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پژوهش های جغرافیای طبیعی - پیاپی 101 (پاییز 1396)

فصلنامه پژوهش های جغرافیای طبیعی
پیاپی 101 (پاییز 1396)

  • تاریخ انتشار: 1396/09/30
  • تعداد عناوین: 11
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  • قاسم عزیزی *، مجتبی رحیمی، حسین محمدی، فرامرز خوش اخلاق صفحات 381-393
    با توجه به نقش کلیدی پوشش برف در تامین منابع آب و اثرگذاری آن بر سیستم‏های زیست‏محیطی، هدف از مطالعه حاضر پایش پوشش برف و تغییرپذیری مکانی و زمانی آن در دامنه های جنوبی البرز است. بدین منظور، از داده های ساعتی و ماهانه ایستگاه های منطقه و محصولات برف‏سنجنده مودیس استفاده شد. ارزیابی دقت داده های سنجنده در مقابل داده های ایستگاهی بیانگر ارتباط مناسب میان آن‏هاست؛ به‏طوری‏که در صورت حذف محدودیت تصاویر (ابرناکی)، دقت محصولات مودیس به‏مراتب افزایش می‏یابد و از حداقل 67 به حداکثر 96 درصد می‏رسد. بررسی روند ماهانه پوشش برف نشان داد که پوشش در اوایل پاییز و اواخر زمستان رو به افزایش و در ژانویه و به‏ویژه فصل بهار به میزان فزاینده‏ای در حال کاهش است؛ به‏طوری‏که مقادیر آماره من‏- کندال در ماه می به 2/2- می‏رسد. این وضعیت نشان‏دهنده کوتاه‏شدن دوره تداوم پوشش برف و افزایش دوره ذوب آن است. از نظر ارتفاعی، پوشش برف در حال پسروی است؛ زیرا روند آن در همه طبقات ارتفاعی، به‏ویژه در ارتفاعات 3000 تا 3500 متر، رو به کاهش است. مقایسه وضعیت پوشش برف با شرایط دما و بارش نشان می‏دهد در بیشتر مواقع ناهنجاری های منفی پوشش برف با ناهنجاری مثبت دما و منفی بارش تطابق دارد.
    کلیدواژگان: البرز جنوبی، برف، سنجش از دور، محصول MOD 10
  • محمد صادق کیخسروی کیانی، سید ابوالفضل مسعودیان * صفحات 395-408
    داشتن آگاهی های بهنگام و درخور اعتماد از منابع اصلی پوشش برف کشور می‏تواند راه‏گشای بسیاری از مشکلات مربوط به مسائل آب در ایران باشد. هدف از پژوهش کنونی شناسایی و بررسی ویژگی های جغرافیایی برف‏خوان‏های ایران است. بدین منظور، داده های دو سنجنده مودیس تررا و مودیس آکوا برای بازه زمانی 13821393 به‏صورت روزانه و در تفکیک مکانی 500 × 500 متر از تارنمای سازمان فضایی امریکا دریافت شد. همچنین، مدل رقومی ارتفاع ایران هماهنگ با تفکیک و سیستم تصویر داده های پوشش برف به‏کار گرفته شد. پیش از به‏کارگیری داده ها برخی پردازش‏ها بر روی داده های روزانه به منظور کاستن از اثر ابرناکی انجام گرفت و درنهایت یک پایگاه نوین بر روی ایران به کمک یک‏کاسه‏سازی داده های سنجنده های مودیس تررا و مودیس آکوا ساخته شد. بررسی ها نشان داد در ایران به طور کلی سه برف‏خوان بزرگ وجود دارد که عبارت‏اند از: برف‏خوان البرز؛ برف‏خوان شمال ‏غرب؛ و برف‏خوان زاگرس؛ و بیشینه شمار روزهای برف‏پوشان در این سه برف‏خوان به‏ترتیب 153، 132، و 127 روز در سال است. بررسی ها نشان داد شرایط ناهمواری نقش برجسته‏ای در پراکنش پوشش در برف‏خوان‏های ایران دارد و اینکه ناگزیر بیشترین پوشش برف در بلندترین بخش‏های کشور وجود دارد تصور درستی نیست.
    کلیدواژگان: ایران، برف خوان، سنجنده مودیس آکوا، سنجنده مودیس تررا
  • مریم بیات ورکشی * علیرضا ایلدرومی_حمید نوری_حمید زارع ابیانه صفحات 409-422
    در این تحقیق، با به‏کارگیری تبدیل موجک، به بررسی روش شبکه عصبی‏- موجک و زمین‏آمار در برآورد توزیع مکانی سه مولفه ارتفاع برف، چگالی برف، و ارتفاع آب معادل برف حوضه های آبریز شمال غرب کشور پرداخته شد. بدین‏ منظور، با مدنظر قراردادن اطلاعات اندازه‏گیری چهارساله (13871387 تا 13901391) سه استان آذربایجان شرقی، آذربایجان غربی، و اردبیل توانایی روش شبکه عصبی- موجک و زمین‏آمار ارزیابی شد. مقایسه روش‏های مختلف زمین‏آمار نشان از برتری روش کریجینگ معمولی با نیم‏تغییرنمای گوسین برای مولفه های چگالی برف، آب معادل برف، و ارتفاع برف با آماره میانگین مجذور مربعات خطای استاندارد (NRMSE) به‏ترتیب 259/0، 429/0، و 390/0 بود. با کاربرد روش شبکه عصبی- موجک خطای برآورد هر سه مولفه بسیار کاهش یافت؛ به‏طوری‏که مقدار NRMSE برای مولفه های چگالی برف، آب معادل برف، و ارتفاع برف به‏ترتیب 122/0، 002/0، و 001/0 به‏دست آمد. ضمن آنکه دقت شبیه‏سازی نقاط حدی مولفه های برف به وسیله روش شبکه عصبی- موجک افزایش یافت. بنابراین، کاربرد شبکه عصبی- موجک در مقایسه با زمین‏آمار در برآورد توزیع مکانی مشخصه های برف توصیه می‏شود.
    کلیدواژگان: آب معادل برف، چگالی برف، زمین آمار، ضخامت برف، عصبی، موجک
  • خدیجه جوان * صفحات 423-439
    گردشگری یکی از بزرگ‏ترین بخش‏های اقتصادی در سطح جهان است. برای گردشگران، آب و هوا یکی از مولفه های اصلی گردشگری است. هدف از این پژوهش ارزیابی شرایط اقلیم گردشگری شهر ارومیه با استفاده از شاخص اقلیم تعطیلات (HCI) و شاخص اقلیم گردشگری (TCI) و مقایسه این دو شاخص برای مشخص‏کردن تاثیر عناصر اقلیمی بر فعالیت گردشگری است. برای محاسبه این شاخص‏ها از داده های روزانه حداکثر دمای هوا، میانگین دمای هوا، حداقل رطوبت نسبی، میانگین رطوبت نسبی، بارش، پوشش ابر، ساعات آفتابی، و سرعت باد در دوره زمانی 19812010 استفاده شده است. نتایج این مطالعه نشان می‏دهد هر دو شاخص TCIو HCI دارای اوج تابستانه‏اند و در ماه های ژوئن، ژوئیه، آگوست، و سپتامبر شرایط ایده‏آلی برای گردشگری و تفریح دارند. مقایسه دو شاخص در ارزیابی شرایط اقلیمی نشان داد که عمدتا امتیازات شاخص HCI در بیشتر مواقع بیشتر از TCI است. علت اختلاف امتیاز بین دو شاخص اختلاف در وزن مولفه ها و سیستم رتبه‏دهی متغیرهاست. در کل، می‏توان گفت شاخص HCI به دلیل ارزیابی دقیق‏تر شرایط آب و هوا برای گردشگری و همچنین توجه به آرای گردشگران در شناسایی شرایط ایده‏آل اقلیمی بهتر از شاخص TCI است.
