فهرست مطالب

نشریه پژوهش های اقلیم شناسی
پیاپی 39-40 (پاییز و زمستان 1398)

  • تاریخ انتشار: 1398/11/01
  • تعداد عناوین: 7
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  • حنانه باروتی، کاظم اسماعیلی*، بیژن قهرمان صفحات 1-20

    تغییرپذیری بالای بارش، پراکندگی شبکه باران سنجی فعال و نیز کیفیت پایین مقادیر بارش ثبت شده، کاربرد مدل های تصادفی زمانی- مکانی بارش در مناطق خشک و نیمه خشک را همواره با چالش روبه رو کرده است. در این پژوهش، از مدل خطی تعمیم یافته به منظور تکمیل و توسعه ی سری زمانی داده های ثبت شده ی بارش روزانه ی 11 ایستگاه سینوپتیک نسبتا پراکنده در زیرحوضه ی رود شور با وسعت 67/22066 کیلومتر مربع (که یکی از زیر حوضه های دریاچه نمک می باشد) در دوره آماری 1991 تا 2010 میلادی استفاده شد، تا بتواند برای مدل های هیدرولوژیکی بکار رود. جهت بیان ساختار زمانی- مکانی بارش از مدل توزیع لجستیک برای رخداد بارش و از توزیع گاما برای مدل کردن مقدار بارش استفاده گردید. صحت سنجی زمانی و مکانی در  یک دوره زمانی مجزا و نیز استفاده از ایستگاه هایی که در برازش مدل وارد  نشده بودند، انجام  آنگاه  تغییر پذیری زمانی- مکانی بارش آزموده شد. اثرات فصلی  بر تغییرات زمانی- مکانی بارش ، همبستگی بارش روزانه و نیز طول، عرض جفرافیایی و ارتفاع ایستگاه های باران سنجی شناسایی شدند که از آن مدل توانست به خوبی ویژگی های بارش را در منطقه مطالعاتی نشان دهد. در هر دو مدل رخداد و مقادیر، اثرات مکانی از مهمترین پیش بینی کننده های بارش و بعد از آن در بین اثرات زمانی، میانگین بارش در یک روز قبل مهمترین متغیر پیش بینی کننده بارش در حوضه شناخته شده است. به علاوه محرک های بارندگی در مقیاس بزرگ (دما و رطوبت نسبی) تا حدودی باعث بیان بهتر تغییرات در هرسال شده و نیز ارتباط بین متغیرهای بزرگ مقیاس و بارش،  زمینه ی ریزمقیاس نمایی خروجی های مدل های جهانی اقلیم را فراهم می نماید. در واقع مدل های تصادفی با رویکرد توسعه ی مجموعه داده های مشاهداتی در ایستگاه های باران سنجی و ریزمقیاس نمایی سناریوهای اقلیمی در مقیاس منطقه ای و جهانی پایه گذاری شده است.

    کلیدواژگان: بارش، نیمه خشک، الگوهای خطی تعمیم یافته، مدل های تصادفی، داده های گمشده
  • معصومه فروغی*، یعقوب دین پژوه، سعید جهانبخش اصل صفحات 21-37

    تغییراقلیم یکی از مهم ترین چالش های زیست محیطی در جهان می باشد.تغییراقلیم، اثرات شدیدی بر کشاورزی، سیستم های هیدرولوژیکی، اکوسیستم و سایر سیستم های مرتبط با آن دارد. تبخیر- تعرق گیاه مرجع (ET0)، یکی از عناصر مهم چرخه ی هیدرولوژیکی است که تحت تاثیر تغییراقلیم می باشد. در مطالعه حاضر، روند تبخیر- تعرق گیاه مرجع (ET0) در مقیاس های زمانی ماهانه و سالانه مورد بررسی قرار گرفت. ET0 با استفاده مدل فائو- پنمن- مانتیث (FAO-PM-56)، در 18 ایستگاه هواشناسی واقع در غرب ایران، برآورد گردید. در مطالعه حاضر، روند تبخیر- تعرق پتانسیل گیاه مرجع (ET0) در مقیاس های ماهانه و سالانه در 18 ایستگاه منطقه غرب ایران بررسی گردید. همه ایستگاه ها بیش از 20 سال داده منتهی به 2015 داشتند. از داده های میانگین دمای حداکثر و حداقل، ساعات آفتابی، سرعت باد، رطوبت نسبی حداکثر و حداقل استفاده شد.  ET0  از مدل فائو- پنمن- مانتیث، و روند آناز روش من- کندال تحلیل شد. شیب خط روند از تخمین گر سن، بدست آمد. نتایج نشان داد که، گرچه هم روند افزایشی معنی دار و هم روند کاهشی در ET0سالانه و ماهانه ایستگاه ها وجود داشت، با این حال، درصد زیادی از سری های زمانی ET0روند رو به بالا داشته اند. در مقیاس ماهانه، تعداد سری های با روند افزایشی نسبت به کاهشی، در بسیاری از ماه های گرم سال بیشتر بود. در مقیاس های ماهانه و سالانه به ترتیب 24/78 و 77/77 درصد سری ها، روند افزایشی (69/47 درصد برای ماهانه و 55/55 برای سالانه معنی دار،  (P<0.10 داشتند. قویترین روند مثبت و منفی برای  ET0سالانه، به ترتیب در کرمانشاه (79/5=Z) و خدابنده (78/1-=Z) مشاهده شد. در مقیاس ماهانه، قویترین روند مثبت و منفی برای  ET0 هر دو در ماه اوت به ترتیب برای کرمانشاه با 43/5=Z و خدابنده با 47/3-=Z مشاهده گردید.

