Identifying suitable locations for the implementation of control and reduction of sheet and rill erosion using the erosion risk assessment model

Eshghizadeh, Masoud; Hayatzadeh, Mehdi

doi:10.22034/gahr.2024.473715.2241

Identifying suitable locations for the implementation of control and reduction of sheet and rill erosion using the erosion risk assessment model

Document Type : Original Article

Authors

Masoud Eshghizadeh ¹

Mehdi Hayatzadeh ²

¹ University of Gonabad

² Ardakan University

10.22034/gahr.2024.473715.2241

Abstract

One of the methods that can be used to determine suitable and effective places in erosion control is to determine the soil erosion risk. In this method, to determine the suitable places for implementing soil conservation, the soil erosion risk was evaluated in 30 years in 1991 and 2020 in the Kalat watershed of Gonabad county. SL190-96 model was used to evaluate soil erosion risk. In this model, three factors of vegetation cover, slope, and land use are used as the main inputs to evaluate the erosion risk. The results showed that 9.4% of the study area has a very high priority and 45.6% has a high priority for the implementation of the soil conservation measures. In general, the erosion risk in 2021 has become more acute than in 1991, and the area of areas with slight, low, and moderate erosion risk has been reduced, and it has been added to high, very high, and extremely high. The greatest reduction in erosion risk was related to the moderate erosion risk class in 1991. The highest increase was related to the very high erosion risk class in 2021. Based on the results, in 2021 compared to 1991 only 0.13% of the studied area improved their erosion risk and 65.14% of them have become more acute.

Keywords

Prioritization

Soil conservation

Vegetation cover

Geographic Information System

Subjects

remote sensing

- Abdolalizadeh, Z., Ghorbani, A., Mostafazadeh, R., and Moameri, M. 2020. Rangeland canopy cover estimation using Landsat OLI data and vegetation indices in Sabalan rangelands, Iran. Arabian Journal of Geosciences. 13(6): 1-13. DOI: 10.1007/s12517-020-5150-1

- Abhinav, M., Shital, Sh., and Shrey, R. 2021. Vegetation Change Analysis using Normalized Difference Vegetation Index and Land Surface Temperature in Greater Gir Landscape. Journal of Scientific Research. 65(3): 1-7. DOI: 10.37398/JSR.2021.650301

- Amellah, O., Morabiti, K. 2021. Assessment of soil erosion risk severity using GIS, remote sensing and RUSLE model in Oued Laou Basin (north Morocco). Soil Science Annual. 72(3): 142530. https://doi.org/10.37501/soilsa/142530

- Aslam, B., Maqsoon, A., Shahzaib, Kazmi, Z.A., Sodangi, M., Anwarf, F., Bakri, M.H., Tufail, R.F., Farooq, D. 2020. Effects of landscape changes on soil erosion in the built environment: application of geospatial- based RUSLE technique. Sustainability. 12(15): 5898.

- Bekele, D.A., Gella, G.W., Ejigu, M.A. 2022. Erosion risk assessment: A contribution for conservation priority area identification in the sub-basin of Lake Tana, north-western Ethiopia. International Soil and Water Conservation Research. 10(1): 46-61. https://doi.org/10.1016/j.iswcr.2021.04.010

- Bhadra, A., Lalramnghaki, H., Kiba, L.G., Bandyopadhyay, A. 2018. Temporal variation in water induced soil erosion by RUSLE model using RS and GIS. EPiC series in engineering. 3: 236.

- Beskow, S., Mello, C.R., Norton, L.D., Curi, N., Viola, M.R., Avanzi, J.C. 2009. Soil erosion prediction in the Grande River Basin, Brazil using distributed modeling. Catena. 79(1): 49–59.

- Bisrat, E., Berhanu, B. 2018. Identification of Surface Water Storing Sites Using Topographic Wetness Index (TWI) and Normalized Difference Vegetation Index (NDVI). Journal of Natural Resources and Development 8, 91–100. https://doi.org/10.5027/jnrd.v8i0.09

- Chen, W.H., Liu, L.Y., Zhang, C., Pan, Y.C., Wang, J.H., Wang, J.D., 2005. The fast method of soil erosion investigation based on remote sensing (in Chinese, with English abstract). Research of Soil and Water Conservation. 12(6): 8–10.

- Deng, Z.Q., Lima, J., Jung, H.S., 2009. Sediment transport rate-based model for rainfall-induced soil erosion. Catena. 76(1): 54–62.

