Dust Storms Trajectories and Identification of the Internal Sources over Hormozgan Province: A Case Study on Kohestak- Bandar Abbas, south of Iran

Document Type : Research Article


1 Assistant Professor, Soil Conservation & Watershed Management Research Institute (SCWMRI), Tehran, Iran

2 Associate Professor, Soil Conservation & Watershed Management Research Institute (SCWMRI), Tehran, Iran


Today, the existence of numerous sources of dust production is one of the environmental challenges of Hormozgan province. Remote sensing and using MODIS data is one of the effective methods for the detection and mapping of dust storms. At first, meteorological data of all synoptic stations in the study area were collected and analyzed. According to the results, the highest frequency of dust occurrence is related to the three months of July, August, and May, which are in spring and summer. October, December and November have the lowest occurrence of dust storms in the study area. Also, autumn with 12.5% has the lowest occurrence of dust storms in all stations in the study area, and spring with 34.4%, and then summer with 33.6% has been recorded as the highest occurrence of the dust storms. This research monitors and evaluates four detecting algorithms for identification of plume and dust source and dust storm emission in the Kostak- Bandar Abbas area in the Hormozgan Province using MODIS satellite data and the HYSPLIT model. Ackerman’s model, Normalized Difference Dust Index (NDDI), Thermal-infrared Dust Index (TDI), and thermal Infrared Integrated Dust Index (TIIDI) were four Algorithm methods for dust source and plume identification using MODIS Level 1B and MODIS Level 2 data. The results show that all of the algorithms except NDDI were successful in detecting dust plumes, but the most effective algorithm for plumes identification varied from event to event. In addition, TDI is the best algorithm comparing its results with those of other three algorithms. The results show that there are a lot of dust sources in the study area that have many negative effects on other populated areas in the Hormozgan province and its neighboring areas. The results indicate that the Flood Plains Deposits (Qal3), Natural Levee Deposits (Qal2), and Coastal Dunes (Qdune) play the most important role in dust production in the study area. The HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) model was used to trace wind flow backward and forward to the study area. The results of the HYSPLIT model show that the dust particles are mainly transported to the study area from three main paths, namely, Northeastern, the West, and the Southwestern part of the study area. The results also, show that dust plumes lifted and dispersed towards different directions including the north and northeast with 30%, the south with 25%, and the east with 20% of the total events in the study period 2000 to 2018. In addition, the results show that the study area has a high potential for the occurrence of dust storms during the year as many dust hotspots have been identified in this area. Also, the occurrence of more than a decade of drought, the presence of erosion-sensitive formations, and the presence of erosion-sensitive sedimentary units in the study area and its surrounding areas, especially in the seasonal wetland Jazmourian has provided conditions to aggravate this situation. Due to the economic and tourism importance of cities such as Bandar Abbas, Qeshm, and Minab, and especially the industrial and economic region west of Bandar Abbas and the existence of active dust sources around this region, the need for executive operations and watershed management activities is highly recommended.


