ارزیابی دو روش ریز مقیاس نمایی آماری LARS-WG و SDSM در برآورد تغییرات پارامترهای اقلیمی (مطالعه موردی:دشت بیرجند)

نوع مقاله : مقاله کامل علمی پژوهشی

نویسندگان

1 دانشگاه بیرجند، دانشکده کشاورزی، گروه مهندسی آب

2 استادیار گروه مهندسی آب، دانشکده کشاورزی، دانشگاه بیرجند

3 دانشیار گروه مهندسی آب، دانشکده کشاورزی، دانشگاه بیرجند

چکیده

سابقه و هدف: در حال حاضر معتبرترین ابزار جهت تولید سناریوهای اقلیمی، مدل‌های سه بعدی جفت شده جوی-اقیانوسی گردش عمومی هوا می‌باشند که به طور مخفف از آن به عنوان AOGCM یاد می‌شود. یکی از مشکلات عمده در استفاده از خروجی مدل‌های AOGCM، بزرگ بودن مقیاس مکانی سلول محاسباتی آن‌ها، نسبت به منطقه مورد مطالعه است و باید نتایج خروجی این مدل‌ها کوچک مقیاس شوند. روش‌های آماری متعددی جهت ریزمقیاس نمودن خروجی‌های مدل‌هایAOGCM برای دستیابی به دقت بیشتر توسعه یافته‌اند. تفاوت دقت روش‌های ریزمقیاس نمایی متناسب با مکان و نوع مدل اقلیمی می‌تواند باعث اختلاف در نتایج شبیه‌سازی گردد. لذا بررسی دقت این روش‌ها از اهمیت بالایی برخوردار است. پژوهشگران زیادی در سرتاسر دنیا به بررسی دقت روش‌های گوناگون در ریزمقیاس‌نمایی پرداخته‌اند. نتایج پژوهشگران در سرتاسر دنیا بیانگر این مطلب می‌باشد که بر اساس نوع خروجی مدل‌هایAOGCM و همچنین کمیت و کیفیت داده‌های مشاهداتی منطقه مورد مطالعه شبیه‌سازی مؤلفه‌های اقلیمی متفاوت خواهد بود. هدف از این پژوهش بررسی دقت روش‌های ریزمقیاس نمایی آماری LARS-WG و SDSM برای بارندگی و متوسط درجه حرارت روزانه و برای ایستگاه سینوپتیک بیرجند می‌باشد.
مواد و روش‌ها: آمار مشاهداتی دوره 2000-1960 از سازمان‌ هواشناسی استان استخراج شد. دوره 1990-1960 برای واسنجی و دوره 2000-1991 برای دوره صحت‌سنجی انتخاب شدند. سری شاخص‌های حدی اقلیمی در دوره صحت‌سنجی برای آمار مشاهداتی ایستگاه سینوپتیک و شبیه‌سازی شده توسط دو روش ریزمقیاس‌نمایی محاسبه شد. به منظور ارزیابی دقت دو روش در محاسبه شاخص‌ها، از آزمون‌های آماری استفاده شد. بدین ترتیب که حساسیت روش‌ها به ناهنجاری‌های بزرگ مقیاس (همبستگی داده‌ها) و توانایی روش‌های ریزمقیاس‌نمایی برای برگرداندن توزیع داده‌های مشاهداتی به ترتیب با آزمون‌های همبستگی پیرسون و رتبه‌ نشان‌دار ویل‌کاکسون مورد ارزیابی قرار گرفت.
یافته‌ها: پس از بررسی نتایج مشخص شد که برتری قابل توجهی در آزمون همبستگی پیرسون بین دو روش وجود ندارد. هرچند که در دو روش نتایج برازش بیش از 50% شاخص‌های مشاهداتی و شبیه‌سازی شده قابل قبول است. نتایج عملکرد دو مدل در آزمون ویل‌کاکسون نشان داد که تفکر مبدل‌های اقلیمی به طور قابل ملاحظه‌ای بالاتر از روش‌های رگرسیون خطی می-باشد. نتایج این آزمون نشان داد که در روش LARS-WG بیش از 90% شاخص‌ها برازش خوبی را دارا می‌باشند. همچنین برازش شاخص‌های دما در روش SDSM-DC در مقایسه با روش LARS-WG بسیار نامطلوب بود.
نتیجه‌گیری: نتایج مطالعه نشان داد که به طور کلی روش LARS-WG در مقایسه با روش SDSM-DC دقت بهتری دارد. این برتری به خصوص در در پیش‌بینی تابع توزیع همسان با داده‌های مشاهداتی محسوس‌تر بود.

کلیدواژه‌ها

عنوان مقاله [English]

assessment of statistical downscaling methods LARS-WG & SDSM in forecast of climate parameter variation

نویسندگان [English]

  • ahmad jafarzadeh 1
  • Abbas Khashei-Siuki 2
  • Ali Shahidi 3
1 Department of Water Engineering, Faculty of Agriculture, University of Birjand, Iran
2
3
چکیده [English]

Background and Objectives: Now most reliable tool to produce climate scenarios is use of Atmosphere-Ocean General Circulation Model outputs which stands as AOGCM. One of the using major problems of AOGCM outputs is computational large cell size of their simulation in any region. So first must their outputs has been downscaled and then they used. Present several stochastically methods for downscaling AOGCM outputs to increase their accuracy in simulate. It should be noted that Deference in downscaling methods can cause deference in simulation results. So assess accuracy of downscaling methods is very necessary in any region. Many researchers around the world to check the accuracy of various downscaling methods have focused. Results of Research study around the world indicates that simulation of climate and hydrological parameters depending on output of AOGCM models and also quality and quantity of observation data are very deferent. The aim of this study is assessment of statistical downscaling methods for precipitation and temperature include LARS-WG and SDSM in Birjand synoptic station.
Materials and Methods: Observation data of Birjand synoptic station include precipitation, maximum and minimum temperature and solar watch daily on 1960-2000 were taken of province Meteorological organization. The period 1960-1990 is used for models calibration (train) and 1991-2000 for validation (test) selected. Series of climate extremes indices evaluated for observed data of synoptic station and simulated by downscaling methods on validation period. Statistical tests are used for evaluation and analysis of downscaling methods performance. The sensitivity of the methods to large-scale anomalies (correlation between observed and simulated data) and their ability to replicate the observed data distribution in the validation period are separately tested for each index by Pearson correlation and Wilcoxon signed rank tests, respectively.
Results: By analysis of results defined that between of downscaling methods there isn’t significant superiority in person correlation test. While in correlation test in both model p-value of more 50% of observation and simulation indices is most of 0.05 and they acceptable. Results of performance models in Wilcoxon test showed that performance of weather generator technic is significantly better than linear regression method. Results of this test showed that more of 90% of indices have a suitable fit in LARS-WG. Also fit of temperature indices in SDSM-DC compared with LARS-WG were very weak.
Conclusion: results of this study showed that LARS-WG method compared with SDSM-DC method is more accurate generally. This accuracy in forecast of distribution function was more tangible.

کلیدواژه‌ها [English]

  • Wilcoxon Signed Rank Test
  • Climate Extremes indices
  • Pearson Correlation
  • HADCM3

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مجله پژوهش‌های حفاظت آب و خاک
دوره 23، شماره 4
مهر 1395
صفحه 309-322

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