ارزیابی حساسیت اراضی جهت تعیین مناطق مستعد تولید گردو غبار (مطالعه موردی: استان البرز)
الموضوعات :
کتایون حجتی
1
,
زهرا عابدی
2
,
بهزاد رایگانی
3
,
مصطفی پناهی
4
1 - دانشجوی دکتری مدیریت محیط زیست – اقتصاد محیط زیست، دانشکده منابع طبیعی و محیط زیست، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران،ایران
2 - عضو هیات علمی دانشکده منابع طبیعی و محیط زیست، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران. * (مسوول مکاتبات)
3 - عضو هیات علمی گروه ارزیابی و مخاطرات محیط زیست، پژوهشکده محیط زیست و توسعه پایدار، سازمان حفاظت محیط زیست، تهران، ایران.
4 - عضو هیات علمی دانشکده منابع طبیعی و محیط زیست، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی.
تاريخ الإرسال : 23 السبت , صفر, 1442
تاريخ التأكيد : 28 الأحد , ربيع الثاني, 1442
تاريخ الإصدار : 18 الجمعة , جمادى الثانية, 1443
الکلمات المفتاحية:
حساسیت به فرسایش,
فرسایش بادی,
مدل‌منطقه ای,
عضویت فازی,
ملخص المقالة :
زمینه و هدف: وسعت بسیار زیاد مناطق خشک و فراوانی پدیده های گرد و غبار در کشور باعث شده است، شناسایی دقیق کانون های تولید گرد و غبار همواره یکی از اهداف اصلی پژوهش ها در زمینه گرد و غبار به شمار آید. هدف اصلی این مطالعه شناسایی منبع طوفان گرد و غبار در استان البرز است.روش بررسی: در این پژوهش از شاخص حساسیت زمین در برابر فرسایش بادی (ILSWE) برای مکان یابی منابع تولید گرد و غبار استفاده شد. شاخص ILSWE از ترکیب پنج فاکتور موثر در فرسایش بادی شامل فرسایش دهندگی اقلیم، فرسایش پذیری خاک، سله خاک، پوشش گیاهی و زبری سطح ایجاد شده است. برای محاسبه این فاکتورها از نقشه های دما، بارش، سرعت باد، درصد شن، سیلت، رس، کربنات کلسیم، EVI و کاربری اراضی استفاده شد. بعد از محاسبه هریک از فاکتورها، با ضرب آنها در هم، شاخص ILSWE محاسبه شد. درنهایت با طبقه بندی این شاخص در نرم افزار Arc GIS مناطق حساس شناسایی شد.یافته ها: نقشه نهایی شاخص ILSWE نشان داد که به طورکلی مناطق جنوبی استان البرز نسبت به دیگر نواحی، به فرسایش بادی حساس تر هستند. نقشه طبقه بندی ILSWE نشان داد که 5/34٪ از منطقه مورد مطالعه در کلاس خیلی کم، 8/26 ٪ در کلاس کم، 3/18٪ در کلاس متوسط، 6/12٪ در کلاس زیاد و 8/7٪ در کلاس خیلی زیاد حساسیت به فرسایش بادی قرار دارد. کلاس حساسیت زیاد به عنوان کانون ایجاد گرد و غبار در نظر گرفته شد که عمدتا در نواحی جنوبی استان البرز قرار دارد. اکثر نواحی کانون ایجاد گرد و غبار، اراضی بایر هستند.بحث و نتیجه گیری: با توجه به نتایج این پژوهش، اراضی بایر نقش مهمی در تولید گرد و غبار استان البرز دارند درنتیجه عملیات تثبیت خاک در این نواحی برای کاهش گرد و غبار لازم است. به طور کلی نتایج این پژهش نشان داد که شاخص ILSWE، یک مدل منطقه ای مناسب جهت تعیین مناطق مستعد و کانون های تولید گرد و غبار است.
المصادر:
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)
_||_
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)
