The Impact of Climate Change and Land use on Soil Erosion Risk using the RUSLE Model (Case Study: Gorganrood Watershed)
Subject Areas : Optimal management of water and soil resources
Saleh Arekhi
1
,
Mohammad Baratzadeh
2
,
Sayed Hussein Roshun
3
1 - Associate Professor, Department of Geography, Faculty of Humanities, University of Golestan, Gorgan, Iran.
2 - MSc. Graduated in Surveying Engineering, Department of Surveying Engineering, Faculty of Engineering Lamei Gorgani Institute of Higher Education, Gorgan, Iran
3 - Ph.D. Watershed Management Science and Engineering, Department of Watershed Management Engineering, Faculty of Natural Resources, Agricultural Sciences and Natural Resources of Sari University, Sari, Iran.
Keywords: Soil loss, land use changes, downscaling, LARS-WG model, Gorganroud watershed,
Abstract :
Introduction: Soil erosion and its impacts on the earth's resources are significant concerns in many countries. The most important effects of soil erosion are loss of soil fertility, water pollution, reduction of agricultural productions and reduction of dam’s useful life. The present study aims to predict the future effects of climate change and land use change on the soil erosion intensity and potential in the Gorganroud watershed. The erosion rates were compared with the RUSLE model in three different scenarios: future climate change, future land use change, and a combination of climate and land use changes for erosion rates in base period.
Methods: Initially, weather data (temperature and precipitation) from existing stations in the catchment area were collected for a 20-year statistical period (2001-2020). The normality, homogeneity, and randomness of the data were examined using the Kolmogorov-Smirnov and run tests, respectively. To address statistical deficiencies, a regression method was employed in SPSS software. The statistical downscaled data from the general circulation model and synthetic data were generated for the future period (2021-2040) using the AIB, A2, and B1 scenarios (optimistic, pessimistic, and moderate, respectively) in the LARS-WG model based on the fifth report of the Intergovernmental Panel on Climate Change, and two models, HADCM3 and GFCM21. Additionally, the land use map of the catchment area was prepared using Landsat 7 and 8 satellite images for the years 2001, 2010, and 2020 and evaluated through Google Earth. Finally, the CA-Markov model in the IDRISI Selva software was used to simulate future land use changes. Soil loss values for the current period and under climate and land use change scenarios were also calculated based on the RUSLE model.
Results: The results showed that soil erosion rates increase under climate change scenarios compared to the base period. Land use changes and coverage will also shift towards a decrease in dense and semi-dense forest areas and an increase in pastures and residential areas. The results indicated that the average annual soil erosion rate in the base period is 41.96 tons per hectare per year. With the consideration of A2, A1B, and B1 scenarios, the erosion rate will increase by 2-4% compared to the base period. By considering the simulated land use in 2040 and the A2, A1B, and B1 scenarios, the erosion rate will increase by 7.5%, 25.5%, and 73.1%, respectively, due to the reduction in natural coverage.
Conclusion: The results showed that land use changes have the most significant impact on soil erosion rates, and therefore, proper management of cover can mitigate the increasing trend of soil erosion in the Gorganroud catchment area. The largest share of land use in creating erosion is related to semi-dense forest use with an average of 115.04 ton/ha/year and dense forest use without considering residential areas has the lowest share with an average value of 51.39 ton/ha/year.
Agarwal, C. (2002). A review and assessment of land-use change models: dynamics of space, time, and human choice. U.S. Department of Agriculture, Forest Service, Northeastern Research Station, 61 Pages.
Anderson, R. L., Rowntree, K. M., & Le Roux, J. J. (2021). An interrogation of research on the influence of rainfall on gully erosion. Catena, 206, 105482. https://doi.org/10.1016/j.catena.2021.105482
Babaei, M., Hosseini, S.Z., Nazari Samani, A. & Almodaresi, S.A. (2016). Assessment of soil erosion using RUSLE 3D, case study: Kan Watershed. Watershed Engineering and Management, 8(2), 165-181. https://doi.org/10.22092/ijwmse.2016.106454 [in Persian]
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, 1783-1798. https://doi.org/10.1007/s11269-008-9253-9
Eekhout, J.P., & De Vente, J. (2020). How soil erosion model conceptualization affects soil loss projections under climate change. Progress in Physical Geography: Earth and Environment, 44(2), 212–232. https://doi.org/10.1177/0309133319871937
Estrada-Carmona, N., Harper, E. B., DeClerck, F., & Fremier, A. K. (2017). Quantifying model uncertainty to improve watershed-level ecosystem service quantification: a global sensitivity analysis of the RUSLE. International Journal of Biodiversity Science, Ecosystem Services & Management, 13(1), 40-50. https://doi.org/10.1080/21513732.2016.1237383
Faridi, P., & Rezaee, P. (2015). Estimation of Soil erosion using RUSLE, GIS and RS (case study: Gabric Watershed, South-eastern of Hormozgan province). The 4th International Conference on Environmental Challenges & Dendrochronology, 14-15 May, Sari Agricultural science and natural resourecs university, Sari, Iran. https://civilica.com/doc/788185/ [in Persian]
Fernández, C., & Vega, J. A. (2016). Evaluation of RUSLE and PESERA models for predicting soil erosion losses in the first year after wildfire in NW Spain. Geoderma, 273, 64-72. https://doi.org/10.1016/j.geoderma.2016.03.016
Gupta, S., & Kumar, S. (2017). Simulating climate change impact on soil erosion using RUSLE model− A case study in a watershed of mid-Himalayan landscape. Journal of Earth System Science, 126, 1-20.
