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پژوهش های جغرافیای طبیعی، جلد ۵۳، شماره ۳، صفحات ۳۵۱-۳۶۴
عنوان فارسی بررسی پراکنش فصلی و روند بی ‌هنجاری دمای سطح زمین روز و شب ایران با استفاده از داده ‌های سنجنده MODIS
چکیده فارسی مقاله بی ‌هنجاری دمای سطح زمین (LSTA) متغیری کلیدی در مطالعات اقلیمی، کشاورزی، و مدیریت منابع آب است. هدف از این مطالعه بررسی تغییرات فصلی و روند بی ‌هنجاری دمای سطح زمین روز و شب ایران است. بی ‌هنجاری دمای سطح زمین برگرفته از سنجنده MODIS ماهواره Terra طی دو بازه زمانی روز و شب برای دوره 2001-2018 بررسی شده است. برای درستی ‌سنجی داده‌ های دمای سطح زمین از داده‌ های هشت ایستگاه همدید با روش رگرسیون خطی استفاده شد که نتایج نشان از دقت بالای این داده‌ ها در کشور را داشته است. نتایج نشان داد بی ‌هنجاری منفی در مناطق خشک کم ‌ارتفاع و بی‌ هنجاری مثبت در مناطق مرتفع و عرض‌ های جغرافیایی بالا دیده می‌ شود. تحلیل روند نشان داد بی‌ هنجاری دمای سطح زمین روز و شب با سرعت متوسط 01/0 و 02/0 درجه سلسیوس به ازای هر سال در حال افزایش است. بیشینه نمره Z آزمون من- کندال (روند مثبت) با 80/3 در فصل تابستان برای شب و روز اتفاق افتاده است. برعکس، روند منفی در بی‌ هنجاری‌ها برای مناطق خشک جنوب ‌شرقی و داخلی و کوهپایه‌ های زاگرس و البرز جنوبی به‌ دست آمده است.
کلیدواژه‌های فارسی مقاله ایران،بی‌ هنجاری دمای سطح زمین،سنجنده MODIS،ماهواره Terra،
عنوان انگلیسی Investigation of seasonal distribution and abnormal trend of day and night surface temperature in Iran using MODIS sensor data
چکیده انگلیسی مقاله "Introduction,Land surface temperature (LST) plays an important role in surface energy balance. A set of environmental parameters, such as temporal and geographical changes, thermal properties, biophysical properties, climatic parameters and subsurface conditions can cause heterogeneous spatio-temporal distribution of LST and its anomalies to be. LULCC-induced surface temperature anomalies have important implications for understanding the physical mechanisms associated with the surface to changes in various biophysical factors, including albido and surface roughness (also known as aerodynamic resistance). The purpose of this study is to evaluate the seasonal changes and abnormalities of daytime and nighttime land surface temperature in Iran based on LST derived from satellite data.Materials and methods,In this study, the following steps were performed:A study area:The whole country of Iran was wanted. To better reveal the behavior of surface temperature anomalies in Iran, the data has been converted to a seasonal scale and also for the first time in the country, surface temperature anomalies have been studied separately for night and day.B) DataB-1) Moderate Resolution Imaging Spectroradiometer(MODIS)To investigate the anomaly of land surface temperature, the MODIS sensor data of Terra satellite MOD_LSTAD and MOD_LSTAN products were used for day and night data with a horizontal separation of 10 km and the statistical period of 2001-2018, respectively.C) Calculate trend and trend slope using non-parametric Mann-Kendall and Sen’s testsIn order to evaluate the abnormal trend of land surface temperature in Iran, non-parametric Mann-Kendall (M-K) test was used. The non-parametric Sen's method was used to estimate the slope of the process in the time series of land surface temperature anomalies and day and night in Iran.Results and discussion,The results showed that the mean anomaly of daytime land surface temperature in Iran (LSTAD) in the three seasons of winter, spring and autumn is negative and in summer is positive. Also, the long-term mean anomaly of night surface temperature (LSTAN) is negative in cold seasons (winter and autumn) and positive in warm seasons. The positive maximum of LSTAD in Iran was 0.172 in summer and its negative maximum was -0.672 in autumn. The same statistical quantity was obtained for LSTAN positive anomaly in summer 0.266 and in autumn 0.244. The minimum LSTAD was calculated between -1.942 to -3.097 and the maximum was calculated between 1.047 to 2.865. For night, it showed a minimum between -0.748 to -1.296 and a maximum between 1.597 to 2.189. The average statistical trend of Iran LSTAD and LSTAN in all seasons except autumn is increasing. This