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Spatial scaling effects of gully erosion in response to driving factors in southern China

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Abstract

Gully erosion, an integrated result of various social and environmental factors, is a severe problem for sustainable development and ecology security in southern China. Currently, the dominant driving forces on gully distribution are shown to vary at different spatial scales. However, few systematic studies have been performed on spatial scaling effects in identifying driving forces for gully erosion. In this study, we quantitatively identified the determinants of gully distribution and their relative importance at four different spatial scales (southern China, Jiangxi province, Ganxian county, and Tiancun township, respectively) based on the Boruta algorithm. The optimal performance of gully susceptibility mapping was investigated by comparing the performance of the multinomial logistic regression (MLR), logistic model tree (LMT), and random forest (RF). Across the four spatial scales, the total contributions of gully determinants were classified as lithology and soil (32.65%) > topography (22.40%) > human activities (22.31%) > climate (11.32%) > vegetation (11.31%). Among these factors, precipitation (7.82%), land use and land cover (6.16%), rainfall erosivity (10.15%), and elevation (11.59%) were shown to be the predominant factors for gully erosion at the individual scale of southern China, province, county, and township, respectively. In addition, contrary to climatic factors, the relative importance of soil properties and vegetation increased with the decrease of spatial scale. Moreover, the RF model outperformed MLR and LMT at all the investigated spatial scales. This study provided a reference for factor selection in gully susceptibility modeling and facilitated the development of gully erosion management strategies suitable for different spatial scales.

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References

  • Achour Y, Pourghasemi H R, 2020. How do machine learning techniques help in increasing accuracy of landslide susceptibility maps? Geoscience Frontiers, 11(3): 871–883.

    Article  Google Scholar 

  • Amiri M, Pourghasemi H R, Ghanbarian G A et al., 2019. 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.

    Article  Google Scholar 

  • Arabameri A, Chen W, Loche M et al., 2019. Comparison of machine learning models for gully erosion susceptibility mapping. Geoscience Frontiers, 11(5): 1609–1620.

    Article  Google Scholar 

  • Arabameri A, Pradhan B, Rezaei K et al., 2018. Spatial modelling of gully erosion using evidential belief function, logistic regression, and a new ensemble of evidential belief function-logistic regression algorithm. Land Degradation & Development, 29(11): 4035–4049.

    Article  Google Scholar 

  • Azareh A, Rahmati O, Rafiei-Sardooi E et al., 2019. Modelling gully-erosion susceptibility in a semi-arid region, Iran: Investigation of applicability of certainty factor and maximum entropy models. Science of The Total Environment, 655: 684–696.

    Article  CAS  Google Scholar 

  • Bernatek-Jakiel A, Poesen J, 2018. Subsurface erosion by soil piping: Significance and research needs. Earth Science Reviews, 185: 1107–1128.

    Article  Google Scholar 

  • Bezerra M O, Baker M, Palmer M A et al., 2020. Gully formation in headwater catchments under sugarcane agriculture in Brazil. Journal of Environmental Management, 270: 110271.

    Article  Google Scholar 

  • Borrelli P, Robinson D A, Panagos P et al., 2020. Land use and climate change impacts on global soil erosion by water (2015–2070). Proceedings of the National Academy of Sciences of the United States of America, 117(36): 21994–22001.

    Article  CAS  Google Scholar 

  • Breiman L, 2001. Random forests. Machine Learning, 45: 5–32.

    Article  Google Scholar 

  • Bui D T, Tuan T A, Klempe H et al., 2016. Spatial prediction models for shallow landslide hazards: A comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides, 13(2): 361–378.

    Article  Google Scholar 

  • Caraballo-Arias N A, Conoscenti C, Di Stefano C et al., 2014. Testing GIS-morphometric analysis of some Sicilian badlands. Catena, 113: 370–376.

    Article  Google Scholar 

  • Castillo C, Gómez J A, 2016. A century of gully erosion research: Urgency, complexity and study approaches. Earth Science Reviews, 160: 300–319.

    Article  Google Scholar 

  • Chaplot V, Brozec E C L, Silvera N, 2005. Spatial and temporal assessment of linear erosion in catchments under sloping lands of northern Laos. Catena, 63(2/3): 167–184.

