Abstract
Soil erosion is one of the most common types of land degradation. To provide useful information for proper management, quantitative soil erosion evaluation and identifying influential factors are needed. However, rare studies have been reported on spatial modeling of soil erosion in connection with affective factors to prioritize the locality and the type of erosion control measures. Hence, the aim of this study was to (1) assess erosion-prone areas in the Talar watershed, Iran, using the revised universal soil loss equation (RUSLE) model and (2) investigate the relationship between soil erosion variability and land-use changes. Toward that, the ordinary least squares (OLS), geographically weighted regression (GWR) models, and principal component analysis (PCA) were used to analyze spatial relationships between soil erosion, land-use, and the RUSLE factors. The results of the OLS and GWR models indicated that these relationships are spatially non-stationary. GWR models had a good predictive performance than OLS with lower Akaike’s Information Criterion (from 254.31 to 276.81 in OLS and from 247.87 to 269.42 in GWR) and higher adjusted R2 values (from 0.12 to 0.54 in OLS, and from 0.36 to 0.66 in GWR). Among the variables mentioned above, LS factor, P factor, forest, and irrigated land were the most influential variables in GWR models. The results of PCA showed that PC1 and PC2 explained 66.2% of the variation in soil erosion concerning land-use and the RUSLE factors. These results provided appropriate references for managers and experts properly planning the study watershed.
Graphical abstract
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The datasets used and analyzed during the present study are available from the corresponding author upon reasonable request.
Code availability
R software was used for this study.
References
Anache JAA, Flanagan DC, Srivastava A, Wendland EC (2018) Land use and climate change impacts on runoff and soil erosion at the hillslope scale in the Brazilian Cerrado. Sci Total Environ 622–623:140–151. https://doi.org/10.1016/j.scitotenv.2017.11.257
Avand M, Mohammadi M, Mirchooli F et al (2022) A new approach for smart soil erosion modeling: integration of empirical and machine-learning models. Environ Model Assess. https://doi.org/10.1007/s10666-022-09858-x
Belayneh M, Yirgu T, Tsegaye D (2019) Potential soil erosion estimation and area prioritization for better conservation planning in Gumara watershed using RUSLE and GIS techniques. Environ Syst Res 8:1–17. https://doi.org/10.1186/s40068-019-0149-x
Brunsdon C, Fotheringham AS, Charlton ME (1996) Geographically weighted regression: a method for exploring spatial nonstationarity. Geogr Anal 28(4):281–298. https://doi.org/10.1111/j.1538-4632.1996.tb00936.x
Chakrabortty R, Pal SC, Sahana M (2020) Soil erosion potential hotspot zone identification using machine learning and statistical approaches in eastern India. Nat Hazards 104:1259–1294. https://doi.org/10.1007/s11069-020-04213-3
Chalise D, Kumar L, Prasad C, Sushil S (2018) Spatial assessment of soil erosion in a hilly watershed of Western. Environ Earth Sci 77:1–11. https://doi.org/10.1007/s12665-018-7842-3
Chaplot V, Giboire G, Marchand P, Valentin C (2005) Dynamic modelling for linear erosion initiation and development under climate and land-use changes in northern Laos. CATENA 63:318–328
Chicas SD, Omine K, Ford JB (2016) Identifying erosion hotspots and assessing communities’ perspectives on the drivers, underlying causes and impacts of soil erosion in Toledo’s Rio Grande Watershed: Belize. Appl Geogr 68:57–67. https://doi.org/10.1016/j.apgeog.2015.11.010
Choudhury BU, Nengzouzam G, Islam A (2022) Runoff and soil erosion in the integrated farming systems based on micro-watersheds under projected climate change scenarios and adaptation strategies in the eastern Himalayan mountain ecosystem (India). J Environ Manag 309:114667
