پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 158 |
| تعداد شماره‌ها | 6,225 |
| تعداد مقالات | 67,685 |
| تعداد مشاهده مقاله | 114,980,577 |
| تعداد دریافت فایل اصل مقاله | 89,652,896 |
الگوهای توزیع فراوانی آب بارش‌پذیرِ ایران‌زمین در دورة مطالعاتی 1381-1396 | ||
| پژوهش های جغرافیای طبیعی | ||
| مقاله 3، دوره 52، شماره 4، دی 1399، صفحه 553-565 اصل مقاله (1.34 M) | ||
| نوع مقاله: مقاله کامل | ||
| شناسه دیجیتال (DOI): 10.22059/jphgr.2021.289267.1007438 | ||
| نویسندگان | ||
| منیژه کیانی پور1؛ سید ابوالفضل مسعودیان* 2؛ حسین عساکره3 | ||
| 1دانشجوی دکتری آب و هواشناسی، گروه جغرافیای طبیعی دانشگاه اصفهان | ||
| 2استاد آب و هواشناسی، گروه جغرافیای طبیعی دانشگاه اصفهان | ||
| 3استاد آب و هواشناسی، گروه جغرافیای دانشگاه زنجان | ||
| چکیده | ||
| رطوبت جو نه‌تنها یک گاز گلخانه‌ای مهم به‌شمار می‌رود، بلکه وردش‌های جهانی اقلیم و چرخة آب نیز به شکل قابل توجهی تحت تأثیر این عامل قرار دارد. در این مطالعه برای بررسی الگوهای توزیع فراوانی آبِ بارش‌پذیر در ایران از فرآوردة آب بارش‌پذیر سنجندة مودیس آکوا برای بازة زمانی 1381-1396 استفاده شد. نتایج حاصل از اجرای روش تحلیل مؤلفة اصلی بر روی آرایة فراوانی آبِ بارش‌پذیر در گسترة ایران نشان داد چهار مؤلفة اول 5/95 درصد پراش کل را تبیین می‌کند. ارتباط هر یک از این مؤلفه‌ها با عامل ارتفاع و فاصله از دریا نیز بررسی شد. نتایج تحلیل مؤلفه نشان داد در بخش‌های داخلی، ارتفاع و در کرانه‌ها شرایط دمایی و فرارفت رطوبت بیشترین نقش را در توزیع فراوانی رطوبت جو ایران بازی می‌کنند؛ به‌طوری‌که آبِ بارش‌پذیر مناطق با ارتفاع بالاتر از 3000 متر کمتر از 6 میلی‌متر و سواحل دریای عمان در 60 درصد اوقات بالای 26 میلی‌متر است. واکاوی پیوند میان ارتفاع و فاصله از دریا با مؤلفه‌های اصلی نیز تأییدی بر یافته‌های فوق بود. | ||
| کلیدواژه‌ها | ||
| آبِ بارش‌پذیر؛ ایران؛ تحلیل مؤلفة اصلی؛ توزیع فراوانی؛ مودیس | ||
| عنوان مقاله [English] | ||
| Frequency Distribution Patterns of Precipitable Water in Iran | ||
| نویسندگان [English] | ||
| Manizheh Kianipour1؛ Seyed Abolfazl Masoodian2؛ Hossein Asakereh3 | ||
| 1PhD Candidate in Climatology, University of Isfahan, Iran | ||
| 2Professor of Climatology, University of Isfahan, Iran | ||
| 3Professor of Climatology, University of Zanjan, Iran | ||
| چکیده [English] | ||
| Introduction Precipitable Water (PW) is highly variable in space and time, being one of the most important abundant greenhouse gases to play a crucial role in the study of climate change, hydrological cycle, energy budget, and numerical weather prediction. Knowledge about spatial and temporal variability of PW is important for understanding climatic processes along with monitoring drought conditions and desertification processes (Kaufman & Gao, 1992). It is, therefore, necessary to obtain the distribution condition of water vapor in the atmosphere and understand the effects of spatiotemporal variations of PW on regional, meso-micro scales as well as global climate change (Wang, 2013). PW has a very short life cycle in atmosphere. This rapid turnover, accompanied by temperature variations with altitude and geography, distance to sea, evapotranspiration, and moisture advection, causes an irregular PW distribution in the atmosphere, both horizontally and vertically. Thus this study aims at identifying the distribution patterns of PW in Iran and correlate these patterns with elevation and distance to sea. Materials and Methods The present research uses MODIS Aqua data (MYD05_L2. A V06) and selects the data with spatial resolution of 1 km (Near Infrared). The selected study period covers from 2002/07/04 to 2017/07/25 (5501 days), taken from NASA web site. These data are erroneous in the range between 5% and 10% (Kaufman & Gao, 2003). The spatial resolution of PW data are 1 km and the temporal resolution, twice per day. Afterwards, by using the functions, these data are converted from Level_2(swath data) to Level_3(grid data) and PW values interpolate on sinusoidal grid in 1800×2700 matrix with 1 km spatial resolution and daily temporal resolution. These data have been extracted for pixels within the political boundary of Iran and result in