بهبود پیش بینی دبی جریان با استفاده از داده گواری در مدل مفهومی Hymod

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکترا مهندسی منابع طبیعی/آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی گرگان.

2 دانشیار / گروه مهندسی منابع طبیعی-آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی گرگان،

3 دانشیار/ گروه مهندسی منابع طبیعی-آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی گرگان.

4 دانشیار / گروه منابع آب، دانشگاه صنعتی دلفت، هلند.

5 استاد/ انستیتو علوم زمین و زیست شناسی، مرکز تحقیقات یولیش، آلمان.

چکیده

پیش بینی دبی جریان توسط مدل های هیدرولوژی، همواره با عدم قطعیت همراه است. به همین دلیل از روش های مختلف از جمله افزایش کیفیت اطلاعات ورودی به مدل، بهبود ساختار مدل، و داده گواری اطلاعات مشاهداتی در دسترس برای کاهش عدم قطعیت مدل ها استفاده شده است. در صورت بدون اشکال فرض کردن ساختار مدل هیدرولوژی،نمی توان از عدم قطعیت ورودی، پارامتر، و شرایط اولیه مدل چشم پوشی کرد. یکی از روش های کاهش عدم قطعیت، داده گواری است که با درنظر گرفتن عدم قطعیت ورودی ها و مشاهدات، و به روزرسانی متغیر حالت، عدم قطعیت را کاهش می دهد. در این پژوهش بهبود پیش بینی دبی جریان برای یک روز آتی با مدل هیدرولوژی Hymod توسط فیلتر کالمن دسته ای (EnKF) که یکی از روش های داده گواری است در آبخیز رودک بررسی شده است. نتایج با استفاده از معیارهای نکویی برازش ناش ساتکلیف (NSE) ، کلینگ گوپتا (KG)، ناش ساتکلیف لگاریتمی (LNSE) و DCpeak بررسی شد. نتایج نشان دهنده افزایش معیارهای نکویی برازش NSE، KG ، DCpeak و LNSE مدل هیدرولوژی Hymod توسط الگوریتم EnKF نسبت به الگوریتم بهینه سازی تکامل مجتمع های مخلوط شده به ترتیب به مقدار 13% ، 5% ، 17% و 94% بوده است. به این ترتیب امکان پیش بینی دبی جریان یک روز آتی با دقت قابل قبولی فراهم شد.

کلیدواژه‌ها


عنوان مقاله [English]

Improving River Discharge Forecasting With the Hymod Conceptual Rainfall-Runoff Model Using Data Assimilation

نویسندگان [English]

  • Maryam Tajiki 1
  • Ali Najafinejad 2
  • Abdoreza Bahremand 3
  • Gerrit Schoups 4
  • Harrie-Jan Hendricks-Franssen 5
1 Ph.D. Candidate, Department of Watershed Management, Gorgan University of Agricultural Science and Natural Resources, Gorgan, Iran.
2 Associate professor, Department of Watershed Management, Gorgan University of Agricultural Science and Natural Resources, Gorgan
3 Associate professor, Department of Watershed Management, Gorgan University of Agricultural Science and Natural Resources, Gorgan, Iran.
4 Associate professor, Department of Water Management, Delft University of Technology, Delft, Netherlands.
5 Professor, Research Centre Jülich, Institute of Bio- and Geosciences: Agrosphere (IBG-3), Germany.
چکیده [English]

Predicting discharge prediction through modeling is inherently associated with important uncertainties.Then uncertainty in hydrological modeling is mostly reduced by increasing the quality of inputs, improving structure of models, and data assimilation. Even if we assume that the physical structure of the model is perfect, uncertainties in parameters, forcing variables and initial conditions will be reflected in the simulation results through complex error propagations. One of the actions that can be taken toward reducing uncertainty in hydrologic predictions is data assimilation. It provide a superior hydrologic state estimate by considering input and observation uncertainties. In the current study, the efficiency of assimilating stream-flow into a hydrologic model using the Ensemble Kalman Filter (EnKF) in the Roudak catchment is investigated. Four evaluation criteria including NSE, KG,LNSE, DCpeak are applied to estimate the predictive performance of results. Results show that EnKF improved estimated stream-flow compared to an offline calibration with SCE-UA as NSE, KG,LNSE, DCpeak are increased by 13%, 5%, 17% and %94 respectively. Also one-day ahead prediction of stream-flow could be estimated by acceptable accuracy.

کلیدواژه‌ها [English]

  • Conceptual Hymod Model
  • Data assimilation
  • Ensemble Kalman filter
  • Roudak Catchment
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