Chuma GB, Bora FS, Ndeko AB (2022) Estimation of soil erosion using RUSLE modeling and geospatial tools in a tea production watershed (Chisheke in Walungu), Eastern Democratic Republic of Congo. Model Earth Syst Environ 8:1273–1289. https://doi.org/10.1007/s40808-021-01134-3
Dadashpoor H, Azizi P, Moghadasi M (2019) Environment Land use change, urbanization, and change in landscape pattern in a metropolitan area. Sci Total Environ 655:707–719. https://doi.org/10.1016/j.scitotenv.2018.11.267
Efthimiou N, Lykoudi E, Psomiadis E (2020) Inherent relationship of the USLE, RUSLE topographic factor algorithms and its impact on soil erosion modelling. Hydrol Sci J. https://doi.org/10.1080/02626667.2020.1784423
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. Remote Sens Appl Soc Environ 13:361–374. https://doi.org/10.1016/j.rsase.2018.12.004
Fayas CM, Abeysingha NS, Nirmanee KGS (2019) Soil loss estimation using RUSLE model to prioritize erosion control in KELANI river basin in Sri Lanka. Int Soil Water Conserv Res 7:130–137. https://doi.org/10.1016/j.iswcr.2019.01.003
Fotheringham AS, Brunsdon C, Charlton M (2002) Geographically weighted regression: the analysis of spatially varying relationships. John Wiley & Sons
Ganasri BP, Ramesh H (2016) Assessment of soil erosion by RUSLE model using remote sensing and GIS—a case study of Nethravathi Basin. Geosci Front 7:953–961. https://doi.org/10.1016/j.gsf.2015.10.007
Gao J, Li S (2011) Detecting spatially non-stationary and scale-dependent relationships between urban landscape fragmentation and related factors using Geographically Weighted Regression. Appl Geogr 31:292–302. https://doi.org/10.1016/j.apgeog.2010.06.003
Gao L, Bowker MA, Xu M (2017) Biological soil crusts decrease erodibility by modifying inherent soil properties on the Loess Plateau, China. Soil Biol Biochem 105:49–58. https://doi.org/10.1016/j.soilbio.2016.11.009
Georganos S, Abdi AM, Tenenbaum DE, Kalogirou S (2017) Examining the NDVI-rainfall relationship in the semi-arid Sahel using geographically weighted regression. J Arid Environ 146:64–74. https://doi.org/10.1016/j.jaridenv.2017.06.004
Gia T, Degener J, Kappas M (2018) Integrated universal soil loss equation (USLE) and Geographical Information System (GIS) for soil erosion estimation in A Sap basin: Central Vietnam. Int Soil Water Conserv Res 6:99–110. https://doi.org/10.1016/j.iswcr.2018.01.001
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
Hickey R (2000) Slope angle and slope length solutions for GIS. Cartography 29:1–8
Kavian A, Mohammadi M, Gholami L, Rodrigo-Comino J (2018) Assessment of the spatiotemporal effects of land use changes on runoff and nitrate loads in the Talar River. Water 10:1–19. https://doi.org/10.3390/w10040445
Koirala P, Thakuri S, Joshi S, Chauhan R (2019) Estimation of soil erosion in Nepal using a RUSLE modeling and geospatial tool. Geosciences. https://doi.org/10.3390/geosciences9040147
Li L, Zha Y, Zhang J (2020) Spatially non-stationary effect of underlying driving factors on surface urban heat islands in global major cities. Int J Appl Earth Obs Geoinf 90:102131. https://doi.org/10.1016/j.jag.2020.102131
Mehri A, Salmanmahiny A, Tabrizi ARM (2018) Investigation of likely effects of land use planning on reduction of soil erosion rate in river basins: case study of the Gharesoo River Basin. CATENA 167:116–129. https://doi.org/10.1016/j.catena.2018.04.026
Mirchooli F, Sadeghi SH, Darvishan AK (2020) Analyzing spatial variations of relationships between land surface temperature and some remotely sensed indices in different land uses. Remote Sens Appl Soc Environ. https://doi.org/10.1016/j.rsase.2020.100359
Mohamadi M, Fallah M, Kavian A (2016) The application of RUSLE model in spatial distribution determination of soil loss hazard. Ecohydrology 3:645–658