Halecki, W., Kruk, E., & Ryczek, M. (2018). Loss of topsoil and soil erosion by water in agricultural areas: A multi-criteria approach for various land use scenarios in the Western Carpathians using a SWAT model. Land Use Policy, 73, 363-372. https://doi.org/10.1016/j.landusepol.2018.01.041
Islam, K., Jashimuddin, M., Nath, B., & Nath, T. K. (2018). Land use classification and change detection by using multi-temporal remotely sensed imagery: The case of Chunati wildlife sanctuary, Bangladesh. The Egyptian Journal of Remote Sensing and Space Science, 21(1), 37-47. https://doi.org/10.1016/j.ejrs.2016.12.005
Lambin, E. F. (1997). Modelling and monitoring land-cover change processes in tropical regions. Progress in physical geography, 21(3), 375-393. https://doi.org/10.1177/030913339702100303
Marzen, M., Iserloh, T., De Lima, J. L., Fister, W., & Ries, J. B. (2017). Impact of severe rain storms on soil erosion: Experimental evaluation of wind-driven rain and its implications for natural hazard management. Science of the Total Environment, 590, 502-513. https://doi.org/10.1016/j.scitotenv.2017.02.190
Meinen, B. U., & Robinson, D. T. (2021). From hillslopes to watersheds: Variability in model outcomes with the USLE. Environmental Modelling & Software, 146, 105229. http://dx.doi.org/10.1016/j.envsoft.2021.105229
Moisa, M. B., Negash, D. A., Merga, B. B., & Gemeda, D. O. (2021). Impact of land-use and land-cover change on soil erosion using the RUSLE model and the geographic information system: a case of Temeji watershed, Western Ethiopia. Journal of Water and Climate Change, 12(7), 3404-3420. https://doi.org/10.21203/rs.3.rs-100340/v1
Mondal, A., Khare, D., Kundu, S., Meena, P. K., Mishra, P. K., & Shukla, R. (2015). Impact of climate change on future soil erosion in different slope, land use, and soil-type conditions in a part of the Narmada River Basin, India. Journal of Hydrologic Engineering, 20(6), C5014003. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001065
Mousavinejad, S.H., Habashi, H., Kiani, F., Shataee, Sh. & Abdi, O. (2017). Evaluation of soil erosion using imagery SPOT5 satellite in Chehel chi catchment of Golestan Province. Journal of Wood and Forest Science and Technologym, 24(2), 73-86. https://doi.org/10.22069/jwfst.2017.1446.1 [in Persian]
Nearing, M. A. (2001). Potential changes in rainfall erosivity in the US with climate change during the 21st century. Journal of Soil and Water Conservation, 56(3), 229-232. https://www.jswconline.org/content/56/3/229
Pal, S. C., & Chakrabortty, R. (2019). Simulating the impact of climate change on soil erosion in sub-tropical monsoon dominated watershed based on RUSLE, SCS runoff and MIROC5 climatic model. Advances in Space Research, 64(2), 352-377. https://doi.org/10.1016/j.asr.2019.04.033
Prasannakumar, V., Vijith, H., Abinod, S., & Geetha, N. J. G. F. (2012). Estimation of soil erosion risk within a small mountainous sub-watershed in Kerala, India, using Revised Universal Soil Loss Equation (RUSLE) and geo-information technology. Geoscience frontiers, 3(2), 209-215. https://doi.org/10.1016/j.gsf.2011.11.003
Rahimi, K., & Mezbaani, M. (2013). Evaluation of Sivand basin erosion by RUSLE Model during 1998 to 200. Quarterly Journal of Environmental Erosion Researches, 3(9), 1-18. [in Persian]
Ranzi, R., Le, T. H., & Rulli, M. C. (2012). A RUSLE approach to model suspended sediment load in the Lo River (Vietnam): Effects of reservoirs and land use changes. Journal of Hydrology, 422, 17-29. https://doi.org/10.1016/j.jhydrol.2011.12.009
Refahi, H. & Nematti, M. (1995). Erodibility assessment of the Alamout subcatchment and its effect on the sediment yield. Journal of Agricultural Sciences, Iran 26, 48–56. [in Persian]
Refahi, H. G. (2003). Water erosion and control. Tehran University Publ. [in Persian]
Renard, K.G., & Freidmund, J.R. 1994. Using monthly precipitation data to estimate the R-factor in the Revised USLE. Journal of Hydrology, 157, 287-306. https://doi.org/10.1016/0022-1694(94)90110-4
Renard, K.G., G.R. Foster, G.A. Weesies, D.K. McCool, & D.C. Yoder. (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. Handbook. No. 703.