    Article  Google Scholar 

  • Chen J L, Zhou M, Lin J S et al., 2018. Comparison of soil physicochemical properties and mineralogical compositions between noncollapsible soils and collapsed gullies. Geoderma, 317: 56–66.

    Article  CAS  Google Scholar 

  • Chen Y P, Wu J H, Wang H et al., 2019. Evaluating the soil quality of newly created farmland in the hilly and gully region on the Loess Plateau, China. Journal of Geographical Sciences, 29(5): 791–802.

    Article  Google Scholar 

  • Conforti M, Aucelli P P C, Robustelli G et al., 2011. Geomorphology and GIS analysis for mapping gully erosion susceptibility in the Turbolo stream catchment (Northern Calabria, Italy). Natural Hazards, 56(3): 881–898.

    Article  Google Scholar 

  • Conoscenti C, Agnesi V, Angileri S et al., 2013. A GIS-based approach for gully erosion susceptibility modelling: A test in Sicily, Italy. Environmental Earth Sciences, 70(3): 1179–1195.

    Article  Google Scholar 

  • Cox R, Zentner D B, Rakotondrazafy A F M et al., 2010. Shakedown in Madagascar: Occurrence of lavakas (erosional gullies) associated with seismic activity. Geology, 38(2): 179–182.

    Article  Google Scholar 

  • de Vente J, Poesen J, 2005. Predicting soil erosion and sediment yield at the basin scale: Scale issues and semi-quantitative models. Earth Science Reviews, 71(1/2): 95–125.

    Article  Google Scholar 

  • Deng Y S, Duan X Q, Ding S W et al., 2020. Effect of joint structure and slope direction on the development of collapsing gully in tuffaceous sandstone area in South China. International Soil and Water Conservation Research, 8(2): 131–140.

    Article  Google Scholar 

  • Descroix L, Barrios J G, Viramontes D et al., 2008. Gully and sheet erosion on subtropical mountain slopes: Their respective roles and the scale effect. Catena, 72(3): 325–339.

    Article  Google Scholar 

  • Drăguţ L, Schauppenlehner T, Muhar A et al., 2009. Optimization of scale and parametrization for terrain segmentation: An application to soil-landscape modeling. Computational Geosciences, 35(9): 1875–1883.

    Article  Google Scholar 

  • Elith J, Leathwick J R, Hastie T, 2008. A working guide to boosted regression trees. Journal of Animal Ecology, 77(4): 802–813.

    Article  CAS  Google Scholar 

  • Erktan A, Cécillon L, Graf F et al., 2016. Increase in soil aggregate stability along a Mediterranean successional gradient in severely eroded gully bed ecosystems: Combined effects of soil, root traits and plant community characteristics. Plant and Soil, 398(1/2): 121–137.

    Article  CAS  Google Scholar 

  • Frankl A, Pretre V, Nyssen J et al., 2018. The success of recent land management efforts to reduce soil erosion in northern France. Geomorphology, 303: 84–93.

    Article  Google Scholar 

  • Fu B J, Zhao W W, Chen L D et al., 2006. A multiscale soil loss evaluation index. Chinese Science Bulletin, 51(4): 448–456.

    Article  Google Scholar 

  • Garosi Y, Sheklabadi M, Pourghasemi H R et al., 2018. Comparison of the different resolution and source of controlling factors for gully erosion susceptibility mapping. Geoderma, 330: 65–78.

    Article  Google Scholar 

  • Gayen A, Pourghasemi H R, Saha S et al., 2019. Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms. Science of The Total Environment, 668: 124–138.

    Article  CAS  Google Scholar 

  • Hayas A, Peña A, Vanwalleghem T, 2019. Predicting gully width and widening rates from upstream contribution area and rainfall: A case study in SW Spain. Geomorphology, 341: 130–139.

    Article  Google Scholar 

  • Hengl T, 2006. Finding the right pixel size. Computational Geosciences, 32(9): 1283–1298.

    Article  Google Scholar 

  • Jaafari A, Janizadeh S, Abdo H G et al., 2022. Understanding land degradation induced by gully erosion from the perspective of different geoenvironmental factors. Journal of Environmental Management, 315: 115181.

