فهرست مطالب

پژوهش های اقلیم شناسی - پیاپی 44 (زمستان 1399)

نشریه پژوهش های اقلیم شناسی
پیاپی 44 (زمستان 1399)

  • تاریخ انتشار: 1400/01/18
  • تعداد عناوین: 9
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  • دینا عبدمنافی*، امیرحسین مشکوتی، سهراب حجام صفحات 1-14

    این پژوهش در طی سال های 1391،1392 و 1394 و برای دو ایستگاه هواشناسی اقدسیه و مهرآباد شهر تهران انجام گرفته است. سختی کل، pH، قلیاییت کل،هدایت الکتریکی (EC) و کل مواد جامد محلول (TDS)، 30 نمونه ی آب بارش، اندازه گیری شدند. در این مقاله از اندازه گیری غلظتگازها و ذرات معلق شرکت کنترل کیفیت هوای تهران و نیز مقادیر سرعت اصطکاکی و باد در ارتفاع 10 متری که از خروجی مدل عددیWRF  بدست آمده اند نیز کمک گرفته شده است. نتایج نشان می دهند که، بارش ها در فصل های پاییز و زمستان بدلیل افزایش غلظت گازهای  و  و وقوع وارونگی در جو اسیدی و دراواخر فصل زمستان، فصل بهار و فصل تابستان بدلیل ازدیاد هواویزهای گرد و خاک در جو قلیایی بودند. پایین ترینpH در ایستگاه مهرآباد (6/4)  بدست آمده است. نتایج سرعت اصطکاکی،TDS،EC و قلیاییت کل نیز فرسایش خاک قبل از وقوع بارش ها در فصل بهار و تابستان را تصدیق می کنند. نوع سامانه، جهت گردش های بزرگ مقیاس، عمق لایه آمیخته، منابع آلاینده های محلی، وجود وارونگی دما، مقدار و جهت سرعت باد و خصوصیات جغرافیایی منطقه مورد مطالعه، از عوامل بسیار مهم و تاثیر گذار بر روی کیفیت آب بارش در شهر تهران به شمار می روند.

    کلیدواژگان: :pH، TDS، EC، قلیاییت کل، سختی کل
  • اعظم عربی یزدی، حسین ثنایی نژاد*، عباس مفیدی صفحات 15-32

    دسترسی به ابزاری که بتواند بر اساس پویایی روابط بین سطح زمین و جو و نه لزوما بارش و دمای هوا، خشکسالی های کشاورزی و هیدرولوژیکی را اندازه گیری کند و آگاهی های زودهنگام را جهت اخذ تصمیم گیری های مدیریتی ارایه دهد، ضروری است. در این راستا، نیاز به انواع داده های شبکه بندی شده ای است که بتواند کمبودهای شبکه غیر یکنواخت داده های زمینی یا محدودیت های داده های ماهواره ای را جبران نماید. مفهوم تقاضای تبخیری جو (EDDI) نشانگر تشنگی جو است و بر اساس محرک های اقلیمی فیزیکی دمای هوا، سرعت باد، تابش خورشیدی و رطوبت به راحتی و در زمان نزدیک به واقعی قابل دسترس است. در این تحقیق به منظور برآورد شاخص خشکسالی تقاضای تبخیری در شرایط مختلف اقلیمی ایران، از داده های شبکه بندی شده تحلیل مجدد مدل ERA-Interim از پایگاه ECMWF  طی سال های 2017-1979استفاده شد و توانایی این شاخص در پایش خشکسالی هیدرولوژیکی در برابر شاخص های رایج خشکسالی SPI و SPEI مورد ارزیابی قرار گرفت. ضرایب همبستگی قوی و معنی دار در مقیاس های ماهانه، فصلی و سالانه بین شاخص EDDI با SPEI مبین نقش مهم تبخیر تعرق در پایش خشکسالی در مناطق خشک و نیمه خشک است و می تواند ضعف شاخصSPI  در مناطق کم بارش را جبران کند. این شاخص توانایی پایش خشکسالی های کوتاه مدت و ماندگار را زودتر از دیگر شاخص های رایج نظیر SPI و SPEI دارد که این شاخص را پیشرو می کند. شاخص EDDI ابزاری آسان برای اجرای هشدار زودهنگام عملیاتی و کنترل طولانی مدت خشکسالی هیدرولوژیکی است. همچنین با پایش در هر مقیاس زمانی(به طور مثال فصلی) می توان از نتایج آن برای پیش آگاهی دوره های طولانی تر(سالانه یا چند ساله) استفاده نمود و شکاف بین پیش بینی های کوتاه مدت و فصلی را جبران می کند.

    کلیدواژگان: خشکسالی ماندگار، شاخص تقاضای تبخیری جو، خشکسالی سریع، پیش بینی بین فصلی، ECMWF
  • بهروز سبحانی*، وحید صفریان زنگیر صفحات 33-48

    خشکسالی از جمله مخاطرات طبیعی می باشد که در دهه های گذشته کشور ایران را با مشکلات و مخاطرات محیطی جدی زیادی مواجع کرده است از جمله این مناطق، بخش های جنوبی ایران می باشد. پژوهش های صورت گرفته در منطقه جنوبی ایران در زمینه مدل سازی آماری خشکسالی به ندرت و خیلی ناچیز می باشد. بنابراین هدف از پژوهش حاضر فازی سازی شاخص S.M.S، مدل سازی و پیش بینی خشکسالی در نیمه جنوبی ایران می باشد.برای انجام این پژوهش از داده 29 ساله دما و بارش در 28 ایستگاه سینوپتیک در نیمه جنوبی ایران در بازه زمانی (2018- 1990) استفاده شد. در این پژوهش، ابتدا سه شاخص خشکسالی SPI, MCZI, SETجداگانه محاسبه و ترکیب شده و شاخص فازی S.M.S به دست آمد سپس در دو مدل شبکه عصبی ANFISو RBFدر نرم افزار MATLABمقایسه و مدل سازی و برای 16 سال آینده پیش بینی شدند و در نهایت با استفاده از مدل تصمیم گیری چند متغیره TOPSISمناطق درگیر خشکسالی برای سال های آتی یعنی 16 سال آینده اولویت سنجی شدند. یافته های پژوهش نشان داد شاخص جدید فازی سه شاخص مذکور خشکسالی را با دقت قابل قبول در خود منعکس کرد. در ارزیابی دو مدل ANFISو RBF، مدل RBFبا مقدار RMSEبرابر با 15/1 و مقدار  R2برابر با 99/0 بیشترین دقت را نسبت به مدل ANFISبرای پیش بینی به خود اختصاص داد. براساس شاخص فازی S.M.S ایستگاه های مانند کرمان، یاسوج و آبادان به ترتیب با درصد خشکسالی (99/0، 97/0 و 89/0) در مناطق مورد مطالعه بیش تر در معرض خشکسالی آینده قرار گرفتند. هم چنین براساس مدل Topsisنیز ایستگاه های مرکزی و شمالی منطقه مورد پژوهش مانند کوهرنگ و صفاشهر به ترتیب (19/0 و 21/0) در سال های آتی در معرض خشکسالی با درصد کم تری قرار گرفتند.