    کلیدواژگان: تبخیر- تعرق گیاه مرجع، تخمین گر سن، تغییراقلیم، پنمن مانتیث، من- کندال
  • جواد بداق جمالی*، سهیلا جوانمرد، سحر تاجبخش صفحات 38-56

    هدف این مقاله بررسی و شناسایی میانگین ماهانه بارش های همرفتی و پوشنی با استفاده از داده های سنجنده TMI  ماهواره  TRMMمبتنی بر میزان گرمای نهان آزاد شده طی  دوره آماری 13 ساله (2010-1998)  می باشد. تفکیک دو رژیم بارشی همراه با ابرهای همرفتی و پوشنی به علت پیچیدگی سازوکار آن ها و در نظر گرفتن داده های دیدبانی ایستگاهی محدود موجود، تا کنون،  در کشور انجام نشده است. به همین منظور، در این مقاله به مطالعه ماهانه توزیع مکانی-زمانی ابرهای همرفتی و پوشنی که می تواند راهنمای مناسبی برای شناسایی رژیم بارش در کشور باشد، پرداخته شده است.  نقشه های پهنه بندی میانگین ماهانه رخداد دو نوع بارش همرفتی و پوشنی با استفاده از داده های TRMM_TMI پردازش شده توسط نرم افزارGrADS  تحت سیستم عامل لینوکس، ترسیم شده اند. نتایج نشان دادند که تمرکز زیاد بارش های همرفتی به ترتیب در ارتفاعات شمال غرب کشور، زاگرس میانی و البرز مرکزی رخ داده است. بارش های پوشنی نیز الگوی مشابهی با بارش های همرفتی دارند اما مقادیر بارش به مراتب کمتر از بارش همرفتی است و با توجه به شرایط فصلی، بین 100 تا 200 میلی متر بین این دو بارش اختلاف دیده می شود. بیشینه مقدار بارش همرفتی فصل بهار (مارس، آوریل، و می)، در مناطق غرب، شمال غرب و سواحل غربی دریای خزر رخ داده است. برای 8 ماه از سال، بیشینه بارش همرفتی کشور در شمال غرب، ارتفاعات کردستان و ارتفاعات زاگرس میانی مشاهده شد. بیشینه بارش پوشنی نیز عمدتا درمناطق مرتفع رشته کوه زاگرس رخ داده است.

    کلیدواژگان: بارش همرفتی، بارش پوشنی، گرمای نهان، ماهواره TRMM، سنجنده TMI
  • ودود صمدی، مرتضی خداقلی*، اکبر شائمی صفحات 57-68

    هدف اصلی این پژوهش، درک ارتباط بین متغیرهای اقلیمی و پراکنش گونه داروئی Nepeta catariaدر شمال غرب کشور بود تا از بین آن ها عوامل اقلیمی مهم تاثیرگذار بر گسترش رویشگاه های این گونه در منطقه مشخص شود. به این منظور، با در نظر گرفتن فاکتورهای پراکنش طبیعی گونه در این منطقه، با استناد به آمارهای هواشناسی 29 ایستگاه سینوپتیک واقع در سه استان اردبیل، آذربایجان شرقی و غربی و استان های مجاور، 46 متغیر اقلیمی که از نظر شرایط اکولوژیک این گونه از اهمیت بیشتری برخوردار بودند، انتخاب شدند و با روش تحلیل عاملی، عوامل موثر در پراکنش این گونه بررسی گردید. یافته ها نشان داد اقلیم منطقه شمال غرب کشور حاصل تعامل پنج عامل دمای گرمایشی، الگوی زمانی بارش، دمای فصل سرد، باد و بارش می باشد که به ترتیب با 9/30 ، 2/19، 2/19، 4/11 و 8/7 درصد و در کل، 5/88 درصد پراش متغیرهای اولیه اقلیم رویشی منطقه را بیان می کنند. با انطباق نقشه پراکنش گونه با نقشه های اقلیمی تهیه شده، تاثیر عامل دمای گرمایشی با میانگین 83/0- در پراکنش گونه با بالاترین میزان و با تاثیر منفی مشخص شد. میانگین ارتفاع از سطح دریا در مناطق دارای این گونه داروئی بالای 2000 متر و میزان بارش سالانه در این مناطق در حدود 381 میلی متر می باشد. میانگین متغیر تعداد روزهای همراه با بارش سالانه در مناطق دارای گونه، 96 روز می باشد.

    کلیدواژگان: پونه سا (Nepeta cataria)، متغیرهای اقلیمی، رویشگاه، شمال غرب کشور
  • حسین بهزادی کریمی*، احمد مزیدی صفحات 69-86

    تخمین دقیق تبخیر و تعرق نقش مهمی در مدیریت منابع آب، برنامه ریزی آبیاری و تامین نیاز آبی گیاهان به ویژه در مناطق نیمه خشک و خشک دارد. هدف از این تحقیق ارائه ی یک مدل رگرسیونی است که با مبنا قرار دادن روش فائو-پنمن-مانتیث بتوان مقدارتبخیر و تعرق گیاه مرجع را با عوامل اقلیمی برآورد کرد. در این پژوهش ابتدا با استفاده از داده های ماهانه ی حداکثر و حداقل دما، متوسط رطوبت نسبی، ساعات آفتابی و متوسط سرعت باد ایستگاه های سینوپتیک واقع در حوضه ی آبریز فلات مرکزی ایران در یک دوره ی آماری 18 ساله (2012-1995)، مقادیر تبخیر و تعرق سالانه و فصلی به روش FAO-56-PMمحاسبه شد. سپس با استفاده از نرم افزار SPSSرابطه ی بین عوامل اقلیمی فوق با مقدار تبخیر و تعرق از طریق رگرسیون خطی چندمتغیره مدلسازی گردید. صحت دقت مدل ها نیز با آزمودن چهار فرضیه ی خطی بودن رابطه، نرمال بودن باقیمانده ها، ثابت بودن واریانس باقیمانده ها و ناهمبسته بودن خطای مدل سنجیده شد. نتایج حاصل از اجرای مدل مبین آن است که رابطه ی قوی بین EToحاصل از رگرسیون چندمتغیره با عوامل اقلیمی وجود دارد؛ بطوریکه در مقطع زمانی سالانه و فصول بهار و تابستان حدود 98درصد تغییرات ETo توسط این پنج متغیر تبیین می شود و در فصول پاییز و زمستان به ترتیب حدود 97 و 96 درصد از پراش تبخیر و تعرق با پراش پنج عامل اقلیمی مشترک است. معادلات رگرسیون استاندارد حاکی از آن است که سهم متغیرهای سرعت باد و حداکثر دما در میزان تبخیر و تعرق سالانه و فصلی بیش از سایر عوامل اقلیمی است.مقایسه ی نقشه های هم تبخیر سالانه و فصلی حوضه ی آبریز فلات مرکزی نشان داد که ازلحاظ مکانی نیز ارتباط نزدیک و قابل قبولی بین روش فائو-پنمن-مانتیث و مدل رگرسیون وجود دارد و شمال غرب حوضه از کمترین و مناطق جنوبی از بیشترین میزان تبخیر و تعرق سالانه و فصلی برخوردار می باشند.

