrated especially in the abandoned bare land areas. The protection of bare soil to reduce soil loss should be ensured by appropriate cultivations. According to the results a large part of severe erosion occurs in the steep areas in the north and southwest of the study area. Agricultural cultivations may change the land cover, leading to poorer vegetation cover or bare land, especially after harvest and thus increase erosion processes and land degradation. Thus, control of soil erosion targeted to the area not only reduces direct costs of soil erosion; however, it also it diminishes the implementation costs of control operations for decreasing soil erosion.
Keywords: Soil Erosion, Modeling, Deposition, Remote Sensing (RS), Esfahan
 

Keywords


Ahmadi, H., 2006. Applied Geomorphology: Volume1 water erotion. University of Tehran Press. 2. Aiello, A., Adamo, M., Canora, F., 2014. Modelling Spatially–Distributed Soil Erosion through Remotely–Sensed Data and GIS, International Conference on Computational Science and Its Applications. ICCSA 2014, pp. 372-385. 3. Arnold, J. G., Srinivasan, R., Muttiah, R. S., Williams, J. R., 1988. Large area hydrologic modeling and assessment part I: Model development1. Wiley Online Library. 4. Asadi, H., Honarmand, M., Vazifedoust, M., Moussavi, A., 2017. Assessment of Changes in Soil Erosion Risk Using RUSLE in Navrood Watershed, Iran. Journal of Agricultural Science and Technology. 19(1): 231-244. 5. Babaei, M., Hosseini, S. Z., Nazari Samani, A., Almodaresi, S. A. , 2016. Assessment of soil erosion using RUSLE 3D, case study: Kan-Soleghan Watershed. Watershed Engineering and Management. 8(2): 165-181. 6. Damian, G., Năsui, D., Damian, F., Ciurte, D., 2014. Erosion Assessment Modeling Using the Sateec Gis Model on the Prislop Catchment. Present Environment and Sustainable Development. 8(1): 217-224. 7. Fathizad, H., Karimi, H., Alibakhshi, S. M., 2014. The estimation of erosion and sediment by using the RUSLE model and RS and GIS techniques (Case study: Arid and semi-arid regions of Doviraj, Ilam province, Iran). International Journal of Agriculture and Crop Sciences. 7(6): 304-314. 8. Flanagan, D., Nearing, M., 1995. USDA-Water Erosion Prediction Project: Hillslope profile and watershed model documentation, NSERL report. 9. Leh, M., Bajwa, S., Chaubey, I., 2013. Impact of land use change on erosion risk: an integrated remote sensing, geographic information system and modeling methodology. Land Degradation & Development. 24(5): 409-421. 10. Lin, C.-Y., Lin, W.-T., Chou, W.C., 2002. Soil erosion prediction and sediment yield estimation: the Taiwan experience. Soil and Tillage Research. 68(2): 143-152. 11. Liu, J., Liu, S., Tieszen, L. L., Chen, M., 2007. Estimating soil erosion using the USPED model and consecutive remotely sensed land cover observations, Proceedings of the 2007 summer computer simulation conference. Society for Computer Simulation International, pp. 16. 12. Miller, W., Baharuddin, M., 1987. Interrill erodibility of highly weathered soils. Communications in Soil Science & Plant Analysis. 18(9): 933-945. 13. Millward, A. A., Mersey, J. E., 1999. Adapting the RUSLE to model soil erosion potential in a mountainous tropical watershed. Catena. 38(2): 109-129. 14. Mitasova, H., Barton, M., Ullah, I., Hofierka, J., Harmon, R., 2013. GIS-based soil erosion modeling, Treatise on Geomorphology. Elsevier Inc. (3): 228-258 15. Mitasova, H., Hofierka, J., Zlocha, M., Iverson, L. R., 1996. Modelling topographic potential for erosion and deposition using GIS. International Journal of Geographical Information Systems. 10(5): 629-641. 