توسعه سامانه پیش‎بینی چند مدلی بارش ماهانه در حوضه آبریز سفیدرود

نوع مقاله : مقاله پژوهشی

نویسندگان

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

چکیده

پیش‌بینی بارش یکی از ابزارهای مهم در برنامه‌ریزی و مدیریت منابع آب به حساب می‌آید. اخیراً از روش‌های جدیدی به نام مدل‌های دینامیکی جو برای پیش‌بینی بسیاری از متغیرهای هیدرو-اقلیمی از جمله بارش استفاده می‌شود. قبل از استفاده از پیش‌بینی‎های این مدل‌ها در برنامه‌ریزی و تصمیم‏گیری، لازم است ارزیابی دقت و تصحیح اریبی آن‌ها انجام شود. از این رو هدف مقاله حاضر، تصحیح اریبی و ترکیب نتایج پیش‎بینی بارش مربوط به مجموعه‌ای از مدل‎های پیش‎بینی دینامیکی جهانی می‎باشد. برای این کار، ابتدا نتایج پیش‎بینی بارش هریک از مدل‎ها به‌صورت جداگانه با داده‎های بارش ایستگاهی منطقه برای دوره تاریخی 1982 تا 2017 مقایسه شدند و خطای سامانمند هریک از آن‌ها به روش نگاشت چندک تصحیح شد. این کار برای افق‌های پیش‌بینی مختلف و برای پیش‌بینی‌های صادره از ماه‌های مختلف انجام شده است. در گام بعدی متناسب با دقت هر یک از مدل‎های پیش‌بینی، سامانه پیش‎بینی ترکیبی یا چند مدلی با استفاده از روش میانگین‌گیری بیزین توسعه داده شد. نتایج نشان داد پس از تصحیح اریبی به روش نگاشت چندک، حداقل یک مدل پیش‌بینی از 78 مدل پیش‌بینی دارای همبستگی نسبتاً بالا در حدود 7/0 می‌باشد. این نتیجه برای افق پیش‌بینی 1 ماه آینده بیشتر دیده شد. بعد از ترکیب 78 عضو پیش‌بینی با استفاده از روش میانگین‌گیری بیزین، این میزان همبستگی به بیشتر از 8/0 افزایش یافت. بنابراین با تصحیح اریبی و ترکیب مدل‌های پیش‌بینی با یکدیگر، دقت بارش پیش‌بینی‌شده به مقدار قابل‌توجهی افزایش می‌یابد.

کلیدواژه‌ها

عنوان مقاله [English]

Development of Monthly Ensemble Precipitation Forecasting System in Sefidrud Basin, IRAN

نویسندگان [English]

  • Hossein Dehban
  • Kumars Ebrahimi
  • Shahab Araghinejad
  • Javad Bazrafshan
Irrigation and Reclamation Engineering Department, Faculty of Agricultural Engineering & Technology, College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran
چکیده [English]

Precipitation forecasting is one of the most important tools in water resources planning and management. Recently, new methods called atmospheric dynamic models have been used to predict many hydro-climate variables including precipitation. Before using the predictions of these models in planning and decision making, the accuracy of the mentioned predictions and their bias correction should be evaluated. Therefore the objective of this study is to ascertain the biases and to combine the results of precipitation forecasting with a set of global dynamic forecasting models. To achieve this aim, firstly the precipitation forecast results of each model were compared separately with the regional recorded precipitation data in the period of 1982 to 2017. Using this approach, the systematic errors were removed and corrected, i.e. using the quantile mapping method. This work was done for different forecast periods and also for different months. Furthermore, based on the accuracy of each model, a hybrid/multi-model prediction system was developed using Bayesian averaging method (BMA). The results showed that after the bias correction, using the quantitative mapping, at least one model among 78 prediction models have a relatively high correlation value of about 0.7. This result was recorded for the next one-month horizon. This correlation was increased to more than 0.8, by combining 78 predictive members, using Bayesian averaging method. Therefore, the accuracy of the predicted precipitation increases significantly using bias correction in tandem with combining the prediction models.

کلیدواژه‌ها [English]

  • Precipitation Forecasting
  • NMME Models
  • Uncertainty
  • Bayesian model averaging
  • Quantile mapping

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تحقیقات آب و خاک ایران
دوره 51، شماره 8
آبان 1399
صفحه 1881-1893

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