    کلیدواژگان: تبخیر و تعرق گیاه مرجع، روش فائو-پنمن-مانتیث، مدل رگرسیون چندمتغیره، حوضه ی آبریز فلات مرکزی ایران
  • مصطفی یعقوب زاده*، یوسف رحمانی صفحات 87-100

    افزایش میزان غلظت گازهای گلخانه ای می تواند منجر به گرم شدن جهانی شود و این امر آب و هوا را تحت تاثیر قرار داده و  منجر به وقوع پدیده تغییراقلیم شود. در این تحقیق سعی شد روند تغییرات بارش، دمای کمینه و دمای بیشینه ایستگاه سینوپتیک بیرجند در طی دوره های زمانی مختلف از سال 2010 تا سال 2100 میلادی با استفاده از داده های گزارش پنجم تغییراقلیم مورد بررسی قرار گیرد. مدل های CSIROMK3.6،  GFDL-ESM2M، GISS-E2-R، IPSL-CM5A-MR و MIROC-ESM به همراه سناریوهای انتشار RCP2.6, RCP4.5, RCP6, RCP8.5ارزیابی تغییرات بارش، دمای کمینه و دمای بیشینه را در دوره زمانی های مختلف آینده نسبت به دوره زمانی پایه برای ایستگاه سینوپتیک بیرجند انجام دادند. برای اطمینان از نتایج مدل ها، ابتدا مقایسه ای بین داده های متغیرهای هواشناسی حاصل از مدل ها با ایستگاه سینوپتیک در دوره زمانی پایه انجام شد. نتایج تحقیق مشخص کرد مدل های GFDL-ESM2M و GISS-E2-R از دقت بیشتری در برآورد متغیرهای هواشناسی در دوره زمانی پایه و آتی برخوردارند. همچنین روند تغییرات بارش از سال 2010 تا 2100 میلادی برای مدل ها و سناریوهای مختلف متغیر خواهد بود. با این وجود، مدل های GFDL-ESM2M و MIROC-ESM در بین مدل ها و سناریو RCP8.5 در بین سناریوها کاهش بارش بیشتری را برآورد می کنند. از مقایسه سناریوها در همه مدل ها نیز مشخص شد که دمای بیشینه در سناریو RCP8.5و RCP2.6 به ترتیب بیشترین و کمترین افزایش را در طی دوره های آتی خواهد داشت. همچنین این تحقیق مشخص کرد مدل های مختلف GCM و سناریوهای انتشار برآورد متفاوتی از متغیرهای هواشناسی خواهند داشت و باید در انتخاب مدل و سناریو برای هر منطقه دقت بیشتری انجام داد.

    کلیدواژگان: گزارش پنجم تغییراقلیم، مدل AOGCM، سناریو انتشار، متغیرهای هواشناسی، دوره آتی
  • اکبر شائمی*، نسرین نیک اندیش، ملیحه حسینی صفحات 101-110

    با توجه به محدودیت منابع، یکی از مهمترین عوامل موثر در جهت مصرف بهینه انرژی در کشت گلخانه ای، توجه به شرایط اقلیمی به ویژه  دما در مکانیابی محل  استقرار واحدهای گلخانه  است. از آنجایی که هر محصول نیاز به شرایط دمایی خاص دارد، می توان با مکان یابی صحیح واحدهای گلخانه بر اساس کشتهای غالب در هر منطقه انرژی مصرفی را به طور قابل ملاحظه ای در این صنعت کاهش داد و از هدر رفت مقادیر زیادی انرژی و صرف هزینه های هنگفت جلوگیری کرد. در این تحقیق برای نیل به این هدف، داده های روزانه دمای 6 ایستگاه هواشناسی در سطح استان قم که دارای اطلاعات کامل در دوره آماری 2015-2005 استفاده شد. در این پژوهش جهت پهنه بندی نیازهای فصلی و سالانه گرمایشی و سرمایشی استان ابتدا در محیط اکسل داده ها پردازش و معادله همبستگی بین نیازهای فوق با ارتفاع تشکیل و سپس با استفاده از نرم افزار سیستم اطلاعات جغرافیایی (GIS و بکارگیری لایه رقومی DEM محدوده مورد مطالعه، نقشه های نیازهای گرمایشی و سرمایشی ترسیم شد. نتایج این پژوهش  نشان میدهد که  میزان نیاز انرژی برای مصارف سرمایش (CDD) و گرمایش (HDD) در فصول مختلف سال، تابع ارتفاع  بوده و در نواحی کوهستانی غرب و جنوب استان ، میزان نیاز به گرمایش سالانه بیشتر بوده و با حرکت  به سمت شرق و شمال استان ، نیاز سرمایشی افزایش می یابد. در واقع الگوی زمانی و مکانی  نیاز سرمایشی و گرمایشی از شرایط ارتفاعی منطقه تبعیت می کند. کمترین درجات  سرمایش سالانه به ترتیب در جنوب به مقدار (06/122) در ایستگاه وشنوه و بیشترین آن در  ایستگاه کوه سفید در شرق استان قم به مقدار (27/796) وجود دارد. همچنین بیشترین و کمترین نیاز گرمایش سالانه در ایستگاه وشنوه و قم به ترتیب به مقدار (37/2782) و (55/1705) دیده می شود. نتایج نشان داد که به ازای هر 100 متر افزایش ارتفاع ،میزان نیازسرمایشی محیط گلخانه62/50 درجه روز کاهش و مقدار میزان نیاز گرمایشی 52/84 درجه روز افزایش می یابد.همچنین دوره سرمایش 4.8 روز کوتاهتر و دوره گرمایش 5.8 روز طولانی تر میشود نتایج و دستاوردهای این تحقیق برای مکانیابی بهینه کشت های گلخانه در جهت توسعه پایدار حائز اهمیت است.