    کلیدواژگان: ارومیه، اقلیم گردشگری، شاخص اقلیم تعطیلات (HCI)، شاخص اقلیم گردشگری (TCI)، گردشگری
  • پریسا کهخامقدم *، معصومه دلبری صفحات 441-455
    هدف از پژوهش حاضر، تعیین پتانسیل انرژی باد و انتخاب نقاط بهینه برای احداث نیروگاه بادی با استفاده از آمار هشت ایستگاه سینوپتیک استان سیستان و بلوچستان است. بدین منظور، از داده های سه‏ساعته سرعت باد در ارتفاع 10 متری از سطح زمین در طی دوره آماری 20052014 استفاده شد. احتمال تجربی داده ها با استفاده از تابع توزیع ویبول محاسبه شد. سپس، با استفاده از قانون یک‏هفتم نیرو، اطلاعات باد در ارتفاع 10 متری به ارتفاع 50 متری تبدیل شد و پتانسیل انرژی باد در هر دو ارتفاع یادشده تحلیل شد. همچنین، بر اساس آمار بلندمدت، روند تغییرات زمانی سرعت باد در مقیاس ماهانه و سالانه بررسی شد. نتایج تحلیل روند نشان داد ایستگاه های ایرانشهر، زابل، زهک، و زاهدان به‏ترتیب دارای بیشترین روند مثبت معنی‏دار ماهانه‏اند. در مقیاس سالانه، فقط زابل و ایرانشهر دارای روند مثبت معنی‏دارند. از طرفی، نتایج نشان داد که ایستگاه های زابل، زهک، و کنارک قابلیت مناسبی برای استقرار توربین‏های تجاری دارند. ایستگاه زابل با حداکثر مقدار چگالی توان باد (513 وات بر متر مربع) در ارتفاع 50 متری و حداکثر احتمال موجودیت باد با سرعت بین 3 تا 25 متر بر ثانیه مناسب‏ترین مکان برای بهره‏برداری از انرژی باد تشخیص داده شد.
    کلیدواژگان: پتانسیل انرژی باد، تحلیل روند، توزیع ویبول، چگالی توان باد
  • جعفر معصوم پور سماکوش *، عبدالله جلیلیان، احترام یاری صفحات 457-475
    امروزه مدیریت منابع آب از اهمیت ویژه‏ای برخوردار است. از این رو، مطالعه بارش، به‏منزله مهم‏ترین منبع تامین آب، در مقیاس زمانی و مکانی بسیار حائز اهمیت است. این پژوهش در پی تحلیل، ارزیابی، و شناسایی رفتار بارش فصلی است. بدین منظور، داده های بارش 67 ایستگاه سینوپتیک کشور با دوره آماری سی‏ساله (19852014) استخراج شد. سری های زمانی بارش بررسی شد و بهترین مدل بر اساس ملاک اطلاع آکائیک به سری داده های هر ایستگاه برازش داده شد. صحت و کفایت مدل‏های ‏‏برازش‏شده به کمک نمودار مانده های استانداردشده، نمودار تابع خودهمبستگی مانده های مدل، و آزمون لیونگ- باکس ارزیابی شد. مرتبه های اتورگرسیو، میانگین ‏متحرک، و مرتبه های تفاضلی فصلی و بین‏فصلی حاصل از مدل‏های برازش‏‏شده برای بررسی وابستگی بارش‏های فصلی و بین‏فصلی و تحلیل روند سری های زمانی بارش فصلی بررسی شد. نتایج نشان داد برای همه ایستگاه های مورد مطالعه (جز بوشهر، شهرکرد، بیرجند، امیدیه ‏آغاجاری، و رشت) مدل ساریما کفایت مناسبی دارد. نتایج به‏دست‏آمده از بررسی مرتبه های فصلی نیز نشان داد که به‏جز ایستگاه های کاشان، آبعلی، دوشان‏تپه، سمنان، و شاهرود در بقیه ایستگاه ها بارش‏ها‏ از الگوهای فصلی تبعیت می‏کنند. همچنین، در بیشتر ایستگاه های مورد مطالعه (93%) روند کاهشی یا افزایشی معناداری در سری زمانی بارش ‏فصلی مشاهده نشده است.
    کلیدواژگان: ایران، بارش فصلی، روند، کفایت مدل، ملاک اطلاع آکائیک، مدل ساریما
  • شعیب آب خرابات، مصطفی کریمی *، امان الله فتح نیا، محمدحامد شام بیاتی صفحات 477-489
    برای بررسی اثرهای دمایی باد 120 روزه سیستان طی دوره 19932012 با استفاده از آزمون‏ تحلیل‏ عاملی و خوشه‏بندی دو الگوی اصلی وزش باد شمالی (باد 120 روزه سیستان) و باد شرقی شناسایی شد و مشخص گردید که در الگوهایی با وزش باد شرقی هسته‏ای از وزش دمایی منفی در شرق ایران و مرکزی از وزش دمایی مثبت در مناطق مرکزی‏تر فلات ایران شکل می‏گیرد. این وزش منفی جنوب ‏شرق ایران را دربر نمی‏گیرد که سبب افزایش دمای منطقه می‏شود. در الگوهایی با وزش باد شمالی هسته‏ای از وزش دمایی منفی در شرق و جنوب ‏شرق ایران شکل می‏گیرد و حرارت را از این مناطق به دریای عمان و بخش شرقی دریای عرب انتقال می‏دهند. این هسته وزش دمایی منفی شکل‏گرفته 46 درجه نسبت به الگوهای باد شرقی در مناطق جنوبی‏تر قرار می‏گیرد و باعث کاهش دمای هوا در مناطق شرقی و جنوب ‏شرقی ایران می‏شود و استوای حرارتی را در این منطقه از کره ‏زمین به عرض‏های جنوبی‏تر جابه‏جا می‏کند. در حالی‏که در الگوهایی با وزش باد شرقی استوای حرارتی در عرض‏های شمالی‏تر و بر روی جنوب ‏شرق و حتی شرق مرکزی ایران مستقر می‏شود و سبب افزایش دمای این مناطق نسبت به غرب، جنوب ‏غرب، و الگوهای وزشی باد شمالی می‏شود.
    کلیدواژگان: ایران، باد 120 روزه سیستان، وزش دمایی، همدید
  • یدالله یوسفی *، فاطمه کاردل، همت الله رورده، مولود محتسبی خلعتبری صفحات 491-501
    تراکم جمعیت در شهرها سبب افزایش دمای ناحیه مرکزی می‏شود. هدف از این مطالعه بررسی اختلاف دمایی ناشی از جزیره حرارتی و اثر آن در تغییرپذیری دمای شهر بابل است. از سه دستگاه دیتالاگر ثبت‏کننده داده در محیط‏های بافت متراکم شهری، حومه، و فضای سبز شهری استفاده شد. تغییرپذیری دما و رطوبت نسبی به مدت هشتاد روز (15 تیر تا 31 شهریور 1394) با گام یک‏ساعته بررسی شد. اختلاف دمای میانگین ایستگاه شهری با حومه 1 و با فضای سبز 8/1 درجه بوده است. کمترین میانگین رطوبت در محیط شهری (67 درصد) و بیشترین در فضای سبز (77 درصد) مشاهده شد. اختلاف روزبه‏روز دمای میانگین (DTD(tmean)) و روز به روز دمای بیشینه ((DTD(tmax) شهر از حومه و فضای سبز بیشتر است، اما اختلاف روزبه‏روز دمای کمینه ((DTD(tmin) در شهر کمتر از دو محیط دیگر است. تفاوت تغییرپذیری دمای بیشینه و کمینه (DTD∆) شهر از دو منطقه دیگر بیشتر بوده و در فضای سبز تقریبا صفر است. این میزان‏ها بیانگر تغییرپذیری بیشتر دمای شهر است. نتایج نشان می‏دهد جزیره حرارتی ایجادشده در بابل در تغییرپذیری روزبه‏روز دمای آن موثر است. در شهرهای متوسط می‏توان اثر جزیره حرارتی بر دما و رطوبت را دید و فضای سبز در کاهش دمای بابل نقش مهمی دارد.
    کلیدواژگان: بابل، بافت شهری، تغییرپذیری روزبه روز دما، جزیره حرارتی، رطوبت نسبی
  • عبدالرضا کاشکی *، عباسعلی داداشی رودباری صفحات 503-521
    در این پژوهش، به ‏منظور واکاوی تعداد روزهای بارانی ایران، از پایگاه داده‏- بارش آفرودیت طی دوره آماری 56ساله استفاده ‏شده است. همچنین، نقش مولفه های جغرافیایی در تعداد روزهای بارانی بررسی شده است. نتایج نشان داد متوسط روزهای بارانی ایران 38 روز است؛ با وجود این، بارش 36/62 درصد از گستره کشور از 38 روز نیز کمتر است. بیشینه روزهای بارندگی ایران با 147 روز در جنوب غرب دریای خزر واقع ‏شده است. از سوی دیگر، کمینه روزهای بارانی ایران با 9 روز در جنوب شرق ایران قرار دارد. بررسی ها و تحلیل‏های آماری نشان داد بهترین تقسیم‏بندی از روزهای بارانی ایران تقسیم کشور به شش پهنه است. این شش پهنه عبارت‏اند از: 1. پهنه خزری با تعداد روزهای بارانی 126 روز؛ 2. پهنه بارشی ایران شامل مناطق کوهستانی غرب، شمال غرب، و شمال شرق با تعداد روزهای بارانی 77 روز؛ 3. پهنه کوهپای های با 57 روز؛ 4. پهنه نواری بین ارتفاعات و مناطق پست داخلی بادپناه داخلی با 38 روز بارانی؛ 5. پهنه ایران مرکزی و نواحی بادپناه داخلی با 27 روز بارانی؛ 6. فقیرترین منطقه بارشی ایران شامل کویرها و چاله های شرقی و نواحی جنوب شرق است با متوسط تعداد روزهای بارانی 17 روز.
