ارزیابی دقت مدل های شناسایی فاز برف از باران در ایستگاه های هواشناسی منطقه کوهستانی زاگرس

نوع مقاله: مقاله پژوهشی

نویسندگان

1 استادیار/ پژوهشکده حفاظت خاک و آبخیزداری، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران.

2 دانشگاه تبریز- گروه آب و هواشناسی

3 دانشکده جغرافیا و برنامه ریزی، گروه اقلیم شناسی، دانشگاه تبریز،تبریز،ایران

4 استاد/دانشکده جغرافیا و برنامه‌ریزی، دانشگاه تبریز.

چکیده

در این پژوهش امکان برآورد برف در منطقه کوهستانی زاگرس با استفاده از مدلهای مختلف شناسایی فاز برف از باران مورد ارزیابی قرار گرفت. برای این منظور از داده‌های روزانه بارش، عمق برف و دمای هوای 36 ایستگاه سینوپتیک و اقلیم شناسی منطقه در دوره آماری 1951-2015 استفاده شد. برای شناسایی فاز برف از باران در ایستگاه‌های مورد مطالعه از هشت مدل مختلف که ورودی همه آنها دما و بارش روزانه است استفاده شد و عملکرد آنها در پیش‌بینی درست برف ایستگاه ها به کمک مجموعه‌ای از سنجه‌های ارزیابی دقت مدل در پیش بینی‌ درست رویدادهای مشاهده‌ای ارزیابی شد. نتایج نشان داد که آستانه دمایی جدایی برف از باران (Tt) در ایستگاههای منتخب بین 7/1- تا 5 درجه سیلسیوس در تغییر است ولی این مقدار در بیش از 75 درصد از ایستگاه ها بین صفر و 2 درجه سیلسیوس است. همچنین مشخص شد که دامنه دمایی (Tr) که در آن هم احتمال ریزش برف و هم احتمال ریزش باران وجود دارد از ایستگاهی به ایستگاه دیگر متفاوت است، هرچند که این مقدار در اغلب ایستگاه ها بین 9 تا 13 درجه سیلسیوس است. سنجه‌های مختلف نشان دادند که هر هشت مدل استفاده شده در این مطالعه قابلیت پیش بینی روزهای برفی در منطقه را دارند و عملکرد مدلهای USACE، Pipes، Keinzel، Motoyama و Tangent در همه ایستگاهها تقریباً همانند است. براساس نتایج سنجه‌های مختلف، مدل USACE در همه ایستگاهها بهترین پیش‌بینی و مدلهای McCabe، Brown (Max, Min) و Brown (Mean) ضعیفترین پیش‌بینی روزهای برفی دارند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Assessing The Accuracy Of Snow-Rain Phase Separation Models at Meteorological Stations of the Mountainous region of Zagros, Iran

نویسندگان [English]

  • T. Raziei 1
  • S. Jahanbakhsh Asl 2
  • Akram Parandeh Khouzani 3
  • B. Sarisarraf 4
1 Assistant professor, Soil Conservation and Watershed Management Research Center (SCWMRI), Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran.
2 Professor Tabriz University, Tabriz, Iran.
3 Department of Climatology,Faculty of Geography and Planning,Tabriz University,Tabriz,IRAN
4 - Professor, Department of Climatology, Faculty of Geography and Planning, University of Tabriz, Tabriz, Iran
چکیده [English]

In this study, the possibility of snow estimation using the snow-rain phase separation models that usually integrated into the snowmelt models or in the numerical weather prediction (NWP) models are evaluated in the Zagros region, Iran. For this purpose, daily precipitation (rain and snow) and temperature from 36 weather stations distributed over Zagros and having data records over the 1951-2015 period were used. The performances of the snow-rain phase separation models in accurately predicting the phase of precipitation at the studied stations were evaluated through constructing contingency tables between the predictions and observations and eventually evaluating the result using a set of statistical skill scores. A statistically significant relationship was found between the phase of precipitation predicted by the models and the snow occurrences observed at all selected stations. The USACE, Pipes and Quick, Hyperbolic tangent function and Kienzle were found as the best models while the Mccabe and Wolock model and the Brown model based on the maximum and minimum of temperature resulted in the weaker predictions. The snow-rain separation temperature threshold at the selected stations varies from -1.7 to 5 degree Celsius, nonetheless, it it is between 0 and 2 degree Celsius in more than 75% of stations. It was also found that the temperature range within which both snow and rain can occur simultaneously varies in all considered stations, but in most of the stations it is between 9 and 13 degrees Celsius.

کلیدواژه‌ها [English]

  • Snow-Rain Phase Separation
  • Snow-Rain Phase Temperature Threshold
  • Snowfall Temperature Range
  • Snow Depth Prediction
  • Snow Melt Models
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