Analysis and zoning of soil erosion rate and its relationship with hydrogeomorphic indicators and vegetation (case study: Khiavchai watershed, MeshkinShahr) | ||
| هیدروژئومورفولوژی | ||
| Volume 11, Issue 38, April 2024, Pages 59-40 PDF (620.2 K) | ||
| Document Type: پژوهشی | ||
| DOI: 10.22034/hyd.2023.58528.1708 | ||
| Authors | ||
| Mousa Abedini* 1; AmirHesam Pasban2 | ||
| 1Professor in Geomorphology Faculty social science University of Mohaghegh Ardabili, Ardabil | ||
| 2department geographi university of Mohaghegh Ardabili, Ardabil | ||
| Abstract | ||
| Soil erosion is one of the serious environmental threats that can affect the political, social and economic aspects of countries. One of the widely used experimental models for estimating the amount of soil erosion is the modified global soil erosion equation known as the RUSLE model. The purpose of this research is to analyze and zonate the amount of soil erosion and its relationship with hydrogeomorphic indicators and vegetation cover of Khiavchai Meshkinshahr watershed in Ardabil province. RUSLE model factors include rain erosion (R), soil erodibility (K), topography (LS), vegetation (C) and protection operations (P). respectively, by using rainfall data, soil texture layer, digital model of height and land use were prepared in the environment of geographic information system (GIS) and after overlapping the layers, the amount of annual soil erosion between 0 and 150.54 tons per hectare per year in The area level was estimated. In the next step, the hydrogeomorphic and vegetation indices that are effective in soil erosion include topographic moisture index (TWI), waterway capacity index (SPI), domain curvature index (Curvatore), section curvature index (Profil Curvatore), surface curvature index (Plan) Curvatore) and Normalized Vegetation Index (NDVI) were created in ArcMap environment and zoning maps were prepared. The results of this research also showed that the topography factor with a correlation coefficient of 0.92% had the greatest impact on the estimation of annual soil erosion by the RUSLE model. In another study, the relationship between hydrogeomorphic indices and vegetation cover with annual soil erosion rate was conducted, and the results showed that normal vegetation cover indices and cross-sectional curvature were the most and least effective with correlation coefficients of 0.57 and 0.05, respectively, compared to other indices.The results of this research confirm the possibility of combining the effective indicators of hydrogeomorphic and vegetation on erosion, as well as the possibility of using other effective indicators and the capabilities of RS and GIS to quantitatively estimate the amounts of soil erosion. | ||
| Keywords | ||
| Soil Erosion; Regression; RUSLE model; Normal Vegetation Index; Hydrogeomorphic; Khiavchai watershed; Northwest of iran | ||
| References | ||
|
Abedini, M., Bahramnia, F., Mostafazadeh, R., Pasban, A. H. (2022). Investigating the impact of land use changes in a period of twenty years on the rate of erosion and sedimentation in RaziChai Basin, Geography and Environmental Studies, 12(45), 114-133. (In Persian). Abedini, M., JavadiAlibabalo, S., Mostafazadeh, R., & Pasban, A.H. (2021). Relationship between vegetation and geomorphic indicators with erosion and sediment values in KozehTopraghi watershed, Hydrogeomorphology, 32(9), 105-128. (In Persian). Allafta, H., & Opp, Ch. (2022). Soil Erosion Assessment Using the RUSLE Model, Remote Sensing, and GIS in the Shatt Al-Arab Basin (Iraq-Iran), Applied sciences, 12, 1-17. Amanpour, S., Obayat, M., Obayat, M., & Obayat, M. (2021). Investigating the effect of land use changes on soil erosion and sediment production in Ramhormoz basin using object-oriented classification and RUSLE model, Iran Water and Soil Research, 52(3), 649-635. (In Persian). Ammar, A.K., Alaa, m., Fadhil, K., Alzahrani, H., & Hamad, S. (2023). Predicting