IMPACT OF THE LS-FACTOR ESTIMATION METHODS ON MEAN ANNUAL SOIL LOSS
Received: 8th November 2023 Revised: 04th December 2023, 26th December 2023, 02nd January 2023 Accepted: 15th November 2023
DOI:
https://doi.org/10.20319/lijhls.2024.9.5161Keywords:
Soil Erosion, Soil Loss, LS-Factor, RUSLE, Nigeria, SlopeAbstract
Soil erosion was assessed in Anambra State using three different LS factor estimation methods. It showed that soil loss from the LS factor based on the slope in percent gives a good result with little underestimation with a PBIAS of -9.04 % while that based on slopes in radians performed best with a slight overestimation with a PBIAS of 2.81 %. The one based on degrees’ slope performed the least with a high underestimation with a PBIAS of -46.43 %. The result from field measurement yielded 27.76 t ha-1yr-1. The coefficient of variations was 241.47 %, 192.01 %, and 157.97 %, respectively, for slope in percent, radians and degree. Soil erosion is a highly variable phenomenon which was reflected by the high coefficient of variations. It shows that modelling soil erosion in the State with the LS factor estimation based on slopes in radians and percent yields better results. We believe this finding will be useful to authorities and scientists interested in soil erosion studies in the State. It is recommended that a similar study be extended to other terrains with moderate slopes.
References
Alewell, C., Borrelli, P., Meusburger, K., & Panagos, P. (2019). Using the USLE: Chances, challenges and limitations of soil erosion modelling. International Soil and Water Conservation Research, 7(3), 203–225. https://doi.org/10.1016/j.iswcr.2019.05.004
Andreoli, R. (2018). Modeling erosion risk using the RUSLE equation. QGIS and Applications in Water and Risks, 4, 245–282. https://doi.org/10.1002/9781119476726.ch8
Benavidez, R., Jackson, B., Maxwell, D., & Norton, K. (2018). A review of the (Revised) Universal Soil Loss Equation ((R) USLE): With a view to increasing its global applicability and improving soil loss estimates. Hydrology and Earth System Sciences, 22(11), 6059–6086. https://doi.org/10.5194/hess-22-6059-2018
CHIRPS. (2022). Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) daily rainfall data (2022nd ed.) [Map]. USGS. https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_DAILY
Desmet, P. J. J., & Govers, G. (1996). A GIS procedure for automatically calculating the USLE LS factor on topographically complex landscape units. Journal of Soil and Water Conservation, 51(5), 427–433. https://www.researchgate.net/publication/233425999_A_GIS_procedure_for_automatically_calculating_the_USLE_LS_factor_on_topographically_complex_landscape_units
Desmet, P. J. J., & Govers, G. (1997). Comment on’Modelling topographic potential for erosion and deposition using GIS’. International Journal of Geographical Information Science, 11(6), 603–610. https://doi.org/10.1080/136588197242211
Egbueri, J. C., Igwe, O., & Ifediegwu, S. I. (2022). Erosion risk mapping of Anambra State in southeastern Nigeria: Soil loss estimation by RUSLE model and geoinformatics. Bulletin of Engineering Geology and the Environment, 81(3), 91. https://doi.org/10.1007/s10064-022-02589-z
iSDASoil. (2022). Innovative Solutions for Decision Agriculture (iSDA), 30 m resolution soil map of Africa. (2nd ed.) [Map]. https://www.isda-africa.com/isdasoil/developer/
Karra, K., Kontgis, C., Statman-Weil, Z., Mazzariello, J. C., Mathis, M., & Brumby, S. P. (2021). Global land use/land cover with Sentinel 2 and deep learning. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 4704–4707. https://doi.org/10.1109/IGARSS47720.2021.9553499
Koirala, P., Thakuri, S., Joshi, S., & Chauhan, R. (2019). Estimation of soil erosion in Nepal using a RUSLE modeling and geospatial tool. Geosciences, 9(4), 147. https://doi.org/10.3390/geosciences9040147