    کلیدواژگان: ایران، پایگاه داده، بارش آفرودیت، پهنه های بارشی، وایازی گام به گام، روز بارانی
  • اسماعیل حقیقی *، محمدحسین قلی زاده، مهدی دوستکامیان، فاطمه قادری صفحات 523-539
    هدف از این مطالعه واکاوی وردش‏های جوی بارش‏های بهاری فراگیر ایران طی نیم قرن اخیر است. بدین منظور داده های بارش روزانه 283 ایستگاه سینوپتیکی طی دوره آماری 1961 تا 2010 از سازمان هواشناسی کشور استخراج و مرتب شد. پس از استخراج بارش‏های روزانه فصل بهار (فروردین، اردیبهشت، و خرداد)، به ‏منظور شناسایی الگوهای بارش فراگیر، داده های فشار سطح زمین از پایگاه داده مرکز ملی پیش‏بینی محیطی و مرکز ملی پژوهش‏های جوی استخراج شد. سپس با اجرای تحلیل خوشه‏ای بر روی داده های فشار سطح زمین الگوهای همدید بارش‏های فراگیر بهاره شناسایی، بررسی، و تجزیه و تحلیل شد. نتایج حاصل از این مطالعه بیانگر آن است که بارش‏های بهاره، ضمن برخورداری از افت‏وخیز روزانه، ضریب تغییرات مکانی بسیار زیادی دارند؛ در این بین، به سمت ماه خرداد این تغییرات چشم‏گیرتر خواهد بود. نتایج حاصل از واکاوی وردش‏های جوی بارش‏های بهاری فراگیر ایران نشان داد که چهار الگوی کم‏فشار عربستان- کم‏فشار ایران مرکزی، کم‏فشار اروپا- کم‏فشار سودان، کم‏فشار خلیج فارس- پرفشار سیبری، و الگوی چندهسته‏ای کم‏فشار خاورمیانه بیشترین نقش را در بارش‏های بهاری فراگیر ایران ایفا می‏کنند.
    کلیدواژگان: بارش فراگیر بهاره، شار رطوبت، جبهه زایی، آب قابل بارش، همگرایی و واگرایی
  • هادی نیری *، خالد اوسطی، پریسا عثمانی صفحات 541-556
    رودخانه تروال، به‏عنوان سرشاخه سفیدرود، در شرق استان کردستان واقع شده است. هدف اصلی در این مطالعه تحلیل تعادل ژئومورفولوژیکی و شناسایی مناطق پایدار و ناپایدار رودخانه تروال است. نخست ظرفیت تعادل رودخانه با روش چهارچوب استیل ‏رود و سپس وضعیت پایداری استیل‏ها بر اساس طبقه‏بندی رزگن تعیین شد. بر اساس چهارچوب استیل رود، استیل‏های سینوزیته کم با مواد ریزدانه و مئاندری با بستر ماسه‏ای دارای ظرفیت تعادل محلی و استیل‏های سینوزیته کم با بستر گراولی، چندمجرایی در چم ازون‏دره، سینوزیته کم با بستر ماسه‏ای در چم تروال و مئاندری با مواد ریزدانه در چم سنگ‏سیاه ظرفیت تعادل بسیار زیادی دارند. برای بررسی پایداری، 34 مقطع در همه استیل‏ها برداشت شد. استیل‏های مئاندری و چندمجرایی با بستر ماسه‏ای، سینوزیته کم با مواد ریزدانه و مئاندری با مواد ریزدانه در چم تروال و استیل آدا با بستر رسی در چم سنگ سیاه پایدارند؛ درحالی‏که استیل‏های سینوزیته کم با بستر گراولی، سینوزیته کم با بستر ماسه‏ای و چندمجرایی ناپایدار بودند. ناپایداری در چم تروال و سنگ‏ سیاه می‏تواند به دلیل گسل‏های متعدد باشد. به‏طورکلی، مناطقی از رودخانه که بر اساس روش استیل رود دارای ظرفیت تعادل زیادی است معمولا نتایج روش رزگن حاکی از ناپایداری وضعیت آن بخش رودخانه است.
    کلیدواژگان: تعادل ژئومورفولوژیکی، چهارچوب استیل رود، رودخانه تروال، طبقه بندی رزگن
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  • Ghasem Azizi *, Mojtaba Rahimi, Hossien Mohammadi, Faramarz Khoshakhlagh Pages 381-393
    Introduction
    Snow cover is one of the most important components of the Earth's surface which has an important role in the global hydroclimate. Snow acts as a temporary reservoir of water and frequently, to maintain the flow of rivers and recharge underground aquifers provide water during the dry season caused billions consumer. Study of spatial and temporal variability of snow cover in arid and semiarid region such as Iran that temporal and spatial variations of precipitation is high and has always suffered from lack of water is important. So that about 60% of surface water and groundwater 57 percent of the country is underway in snowy areas. Therefore, this study aimed to evaluate the accuracy of MODIS snow products and combines remote sensing and terrestrial data to investigate the spatiotemporal changes of snow cover in South Central Alborz slopes and ultimately the relevance of this change is done with climatic elements.
    Data and
    Methods
    In the present study, we used data from 16 synoptic stations located in the study area and MODIS data. At first, to extract snow cover, the products MOD10A1 and MOD10A2 were used. Then, to evaluate the accuracy of MODIS and relationship between snow cover changes with climatic elements of the data of snow depth, precipitation and temperature (on a scale of hourly and monthly) were selected stations. MODIS images for the detection of snow cover in the index NDSI = (band4-band 6) / (band4 band6) is used. Band4 the spectral reflectance in the visible band (0.555 micrometers) and band6 is the spectral reflectance in the intermediate-infrared band (1.64 micrometers). In this sensor products addition to snow other phenomena is indivisible. Therefore, in order to separate and identify different phenomena pixels, images were processed in GIS software. For evaluation of pictures, daily product images (MOD10A1) to winter (December to February), three years (2007-2009) and precision processing station were evaluated by snow depth data. If one centimeter snow depth at the station and more, pixel located at the station as snow cover and otherwise were considered as no snow. The agreement between the image and the correct classification of stations coefficient that as the number of days (without snow- snow-snow and no snow) divided the total number of days per month in percent, were defined. Finally, the variability of snow cover was evaluated by Mann-Kendall test. To examine the relationships anomalies snow cover and climatic conditions Z index was used.
    Results And Discussion
    The percentage of agreement between the earth data and satellite images for three months of winter are respectively from December to February, 81, 67 and 75 percent. However, the mean of all stations is used in snow-prone areas even mean reduced image accuracy up to 40 percent. Studies show that errors are often caused by clouds in the pixel location. Thus the second stage evaluation was performed by removing the cloudy days. The results show that at this stage much increased image accuracy and agreed percentage for each quarter to more than 95 percent. Therefore, according to the movement of the clouds, the product of eight days this sensor was used to monitor and evaluate changes in snow cover. Snowfall in the region since October began with a decrease in air temperature and snow accumulation increased in the period leading up to January 9, the maximum is 34%. January and December respectively 31.5% and 24.8% accounted for the highest monthly snow cover. The highest and lowest snow cover these two months, with respectively 76.2 percent and 5.8 percent is owned by the January 9, 2008 and December 27, 2010. Study shows the monthly change in snow cover, in October, November and March of this phenomenon is on the rise, although not significant. While in December, January, April and especially May in most classes, snow cover has decreased over the last 15 years. This negative trend in the months -2.18 score is significant. Compare snow cover with temperature and precipitation shows, snow cover mostly positive anomalies with positive precipitation anomalies and negative temperature anomalies are consistent and vice versa.