Soil Erosion Rate at Transboundary Sub-Watersheds in Ali Al-Gharbi, Southern Iraq, Using RUSLE-Based GIS Model, Sustainability, 15,1776. Arekhi, S., Barani, Sh., Emadaddian, S. (2022). Zoning Erosion Hazard and Sediment Estimation in Cham Gardalan basin (Ilam province) using the Revised Universal Soil Erosion Equation (RUSLE), Journal of Natural Environmental Hazards, 11(34), 35-56. Arkhi, S., & Niazi, Y. (2019). Investigating the use of GIS and RS to estimate soil erosion and sediment load using the RUSLE model (case study: upstream basin of Ilam Dam), Water and Soil Conservation Research, 17(2), 1-27. (In Persian). Arnoldus, H.M.J. (1980). approximation of the rainfall factor in the Universal Soil Loss Equation M, De Boodt, D. Gabriels (Eds.), Assessment of Erosion, Wiley, Chichester, 127-132. Bablimokher, H., Taghian, A., R., & Shirani, K., (2017). Evaluation of the landslide susceptibility zoning map using the combined method of confidence factor and logistic regression using geomorphic indices, Quantitative Geomorphology Research, 7(6), 116- 91. (In Persian). Buryak, Z.A., Narozhnyaya, A.G., Gusarov, A.V., & Beylich, A.A. (2022). Solutions for the Spatial Organization of Cropland with Increased Erosion Risk at the Regional Level: A Case Study of Belgorod Oblast, European Russia, Applied sciences, 11, 1492. Choudhury, M.K., & Nayak, T. (2003). Estimation of soil erosion in Sagar Lake catchment of Central India Proc, International Conference on Water and Environment, 387-392. Dabral, p.p., Baithuri, N., & Pandey, A. (2008). Soil erosion assessment in a hilly catchment of North Eastern India using USLE, GIS and remote sensing, Water Resources Management, 22(12), 1783-1798. El Jazouli, A., Barakat, A., Khellouk, R., Rais, J., & El Baghdadi, M. (2019). Remote Sensing and GIS Techniques for Prediction of Land Use Land Cover Change Effects on Soil Erosion in the High Basin of the Oum Er Rbia River (Morocco), Remote Sensing Applied sciences Environment, 13, 361–374. Elsayed, A., Mostafa, A., Farag, O., Ahmad, B., Dmitry, E., & Mohamad, S. (2023). Integration of RUSLE Model, Remote Sensing and GIS Techniques for Assessing Soil Erosion Hazards in Arid Zones, Agriculture, 13(35), 1-19. Faizizadeh, b. (2016). Modeling land use changes and its effects on the erosion system in the Alaviyan dam basin using remote sensing and GIS techniques, Hydrogeomorphology, 3(11), 21-38. (In Persian). Ganasri, B.P. 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. Gao, J., & Wang, H. (2018). Temporal analysis on quantitative attribution of karst soil erosion: A case study of a peak-cluster depression basin in Southwest China, Water Resources Management, 172, 369–377. Guzzetti, F., Reichenbach, P., Cardinali, M., & Galli, M. (2000). Probabilistic landslide hazard assessment: A review of methods and applications, Natural Hazards, 22(1), 1-59. Hessel, R., Wyseure, G., Panagea, I.S., Alaoui, A., Reed, M.S., van Delden, H., Muro, M., Mills, J., Oenema, O., & Areal, F., (2022). Soil-Improving Cropping Systems for Sustainable and Profitable Farming in Europe. Land, 11, 780. Huang, L., McDonald-Buller, E.C., McGaughey, G., Kimura, Y., & Allen, D.T. (2016). The impact of drought on ozone dry deposition over eastern Texas Atmos. Environment, 127, 176-186. Laougue, I., Mbaindogoum, D., & Mahamat, A. (2023). Evaluation of Soil Erosion by RUSLE Model in Mount Guera, Open Access Library Journal, 10, 1-19. DOI: 10.4236/oalib.1109888. Luca, F., Conforti, M., & Robustelli, G. (2011). Comparison of GISbased gullying susceptibility mapping using bivariate and multivariate statistics: Northern Calabria, South Italy, Geomorphology, 134, 297–308. Maddi, A., Pasban, A.H., & Nazaft Tekle, B. (2022). Investigating and evaluating the amount of soil loss in land uses in the Atashgah watershed using the RUSLE model and Landsat satellite images (OLI meter), Environmental Science Studies, 8(2), 6625-6612. (In