Merchán, L., Martínez-Graña, A. M., Alonso Rojo, P., & Criado, M. (2023). Water Erosion Risk Analysis in the Arribes del Duero Natural Park (Spain) Using RUSLE and GIS Techniques. Sustainability, 15(2), 1627. https://doi.org/10.3390/su15021627
Mitasova, H., Hofierka, J., Zlocha, M., & Iverson, L. R. (1996). Modelling topographic potential for erosion and deposition using GIS. International Journal of Geographical Information Systems, 10(5), 629–641. https://doi.org/10.1080/02693799608902101
Moisa, M. B., Negash, D. A., Merga, B. B., & Gemeda, D. O. (2021). Impact of land-use and land-cover change on soil erosion using the RUSLE model and the geographic information system: A case of Temeji watershed, Western Ethiopia. Journal of Water and Climate Change, 12(7), 3404–3420. https://doi.org/10.2166/wcc.2021.131
Moore, I. D., & Burch, G. J. (1986a). Modelling erosion and deposition: Topographic effects. Transactions of the ASAE, 29(6), 1624–1630. https://doi.org/10.13031/2013.30363
Moore, I. D., & Burch, G. J. (1986b). Physical basis of the length-slope factor in the universal soil loss equation. Soil Science Society of America Journal, 50(5), 1294–1298. https://doi.org/10.2136/sssaj1986.03615995005000050042x
Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885–900. https://doi.org/10.13031/2013.23153
Naipal, V., Reick, C., Pongratz, J., & Van Oost, K. (2015). Improving the global applicability of the RUSLE model–adjustment of the topographical and rainfall erosivity factors. Geoscientific Model Development, 8(9), 2893–2913. https://doi.org/10.5194/gmd-8-2893-2015
Nakil, M., & Khire, M. (2016). Effect of slope steepness parameter computations on soil loss estimation: Review of methods using GIS. Geocarto International, 31(10), 1078–1093. https://doi.org/10.1080/10106049.2015.1120349
NASA. (2022). Shuttle Radar Topographic Mission (SRTM) 30 m Digital Elevation Model (DEM) [Tiff]. https://dwtkns.com/srtm30m/
Panagos, P., Borrelli, P., & Meusburger, K. (2015). A new European slope length and steepness factor (LS-Factor) for modeling soil erosion by water. Geosciences, 5(2), 117–126. https://doi.org/10.3390/geosciences5020117
Renard, K. G., Foster, G. R., Weesies, G. A., McCool, D. K., & Yoder, D. C. (1997). Predicting soil erosion by water: A guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). Agriculture Handbook, 703, 407. https://www.ars.usda.gov/arsuserfiles/64080530/rusle/ah_703.pdf
Roose, E. J. (1977). Use of the universal soil loss equation to predict erosion in West Africa. In Soil erosion: Prediction and control (Vol. 21, pp. 60–74). Soil Conservation Society of America Ankeny, IA. https://horizon.documentation.ird.fr/exl-doc/pleins_textes/pleins_textes_5/b_fdi_08-09/09135.pdf
Tsige, M. G., Malcherek, A., & Seleshi, Y. (2022). Improving the Modified Universal Soil Loss Equation by Physical Interpretation of Its Factors. Water, 14(9), 1450. https://doi.org/10.3390/w14091450
USGS. (2023). The United States Geological Survey (USGS) 30 m resolution Landsat 7 and 8 images [Map]. https://earthexplorer.usgs.gov/
Wischmeier, W. H., & Smith, D. D. (1978). Predicting rainfall erosion losses: A guide to conservation planning. Department of Agriculture, Science and Education Administration. https://www.researchgate.net/profile/Heriansyah-Putra-2/post/Which-formula-is-correct/attachment/59d64d4e79197b80779a6e2c/AS%3A487650378424321%401493276318482/download/USLE.pdf
Ziadat, F. M., & Taimeh, A. Y. (2013). Effect of rainfall intensity, slope, land use and antecedent soil moisture on soil erosion in an arid environment. Land Degradation & Development, 24(6), 582–590. https://doi.org/10.1002/ldr.2239
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