    کلیدواژگان: تحلیل آماری، مخاطره، مدل هایRBF و ANFIS، شبیه سازی، فازی سازی
  • نادر نقشینه*، رقیه معصوم پور امیرآبادی، فاطمه فهیم نیا، میترا صمیعی صفحات 49-62

    موانع استفاده پذیری از اطلاعات هواشناسی کشاورزی به معنای محدودیت هایی است که به هر دلیلی با بهکارگیری آن، اطالعات هواشناسی کشاورزی برای برنجکاران قابل استفاده نخواهد بود. هدف پژوهش شناسایی موانع استفاده پذیری اطلاعات هواشناسی کشاورزی است که با تمرکز بر رفتار و نیاز اطلاعاتی برنجکاران در سه مرحله کاشت، داشت و برداشت انجام شده است. پژوهش از نوع کاربردی و با رویکرد آمیخته به ترتیب در دو بخش کیفی و کمی اجرا شد. در بخش اول تحلیل کیفی داده ها از طریق مصاحبه عمیق با 02 نفر از برنجکاران نمونه در استانهای شمالی کشور گردآوری شده است. نمونه گیری به روش هدفمند انجام گرفت و تا اشباع اطلاعات ادامه یافت. کدگذاری طی سه مرحله کدگذاری باز، محوری و گزینشی انجام شد و نظریه زمینهای مبنای این پژوهش بود تحلیل نتایح کیفی نشان داد که پنج مقوله شامل عدم کاربردی بودن خدمات، ناآگاهی از خدمات هواشناسی کشاورزی، ضعف باورپذیری به اطالعات هواشناسی و عدم دسترسی متناسب اطالعات و کاربرمحوری فوریکا از مهمترین موانع استفاده پذیری خدمات هواشناسی کشاورزی در بین برنجکاران است.در بخش دوم با روشکمی و با یک پرسشنامه محقق ساخته، مدل کیفی طراحی شده بخش اول، مورد ارزیابی قرارگرفت. ابزار تحقیق در بخش کمی، پرسشنامه حاصل از مقوله های کیفی است. نتایج تحلیل کمی نشان داد بیاطالعی از سامانه تهک ، قطع سامانه تهک و ناآگاهی کشاورزان از خدمات مروجین و عدمدسترسی به خدمات نقطه ای هواشناسی کشاورزی نشان دهنده این مطلب است اطلاعات هواشناسی کشاورزی زمانی از نظر برنجکاران ارزشمند و قابل استفاده خواهد بود که خدمات به شکل نقطه ای اشاعه یابد که برنجکاران حداکثر سود را از کاربرد محتوای اطلاعات، به دست آورند. اصالت موضوع مورد مطالعه و استفاده از رویکرد آمیخته بیانگر اصالت و ارزش این پژوهش است و شناسایی موانع استفاده پذیری، کمک شایانی به سیاستگذاری و تدوین خط مشیهای واقع بینانه میکند.

    کلیدواژگان: هواشناسی کشاورزی، موانع استفاده پذیری خدمات، تجارب برنج کاران
  • رضا اسماعیلی*، فرخ لقا امینی صفحات 63-78

    امروزه آلودگی هوابه بزرگترین معضل زیست محیطی در کلانشهر ها تبدیل شده است. بر اساس برآورد سازمان بهداشت جهانی (WHO) از هر 10 نفر 9 نفر هوای ناسالم تنفس می کنند. بر اساس این گزارش سالانه بالغ بر 7 میلیون نفر قربانی آلودگی هوا می گردند که هزینه این مرگ و میر 225 میلیارد دلار برآورد شده است. در این تحقیق الگوی پراکنش pm2.5 به عنوان آلاینده اصلی کلانشهر مشهد با استفاده از تحلیل خود همبستگی فضایی مورد بررسی قرار گرفته است. بدین منظور برای یک دوره 5 ساله60 نقشه ماهانه پراکنش pm2.5  ترسیم گردید سپس نقشه های فصلی و سالانه از ترکیب نقشه ها ماهانه بدست آورده شد. با استفاده از آماره محلی موران (LMI) و شاخص گتیس ارد جی (Getis -Ord-Gi) نقاط داغ غلظت ذرات معلق شهر مشهد شناسایی شد. بر اساس یافته های تحقیق نقاط داغ (Hot Spot) با 22.3 درصد از مساحت کل شهر در شرق و جنوب شرق و نقاط سرد (Cold Spot) با 25.5 درصد در شمال غرب مشهد شکل گرفته اند. یک چنین الگویی در مقیاس فصلی نیز وجود دارد. نتایج محاسبات انجام شده بین دو لکه متمایز داغ و سرد نشان داد ، تعداد روزهای ناسالم در نقاط داغ چهار برابر نقاط سرد است. این نسبت در غلظت NO2حدودا چهار برابر، SO2 سه برابروPM2.5,10 و CO دو برابر بیشتر است. همچنین 21 درصد جمعیت بیشتر، 30 درصد مساحت و تعداد کاربری های صنعتی و خدماتی بیشتر در نقاط داغ نسبت به نقاط سرد بیشتر است. از طرفی برخورداری کمتر از (70 درصد کمتر) پارک ها و فضاهای سبز (62 درصد کمتر) و همچنین ارتفاع 114 متری کمتر بین دو منطقه را می توان در تراکم بالای PM2.5  و شکل گیری نقاط داغ در شرق و جنوب شرق مشهد موثر دانست.

    کلیدواژگان: هواشناسی کشاورزی، موانع استفاده پذیری خدمات، تجارب برنج کاران
  • فرزانه مرادی، غلامعباس فلاح*، منصور چترنور صفحات 79-90

    افزایش غلظت گازهای گلخانه‍ای طی چند دهه اخیر باعث ایجاد اثر گلخانه‍ای در جو زمین و گرمتر شدن هوای آن شده است. پژوهش حاضر یک بررسی کاربردی تحلیلی است که با کمک مدلDAYCENT جهت مطالعه شار گازهای متان (CH4)، نیتروس اکساید (N2O) ونیتریک اکساید ((NO درگندمزارهای شوش و شالیزارهای باغملک در خوزستان استفاده شد. همچنین تغییرات متوسط دمای سالانه هوا(oC1، 5/2 و 2/4) و بارش (2-، 7 و 14٪) جهت بررسینرخ شار گازهای متان، نیتروس اکساید و نیتریکاکساید شبیه‍سازی گردید. نتایج میانگینشار گاز متان در ایستگاه باغملک، 369/1، اکسید نیتروس 01/0 و اکسید نیتریک 01/0 تن در هکتار  در سال به دست آمد و برای ایستگاه شوش شار متان 106/0، اکسید نیتروس 101/0 و اکسید نیتریک 111/0 تن در هکتار در سالتعیین شد.گاز متان بیش ترین مقدار شار را در ایستگاه باغملک داشت. همچنین ایستگاه شوش،شار نیتروس اکساید و نیتریک اکساید بالاتری را نسبت بهایستگاه باغملک نشان داد. در ادامه بر اساس تغییرات دما در ایستگاه باغملک مقدار شار متان تقریبا به صورت ثابت و مقدار تغییرات شار نیتروس اکساید و نیتریک اکساید بسیار ناچیز بدست آمد. در حالی که در ایستگاه شوش مقدار شار هر سه گاز با افزایش دما تغییر محسوسی را نشان داد. همچنین بر اساس تغییرات بارش در ایستگاه باغملک، شار متان مقدار ثابتی داشت، ولی شار نیتروس اکساید و نیتریکاکساید با زیاد شدن بارش، افزایش یافت. اما در ایستگاه شوش شار هر سه گاز با کاهش بارندگی کم و با افزایش آن زیاد شد. اختلاف بین مزارع برنج و گندم از نظر شار گازها به دلیل وضعیت هوازی و غرقابی بودن آنهاست.