Persian). Moore, I.D., & Grayson, R.B. (1991). Landson. Digital terrain Modeling: A review of hydrological, Geomorphological and Biological application. Hydrology, 5, 3-30. Moore, I.D., & Grayson, R.B. (1991). Landson. Digital terrain Modeling: A review of hydrological, Geomorphological and Biological application. Hydrology, 5, 3-30. Olorunfemi, I.E., Komolafe, A.A., Fasinmirin, J.T., Olufayo, A.A. & Akande, S.O. (2020). A GIS-based assessment of the potential soil erosion and flood hazard zones in Ekiti State, Southwestern Nigeria using integrated RUSLE and HAND models CATENA, Land, 194, 104725. Pandey, A., Chowdary, V.M., & Mal, B.C. (2007). Identification of critical erosion prone areas in the small agricultural watershed using USLE, GIS and remote sensing, Water Resources Management, 21(4), 729-746. Rawat, K. S., & Singh, S. K. (2018). Appraisal of Soil Conservation Capacity Using NDVI Model-Based C Factor of RUSLE Model for a Semi Arid Ungauged Watershed: a Case Study, Water Conservation Science and Engineering, 3(1), 47-58. Rejith, R.G., & Anirudhan, s. (2019). Delineation of Groundwater Potential Zones in hard rock Terrain Using Integrated Remote Sensing GIS and MCDM Techniques A Case Study From Vamanapuram River Basin, Kerala, India, Gis and Geostatistical Techniques for Groundwater science, 349-364. Renard, K.G. & Freidmund, J.R. (1994). Using monthly precipitation data to estimate the R-factorin the RUSLE, Journal of Hydrology, 157, 287-306. Renard, K.G., Foster, G.R., Weesies, G.A., & Porter, J.P. (1991). RUSLE: Revised universal soil loss equation, Journal of Soil and Water Conservation, 46 (1), 30-33. Santos, J.C.N., Andrade, E.M., Medeiros, P.H.A., & Joao, M. (2017). Land use impact on soil erosion at different scales in the Brazilian semi-arid. Revista Ciencia Agronomica, 48(2), 251-260. Serbaji, M., Bouaziz, M., & Weslati, O. (2023). Soil Water Erosion Modeling in Tunisia Using RUSLE and GIS Integrated Approaches and Geospatial Data, Land, 12(548), 13-22. Sharma, A. (2010). Integrating Terrain and Vegetation Indices for Identifying Potential soil Erosion Risk Area, Geo-Spatial Information Science, 13(13), 201-209. Shin, G.J. (1999). The analysis of soil erosion analysis in watershed using GIS. Ph.D. thesis, Department of Civil Engineering, Gang-won National University, 47. Singh, S., Bhardwaj, A., & Verma, V. (2020). Remote sensing and GIS based analysis of temporal land use/land cover and water quality changes in Harike wetland ecosystem, Punjab, India. Journal of Environmental Management, 262, 11035. Vaezi, A.R., Abbasi, M., & Hajimaleki, Kh. (2016). Evaluation of RUSLE model combined with remote sensing and geographic information system in small drainage areas in the semi-arid region, northwest of Iran. Iran Watershed Science and Engineering, 11(38), 1-10. (In Persian). Vijith, H., Seling, L.W., & Dodge-Wan, D. (2018). Estimation of soil loss and identification of erosion risk zones in a forested region in Sarawak, Malaysia, Northern Borneo, Environment, Development and Sustainability, 20(3), 1365-1384. Wang, S., Wente, G.Z., & Gertner, A. (2002). Improvement in mapping vegetation cover factor for the universal soil loss equation by geostatistical methods with Landsat Thematic Mapper images Int. Remote Sensing, 23 (18), 3649-3667. Waseem, M., Iqbal, F., Humayun, M., Latif, M., Javed, T., & Leta, M. (2023). Spatial Assessment of Soil Erosion Risk Using RUSLE Embedded in GIS Environment: A Case Study of Jhelum River Watershed, Applied sciences, 13, 1-16. Whittington, D. (2022). Improving the Performance of Contingent Valuation Studies in Developing Countries, Environmental and Resource Economic, 22, 323–367. Wischmeier, W.H., & Smith, D.D. (1978). Predicting rainfall erosion losses: a guide to conservation planning (No. 537). Department of Agriculture, Science and Education Administration. | ||
|
Statistics Article View: 600 PDF Download: 382 |
||