    کلیدواژگان: استان قم، سرمایش، گرمایش، کشت گلخانه ای
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  • Hananeh Barooti, Kazem Esmaili *, Bijan Ghahraman Pages 1-20

    Introduction:

     Long sequences of rainfall data are required as inputs to many water resource and flood management applications. However, observed rainfall data are rarely sufficient to characterize the full range of rainfall variability at the temporal and spatial scales of interest particularly when the purpose is to make long-term predictions such as those needed for climate change assessment as well as in spatially distributed modelling. To overcome this, stochastic models are commonly used to generate synthetic sequences of rainfall that are statistically consistent with the observed record at one or more gauged sites. Stochastic models also have the potential to quantify uncertainty due to missing values in the observed record and to downscale the outputs of global climate models. a particular challenge for these models is the application to arid and semi-arid regions because of the generally high variability in rainfall, sparse networks of rain gauges and potential data quality problems.

    Methodology:

     In this article, generalized linear model is applied to develop time-series data records of daily rainfall from11 synoptic stations in a 22066.67 km2 relatively sparse sub-basin of Shoor River basin for period of 1991–2010,to be used in hydrological models. To set temporal and spatial rainfall structures ,we used GLMs model .A GLM for daily rainfall is specified in two parts. The pattern of wet and dry days (rainfall occurrence) at a site is typically modelled using logistic regression. Second, a distribution function is fitted to the amount of rainfall on each wet day. For both precipitation occurrence and amounts models, model parameters are determined using the maximum-likelihood estimation (MLE) method. Also model performance can be assessed using simple but informative tests, such as the Pearson residuals. By defining suitable dependence structures between sites, it is possible to build a multivariate GLM. For the occurrence model, we have used a beta-binomial distribution for the number of wet sites on any day. For the amounts model, we have used the inter-site correlation structure of the Anscombe residuals. Results and discussion Following the procedure explained earlier, precipitation occurrence and amounts were modelled sequentially. In the occurrence model, most predictors are temporal predictors and interactions thereof (12 predictors out of 19). while in the amounts model it is the spatial predictors and their interactions which are more significantIn the final model the absolute magnitude of the ratio of the parameter’s value to its standard error can be regarded as an indicator of the covariate’s strength in the model. Aside from these indicators, we need to check that the Pearson residuals are within the 95% confidence interval. Among the external covariates, Humidity, temperature from reanalysis data were significant external predictors in the occurrence model. In addition, mean Pearson residuals are used to check the systematic model components. almost all the mean Pearson residuals by month, year and site are within the 95% confidence intervals for both occurrence and amounts models. However In the annual plots, it can be seen that some years have residuals placed outside the confidence interval indicating that these years may not be well represented by the models (but, results in Figure 4 show that observed annual and seasonal rainfall in these years are within the simulated bounds implying that the combined occurrence and amounts models produce reasonable annual totals even within these extreme years). This can be considered as evidence of good model performance, and shows that the structure of the precipitation characteristics by month, year and site are all well captured by the models. To indicate the spatial structure of errors, bubble maps for mean residuals by sites for both occurrence and amount models are plotted. Random distribution of residuals in space and lack of systematic structure in either of these plots can be interpreted that spatial variation of both rainfall occurrence and amount are well captured by the fitted models. To further check whether the GLM developed for this basin has captured the temporal structure of the observed data, we used the GLM to simulate rainfall at sites G5, G12 and G14 for the period 1981–1990, which is outside the fitting period. These particular sites were chosen on the basis of data availability prior to the period used for fitting. For spatial validation, rainfall data were simulated for the period 1991–2010 at stations G3, G11 and G13, which were not used at all for fitting. Again, from this plot, it can be seen that in general the GLM seems to adequately simulate the observed data.

    Summary and conclusions:

     The aim of this work was to investigate the potential applicability of a GLM for stochastic simulation of multisite daily rainfall in semi-arid areas and to develop a model that can be used to infill and extend historical rainfall data. In our application, logistic regression was used to simulate rainfall occurrence and two-parameter gamma distributions were used to simulate amounts on wet days. Inter-site dependence models were included for both occurrence and amounts. The main predictors of rainfall in the Shoor case study were found to be location effects, seasonality and temporal dependence. Humidity, temperature and from re-analysis data were significant external predictors. Analysis of model residuals showed that in general the model captured the seasonal, annual and spatial structure of rainfall in the basin. The simulation results indicated that in general the model results were consistent with the observed rainfall properties especially when we used from external predictors. Spatial and temporal validation tests showed that the GLM adequately simulated rainfall for the periods and gauges not used during model fitting. It can be concluded that the GLM provides a useful tool for simulating multi-site rainfall in the semi-arid Shoor basin for water resources purposes and may potentially be applicable to climate change analysis and to other semi-arid regions.