    کلیدواژگان: باغملک، شوش، گازهای گلخانهای، مدل DAYCENT
  • سارا کرمی، نسیم حسین حمزه*، حسین سبزه زاری، محسن لو علیزاده صفحات 91-103

    پدیده گردوخاک یکی از مخاطرات طبیعی است که امروزه بخش وسیعی از کشورهای دنیا را تحت تاثیر قرار داده و سبب بروز خسارات مالی و جانی فراوانی می شود. تغییر الگوی بارش و دما و وقوع خشکسالی های طولانی و پی درپی ناشی از تغییر اقلیم در سال های اخیر سبب وقوع پدیده های شدید و فراگیر گردوخاک در کشورهای منطقه خاورمیانه شده است. استان خوزستان از دیرباز با پدیده گردوخاک مواجه بوده است. پدیده گردوخاک در استان خوزستان بویژه در سال های اخیر بسیاری از سازوکارهای اجتماعی، اقتصادی و حتی اداری این منطقه را دچار اخلال نموده است. در این مطالعه داده های 11 ایستگاه هواشناسی همدیدی استان خوزستان در دوره 25 ساله (سال های 1372 تا 1396) مورد بررسی قرار گرفته است. همچنین برخی نتایج بدست آمده با داده های عمق نوری و شاخص NDVI در سطح استان خوزستان مقایسه شده اند. از میان 11 ایستگاه موردمطالعه، ایستگاه های اهواز، بستان و آبادان بیشترین فراوانی روزهای همراه با گردوخاک را دارا بوده در حالی که ایستگاه های بهبهان و رامهرمز کمترین فراوانی روزهای گردوخاک را دارند. میانگین روزهای همراه با گردوخاک سالانه در استان خوزستان در دوره 25 ساله معادل 3/37 روز در سال بوده که در این راستا سال های 1387، 1388 و 1390 بیشترین فراوانی روزهای گردوخاک استان را به خود اختصاص داده اند. بیشترین مقادیر AOD و کمترین مقدار شاخص NDVI نیز در سال های 1387 و 1388 مشاهده شده که با تعداد روزهای همراه با گردوخاک در استان خوزستان در توافق است. همچنین در ماه های تیر و خرداد با بیشترین دما و کمترین مقدار بارش و رطوبت نسبی، بیشترین رخداد گردخاک و در ماه آذر کمترین تعداد پدیده گردوخاک در کل استان رخ داده است. به طور کلی ضریب روند سالانه تعداد روزهای همراه با گردوخاک استان خوزستان 39/1 بوده که بیانگر روند افزایشی است. بررسی نتایج آزمون من کندال، این روند را در سطح اعتماد 5 درصد معنی دار نشان می دهد. با بررسی فصلی نیز می توان نتیجه گرفت که روند تعداد روزهای همراه با گردوخاک استان خوزستان در فصل بهار در سطح اعتماد 5 درصد معنی دار است. این روند در فصل تابستان معنی دار نبوده ولی در فصل پاییز در سطح اعتماد 10 درصد و در فصل زمستان در سطح اعتماد 1 درصد معنی دار است و در هر دو فصل روند مثبت بوده است.

    کلیدواژگان: تغییر اقلیم، گردوخاک، روند ناپارامتریک، من کندال، سطح اطمینان
  • مژگان باقری نیا، مجید رحیم زادگان* صفحات 105-119

    از اثرات عمده تغییر اقلیم، تاثیر آن بر کیفیت محصولات کشاورزی می باشد و انگور یکی از محصولات باغی استراتژیک کشاورزی می باشد. مقادیر دما و بارش روزانه ایستگاه گلمکان براساس مدل HadCM3 در دوره پایه (2005-1987) و آینده نزدیک (2050-2020) تحت سناریوهای RCP8.5,RCP4.5 با استفاده از روش عامل تغییر، ریزمقیاس شدند سپس با استفاده از سه سری داده های پایه هواشناسی، ریزمقیاس نمایی و کیفیت مشاهداتی انگور، کیفیت انگور برای آینده با بکارگیری شبکه عصبی پرسپترون در Matlab 2019A شبیه سازی شده است. مدل اقلیمی، افزایش دما و کاهش بارندگی در آینده را تحت سناریوهای RCP8.5,RCP4.5 نسبت به دوره پایه نشان داد. دمای حداکثر به ترتیب 3، 9 و 4.7 درجه سانتی گراد افزایش و دمای حداقل به ترتیب 3.8 و 4.4 درجه سانتی گراد افزایش و بارش به ترتیب 3/0 و 8/0 میلیمتر کاهش را دارد. هر یک از متغیرهای مستقل دمای کمینه، بیشینه، و بارش با هر یک از متغیرهای وابسته سن درخت، قند، وزن خوشه، اندازه خوشه، طول میوه، عرض میوه، اسیدیته، pH و TSS رابطه معناداری را بر پایه آزمون پیرسون نشان می دهند. تحت هر دو سناریو وزن خوشه، اندازه خوشه، طول میوه، عرض میوه، قند، pH، TSS بریکس، اسیدیته و وزن حبه به صورت کاهشی پیش بینی می شود. در RCP8.5 میزان تغییرات بیشتر از RCP4.5 می باشد. در خصوصیات رنگ آبمیوه، رنگ گوشت، طعم میوه، انبارداری، بازارپسندی و حمل و نقل در دو سناریو بدون تغییر است. آزمون T-Test تغییر در متغیرهای pH، قند، اسیدیته، وزن خوشه، طول میوه و طول در عرض خوشه در دو سناریو معنادار بوده است. متغیرهای وزن حبه و عرض میوه در دو سناریو 4.5 و 8.5، اندازه خوشه سناریو 8.5 و طول در عرض حبه سناریوی 4.5 فاقد تغییرات معنی داری است. نتایج نشان می دهد، دراثر افزایش دما و کاهش بارندگی در اقلیم آتی، برخی متغیرهای کیفت انگور در آینده با روند کاهش معنی داری مواجه خواهند شد.

    کلیدواژگان: اسپکترورادیومتر تصویربردار چندزاویه ای (MISR)، داده های عمق نوری هواویز (AOD)، ذرات معلق با قطر کمتر از 5، 2 میکرون (PM2.5)، ذرات معلق با قطر کمتر از 10 میکرون (PM10)، روش شبکه عصبی مصنوعی
  • لیلا تیموری یگانه*، مریم تیموری یگانه صفحات 121-132