    Conclusion
    The results of evaluating the accuracy of satellite images showed snow produce of MODIS has the ability to appropriate estimate an area of snow cover. But the cloud is one of the main limitations of MOD10A1. So that in present study after removing the cloudy days, the average accuracy of this images from 67% to 95% and even in snow-prone stations reached to 100 percent. Because the clouds are moving quickly changing daily, but change snow is gradual so for long-term monitoring of snow cover the product (MOD10A2) this sensor is used. The monitoring results showed January and December are the greatest area of snow cover. In terms of spatial continuity and extent of snow cover decreases from West to East in the study area. So that, percentage of snow covers in the Shahrood and Karaj river basins are more than the Semnan and Hablehrood basins. The results of the trend reveal, though for most months and zones of elevation changes of snow cover has a negative trend, but rarely this trend is significantly. In timescale highest positive trend in October with a score of 1.68 and the severest decline in May with a score of -2.18 was observed. In terms of spatial the greatest decline was observed in the elevation 2500 to 3750 meters. In most years positive anomalies of snow cover coincides with positive (negative) anomalies of precipitation (temperature).
    Keywords: Snow, Remote Sensing, MOD 10, southern Alborz
  • Mohammad Sadegh Keikhosravi Kiany, Seyed Abolfazl Masoodian * Pages 395-408
    Introduction
    At the high elevations of river basins, precipitations are mainly in the form of snow and its accumulation provides water of the rivers in warm seasons. Having accurate and on time information is of great importance for flood controlling, estimation of snow water equivalent. The extent of snow cover and its variations are important parameters in hydrologic and climatic systems. Suitable and accurate evaluation of snow cover on both small and large scales is very crucial. Lack of information on the high elevations is an issue which cause increasing concerns due to climate change as a great number of large rivers originate from these highlands. In the last few decades many researches have been carried out on the study of snow cover using remote sensing data. For instance Maskey et al (2011) used MODIS Terra data to examine seasonal snow cover in Nepal for the period from 2000 to 2008. His findings revealed that snow cover is more in the elevation zone of 3000 to 4000 meters compared to the elevation zone of 4000 to 5000 and 5000 to 6000. Khadka et al (2014) evaluated snow cover in different seasons in Tamakoshi in the highlands of Himalaya using MODIS data for the time coverage from 2000 to 2009. The results indicated that snow cover below the elevation of 4500 meters above sea level is not much significant. In winter and spring at the elevation above 4500 meters the snow cover areas are very noticeable. However in summer the elevation zones above 5500 meters have significant extent of snow cover.
    Materials And Methods
    In the present paper MODIS Terra and MODIS Aqua data were used to detect snow reservoirs of the country. The selected study period covers the years from 1382 to 1393. As MODIS Aqua data are missing before the year 1382, we had to limit the study period only to the aforementioned years. Before the analysis of the data, we applied two different algorithms to minimize cloud contamination that is a big obstacle against snow cover monitoring. One of the applied algorithms is based on three days filtering and the second is made on the combination of the two products. By merging the two products we managed to develop a regional snow cover data set over Iran. We also used a Digital Elevation Model that was exactly like the snow cover data both on the special resolution and projection system.
    Results And Discussion
    The findings of the present study revealed that there are three principle snow reservoirs that are very suitable for the accumulation of snow cover. The snow reservoir is an area which is snow-covered in long period of time in a year. The three main snow reservoirs of the country are Alborz, North-west and Zagrous and the most number of snow covered days on the heart of these snow reservoir is 153, 132 and 127 days respectively. The analysis revealed that the heart of Alborz snow reservoir is a point in Alamkooh which has a north facing slopes that is suitable for snow cover accumulation. The findings also revealed that in the snow reservoir of Zagrous the relation between snow covered days and elevation is not very matched in west to east direction. And this is due to decrease of precipitation from west to east in this area.
    Conclusion
    In this study the daily time series of MODIS Terra and MODIS Aqua data were applied to detect snow reservoirs of the country. Before using the daily data, some cloud removal technics were applied on the raw daily data to minimize cloud cover effects. The findings revealed that in Iran there are three main snow reservoirs which are Alborz, North-west and Zagrous. The most number of snow covered days was detected to be on Alborz snow reservoirs. It has been detected in this study that in eastern Zagrous the changes of snow cover with elevation is not a positive direct relation and it tends to be reduced as elevation increase. We also concluded that the most number of snow covered days are not necessarily seen on the highest mountains in Iran but in lower elevations. It was discovered that role of topographic conditions is of great importance for the accumulation of the snow cover. The eastern and northern aspects are suitable for the persistence of snow cover days and the highest number of snow covered days was detected in these aspects in the country.
    Keywords: Snow reservoirs, MODIS Terra, MODIS Aqua, Iran
  • Maryam Bayat Varkeshi *, Alireza Ildoromi, Hamid Nouri, Hamid Zare Abyaneh Pages 409-422
    Introduction
    Snow is an important hydrological phenomenon and snow water equivalent is suitable water resource in many parts of the world. Snow and snow water equivalent have a significant contribution in streamflow and groundwater recharge. For this reason, it is important modeling of snow accumulation and melting. So, estimation of snow spatial distribution in different time scales is one of key stages in water resources studies.
    Due to the successful application of geostatistical methods in different sciences, the purpose of this study is mapping of snow characteristics. In this study, the spatial analysis of snow water equivalent, snow depth and snow density, which is one of major components of the water balance, is evaluated in watershed of the country north-west.
    Materials And Methods
    In this study, using geostatistical methods, spatial distribution of snow height, snow density and snow water equivalent were estimated. So, by measurement data of three Azabbayjan- Sharghi, Azarbayjan- Gharbi and Ardebi provinces during four years (2008-2012) in north-west, capability of Kriging, radial basis function and inverse distance weight method were evaluated. The Figure 1 shows location of study area and used data.
    For estimation of snow characterizes in non-measurement estimated points was used evaluation, longitude and latitude parameters. The results comparison of each geostatistical methods was done by the Normal Root Mean Square Error (NRMSE) criterion.
    (1) where Xi , Yi: ith estimated snow data , n: number examples. The drawing of zoning maps was done by ArcGIS software.
    Results And Discussion
    The before zoning, correlation coefficient value of snow density, snow height and snow water equivalent as depended with geographic characterizes was obtain in SPSS software (Table 1).
    Table 1. the correlation coefficient matrix of used variables
    Longitude Latitude Elevation Snow density Snow water equivalent Snow height
    Longitude 1
    Latitude -0.456** 1
    Elevation 0.276* 0.105 1
    Snow density 0.167 -0.053 0.221 1
    Snow water equivalent 0.270* -0.107 0.489** 0.410** 1
    Snow height 0.218 -0.103 0.500** 0.035 0.893** 1
    In addition to Table 2, elevation and longitude with correlation coefficient of 0.489 and 0.270, respectively, have the most effect on snow water equivalent. the positive sign indicates straight relative between elevation and longitude with snow water equivalent.
    As a general result, each three snow characterizes have positive relative with elevation. it is because of elevation is an important topography factor and increased height leads decreased air temperature and enhancement snow.
    The results indicated that in 100% cases, the ordinary kriging method with Gaussian semi variogram had the best results than all methods. the results of inverse distance weight method showed that this method had the least accurate in zoning. Even, power increased dont lead than accurate enhancement. Nikbakht and Delbari (2013) applied different interpolation methods for estimated ground water table in Zahedan plain. in this study reported preference of kriging method with Gaussian semi variogram. McKenna (2000) reported that kriging geostatistical method is suitable, also can be used low number data. The results indicated that mean accurate of kriging method with Guassian semi variogram for snow density, snow water equivalent during four years on base Normal Root Mean Square Error (NRMSE) were 6.76, 3.18 and 10.57, respectively. the results of mapping showed that the most zones of snow characterizes were located in two middle classis and minimum and maximum zones were as small spots.
    Conclusion
    The purpose of this research was to develop interpolation methods to assess the estimation snow components in the non-measurement points. In addition to equipment and preparation problems of snow stations, it is necessary to use modern methods to inform from the snow spatial distribution. The results of this study showed that in the study area and in four years time, ordinary kriging gave better results than other methods. the different interpolation methods had different accurate and can not extend results of an area easily to other areas. But the advantage of this research was in using four years data to an area with 100,861 km2 zone and for three snow important features. Other features of the study was in using longitude, latitude and elevation in non-measured points. Since the used independent variables located in readily available variables categories (access quick to the data at a lower cost and higher accuracy), so we can expect good results with high accurate.