    Keywords: Precipitation, semi-arid, generalized linear models, Statistical models, Missing data
  • Masomeh Foroghi *, Yagob Dinpashoh, Saeed Jahanbakhsh Asl Pages 21-37
    Introduction

    A better understanding of trends in reference crop evapotranspiration (ET0) is crucial, in scientific management of water resources, in arid and semiarid regions. According to the fifth assessment report of the Intergovernmental Panel on Climate Change (IPCC), the global warming trend, which is mainly caused by the increasing amount of greenhouse gas emissions, will be continued. Climate change is known as a global environmental challenge facing humanity with implications for food production, natural ecosystem, and fresh water supply. The expected climate change is thought to have a severe impact on different elements of natural systems such as agriculture, hydrology, and ecosystem. reference crop evapotranspiration is one of the main elements of hydrological cycle affected by climate change. reference crop evapotranspiration can be considered as a measure of atmospheric evaporative demand. It is independent of crop type, crop development, and management practices. It provides a measure of the integrated effect of several meteorological parameters such as radiation, wind speed, air temperature, and humidity. reference crop evapotranspiration variability in a single station is affected by these climatic parameters. Estimating reference crop evapotranspiration is one of the important steps for calculating crop water requirements that has a special economic importance in the rationalization of water consumption in the agricultural field under current and future climate conditions. Moreover, this parameter plays an important role in the energy balance of basin ecosystems. Several hydrological processes, such as soil moisture dynamics, groundwater recharge, and runoff generation affected by ET0 fluctuation. 

    Materials and methods

    The data of the 18 synoptic stations located in the west and Northwest of Iran were obtained from the Islamic Republic of Iran Meteorological Organization (IRIMO). In this study, the stations which had at least 20 years of daily data (up to 2016) were chosen for analysis. The gathered data including the meteorological parameters namely maximum air temperature (Tmax), minimum air temperature (Tmin), mean air temperature (Tmean), wind speed in 10 m height (U), maximum relative humidity (RHmax), minimum relative humidity (RHmin), sunshine hours duration (n), and actual vapor pressure (ea). Each of the mentioned data was checked for quality, separately. These data were used to estimate the daily values of ET0 in selected stations using the FAO-56 PM method. The trends of ET0 were detected by using the Mann–Kendall test. The slopes of the trend lines were computed by using the Sen’s slope estimator. And, to investigate the spatial and temporal variability the IDW interpolation method was used

    Results and Discussion

    The results showed significant increasing as well as decreasing trends in the annual and monthly ET0. Although, the increasing trends in ET0 was more pronounced than the decreasing trends. In the monthly scale, during the warmer months of the year, the observed increasing trends were more than the decreasing trends of the ET0. In the annual scale, the stronger positive trend in ET0 magnitude was found at Kermanshah stations (Z=5.79), and the strongest negative trend was found at Khodabandeh station (Z=-1.78). Also, in the monthly scale and in the warm season, the strongest positive trend magnitude was found in August at Kermanshah station, (Z= 5.43), and the monthly strongest negative trend magnitude was found in August at Khodabandeh station, (Z=-3.47). In general, it is possible to conclude that, in the recent decades the required water of the plants is increased in the studied area.

    Conclusion

    Reference crop evapotranspiration is one of the main elements of hydrologic systems. Climate change in different parts of the Earth impacted natural systems in a different way. This study examined the trends of ET0 in 18 weather stations selected in west and northwest of Iran. The FAO-56 PM method used to calculate the ET0. The MK method was used to detect the trends in monthly and annual ET0 time series.The Sen’s estimator was used to estimate the magnitude of the trends. Results indicated that most of the monthly ET0 time series had upward trends. In annual time scale, Most of the stations showed increasing annual ET0 trends, which were significant at 10 percent level. Therefore, it can be concluded that in all water-related activities, especially in agriculture, fresh water should be used scientifically. Otherwise, all of the water-dependent activities might be adversely affected in future.

    Keywords: Climate change, Mann–Kendall, Penman Monteith method (FAO-56 PM), Reference crop Evapotranspiration, Sen’s Estimator
  • Javad Bodghjamali *, Soheila Javanmard, Sahar Tajbakhsh Pages 38-56

     Precipitation is one of the most important component of climate type specification which has been attracted specialists in different fields. It also plays an important role in hydrological cycle and world climate and has application in various sectors such as; weather forecasting, environment, agriculture, water basin management, flood probability occurrence and climate change. Traditional methods which have been applied for precipitation measurement are based on the synoptic, climatological, and raingauge meteorological stations and have difficulties such as high expense, shortage of number of stations, and lack of raingauge over impassable regions. Since the prominent climate of Iran is dry and semi-arid, knowledge of amount and temporal variation of precipitation in each region could be essential for planning and management of surface water resource. In this regard, precipitation estimation using TRMM satellite is one of the modern precipitation product approaches which have been considerably applied in meteorological studies now. The Tropical Rainfall Measuring Mission’s (TRMM) Microwave Imager (TMI) is a nine channel passive microwave sensor designed to provide quantitative rainfall information over a wide swath under the TRMM satellite. By carefully measuring the minute amounts of microwave energy emitted by the Earth and its atmosphere, TMI is able to quantify the water vapor, the cloud water, and the rainfall intensifying the atmosphere. The data used in this study are TRMM-TMI monthly products (3A-12 V6) which are global belt (40°S - 40°N) monthly average of surface rain rate (mmh-1), convective surface rain rate (mmh-1), stratiform surface rain rate (mmh-1) and 14 vertical layers (surface until 18 km above surface) hydrometeor contents (cloud liquid water (gm-3) precipitating water (gm-3), cloud ice (gm-3), precipitating ice (gm-3) and latent heat (degh-1) for 0.5 x 0.5 degree grids. In this research TRMM-TMI monthly data have been downloaded from the website This paper is aimed at investigating and recognizing of monthly mean of convective and stratiform precipitation using TMI sensor of TRMM satellite data based on rate latent heat release during 13 years (1998-2010). Recognition of two precipitation regime associated with convective and stratiform clouds have not ever been carried out in the country due to their conflict mechanisms and considering of restricted existing observation stations. On the other hand, the best recognition of the two precipitation regimes has been presented by TRMM satellite and the other thing has not been replaced up to now. It should also be noted that in view point of cloud seeding feasibility study, convective clouds have higher potential for cloud seeding compared with stratiform clouds due to the more precipitable water of it. Therefore, detection and distinction of convective and stratiform precipitation type is very important and necessary. As a result, considering of the importance of precipitation regime which includes cloud type indirectly is one of essential indices in cloud seeding feasibility studies.  For this purpose, in this paper, monthly mean spatial distribution of two types of convective and stratiform precipitation have been drawn up for 12 months, from January through December using TRMM_TMI data processed by GrADS software under  Linux Operating System. The results showed that high concentration of convective rainfall have been occurred over the high elevations of Northwest, middle of Zagros and Central Alborz respectively. The pattern of stratiform and convective rainfall are similar but the amount of stratiform rainfall is far less than convective rainfall and considering the seasonal condition, the difference of the maximum and minimum of stratiform and convective rainfalls have been observed about 100 to 200 mm per month respectively. Considering the separation of two precipitation types algorithm which is based on the evaporation latent heat mechanism, the precipitation which have had convective mechanism (including frontal, cyclonic, mountainous precipitation) all have been assumed convective. Therefore, more portion of precipitation belongs to the convective precipitation Maximum convective precipitation amount have been occurred in spring (March, April, and May) over west, northwest, and western coast of Caspian Sea. Since, the winter synoptic systems type is in transition to summer type in spring, accordingly, the air near the surface warms and atmospheric condition would be suitable for convection.  The maximum of convective rainfall have been observed over northwest, and Kordestan’s elevation and central Zagros elevation for 8 months of year. The maximum of stratiform rainfall have also been occurred over high elevations of Zagros mountains. Due to the shortage of observation stations over high elevations and also their unsuitable dispersion, the satellite data could be able to complete the lack of data. However, they would be associated with some errors in some cases. For example, it could not be observed suitable estimation of precipitation over southwest region of Iran based on the TRMM data.

