ارزیابی کارایی سامانه همادی چندگانه برای بهبود مهارت پیش بینی مدل های عددی بارش

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی آب، دانشکده فناوری کشاورزی، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران، تهران، ایران.

2 گروه برنامه‌ریزی و مدیریت محیط زیست، دانشکده محیط زیست، دانشگاه تهران، تهران، ایران.

3 گروه مدیریت منابع آب و خاک، دانشکده مهندسی عمران، دانشگاه فنی اسلواکی، براتیسلاوا، اسلواکی.

10.22059/jwim.2023.350086.1025

چکیده

زمان پیشهشدار و دقت پیش‌بینی‌های بارندگی اثر قابل ملاحظه‌ای بر سیستم‌های پیش‌بینی و هشدار سیلاب دارند. کاربرد پیش‌بینی‌های همادی بارندگی مستخرج از مدل‌های عددی بارش به دلیل تأثیری که بر افزایش زمان پیش‌هشدار سیلاب دارند، توسعه یافته است. هدف این تحقیق، بهبود مهارت پیش‌بینی مدل‌های عددی بارش توسط تکنیک‌های پس‌پردازش است. بدین ترتیب پیش‌بینی همادی بارندگی سه مدل هواشناسی NCEP، UKMO و KMA برای شش رویداد بارش مولد سیلاب در حوضه دز استخراج گردید. جهت پس‌پردازش پیش‌بینی‌های همادی بارش از رویکردهای آماری و مدل داده محور استفاده شد. بدین‌منظور، پیش‌بینی خام هر مدل منفرد با استفاده از مدل‌های رگرسیونی خطی و توانی تصحیح گردید. سپس خروجی‌ تصحیح شده مدل‌های منفرد توسط مدل پیشنهادی کنترل گروهی داده ها (GMDH) ترکیب شدند. نتایج نشان داد برای اصلاح پیش‌بینی‌های خام، عملکرد مدل‌های توانی بهتر از خطی است. پس از تصحیح برونداد مدل‌ها، نتایج دقیق‌تری با استفاده از مدل‌های NCEP و UKMO به دست آمد. همچنین، سامانه همادی چندگانه ساخته شده توسط مدل GMDH اثر قابل‌ ملاحظه‌ای بر مهارت پیش‌بینی مدل‌های عددی بارش داشت، به‌گونه‌ای که معیارهای ارزیابی نش-ساتکلیف و خطای نرمال شده به طور متوسط 23% و 11% نسبت به مدل‌های توانی بهبود یافتند. ارزیابی مقایسه‌ای قابلیّت تفکیک‌پذیری مدل‌های همادی چندگانه با مدل‌های منفرد توسط منحنی ROC در دو سطح آستانه 5/2 و 10 میلی‌متر بیانگر توانایی تفکیک‌پذیری بالاتر مدل‌های همادی چندگانه در هر دو سطح آستانه بارش بود. پیش بینی-های پس پردازش شده بارندگی همادی به‌عنوان یک ورودی قابل اعتماد برای مدل‌های هیدرولوژیکی جهت پیش‌بینی وقایع حدی به کار می‌آید.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Investigating the efficiency of the multi-model ensemble system to improve the forecast skill of the numerical precipitation models

نویسندگان [English]

  • Mitra Tanhapour 1
  • Jaber Soltani 1
  • Bahram Malekmohammadi 2
  • Kamila Hlavčova 3
  • Mohammad Ebrahim Banihabib 1
1 Department of Water Engineering, Faculty of Agricultural Technology, University College of Agriculture and Natural Resources, University of Tehran, Tehran, Iran.
2 Department of Environmental Planning and Management, Graduate Faculty of Environment, University of Tehran, Tehran, Iran.
3 Department of Land and Water Resources Management, Faculty of Civil Engineering, Slovak University of Technology, Bratislava, Slovakia.
چکیده [English]

The lead-time and accuracy of the precipitation forecasts have a substantial influence on the flood forecast and warning systems. The application of Ensemble Precipitation Forecasts (EPFs) derived from numerical precipitation models has been developed due to their impact on increasing flood lead-time. This research aims to improve the skill of numerical precipitation models using post-processing techniques. In this regard, EPFs of three meteorological models, e.g., NCEP, UKMO, and KMA, were extracted for sex precipitation events leading to flood in the Dez river basin during 2013-2019. The statistical approaches and data-driven model were applied to post-process the EPFs. For this purpose, the raw forecast of every single model was corrected using linear and power regression models. Then, the corrected output of single models was combined using the proposed model of Group Method of Data Handling (GMDH). The results indicated that Power Regression Model (PRM) outperformed the linear models to correct raw forecasts. After correction of models' output, more accurate results were obtained by NCEP and UKMO models. Moreover, the Multi-Model Ensemble (MME) system constructed by the GMDH model (MME_GMDH) had a great effect on the skill of numerical precipitation models, so that the Nash–Sutcliffe and normalized error (NRMSE) efficiency criteria for MME_GMDH respectively were improved on average 23% and 11% in comparison with the PRM. A comparative assessment of the discrimination capability of MME with single ensemble models using ROC curve at the thresholds of 2.5 and 10 mm represented a higher discrimination ability by MME_GMDH for both thresholds. Post-processed EPFs exert as a reliable input to the hydrological models for extreme events forecast.

کلیدواژه‌ها [English]

  • Ensemble precipitation forecasts
  • GMDH model
  • Multi-model ensemble
  • Post-processing techniques
  • Regression models

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مدیریت آب و آبیاری
دوره 13، شماره 1
فروردین 1402
صفحه 275-293

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