Investigation of Geomorphometric Parameters to Simplify Water Erosion Modelling (a Case Study: Emamzadeh Watershed, Iran)

Ataallah Khademalrasoul, Hadi Amerikhah

Abstract


In recent decades, water erosion potential has been recognized as a severe threat against soil sustainability and water resources. The present study was conducted to investigate the relation between geomorphometric parameters and soil type to simulate water erosion in the Emamzadeh watershed located in the northeast of Khuzestan Province. The primary and secondary geomorphic parameters, including slope, plan curvature, profile curvature, flow length, flow accumulation, flow direction, and stream power index (SPI) calculated based on the digital elevation model (DEM). The water erosion measured using available data and laboratory analyzes, then it was predicted with water erosion prediction project (WEPP) model. Our results revealed that the measured soil erosion does not show any relation with geomorphic parameters, while some of the geomorphometric parameters depicted a significant relation with WEPP model’s predictions. A model with an excellent explanation coefficient obtained using multivariate linear regression to predict water erosion. The geomorphometric parameters application allows an estimation of erosion based on simple linear models (R2: 0.934, sig: 0.000). Moreover, for SPI, the total curvature was -0.794, plan curvature was -0.658, and profile curvature was 0.746. Therefore, there was a relation between curvature and SPI. Our results showed no specific relation between sediment transport index (STI) and water erosion. The low amount of STI represents the sedimentation areas in the watershed. Generally, application of geomorphometric parameters simplify the soil erosion prediction.  


Keywords


DEM; geomorphometric parameters; regression; soil great group; water erosion

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Arabameri, A., Tiefenbacher, J.P., Blaschke, T., Pradhan, B., Tien Bui, D., 2020. Morphometric analysis for soil erosion susceptibility mapping using novel GIS-based ensemble model. Remote Sensing, 12(5): 874.

Ballerine, C., 2017. Topographic Wetness Index Urban Flooding Awareness Act Action Support. Illinois State Water Survey Contract Report 2017-02, Champaign, IL.

Beven, K.J., Kirkby, M.J., 1979. A physically based variable contributing area model of basin hydrology. Hydrological Science Bulletin, 24: 43–69.

Borough, P.A., 1991. Geographical Information Systems, vol. 2. Longmans, Harlow.

Borrelli, P., Alewell, C., Alvarez, P., Anache, J.A.A., Baartman, J., Ballabio, C., ... Panagos, P., 2021. Soil erosion modelling: A global review and statistical analysis. Science of the Total Environment, 780: 146494.

Cavazzi, S., Corstanje, R., Mayr, T., Hannam, J., Fealy, R., 2013. Are fie resolution digital elevation models always the best choice in digital soil mapping? Geoderma, 195: 111–121.

Chaplot, V., Walter, C., Curmi, P., 2000. Improving soil hydromorphology prediction according to DEM resolution and available pedological data. Geoderma, 97(3): 405–422.

Danielson, T., 2013. Utilizing a High Resolution Digital Elevation Model (DEM) to Develop a Stream Power Index (SPI) for the Gilmore Creek Watershed in Winona County, Minnesota. Vol. 15, Papers in Resource Analysis. 11 p. Saint Mary’s University of Minnesota University Central Services Press. Winona, MN.

Deng, Y., Wilson, J.P., Bauer, B.O., 2007. DEM resolution dependencies of terrain attributers across a landscape. International Journal of Geographical Information Science, 21(2), 187–213.

Ding, Z., Zhang, Z., Li, Y., Zhang, L., Zhang, K., 2020. Characteristics of magnetic susceptibility on cropland and pastureland slopes in an area inflenced by both wind and water erosion and implications for soil redistribution patterns. Soil and Tillage Research, 199: 104568.

Flanagan, D.C., Livingston, S.J., (Eds.), 1995. WEPP user summary. NSERL Report No. 11. USDA-ARS NSERL, West Lafayette.

Florinsky, I., 2016. Digital Terrain Analysis in Soil Science and Geology (2nd ed.), Academic Press, Amsterdam.

Hennrich, K., Schmidt, J., Dikau, R., 1999. Regionalization of geomorphometric parameters in hydrological modeling using GIS. In: B. Diekkruger, M.J. Kirkby U. Schroder (Eds.), Regionalization in Hydrology (pp. 181–199). IAHS Publ. 254. IAHS Press, Wallingford, UK.

Gholami, V., Booij, M.J., Tehrani, E.N., Hadian, M.A., 2018. Spatial soil erosion estimation using an artifiial neural network (ANN) and fild plot data. Catena, 163: 210–218.

Iqbal, J., Read, J.J., Thomasson, A.J., Jenkins, J.N., 2004 Relationships between soil-landscape and dryland cotton lint yield. Soil Science Society of America Journal, 69: 1–11.

Johnson, C.W., Gebhardt, K.A., 1982. Predicting sediment yield from sagebrush rangelands. In: Proceedings of the workshop on estimating erosion and sediment yield on rangelands, March 7–9, Tuscon, Department of Agriculture, Agricultural Reviews and Manuals, Western Series, vol. 26, pp 145–156.