amount, despite being incremental, is not significant. During the day, the maximum average trend of increasing abnormality is obtained in summer (0.744) and at night in spring (1.038). The minimum and maximum trends in both day and night in Iran are significant at the alpha level of 0.01 and in terms of trend intensity, the warm seasons are more intense. The highest computational Z-score of Mann-Kendall test was obtained at night with the value of 4.097 (spring). Also, the same maximum amount per day was calculated with the amount of 3.917 in summer.Conclusion,In this study, we have evaluated the day and night land surface temperature anomaly of Iran using Terra satellite MODIS sensor data during a long-term statistical period (2001-2018). The non-parametric Mann-Kendall test was used to study the trend and the non-parametric Sen test was used to calculate the trend slope. The positive anomaly of Iran's land surface temperature is higher at night than during the day and this amount is also significant in the warm seasons of the year. The maximum positive anomaly was obtained during the day during the summer with a value of 0.172 degrees Celsius and for the night with a value of 0.266 degrees Celsius. The average anomaly trend of land surface temperature during the day and night in winter to summer is increasing and only in autumn this amount is decreasing. The minimum and maximum trend in each period of time is significant at the alpha level of 0.01 and the intensity of the trend is more at night than during the day. The main focus of negative anomalies is recognizable in low-lying dry areas, inland arid regions located in the east and southeast of Iran and inland holes of Iran. While the increasing anomaly in the highlands and high latitudes of Iran is significant. Also, the dominant upward trend can be seen in the highlands of Iran, except in autumn; In this regard (Fallah Ghalhari, Shakeri and Dadashi Roudbari,2019) who used three methods of microcirculation SDSM, MarkSimGCM and CORDEX simulated the minimum and maximum temperature of Iran under the models CanESM2, GFDL-ESM2M and MPI-ESM-LR up to 2100 ; It was concluded that the annual temperature anomalies of the selected models are at high latitudes and mountainous highlands, which is in line with the results obtained in this study. One of the most important roles of land surface temperature and its anomaly is changes in convective processes, mixture layer depth and wind speed. Therefore, increasing the anomaly of land surface temperature in Iran can increase convection on the one hand and change the regional wind speed. (Dadashi Roudbari,1399) in explaining the role of surface temperature and climate change has stated that the warm surface of convection increases and causes the mixing of surface air and high surface air. Since the velocities of horizontal winds at land level are zero and at higher levels, the vertical mixing of horizontal winds causes wind speeds close to the earth's land surface to increase and wind speeds at high levels to decrease. Variability in surface temperature also changes the air temperature near the surface. In addition to what has been said, land surface warming in the highlands of Alborz and Zagros also affects the carbon cycle; Because surface heating accelerates the melting of snow and ice in these areas, resulting in the release of excess carbon (Fili, Roir, Gotha, & Pregent, 2003). Therefore, it is worthwhile to pay more attention to policies related to carbon stabilization as well as programs related to water resources and dam construction based on what was addressed in this study.
کلیدواژه‌های انگلیسی مقاله ایران,بی‏ هنجاری دمای سطح زمین,سنجندة MODIS,ماهوارة Terra
نویسندگان مقاله محمود احمدی |
دانشیار آب‏ و هواشناسی، دانشگاه شهید بهشتی، دانشکدة علوم زمین، تهران

زهرا سادات میرزایی |
کارشناس ‏ارشد آب ‏و هواشناسی، داانشگاه شهید بهشتی، دانشکدة علوم زمین، تهران

عباسعلی داداشی رودباری |
پژوهشگر پسادکتری آب ‏و هواشناسی، دانشگاه فردوسی مشهد، مشهد

نشانی اینترنتی https://jphgr.ut.ac.ir/article_83587_74d07d20f589639114893c7e485d7a3c.pdf
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