    Keywords: ArcGIS, Geostatistical, Snow water equivalent, Snow density, Snow depth
  • Khadijeh Javan * Pages 423-439
    Introduction; Tourism has become one of the largest global economic sectors in the world and contributes significantly to national and local economies. Climate has a significant influence for tourists’ decision-making process, and it is a key factor considered by the tourists either explicitly for the purpose of travel planning or as a primary motivator. The first attempt to develop a numerical index for evaluating climate for tourism purposes was by Mieczkowski (1985) who designed the ‘Tourism Climate Index’ (TCI). The purpose of the TCI was to present a quantitative composite measure to evaluate the world’s climate for general tourism activities by integrating all climatic variables relevant to tourism into a single index.
    The TCI has been widely applied to assess the future climate suitability of destinations. Despite the TCI’s wide application, it has been subject to substantial critiques. The four key deficiencies of the TCI include: (1) the subjective rating and weighting system of climatic variables; (2) it neglects the possibility of an overriding influence of physical climatic parameters; (3) the low temporal resolution of climate data (i.e., monthly data) has limited relevance for tourist decision-making; and (4) it neglects the varying climatic requirements of major tourism segments and destination types. To overcome the above noted limitations of the TCI, a Holiday Climate Index (HCI) was developed to more accurately assess the climatic suitability of destinations for tourism. The main purpose of this study is to evaluated and compared tourism climatic condition in Urmia by using holiday Climate Index (HCI) tourism climate index (TCI).
    Materials And Methods
    In this study, two tourism climate indices, the Tourism Climate Index (1985) and newly designed Holiday Climate Index have been applied. Daily data of air temperature, relative humidity, precipitation, cloud cover, sunshine and wind speed were obtained to calculate both indices. The TCI was designed by Mieczkowski (1985) as a method to evaluate climate suitability for general tourism activities. The TCI assesses a location’s climate suitability for tourism by grouping seven climatic variables into five sub-indices. In this study, daily climatic data was used as the TCI’s input for the purpose of comparing the rating differences between the two tourism climate indices. The index score calculated according to the TCI formula was then adapted to the classification scheme designed by Mieczkowski (1985) to describe a location’s climate suitability for tourism. A new tourism climate index, the Holiday Climate Index (HCI) was designed with the purpose of overcoming all identified deficiencies and limitations of the TCI. The HCI uses five climatic variables related to the three facets essential to tourism: thermal comfort (TC), aesthetic (A), and physical (P) facet. A major advancement of the HCI is that its variable rating scales and the weighting component system were designed based on the available literature on tourists’ climatic preferences that have been obtained from a range of surveys from the last 10 years.
    Results And Discussion
    Current climatic conditions (1981-2010) of Urmia were rated using both TCI and HCI. This station has a summer peak climate distribution when rated by the TCI. This means that summer months have the most suitable climate for urban tourism. Similar to the TCI ratings, Urmia has a summer peak climate distribution when rated by the HCI. By comparing the HCI and TCI monthly, it can be seen from that rating differences between the two indices are more prominent in winter months, and that the HCI rates the climate for tourism higher. then rating differences between the two indices in thermal, aesthetic and physical facet compared. When the HCI was compared with the TCI in assessing climatic conditions of Urmia, rating differences were observed from temporal aspects. The HCI ratings are generally higher than the TCI ratings in most months of the year. Seasonally, a major disagreement between the two indices exists in the rating of winter climate conditions, as winter has the widest gap in ratings between the TCI and HCI. When temperatures become warmer, the gap between the two indices becomes narrower.
    Conclusion
    In assessing a destination’s climatic suitability for tourism, the Tourism Climate Index (TCI) has a dominant place literature. An ideal tourism climate index would integrate the effects of all facets of climate, simple to calculate, easy to use and understand, recognize overriding effect of certain weather conditions and most importantly, based on actual tourist preferences. This paper intended to fill this gap by introducing a new tourism climate index, the Holiday Climate Index (HCI). By comparing the rating differences between the two indices under specified weather conditions and comparing the ratings against visitation data, a reasonable conclusion could be drawn regarding to whether the HCI is a better index than the TCI in rating the climate suitability for tourism and whether existing studies using the TCI to assess tourism climate resources should be reassessed.
    Keywords: Tourism Climate Index (TCI), Holiday Climate Index (HCI), climate index, tourism, Urmia
  • Parisa Kahkha Moghaddam *, Masoomeh Delbari Pages 441-455
    Energy is one of the most important demands in the development of human societies. As world population continues to growth and the limited and non-renewable resources of fossil fuels begin to diminish, countries must take action to facilitate a greater use of renewable energy resources, such as geothermal and wind energy. Iran has a high wind energy potential, but except in a few specific regions such as Binalud and Manjil, the use and exploitation of such clean renewable source is still not addressed enough. Wind speed in Sistan and Baluchistan province especially in cities like Zabol is very high and sometimes it goes near 120 km/h. So this study aims to investigate the feasibility of wind harvesting in synoptic stations of Sistan and Baluchestan province. Moreover, the trend analysis of wind data is investigated in this paper.
    Materials And Methods
    This study is based on wind data in 8 synoptic stations, for a period 10 years (2005-2014). The analysis was based on 3 hours interval wind speed data measured in 10 m height above ground surface.
    The most widely used model to describe the wind speed distribution is the Weibull two- parameter. These two parameters include k and c: the first is the shape parameter and the second is the scale parameter. There are several methods for calculating these parameter.In this paper, these two parameter were determined through the maximum likelihood (ML) technique.The Weibull distribution functionis expressed mathematically as: (1) where f(v) is the probability density function, k is the shape parameter, c is the scale parameter (m/s), and v is the wind speed (m/s).The probability of having a wind speed between two values of interest V1 and V2 is given by the equation
    P(V_1
    Results And Discussion
    Monthly mean and standard deviation of wind speed data were calculated for the selected stations during 2005-2014. The results showed that the monthly variation (2 to 5 m/s) of mean wind speed for all years is similar as the highest and lowest mean wind speed was happened in winter and autumn, respectively.
    The wind speed characteristics required to evaluate the feasibility of wind energy utilization were calculated for selected stations. The results showed that the maximum wind power density was seen for Zabol with amounts of 257.227 W/m2 and 512.713 W/m2 in 10 m and 50 m heights, respectively. The lowest wind power density was seen in Iranshahr with amounts of 40.196 W/m2 and 80.12 W/m2 in 10 m and 50 m heights, respectively. Comparing these data and data calculated for other stations with the standard classification criteria indicated that Zabol, Zahak and Konarak are the most suitable sites for wind turbines installation. Moreover, Zabol has the maximum probability of having the wind speed of 3 to 25 m/s, i.e. 0.71 and 0.82 for 10 m and 50 m heights, respectively. Therefore given a wind turbine installed in 50 m height, the probability of blowing wind with the speed of 3 to 25 m/s, is about 0.82 multiply by total hours of wind existence during a year (82*7566 hrs/year), i.e. 5822 hrs/year.
    The results of trend analysis by Mann-Kandal test showed that there are either an increasing trend or decreasing trend in selected stations, however, increasing trends (e.g. Zabol, Iranshahr, Zahedan and Konarak) were more often. Wind speed in Zabol has shown a positive trend for all months (except September). However the trend was significant in 41.6 percent of times.In annual basis, wind speed in Zabol has positively increased at a significance level of 5%. Wind speed in Iranshahr showed a significant positive trend in both monthly (except April) and annual scale. Overall, annual wind speed showed a positive trend in half the stations considered and a negative trend in others.
    Conclusion
    According to findings achieved in this study, wind speed is lower in the last months of the year for all stations in Sistan and Baluchistan province. The highest variation of wind speed was seen for Zabol. Based on trend analysis, some significant positive trend of annual and monthly wind speed was seen in Iranshahr, Zabol, Zahedan, and Konarak in descending order. According to the results, the highest wind power density in 50 m height was seen for Zabol (513 W/m2) and Zahak (434 W/m2) and the lowest one was seen for Iranshahr (80 W/m2). Overall, based on wind speed existence and its annual continuity, three stations Zabol, Zahak and Konarak were realized to be appropriate for installing wind turbines.
    Keywords: Trend analysis, Weibull distribution, Wind energy potential, Wind power density
  • Jafar Masoumpour Samakosh *, Abdollah Jalilian, Ehteram Yari Pages 457-475
    Introduction
    Due to the increasing significance of water supplement in Iran, the management of water resources is on a particular importance. Precipitation is regarded as the most considerable source of water having a lot of temporal (daily, monthly, seasonal and yearly) and spatial changes among other climatic factors. Therefore, the studies, focusing extensively on this issue, are really useful, for they would provide the ways for optimal use and water management in the temporal and spatial scales.