    Article  Google Scholar 

  • Kadavi P R, Lee C W, Lee S, 2019. Landslide-susceptibility mapping in Gangwon-do, South Korea, using logistic regression and decision tree models. Environmental Earth Sciences, 78(4): 116.

    Article  Google Scholar 

  • Kheir R B, Chorowicz J, Abdallah C et al., 2008. Soil and bedrock distribution estimated from gully form and frequency: A GIS-based decision-tree model for Lebanon. Geomorphology, 93(3/4): 482–492.

    Article  Google Scholar 

  • Khosravi K, Binh T P, Chapi K et al., 2018. A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Science of The Total Environment, 627: 744–755.

    Article  CAS  Google Scholar 

  • Kropat G, Bochud F, Jaboyedoff M et al., 2015. Predictive analysis and mapping of indoor radon concentrations in a complex environment using kernel estimation: An application to Switzerland. Science of The Total Environment, 505: 137–148.

    Article  CAS  Google Scholar 

  • Kursa M B, Rudnicki W R, 2010. Feature selection with the Boruta package. Journal of Statistical Software, 36(11): 1–13.

    Article  Google Scholar 

  • Lana J C, Amorim P D C, Lana C E, 2022. Assessing gully erosion susceptibility and its conditioning factors in southeastern Brazil using machine learning algorithms and bivariate statistical methods: A regional approach. Geomorphology, 402: 108159.

    Article  Google Scholar 

  • Landwehr N, Hall M, Frank E, 2003. Logistic model trees. In: European Conference on Machine Learning. Springer: 241–252.

  • Lei X X, Chen W, Avand M et al., 2020. GIS-based machine learning algorithms for gully erosion susceptibility mapping in a semi-arid region of Iran. Remote Sensing, 12(15): 2478.

    Article  Google Scholar 

  • Liao D L, Deng Y S, Duan X Q et al., 2022. Variations in weathering characteristics of soil profiles and response of the Atterberg limits in the granite hilly area of South China. Catena, 215: 106325.

    Article  CAS  Google Scholar 

  • Liao Y S, Yuan Z J, Zheng M G et al., 2019. The spatial distribution of Benggang and the factors that influence it. Land Degradation & Development, 30(18): 2323–2335.

    Article  Google Scholar 

  • Liaw A, Wiener M, 2002. Classification and regression by random forest. R News, 2(3): 18–22.

    Google Scholar 

  • Ließ M, Glaser B, Huwe B, 2012. Uncertainty in the spatial prediction of soil texture: Comparison of regression tree and random Forest models. Geoderma, 170: 70–79.

    Article  Google Scholar 

  • Lin J S, Huang Y H, Wang M K et al., 2015. Assessing the sources of sediment transported in gully systems using a fingerprinting approach: An example from Southeast China. Catena, 129: 9–17.

    Article  Google Scholar 

  • Liu G, Zheng F L, Wilson G V et al., 2021. Three decades of ephemeral gully erosion studies. Soil & Tillage Research, 212: 105046.

    Article  Google Scholar 

  • Lucà F, Conforti M, Robustelli G, 2011. Comparison of GIS-based gullying susceptibility mapping using bivariate and multivariate statistics: Northern Calabria, South Italy. Geomorphology, 134(3/4): 297–308.

    Article  Google Scholar 

  • Micheletti N, Foresti L, Robert S et al., 2013. Machine learning feature selection methods for landslide susceptibility mapping. Mathematical Geosciences, 46(1): 33–57.

    Article  Google Scholar 

  • Millares A, Gulliver Z, Polo M J, 2012. Scale effects on the estimation of erosion thresholds through a distributed and physically-based hydrological model. Geomorphology, 153: 115–126.

    Article  Google Scholar 

  • Mokarram M, Zarei A R, 2021. Determining prone areas to gully erosion and the impact of land use change on it by using multiple-criteria decision-making algorithm in arid and semi-arid regions. Geoderma, 403: 115379.

    Article  Google Scholar 

  • Naghibi S A, Pourghasemi H R, Dixon B, 2015. GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environmental Monitoring and Assessment, 188(1): 44.