Main Subjects

جبالی، ع.، اختصاصی، م. و جعفری، ر.، 1398، ارزیابی عملکرد الگوریتم‌های آشکارساز طوفان‌های گردوغبار در مناطق خشک (مطالعه موردی استان یزد). مجله علمی پژوهشی مهندسی اکوسیستم­ بیابان، سال هشتم، شماره 23، 85-105.
خیر­اندیش، ز.، بداق جمالی، ج. و رایگانی، ب.، 1397، شناسایی بهترین الگوریتم تشخیص گردوغبار با کمک داده‌های مودیس، مجله مخاطرات محیط طبیعی، دوره هفتم، شماره 15، 205-218.
دمی­زاده، م.، مهدوی، ر.، نوروزی، ع.، حلی‌ساز، ا. و غلامی، ح.، 1400، آشکارسازی و واکاوی گردوغبار در استان هرمزگان، مجله مهندسی و مدیریت آبخیز،دوره 13، شماره 1، صص 111-124.
سبحانی، ب.، صفریان، ز.، و. و فیض‌اله‌زاده، س.، 1399، مدل‌سازی و پیش‌بینی گردوغبار در غرب ایران، پژوهش‌‌های جغرافیای طبیعی، دورۀ 25 ، شمارۀ 1.
رایگانی، ب. و خیراندیش، ز.، 1396، بهره­گیری از سری زمانی داده‌های ماهواره­ای به‌منظور اعتبارسنجی کانون‌های شناسایی­ شده تولید گردوغبار استان البرز، نشریه تحلیل فضایی مخاطرات طبیعی، سال چهارم شماره4، 18-1.
کارگر، ا.، بداق‌جمالی، ج.، رنجبرسعادت­آبادی، ع.، معین­الدینی، م. و گشتاسب، ح.، 1395، شبیه‌سازی و تحلیل عددی طوفان گردوغبار شدید شرق ایران، نشریه تحلیل فضایی مخاطرات محیطی، شماره4، 101-119.
قادری‌نسب، ف. و راهنما، م. ب.، 1397، آشکارسازی گردوغبار در حوضه آبریز جازموریان با استفاده از تکنیک­های چند طیفی در تصاویر سنجنده مودیس، مجله پژوهش­های جغرافیایی، دوره5 شماره 3، 545-562.
مهرابی، ش.، جعفری، ر.، سلطانی‌کویانی، س.، 1394، بررسی کارایی شاخص NDDI در پهنه‌بندی طوفان گردوغبار (مطالعۀ موردی: استان خوزستان)، مجله علمی پژوهشی مهندسی اکوسیستم بیابان، سال چهارم، شماره 8، 1-10.
ملکوتی، ح.، باباحسینی، س.، نوحه­گر، ا.، آزادی، م. و محمدپور، م.، 1392، مطالعه همدیدی و عددی نشر، انتقال و شناسایی چشمه یک طوفان گردوغبارسنگین در منطقه خاورمیانه. فصلنامه علمی –پژوهشی پژوهشهای فرسایش محیطی سال سوم، شماره 12، 69-80.
Ackerman, S. A., 1997, Remote sensing aerosols using satellite infrared observations. Journal of Geophysical Research: Atmospheres 102(D14), 17069-17079.
Ackerman, S., Strabala, K., Menzel, W., Frey, R., Moeller, C., Gumley, L., Baum, B., Seemann, S. and Zhang, H., 2002, Discriminating clear-sky from cloud with MODIS—algorithm theoretical basis document. (MOD35), ATBD Reference Number: ATBD-MOD-06. Goddard Space Flight Center.
Baddock, M. C., Bullard, J. E. and Bryant, R. G., 2009, Dust source identification using MODIS: A comparison of techniques applied to the Lake Eyre Basin, Australia. Remote Sensing of Environment 113(7), 1511-1528.
Darmenov, A. and Sokolik, I. N., 2005, Identifying the regional thermal-IR radiative signature of mineral dust with Modis, Geophisical Research Letters, 32, 16803, doi: 10.1029/2005GL023092.
Hamish, M. and Andrew, C., 2008, Identification of dust transport pathways from Lake Eyre, Australia using Hysplit, Atmospheric Environment 42 (29) 6915-6925, doi.org/10.1016/j.atmosenv.2008.05.053
Ganbat, G. and Jugder, D., 2019, Observations and transport modeling of dust storm event over Northeast Asia using HYSPLIT.E3S Web of Conferences; Les Ulis Vol. 99, doi.org/10.1051/e3sconf/20199902002.
Hao, X. and Qu, J. J., 2007, Saharan dust storm detection using moderate resolution imaging spectroradiometer thermal infrared bands. Journal of Applied Remote Sensing 1(1), 013510.
Javadian, M., Behrangi, A. and Sorooshian, A., 2019, Impact of drought on dust storms: case study over Southwest Iran. Environal Research Letters, 14, 124029 https://doi.org/10.1088/1748-9326/ab574e.
