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SUSLE: a slope and seasonal rainfall-based RUSLE model for regional quantitative prediction of soil erosion

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Abstract

The Revised Universal Soil Loss Equation (RUSLE) models are most widely used for quantitative prediction of soil erosion. However, these models have many shortcomings. For example, the annual total rainfall is often adopted, ignoring the inhomogeneity of seasonal rainfall. The adopted vegetation coverage indexes (VCIs) are usually the annual average vegetation coverage or VCIs obtained by monitoring on a specific day, ignoring the seasonal changes in VCIs during the year. In addition, the impact of slope on the conservation practices factor is not considered. To overcome these problem, this study aims to propose a seasonal and slope factor-based RUSLE (SUSLE) model that considers the seasonal changes in rainfall and VCIs and the effect of slope on the conservation practices factor. Based on GIS and remote sensing, the quantitative prediction of soil erosion in Ningdu County, Jiangxi Province, in 2017 is taken as a case study. The traditional RUSLE model and the proposed SUSLE model are analyzed and compared. Results show that the overall distribution characteristics of soil erosion in the two models are similar that the SUSLE model is more consistent than the RUSLE model in all erosion levels and that the prediction performances of the SUSLE model in the very low, moderate, and high erosion levels are better than those of the RUSLE model. The distribution characteristics of soil erosion in different periods and the relationships between soil erosion and environmental factors (e.g., slope and land use) under the SUSLE model are discussed. The results show that the maximum erosion area occurred in spring and the minimum area in autumn; the soil erosion amount on slopes of 8~25° reached 65.14% of the total amount; bare grassland and cultivated land are the main land cover types impacted by soil erosion in Ningdu County.

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References

  • Addis HK, Klik A (2015) Predicting the spatial distribution of soil erodibility factor using USLE nomograph in an agricultural watershed, Ethiopia. International Soil and Water Conservation Research 3(4):282–290

    Google Scholar 

  • Bagherzadeh A (2012) Estimation of soil losses by USLE model using GIS at Mashhad plain, northeast of Iran. Arab J Geosci 7(1):211–220

    Google Scholar 

  • Baumann V, Bonadonna C, Cuomo S, Moscariello M (2020) Modelling of erosion processes associated with rainfall-triggered lahars following the 2011 cordon caulle eruption (chile). J Volcanol Geotherm Res:390

  • Belasri A, Lakhouili A (2016) Estimation of soil erosion risk using the universal soil loss equation (USLE) and geo-information technology in Oued El Makhazine watershed, Morocco. J Geogr Inf Syst 8(1):98–107

    Google Scholar 

  • Buttafuoco G, Conforti M, Aucelli PPC, Robustelli G, Scarciglia F (2011) Assessing spatial uncertainty in mapping soil erodibility factor using geostatistical stochastic simulation. Environ Earth Sci 66(4):1111–1125

    Google Scholar 

  • Cai C (2000) Study of applying USLE and geographical information system IDRISI to predict soil erosion in small watershed. J Soil Water Conserv 14(2):19–24

    Google Scholar 

  • Cao C, Sun Y, Sun H, Song Y (2018) Erosion resistance and scouring depth of fine-grained seabed of the Huanghe river estuary, China. Bull Eng Geol Environ 77(3):897–910

    Google Scholar 

  • Cerdà A (1998) The influence of aspect and vegetation on seasonal changes in erosion under rainfall simulation on a clay soil in Spain. Can J Soil Sci 78(2):321–330

    Google Scholar 

  • Chang Z, Du Z, Zhang F, Huang F, Chen J, Li W, Guo Z (2020) Landslide susceptibility prediction based on remote sensing images and GIS: comparisons of supervised and unsupervised machine learning models. Remote Sens 12(3):502

    Google Scholar 

  • Chen YB, Liang JM, Wang SY (2015) The characteristics of spatial-temporal differentiation of soil erosion based on RUSLE model: a case study of Chaoyang city, Liaoning province. Sci Geogr Sin 35(3):365–372

    Google Scholar 

  • Cuomo S, Della Sala M (2013) Rainfall-induced infiltration, runoff and failure in steep unsaturated shallow soil deposits. Eng Geol 162:118–127