Kennelly, P.J., 2008. Terrain maps displaying hillshading with curvature. Geomorphology, 102(3–4): 567–577.

Khademalrasoul, A., Amerikhah, H., 2020. Assessment of soil erosion patterns using RUSLE model and GIS tools (case study: the border of Khuzestan and Chaharmahal Province, Iran). Modeling Earth Systems and Environment, 7: 885–895.

Khanifar, J., Khademalrasoul, A., Amerikhah, H., 2020. Effects of digital elevation model (DEM) spatial resolution on soil landscape analysis (case study Raakat watershed of Izeh, Khuzestan Province). Applied Soil Research, 8(1): 121–135. (in Persian)

Khanifar, J., Khademalrasoul, A. 2020. Multiscale comparison of LS factor calculation methods based on different flw direction algorithms in Susa Ancient landscape. Acta Geophysica, 68(3): 783–793. https://doi.org/10.1007/s11600-020-00432-1.

Khanifar, J., Khademalrasoul, A. 2021. Effects of neighborhood analysis window forms and derivative algorithms on the soil aggregate stability – landscape modeling. Catena, 198: 105071. https://doi.org/10.1016/j.catena.2020.105071.

King, C., Baghdadi, N., Lecomte, V., Cerdan, O., 2005. The application of remote-sensing data to monitoring and modeling of soil erosion. Catena, 62: 79–93. https://doi.org/10.1016/j.catena.2005.05.007.

Kumhálová, J., Matejkova, S., Fifernová, M., Lipavsky, J., Kumhála, F., 2008. Topography impact on nutrition content in soil and yield. Plant Soil and Environment, 54(6): 255.

Li, Z., Zhu, Q., Gold, C., 2005. Digital Terrain Modeling: Principles and Methodology. CRC Press, Boca Raton.

Martinez, L.J., Correa, N.A., 2016. Digital elevation models to improve soil mapping in mountainous areas: case study in Colombia. In: J.A. Zinck, G. Metternicht, G. Bocco, H.F. del Valle (Eds.), Geopedology an Integration of Geomorphology and Pedology for Soil and Landscape Studies (pp. 377–388). Springer International Publishing, Cham.

Martinez-Casasnovasa, J.A., Ramosa, M.C., Poesen, J., 2004. Assessment of sidewall erosion in large gullies using multitemporal DEMs and logistic regression analysis. Geomorphology, 58: 305–321.

Moore, I.D., Grayson, R.B., Ladson, A.R., 1991. Digital terrain modeling: A review of hydrological, geomorphological and biological applications. Hydrological Processes, 5: 3–30.

Munar-Vivas, O.J., Martínez, M.L.J., 2014. Relief parameters and fuzzy logic for land evaluations of mango crops (Mangifera indica L.) in Colombia. Agronomía Colombiana, 32(2): 238–245.

Neteler, M., Mitasova, H., 2008. Open Source Software and GIS. In: Open Source GIS (pp. 1–6). Springer, Boston.

Pelacani, S., Marker, M., Rodolfi G., 2008. Simulation of soil erosion and deposition in a changing land use: a modeling approach to implement the support practice factor. Geomorphology, 99: 329–340.

Tovar-Pescador, J., Pozo-Vázquez, D., Ruiz-Arias, J.A., Batlles, J., López, G., Bosch, J.L., 2006. On the use of the digital elevation model to estimate the solar radiation in areas of complex topography. Meteorological Applications, 13(3): 279–287.

Sartori, M., Philippidis, G., Ferrari, E., Borrelli, P., Lugato, E., Montanarella, L., Panagos, P., 2019. A linkage between the biophysical and the economic: Assessing the global market impacts of soil erosion. Land Use Policy, 86: 299–312.

Schaetzl, R.R.J., Anderson, S., 2005. Soils Genesis and Geomorphology. Cambridge University Press, New York.

Suriyaprasit, M., 2008. Digital terrain analysis and image processing for assessing erosion prone areas. Unpublished MSc. thesis, International Institute for Geo-Information Science and Earth Observation (ITC), Enschede, the Netherlands.

Wilson, P.J., Gallant, J.C., 2000. Digital Terrain Analysis. Principles and Applications. John Wiley & Sons, New York.

Wilson, P.J., 2018. Environmental Application of Digital Terrain Modeling. John Wiley & Sons, New York.

Wolock, D.M., McCabe, G.J., 1995. Comparison of single and multiple flw direction algorithms for computing topographic parameters in TOPMODEL. Water Resources Research, 31(5): 1315–1324.

Zinck, J.A., 1988. “Physiography and Soils”, ITC Lecture Note SOL. 4.1. International Institute for Geoinformation Science and Earth Observation (ITC). Enschede, 156 p.

Zhang, X.C., Liu, W.Z., 2005. Simulating potential response of hydrology, soil erosion, and crop productivity to climate change in Changwu tableland region on the Loess Plateau of China. Agricultural and Forest Meteorology, 131(3–4): 127–142.




DOI: http://dx.doi.org/10.17951/pjss.2022.55.1.1-18
Data publikacji: 2022-06-27 10:25:02
Data złożenia artykułu: 2021-02-02 10:05:22


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