Karimi, K., Moridnejad, A., Golian, S., Mohammad Vali Samani, J., Karimi, D. and Javadi, S., 2012, Comparison of dust source identification techniques over land in the Middle East region using MODIS data. Canadian Journal of Remote Sensing, 38, 5, 586_599.
Khalidy, R., Salmabadi, H. and Saeedi, M., 2019, Numerical Simulation of a Severe Dust Storm over Ahvaz Using the HYSPLIT Model. International Journal of Environmental Research, 13, 161–174.
Lee, Y. C., Yang, X. and Wenig, M., 2010, Transport of dusts from East Asian and non-East Asian sources to Hong Kong during dust storm related events 1996- 2007. Atmospheric Environment. Vol. 44, 3728-3738.
Liu, Y. and Liu, R., 2011, A thermal index from modis data for dust detection, 2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, BC, Canada.
Miller, S. D., 2003, A consolidated technique for enhancing desert dust storms with MODIS, Geophysical Research Letters 30(20).
Mohamed, F. Y., Sarah K. A. and Ali A. H., 2018, Dust storms backward Trajectories' and source identification over Kuwait. Atmospheric Research, Vol. 212, 158-171.
Qu, J. J., Hao, X., Kafatos, M. and Wang, L., 2006, Asian dust storm monitoring combining Terra and Aqua MODIS SRB measurements. IEEE Geoscience and Remote Sensing Letters 3(4), 484-486.
Rajaee, T., Rohani, N., Jabbari, E. and Mojaradi, B., 2020, Tracing and assessment of simultaneous dust storms in the cities of Ahvaz and Kermanshah in western Iran based on the new approach. Arabian Journal of Geosciences,13,461.
Roskovensky, J. K. and Liou, K. N., 2003, Detection of thin cirrus from 1.38 μm/0.65 μm reflectance ratio combined with 8.6–11 μm brightness temperature difference. Geophysical Research Letters 30(19).
Sarikhani, A., Dehghani, M., Karimi-Jashni, A. and Saadat, S., 2020, A New Approach for Dust Storm Detection Using MODIS Data. Iranian Journal of Science and Technology, Transactions of Civil Engineering.
Sugimoto, N., Shimizu, A., Nishizawa1, T., Jin, Y. and Yumimoto, K., 2020, Long-Range-Transported Mineral Dust from Africa and Middle East to East Asia Observed with the Asian Dust and Aerosol Lidar Observation Network (AD-Net). The 29th International Laser Radar Conference (ILRC 29), 237, https://doi.org/10.1051/epjconf/202023705009
Yu, Y., Kalashnikova, O. V., Garay, M. J. And Notaro, M., 2019, Climatology of Asian dust activation and transport potential based on MISR satellite observations and trajectory analysis. Atmospheric Chemistry and Physics, 19, 363–378, https://doi.org/10.5194/acp-19-363.
Wang, H., Zhang, X., Gong, S., Chen, Y., Shi, G. and Li, W., 2010, Radiative feedback of dust aerosols on the East Asian dust storms. Journal of Geophysical Research, Atmospheres 115(D23).
Zandkarimi, A., Fatehi, P. and Shah-Hoseini, R., 2020, An improved dust identification index (IDII) based on MODIS observation. International Journal of Remote Sensing, 41(20), 8048-8068. https://doi.org/10.1080/ 01431161.2020.1770366.
Zhang, Z., Zhang, M., Bilal, M., Su, B., Zhang, C. and Guo, L., 2020, Comparison of MODIS- and CALIPSO-Derived Temporal Aerosol Optical Depth over Yellow River Basin (China) from 2007 to 2015. Earth Systems and Environment, 4, 535–55.
Zhao, T. X.-P., Ackerman, S. and Guo, W., 2010, Dust and Smoke Detection for Multi-Channel Imagers. Remote Sensing 2(10), 2347.