    Generally, there are a lot predictive methods trying to determine the relationship between dependent and independent variables. Moreover, different statistical models have been applied to predict climatic variables. In recent years, the analysis of time series has been extensively used in scientific issues.
    As a matter of fact, the analysis of a time series provides the ways to determine its possible structure, recognize its components to analyze and predict the process and future values. Therefore, the investigation and prediction of precipitations in different temporal dimension (daily, monthly, seasonal, and yearly) for each region and watershed are considered as the most important climatic parameters for optimal use of water resources affecting temporal and spatial distribution of other climatic factors. Accordingly, it is necessary to recognize the seasonal pattern of precipitation, and spatial similarities and differences of this time pattern, especially when they are not the same for different regions of Iran. The present research aims at studying the seasonal precipitation of Iran. It turns out that the precipitation does not follow a distinct unique pattern in each part of Iran, so the recognition of seasonal precipitation, separating different region, would help the authorities for environmental planning and management. Moreover, it even can lead to more successful predictions.
    Materials And Methods
    In the present study, seasonal precipitation time series of synoptic stations (during the statistical period of 1985- 2014) is modeled applying SARIMA model. The accuracy of the fitted models to the data series for each station is evaluated by the standardized residuals graph, autocorrelation graph of residuals models and Ljung-Box test (in the significance level of 0.05). Then, the appropriate model for seasonal precipitations is presented for each station (Table. 1) according to Akaik Information Criterion (AIC). Furthermore, seasonal and interseasonal autoregressive rate (P, p) and moving average rate (Q, q), which were found by fitted models, are studied to investigate the seasonal and interseasonal precipitations relationship in each station. At the end, the relationship of seasonal precipitation patterns is mapped by applying GIS software.
    Moreover, all statistical tests and temporal series computations are done in the environment of R software.
    Results And Discussion
    Evaluating the adequacy of the fitted models, it was revealed that the model of correlation structure is able enough to describe the data for all studying stations (except for Booshehr, Shahr-e-Kurd, Birjand, Omidiye Aghajari, and Rasht) analyzing seasonal precipitations for the stations correctly. Therefore, it is adequate enough. Seasonal and interseasonal autoregressive rate (P. p) and moving average (Q, q) from the fitted models are used to determine the relationship of seasonal and interseasonal precipitation for each station. Except for Kashan, Abali, Doushantape, Semnan and Shahroud stations, the other 62 studying stations (93%) follow the seasonal pattern showing seasonal behavior. Furthermore, the rate of seasonal part of the model (P) shows that there is a direct relationship between the precipitation s of each season and the precipitations of that season in the previous years (1 to 2). The (Q) rate reveals that random oscillation of seasonal precipitations of 1 to 2 years before is also indirectly effective for some stations. The rates of interseasonal difference (d) were investigated to analyze the process of time series of precipitation for the studying stations. It demonstrates that the stations of Maraghe, Sanandaj, Hamedan-Nouzhe, and Ferdos have a decreasing process in their data, while, in the other stations, seasonal precipitation does not follow a decreasing or increasing process. In fact, it follows a constant process having no static process.
    Conclusion
    Applying SARIMA model, the relationship of seasonal and interseasonal precipitations of Iran was recognized. In this regard, first, the adequacy of SARIMA model was evaluated. The findings prove that the aforesaid model can describe the correlation structure of the data for the studying stations (except for Booshehr, Shahr-e-Kurd, Birjand, Omidiye Aghajari and Rasht) well and it is adequate enough. This fact is in accordance with the findings of Alijani and Ramezani (2002), Golabi et al (2013), Chang et al (2012), Bari et al (2015) who used SARIMA model to predict drought and temporal series of precipitation proving its adequacy.
    The investigation of seasonal and interseasonal precipitation dependency and the analysis of temporal series process of seasonal precipitation in each station show that, according to seasonal autoregressive rate (P) in all studying stations (except for Kashan, Abali, Doushantape, Semnan and Shahroud), the precipitations of each season has a direct dependency with the precipitations of that seasons in the previous years (1 to 2). Besides, the random oscillation of seasonal precipitation of the previous years (1 to 2) also affects the seasonal precipitations on some stations. So, it is concluded that the precipitations of the stations (93%) follow the seasonal patterns showing seasonal behavior. Furthermore, the findings of interseasonal autoregressive rate (p) for all stations prove that the precipitations of each season have a direct relationship with the precipitations of the previous season for 19 stations (28%).
    Analyzing the process of seasonal precipitations, it is found that, except for Maraghe, Sanandaj, Hamedan-Nouzhe and Ferdos stations, time series of seasonal precipitation has no process (random or non-random) in the stations. This process has a decreasing process for these 4 stations, while it is static in the other stations.
    Keywords: Seasonal Precipitation, SARIMA Model, Model Evaluation, Trend, Iran
  • Shoaieb Abkharabat, Mostafa Karimi *, Amanallah Fathnia, Mohammad Hamed Shambaiati Pages 477-489
    Introduction
    120-day winds of Sistan are considered as the most important and well-known climatic factors in eastern regions of Iran during hot period. They have various effects on the region. For example, these winds make dust storm, more evapotranspiration and sand prairie in this region. Generally, as these winds have great impacts on the environment and human life, they should be studied from different climatic aspects. Considering the importance of them, this study aimed at evaluating the role of these winds in decreasing the temperature of the region. The results will prove one of the positive environmental aspects of these winds during hot period of year in eastern dessert regions of Iran. Although, most effects of these winds were negative considered as one of the life limiting factors through the east of Iran.
    Matarials and
    Methods
    The period used in this study was the 2480 days in 22 years (2012-1993) from May until end of September. The atmospheric circulation types were extracted using daily mean of the 850 hPa geopotential height data for these. Then the agglomerative hierarchical cluster analysis with the ward algorithm and Euclidean distance used to identify atmospheric circulation types over Iran in mentioned period of years. Finally, 5 atmospheric circulation type were identified in this period of years. Then wind speed and direction, as well as the wind thermal advection in levels of 1000, 925, 850 and 700 hPa and also the thermal advection of atmospheric vertical profiles were analyzed.
    Results And Discussion
    The Position of Maximal Cores of Temperature (Thermal Equator) Figures 4, 5, and 6 present maximal cores of temperature in 1000 hPa level through quintet patterns. As a matter of fact these maximal cores of temperatureimply the position of earth thermal equator. Pattern 1, in which 120- day winds of Sistan cover east and southeast of Iran more intensely and more widely, reveals that maximal temperature covers Iraq and west of Iran, while in the same latitude there is cool weather in east of Iran. Pattern 2 also, in which 120-day winds of Sistan have less intensity and expansion, shows that thermal equator belt of east is penetrating northern latitudes, even though lower temperature is still recorded in east and southeast of Iran, compared with west of Iran and Iraq. Generally, these 2 synoptic patterns reveal that 120-day winds of Sistan with northern direction lead to a decrease in the temperature of east and southeast of Iran. Besides it makes thermal equator belt move to southern latitudes. Figures 5 and 6 show the patterns 3, 4, and 5. In these patterns, 120-day Sistan winds are not dominant in the area which leads to an increase in the temperature of the area and a core formation of maximal temperature in east and southeast of Iran. Unlike patterns 1 and 2, in these patterns eastern regions of Iran have higher temperature than Mesopotamia and west of Iran. As a result, the advection of eastern winds in the region makes thermal equator penetrate northern altitudes as it covers east and southeast of Iran. Due to this phenomenon, eastern regions records much higher temperature than the regions in Mesopotamia and west of Iran, although they are in the same latitude.
    Conclusion
    Wind advection in eastern and southeastern regions of Iran during hot period of year is considered as one of the most important and most effective climatic phenomena having great impacts on environment and communities. There are two advection orders during this period of year, including the advection of northern winds (120- day winds of Sistan) and eastern winds. Eastern winds mostly cover eastern and northeastern regions of Iran, while northern winds mostly cover eastern and southeastern regions. The calculation of thermal advection during the existence of each wind demonstrates that during the advection of northern winds, a core of negative thermal advection is made in east and southeast of Iran (Fig. 1). As these winds are intensified, the intensity of this negative thermal core increases too (Fig. 1 a). This phenomenon reveals that this is the heat transmission from the dominant regions of this negative thermal advection to surrounding regions which provide cool weather in east and southeast of the country. Besides, vertical profile of atmosphere also proves the altitudinal expansion of this core of negative thermal advection through higher levels. A core of negative thermal advection is made during the advection of eastern winds (Fig. 2 and 3), althoughthis core dominates less regions limiting to eastern regions of Iran. Besides, a core of positive thermal advection is made in southeast of Iran. This phenomenon not only leads to heat aggregation, but also makes a core of maximal temperature in the region and transfers thermal equator to east and southeast of Iran.Moreover, eastern half of Iran shows lower temperature than west of Iran and Mesopotamia during the advection of 120- day winds, while this region shows higher temperature than west of Iran and Mesopotamia in the absence of 120- day winds. Therefore, the advection of northern winds (120- day winds of Sistan) makes thermal equator of the earth move to southern latitudes in southeast of Iran decrease the temperature of the region.