    Keywords: convective precipitation, stratiform rainfall, Latent heat, TRMM Satellite, TMI sensor
  • Vadood Samadi, Morteza Khodagholi *, Akbar Shaemi Pages 57-68
    Introduction

    Medicinal plant is defined as a plant with at least a part having medicinal and healing properties for living organisms. Iran has a rich collection of medicinal plants and is known as one of the best places in the world in terms of climatic, geographical and growth condition for such valuable plants. Population growth and emergence of pharmaceuticals companies that produce medicines from herbs has led to an increasing rate of harvesting of these plants on their natural habitats, intensifying the pressure, habitat destruction and the extinction of some plant species of the country. An alternative option to prevent this problem is to plant and crop wild species. The present research aims to update information and develop insights on climatic characteristics of habitats of species Nepeta cataria in the northwest of Iran. This species is the most famous species of Nepeta in the world which widely grown in large parts of Europe and Asia such as Iraq, Iran, Afghanistan, Pakistan and India but in the US it is widely cultivated. Nepeta cataria has been used as a medicinal plant to urination, pulmonary vasodilatation and blocked uterus opening and disposal of body worms. Essential oil is main constituents extracted from its flowering branches and inflorescence.

    Materials and Methods

    Study area: Natural habitats in northwest of Iran in three provinces of West Azarbaijan, East Azarbaijan and Ardebil with an area exceeding 100000km2 (about 6% of the country), were considered as study area. The spatial distribution of the species and ecological variety in the northwestern of the country, is one of the reasons for choosing this study population.Data: Preliminary data, used in this study, were included meteorological data at 29 synoptic stations in three provinces of Ardabil, East Azerbaijan and West and neighboring provinces (including in southern Kurdistan, Zanjan and Gilan provinces in the South East in study area) in point-data format with the period from the beginning of the recorded date to the end of 2005, were measured and recorded. Annual and monthly climatic variables included 30 variables in temperature, 6 in humidity, 12 in precipitation, 6 in wind and 6 variables in sunshine group.

    Methodology

    In this study, a kriging interpolation method was used to generalize the data and mapping the spatial distribution. As a result, grids with dimensions of 10 × 10 km within the study area was established and finally matrix with 75 columns and 1055 rows (the intersection of grid lines) for the entire study area was prepared. Factor analysis was used for reduction of data matrix dimensions. Before using factor analysis, KMO (Kaiser-Mayer-Olkin) index and Bartlett's test of sphericity, were tested. Principal Components Analysis with correlation matrix type and up to 25 rotations was performed to extract the maximum operating frequency for convergence model. A Varimax method was used to improve relationship between variables and initial factors and apply special transformations on the factors, one of the most common methods is orthogonal to maintain independence between extracted factors. In this study, regression analysis was used to calculate the factor scores where, regardless of rotations, the number of related factors are estimated. The factor loadings with values greater than 0.6 were considered.

    Results

    The climate type of northwest of Iran is resulted from the interaction of five different factors. They cover 88.5 % of total variance including thermal temperature (30.9), temporal rainfall pattern (19.2%), temperature of the cold season (19.2%), wind (11.4%), and rainfall (7.8%). The results of cluster analysis demonstrated that six climatic zones were detected in the study area as follows: temperate high rainfall, temperate semi-arid, cold semi-arid, temperate and windy semi-arid, and humid temperate. According to the results obtained from the adaptation of species distribution and obtained climate maps, thermal temperature, with an average value of -0.83, was identified as the most negative influential factor affecting the species distribution. The average height in the areas of medical plants is about 2000 meters from sea level with annual precipitation of 381 mm. The number of rainy day is 96 days per year in this area.