    پیش بینی فرایندهای آب و هوایی ابزار مناسبی در اختیار مدیران حوضه های مختلف قرار می دهد، تا با در نظر گرفتن این پیش بینی ها، سیاست های آینده را در جهت بهینه نمودن صرف هزینه ها و امکانات بهره وری حداکثر طرح ریزی کنند. پیش بینی بارش برای اهداف مختلفی نظیر برآورد سیلاب، خشکسالی، مدیریت حوضه آبریز، کشاورزی و... دارای اهمیت بسیاری است. در این تحقیق، جامعه آماری شامل میزان بارش در ایستگاه های سینوپتیکی استان کرمانشاه، کنگاور، سرپل ذهاب و اسلام آباد غرب می باشد. روش مطالعه به صورت مقطعی و حجم نمونه نیز تمام داده های میزان بارش طی سال های 1365 تا 1397 می باشد. به منظور تجزیه و تحلیل داده ها از روش آریما برای برازش مدل سازی سری زمانی و در انتها بعد از آزمون مدل های موجود بهترین مدل برای پیش بینی میزان بارش تعیین گردید. نتایج بررسی ها نشان داد که مدل سری زمانی آریما بهترین کارایی را داشته و روند کاهشی بارش به اندازه 2/0 را خواهد داشت. در بررسی های حاضر با استفاده از داده های 32 ساله (97-65) ایستگاه کرمانشاه، اسلام آباد، کنگاور و سرپل ذهاب و همچنین مدل های سری زمانی اقدام به مدلسازی و پیش بینی بارش گردید. براساس نتایج بدست آمده از نمودارهای خود همبستگی و خود همبستگی جزیی، بهترین مدل برازش شده بر داده ها مدل بود. در نهایت با توجه به تصادفی بودن و همچنین تاخیر زمانی خارج از محدوده صفر براساس باقیمانده خود همبستگی جزیی و باقیمانده خود همبستگی در مدل پیش بینی داده ها کمتر از 05/0 می باشد. پس مدل پیش بینی قابل اطمینان برآورد شد. و براساس مدل برازش شده بارش به اندازه 2/0 روند کاهشی را خواهد داشت.

    کلیدواژگان: بارش ماهانه، سری زمانی، کرمانشاه، پیش بینی
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  • Dina Abdemanafi *, Amirhossein Meshkatee, Sohrab Hejam Pages 1-14

    Precipitation is one of the most efficient mechanisms for washing the atmospheric pollutants, specifically particulate ones out. The process has two very important consequences (one good and one bad).  The good one is that it clears atmosphere from many substances that have adverse impacts on many aspects of life whereas the bad one is that their reaction with rain water leads to the formation of some dangerous and destructive substances that have great impacts on both, the natural environment as well as manmade constructions.  Precipitation chemistry plays an important role in understanding the air quality in a study area, because the concentrations and distribution of chemical components in rain depend on a variety of emission sources. Realizing that, a lot of effort has been made in order to identify the nature and type of those particulates, especially in urban environment. Study upon the atmospheric pollutants and their nature in Tehran atmosphere goes back to 1991, when Ismaeili Sari, initially attempted to identify the nature of atmospheric pollutants over Tehran by sampling rain waters from  synoptic and climatology stations (also, he did the same research for one station in Tehran. The results for two researches have shown and also compared with present study in the section of results and discussion. Tehran city located in central part of the Alborz Mountain Range and characterized by its complex topography and diverse climate in an area of about 700 Km2. In this article, two Mehrabad and Aghdasiyeh synoptic stations were selected for rain water sampling. Aghdasieh synoptic station started to work in 1988 and is located in the northeast of Tehran, the area with highest annual average of precipitation among other areas of the city. According to Iranian Meteorological Organization records, the annual average of precipitation in this area is 420mm. Mehrabad synoptic station is the oldest meteorological station of the city that started to work in 1951 and is located in Mehrabad airport at the west of Tehran.  Recent years observations indicate that some unprecedented behavior in the city’s precipitation regime has occurred. The city fast growth of population, area, industrial activities and the number of motor vehicles alongside with substantial changes in the constructions from horizontal to vertical, has Considered responsible to that observed changes. Accordingly, to do that 30 rainwater samples from two Mehrabad and Aghdasieh synoptic stations (15 samples from each station) collected during the autumn and winter of 2012 and spring and summer of 2013 and 2015. During the examination of the chemical properties of the collected samples rain water total hardness, total dissolved solids (TDS), PH, total alkalinity and electrical conductivity (EC). Measured PH showed that autumn and winter precipitation were acidic while the springs' were mostly alkaline.  Dust highest concentration in spring and summer may be the main reason for rain water alkalinity and higher TDS, EC, and hardness relative to the winter samples. In order to analyze the causes of acid and alkaline precipitation were measured Concentrations of gases and aerosols (SO2,NOx, PM10 and PM2.5) Air Quality Control Company of Tehran and the friction velocity values obtained from the output of the numerical model WRF also taken. The results show that rainfall in autumn and winter seasons were acidic because of the increased concentration of gases in the atmosphere (SO2 and NOx) and inversion. In late winter and early spring due to increasing alkaline dust in the atmosphere precipitation were alkaline. Acidity of rain waters was higher in Mehrabad samples (with minimum amount of 4.6).The pH ranged from 4.6 to 8.1 with a mean of 6.3. Results show that in Aghdasieh the lowest measured pH was 4.72 and the highest was 7.95.  The highest calculated Alkalinity was 33.58ppm.  The same studies for Mehrabad indicate that the lowest and highest measured pH were 4.61 and 8.12, respectively.  Again, the highest calculated Alkalinity was 40ppm.  EC, TDS and Total Hardness were slightly higher at Mehrabad than Aghdasieh. They were 135μs/cm, 110ppm, and 57.95ppm in Aghdasieh and 195μs/cm, 130ppm, and 76.01ppm in Mehrabad, respectively. The results have shown the friction velocity, TDS, EC, and alkalinity of the soil erosion occurring before the spring rains. System type, the large-scale circulation, mixed layer depth, local pollution sources, the inversion temperature, wind speed and direction and the geographical features of the region, the important factors affecting the pH precipitation in Tehran considered.

    Keywords: pH, TDS, EC, Total Alkalinity, Total hardness
  • Azam Arabi Yazdi, Seyed Hossein Sanaei Nejad *, Abas Mofidi Pages 15-32
    Introduction

    Drought is a creeping and gradual phenomenon that can cause irreparable damage in many fields such as agriculture, food security, water resources management and the economy. It is essential to accessing a tool that can measure agricultural and hydrological droughts based on the dynamics of the relationship between land surface-atmosphere -not necessarily precipitation and air temperature- and provide early awareness for managerial decision-making. What makes drought monitoring important to us is the impacts on the agricultural sector (agricultural productivity, access to food security, agricultural and livestock insurance), water supply management and economic and social impacts. The concept of evaporative demand drought index (EDDI) reflects thirst for the atmosphere and is easily and realistically available in near-real-time based on physical climatic drivers of air temperature, wind speed, solar radiation and humidity, providing comprehensive information on drought dynamics.  The use of an indicator that can indicate the dynamics of drought in the shortest possible time will help to make managerial decisions and different levels of policy making to announce early operational warnings in the field of agriculture and reduce the social and economic consequences of this phenomenon. In this regard, there is a need for gridded data that can provide the required data set and compensate for non-uniform network data gaps or satellite data limitations. near-real-time networked data such as ERA-Interim is also useful in regional and extensive drought monitoring.

    Material and methods

    In this study, the ERA-Interim reanalysis data from ECMWF database was used to estimate the evaporative demand drought index in different climatic conditions of Iran and its ability in drought monitoring was investigated. Using probabilistic methods, the ASCI-PM method to estimate atmospheric evaporation demand, the EDDI index was calculated as the index of hydrological drought in Iran in different time scales during 1979- 2017. Also, the EDDI index was evaluated against the SPI and SPEI common drought indices.