    Keywords: Sistan 120 days wind, Synoptic, Thermal advection, Iran
  • Yadollah Yousefi *, Fatemeh Kardel, Hematolah Roradeh, Molod Mohtasebi Khalatbari Pages 491-501
    Introduction
    The rapid growth of urbanization formed in the 19th century after the industrial revolution in the developed countries and after that the urban area of many cities extended rapidly. In those cities many different activities such as transportation, industrial and construction activities is much higher than rural environments, while in that environments vegetation and green spaces are lower. Thus, these activities cause the increased level of temperature in the urban environments compared to surrounding areas which this phenomenon is called “urban heat island” (Oke, 1973). This phenomenon is one of the main concerns in the urban environments due to its impact on biological, meteorological, environmental, social and economical aspects. For instance, urban head island causes earlier bloom and the blossom of plants and trees, prolongation of the growing season. Thus, Babol is one of the densely built cities of Mazandaran province, which is confronted with rapid population growth during few last decades. The aim of this research is to investigate the impact of urbanization on heat island and day to day temperature variation in that city.
    Materials And Methods
    The study area was conducted in the Babol city, Mazandaran. Due to the lack of meteorological station in the Babol city, three devices with data logger (MIC 98583 USB-Data Logger, Taiwan) were equipped to record temperature and relative humidity in three environments urban, suburban, and green. The temperature and relative humidity were recorded every hour throughout the course of 80 days (July 6 to September 22) in 2015.
    The monitoring boxes were placed at a height of about 2.5 m above the ground surface. This study is investigated the day to day temperature variation with respect to the impact of urbanization on temperature variations. For this purpose, the two following integrated methods were used: 1) the day to day temperature variation (DTD); 2) the difference between day to day variability of daily maximum temperature (DTD max) and day to day variability of daily minimum temperature (DTDmin) (Tam et al., 2015).
    The day to day temperature variation is based on the following equations: Equation 1: Where Σ is the sum over all n data elements, t is daily temperature, i is the counter that marches through the days in a time period (e.g. a month),| | is the absolute value, and n is the number of days elements.
    Equation 2: ΔDTD is the difference between day to day variability of daily Tmax (DTDtmax) and day to day variability of daily Tmin (DTDtmin). A positive value indicates greater day-time day to day temperature variation and a negative value indicates greater night-time day to day temperature variation (Tam et al., 2015). The significant difference for temperature (maximum and minimum) and daily relative humidity in different environments was tested using the one-way analysis of variance (ANOVA); and then the day to day variability of temperature was calculated based on a DTD and ΔDTD equations for all three environments. Analysis of the data was conducted using Excel and R software.
    Results And Discussion
    The difference between the mean temperature of urban and suburban environments in our study area is around 1°C and this difference between urban and green environments is around 1.8°C. The mean relative humidity in the urban and green environments are minimum (67%) and maximum (77%), respectively. Day to day temperature variation of daily temperature DTD(tmean) and temperature maximum DTD(tmax) in the urban environment is higher than suburban and green environments, but the day to day temperature variation of daily temperature minimum DTD(tmin) is less than the two other environments. The difference of DTD(tmax) and DTD(tmin) in the urban environment is higher than the two other environments and is nearly zero for the green environment. These values indicate higher variation of the daily temperature in the urban and a very small difference in the daily and nightly temperatures variation for the green environment. The results of this research demonstrate that the heat island not only affects the temperature in Babol city, but also influences its day to day temperature variation.
    Conclusion
    Based on these results, one could observe the impact of urbanization on climatic parameters in particular temperature and humidity. Moreover, the green environment can play an important role on the climate change of Babol city.
    Keywords: urban heat island, temperature, day to day temperature variation, relative humidity, Babol city
  • Abdol Reaza Kashki *, Abbas Ali Dadashi Roudbari Pages 503-521
    Introduction
    Knowledge of the amount, spatial and temporal distribution of precipitation days is essential to plan.as a strategic axis for the future planning should be considered. The vast territories of Iran between Siberia in the north, the Mediterranean in the West, the deserts of Africa in Saudi Arabian Sea and the largest country in the South West and the East India factor for the interaction of different weather systems on Iran; and one each in the range of years according to a system to bring Iranian, Iran affect climate. Deep interaction, complex and continuous of precipitation caused by climate change and other elements of diversity in space and time was this element is; However, climate studies will be valuable when researchers have provided real time data; weather stations valuable information about the amount and frequency of rainfall courses are available to researchers, however, measurement stations rainfall is usually in population centers or research centers have been special. The aim of this study is knowing what Analyze number of days of rain before Iran, with output in the range of 56 database-annual Aphrodite (01/01/1951 to 31/12/2007 AD) is
    Materials And Methods
    In this study, data from the database to Analyze rainy day Iran - is Aphrodite, the Middle East (APHRO_ME) of the final product of this database as v1101, by resolution 25/0 × 25/0 by 56-year period (1 / 1/1951 to 31/12/2007 AD) is used. To divide the country into zones rainfall zone stepwise regression methods as well as for zoning rainy days rainy days kriging method has been optimized.
    Results And Discussion
    Has in every area of rainfalls and the time is different. Skewness provided shows that the spatial distribution of precipitation is skewed to the right, the low-rainfall areas than in areas with high rainfall. According to the terms dynamic and thermodynamic systems, causing precipitation and depending on your geographic location, in dealing with local conditions can cause precipitation regions differently. Therefore, the amount of precipitation has statistical parameters will be different. The difference in median, mean and deviation indicates that the data does not follow a normal distribution.
    The number of rainy days between 9 to 147 days. Iran is the average number of rainy days is 38 days, while the number of rainy days is 36/62% of the area of the country less than 38 days. The region's rainy day in terms of number of rainy days 147 South West of the Caspian Sea (32 km south of West synoptic Bandar Anzali) is located. Similarly, the lowest number of rainy days with 9 days in South East Iran Iran is located 116 kilometers East of Khash synoptic stations.
    Conclusion
    The results showed that the average of rainy days is 38 days, however, the number of rainy days is 36/62% of the area of the country less than 38 days. The maximum rainfall Iran on the Caspian Sea is located at 147 days in the South West, on the other hand a minimum of 9 days rainy days in the South East is Iran. Iran was divided into four zones Likewise, Iran's biggest drawback is divided into four zones rainy days of the entire north coast, the northern part of North Khorasan, North West and West Highlands in a group. Finally, the division offered the best division's zone of rainy days, divide the country into six zones were detected. The six zones are zones Khzrry with rainy days 126 days, across the mountainous regions of West, North West and Northeast with rainy days 77 days across mountainous 57 days, zone bar between the highlands and lowlands of leeward of the 38 Days of Rain, Finally, the relationship between the number of rainy days with latitude and height above sea level for the entire zone between Iran and six zones was rainy. Relationship provided for the entire region of Iran was presented by a factor of 0.57 determine the most important factor in the equation, longitude were identified. Although differences between the average and maximum number of rainy days Iran with other research such as (Alijani, 1389) and Masoodian (1390), which it named difference with this study is the research database, but acknowledged evaluate.
    Keywords: rainy day, shear zones, stepwise regression, Aphrodite, Iran
  • Esmaeil Haghighi *, Mohammad Hossein Gholi Zadeh, Mehdi Dostkamiyan, Fatemeh Ghaderi Pages 523-539
    Introduction
    Atmospheric circulation patterns play a major role in the distribution and geographical distribution of precipitation. According to the researchers views Changes climatic circulation patterns are Controller swing shift and also intensity of precipitation and atmospheric moisture content changes (Trenberth et al., 2003: 327-339; Emori and Brown, 2005). So that increases in atmospheric temperature will follow moisture content increases (Trenberth et al., 2003; Meehl et al., 2007). On the other hand, changes in precipitation patterns may be affected by carbon dioxide. That the increase in greenhouse gases how much of the affect climate processes is still in question. But is obvious that the density increases the concentration of greenhouse gases directly or indirectly climate elements, both spatially and in terms of time is affected (colins 2013: 126; Chadwick et al 2013: 610-615). However, many studies have shown that rainfall patterns in tropical areas, especially over the oceans are heavily influenced by changes in temperature (SST) patterns of sea level (Xie et al., 2010; Chadwick et al., 2013: 3803-3822; Ma and Xie, 2013: 2482-2501; Huang et al., 2013: 966-986).