    Article  Google Scholar 

  • Ngo P, Panahi M, Khosravi K et al., 2021. Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran. Geoscience Frontiers, 12(2): 505–519.

    Article  Google Scholar 

  • Nyssen J, Poesen J, Moeyersons J et al., 2002. Impact of road building on gully erosion risk: A case study from the northern Ethiopian Highlands. Earth Surface Processes & Landforms, 27(12): 1267–1283.

    Article  Google Scholar 

  • Peeters I, Van Oost K, Govers G et al., 2008. The compatibility of erosion data at different temporal scales. Earth and Planetary Science Letters, 265(1/2): 138–152.

    Article  CAS  Google Scholar 

  • Poesen J, 2018. Soil erosion in the anthropocene: Research needs. Earth Surface Processes & Landforms, 43(1): 64–84.

    Article  Google Scholar 

  • Poesen J, Nachtergaele J, Verstraeten G et al., 2003. Gully erosion and environmental change: Importance and research needs. Catena, 50(2–4): 91–133.

    Article  Google Scholar 

  • Pourghasemi H R, Rahmati O, 2018. Prediction of the landslide susceptibility: Which algorithm, which precision? Catena, 162: 177–192.

    Article  Google Scholar 

  • Pourghasemi H R, Yousefi S, Kornejady A et al., 2017. Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling. Science of The Total Environment, 609: 764–775.

    Article  CAS  Google Scholar 

  • Rahmati O, Tahmasebipour N, Haghizadeh A et al., 2017. Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion. Geomorphology, 298: 118–137.

    Article  Google Scholar 

  • Song C, Wang G, Sun X et al., 2016. Control factors and scale analysis of annual river water, sediments and carbon transport in China. Scientific Reports, 6: 25963.

    Article  CAS  Google Scholar 

  • Sonneveld M P W, Everson T M, Veldkamp A, 2005. Multi-scale analysis of soil erosion dynamics in Kwazulu-Natal, South Africa. Land Degradation & Development, 16(3): 287–301.

    Article  Google Scholar 

  • Sun W Y, Shao Q Q, Liu J Y, 2013. Soil erosion and its response to the changes of precipitation and vegetation cover on the Loess Plateau. Journal of Geographical Sciences, 23(6): 1091–1106.

    Article  Google Scholar 

  • Tao Y, He Y, Duan X et al., 2017. Preferential flows and soil moistures on a Benggang slope: Determined by the water and temperature co-monitoring. Journal of Hydrology, 553: 678–690.

    Article  Google Scholar 

  • Tarolli P, Sofia G, 2016. Human topographic signatures and derived geomorphic processes across landscapes. Geomorphology, 255(4): 140–161.

    Article  Google Scholar 

  • Valentin C, Poesen J, Li Y, 2005. Gully erosion: Impacts, factors and control. Catena, 63(2/3): 132–153.

    Article  CAS  Google Scholar 

  • Vanmaercke M, Panagos P, Vanwalleghem T et al., 2021. Measuring, modelling and managing gully erosion at large scales: A state of the art. Earth Science Reviews, 218: 103637.

    Article  Google Scholar 

  • Vanmaercke M, Poesen J, van Mele B et al., 2016. How fast do gully headcuts retreat? Earth Science Reviews, 154: 336–355.

    Article  Google Scholar 

  • Wang H L, Luo J, Qin W et al., 2020. Effect of spatial scale on gully distribution in northeastern China. Modeling Earth Systems and Environment, 7(3): 1611–1621.

    Article  CAS  Google Scholar 

  • Wang J, Zhong L N, Zhao W W et al., 2018. The influence of rainfall and land use patterns on soil erosion in multi-scale watersheds: A case study in the hilly and gully area on the Loess Plateau, China. Journal of Geographical Sciences, 28(10): 1415–1426.

    Article  Google Scholar 

  • Wang Y, Cao L X, Fan J B et al., 2017. Modelling soil detachment of different management practices in the red soil region of China. Land Degradation & Development, 28(5): 1496–1505.

    Article  Google Scholar 

  • Wang Z, Zhang G, Wang C et al., 2022. Assessment of the gully erosion susceptibility using three hybrid models in one small watershed on the Loess Plateau. Soil & Tillage Research, 223: 105481.