Mohammadi M, Khaledi Darvishan A, Bahramifar N (2019) Spatial distribution and source identification of heavy metals (As, Cr, Cu, and Ni) at sub-watershed scale using geographically weighted regression. Int Soil Water Conserv Res 7:308–315. https://doi.org/10.1016/j.iswcr.2019.01.005
Nazari MZAA, Mohammady SM (2017) Investigating effects of land use change scenarios on soil erosion using CLUE-s and RUSLE models. Int J Environ Sci Technol 14:1905–1918. https://doi.org/10.1007/s13762-017-1288-0
Nearing MA, Foster GR, Lane LJ, Finkner SC (1989) A process-based soil erosion model for USDA water erosion prediction project. Transactions of ASAE. Trans ASAE 32:1587–1593
Nguyen KA, Chen W, Lin BS, Seeboonruang U (2021) Comparison of ensemble machine learning methods for soil erosion pin measurements. ISPRS Int J Geo-Inf 10:42. https://doi.org/10.3390/ijgi10010042
Nunes AN, De AAC, Coelho COA (2011) Impacts of land use and cover type on runoff and soil erosion in a marginal area of Portugal. Appl Geogr 31:687–699. https://doi.org/10.1016/j.apgeog.2010.12.006
Nyesheja EM, Chen X, El-Tantawi AM (2019) Soil erosion assessment using RUSLE model in the Congo Nile Ridge region of Rwanda. Phys Geogr 40:339–360. https://doi.org/10.1080/02723646.2018.1541706
Ochoa PA, Fries A, Mejía D (2016) Effects of climate, land cover and topography on soil erosion risk in a semiarid basin of the Andes. CATENA 140:31–42. https://doi.org/10.1016/j.catena.2016.01.011
Park KN (2012) Impacts of land use changes on soil erosion in Pa Deng sub-district, adjacent area of Kaeng. Soil Water Resour 7:10–17
Pejman A, Nabi Bidhendi G, Ardestani M (2015) A new index for assessing heavy metals contamination in sediments: a case study. Ecol Ind 58:365–373. https://doi.org/10.1016/j.ecolind.2015.06.012
Prasannakumar V, Shiny R, Geetha N, Vijith H (2011) Spatial prediction of soil erosion risk by remote sensing, GIS and RUSLE approach : a case study of Siruvani river watershed in Attapady valley, Kerala, India. Environ Earth Sci 64:965–972. https://doi.org/10.1007/s12665-011-0913-3
Renard KG, Freimund JR (1994) Using monthly precipitation data to estimate the R-factor in the revised USLE. J Hydrol 157:287–306
Renard KG, Foster GR, Weesies GA, Porter JP (1991) RUSLE: revised universal soil loss equation. J Soil Water Conserv 46:30–33
Ruysschaert G, Poesen J, Verstraeten G, Govers G (2007) Soil loss due to harvesting of various crop types in contrasting agro-ecological environments. Agr Ecosyst Environ 120:153–165. https://doi.org/10.1016/j.agee.2006.08.012
Sahour H, Gholami V, Vazifedan M, Saeedi S (2021) Machine learning applications for water-induced soil erosion modeling and mapping. Soil Tillage Res 211:105032
Senanayake S, Pradhan B, Alamri A, Park H-J (2022) A new application of deep neural network (LSTM) and RUSLE models in soil erosion prediction. Sci Total Environ 845:157220
Setyawan C, Lee CY, Prawitasari M (2019) Investigating spatial contribution of land use types and land slope classes on soil erosion distribution under tropical environment. Nat Hazards 98:697–718. https://doi.org/10.1007/s11069-019-03725-x
Sharma A, Tiwari KN, Bhadoria PBS (2011) Effect of land use land cover change on soil erosion potential in an agricultural watershed. Environ Monit Assess 173:789–801. https://doi.org/10.1007/s10661-010-1423-6
Taghipour Javi S, Malekmohammadi B, Mokhtari H (2014) Application of geographically weighted regression model to analysis of spatiotemporal varying relationships between groundwater quantity and land use changes (case study: Khanmirza Plain, Iran). Environ Monit Assess 186:3123–3138. https://doi.org/10.1007/s10661-013-3605-5
Tang Q, Xu Y, Bennett SJ (2015) Assessment of soil erosion using RUSLE and GIS: a case study of the Yangou watershed in the Loess Plateau, China. Environ Earth Sci 73:1715–1724. https://doi.org/10.1007/s12665-014-3523-z
