ارزیابی مدل برف طرحواره سطح NOAH-MP جفت‌شده با مدل منطقه‌ای WRF در بارش‌های سنگین برف در شمال و غرب ایران

نوع مقاله: مقاله تحقیقی‌ (پژوهشی‌)

نویسندگان

1 گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه بوعلی سینا

2 مؤسسه ژئوفیزیک دانشگاه تهران

3 گروه علوم مهندسی آب، دانشکده کشاورزی، دانشگاه بوعلی سینا

4 آب منطقه ای کردستان

چکیده

کسر پوشش برف، به دلیل افت‌وخیزهای شدید زمانی و مکانی، ضریب آلبیدوی بالا و رسانایی حرارتی بسیار کم نقش مهمی را در پارامتره­سازی برف در طرحواره­های سطح بر عهده دارد. این تحقیق به ارزیابی مدل برف طرحواره سطح NOAH-MP جفت‌شده با مدل WRF با فاکتور ذوب برف پیش­فرض مدل می­پردازد. منطقه مورد مطالعه نواحی شمالی (استان­های اردبیل، گیلان و مازندران) و غربی ایران (استان­های کردستان و همدان) است که به پنج ناحیه جنگلی، مرتع، پست و کم­ارتفاع و کوهستانی با شیب­های کم و زیاد تقسیم شد. مدل با گام مکانی 15 کیلومتر و 5 کیلومتر برای شبکه‌های مادر و داخلی، در بارش‌های برف سنگین در زمستان سال­های 2013 و 2014 اجرا شد و تصاویر روزانه سنجنده مودیس برای ارزیابی کسر پوشش برف استفاده شد. مدل در برآورد کسر پوشش برف و عمق برف در نواحی پست و کم‌ارتفاع با بالاترین ضرایب کارایی (به­ترتیب 64/0 و 37/0) و همبستگی (82/0 و 69/0)، کوچک­ترین خطای اریبی (4/2- و  cm1/3-) و میانگین مطلق خطا (9/4 و  cm5/6) بهترین عملکرد را دارد؛ درحالی‌که در برآورد کسر پوشش برف در نواحی مرتع و کوهستانی با شیب زیاد و عمق برف در نواحی جنگلی و کوهستانی با شیب زیاد، با منفی بودن ضریب کارایی، ناموفق است. عملکرد نسبی مدل در پیش­بینی وقوع بارش برف در اکثر نواحی، به­جز ناحیه مرتع با سطح مهارتی مناسب، در سطح مهارتی خوب است. مدل در برآورد کمینه دمای هوا در تمام نواحی، با مثبت بودن ضریب کارایی (محدوده 29/0 تا 88/0)، موفق است. نتایج این پژوهش بیانگر موفقیت مدل WRF-NOAHMP در پیش­بینی کمینه دمای هوا در تمام نواحی است؛ درحالی‌که هنوز هم در پارامتره­سازی کسر پوشش برف و عمق برف در نواحی کوهستانی با توپوگرافی پیچیده و دارای سطح ناهمگن و پارامتره­سازی برف تاج ­پوشش گیاهی دارای عدم قطعیت بالایی است.

کلیدواژه‌ها


عنوان مقاله [English]

The evaluation of snow model in NOAH-MP coupled with WRF model during the periods of heavy snow over the northern and western regions of Iran

نویسندگان [English]

  • Mehraneh Khodamorad Pour 1
  • Parviz Irannejad 2
  • Samira Akhavan 3
  • Khaled Babei 4
1 Faculty of Agriculture Bu-Ali Sina University
2 Institute of Geophysics, University of Tehran
3 Faculty of Agriculture Bu-Ali Sina University
4 Water Organization
چکیده [English]

Land surface schemes have considerable significance in the regional climate models. Due to their role in both surface’s energy and water budget, snow processes are among the most important components of the surface schemes. Snow cover fraction, because of extreme temporal and spatial changes and various features, including high albedo coefficient and very low conductivity, plays an important role in the snow models. This research evaluates snow parameterization in the Advanced Weather Research and Forecasting model (WRF) coupled with the NOAH-MP as a land surface scheme, improved NOAH scheme, through the advanced canopy, snow, and runoff modeling. The snow cover fraction of this scheme is estimated through the hyperbolic tangent relationship between snow height, snow density, and snow melt factor. The snowmelt factor in this model is pre-determined as one since its calibration is difficult due to the lack of access to the observational data at weather stations, using satellite images, and lack of images at most of the snowfall time periods because of the cloud coverage in most parts. For this reason, in this research, the snow cover fraction is evaluated with the default snowmelt coefficient of the model. The WRF-NOAHMP model runs in two separate zones, the northern (Ardebil, Gilan, and Mazandaran provinces) and the western (Kurdistan and Hamedan provinces) regions of Iran, through one-way nesting method with the spatial resolution of 15 kilometers and 5 kilometers for mother and inner domains and during the several periods of heavy snow in the winter of 2013 and 2014. The daily Modis images of the Terra Satellite were used to evaluate the snow cover fraction. Based on the digital elevation model and land use maps, the study area is categorized into five areas, including forests, rangelands, low lands, and mountainous regions with high and low slopes.
The WRF-NOAHMP model is successful in predicting the snow cover fraction in most areas, except mountainous areas with high slopes and rangeland areas; however, the model’s best performance is for low lands due to the highest efficiency coefficient (0.64), the smallest Bias error (-2.4), and Mean Absolute Error (9.4). Moreover, the skill level of the model’s performance (using the area under ROC curve) is good in predicting snowfall in most areas, except for the rangeland area. The WRF-NOAHMP is unsuccessful in estimating the snow depth in forests and mountainous areas with high slopes due to the negative efficiency coefficient, while it has the highest efficiency in estimating snow depth in low lands and mountainous areas with a low slope. Evaluation of the simulated minimum temperature by the model indicates the model’s success in estimating the minimum temperature in all studied areas because of the positive efficiency coefficients. The results of this study show the success of the WRF-NOAHMP in the prediction of the minimum temperature in different regions, while it still has a great deal of uncertainty in the parameterization of the snow cover fraction and the snow depth in mountainous areas with complex topography and areas with surface heterogeneity as well as the parameterization of the snow canopy.

کلیدواژه‌ها [English]

  • snow cover fraction
  • NOAH-MP land surface scheme
  • WRF
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