16. Mitasova, H., Mitas, L., Brown, W., Johnston, D., 1998. Multidimensional soil erosion/deposition modeling and visualization using GIS. Final report for USA CERL. University of Illinois, Urbana-Champaign, IL. Available at: http://www4.ncsu.edu/~hmitaso/gmslab/protected/hohen/hoh4.html (visited 24 Februray 2018). 17. Mitasova, H., Mitas, L., Brown, W. M., Johnston, D. M., 1999. Terrain modeling and soil erosion simulations for Fort Hood and Fort Polk test areas. Geographic Modeling and Systems Laboratory, University of Illinois at Urbana-Champaign. Available at: http://www4.ncsu.edu/~hmitaso/gmslab/reports/CerlErosionTutorial/denix/Advanced/ErosionRep99/cerl99/rep99.html (visited 24 Februray 2018). 18. Mitasova, H., Mitas, L., M. Brown, W., M. Johnston, D., 2003. Terrain Modeling and Soil Erosion Simulation Final Report, University of Illinois, Urbana-Champaign, IL. 19. Mohammadi, Sh., 2016. Estimating of erosion and sediment in the Menderjan watershed by RS and GIS,. M.Sc. thesis, isfahan university of technology.107 pp. 20. Mohammadi, S., Karimzadeh, H. R., Habashi, K., 2017. Soil erosion assessment using ICONA model (case study: the Zayandehroud basin, Menderjan sub-basin), The 1st International Conference Of SilkGIS, 24-26 May, Isfahan University of Technology, Esfahahan, Iran. 21. Mohammadi, S., Karimzadeh, H. R., Habashi, K., 2017. Soil erosion assessment using CORIN model (case study: the Zayandehroud basin, Menderjan sub-basin), The 1st International Conference Of SilkGIS, 24-26 May, Isfahan University of Technology, Esfahahan, Iran. 22. Moore, I. D., Burch, G. J., 1986. Physical basis of the length-slope factor in the Universal Soil Loss Equation. Soil Science Society of America Journal. 50(5): 1294-1298. 23. Nohegar, A., Kazemi, M., 2013. Assessment Water Erosion Using ICONA Model (case study: Tang Bostanak Basin). Journal of Geographical Research. 28(4): 1-14. 24. Pistocchi, A., Cassani, G., Zani, O., 2002. Use of the USPED model for mapping soil erosion and managing best land conservation practices. 1st International Congress on Environmental Modelling and Software - Lugano, Switzerland - June 2002. 25. Renard, K. G., Foster, G. R., Weesies, G., Mccool, D., Yoder, D., 1997. Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). US Department of Agriculture, Agricultural Research Service Washington. USA. 26. Renard, K. G., Freimund, J. R., 1994. Using monthly precipitation data to estimate the R-factor in the revised USLE. Journal of hydrology. 157(1): 287-306. 27. Rezaei, P., Faridi, P., Ghorbani, M., Kazemi, M., 2014. Estimation of Soil Erosion Using the RUSLE Model and Identification of the Most Effective factor in the Gabrice Basin - Hormozgan Province. Quantitative Geomorphological Researches. 3(1): 97-113. 28. Shirazi, M. A., Boersma, L., 1984. A unifying quantitative analysis of soil texture. Soil Science Society of America Journal. 48(1), 142-147. 29. Teng, H., Rossel, R. a. V., Shi, Z., Behrens, T., Chappell, A., Bui, E., 2016. Assimilating satellite imagery and visible–near infrared spectroscopy to model and map soil loss by water erosion in Australia. Environmental Modelling & Software. 7(7), 156-167. 30. Veihe, A., 2002. The spatial variability of erodibility and its relation to soil types: a study from northern Ghana. Geoderma. 106(1): 101-120. 31. Wischmeier, W. H., Smith, D. D., 1978. Predicting rainfall erosion losses-A guide to conservation planning. Predicting rainfall erosion losses-A guide to conservation planning. USDA, Hyattsville, Maryland, USA. 32. Zakerinejad, R., Maerker, M., 2015. An integrated assessment of soil erosion dynamics with special emphasis on gully erosion in the Mazayjan basin, southwestern Iran. Natural Hazards. 79(1): 25-50