    Result and discussion

    The results of evaporation demand estimates in the country show that seasonal variation and climate variability are factors that change the rate of evaporation demand. As a result of the interaction of the governing climatic factors, different climatic zones are created throughout the country and each region experiences different evaporation rates throughout the year. The EDDI index compensates for the gap between the theory of drought and operational drought management in determining and monitoring persistent drought as soon as possible between the occurrence of the phenomenon and the available data available. Significant correlation coefficients at monthly, seasonal and annual scales between EDDI and SPEI index indicate the important role of evapotranspiration in drought monitoring at arid and semi-arid regions so can offset the weakness of SPI in low rainfall areas. It has the ability to monitor short, medium and long term droughts earlier than other common indices, such as SPI and SPEI. The EDDI is capable of reporting a variety of persistent droughts without the need for precipitation data. Rapid response to environmental drying and humidification, processes caused by interactions between the atmosphere and the Earth's surface, making this index more flexible and advanced than other common indices. The longer the cumulative period of drought, the greater the time the indicator progresses.

    Conclusion

    The EDDI indicator is an easy tool for operational early warning, fire hazards, seasonal to seasonal drought prediction and long-term hydrological drought Monitoring at any time scale (e.g. seasonally). It can also be used to predict longer periods (annual or multi-year), so compensates the gap between sub-seasonal to seasonal forecasts. An important advantage of using networked data in calculating the EDDI index is that applicable at all times of the year - on cloudy days or for areas with snow cover, to complete the data due to satellite transit times and Delay in data access - no restrictions. Since atmospheric evaporation demand in the EDDI index is considered to be the cause of the drought, when combined with satellite data, EDDI and ESI (Green Water Index) combine to show real drought stress. Give. It is also recommended that MODIS, LANDSAT and ALEXI products be used to evaluate transpiration values and compare the EDDI index with satellite data and compare the results. The EDDI index is capable of decomposing to atmospheric governing factors such as radiation and advection components. By sensitivity analysis, it is possible to determine the main governing factors such as temperature, wind, short wavelength radiation and specific humidity on drought in each region, Determining and provided additional understanding of the dynamics and assessment of drought.

    Keywords: Sustainable Drought, Evaporative Demand Drought Index, Flash Drought, gridded data, ECMWF
  • Behrouz Sobhani *, Vahid Safarian Zengir Pages 33-48
    Introduction

    Today, drought is one of the most important natural hazards that has direct and indirect consequences in different parts of the planet (barqi et al., 2018: 141). Nevertheless, drought is one of the environmental events and an integral part of climate fluctuations. This phenomenon is one of the main characteristics and recurrence of different climates (Hejazizadeh and Javizadeh, 2019: 251 ) The purpose of this study was to analyze the temperature and precipitation data first, then, using ANFIS and RBF model model, a model-comparative model was developed and the new S.M.S drought index was designed. Finally, in order to better visibility of the drought situation for the future, in areas affected by drought in southern regions of Iran were predicted.

    Material and method

    In this study, after the 29-year data on temperature and precipitation data for 28 stations in the drought areas of Iran, the data were first analyzed, then normalized and the stations with abnormal data were normalized. After normalizing the temperature and precipitation data, using two new and powerful applied models for modeling and forecasting in climateology, namely ANFIS and RBF neural network models, were modeled. Then, the two models were compared for accurate prediction for the future, and after training three SPI, MCZI, and SET data, they predicted a new drought index called SMS, for the coming years, and Finally, using the TOPSIS multivariate decision making model, the areas most involved with the drought risk phenomenon were prioritized and ArcGIS software delimited the output data.

    Results

    Drought is a natural hazard, which is evident gradually over the long years due to climate change in its affected areas. Which effects itself on different parts of the living environment of living organisms. One of these areas in Southwest Asia is Iran, which in recent years has shown drought in its regions, especially the southern regions of high intensity. According to the comparisons of ANFIS and RBF neural network models, the two models were able to predict the drought. The results obtained from the training of the ANFIS neural network model were, at best, RMSE values equal to 9.64 and R2 values equal to 0.0681. But the results obtained from the training of the RBF neural network model were, at best, RMSE equal to 1.15 and the R2 value was 0.9961By comparing these two models, it was finally concluded that the performance of the RBF neural network model was better. According to the modeling and the results obtained from the comparison of the models, the accuracy and reliability of the RBF neural network model was confirmed for prediction. The prediction of the RBF neural network model was used. Modeling and predicting droughts in 28 synoptic stations in southern regions of Iran were compared using SMS fuzzy new index and ANFIS, RBF models. The methods used in this study, in most studies, Monitoring, Modeling and Comparison. Among these, studies have been done in Iran: Zeinali and Safarian-zengir (2017) by studying drought monitoring in the Lake Urmia basin using Fuzzy index; Babayan et al. (2018), the monthly forecast of drought in the southwestern basin of the country Using the CFSv.2 model, they confirmed the model's acceptable accuracy. However, with all the comparisons of different models and indices in these researches, the new SMS fuzzy index and two ANFIS and RBF models used in this study, namely, modeling and predicting the natural hazards of drought In the southern regions of Iran, it has an acceptable performance.

    Conclusion

    The purpose of this study was to model and investigate the possibility of drought prediction in the southern half of Iran. To do this, the fuzzyization of the SMS index, based on the three SPI, MCZI, SET, comparisons and the results of two new simulation models in Climatology, the ANFIS and RBF neural network models, as well as the TOPSIS multivariate decision making model. The results showed that the S.M.S index reflected the three SPI, MCZI, and SET indices. Comparing two models of ANFIS and RBF neural networks, the RBF model is more accurate than the ANFIS model. As a result, for prediction of drought, RBF model was used for future years. The results showed that the S.M.S index reflected the three SPI, MCZI, and SET indices. Comparing two models of ANFIS and RBF neural networks, the RBF model is more accurate than the ANFIS model. As a result, for prediction of drought, RBF model was used for future years. The accuracy of the RBF model at best was RMSE equal to 1.15 and the R2 value was 0.99 The results of the fuzzification of the SMS index showed that the central and western parts of the study areas such as Kerman, Yasuj and Abadan, with the SMS drought percentage (0.99, 0.97 and 0.89), respectively, were higher Exposed to the drought.

    Keywords: statistical analysis, hazard, RBF, ANFIS Models, Simulation, Fuzzy
  • Nader Naghshineh *, Roghayyeh Masoumpour Amirabadi, Fateme Fahim Nia, Mitra Samiei Pages 49-62

    The purpose of this study is to investigate the barriers to the use of agricultural meteorological information. This has been done by focusing on the information behavior and information needs of rice farmers in three phases of planting, holding and harvesting.This study is conducted with a mixed approach in both  qualitative and quantitative parts, respectively. In the first part, the qualitative content of the data has been analyzed  by in-depth interviewing with 20 sample rice farmers in the northern provinces of Iran. Sampling is performed purposefully until the saturation of information, and the qualitative data is interpreted by grounded theory analysis. In the second part, the qualitative model designed in the first part is evaluated with a quantitative method and a researcher-made questionnaire. In the quantitative part, the research tool is a questionnaire derived from qualitative categories. Sequential exploratory plan including data collection and analysis by qualitative research method in the first stage and then using its results to collect and analyze the quantitative data, which is finally an interpretation and time collection by collecting information. he results of the analysis showed that according to therice growers’ ideas on average unawareness about the services of the agricultural meteorological system in the country is the main barrier and the lack of desire to the training classes on agricultural meteorological services is the lowest barriers. The main non-use of agricultural meteorological services was identified in the rice planting phase. On average, based on the rice holding phase, unawareness about the services of the agricultural meteorological system in the country was identified as the most important barrier and tedious agricultural work was known as the lowest main obstacle in not using the agricultural meteorological services. In the rice harvesting phase, the weak insurance and political support  in the country and the ignorance of the promoters of agricultural jihad are the most important barrier and the lack of desire to training classes of agricultural meteorological services is the less main obstacle for not using agricultural meteorological services.

