Assessment of land sensitivity to determine areas dust sources (Case study: Alborz province)
Subject Areas :
ارزیابی پی آمدهای محیط زیستی
katayoon hojati
1
,
Zahra Abedi
2
,
behzad raigani
3
,
mustafa panahi
4
1 - PHD student of environment management, enviro-economy Islamic Azad University Science and Research Branch, Tehran, Iran.
2 - member of science board in environment and Natural resources faculty, Islamic Azad University, Tehran, Iran(in charge of correspondence)
3 - Faculty member, Research group of Environmental Assessment and Risks, Research center of Environment and sustainable Development(RCESD), Department of Environment, Tehran, Iran.
4 - member of science board in environment and Natural resources faculty, Islamic Azad University, Tehran, Iran.
Received: 2020-10-10
Accepted : 2020-12-13
Published : 2022-01-21
Keywords:
Fuzzy membership,
Wind Erosion,
Sensitivity to erosion,
Regional model,
Abstract :
Background and Objective: The vastness of arid areas and the abundance of dust storms in the country have made the accurate identification of dust production centers always one of the main goals of research in the field of dust. The primary objective of this study is dust storm source identification in Alborz ProvinceMaterial and Methodology: In this study, the Index of Land Susceptibility to Wind Erosion (ILSWE) was used to locate dust sources. The ILSWE was created by combining five influential wind erosion factors including climatic erosivity, soil erodibility, soil crust, vegetation cover & surface roughness. Temperature, precipitation, wind speed, sand percentage, silt, clay, calcium carbonate, EVI and land use maps were used to calculate these factors. After calculating each of the factors, by multiplying them together, the ILSWE index was calculated. Finally, by classifying this index in Arc GIS software, sensitive areas were identified.Findings: The final map of ILSWE index showed that in general, the southern regions of Alborz province are more sensitive to wind erosion than other regions. The ILSWE classification map showed that 34.5% of the studied area falls within the very low class, 26.8% in the low class, 18.3% in the medium class, 12.6% in the high class and 7.8% in very high sensitivity to wind erosion class. Very high sensitivity class was considered as dust sources, which is mainly located in the southern parts of Alborz province. Most sources of dust are barren lands.Discussion and Conclusion: According to the results of this study, barren lands have an important role in dust production in Alborz province; therefore soil stabilization operations in these areas are necessary to reduce dust. In general, the results of this study showed that the ILSWE index is a suitable regional model for determining susceptible areas and centers of dust production.
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Fryrear, D.W., Saleh, A., Bilbro, J.D., Schomberg, H.M., Stout, J.E., Zobeck, T.M., 1998. Revised Wind Erosion Equation (RWEQ). Wind Erosion and Water Conservation Research Unit, USDA-ARS, Southern Plains Area Cropping Systems Research Laboratory. Technical Bulletin No. 1. pp. 185. (accessed 2019). https://www.csrl.ars.usda.gov/wewc/rweq/rweq.pdf.
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TA-Luft., 2001. Erste Allgemeine Verwaltungsvorschrift zum Bundes-Immissionsschutzgesetz. Technische Anleitung zur Reinhaltung der Luft – TA-Luft Stand 12.06.2001.
Memarian-Fard, M., Mokhtari, H., Kouhzadbighi, B. and Zolfaghari, H., 2017. Investigating the microflora and dust, its effects and its containment methods. Third International Conference on Environment, Energy and Bio-Defense.ScientificQuartely Journal, Geosciences, 27: 105-116. (In Persian)
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Boloorani, A. D., Samany, N. N., Mirzaei, S., Bahrami, H. A., & Alavipanah, S. K., 2020. Remote Sensing and GIS for Dust Storm Studies in Iraq. In Environmental Remote Sensing and GIS in Iraq. Springer, Cham, 2020. 333-375.
