Soil Erosion Risk Assessment Using CORINE Model in Kharestan Watershed, Fars Province

Authors

10.22052/deej.2020.9.29.41

Abstract

Introduction: Soil erosion is the most important factor in damaging and decreasing the productivity of agricultural soils. Moreover, as the transfer of sediments rich in nutrients through the soil leads to soil erosion and in turn to the decrease in the dams’ reservoirs’ storage capacity, bringing about adverse economic and eco-environmental consequences such as damage to land resources and decrease in land productivity, soil erosion is regarded as a serious problem in the world. Therefore, estimation of the soil erosion risk is necessary for preserving agricultural lands and achieving sustainable management of watersheds.
Materials and Methods: This study investigated the potential and actual erosion risk in Kharestan region, using CIRINE model together with GIS and RS. The parameters are presented as four separate indices. The model consists of the following steps. First, soil texture, depth, and the percentage of stones and pebbles determine the soil’s erodibility map. Second, Fournier and Bagnouls-Gaussen aridity indices which are calculated based on meteorological data would be used to form the erosivity layer. Third, slope classes would be identified according to the DEM of the study area, and then the potential soil erosion risk (PSER) layer is produced through the overlaying of soil erodibility, erosivity, and slope layers. Fourth, the LULC layer is formed based on the Landsat ETM7 images. Finally, the LULC and the PSER layers are combined to identify the actual soil erosion risk (ASER).
Results: The study’s findings indicated that there existed 5 types of soil textures in Kharestan region, including Silty clay, clay lomy, silty clay lomy, sandy lomy, and lomy. It was also found that 75.28% of the study area included clay silty (14.36%), clay loamy and silty loamy clay (60%), and 24.71% of the region comprised of sandy loam and loamy texture. Moreover, three classes were observed for the soil depth of the area, including over 75 cm (60.54%), 25-75 cm (5.92), and below 25 cm (33.53). The findings also indicated that 88.42% of the area was covered with over 10% pebbles, while the rest of the area comprised of below 10% pebbles (plains and hills). In terms of erodibility as a function of soil texture, depth, and percentage of pebbles, 39.24% of the study area was covered by highly erodible soils, while 32.26% of the area was covered by low and 28.48% of it was covered by moderately erodible soil, respectively. In total, about 68% of the study area was erodible. The Modified Fournier Index (MFI) was classified in two classes, the highest value of which was observed in the study area’s class 3, indicating a moderate rainfall. In addition, the Bagnouls-Gaussen Aridity index (BGI) was classified into two classes, with its highest value found in Class 3, suggesting the dryness of the area. Comprised of MFI and BGI, soil erosivity index showed that 60.47% of the region had been located in medium erosivity class and 39.55% of the area had been located in low erosivity class. In terms of potential soil erosion risk, 29.125% of the study area was located in low class, 29.8% in moderate class, and 41.7% in high class. The slope map extracted from the study areas’ DEM showed that 68.38 % of the area was located at slope class of below 15% and 31.62 % of the area was located at slope class of over 15%. The actual soil erosion risk was extracted from the overlap of the potential soil erosion risk and vegetation. Surface vegetation was classified into fully protected (8.71%) and unprotected areas (91.69%). The outcome was a map depicting non-erosion, and, low, moderate, and high actual soil erosion risk in the study area. In terms of actual soil erosion risk, 31.14% of the study area were found to be in low class, 31.11% in medium class, and 37.78% in high class areas. The areas high erosion risk areas within the potential erosion risk map were reduced from 41.07% to 37.78% in actual soil erosion risk map after overlapping the vegetation layer.
Discussion and Conclusion: Soil erodibility depends primarily on the structural stability of the soil (and hence its resistance to particle detachment by rain-splash or runoff) and its ability to absorb rainfall. As soils with medium to fine texture have low infiltration rates, the are less transferred when exposed to high velocity of runoff water. Moreover, in shallow soils, the rate of erosion increases due to lower potentials for water holding water and higher overland flow. The study’s findings also indicated that the areas with moderate–high erosion risk are located in northern, southeastern, western, and northwestern parts of the region, where the soil and water preservation practices should be focused. Thus, protection and restoration of vegetation in the Kharestan is highly important for the preservation of water and water resources. In the southern, central, and eastern parts of the region, the slope is generally slight and the soil is shallow. However, due to the mountainous nature of the region and the low involvement of human activities, the erosion risks lie in low-class areas.
Considering the application erodibility, erosivity, slope and surface coverage, the use of CORINE model for determining the potential and actual erosion risk maps could well show different erosion classes in Kharestan region. Moreover, the study’s findings confirm the application of the CORINE model in natural resource lands. The study suggested that together with GIS and RS techniques, the CORINE model possesses great potential for producing useful and inexpensive erosion risk information in Iran with low data and in relatively short time.