    Materials And Methods
    In order to perform Analysis an extensive rains of spring in Iran In this study, is used two groups different environmental data that the data are as follows: 1. Environmental Data: This group of data through interpolation of Daily rainfall spring quantities station in April (April), May (May) and June (June) throughout the country And for a period of1961 to 2010 (4650 days), Using daily precipitation data from 551 Synoptic and climatology stations Had been received from Meteorological Organization Country, were Sort and Interpolation. Finally, by combining these three matrices, Matrix dimensions was obtained studied period (4650×7187). In this study, is said rainy day to day At least one millimeter per day and more experienced. After identifying rainy days percent coverage (pervasive of precipitation) was considered Which for this purpose, given that most researchers in their studies Have chosen Fifty percent coverage for pervasive This day was chosen to cover 50% And finally selected 265 days and was examined and analyzed. Atmospheric data: This data is consists of data sea level pressure and geopotential height at 500 hPa zonal and meridional wind data Which has been received from the database National Center for Environmental Prediction and of the National Center for Atmospheric Research (National Centers for Environmental Prediction / National Center for Atmospheric Research).
    Conclusion
    The results of this study showed that: The results of the descriptive analysis spring rainfall shows that pervasive and spring rains Towards June While the had a significant reduction is high Spatial Coefficient of variation In between central and southern parts from spatial variation have been more.
    The results of the study showed that Four pattern Saudi low pressure- Iran central low pressure, Europe low pressure - Sudan low pressure, Persian Gulf low pressure- Siberian high pressure pattern and multi-core pattern Middle East low pressure The largest role have in pervasive rainfall in spring. In all four pattern Cold air advection high latitudes on Thermal low pressures Low latitudes is causes Extreme Temperature and pressure gradient Resulting in prepare the frontogenesis. Also convergence centers 1000 hPa in these patterns is consistent with low pressure systems. Air high pressure centers to take his side and in contrast to the cold weather tabs high pressure centers due to rising air and create instability in areas under their influence, especially Iran. In 1000 hPa level moisture flux maps humidity injection has been done mainly through the anticyclone on the Arabian Sea into the Sudanese system and strengthen it by injecting humidity from the Red Sea and the Persian Gulf. So that the Cold air advection high pressure centers tabs on the system have been due to its dynamic and moisture transport in the North to the Persian Gulf and Iraq (just in front trough Mediterranean at 500 hPa level). Also, according to the maps Front Genesis at 850 hPa at All patterns are frontogenesis mainly in accordance with the transmission path is hot and humid weather southern extends north.
    Conclusion
    Spring rains also have daily rise and fall with high spatial variation coefficients In between towards month June this will be more significant changes. The results of the study showed that Four pattern Saudi low pressure- Iran central low pressure, Europe low pressure - Sudan low pressure, Persian Gulf low pressure- Siberian high pressure pattern and multi-core pattern Middle East low pressure The largest role have in pervasive rainfall in spring.
    Keywords: spring pervasive precipitation, Moisture flux, Frontogenesis, Perceptible water, convergence, divergence
  • Hadi Nayyeri *, Khaled Osati, Parisa Osmani Pages 541-556
    1-
    Introduction
    Rivers, under influences of different factors, change in terms of size, shape, direction and pattern. These changes indicate that rivers tend to equilibrate. Tarwal River, as the main branches of SefidRud, is located in the eastern part of Kurdistan province. Equilibrium, stability and the role of environmental variables on Tarwal River have not studied yet as there is no proper knowledge of its actions and reactions. Therefore, this case study is trying to find out the answers the questions related to such issues in Tarwal basin. The balance between erosion and deposition in a river defined as equilibrium (Davis 1902) while The stability of stream morphology defined as the ability to preserve existing conditions for a long time (Bureau of technical Vice Presidency for Strategic Planning and Supervision, 2012). Doyle and Harbor (2003) showed that the type of bed sediments has a great impact on the equilibrium, so that time needed for a channel to equilibrate with sand bed is almost half the time needed for a channel with gravel bed. It’s because sediment transport more rapidly in channels with a sandy bed and these channels equilibrate more rapidly than those with fine sediments. One way to evaluate river stability is Rozgen classification system. Savery et al (2007) declared that Rozgen classification system is applicable for flat and low steep areas. This method is usable in the engineering designs, management issues and stream restoration (Rustaei et al. 2013). River Style Framework method is also another applicable method to evaluate river stability.
    2-
    Methodology
    For each style of Tarwal River network, equilibrium capacity and geomorphic condition was determined by river style framework using three parameters of channel properties, channel planform and bed characteristics. In the next step, stability of Tarwal river was determined based on Rozgen classification system using 15 parameters (bank vegetation, channel capacity, section cuts, aggradation, degradation, sediment and …). Therefore, in addition to extensive field campaigns, topographic maps, aerial photos and Google Earth software used to determine cross section dimensions and vegetation condition as well as trenches and terraces in the study areas. During field campaigns, cross sections dimensions token by a laser meter Leica D5 and locations of each sections and trenches recorded by GPS. In addition, collections of photos token from different features. Then data digitized to Arc GIS software.
    Results And Discussion
    Based on river style framework, styles of low sinuosity with fine grained, ada clay bed and meandering sand bed show a local equilibrium capacity. These rivers styles have limited vertical and lateral adjustment and sorts sediments well, while styles of low sinuosity gravel bed, multichannel sand bed in Ozon Darreh River, low sinuosity sand bed in Tarwal River and meandering fine grained in Sang Siah River show a high equilibrium capacity because of vertical and lateral adjustment and non-homogeneous sediments. In meandering fine grained style of Sang Siah River, equilibrium capacity is increased because of vertical (bed incision) and lateral adjustments in the form of channel contraction (Alluvial terraces) where it dominated by vertical adjustment. These results support the results of Nayyeri and Rezaei moghadam (2005) in meandering river of Simineh rood where bed equilibrium reported in the form of bed incision. In River Style Framework method, the geomorphic conditions of river assessed through river characteristics and behavior. Styles of low sinuosity gravel bed in Sis River and a small tributary (in the northeast of Tarwal River), low sinuosity sand bed in Tarwal River and multichannel sand bed in Ozon Darreh River showed a relatively high width to depth ratio, low sinuosity, compound and irregular channel shapes and erosional banks. These styles show poor geomorphic conditions because of non-homogenous, poor-sorting sediments and bed erosion. Styles low sinuosity fine-grained bed in Esmail Jamal, Jorvandi, Ozon Darreh and Tarwal Rivers, ada clay bed in Sang Siah and Jorvandi Rivers and meandering sand bed in Tarwal River show a good geomorphic condition because of well sorting sediments, considerable vegetation cover and lack of erosion bank.
    The stability of all sections analyzed based on Rozgen classification system. Therefore, in each reach style, 1-3 sections and totally 34 sections picked up. Styles of ada clay bed in Sang Siah River and meandering sand bed, multichannel sand bed, low sinuosity fine grained and meandering fine grained in Tarwal Main River are stable while styles of low sinuosity gravel bed in Sis and a small tributary (in the northeast of Tarwal River), low sinuosity sand bed in Tarwal River and multichannel sand bed in Ozon Darreh River are unstable. This instability is because of significant incision, high roundness of sediments, poor sorting and filled pools. .
    Conclusion
    Section instability in Downstream of Jorvandi and Ozon Darreh Rivers and in upstream of Tarwal and Sang Siah Rivers can be resulted from numerous faults in some sections as the slope has increased in current period results in increasing stream power and unstable reaches. Increasing particle size is a good evidence of activity of faults in the section. Faults have increased stream power and causes extensive move of particles. Fine particles removed and only coarse particles remain in current periods. Such evidences clarify that the river bed has affected by a tectonic activity in a small scale (Ramesht, 2012). In this case study, Tarwal River were analyzed by River Style Framework method, Rozgen classification system and field campaigns. Due to non steepness of the study area, Rozgen classification system well fitted to the field data which support Savery et al (2007) recommendation about applicability of Rozgen classification system in flat areas. The results of this study can help to improve the evaluation of watershed management activities, hydrological designs and river adjacent activities such as land use changes, sand and gravel mining, flood plain management, flow adjustment by storage and diversion dams and river rehabilitation.
    Keywords: geomorphologic equilibrium, River Style Framework, Rozgen classification system, Tarwal River