Waggoner, D.G., Sokolik, I. N., 2010. Seasonal dynamics and regional features of MODIS-derived land surface characteristics in dust source regions of East Asia. Remote Sensing of Environment. 114(10), 2126-2136.
Huang, J., Li, Y., Fu, C., Chen, F., Fu, Q., Dai, A., ... & Zhang, L., 2017. Dryland climate change: Recent progress and challenges. Reviews of Geophysics, 55(3), 719-778.
Alizadeh‐Choobari, O., Ghafarian, P., & Owlad, E., 2016. Temporal variations in the frequency and concentration of dust events over Iran based on surface observations. International Journal of Climatology, 36(4), 2050-2062.
Zolfaghari, H., Masoumpour Samakosh, J., Shaygan Mehr, Sh., Ahmdi, M., 2010. A Synoptic Investigation of Dust Storms in Western Regions of Iran during 2005- 2010 (A Case Study of Widespread Wave in July 2009). Geography and Environmental Planning, 22(3), 17-34. (In Persian)
Beyranvand, A., Azizi, G., Alizadeh-Choobari, O., & Boloorani, A. D., 2019. Spatial and temporal variations in the incidence of dust events over Iran. Natural Hazards, 1-13.
Mei, D., Xiushan, L., Lin, S., & Ping, W. A. N. G., 2008. A dust-storm process dynamic monitoring with multi-temporal MODIS data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37.
Huang, M., Peng, G., Zhang, J., & Zhang, S., 2006. Application of artificial neural networks to the prediction of dust storms in Northwest China. Global and Planetary change, 52(1-4), 216-224.
Effati, M., Bahrami, H. A., Gohardoust, M., Babaeian, E., & Tuller, M., 2019. Application of Satellite Remote Sensing for Estimation of Dust Emission Probability in the Urmia Lake Basin in Iran. Soil Science Society of America Journal, 83(4), 993-1002.
Qu, J. J., Hao, X., Kafatos, M., & Wang, L., 2006. Asian dust storm monitoring combining Terra and Aqua MODIS SRB measurements. IEEE Geoscience and remote sensing letters, 3(4), 484-486.
Middleton, N., & Kang, U., 2017. Sand and dust storms: impact mitigation. Sustainability, 9(6), 1053.
Rayegani, B., kheirandish, Z., Kermani, F., Mohammdi Miyab, M., Torabinia, A., 2017. Identification Of Active Dust Sources Using Remote Sensing Data And Air Flow Simulation (Case Study: Alborz Province). Desert Management, 4(8), 15-26. (In Persian)
Fenta, A. A., Tsunekawa, A., Haregeweyn, N., Poesen, J., Tsubo, M., Borrelli, P., ... & Kawai, T., 2020. Land susceptibility to water and wind erosion risks in the East Africa region. Science of the Total Environment, 703, 135016.
Funk, R., Reuter, H.I., 2006. Wind erosion. In: Boardman, J., Poesen, J. (Eds.), Soil erosion in Europe. Wiley, Chichester, UK, pp. 563–582.
Mehrabi S, Soltani S, Jafari R., 2015. Analyzing the Relationship Between Dust Storm Occurrence and Climatic Parameters. Journal of Water and Soil Science. 19 (71) :69-81
Rayegani, B., Barati Ghahfarokhi, S., Khoshnava, A., 2019. Dust & Sand Source Identification Using Remotely Sensed Data: a comprehensive Approach. Journal of Range and Watershed Managment, 72(1), 83-105. (In Persian)
Rayegani, B., Barati, S., Goshtasb, H., Gachpaz, S., Ramezani, J., & Sarkheil, H., 2020. Sand and dust storm sources identification: A remote sensing approach. Ecological Indicators, 112, 106099.
Feuerstein, S., & Schepanski, K., 2019. Identification of Dust Sources in a Saharan Dust Hot-Spot and Their Implementation in a Dust-Emission Model. Remote Sensing, 11(1), 4.
