واکاوی زمانی-مکانی سپیدایی روشن (White Sky Albedo)بالاتر از میانگین در ایران

نوع مقاله : مقاله کامل

نویسندگان

1 دانشجوی دکتری آب‏ وهواشناسی، دانشکدة علوم جغرافیایی، دانشگاه خوارزمی، تهران

2 استاد آب ‏وهواشناسی، دانشکدة علوم جغرافیایی، دانشگاه خوارزمی، تهران

3 استاد آب‏ وهواشناسی، دانشکدة علوم جغرافیایی، دانشگاه اصفهان

چکیده

هدف از پژوهش حاضر واکاوی رفتار زمانی- مکانی سپیدایی روشن در ایران است. سنجندة مودیس سپیدایی تیره را برای تابش مستقیم و سپیدایی روشن برای تابش پراکندة همسانگرد در ظهر محلی ارائه می‏دهد. برای این منظور، داده‏های ترکیبی سپیدایی سنجندة مودیس تررا- آکوا (MCD43A3v006) برای بازة زمانی 1378-1398 به‏صورت روزانه و در تفکیک مکانی 500×500 متر بهکار گرفته شد. تغییرات زمانی- مکانی مقادیر سپیدایی روشن با استفاده از تحلیل مؤلفة اصلی واکاوی شد. نتایج نشان داد سه مؤلفة اصلی قادر به تبیین 7/97 درصد از پراش داده‏هاست. واکاوی مکانی سپیدایی روشن حاکی از آن است که سپیدایی‏های بالاتر از میانگین در نواحی مرتفع و کوهستانی ایران، همچون رشته‏کوه‏های زاگرس و البرز، ارتفاعات شمال غرب کشور مانند قله‏های سبلان و سهند وجود دارد که در ارتباط با پوشش برفی است. بنابراین، مؤلفة اول پوشش برفی نام‏گذاری شد. در مؤلفة دوم سپیدایی در سه فصل بهار، تابستان، و پاییز برابر است. واکاوی مکانی مؤلفة دوم نشان داد سپیدایی‏های بالاتر از میانگین در ارتباط با پوشش نمکی است. بنابراین، مؤلفة دوم پوشش نمکی نام‏گذاری شد. واکاوی تغییرات زمانی سپیدایی در مؤلفة سوم حاکی از آن است که سپیدایی‏های بالاتر از میانگین در برف‏خوانهاست. درنتیجه، مؤلفة سوم برف‏خوان نام‏گذاری شد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Spatiotemporal variations of white sky albedo upper of average in Iran

نویسندگان [English]

  • ali reza karbalaee doree 1
  • zahra Hedjazizadeh 2
  • Seyed Abolfazl Masoodian 3
1 PhD Student of Climatology, Faculty of Geographical Sciences, University of Kharazmi Tehran
2 Professor of Climatology, Faculty of Geographical Sciences, University of Kharazmi
3 Professor of Climatology, Faculty of Geographical Sciences, University of Isfahan.
چکیده [English]