    Keywords: catnip (nepta cataria), climatic, factors, Habitat, Northwestern Iran
  • Hossein Behzadi Karimi *, Ahmad Mazidi Pages 69-86
    Introduction

    The precise estimation of evapotranspiration plays an important role in managing water resources, planning irrigation and providing water needed for plants, especially in semi-arid and dry areas. The FAO-Penman-Monteith method is proposed as the only standard method for calculating reference evapotranspiration from climate data and for evaluating other methods (Allen et al., 1998). In the last few decades, multivariate regression has been used a one of the important methods for recognizing the interrelationship among variables and determining the correlation between evapotranspiration and a set of climatic factors. In this method, evapotranspiration was the dependent variable and various climatic elements were included as independent variables to the model. The best model is a model that can provide a better estimate of two or more other dependent variables. Arab Solghar et al. (2010) predicted annual evapotranspiration using multivariate regression in a number of subtropical stations in Iran. The results showed association between estimated evapotranspiration using Penman-Monteith equation and the method had a relatively small error level. Therefore, the aim of this study was to calculate evapotranspiration in different months of the year and subsequently in annual and seasonal periods for stations located in the central watershed of Iran during 1995-2012 using the FAO-Penman-Monteith method and modeling and estimating ETo values based on five climatic factors (maximum and minimum temperature, relative humidity, suny hours, and average wind speed at 2 meters above ground level) and the FAO-56-PM equation.

    Materials and methods

    In this study, first, annual and seasonal evapotranspiration were measured by the FAO-56-PM method using Cropwat software based on monthly data of maximum and minimum temperature, average relative humidity, sunny hours and average wind speed of synoptic stations located in the central watershed of Iran during a statistical period of 18 years (1995-2012). Then, SPSS software was used to model the relationship between the above climatic factors and evapotranspiration through multivariate linear regression. The accuracy of the models was evaluated by testing the four hypotheses of the linearity of the relationship, normality of the residuals distribution, constant variance of the residuals, and statistical independence of the errors. In the end, the GIS software capability was used for zoning the annual and seasonal evapotranspiration of the central plateau watershed, and preparing plans related to ETo values ​​derived from the FAO-Penman-Monteith equation and multivariate regression equation. The two methods were also spatially compared and analyzed.

    Results and discussion

    The results of the model show a strong correlation between ETo obtained from multivariate regression and the five climatic factors, so that about 98% of changes in ETo at annual scale and in the spring and summer were explained by these five variables, and approximately 97 and 96 percent of evapotranspiration changes were associated with changes in climatic factors in the autumn and winter seasons. Standard regression equations showed that the contribution of wind speed and maximum temperature variables in annual and seasonal evapotranspiration was more than other climatic factors. In summer, sunny hours and in other seasons, as well as at annual time scale, relative humidity had the least effect on the amount of evapotranspiration. Also, the highest effect of wind speed area and maximum temperature calculated in the study was 0.81 in the summer season and 0.41 in the spring. Comparison of the annual and seasonal evaporation maps of the central plateau watershed showed an acceptable correlation between the FAO-Penman-Monteith method and the regression model. In spring and summer, the highest ETo observed in both FAO-Penman-Monteith and regression methods occurred in the southern and middle regions of the central plateau watershed and the lowest ETo was in the northwest of the watershed area. In winter, ETo was increasing from the center of the watershed towards lower latitudes due to the relative increase in temperature and wind speed. The annual ETo value was lower in the northern regions but higher in the southern regions. Wind speed, average maximum temperature and minimum temperature had the greatest effect on increasing ETo respectively. The results of this study are consistent with studies by Azizi et al. (2009) on estimating ETo through multivariable regression in Isfahan province; Arab Solghar et al. (2011), in predicting annual ETo using meteorological data in a number of stations in semi-arid areas of Iran; Sheikholeslami et al. (2014) on modeling ETo using daily data in Khorasan Razavi; and Bakhtiari et al. (2015) on the estimation of daily ETo in selected semi-arid climates of Iran.

    Conclusion

    The purpose of this study was to provide a regression model based on the FAO-Penman-Monteith method that can estimate ETo using climatic factors. In this study, the maximum and minimum temperature, average relative humidity, sunny hours and average wind speed were measured monthly at 2 meter height from the ground surface of the synoptic stations via Cropwat software.
    ETo values in the watershed of the central plateau of Iran ​​were calculated in annual and seasonal time scales using the FAO-56-PM method during the statistical period of 18 years (1995-2012). Using SPSS 20, the relationship between the above climatic factors and ETo was modeled through multivariable regression. The results of the model showed a significant relationship between ETo obtained from multivariable regression and the climatic factors, in which the contribution of wind speed and maximum temperature variables in annual and seasonal ETo were more than other climatic factors. Comparison of annual and seasonal ETo maps of the central plateau watershed showed a close and acceptable association between the FAO-Penman-Monteith method and the regression model at spatial level, so that the northwest of the watershed had the lowest and the southern regions had the highest annual and seasonal ETo. Therefore, due to the acceptable results of this study in Iran’s central plateau watershed, it is recommended that regression equations be used in other watersheds of the country with insufficient or lack of lysimeter data to predict ETo with acceptable accuracy, which plays a very important role in determining the water requirement of plants.

    Keywords: plants
  • Mostafa Yaghoobzadeh *, Yousef Rahmani Pages 87-100

    Introduction:

     According to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, the atmospheric concentrations of the greenhouse gases, i.e., carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), have increased to unprecedented levels in the last 800 000 years. An increase in the levels of GHGs (greenhouse gases) can lead to greater warming, which, in turn, can influence the world’s climate that leading to the phenomenon climate change. It is predicted that climate change induced weather extremes, extreme heat, severe drought and heavy precipitation that will have significant impacts on agriculture. In support of the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC), the fifth phase of the Coupled Model Intercomparison Project 5 (CMIP5) provides a new suite of coordinated climate model experiments focusing on major gaps in understanding of historical and future climate changes. Most of the present day CMIP5 models show good performances in reproducing the present climatology, climate variability and climate extremes. In this research, the trend of rainfall changes, minimum temperature and maximum temperature of the synoptic station of Birjand during the decades and different periods from 2010 to 2100 years was studied using the data of CMIP5.