a matrix with 1884080 rows (locations) and 200 columns (PW classes). Then on the matrix’ base, frequency distribution is calculated in 1 mm intervals from 0-199 mm for every pixel (1884040×200). Finally, Principal Component Analysis (PCA) is performed, allowing the identification of frequency distribution patterns in Iran. The effects of altitude and distance to sea on these patterns are analyzed. The special program is developed and employed in MATLAB software for data analysis. Results and Discussion The spatial distribution of atmospheric humidity in Iran is controlled by the altitude, distance to sea, and moisture advection. Based on the results, the mean annual PW of the country is about 12 mm. PW is maximum near the southern and northern coasts of the country, with the highest and lowest PW value observed near the Oman Sea coast (31 mm) and the peak of Damavand (3 mm), respectively. Results from PCA show that 95% of spatial variation of PW can be explained through 4 components. Accordingly, local factors like distance to sea and altitude are the most important ones in spatial distribution of PW. The study of the correlation between distance from the sea and frequency distribution patterns of PW shows the effect of distance and proximity to the sea in the frequency distribution patterns. This is more evident in the first and second components. As expected, up to a distance of approximately 250 kilometers in the first component and 150 kilometers in the second, the amount of PW will gradually decline. From then on, the spatial pattern of PW is affected by altitude and morphology rather than by distance from sea and sea\land breeze. In the third component, due to the formation of a moisture convergence belt at approximately 11 and 4 km, respectively, on the south and north coasts, the amount of atmospheric moisture is maximum. Then from 11 to 66 kilometers due to the Alborz Range, which is a short distance from the Caspian Sea, the amount of PW is minimal. Minimal atmospheric humidity on the southern coast occurs approximately at 250 kilometers away from the sea. In the South Coast, moisture penetrates the country further away from the coast, as it is smoother than the North Coast. Thus, sea moisture enters through the straits of Kahnouj Area into Jazmourian Plain, distinguishing it from its surrounding areas in terms of moisture. Moisture in the Caspian Sea enters the Tarom Valley through Manjil Strait. The spatial distribution of moisture in the western, middle, and eastern Persian Gulf coasts does not have a similar pattern. This difference is because of factors like the dominance of sea-land breeze in the eastern areas of Bushehr and the presence of small firth and bays in the area that increase its atmospheric moisture, in comparison to the surrounding areas. The amount of moisture in the coasts of Oman Sea is clearly different from PW of the Persian Gulf. PW MODIS is also overestimated in places such as near beaches with high temperatures and humidity. In addition to the altitude and distance to sea, the role of moisture advection should not be ignored. In the coastal region, the variability is caused by high temperature and moisture advection, whereas in areas far from coastline, it is the altitude that causes many spatial differences in moisture distribution. Conclusion Although Iran is bounded from the north and south to the sea, atmospheric moisture is very low in the country. According to the results from this paper, minimum and maximum difference of PW is about 27 mm. Thus, in a region above 3000 m from the sea level, PW falls below 6 mm, and the coasts of Oman Sea are above 26 mm, 60% of the time. This means that in spite of the great sources of water both in the south and the north, Iran’s atmosphere suffers from poor moisture. Topography acts as a barrier for moisture to enter inland regions from both north and south seas. Inland, the altitude plays a crucial role for frequency distribution of PW, while in the coastal regions, both moisture advection and temperature are culprits. In this way, moisture advection is an important factor to justify spatiotemporal variations of PW in Iran well. And it is this parameter that affects water budget. | ||