Extended abstract
Introduction:
The main sources of albedo change are variations in snow cover, variations in soil moisture, droughts, and variations in vegetation phenology, forest fires, and land use/ cover changes directly related to human activities, such as deforestation, irrigation, and urbanization. Forests obtain lower albedo values than shrubs, dry crops, grasslands, and bare soils. As a result, the conversion of forests to these land cover types leads to increases in surface albedo. This potentially has local and regional feedback, since an increase in surface albedo leads to a reduction in net radiation, turbulent heat fluxes, convective clouds, and precipitation, leading to a drier atmosphere Furthermore, black carbon decreases the surface albedo when deposited on snow and glaciers because it is incorporated in snowflakes, darkening snow and ice surfaces and increasing surface melt. Aerosols like dust transferred into the atmosphere and transported by the wind into the mountains where it settles on snow and glaciers, reducing albedo and leading to enhanced warming at higher elevations. It is noted that even though precipitation is the main driver of variations in soil moisture, its impact on albedo is controlled by evaporation, soil type, irradiation, vegetation, and topography. The present paper aims to evaluate the spatiotemporal variations of white sky albedo in Iran. For this, daily Albedo datasets from Moderate Resolution Imaging Spectroradiometer (MODIS) from onboard Aqua and Terra (MCD43A3v006) were applied from 2000 to 2019 with a spatial resolution of 500 × 500 m. MODIS provides black-sky albedo for direct and white-sky albedo for isotropic diffuse radiation at local solar noontime. For this, daily white sky albedo datasets from Moderate Resolution Imaging Spectroradiometer (MODIS) from onboard Aqua and Terra (MCD43A3v006) were applied. One of the main applications of the principal component analysis (PCA) is climatic zoning which is a method of determining environmental changes in temporal dimensions. A plethora of studies have been conducted using principal component analysis in the field of climatology but little has been done in relation to the albedo variation. To the best of the authors’ knowledge, this study uses a technique that has not been applied in scientific texts related to Modis albedo data. The questions that we will address in this study include: what is the temporal-spatial behavior of white sky albedo in Iran? How many components explain the variation of white Sky Albedo? What factors will distinguish white sky albedo in Iran?


Materials and methods

In this investigation, daily white sky albedo datasets from Moderate Resolution Imaging Spectroradiometer (MODIS) from onboard Aqua and Terra (MCD43A3v006) were applied for the period of 2000-02-24 to 2019-06-03 (7040 days) with a spatial resolution of 500 × 500 m. Among the various MODIS datasets, white sky albedo datasets were extracted. The daily white sky albedo was averaged over the 19-year period for each pixel inside the border of Iran. The size of this array was 7541502 pixel *12month. Long-term monthly and seasonal means were also calculated by the available time series data. In the next step, the PCA method was applied to analyze the spatio-temporal variations of albedo in Iran. PCA is a method to reduce the number of the data and convert them into several finite components so that these few components explain the largest amount of the variance. This procedure is searching for the variable with the largest amount of the variance in space (PCA was invented in 1901 by Karl Pearson , and it was later developed by Harold Hotelling in the 1930s. In this method, initial variables are converted into n principal components each being a linear combination of the variables. In this way, the first principal component has the largest possible variance, and the components afterward explain a smaller percentage of the variance. Principal component analysis leads to the analysis of space-time array into two time-array and space-array. In this case, it is possible to identify what important spatial patterns the primary data have and at what time periods each of these patterns has been active or inactive. Because the principal components are finite, the temporal and spatial patterns introduced by the first component are more important than the temporal and spatial patterns of subsequent components

Results and discussion
The long-term average of Iran's white sky albedo was calculated; The results showed that the average albedo of spring, summer, autumn and winter in Iran 14.99%, 16.06% , 15.53%, and 19.58%, respectively. The evaluated long-term mean white sky albedo for each season showed that the highest value had occurred in winter. The dramatic increase in this value was placed along the Zagros, Alborz, Sahand, and Sabalan Mountains which exceeds 90 to 100 percent in some places. In the next step, the temporal-spatial variations of white sky albedo values in Iran were analyzed using principal component analysis, and the results showed that the three main components are able to explain 97.7% of the data variation. The first component explains more than 73%, of the total changes, the second component more than 20.8% and finally the third component explains more than 3.9% of the changes.

Conclusion
Spatial analysis revealed that the values which are higher than the mean are places in highlands and mountainous regions of Iran, such as the Zagros and Alborz Mountains, Sabalan, Sahand mountains and Zard Kuh-e Bakhtiari, which are associated with snow cover Therefore, the first component was named as snow cover as the maximum variance of albedo was explained by snow cover. The spatial analysis of the second component revealed that higher values were placed in small areas across Iran including, Hajaligholi desert Gavkhuni wetland, Qom salt lake, Sirjan salt lake parts of Loot desert. In the second component, most of the cell's scores upper of average in Iran corresponded to areas covered with salt. As the maximum variance is explained with salt cover, therefore, it can be named as the salt land. Spatial analysis indicated that in very limited parts of Alborz, Zagros, Alam-Kuh Mountain , Sahand, and Sabalan mountains Kino Mountain values are mostly positive which is related to the glaciers (regions with appropriate conditions to keep the snow cover in most of the year) and is the origin of the seasonal or permanent rivers Therefore, according to the cell scores (upper of average in Iran) in the third component, it was found that these cells corresponded to the , so it was named as the glacier component.
Keywords: MODIS, white Sky Albedo, principal component analysis, snow cover