    Google Scholar 

  • Cuomo S, Chareyre B, d'Arista P, Della Sala M, Cascini L (2016) Micromechanical modelling of rainsplash erosion in unsaturated soils by discrete element method. Catena 147:146–152

    Google Scholar 

  • Dai Z, Huang Y, Xu Q (2019) A hydraulic soil erosion model based on a weakly compressible smoothed particle hydrodynamics method. Bull Eng Geol Environ:1–12

  • de Lollo JA, Sena JN (2013) Establishing erosion susceptibility: analytical hierarchical process and traditional approaches. Bull Eng Geol Environ 72(3–4):589–600

    Google Scholar 

  • Fang G, Yuan T, Zhang Y, Wen X, Lin R (2019) Integrated study on soil erosion using RUSLE and GIS in Yangtze river basin of Jiangsu province (China). Arab J Geosci 12(5). https://doi.org/10.1007/s12517-019-4331-2

  • 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(6):953–961

    Google Scholar 

  • Hancock GR, Verdon-Kidd D, Jbc L (2017) Soil erosion predictions from a landscape evolution model-an assessment of a post-mining landform using spatial climate change analogues. Sci Total Environ 601-602:109

    Google Scholar 

  • Heng kai LI, Liu XS, Bo LI, Fa shuai LI, Beijing T (2014) Vegetation coverage variations and correlation with geomorphologic factors in red soil region: a case in south Jiangxi province. Sci Geogr Sin 34(1):103–109

    Google Scholar 

  • Huang F, Chen L, Yin K, Huang J, Gui L (2018) Object-oriented change detection and damage assessment using high-resolution remote sensing images, Tangjiao landslide, three Gorges reservoir, China. Environ Earth Sci 77(5):183

    Google Scholar 

  • Huang F, Cao Z, Guo J, Jiang S-H, Li S, Guo Z (2020) Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping. CATENA 191:104580. https://doi.org/10.1016/j.catena.2020.104580

    Article  Google Scholar 

  • Hui L, Xiaoling C, Lim KJ, Xiaobin C, Sagong M (2010) Assessment of soil erosion and sediment yield in liao watershed, Jiangxi province, China, using USLE, GIS, and RS. J Earth Sci 21(6):941–953

    Google Scholar 

  • Irvem A, Topaloglu F, Uygur V (2007) Estimating spatial distribution of soil loss over Seyhan river basin in Turkey. J Hydrol 336(1):30–37

    Google Scholar 

  • Jiang L, Yao Z, Liu Z, Wu S, Wang R, Wang L (2015) Estimation of soil erosion in some sections of lower Jinsha river based on RUSLE. Nat Hazards 76(3):1831–1847

    Google Scholar 

  • Kang G-o, Do TM, Lim J-s, Kim Y-s (2019) Evaluation of erosion resistance capacity on compacted weathered granite soil using non-destructive tests. Bull Eng Geol Environ:1–17

  • Karamage F, Zhang C, Kayiranga A, Shao H, Fang X, Ndayisaba F, Nahayo L, Mupenzi C, Tian G (2016) USLE-based assessment of soil erosion by water in the Nyabarongo river catchment, Rwanda. Int J Environ Res Public Health 13(8):835

    Google Scholar 

  • Kinnell PIA (2018) Determining soil erodibilities for the USLE-MM rainfall erosion model. Catena 163:424–426. https://doi.org/10.1016/j.catena.2018.01.008

    Article  Google Scholar 

  • Kinnell PIA (2019) Determining soil erodibility for the USLE-MM rainfall erosion model (vol 167, pg 444, 2018). Catena 179:220–220. https://doi.org/10.1016/j.catena.2019.03.045

    Article  Google Scholar 

  • Lal R (2001) Soil degradation by erosion. Land Degrad Dev 12:519–539

    Google Scholar 

  • Li H, Yang L, Lei J (2016) Soil erosion analysis in red soil hilly region by using HJ-CCD: a case study in Ganzhou. Remote sensing information. Remote Sensing Information 31(3):122–129

