توسعه الگوی ارزیابی اثر تغییر اقلیم بر سطح پوشش برف با استفاده از داده‌های سنجش از دور

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دکتری مهندسی عمران گرایش محیط زیست، دانشکده مهندسی عمران، پردیس دانشکده‌های فنی، دانشگاه تهران، تهران، ایران.

2 دانش‌آموخته کارشناسی ارشد مهندسی عمران گرایش محیط زیست، دانشکده مهندسی عمران، پردیس دانشکده‌های فنی، دانشگاه تهران، تهران، ایران.

3 دانشجوی دکتری، گروه سیستم‎های مهندسی و محیط زیست، دانشگاه ویرجینیا، شارلوتزویل، ویرجینیا، ایالات متحده آمریکا.

4 دانشیار دانشکده مهندسی عمران، پردیس دانشکده‌های فنی، دانشگاه تهران، تهران، ایران.

چکیده

وقوع پدیده تغییر اقلیم و اثر آن بر چرخه هیدرولوژیک در سال‌های اخیر به تأئید رسیده است و به نظر می‌رسد این اثرات رو به تشدید است. نظر به اهمیت پوشش‌ برفی در تغذیه منابع آب به‌ویژه در مناطق خشک و نیمه‌خشک، ارزیابی اثرات تغییر اقلیم بر این متغیر موردتوجه قرارگرفته است. در این مطالعه، رویکردی برای بررسی اثرپذیری پوشش برف از تغییر اقلیم ارائه شده است. اطلاعات سطح پوشش برف از محصولات برف سنجنده MODIS تأمین شده است. به‌منظور تعیین اثرگذاری تغییر اقلیم بر پوشش برف، نقاط شکست سری زمانی با آزمون همگنی نرمال استاندارد محاسبه‌شده است و به‌منظور تعیین روند، از آزمون‌های من-کندال و شیب سن استفاده شد. همچنین، به‌منظور شبیه‌سازی سطح پوشش برف در دوره آتی (2099-2021) تحت اثر تغییر اقلیم از شبکه عصبی مصنوعی با ورودی بارش و دما استفاده شد. رویکرد پیشنهادی در زیرحوضه‌های آبخیز ایران مورد ارزیابی قرارگرفته است. بر اساس نتایج به‌دست‌آمده، نقطه شکست سطح پوشش برف ایران در دوره زمانی 2020-2000 تنها در دو زیرحوضه مرکزی طشک-بختگان-مهارلو و گاوخونی در سال‌های 2007 و 2008 رخ ‌داده ‌است هرچند که سطح پوشش برف در فصل زمستان در اکثر زیرحوضه‌ها روند منفی معناداری را تجربه ‌می‌کند. همچنین، تحت تمام سناریوهای تغییر اقلیم و در اکثر زیرحوضه‌های آبخیز موردبررسی سطح پوشش برف روند کاهشی را تجربه خواهد کرد به طوری‌که متوسط حداکثر سطح پوشش برف آینده تحت سناریوهای RCP 4.5 و RCP 8.5 به ترتیب %100 افزایش و %20 کاهش می‌یابد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Developing an Algorithm for Evaluating the Impact of Climate Change on Snow Cover Area Using Remote Sensing Data

نویسندگان [English]

  • Hamed Tavakolifar 1
  • Mohammad masoud Mohammadpour Khoie 2
  • Saeed Ashrafi 3
  • Sara Nazif 4
1 Ph.D. Graduate of Civil and Environmental Engineering, Department of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran.
2 M.Sc. Graduate of Civil and Environmental Engineering, Department of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran.
3 Ph.D. Student, Department of Engineering Systems and Environment, University of Virginia, Charlottesville, Virginia, USA
4 Associate Professor, Department of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran.
چکیده [English]

Climate change and its intensifying impact on the hydrological cycle have been acknowledged in recent years. Considering the importance of available snow to the water resources recharge, especially in semiarid regions, evaluating climate change impacts on snow cover area (SCA) has gained significant attention. In this study, a methodology is presented which is capable of evaluating the impact of climate change on SCA. To collect historical data, MODIS snow products are used. To evaluate the impact of climate change on SCA, the turning points of SCA time series are detected using the Standard Normal Homogeneity Test; and the trend of historical data is analyzed using Mann-Kendall and Sen’s slope tests. To project the future SCA (2021-2099) under climate change considering precipitation and temperature as inputs an Artificial Neural Network-based model is developed. The proposed methodology is tested in sub-basins in Iran. Analyzing the present SCA (2000-2020) the results showed that the SCA’s turning points are only detected in two sub-basins of Tashk-Bakhtegan-Maharloo in 2007 and Gavkhooni in 2008, respectively. Moreover, a significant decreasing trend of SCA is detected during winter in the majority of sub-basins. According to the results, most sub-basins will experience a significant reduction in their future SCA under all climate change scenarios. In other words, compared to the historical SCA, the average future SCA will increase by 100 percent in RCP 4.5 scenario while decreasing by 20 percent in RCP 8.5 scenario.

کلیدواژه‌ها [English]

  • Snow Cover Area
  • Climate Change
  • Artificial Neural Network
  • Remote Sensing
  • Trend Analysis
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