    Article  Google Scholar 

  • Wei Y J, Yu H L, Wu X L et al., 2021a. Identification of geo-environmental factors on Benggang susceptibility and its spatial modelling using comparative data-driven methods. Soil & Tillage Research, 208: 104857.

    Article  Google Scholar 

  • Wei Y J, Liu Z, Wu X L et al., 2021b. Can Benggang be regarded as gully erosion? Catena, 207: 105648.

    Article  Google Scholar 

  • Wei Y J, Liu Z, Zhang Y et al., 2022. Analysis of gully erosion susceptibility and spatial modelling using a GIS-based approach. Geoderma, 420: 115869.

    Article  Google Scholar 

  • Woo M, Huang L, Zhang S et al., 1997. Rainfall in Guangdong province, South China. Catena, 29(2): 115–129.

    Article  Google Scholar 

  • Wu Z L, Deng Y S, Cai C F et al., 2021. Multifractal analysis on spatial variability of soil particles and nutrients of Benggang in granite hilly region, China. Catena, 207: 105594.

    Article  CAS  Google Scholar 

  • Xia J, Cai C, Wei Y et al., 2019. Granite residual soil properties in collapsing gullies of south China: Spatial variations and effects on collapsing gully erosion. Catena, 174: 469–477.

    Article  CAS  Google Scholar 

  • Yuan X F, Han J C, Shao Y J et al., 2019. Geodetection analysis of the driving forces and mechanisms of erosion in the hilly-gully region of northern Shaanxi province. Journal of Geographical Sciences, 29(5): 779–790.

    Article  Google Scholar 

  • Zabihi M, Mirchooli F, Motevalli A et al., 2018. Spatial modelling of gully erosion in Mazandaran province, northern Iran. Catena, 161: 1–13.

    Article  Google Scholar 

  • Zabihi M, Pourghasemi H R, Pourtaghi Z S et al., 2016. GIS-based multivariate adaptive regression spline and random forest models for groundwater potential mapping in Iran. Environmental Earth Sciences, 75(8): 1–19.

    Article  Google Scholar 

  • Zhang H, Wang L, Yu S et al., 2021. Identifying government’s and farmers’ roles in soil erosion management in a rural area of southern China with social network analysis. Journal of Clean Production, 278: 123499.

    Article  Google Scholar 

  • Zhang S, Han X, Cruse R et al., 2022. Morphological characteristics and influencing factors of permanent gully and its contribution to regional soil loss based on a field investigation of 393 km2 in Mollisols region of northeast China. Catena, 217: 106467.

    Article  Google Scholar 

  • Zhong B L, Peng S Y, Zhang Q et al., 2013. Using an ecological economics approach to support the restoration of collapsing gullies in southern China. Land Use Policy, 32: 119–124.

    Article  Google Scholar 

  • Zhou Y, Yang C Q, Li F et al., 2021. Spatial distribution and influencing factors of Surface Nibble Degree index in the severe gully erosion region of China’s Loess Plateau. Journal of Geographical Sciences, 31(11): 1575–1597.

    Article  Google Scholar 

  • Zhou X Q, Wei Y J, He J et al., 2023. Estimation of gully erosion rate and its determinants in a granite area of southeast China. Geoderma, 429: 116223.

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. College of Resources and Environment, Huazhong Agricultural University, Wuhan, 430070, China

    Zheng Liu, Yujie Wei, Tingting Cui, Hao Lu & Chongfa Cai

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  1. Zheng Liu
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  2. Yujie Wei
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  3. Tingting Cui
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  4. Hao Lu
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  5. Chongfa Cai
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Correspondence to Yujie Wei.

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Foundation: National Natural Science Foundation of China, No.42277329, No.41807065, No.42077067; China Postdoctoral Science Foundation, No.2018M640714; Fundamental Research Funds for the Central Universities, No.2662021ZHQD003

Author: Liu Zheng (1994–), PhD Candidate, specialized in soil erosion and gully susceptibility assessment.

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Liu, Z., Wei, Y., Cui, T. et al. Spatial scaling effects of gully erosion in response to driving factors in southern China. J. Geogr. Sci. 34, 942–962 (2024). https://doi.org/10.1007/s11442-024-2234-y

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