Roy, P. (2019). Application of USLE in a GIS environment to estimate soil erosion in the Irga watershed, Jharkhand, India. Physical Geography, 40(4), 361-383. https://doi.org/10.1080/02723646.2018.1550301
Sadeghi, S. H., Mostafazadeh, R., & Sadoddin, A. (2015). Response of Sediment graphs and Sediment Rating Loops to Land Use Type and Spatial Pattern. Watershed Engineering and Management, 7(1), 15-26. doi: 10.22092/ijwmse.2015.100900 [in Persian]
Salehi, M.H., Esfandyarpour Broujeni, E., Mohajer, R. & Bagheri Bodaghabadi, M. (2014). Supplement soil and water conservation. Payam Noor University Publication, 210 pages. [in Persian]
Shi, W., & Huang, M. (2021). Predictions of soil and nutrient losses using a modified SWAT model in a large hilly-gully watershed of the Chinese Loess Plateau. International Soil and Water Conservation Research, 9(2), 291-304. https://doi.org/10.1016/j.iswcr.2020.12.002
Smoot, J. L., & Smith, R. D. (1999). Soil erosion prevention and sediment control. The University of Tennessee, Knoxville. Water Resources, Civil and Environmental Engineering.
Solomon, S.D., Qin, M.M., Chen, M.M., Marquis, K.B., Averyt, M.T., & Millers, H.L. (2007). IPCC. Summary for policy makers. In Climate Change 2007: The Physical Science Basis; Proceedings of the 10th Working Group I Session, Paris, 29 January–1 February 2007; Cambridge University Press: Cambridge, UK, New York, NY, USA.
Teimouri, F., Bazrafshan, O., & Rafiei sardoei, E. (2019). Assessment of Climate Change and Land Use Change on Soil Erosion (Case study: Kondaran watershed). Iranian journal of Ecohydrology, 6(2), 353-368. doi: 10.22059/ije.2019.274886.1038 [in Persian]
Tangestani, M. H. (2006). Comparison of EPM and PSIAC models in GIS for erosion and sediment yield assessment in a semi-arid environment: Afzar Catchment, Fars Province, Iran. Journal of Asian earth sciences, 27(5), 585-597. https://doi.org/10.1016/j.jseaes.2005.06.002
Vescovi, F. D., Park, S. J., & Vlek, P. L. (2002). Detection of human-induced land cover changes in a savannah landscape in Ghana: I. Change detection and quantification. In 2nd Workshop of the EARSeL Special Interest Group on Remote Sensing for Developing Countries. Bonn, Germany, 1-8.
Weng, X., Zhang, B., Zhu, J., Wang, D., & Qiu, J. (2023). Assessing land use and climate change impacts on soil erosion caused by water in China. Sustainability, 15(10), 7865.
Wischmeier, W. H., Johnson, C. B., & Cross, B. V. (1971). A soil erodibility nomograph for farmland and construction sites. Journal of Soil and Water Conservation, 26(5), 189-193.
Wischmeier, W.H. & Smith D. D. (1978). Predicting rainfall erosion losses. A guide to conservation planning. USDA Agriculture Handbook, 537. 58p.
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.
https://doi.org/10.1007/s11069-015-1700-3
Zandi, J., Habibnejad Roshan, M. & Solaimani, K. (2013). Soil erosion risk assessment and its relationship with some environmental parameters (Case study: Vazroud watershed, Mazandaran). Journal of Range and Watershed Management, 66(3), 401-415. https://doi.org/10.22059/jrwm.2013.36516 [in Persian]