Keywords

References

1. Akbari, M., Ownegh, M., Asgari, H. R., Sadoddin, A. and Khosravi, H., 2016. Soil Erosion Risk Assessment Using the CORINE Model (Case Study: Semi-Arid Region in Golestan Province). Desert Ecosystem Engineering Journal 5(12), 63-78. 2. Al Sayah, M.J., Nedjai, R., Kaffas, k., Chadi Abdallah, C. and Khouri, M., 2019. Assessing the Impact of Man–Made Ponds on Soil Erosion and Sediment Transport in Limnological Basins. Water 11, 2526; doi: 10.3390/w11122526. 3. Alexakis, D.D., Hadjimitsis, D.G. and Agapiou, A., 2013. Integrated use of remote sensing, GIS and precipitation data for the assessment of soil erosion rate in the catchment area of "Yialias" in Cyprus. Atmos. Res. doi: 10.1016/j.atmosres.2013.02.013. 37. 4. Ananda, J. and Herath, G., 2003. Soil erosion in developing countries: a socio-economic appraisal. J Environ Manage 68, 343–353. 5. Aydýn, A. and Tecimen, H.S., 2010. Temporal soil erosion risk evaluation: a CORINE methodology application at Elmalý dam watershed, Istanbul. Environ Earth Sci 61, 1457–1465. 6. Bargiel, D., Herrmann, S. and Jadczyszyn, J. 2013. Using high-resolution radar images to determine vegetation cover for soil erosion assessments. J. Environ. Manag.124, 82–90. 7. Cilek, A., Berberoglu, S., Kirkby, M., Irvine, B., Donmez, C. and Erdogan, M.A., 2015. Erosion modelling in a Mediterranean subcatchment under climate change scenarios using Pan-European Soil Erosion Risk Assessment (PESERA). ISPRS - Int. Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL–7/W3: 359–365. 8. CORINE, 1992. CORINE: soil erosion risk and important land resources in the Southeastern regions of the European community. EUR 13233, Luxembourg, Belgium, pp. 32–48. 9. Denguz, O. and Akgul, S., 2004. Soil Erosion Risk Assessment of the Golbasi Environmental Protection Area and Its Vicinity Using the CORINE Model, Turk. J. Agric 29, 439-448. 10. Drăguţ, L. and Eisank, C., 2012. Automated object-based classification of topography from SRTM data. Geomorphology, 141-142, 21–33 11. Dwivedi, R.S., Ravi Sankar, T., Venkataratnam, L., Karale, R.L., Gawande, S.P., Seshagiri Rao, K.V., Senchaudhary, S.,Bhaumik, K.R. and Mukharjee, K.K., 1997. The inventory and monitoring of eroded lands using remote sensing data. Int. J. Remote Sens 1, 107–119. 12. Edosomwan, N. L., Obazuaye, E. and Uwa Edosomwan, E., 2013. Impacts of dam on characteristics of tropical rainforest soils and sediments in south central Nigeria. Indian J. Agric. Res 47(1), 73-77. 13. El-Nady, M.A. and Shoman, M.M., 2017. Assessment of Soil Erosion Risk in The Basin of Wadi Maged in Northern West Coast of Egypt Using CORINE Model and GIS Techniques. Egypt. J. Soil Sci 57(1), 31 – 45. 14. Entezari, M. and Heaydari, H., 2013. Comparison of SLEMSA and KORINE models in soil erosion assessment (Case study: Tang-e-Sorkh, Shiraz). The Journal of Spatial Planning, 18(3). 1-28. 15. Eroglu, H., Cakir, G., Sivrikaya, F. and Akay, A. E., 2010. Using high resolution images and elevation data in classifying erosion risks of bare soil areas in the Hatila Valley Natural Protected Area, Turkey. Stoch Environ Res Risk Assess 24, 699–704. 16. Everest, T. and Özcan, H., 2017. Determination of soil erosion risk of Dümrek basin downstream with CORINE methodology. COMU Journal of Agriculture Faculty, 5(1), 39-47. 17. Foster, G.R., Young, R.A., Ronkens, M.J.M. and Onstad, C.A., 1985. Processes of soil erosion by water. In: Follett FR, Stewart BA (eds) Soil erosion and crop productivity. American Society of Agronomy and Crop Science Society of America, Madison, WI, pp. 137–162. 18. Gurebiyaw, K., Kendie Addis, H. and Teklay, A., 2018. Assessment of Spatial Soil Erosion Susceptibility Based on the CORINE Model in the Gumara-Maksegnit Watershed, Ethiopia. Journal of natural resources and development 8, 38-45. 19. Hassan, M.F., Ahmed, A.S., Imad-eldin, A.A. and Shinobu L., 1999. Use of remote sensing to map gully erosion along the Atbara River, Sudan. Int. J. Appl. Earth Obs. Geoinf 3–4, 175–180. 8. 20. Ibraheem A., Yousif, H., el dine, E., Tealab1, M.A., Abdel Hady, A.A. and Aldabaa, A.A., 2020. Spatial soil erosion risk assessment using CORINE Model: A case study in wadl el raml. Plant Archives 20(1), 705-714. 21. Jakhar, P., Barman, D., Home Gowda, C.H. and Madhu, M., 2012. Multitier cropping system for profitable resource conservation and sustainable management of sloping lands of eastern India. Indian J. Agric. Res, 46 (4), 309 – 316. 