پیش‌بینی جریان ماهانه با استفاده از مدل ECMWF، مطالعه موردی: حوضه آبریز سفیدرود

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی آبیاری و آبادانی، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران

2 گروه مهندسی آبیاری و آبادانی، دانشگاه تهران

3 استادیار گروه علوم و مهندسی آب، دانشگاه فردوسی مشهد

چکیده

پیش‌بینی جریان در مقیاس زمانی ماهانه برای مدیریت و برنامه‌ریزی بهینه منابع آب ضروری است. در این مقاله با استفاده از پیش‌بینی‌های حاصل از مدل‌ اقلیمی ECMWF، پیش‌بینی جریان ماهانه در زیر حوضه شاهرود واقع در حوضه آبریز سفیدرود در شمال غرب کشور انجام شد. برای این منظور با استفاده از پیش‌بینی بارش ماهانه حاصل از مدل اقلیمی ECMWF و مدل‌سازی داده محور SVR به‌عنوان مدل بارش- رواناب، بارش پیش‌بینی‌شده به جریان تبدیل شد. ابتدا نتایج مربوط به پیش‌بینی بارش در دوره تاریخی حاصل از مدل‌ اقلیمی ECMWF تا افق پیش‌بینی 3 ماهه برای محدوده مورد مطالعه، از درگاه اینترنتی Climate Data Store دریافت شد. سپس با استفاده از مدل داده محور‌ SVR، مدل ترکیب‌شده اقلیمی- داده محور برای پیش‌بینی جریان تا افق پیش‌بینی 3 ماه آینده توسعه داده شد. نتایج نشان داد که پیش‌بینی جریان مبتنی بر مدل‌های پیش‌بینی اقلیمی برای افق پیش‌بینی 1 ماه آینده نسبت به دو افق پیش‌بینی 2 و 3 ماه آینده دقیق­تر است. به‌طوری‌که افق پیش‌بینی 1 ماه آینده بیشترین ضریب نش- ساتکلیف در واسنجی مساوی 77/0 و در مرحله صحت‌‌سنجی 48/0، بیشترین ضریب همبستگی در واسنجی 87/0 و در صحت‌سنجی 69/0، کمترین مقدار جذر میانگین مربعات خطا در واسنجی 8/6 میلیون مترمکعب و صحت‌سنجی 3/6 میلیون مترمکعب و بهترین مقدار اریبی نسبی برای واسنجی 96/0 و صحت‌سنجی 1/1 را داشته است. همچنین نتایج نشان داد که بر اساس دو شاخص ارزیابی احتمالاتی POD و FAR، مدل پیش‌بینی توسعه‌یافته، توانایی بالایی در تشخیص وقایع مختلف جریان به‌خصوص جریان‌های کم و زیاد را دارد.
 

کلیدواژه‌ها


عنوان مقاله [English]

Monthly Stream-flow forecasting using the ECMWF model, case study: Sefidrud basin-Iran

نویسندگان [English]

  • Hossein Dehban 1
  • Kumars Ebrahimi 2
  • Shahab Araghinejad 1
  • Javad Bazrafshan 1
  • Fereshteh Modaresi 3
1 Irrigation and Reclamation Engineering Department, Faculty of Agricultural Engineering & Technology, College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran
2 Irrigation and Reclamation Engineering Department
3 Assistant Professor, Department of Water Science and Engineering, Ferdowsi University of Mashhad (FUM)
چکیده [English]

Stream flow forecasting on a monthly time scale is essential for optimal water resources management and planning. In this paper using the predictions obtained from the ECMWF climate model, monthly stream flow forecast was made in Shahroud river Subbasin, part of Sefidrood basin northwest of Iran. To achieve this aim, using monthly precipitation forecasts from ECMWF climate model in tandem with SVR data-driven modeling, as a rainfall-runoff model, the stream flow was predicted based on the predicted precipitations. First, the results of precipitation forecast, for the desired historical period, up to a 3-month forecast horizon for the study area were obtained from the Climate Data Store. Then, by using the SVR driven model, a linked Climate-Data-driven model was developed to predict the flow up to a 3-month forecast horizon. The results showed that flow forecasting based on climate forecasting models is more accurate for the forecast horizon of the next month than two and three months. So that the forecast horizon of the next month has the highest Nash-Sutcliffe coefficient, in calibration 0.77 and in validation 0.48. The highest correlation coefficient in calibration 0.87 and validation 0.69, the lowest root mean square error in calibration 6.8 and validation 6.3 million cubic meters and also has the best relative bias value for calibration 0.96 and validation 1.1. Also the results, based on the POD and FAR probabilistic indices, showed that the developed predictive model has a high ability to detect different states of stream flow events, especially for extreme flows event.

کلیدواژه‌ها [English]

  • Climate Models
  • SVR
  • Rainfall-runoff modeling
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