    Google Scholar 

  • Liangsong Z, Guohui D, Jiachuang GU (2015) Dynamic changes of soil erosion in the Chaohu watershed from 1992 to 2013. Acta Geograph Sin

  • Liu W, Wan S, Huang F, Luo X, Fu M (2019) Experimental study of subsurface erosion in granitic under the conditions of different soil column angles and flow discharges. Bull Eng Geol Environ:1–12

  • Lu J, Chen X, Li H, Liu H, Xiao J, Yin J (2011) Soil erosion changes based on GIS/RS and USLE in Poyang lake basin. Transactions of the Chinese Society of Agricultural Engineering 27(2):337–344

    Google Scholar 

  • Lui BY, Nearing MA, Risse LM (1994) Slope gradient effects on soil loss for steep slopes. Trans ASAE 37(6):1835–1842

    Google Scholar 

  • McCool DK (1987) Revised slope steepness factor for the universal soil loss equation. Trans ASAE 30(5):1387–1396 Retrieved from https://ci.nii.ac.jp/naid/80003703585/en/

    Google Scholar 

  • Ministry of Water Resources P R (2008) China (2008) soil erosion rate standard, technological standard of soil and water conservation sl190-2007. China Water Power Press, Beijing, pp 1–26

    Google Scholar 

  • Mushi CA, Ndomba PM, Trigg MA, Tshimanga RM, Mtalo F (2019) Assessment of basin-scale soil erosion within the Congo river basin: a review. Catena 178:64–76. https://doi.org/10.1016/j.catena.2019.02.030

    Article  Google Scholar 

  • Pimentel D (2006) Soil erosion: a food and environmental threat. Environ Dev Sustain 8(1):119–137

    Google Scholar 

  • Renard KG, Foster GR, Weesies GA, Mccool DK, Yoder DC (1997) Predicting soil erosion by water: a guide to conservation planning with the revised universal soil loss equation (RUSLE). Agricultural Handbook

  • Renlin Z (2010) Analysis of regional distribution of soil resistance index to erosion in Jiangxi province[d]. Changjiang academy of sciences, Wuhan

    Google Scholar 

  • Risal A, Bhattarai R, Kum D, Park YS, Yang JE, Lim KJ (2016) Application of web erosivity module (WERM) for estimation of annual and monthly R factor in Korea. Catena 147:225–237

    Google Scholar 

  • Seeber C, Hartmann H, Xiang W, King L (2010) Land use change and causes in the Xiangxi catchment, three Gorges area derived from multispectral data. J Earth Sci 21(6):846–855

    Google Scholar 

  • Sharpley AN, Williams JR (1990) Epic-erosion/productivity impact calculator: 2. User manual Technical Bulletin - United States Department of Agriculture, 4(4), 206–207

  • Shi D, Shi X, Li D (1996) Study on dynamic monitoring of soil erosion using remote sensing technique. Acta Pedol Sin 33(1):48–58

    Google Scholar 

  • Shi ZH, Cai CF, Ding SW, Wang TW, Chow TL (2004) Soil conservation planning at the small watershed level using RUSLE with GIS: a case study in the three Gorge area of China. Catena 55(1):33–48

    Google Scholar 

  • Vezina K, Bonn F, Van CP (2006) Agricultural land-use patterns and soil erosion vulnerability of watershed units in Vietnam’s northern highlands. Landsc Ecol 21(8):1311–1325

    Google Scholar 

  • Wang L, Li X-A, Li L-C, Hong B, Liu J (2019) Experimental study on the physical modeling of loess tunnel-erosion rate. Bull Eng Geol Environ:1–14

  • Wischmeier WH (1976) Use and misuse of universal soil loss equation. J Soil Water Conserv 31(1):5–9

    Google Scholar 

  • Wischmeier WJC, Cross B (1971) Soil erodibility nomograph for farmland and construction sites. J Soil Water Conserv 26(5):189–193 Retrieved from https://ci.nii.ac.jp/naid/10006070628/en/

    Google Scholar 

  • Wischmeier WH, Smith DD (1978) Predicting rainfall erosion losses-a guide to conservation planning. Agric Handbook 537