    Keywords: Agricultural Meteorology, barriers to service usability, experiences of rice farmers
  • Reza Esmaili *, Farrokh Legha Amini Pages 63-78
    Introduction

    Air pollution has become a major environmental problem in most parts of the world, especially in the metropolises of developing countries, along with population growth, agricultural development, urban development, industrial development and increasing motor transportation (Joulaei et al., 2017).Among air pollutants, PM2.5 is one of the most dangerous pollutants. Numerous studies have shown that PM2.5 can damage human lung tissue and increase the risk of chronic respiratory, cardiovascular and cancer diseases (Wang and etal, 2019; Vinikoor-Imler and etal, 2011). Statistical and spatial methods have been widely used to identify Spatial-temporal patterns of various air pollutants (Alijani, 2015) The purpose of this study was to use temporal and spatial analysis methods and Geographic Information System (GIS) affecting the emission of PM2.5 in Mashhad in a period of 5 years.

    Materials and Methods

    Mashhad City in Northeastern of Iran, the second largest metropolis in Iran, has 23 air quality monitoring stations. In this study, was used of Geographic Information System (GIS), geo-statistical functions, spatial analysis, geographical processes and sub-tools of each  to identified the spatial and temporal patterns of the main air pollutant in Mashhad.Daily data of particulate matter smaller than 2.5 microns (PM2.5) were collected from air quality stations in the 5-year period (2014-2019). Initially, the data was verified. Then the PM2.5 concentrations were calculated for each station daily and monthly scale. Maps were used by Geographic Information System (GIS). In the next step, local spatial correlation analysis or Local Moran Index (LMI) and hot spots analysis was performed by Getis –Ord-Gi statistic for this pollutant.

    Results  :

    Daily and monthly average concentration of pollutants in the late spring and early summer, showed sometimes winds with dust, so that the PM2.5 concentration is increased. Seasonal maps were prepared using the Algebra Map function.In the spring and summer, the Western and North-Western regions of Mashhad have the lowest density of particulars maters (19.3 μg / m3). The comparison of the average concentration of PM2.5 in different seasons of the year shows that autumn has the highest concentration.In the winter, the intensity of PM2.5 concentration has decreased. However, in the winter, with an average concentration of 30 μg / m3 and it is in the second rank of air pollution after autumn and the lowest concentration is located in the North-West and West of Mashhad as in previous seasons.According to the local spatial autocorrelation analysis, the concentration of particulate matter in the Eastern areas of the city is significantly higher than its neighboring areas, which is marked on the map with high cluster (HH) and low clusters (LL) are spread in the North-West.The annual map of PM2.5 concentration was drawn by combination of seasonal maps in a 5-year period.This map provides a more complete understanding of how PM2.5 is distributed in the air of Mashhad. According to this map, the eastern and southeastern regions of Mashhad with an average concentration of 36.8 μg / m3 have the highest concentration than the Western and North-Western.The study of effective factors in the emission of PM pollutants in the Mashhad city showed that the total length of the urban transportation network in hot spots is 283.287 meters and the average speed of vehicles in this section is 36 kilometers per hour. While, the total length of the network in the cold spot is 424342 meters and the average speed of cars is 46 kilometers per hour.Emissions in cold spots are also lower than in high-traffic areas due to longer communication lines, faster vehicle speeds and light traffic.Geographical point of view show that, Mashhad plain is located between two the Binalood mountains in the south and the Hezar Masjed mountains in the north, which causes the formation of a special pattern and canalization of wind from the South-East to the North-West. High density of suburban population, more agricultural lands, rural roads, sand mines, brick kilns and cement factories in the East and South-East of the city due to high concentration of PM2.5 and the formation of hot spots in this area. However, the location of the cold spot in the North-Western regions of Mashhad was due to lower population density, much more green space per capita, higher altitude, greater distance from industries, adjective to Torqabeh and Shandiz areas.

    Conclusion

    Spatial autocorrelation analysis showed that the most Particulate Matter (PM2.5) or hot spots in East and Southeast Mashhad, and 22.3 percent of the total area of the city. On a seasonal scale, hotspots are the largest in spring with 25% of the total area of the city, then winter with 24%, summer with 21.8% and finally autumn with 17.2%. Against, areas with low concentrations of PM2.5 (cold spots) have been formed in the North-West of Mashhad. On an annual scale, 25.5% of the city is in the cold spots and 22.3% is in the hot spots. The population is 21% and the area of industrial - service uses is 30% more in the hot spot than the cold spot, which directly increases the emission of air pollutants in hot spots. While, in the cold spots the parks area is 70%, residential land use is 67%, the total area of green spaces is 62% is more than hot spots. On the other hand, other factors such highways and main roads, the length of the transportation network, increase the speed of vehicles in these areas.

    Keywords: Air pollution, Spatial Autocorrelation Analysis, Particular Materials smaller than 2.5 microns, Hot Spot
  • Farzaneh Moradi, Gholamabbas Fallah *, Mansour Chatrenour Pages 79-90
    Introduction

     Increasing concentration of greenhouse gases in recent decades has caused a greenhouse effect on the Earth's atmosphere and warmer air. One of  the main causes of global climate change and biodiversity is emission of greenhouse gases from various sources, especially from agricultural sector. Use of chemical fertilizers and pesticides, fossil fuels, crop soil management, livestock manure management in farms and incineration of organic residues are the most important sources of greenhouse gas emissions in agricultural sector. The most important of these gases are methane (CH4), nitrous oxide (N2O) and nitric oxide (NO). Aim of this study was to determine rate of emission of greenhouse gases in Shush wheat fields and Baghmalek paddy fields in Khuzestan province using DAYCENT model. Also, mean annual temperature changes (1, 2.5 and 4.2 oC) and precipitation (-2, 7 and 14%) were simulated to investigate emission rates of methane, nitrous oxide and nitric oxide.

    Materials and methods

     In this study, using existing data and documented sources, we retrieved history information in both study areas. Then, according to agricultural history, each area was written into agricultural daily events program for major land cultivation and both crops. As a result, several DAYCENT application files were created. DAYCENT model program is written in FORTRAN and C programming language and is used with UNIX / Linux platform.