Thomas J, Joseph S, Thrivikramji KP (2018) Estimation of soil erosion in a rain shadow river basin in the southern Western Ghats, India using RUSLE and transport limited sediment delivery function. Int Soil Water Conserv Res 6:111–122. https://doi.org/10.1016/j.iswcr.2017.12.001
Thornes JB, Wainwright J (2004) Environmental issues in the Mediterranean: processes and perspectives from the past and present. Routledge
Thornes JB (1990) The interaction of erosional and vegetational dynamics in land degradation: spatial outcomes. Vegetation and erosion. Processes and environments, 41–53
Torabi Haghighi A, Menberu MW, Darabi H (2018) Use of remote sensing to analyse peatland changes after drainage for peat extraction. Land Degrad Dev 29:3479–3488
Tu J (2011) Spatially varying relationships between land use and water quality across an urbanization gradient explored by geographically weighted regression. Appl Geogr 31:376–392. https://doi.org/10.1016/j.apgeog.2010.08.001
Vanacker V, Ameijeiras-mariño Y, Schoonejans J (2019) Land use impacts on soil erosion and rejuvenation in Southern Brazil. CATENA 178:256–266. https://doi.org/10.1016/j.catena.2019.03.024
Wang D, Fu B, Zhao W (2008) Multifractal characteristics of soil particle size distribution under different land-use types on the Loess Plateau. China 72:29–36. https://doi.org/10.1016/j.catena.2007.03.019
Wang Q, Ni J, Tenhunen J (2005) Application of a geographically‐weighted regression analysis to estimate net primary production of Chinese forest ecosystems. Glob Ecol Biogeogr 14(4):379–393. https://doi.org/10.1111/j.1466-822X.2005.00153.x
Wang K, Zhang CR, Li WD, Lin J, Zhang DX (2014) Mapping soil organic matter with limited sample data using geographically weighted regression. J Spat Sci 59(1):91–106. https://doi.org/10.1080/14498596.2013.812024
Wischmeier WH (1976) Use and misuse of the universal soil loss equation. J Soil Water Conserv 31:5–9
Zare M, Panagopoulos T, Loures L (2017) Simulating the impacts of future land use change on soil erosion in the Kasilian watershed, Iran. Land Use Policy 67:558–572. https://doi.org/10.1016/j.landusepol.2017.06.028
Zema DA, Carrà BG, Lucas-Borja ME (2022) Modelling water flow and soil erosion in mediterranean headwaters (with or without Check Dams) under land-use and climate change scenarios using SWAT. Water 14:2338. https://doi.org/10.3390/w14152338
Zerihun M, Mohammedyasin MS, Sewnet D, Adem AA, Lakew M (2018) Assessment of soil erosion using RUSLE, GIS, and remote sensing in NW Ethiopia. Geoderma Reg 12:83–90. https://doi.org/10.1016/j.geodrs.2018.01.002
Zhao C, Jensen J, Weng Q, Weaver R (2018a) A geographically weighted regression analysis of the underlying factors related to the surface urban heat island phenomenon. Remote Sens 10:1–18. https://doi.org/10.3390/rs10091428
Zhao H, Ren Z, Tan J (2018b) The spatial patterns of land surface temperature and its impact factors : spatial non-stationarity and scale effects based on a geographically-weighted regression model. Sustainability 10:1–21. https://doi.org/10.3390/su10072242
Funding
The partial support of the Agrohydrology Research Group of Tarbiat Modares University (Grant No. IG-39713), Iran, concerning the corresponding author, is acknowledged.
Author information
Authors and Affiliations
Contributions
All authors contributed to the study’s conception and design. FM and MMK performed material preparation, data collection, and analysis. FM and MMK wrote the first draft of the manuscript, and SHS commented on the original versions. All authors read and approved the final manuscript. SHS reviewed and edited the final manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Mirchooli, F., Mohammadi, M. & Sadeghi, S. Spatial modeling of relationship between soil erosion factors and land-use changes at sub-watershed scale for the Talar watershed, Iran. Nat Hazards 116, 3703–3723 (2023). https://doi.org/10.1007/s11069-023-05832-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11069-023-05832-2