کلیدواژه‌ها [English]

  • MODIS
  • White Sky Albedo
  • principal component analysis
  • snow cover
ادب، حامد؛ امیراحمدی، ابوالقاسم و عتباتی، آزاده (1393). ارتباط پوشش گیاهی با دما و آلبدوی سطحی در دورة گرم سال با استفاده از داده‏های مودیس در شمال ایران، پژوهشهای جغرافیای طبیعی، 46(4): 419-434. doi: 10.22059/jphgr.2014.52994.
حجازیزاده، زهرا؛ بزمی، نسرین؛ رحیمی، علیرضا؛ طولابینژاد، میثم و بساک، عاطفه (۱۳۹۶). مدلسازیِ فضایی- زمانیِ آلبدو در گسترة ایرانزمین، نشریة تحقیقات کاربردی علوم جغرافیایی، ۱۷(۴۷): ۱-۱۷.
غیور، حسنعلی و منتظری، مجید (۱۳۸۴). پهنه‏بندی رژیم‏های دمایی ایران با مؤلفه‏های مبنا و تحلیل خوشه‏ای، مجلة جغرافیا و توسعه، ۲(۴): ۲۱. .magiran.com/p303074
خسروی، محمود (1396). اقلیم‏شناسی چندمتغیره، کاربرد تحلیل‏های چندمتغیره در جغرافیای طبیعی و اقلیم‏شناسی، انتشارات دانشگاه سیستان و بلوچستان.
عساکره، حسین و بیات، علی (1392). تحلیل مؤلفة اصلی مشخصات بارش سالانة شهر زنجان، نشریة علمی- پژوهشی جغرافیا و برنامهریزی، 17(45): 121-142.
قائمی، هوشنگ؛ ذرین، آذر و خوشاخلاق، فرامرز (1392). اقلیمشناسی مناطق خشک، تهران: سمت.
کارتر، سریواستاوا (1370). آمار چندمتغیرة کاربردی، ترجمة ناصر ارقامی و ابوالقاسم بزرگنیا، انتشارات بنیاد فرهنگ رضوی.
کیخسروی کیانی، محمدصادق (1395). آبوهواشناسی پوشش برف در ایران با بهرهگیری از دادههای دورسنجی، رسالة دکتری جغرافیایی طبیعی گرایش آبوهواشناسی دانشگاه اصفهان، استاد راهنما سیدابوالفضل مسعودیان.
کیخسروی کیانی، محمدصادق و مسعودیان، سیدابوالفضل (1396). شناسایی برفخوان‏های ایران، پژوهشهای جغرافیای طبیعی، 49(3): 395-408. doi: 10.22059/jphgr.2017.212604.1006908
مسعودیان، سیدابوالفضل (1390). آبوهوای ایران، انتشارات شریعة توس.
Berge, H. F. M. (1986). Heat and water transfer at the bare soil surface: aspects affecting thermal imagery. (PhD thesis), Landbouwhogeschool te Wageningen.
Bethere, L.; Sennikovs, J. and Bethers, U. (2017). Climate indices for the Baltic states from principal component analysis. Earth System Dynamics, 8(4): 951.
Chakravarty, P. and Kumar, M. (2019). Floral Species in Pollution Remediation and Augmentation of Micrometeorological Conditions and Microclimate: An Integrated Approach. In Phytomanagement of Polluted Sites (pp. 203-219). Elsevier.
Chrysoulakis, N.; Mitraka, Z. and Gorelick, N. (2019). Exploiting satellite observations for global surface albedo trends monitoring. Theoretical and Applied Climatology, 137(1-2): 1171-1179.
Coakley, J. A. (2003). Reflectance and albedo, surface. Encyclopedia of the Atmosphere, 1914-1923.
Eltahir, E. A. )1998(. A soil moisture-rainfall feedback mechanism 1. Theory and observations. Water Resour. Res, 34(4): 765-776.
Firozjaei, M. K.; Alavipanah, S. K.; Liu, H.; Sedighi, A.; Mijani, N.; Kiavarz, M. and Weng, Q. (2019). A PCA–OLS Model for Assessing the Impact of Surface Biophysical Parameters on Land Surface Temperature Variations. Remote Sensing, 11(18): 2094.
