Articles | Volume 9, issue 2
https://doi.org/10.5194/soil-9-411-2023
https://doi.org/10.5194/soil-9-411-2023
Original research article
 | 
13 Jul 2023
Original research article |  | 13 Jul 2023

Mapping land degradation risk due to land susceptibility to dust emission and water erosion

Mahdi Boroughani, Fahimeh Mirchooli, Mojtaba Hadavifar, and Stephanie Fiedler

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Cited articles

Amiri, M., Pourghasemi, H. R., Ghanbarian, G. A., and Afzali, S. F.: Assessment of the importance of gully erosion effective factors using Boruta algorithm and its spatial modeling and mapping using three machine learning algorithms, Geoderma, 340, 55–69, https://doi.org/10.1016/j.geoderma.2018.12.042, 2019. 
Anache, J. A. A., Flanagan, D. C., Srivastava, A., and Wendland, E. C.: Land use and climate change impacts on runoff and soil erosion at the hillslope scale in the Brazilian Cerrado, Sci. Total Environ., 622, 140–151, https://doi.org/10.1016/j.scitotenv.2017.11.257, 2018 
Arabameri, A., Chen, W., Loche, M., Zhao, X., Li, Y., Lombardo, L., Cerda, A., Pradhan, B., and Bui, D. T.: Comparison of machine learning models for gully erosion susceptibility mapping, Geosci. Front., 11, 1609–1620, https://doi.org/10.1016/j.gsf.2019.11.009, 2019a. 
Arabameri, A., Pradhan, B., and Rezaei, K.: Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in GIS, J. Environ. Manag., 232, 928–942, https://doi.org/10.1016/j.jenvman.2018.11.110, 2019b. 
Avand, M., Moradi, H. R., and Lasboyee, M. R.: Spatial prediction of future flood risk: An approach to the effects of climate change, Geosciences, 11, 1–20, https://doi.org/10.3390/geosciences11010025, 2021. 
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Short summary
The present study used several different datasets, conducted a field survey, and paired the data with three different machine learning algorithms to construct spatial maps for areas at risk of land degradation for the Lut watershed in Iran. According to the land degradation map, almost the entire study region is at risk. A large fraction of 43 % of the area is prone to both high wind-driven and water-driven soil erosion.
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