    Results and Discussion

     Using DAYCENT model, emission rates of methane, nitrous oxide and nitric oxide were simulated in both Baghmalek and Shousha stations. Simulated methane flux results of Baghmalek station, mean, minimum and maximum were 1.369, 0.805 and 1.701 tons per hectare per year, respectively. Percentage of change coefficient for methane emission in Baghmalek station was 3.6. Results of emitted nitrous oxide gas at Baghmalek station, mean, minimum and maximum were determined to be 0.01, 0.001 and 0.015 tons per hectare per year, respectively. Percentage of change coefficient for nitrous oxide emission in Baghmalek station was 1.2. Results of emitted nitric oxide gas at Baghmalek station were average, minimum and maximum of 0.01, 0.001 and 0.011 tons per hectare per year, respectively. Percentage change coefficient for nitric oxide emission in Baghmalek station was 0.5. Results of simulated methane emissions at Shush station, mean, minimum and maximum were determined to be 0.106, 0.043 and 0.101 tons per hectare per year, respectively. Percentage change coefficient for methane emission at Shush station was 3.9. Results of simulated nitrous oxide gas emission at Shush station were average, minimum and maximum of 0.101, 0.070 and 0.200 tons per hectare per year, respectively. Percentage change coefficient for nitrous oxide emission at Shush station was 67.3. Results of simulated nitric oxide gas emission at Shush station were determined as average, minimum and maximum of 0.111, 0.085 and 0.242 tons per hectare per year, respectively. Percentage change coefficient for nitric oxide emission at Shush station was 56.5. Results of the average methane emission at Baghmalek station (1.369 tons per hectare per year) were higher than Shush station (0.106 tons per hectare per year). For Baghmalek station, average emission of nitrous oxide and nitric oxide gas was equal (0.01 tons per hectare per year) and less than Shush station. Difference between rice and wheat fields in terms of emissions of studied gases is due to their aerobic status and flooding. Then, based on average, minimum and maximum emissions of methane, nitrous oxide and nitric oxide, with increasing average annual temperature of 1, 2.5 and 4.2 oC in Baghmalek paddy fields, trend of methane emissions was determined to be almost constant. And trend of nitrous oxide and nitric oxide emission changes was very small. Also, based on average, minimum and maximum emissions of methane, nitrous oxide and nitric oxide with increasing the average annual temperature of 1, 2.5 and 4.2 oC in Shush wheat fields, emission trend of all three gases changes significantly with increasing temperature. This trend reaches a maximum in increasing temperature by 4.2 oC and has an increasing trend. Then, based on average, minimum and maximum emissions of methane, nitrous oxide and nitric oxide with changes in precipitation of 2-, 7 and 14% in Baghmalek paddy fields with a change in precipitation, amount of methane emissions has a constant trend. And emissions of nitrous oxide and nitric oxide increase with increasing precipitation. Also, based on average, minimum and maximum emissions of methane, nitrous oxide and nitric oxide with changes in precipitation of 2-, 7 and 14% in Shush wheat fields with a change of -2%, we have a decrease in methane emissions. This trend increases with increasing rainfall by 7 and 14%. Also, emission of nitrous oxide and nitric oxide increases with decreasing rainfall and increases with increasing.

    Conclusions

     Based on comparison results of wheat and rice, the highest amount of methane was emitted in Baghmalek station (rice), the highest amount of nitrous oxide and nitric oxide was obtained from Shush station (wheat). According to results obtained from other studies difference between rice and wheat fields in terms of gas emissions is due to their aerobic status and flooding. In rice cultivation field with anaerobic conditions for a long time, it was observed that we have a high methane emission rate. Baghmalek rice paddies prevent release of nitrous oxide and nitric oxide because they are flooded. In these fields, because water content in the soil is higher than soil capacity, nitrous oxide is reduced to nitrogen. Nitric oxide is also released in rice paddy fields in form of pulses after fertilization and heavy rains. For these reasons, flux rates of nitrous oxide and nitric oxide were higher in wheat fields of Susa. Also, results of Shush wheat fields are consistent with results of other researchers.

    Keywords: Baghmalek, Shush, Greenhouse Gases, DAYCENT model
  • Sara Karami, Nasim Hossein Hamzeh *, Hosein Sabzezari, Mohsen Lo Alizadeh Pages 91-103
    Introduction

    Dust storms are one of important natural disasters that affect vast areas in the world and makes damage to people lives and causes many financial problems. Recently, change in precipitation and temperature pattern happened in the Persian Gulf countries due to climate change. Every year, dust storms causes many problems in different regions of Iran especially in Khuzestan province and this phenomenon frequently disrupt social, economic and official mechanism of the province. The areas are affected by Syria and Saudi Arabia dust particles. So analysis and simulation of dust storms will be helpful to investigate the potential sources and transport mechanism of dust particles in this area.In this research, 11 synoptic weather stations in Khuzestan province were investigated in 25years duration. Also results were investigated by AOD and NDVI indexes in all areas of the province. Ahvaz, Bostan, Abadan stations had the most dust storms frequency and Behbahan and Ramhormoz had the least. The average dusty days was 37.3 days per year and 2008,2009 and 2011 years had the most number of dusty days. The maximum amount of AOD and minimum amount of NDVI were in 2008 and 2009 that is in good agreement with number of dusty days in these years.

    Materials and methods

    In this study, the trend of monthly and yearly dusty day frequency investigated in some stations in Khuzestan province in 25years duration (1993 to 2017). Then The Mann-Kendall Test is used for all of them. For this purpose, dust codes (06 and 07) separated in Khuzestan weather stations in 25years duration. Furthermore, reported visibility must be 5000m or less in dusty days. In the next steps, the MODIS Terra AOD was used to investigate Aerosol optical depth in the atmosphere. AOD is available daily and at a spatial resolution of 1 degree. Finally, the normalized difference vegetation index (NDVI) was investigated in the study area. The index investigates vegetation coverage changes in the study area and it used red and near-infrared channels.

    Conclusion

    In this case study, dust storms were investigated climatically in 25years duration (1993 to 2017). Monthly average number for dusty days showed that the maximum temperature and the minimum precipitation happened in June and July in Khuzestan province. Also the maximum frequency of dusty days happened in that two months and the minimum of dust frequency happened in November.The maximum amount of monthly AOD was in June from 2000 to 2018 in Khuzestan province that it is in good agreement with maximum average number of dusty days. Ahvaz, Bostan, Abadan weather stations had the maximum frequency of dusty days, but Behbahan and ramhormoz stations had the minimum frequency of dusty days.The seasonal average number of dusty days showed that the maximum frequency of dusty days was in spring and summer and the minimum frequency was in autumn. Also the maximum number of dusty days happened in 2008, 2009 and 2011. The maximum amount of AOD was in 2008 and 2009 in 19years duration (2000 to 2018). The mean annual NDVI index was the least amount in 2008 and 2009 that it is in good agreement with AOD and dusty days in the province.Totally, annual trend coefficient of dusty days was 1.39 in Khuzestan province which shows and increasing trend. Investigation of Mann Kendall Test in dusty days of Khuzestan weather stations confirms 1 to 10 percent confidence levels of the observation trend at 7 stations, but it was meaningless in 4 weather stations.  Seasonal analysis showed that the trend of number of dusty days was significant at 5% confidence level in the spring. This trend was not significant in summer but it was significant at 10% confidence level in autumn and 1% in winter.

    Keywords: climate change, Dust Storms, Khuzestan, Mann Kendall Test, confidence level
  • Mozhgand Bagherinia, Majid Rahimzadegan * Pages 105-119