22. King, C., Baghdadi, N., Lecomte, V. and Cerdan, O., 2005. The application of remote-sensing data to monitoring and modelling of soil erosion. Catena 2, 79–93. 23. Kosmas, C., Tsara, N., Moustakas, N. and Karavitis. C., 2003. Identification of indicators for desertification. Ann. Arid Zone 42, 393-416. 24. Lakkad, A. P., Patel, G. R., Sondarva, K.N. and Shrivastava, P.K., 2018. Estimation of sediment delivery ratio at subwatershed level using revised and modified USLE. Agric. Sci. Digest 38 (1), 11-16. 25. Lu, D., Li, G., Valladares, G.S. and Batistella, M., 2004. Mapping soil erosion risk in Rondonia, Brazilian Amazonia: using RULSE, remote sensing and GIS. Land Degrad Dev 15, 499–512. 26. Nikkami, D., Shabani, M. and Ahmadi, H., 2009. Land use scenarios and optimization in a watershed. J Appl Sci 9(2):287–295. 27. Omuto, C. T. and Vargas, R.R., 2009. Combining pedometrics, remote sensing and field observations for assessing soil loss in challenging drylands: a case study of northwestern Somalia. Land Degrad Dev, 20, 101–115. 28. Ozsoy, G., Aksoy, E., Dirim, M.S. and Tumsavas, Z., 2012. Determination of soil erosion risk in the Mustafakemalpasa river basin, Turkey, using the revised universal soil loss equation, geographic information system, and remote sensing. Environ. Manag 4, 679–694. 29. Panagos, P., Karydas, C.G., Gitas, I.Z. and Montanarella, L., 2012. Monthly soil erosion monitoring based on remotely sensed biophysical parameters: A case study in Strymonas river basin towards a functional pan-European service. Int. J. Dig. Earth 6, 461–487. 34. 30. Pandey, A., Chowdary, V.M. and Mal, B.C., 2007. Identification of critical erosion prone areas in the small agricultural watershed using USLE, GIS and remote sensing. Water Resour. Manag. 21, 729–746. 36. 31. Peng, W., Zhou, J., Wen, L., Xue, S.H. and Dong, L., 2017. Land surface temperature and its impact factors in Western Sichuan Plateau, China. Geocarto International, 32(8): 919-934. 32. Pradhan, B., Chaudhari, A., Adinarayana, J. and Buchroithner, M.F., 2012. Soil erosion assessment and its correlation with landslide events using remote sensing data and GIS: A case study at Penang Island, Malaysia. Environ. Monit. Assess 2, 715–727. 33. Rastegarlari, A., Urmia, A. and Kayhanfard, M., 1998. Zoning of Erosion Risk and Estimation of Sedimentation in Dorodzan Dam Basin, 3rd Conference of Iranian Geological Society, Shiraz. 34. Rohani, M. R., Rangavar, A., Javadi, M. R. and Ziyaee, A., 2013. A new mathematical mode for estimation of soil erosion, Intl. Res. J. Appl. Basic. Sci 5 (4), 491-497. 35. Sadegh Kamali, K., Moeini, A. and Ahmadi, H., 2018. Investigating the Efficiency of the Corine Model for Estimating Soil Erosion (Case Study: Parwan Watershed). 13th National Conference on Watershed Management Science and Engineering of Iran and the Third National Conference on the Protection of Natural Resources and the Environment. 10 and 11 October 2018 Mohaghegh Ardabili University. 36. Shabani, M., 2010. The role of optimal land use management in reducing soil erosion and increasing the profitability of watersheds (Case study: Kharestan Fars watershed). National Conference on Watershed Management Science and Engineering of Iran (Sustainable Natural Disaster Management). Gorgan University of Agricultural Sciences and Natural Resources. 37. Tayebi, M., Tayebi, M. and Sameni, A., 2016. Soil erosion risk assessment using GIS and CORINE model: A case study from Western Shiraz, Iran, Archives of Agronomy and Soil Science 63(8), 1163-1175. 38. Trimble, S.W., 1990. Geomorphic effects of vegetation cover and management: some time and space considerations in prediction of erosion and sediment yield. In: Thornes JB (ed) Vegetation and erosion. John Wiley & Sons, Chicherster, New York. 39. Vrieling, A., 2006. Satellite remote sensing for water erosion assessment: A review. Catena, 1, 2–18. 40. Wu, S., Yu, M. and Chen, L. 2017. Nonmonotonic and spatial-temporal dynamic slope effects on soil erosion during rainfall-runoff processes. Water Resources Research, 53(2), 1369–1389. 41. Yuksel, A., Gundogan, R. E. and Akay, A., 2008. Using the Remote Sensing and GIS Technology for Erosion Risk Mapping of Kartalkaya Dam Watershed in Kahramanmaras, Turkey. Sensors 8, 4851-4865. 42. Zhu, M., 2012. Soil erosion risk assessment with CORINE model: case study in the Danjiangkou Reservoir region, China, Stochastic Environmental Research and Risk Assessment 26 (6), 813-822.
Desert Ecosystem Engineering
Volume 9, Issue 29
February 2021
Pages 59-74

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