He, T.; Liang, S. and Song, D. X. (2014). Analysis of global land surface albedo climatology and spatial‐temporal variation during 1981–2010 from multiple satellite products. Journal of Geophysical Research: Atmospheres, 119(17): 10281-10298.
Hotelling, H. (1992). Relations between two sets of variates. In Breakthroughs in statistics (pp. 162-190). Springer, New York, NY.
Hu, Y.; Hou, M.; Zhao, C.; Zhen, X.; Yao, L. and Xu, Y. (2019). Human-induced changes of surface albedo in Northern China from 1992-2012. International Journal of Applied Earth Observation and Geoinformation, 79: 184-191.
Kharbouche, S. and Muller, J. P. (2019). Sea Ice Albedo from MISR and MODIS: Production, Validation, and Trend Analysis. Remote Sensing, 11(1): 9.
Li, Z.; Yang, J.; Gao, X.; Yu, Y.; Zheng, Z.; Liu, R.; ... and Wei, Z. (2019). The relationship between surface spectral albedo and soil moisture in an arid Gobi area. Theoretical and Applied Climatology, 136(3-4): 1475-1482.
Loranty, Michael M.; Goetz, Scott, J. and Beck. Pieter S.A. (2011). Tundra Vegetation Effects on Pan-Arctic Albedo. Environmental Research Letters, 6(2): 1-7.
Song, R.; Muller, J. P.; Kharbouche, S. and Woodgate, W. (2019). Intercomparison of surface albedo retrievals from MISR, MODIS, CGLS using tower and upscaled tower measurements. RemoteSensing, 11(6): 644.
Roxy, M.; Sumithranand, V. and Renuka, G. (2010). Variability of soil moisture and its relationship with surface albedo and soil thermal diffusivity at Astronomical Observatory, Thiruvananthapuram, south Kerala. J. Earth. Syst. Sci., 119(4): 507-517.
Timm, N. H. (2002). Applied multivariate analysis. URL http://link. Springer. com/content/pdf/10.1007/b98963. pdf.
Wang, K.; Liu, J.; Zhou, X.; Sparrow, M.; Ma, M.; Sun, Z. and Jiang, W. (2004). Validation of the MODIS global land surface albedo product using ground measurements in a semidesert region on the Tibetan Plateau. Journal of Geophysical Research: Atmospheres, 109(D5).
Whetton, P. H. (1988). A synoptic climatological analysis of rainfall variability in south-eastern Australia. Journal of Climatology, 8(2): 155-177. doi:10.1002/joc.3370080204.
Zhang, X.; Liang, S.; Wang, K.; Li, L. and Gui, S. (2010). Analysis of global land surface shortwave broadband albedo from multiple data sources. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3(3): 296-305.
دوره 53، شماره 1
این شماره با همکاری و مشارکت «انجمن ایرانی ژئومورفولوژی» منتشر شده است، بدینوسیله از مشارکت این انجمن در «داوری مقالات» ، «معرفی داوران» و «دبیران تخصصی » و «شرکت در جلسات و نشست های مرتبط» تشکر می گردد.
فروردین 1400
صفحه 141-155
  • تاریخ دریافت: 05 اردیبهشت 1399
  • تاریخ بازنگری: 22 آذر 1399
  • تاریخ پذیرش: 22 آذر 1399