- Ding, Y., Zheng, X., Zhao, K., Xin, X., Liu, H. 2016. Quantifying the impact of NDVI soil determination methods and NDVI soil variability on the estimation of fractional vegetation cover in Northeast China. Remote Sensing. 8(1): 1–15. https://doi.org/10.3390/rs8010029

- Ebadi, E., Esfandyari, F., Asgari, S., Mostafazadeh, R. 2024. Evaluation of Soil Erosion Potential in the Firouzabadchay Catchment Area using Geomorphic Indicators and Fuzzy Logic. Geography and Human Relationships, 7(1): 178-185. DOI: 10.22034/gahr.2024.430119.2005

- Greenwood, Philip. 2017. A Review of the Radionuclide, Cobalt-60, as a Fine-Sediment Tracer. Cobalt. InTech. DOI:10.5772/intechopen.71304.

- Jahad Sazandegee. 1992. Watershed studies of the four Gonabad dams, implementation justification stage. Third part. geomorphology. 26 p.

- Madadi, A., and Sharifi, Z. 2024. Integration of Geomorphometric and Vegetation Indices for Soil Erosion Hazard Assessment in Samian Watershed, Ardabil Province. Geography and Human Relationships, (). DOI: 10.22034/gahr.2024.451130.2083

- Michalopoulou, M., Depountis, N., Nikolakopoulos, K., Boumpoulis, V. 2022. The Significance of Digital Elevation Models in the Calculation of LS Factor and Soil Erosion. Land. 11(9): 1592. https://doi.org/10.3390/land11091592

- MWR (Ministry of Water Resources, China). 1997. National professional standards for classification and gradation of soil erosion, SL 190-1996 (in Chinese). Beijing, the People's Republic of China.

- Phua, M., Minowa, M. 2005. A GIS-based multi-criteria decision making approach to forest conservation planning at a landscape scale: a case study in the Kinabalu area, Sabah, Malaysia. Landscape and Urban Planning. 71: 207–222.

- Reis, M., Akay, A.E., Savaci, G. 2016. Erosion Risk Mapping Using CORINE Methodology for Goz Watershed in Kahramanmaras Region, Turkey. Journal of Agricultural Science and Technology. 18: 695-706

- Ruksajai, N., Konyai, S., Sriboonlue, V. 2023. Forecasting Soil Erosion Risk Using GIS and Remote Sensing for the Nam Un Basin, Sakon Nakhon Province, Thailand. Polish Journal of Environmental Studies. 32(2): 1767-1780. DOI: 10.15244/pjoes/156791

- Samarin, M., Zweifel, L., Roth, V., Alewell, C. 2020. Identifying Soil Erosion Processes in Alpine Grasslands on Aerial Imagery with a U-Net Convolutional Neural Network. Remote Sensing. 12(24): 4149. https://doi.org/10.3390/rs12244149

- Silva, V.A.M., Mello, K., Vettorazzi, C.A., Costa, D.R.de., Valente, R.A. 2017. Priority Areas for Forest Conservation, Aiming at the Maintenance of Water Resources, through the Multicriteria Evaluation. Revista Árvore. 41(1): e410119. https://doi.org/10.1590/1806-90882017000100019

- Thorat, S.S., Rajendra, Y.D., Kale, K.V., Mehrotra, S.C. 2015. Estimation of Crop and Forest Areas using expert system-based knowledge classifier approach for Aurangabad District. International Journal of Computer Applications. 121(23): 43–46. https://doi.org/10.5120/21845-5153

- U.S. Geological Survey. 2021. Earth Resources Observation and Science: U.S. Geological Survey database, Earth Explorer, http://earthexplorer.usgs.gov.

- Valente, R.O.A., Vettorazzi, C.A., 2008. Definition of priority area for forest conservation through the ordered weighted averaging method. Forest Ecology and Management. 256(6): 1408–1417.

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

- Vrieling, A., De Jong, S.M., Sterk, G., Rodrigues, S.C. 2008. Timing of erosion and satellite data: a multi-resolution approach to soil erosion risk mapping. International Journal of Applied Earth Observation and Geoinformation. 10(3): 267–281.

- Zhang, X., Wu, B., Ling, F., Zeng, Y., Yan, N., Yuan, Ch. 2010. Identification of priority areas for controlling soil erosion. Catena. 83: 76–86