پیش‌بینی احتمال مکانی وقوع برف با استفاده از داده‌های سنجش از دور و زنجیرة مارکوف مرتبة یک

نوع مقاله : علمی - پژوهشی

نویسنده

دانشگاه صنعتی شریف

چکیده

ارزیابی و برآورد ذخایر برفی در مطالعات بیلان آب و بهره‌برداری بهینه از منابع آب در مناطق خشک و نیمه‌خشکی چون ایران که دارای ریزش‌های فصلی برف هستند، اهمیت فراوانی دارد. در حوضه‌های آبریز حوالی دامنه‌های برف‌گیر نظیر زاگرس که سیلاب‌های بهاره سهم عمدة جریان‌های سطحی را تشکیل می‌دهند، پیش‌بینی احتمالاتی ذخیرة برفی پایان سال ضروری است. در پژوهش‌ حاضر، پیش‌بینی احتمالی وقوع برف در حوضة آبریز رودخانه‌های کرخه، دز، کارون و بخشی از حوضة مارون با استفاده از مدل زنجیرة مارکوف مرتبة یک بررسی شد. برای این منظور از داده‌های سطح برف استخراج‌شده از تصاویر ماهواره‌ای سنجندة NOAA-AVHRR در طول سال‌های آبی 1367 تا 1383 استفاده شد. حالت‌های ممکن در نقشه‌های برف به‌صورت وجود (عدد یک) و نبود برف (عدد صفر) تعریف شد. سپس با اعمال فرایند زنجیرة مارکوف، پیش‌بینی احتمال مکانی وقوع برف برای اسفندماه سال‌های 83-1379 صورت گرفت. نتایج نشان دادند که پیش‌بینی احتمالاتی سطح برف در اسفندماه تطبیق مناسبی با نقشه‌های حداکثر پوشش سطحی برف به‌دست‌آمده از تصاویر ماهواره‌ای دارد. وضعیت پوشش سطح در بیش از 60 درصد سطح حوضه با احتمال 100 درصد و در 80 درصد سطح حوضه، با احتمال 50 تا 90 درصد به‌درستی پیش‌بینی شده است. افزون بر این، به‌منظور ارزیابی کمی عملکرد مدل پیش‌بینی از روش جداول وابستگی استفاده شد. نتایج ارزیابی مدل برمبنای سه معیار احتمال ردیابی (POD)، نسبت هشدار غلط (FAR) و موفقیت بحرانی (CSI)، نیز توانمندی مدل زنجیرة مارکوف را در پیش‌بینی سطوح برفی نشان می‌دهند.    کلید‌واژه‌ها: احتمال وقوع برف، زنجیرة مارکوف مرتبة یک، ماتریس احتمالات انتقال.  

کلیدواژه‌ها

عنوان مقاله [English]

Spatial Stochastic Forecasting of Snow Cover Using First Order Markov Chain

چکیده [English]

Evaluation of snow storage is of high importance in water balance studies and optimum operation of water resources in arid and semi-arid regions like Iran. Particularly in the river basins nearby Zagrous Mountains where surface water flows mainly consist of spring runoffs, stochastic forecasting of the snow storage at the end of the year is necessary. In this study stochastic forecasting of snow in river basins of the Karkheh, Dez, Karun and some parts of the Marun was investigated using the first order Markov Chain model. Snow cover data retrieved from NOAA-AVHRR satellite images between 1989 and 2004 were applied as inputs to the model. Two possible states were defined for each snow cover map including existence (1) and non-existence (0) of snow. Through applying the Markov Chain process, snow cover maps of the study area were predicted for March 2000 to 2004. Results show that stochastic forecasts of snow cover properly consist with satellite derived maximum snow cover maps.So that, not only the area of snow covered lands was successfully estimated, but also the exact location of the snow or dry covers was appropriately predicted in more than 80% of the pixels. The performance of the model was assessed using contingency tables and three measures including: Probability of Detection, False Alarm Ratio and Critical Success Index. Results reveal the promising capability of the first order Markov Chain model to forecast snow covered area. Keywords: Snow Probability, First-Order Markov Chain, State Transition Matrix.

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