Borrelli, P., Panagos, P., & Montanarella, L., 2015. New insights into the geography and modelling of wind erosion in the european agricultural land. Application of a spatially explicit indicator of land susceptibility to wind erosion. Sustainability, 7(7), 8823-8836.
Borrelli, P., Panagos, P., Ballabio, C., Lugato, E., Weynants, M., & Montanarella, L., 2016. Towards a pan‐European assessment of land susceptibility to wind erosion. Land Degradation & Development, 27(4), 1093-1105.
Rayegani, B., Khirandish, Z., 2018. Utilization of time series of satellite data in order to validate the identified dust storm sources in Alborz province. Jsaeh. 2018; 4 (4) :1-18 . (In Persian)
, 1979. A provisional methodology for soil degradation assessment. Food and Agriculture Organization, Rome, Italy.
Fryrear, D.W., Krammes, C.A., Williamson, D.L., Zobeck, T.M., 1994. Computing the wind erodible fraction of soils. J. Soil Water Conserv. 49, 183–188.
Hengl, T., de Jesus, J.M., Heuvelink, G.B., Gonzalez, M.R., Kilibarda, M., Blagotic´ , A., Shangguan, W., Wright, M.N., Geng, X., Bauer-Marschallinger, B., Guevara, M.A., 2017. SoilGrids250m: Global gridded soil information based on machine learning. PLoS one 12, e0169748. https://doi.org/10.1371/journal.pone.0169748.
FAO/IIASA/ISRIC/ISSCAS/JRC., 2012. Harmonized World Soil Database (version 1.2). FAO, Rome, Italy and IIASA, Laxenburg, Austria. (accessed 17 May 2019). http://www.fao.org/3/aq361e/aq361e.pdf.
Zobeck, T.M., 1991. Soil properties affecting wind erosion. J. Soil Water Conserv. 46, 112–118.
Fryrear, D.W., Bilbro, J.D., Saleh, A., Schomberg, H.M., Stout, J.E., Zobeck, T.M., 2000. RWEQ: improved wind erosion technology. J. Soil Water Conserv. 55, 183–189.
Zhang, K.L., Li, S., Peng, W., Yu, B., 2004. Erodibility of agricultural soils on the Loess Plateau of China. Soil Till. Res. 76, 157–165.
Borrelli, P., Ballabio, C., Panagos, P., Montanarella, L., 2014. Wind erosion susceptibility of European soils. Geoderma 232, 471–478.
Fryrear, D.W., Saleh, A., Bilbro, J.D., Schomberg, H.M., Stout, J.E., Zobeck, T.M., 1998. Revised Wind Erosion Equation (RWEQ). Wind Erosion and Water Conservation Research Unit, USDA-ARS, Southern Plains Area Cropping Systems Research Laboratory. Technical Bulletin No. 1. pp. 185. (accessed 2019). https://www.csrl.ars.usda.gov/wewc/rweq/rweq.pdf.
Wever, N., 2012. Quantifying trends in surface roughness and the effect on surface wind speed observations. J Geophys Res Atmos. 117 (D11).
Hansen, S.V., 1993. Surface roughness lengths. ARL Technical Report U. S. Army, White Sands Missile Range, NM 88002-5501, pp. 51. (accessed 4 December 2018).
Funk R, Reuter HI. 2006. Wind erosion. In Soil erosion in Europe, Boardman J, Poesen J. (eds). Wiley: Chichester; 563–582.
TA-Luft., 2001. Erste Allgemeine Verwaltungsvorschrift zum Bundes-Immissionsschutzgesetz. Technische Anleitung zur Reinhaltung der Luft – TA-Luft Stand 12.06.2001.
Memarian-Fard, M., Mokhtari, H., Kouhzadbighi, B. and Zolfaghari, H., 2017. Investigating the microflora and dust, its effects and its containment methods. Third International Conference on Environment, Energy and Bio-Defense.ScientificQuartely Journal, Geosciences, 27: 105-116. (In Persian)