| کلیدواژه‌ها [English] | ||
| Precipitable Water, Principal Component Analysis, Frequency Distribution, MODIS, Iran | ||
| مراجع | ||
|
احمدی، م.؛ داداشی رودباری، ع.؛ احمدی، ح. و علی‌بخشی، ز. (1397). واکاوی ساختار دمای ایران مبتنی بر برون‌داد پایگاه دادة مرکز پیش‌بینی میان‌مدت هواسپهر اروپایی (ECMWF) نسخة ERA Interim، پژوهش‌های جغرافیای طبیعی، 50(2): ۳۵۳-372. دارند، م. (1394). واکاوی وردایی زمانی- مکانی رطوبت جوی ایران‌زمین طی بازة زمانی 1979-2013، پژوهش‌های جغرافیای طبیعی، 47(2): ۲۱۳-239. دوستکامیان، م.؛ جلالی، م. و طاهریان، ا.م. (1397). واکاوی شار همگرایی رطوبت و آب قابل بارش جو بارش‌های بهارۀ ایران، جغرافیا و مخاطرات محیطی،7(25): ۱۳۱-152. عساکره، ح. و بیات، ع. (1392). تحلیل مؤلفه‌های اصلی مشخصات بارش سالانة شهر زنجان، جغرافیا و برنامه‌ریزی، 45: ۱۲۱-142. عساکره، ح. و دوستکامیان، م. (1393). تغییرات زمانی و مکانی آب قابل بارش در جو ایران، تحقیقات منابع آب ایران، 10(1): ۷۲-86. عساکره، ح. و دوستکامیان، م. (1395). ناحیه‌بندی اقلیمی آب قابل بارش جو ایران‌زمین، نشریة جغرافیا و برنامه‌ریزی، 58: ۱۸۱-202. عساکره، ح.؛ دوستکامیان، م. و قائمی، ه. (1393). تحلیل تغییرات ناهنجاری‌ها و چرخه‌های آبِ بارش‌پذیر جو ایران، پژوهش‌های جغرافیای طبیعی، 46(4): ۴۳۵-444. فلاح قالهری، غ. ع.؛ اسدی، م. و داداشی رودباری، ع. (1394). تحلیل فضایی پراکنش رطوبت در ایران، پژوهش‌های جغرافیای طبیعی، 47(4): ۶۳۷-650. مباشری، م.؛ پورباقر کردی، س.م؛ فرج‌زاده اصل، م. و صادقی نائینی، ع. (1389). برآورد آبِ بارش‌پذیر کلی با استفاده از تصاویر ماهواره‌ای مودیس و داده‌های رادیوساند ناحیة تهران، فصل‌نامة مدرس علوم انسانی، 14(1): ۱۰۷-126. مسعودیان، س. ا. (1390). آب‌وهوای ایران، مشهد: انتشارات شریعة توس. موسوی بایگی، م. و اشرف، ب. (1389). بررسی و مطالعة نمایة قائم هوای منجر به بارندگی‌های مخرب تابستانه (مطالعة موردی: مشهد)، آب‌وخاک، 24(5): ۱۰۳۶-1048. یارنال، ب. (1385). اقلیم‌شناسی همدید و کاربردهای آن در مطالعات محیطی، ترجمة س. ا. مسعودیان، اصفهان: انتشارات دانشگاه اصفهان. Acheampong, A. A.; Fosu, C.; Amekudzi, L. K. and Kaas, E. (2015). Comparison of precipitable water over Ghana using GPS signals and reanalysis products, Journal of Geodetic Science, 5: 163-170. Ahmadi, M.; Dadashi Roudbari, A.; Ahmadi, H. and Alibakhshi, Z. (2018). Analysis of Iran Temperature Structure Based on ECMWF, ERA Interim Version, Physical Geography Research Quarterly,50(2): 353-372 (in Persian). Asakereh, H. and Bayat, A. (2013). The Analysis of the Trend and the Cycles of Annual Precipitation Characteristics of Zanjan, Journal of Geography and Planning, 17(45): 121-142 (in Persian). Asakereh, H. and Doostkamian, M. (2014). Tempo-Spatial Changes of Perceptible Water in the Atmosphere of Iran, Iran-Water Resources Research, 10(1): 72-86 (in Persian). Asakereh, H. and Doostkamian, M. (2017). Climate Regionalization of Atmospheric Perceptible Water in Iran, Journal of Geography and Planning, 20(58): 181-202 (in Persian). Asakereh, H.; Doostkamian, M. and Qaemi, H. (2014). Analysis of anomalies and perceptible water cycles in Iran atmosphere, Physical Geography Research Quarterly, 46(4): 435-444 (in Persian). Barman, P.; Jade, S.; Kumar, A. and Jamir, W. (2017). Inter annual, spatial, seasonal, and diurnal variability of precipitable water vapour over northeast India using GPS time series, International journal of remote sensing, 38: 391-411. Callahan, J. A. (2014). Estimation of precipitable water over the Amazon Basin using GOES imagery, Doctoral dissertation, University of Delaware. Darand, M. (2015). Analysis of Spatio-Temporal Variation of Atmospheric Humidity in Iran during 1979-2013, Physical Geography Research Quarterly, 47(2): 213-239 (in Persian). Doustkamian, M.; Jalali, M. and Taherian, A.M. (2018). Analysis of Moisture Flux Convergence and Precipitation Spring Precipitable Water in Iran, Geography and Environmental Hazard, 7(25): 131-152 (in Persian). Fallah Ghalhari, Gh.; Asadi, M. and Dadashi Roudbari, A.A. (2016). Spatial