  • Xue J, Lyu D, Wang D, Wang Y, Yin D, Zhao Z, Mu Z (2018) Assessment of soil erosion dynamics using the GIS-based RUSLE model: a case study of Wangjiagou watershed from the three Gorges reservoir region, southwestern China. Water 10(12):1817

    Google Scholar 

  • Yu B, Rosewell CJ (1996) Technical notes: a robust estimator of the r-factor for the universal soil loss equation. Transactions of the ASAE, 39(2):559–561

  • Zhang W, Xie Y, Liu B (2002) Rainfall erosivity estimation using daily rainfall amounts. Sci Geogr Sin 22(6):705–711

    Google Scholar 

  • Zhang H, Wei J, Yang Q, Baartman JEM, Gai L, Yang X, Li S, Yu J, Ritsema CJ, Geissen V (2017) An improved method for calculating slope length (λ) and the LS parameters of the Revised Universal Soil Loss Equation for large watersheds. Geoderma 308:36–45

    Google Scholar 

  • Zhang HD, Zhang RH, Qi F, Liu X, Niu Y, Fan ZF, Zhang QH, Li JZ, Yuan L, Song YY et al (2018) The CSLE model based soil erosion prediction: comparisons of sampling density and extrapolation method at the county level. Catena 165:465–472. https://doi.org/10.1016/j.catena.2018.02.007

    Article  Google Scholar 

  • Zhang L, Wu F, Zhang H, Zhang L, Zhang J (2019) Influences of internal erosion on infiltration and slope stability. Bull Eng Geol Environ 78(3):1815–1827

    Google Scholar 

  • Zhao X, Zhang B, Wu P (2013) Changes in key driving forces of soil erosion in the middle Yellow River basin: vegetation and climate. Nat Hazards 70(1):957–968. https://doi.org/10.1007/s11069-013-0849-x

    Article  Google Scholar 

  • Zhong R, Zhong P (2011) Calculation methods of soil anti-erodibility index of red soil and purple soil. Bulletin of Soil & Water Conservation 31(6):95–98

    Google Scholar 

  • Zhou Q, Yang S, Zhao C, Cai M, Ya L (2014) A soil erosion assessment of the upper Mekong River in Yunnan province, China. Mt Res Dev 34(1):36–47

    Google Scholar 

  • Zhu M (2014) Soil erosion assessment using USLE in the GIS environment: a case study in the Danjiangkou reservoir region, China. Environ Earth Sci 73(12):7899–7908

    Google Scholar 

  • Zhu L, Huang L, Fan L, Huang J, Huang F, Chen J, Zhang Z, Wang Y (2020) Landslide susceptibility prediction modeling based on remote sensing and a novel deep learning algorithm of a cascade-parallel recurrent neural network. Sensors 20(6):1576 Retrieved from https://www.mdpi.com/1424-8220/20/6/1576

    Google Scholar 

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Funding

This research is funded by the National Key Research and Development Program (NO. 2017YFC1502505); the National Natural Science Foundation of China (No.41807285, 41762020, 51879127, and 51769014); the National Science Foundation of Jiangxi Province, China (20192BAB216034, 20192ACB2102, 20192ACB20020); the Postdoctoral Science Foundation of China (NO.2019M652287); and the Jiangxi Provincial Postdoctoral Science Foundation (NO. 2019KY08).

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Authors and Affiliations

  1. School of Civil Engineering and Architecture, Nanchang University, Nanchang, 330031, China

    Faming Huang, Jiawu Chen, Chi Yao, Zhilu Chang & Qinghui Jiang

  2. Changjiang Institute of Survey, Planning, Design and Research co., LTD, Wuhan, 430010, China

    Shu Li

  3. Faculty of Engineering, China University of Geosciences, Wuhan, 430074, China

    Zizheng Guo

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  1. Faming Huang
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  2. Jiawu Chen
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  3. Chi Yao
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  4. Zhilu Chang
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Correspondence to Chi Yao.

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Huang, F., Chen, J., Yao, C. et al. SUSLE: a slope and seasonal rainfall-based RUSLE model for regional quantitative prediction of soil erosion. Bull Eng Geol Environ 79, 5213–5228 (2020). https://doi.org/10.1007/s10064-020-01886-9

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