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Warianty tytułu
Języki publikacji
Abstrakty
Soil Erodibility Factor (K-factor) is a crucial component of a widely used equation for soil erosion assessment known as the USLE (Universal Soil Loss Equation) or its revised version – RUSLE. It reflects the potential of the soil of being detached due to rainfalls or runoffs. So far, an extensive number of researches provide different approaches and techniques in the evaluation of K-factor. This study applies soil erodibility estimation in the soils of the South Caucasian region using soil data prepared by the International Soil Reference and Information Centre (ISRIC) with 250 m resolution, whereas the recent K-factor estimation implemented in the EU scale was with 500 m resolution. Soil erodibility was assessed using an equation involving soil pH levels. The study utilises Trapesoidal equation of soil data processing and preparation, as suggested by ISRIC, for various layers of surface soil data with up to 0-30 cm depth. Both usage of SoilGrids data and its processing as well as estimation of K-factor applying soil pH levels have demonstrated sufficient capacity and accuracy in soil erodibility assessment. The final output result has revealed the K-factor values varying from 0.037 and more than 0.060 t ha h/MJ mm within the study area.
Wydawca
Czasopismo
Rocznik
Tom
Strony
44--55
Opis fizyczny
Bibliogr. 22 poz., rys., tab.
Twórcy
autor
- Azerbaijan National Academy Sciences, Baku, Azerbaijan
autor
- Baku State University, Baku, Azerbaijan
Bibliografia
- [1] Adornado, H.A., Yoshida, M. and Apolinares, H. (2009). Erosion Vulnerability Assessment in REINA, Quezon Province, Philippines with Raster-based Tool Built within GIS Environment. J. Agric. Res.
- [2] Arrouays, D., Grundy M.G., Hartemink A.E. et al. (2014). Chapter Three – GlobalSoilMap: Toward a Fine-Resolution Global Grid of Soil Properties. In: Sparks D.L. (Eds.) Soil carbon. Advances in Agronomy, vol. 125. United States: Academic Press.
- [3] Bayramov, E., Schlager, P., Kada, M. et al. (2019). Quantitative assessment of climate change impacts ontopredicted erosion risks and their spatial distribution within the landcover classes of the Southern Caucasus using GIS and remote sensing. Mod. Earth Sys. and Env., V5.
- [4] Benavidez, R., Bethanna, J., Deborah, M. et al. (2018). A review of the (Revised) Universal Soil Loss Equation ((R)USLE). Hydrol. Earth Syst. Sci., 22.
- [5] Buchhorn, M., Smets, B., Bertels, L. et al. (2019). Copernicus Global Land Service: Land Cover 100m, epoch “year”. Globe (V2.0.2).
- [6] Chen, L., Qian, X. and Shi, Y. (2011). Critical Area Identification of Potential Soil Loss in a Typical Watershed of the Three Gorges Reservoir Region. Water. Resour. Manage. 25, 3445. DOI:10.1007/s11269-011-9864-4.
- [7] David, W. P. (1988). Soil and Water Conservation Planning: Policy Issues and Recommendations. J. Philipp. Dev., 15, 47–84.
- [8] Hengl, T, de Jesus, J.M., MacMillan, R.A. et al. (2014). SoilGrids1km – global soil information based on automated mapping. PLoS One, 9(8):e105992.
- [9] Hengl, T., de Jesus, M.J., Heuvelink, G.B.M. et al. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS One, 12(2):e0169748.
- [10] Hernandez, E.C., Henderson, A. and Oliver, D.P. (2012). Effects of changing land use in the Pagsanjan–Lumban catchment on suspended sediment loads to Laguna de Bay, Philippines, Agric. Water Manag., 106, 8–16.
- [11] Kirchmeir, H. and Michael, H. (2016). Remote Sensing Concepts on Erosion Control and Pasture Management Report. Integrated Erosion control measures in Ismayilli, Azerbaijan. IBIS, GIZ, 11.
- [12] Kirchmeir, H. and Berger, V. (2019). Development of land cover and erosion risk map based on remotesensing for Tusheti protected areas. E.C.O. Institute of Ecology, GIZ.
- [13] Lin, B.S., Chen, C.K, Thomas, K. et al. (2019). Improvement of the K-Factor of USLE and Soil Erosion Estimation in Shihmen Reservoir Watershed. Sustainability, 11(2), 355. DOI: 10.3390/su11020355.
- [14] Morgan, R.P.C. (2005). Soil Erosion and Conservation, National Soil Resources Institute. Cranfield University, ch22.
- [15] Ozsoy, G., Aksoy, E., Dirim, M.S. et al. (2012). Determination of soil erosion risk in the Mustafakemalpasa river basin, Turkey, using the revised universal soil loss equation, geographic information system, and remote sensing. Environ. Manage., 50.
- [16] Panagos, P., Meusburger, K., Ballabio, C. et al. (2014). Soil erodibility in Europe: A high-resolution dataset based on LUCAS. Sci. Total Environ., 5, 461–487.
- [17] Renard, K., Foster, G., Weesies, G. et al. (1997). Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). United States: Agricultural Research Services.
- [18] Yuanyuan, Y., Ruiying, Zh., Zhou, Sh. et al. (2018). Integrating multi-source data to improve water erosion mapping in Tibet, China. CATENA, 169, 31–45.
- [19] Wawer, R., Nowocien, E. and Podolski, B. (2005). Real and calculated K-USLE erodibility factor for selected Polish soils. Polish J. Environ. Studies, 14(5), 655–658.
- [20] Williams, R.J. and Renard, K.G. (1983). EPIC – a new method for assessing erosions effect on soil productivity. J. Soil Water Conserv., 38, 381–383.
- [21] Wischmeier, W.H. and Mannering, J.V. (1969). Relation of Soil Properties to its Erodibility. Soil and Water Management and Conservation. Soil Sci. Soc. Am. J., 15, 131–137. DOI: 10.2136/ss-saj1969.03615995003300010035x.
- [22] Wischmeier, W.H. and Smith, D.D. (1978). Predicting rainfall erosion losses. United States: Agricultural Research Services.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-29dc819a-2bfd-47f5-9c54-c4c4b4ee2385