    Materials and Methods :

    This research was conducted to determine climate changes  condition in Birjand Synoptic Station located at longitude between 59˚ 7’ N and latitude between 32˚ 52’E, Iran. AR5 Global Climate Models of CSIROMK3.6, GFDL-ESM2M, GISS-E2-R, IPSL-CM5A-MR and MIROC-ESM with RCP2.6, RCP4.5, RCP6 and RCP8.5 Emission scenarios performed precipitation, minimum temperature and maximum temperature in the coming decades for the Birjand Synoptic Station.  Also, the process of changes in meteorological variables in the two form of decades-decades from 2010 to 2100 and between the next three periods of 2040-2010, 2070-2070, and 2070-2100 were taken. The changes in meteorological variables of the future periods were investigated to the base period (1970-2000) of station.

    Results and Discussion :

    The results of the study indicated that GFDL-ESM2M and GISS-E2-R models have a more accurate estimation of meteorological variables over the base and future time periods. Also The trend of precipitation from 2010 to 2100 year for various models and scenarios in different decades. However, the GFDL-ESM2M and MIROC-ESM models estimate a further decrease in rainfall between models and RCP8.5 among scenarios. Also Comparison of scenarios in all models showed that the maximum temperature in the RCP8.5 and RCP2.6 respectively with the highest and the least increase in the coming years. This increase in temperature for the scenario is RCP8.5 and the two MIROC-ESM and IPSL-CM5A-MR models will be more than 6 degrees Celsius in 2100 year in compared to 2010 year.The maximum and minimum temperatures Changes unlike precipitation are steadily increasing  and except in the near future  period (2040-2010), in the next two periods, the models estimate the trend of temperature rise in the future relative to the base period. The MIROC-ESM and IPSL-CM5A-MR models and the MIROC-ESM and GFDL-ESM2M models estimate the highest maximum temperature and minimum temperature increase in the future compared to the base period. In Comparing periods, the far future (2070-2100) and mid-term (2040-2070) period estimated a higher increase in temperature and precipitation, respectively. The GISS-E2-R model estimates the minimum maximum and maximum temperature rise for the three periods.
    Conclusions The research has determined that models are uncertain in the estimation of weather variables, which makes it difficult to select the appropriate model for use in research. Changes in scenarios are also different and the 8.5 scenario estimates higher-risk conditions that including higher temperature increases and further reductions for the future. Also, in comparison to periods, highest rainfall and temperature increase was estimated in the 2055 period and 2085 period respectively. The use of climate change Fifth Report data and the study of variations in meteorological variables over the next three periods can determinate what will happen in the future in accordance with the climate change scenarios. Also, the results of this research can be helpful in considering future weather variables in planning for management of different sectors of agriculture, water resources and environment in the future.

    Keywords: Fifth Assessment Report, AOGCM model, Emission scenarios, Meteorological variables, future period
  • Akbar Barzuk *, Nasrin Nik Andish, Malih Hisseini Pages 101-110
    Introduction

    Energy consumption monitoring plays a major role in the sustainable development and optimization of designing applications with the least energy consumption. One of the basic human problems is food supply. Countries can grow and develop, focusing on three principles of access to energy, attention to human resources and the provision of food. Because to the limited resources and energy in the future, attention to energy optimization is important. In Iran, taking into account dry condition and increase of demand for various productions  , greenhouse crops have been booming. Considering the importance of supplying and managing energy consumption in greenhouse crops, consideration of optimal areas location is reduce energy consumption is account in applied planning. Therefore, in this research, with emphasis on environmental and climatic condition, tries to investigate the appropriate location with consideration of amount of heating and cooling requirements.

    Materials and methods

    In this study we  uses of documentary-statistical method. Datas of daily temperature are analyses based on a current period of 2005 to 2015 in 6 stations in the Qom Province .In this study, the location of the time - the energy required for cooling and heating, using the optimize threshold. We uses of ArcGIS 10/2 software and DEM map for to draw the annual and seasonal plans of heating and cooling of selected stations in Qom province.. First, the data were collected and adjusted in Excel, and then the correlation between the height and indicators of the annual and seasonal heating and cooling requirements of the formulation was rune in the GIS software.

    Results and discussion

    The results showed that the amount of cooling and heating demand is changed each season. In winter, the most needed heating needs at veshnaveh station with 1527 °  degree day. The lowest heating requirement  is at the Qom station with 1092 degree day. On average, in winter, more than 1100 heating degree day is needed to adjust the temperature of the environment. In the fall, according to the location of the stations located in the west of the province, due to the higher elevation, more heat demand is needed to adjust the temperature of the environment. In the summer, only the cooling day temperature is necessary to adjust the temperature of the environment. In this season, the minimum CDD is at the veshnaveh Station and the highest amount at Qom station is required to 569 ° C. In the spring, as in other seasons, according to climate and location, the CDD and HDD status changes.The winter season, like the fall season, requires cooling at all stations to be zero, but the need for heating at all stations is very high, with the highest being at Wandshake Station with (1524.22) at a higher elevation. As shown in the following maps, in the autumn, the need for cooling other than the South and the West, which is zero, is very low in the rest of the region as compared to the need for heating. So that the heating demand in this season in the south with the highest (810.24) and the center with the lowest (484.13) have the lowest.

    Conclusion

    In this study, optimal location of greenhouse cultivation of cultivated vegetables in Qom province (with emphasis on optimal energy consumption) was investigated. The results indicated that the factor that can be considered as one of the most important basic information in estimating the amount of energy needed to warm the greenhouse in the cold season or cooling it in the warm season is information that Utilizing them will have fruitful results in planning and decision making in the enTherefore, according to the heating and cooling maps and charts, the annual amount of Qom stations can be said that the amount of heating and cooling required is a function of height. As far as the west of the province and south of the province are concerned, the height increases, CDD decreases and the HDD increases. The vast parts of the east of the province, which have desert and plain climates, require the highest amount (CDD) and the least amount (HDD) per year in the region.ergy sectorB. The results show that for each 100 m elevation, the amount of cooling medium (50.62) decreases and the ambient warming (84.52) increases..