    Aerosols or airborne particulate matter (PM) with different sizes from both natural and anthropogenic emission sources have substantial influences on climate, environment and human health. Particulate matter is characterized by the size (PM10 andPM2.5; particles having an aerodynamic diameter less than 10 and 2.5 μm). However, due to limited spatial coverage and high operational cost, in situ observations are insufficient to capture high resolution, tempo-spatial variation of PM concentration, especially for many developing countries such as Iran. Satellite remote sensing technology, on the other hand, provides a cost-effective way for epidemiological studies and PM monitoring. at various scales by measuring satellite-derived aerosol optical depth (AOD), especially for places where ground-level monitoring is not available. Satellite-derived AOD is related to ground-level PM concentration and can be empirically converted into PM mass. Therefore, a number of empirical models have been developed to predict ground-level PM2.5 or PM10 concentration from various satellite-derived AOD products, such as Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging Spectro Radiometer (MISR). There are a few studies about the AOD-PM relationship in Iran and much fewer studies use MISR instrument.  level2 product of AOD retrieved by MISR with improved resolution of 4.4 km, is a suitable instrument for estimating particular matter. Therefore, the main aim of this study was investigating empirical linear and nonlinear regression models along with artificial neural network model for estimating particulate matter (containing PM2.5 and PM10) using AOD from the MISR 4.4 km aerosol product. In this regard, Tehran one of the largest industrial cities of Iran during 2016 and 2017 was selected as the study area.   To assess ground level PM2.5 and PM10, a number of data sets from various sources were collected for this research, including 2 years daily PM2.5 and PM10 mass concentration at 13 air quality aground stations in Tehran which had sufficient records. Also we used 3 meteorological stations including Geophysic, Mehrabad, and Shemiran. Ground-based meteorological parameters (a total of five parameters), including surface wind speed (SPD), surface temperature (ST), visibility (Vis), and surface relative humidity (SRH), and dew point temperature (Td) were obtained from the meteorological stations.  MISR instrument onboard the EOS-Terra satellite measures the reflected and scattered sunlight from the Earth in four spectral bands of 446, 558, 672, and 866 nm at each of the nine viewing angles (nadir, ±26.1, ±45.6, ±60.0, and ±70.5°). It has a much narrower swath of about 380 km compared with that of about 2330 km of MODIS. The level 2 MISR AOD has the spatial resolution of 4.4 km and the temporal coverage is about once per week in mid latitude. The latest global MISR AOD product (version 22) for 2013 can be downloaded from the Atmospheric Sciences Data Center at NASA/Langley Research Center (http:// eosweb.larc.nasa.gov). Since the data stations are dispersed, PM monitoring sites were matched with meteorological stations based on the neighboring. On the other hand, as MISR pixels are not distributed equally all over the region, we match them to closest PM monitoring site. Previous studies show that the distance between AOD pixels and closest PM monitoring ground stations must be within 25 km. In order to retrieve the ground-level PM value proper conversion should be made first. Related researches have shown that the meteorological conditions (such as relative humidity) can strongly impact models for the AOD–PM relationship, as particle extinction properties can change substantially with different vertical mixing and aerosol hygroscopic growth. Thus, |there is a number of methods which proposed to use meteorological factors to improve the relationship between AOD and ground-level PM. In this study, we developed linear and nonlinear models using MISR AOD values coupled with meteorological parameters for estimating daily ground-level PM concentration:  where PM is daily ground-level PM2.5 or PM10 mass concentration (μg/m3), and AOD is MISR-derived AOD (unit less). c1 is the intercept Regression coefficients. c2–c7 are associated with predictor variables, including AOD, visibility (km), surface wind speed (m/s), surface temperature (°C), surface relative humidity (%), and dew point(°C). In some studies, an exponential function is used for visibility, relative humidity and dew point temperature. Furthermore, surface meteorological conditions including surface temperature and surface wind speed are employed in the model to further amend the corrected AOD to obtain a better relationship between AOD and PM. Here is the none linear model:  As the third approach, neural network algorithms - black-box models of artificial intelligence- were employed. We used the artificial neural network (ANN) algorithm to model ground-level PM concentration based on the meteorological variables and satellite-derived AOD data. We used a back-propagation neural network (BPNN) algorithm to build the ground-level PM estimation model for predicting the PM concentration. The estimation of the ground-level PM concentration model consists of six in the input layer, six neurons in the hidden layer, and one neuron in the output layer. The seven parameters in the input layer include, surface temperature, wind speed, relative humidity, visibility, dew point temperature and MISR AOD products. The neuron in the output layer is PM concentration. Cross validation (CV) of all models was conducted by leaving 10 percent of entire PM monitoring site out, fitting the model without this part, and predicting daily PM from 2016 to 2017 for the left out data with the fitted model. This was iterated so that every data was left out one at a time, and the associations between the predicted values and the observed values were assessed to examine model performance.   Linear and nonlinear regression models had similar predictability for ground-level PM10 (respectively correlation coefficients of 41% and 45%) and PM2.5 (respectively correlation coefficients of 46% and 42%). Artificial neural networks improved the estimation of surface PM2.5 and PM10 significantly and the correlation coefficients improved to 60% and 65%. for PM2.5 and PM10, respectively. It is clear that the neural network model enhanced the regression models result about 19 to 23 percent. The result can use in the regions which there is not sufficient air quality stations.

    Keywords: Multi-angle Imaging SpectroRadiometer (MISR), Aerosol Optical Depth (AOD), PM10, PM2.5, Artificial Neural Network
  • Leila Teymouri Yeganeh *, Maryam Teymouri Yeganeh Pages 121-132
    Introduction

    In recent years, limited water resources to supply water for agricultural and non-agricultural needs have caused many problems and rain is one of the important sources of water supply. On the other hand, rainfall is one of the most important components of input to hydrological systems that its study and measurement in most cases is necessary for studies of runoff, drought, groundwater, flood, sediment, etc. Therefore, forecasting and estimating rainfall for each region and watershed is considered as one of the important climatic parameters in the optimal use of water resources. One of the methods of estimating and predicting precipitation is the use of time series.

    Materials and methods

    In this study, the statistical population includes the amount of precipitation in synoptic stations of Kermanshah, Kangavar, Sarpole-Zahab and Islamabad -Gharb provinces. The data has been prepared from the meteorological website at www.kermanshahmet.ir. The study method is cross-sectional and the sample size is all rainfall data during the years 1986 to 2018. In order to analyze the data in this study, spss16 and minitab18 statistical software for time series modeling fitting and finally after testing the existing models, the best model for predicting precipitation was determined.

    Results and discussion

    In order to analyze the data from Arima method for fitting time series modeling and finally after testing the existing models, the best model for predicting precipitation was determined. The results showed that Arima time series model has the best performance and will have a decreasing trend of precipitation by 0.2. In the present studies, using 32-year data (1986-2018) of Kermanshah, Islamabad, Kangavar and Sarpole-Zahab stations as well as time series models, precipitation was modeled and predicted. Based on the results of autocorrelation and partial autocorrelation diagrams, the best model fitted to the data was the model Arima(2,1,1). Finally, due to randomness and time delay outside the range of zero based on partial autocorrelation residual and residual autocorrelation in the data prediction model is less than 0.05. The model was then estimated to be reliable. And according to the fitted model, precipitation will have a decreasing trend of 0.2.

    Conclusion

    The analysis of random phenomena in the realm of statistics and probability is a subset of hydrology and meteorology. Due to the fact that meteorological processes are random, so the basis for the analysis of these phenomena is meteorology, statistics and probability. Accordingly, time series are used. It is natural that the existence of appropriate statistical data in the study area as input to models in processing problems and receiving reliable outputs is very important and effective. In the present studies, using 32-year data (1986-2018) of Kermanshah, Islamabad, Kangavar and Sarpole-Zahab stations as well as time series models, precipitation was modeled and predicted in the software minitab18.Based on the results obtained from the autocorrelation and partial autocorrelation diagrams, the best fit model on the data was the model Arima(2,1,1). Finally, due to randomness and time delay (Lag-time) outside the range of zero based on the residual of the partial autocorrelation function (PACF ) and the residual of the autocorrelation function (ACF ) in the data prediction model is less than 0.05, so the model Reliable forecast was estimated and according to the fitted model, precipitation will decrease by 0.2.

    Keywords: Monthly Rainfall, time series, Kermanshah, forecast