Analysis of Humidity Propagation over Iran, Physical Geography Research Quarterly, 47(4): 637-650 (in Persian). Gao, B. C. and Kaufman, Y. J. (2003). Water vapor retrievals using Moderate Resolution Imaging Spectroradiometer (MODIS) near‐infrared channels, Journal of Geophysical Research: Atmospheres, 108(D13). Gourbaz, G. and Jin, S. (2017). Long-time variations of precipitable water vapour estimated from GPS, MODIS and radiosonde observations in Turkey, International Journal of Climatology, 37: 5170-5180. Kaufman, Y. J. and Gao, B. C. (1992). Remote sensing of water vapor in the near IR from EOS/MODIS, IEEE Transactions on Geoscience and Remote Sensing, 30: 871-884. Kern, A.; Bartholy, J.; Borbás, É. E.; Barcza, Z.; Pongrácz, R. and Ferencz, C. (2008). Estimation of vertically integrated water vapor in Hungary using MODIS imagery, Advances in Space Research, 41: 1933-1945. Li, X.; Zhang, L.; Cao, X.; Quan, J.; Wang, T.; Liang, J. and Shi, J. (2016). Retrieval of precipitable water vapor using MFRSR and comparison with other multisensors over the semi-arid area of northwest China,Atmospheric research, 172: 83-94. Lins, H. F. (1997). Regional streamflow regimes and hydroclimatology of the United States, Water Resources Research, 33: 1655-1667. Liu, H.; Tang, S.; Hu, J.l Zhang, S. and Deng, X. (2017). An improved physical split-window algorithm for precipitable water vapor retrieval exploiting the water vapor channel observations, Remote sensing of environment, 194: 366-378. Liu, Z.; Wong, M. S.; Nichol, J. and Chan, P. W. (2013). A multi‐sensor study of water vapour from radiosonde, MODIS and AERONET: a case study of Hong Kong, International Journal of Climatology, 33: 109-120. Maghrabi, A. and Al Dajani, H. M. (2013). Estimation of precipitable water vapour using vapour pressure and air temperature in an arid region in central Saudi Arabia, Journal of the Association of Arab Universities for Basic and Applied Sciences, 14: 1-8. Malmusi, S. and Boccolari, M. (2010). Upper and middle precipitable water calculated from METEOSAT-8/-9 tropospheric humidity and NCEP/NCAR temperatures, Atmospheric Research, 95: 8-18. Means, J.D. (2011). GPS precipitable water measurements used in the analysis of California and Nevada climate, University of california, san diego. Mobasheri, M.R.; Poorbager kordi, S.M.; Farajzadeh Asl, M.; Sadeghi Naeini, A. (2010). Total Precipitable Water (TPW) Assessment Using MODIS Images Radiosonde Data (Case study: Tehran District), The Journal of Spatial Planning, 14(1): 107-126 (in Persian). Mousavi Baygi, M. and Ashraf, B. (2010). Investigation and study of vertical weather profile leading to destructive summer rainfall (case study: Mashhad), Journal of water and soil, 24(5): 1036-1048 (in Persian). Prasad, A. K. and Singh, R. P. (2009). Validation of MODIS Terra, AIRS, NCEP/DOE AMIP‐II Reanalysis‐2, and AERONET Sun photometer derived integrated precipitable water vapor using ground‐based GPS receivers over India, Journal of Geophysical Research: Atmospheres, 114(D5). Reitan, C. H. (1963). Surface dew point and water vapor aloft, Journal of Applied Meteorology, 2(6): 776-779. Seidel, D. J. (2002). Water vapor: Distribution and trends, Encyclopedia of Global Environmental Change, 750-752. Trenberth, K. E.; Fasullo, J. and Smith, L. (2005). Trends and variability in column-integrated atmospheric water vapor, Climate dynamics, 24: 741-758. Wang, H.; Wei, M.; Li, G.; Zhou, S. and Zeng, Q. (2013). Analysis of precipitable water vapor from GPS measurements in Chengdu region: Distribution and evolution characteristics in autumn, Advances in Space Research, 52: 656-667. Wong, M. S.; Jin, X.; Liu, Z.; Nichol, J. and Chan, P. W. (2015). Multi‐sensors study of precipitable water vapour over mainland China,International Journal of Climatology, 35: 3146-3159. Yarnal, B. (2006). Translated by: Masoodian, S.A. Synoptic Climatology: An Environmental Studies, Isfahan University Press. | ||
|
آمار تعداد مشاهده مقاله: 591 تعداد دریافت فایل اصل مقاله: 313 |
||