کارایی الگوهای ریزمقیاس نمایی آماری SDSM و LARS-WG در شبیه‌سازی متغیرهای هواشناسی در حوضۀ آبریز دریاچۀ ارومیه

نوع مقاله: مقاله علمی پژوهشی

نویسندگان

1 دانشیار دانشکدة علوم انسانی، دانشگاه محقق اردبیلی

2 دانشجوی دکتری آب‌و‌هواشناسی، دانشگاه محقق اردبیلی و کارشناس آمار و کنترل داده‌های مرکز تحقیقات هواشناسی کاربردی تبریز

3 استادیار پژوهشکدة اقلیم‌شناسی، سازمان هواشناسی کشور،‌ مشهد

چکیده

در این پژوهش، نتایج دو الگوی ریزمقیاس نمایی SDSM و مولد آب‌و‌هوایی LARS-WG با درنظر گرفتن تحلیل عدم قطعیت روی بارش روزانه، کمینه و بیشینه دمای روزانه مقایسه می‌شود. منطقة این پژوهش شامل ایستگاه‌های هواشناسی تبریز و ارومیه به‌مثابة نمایندة حوضة آبریز دریاچة ارومیه است که آمار بلندمدت آنها موجود است. دورة پایه در این الگوها، داده‌های دما و بارش روزانة ایستگاه‌های تبریز و ارومیه در دورة بلندمدت 1990-1961 است. پس از بررسی اولیة داده‌های روزانه، تحلیل عدم قطعیت روی دو الگوی یادشده در دورة پایه انجام گرفت. در این مقاله، از روش‌های نموداری و آماری برای مقایسة عملکرد دو روش ریزمقیاس نمایی استفاده شد. در روش نموداری قدرمطلق اختلاف داده‌های الگوشده و مشاهده‌شده به‌صورت ماهانه برای هریک از مؤلفه‌های بررسی‌شده، روی نمودار آورده و تحلیل شد. نتایج کلی نشان داد الگوی SDSM در دو ایستگاه بررسی‌شده، برای کمینه و بیشینه دمای روزانه عملکرد بهتری نسبت به الگوی LARS-WG دارد؛ درحالی‌که برای بارش روزانه نتایج عملکرد دو الگو تاحدودی در دو ایستگاه مشابه بود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Efficiency of Statistical Downscaling Models of SDSM and LARS-WG in the Simulation of Meteorological Parameters in Lake Urmia Basin

نویسندگان [English]

  • Behrooz Sobhani 1
  • Mehdi Eslahi 2
  • Iman Babaeian 3
1 Associate Professor, Department of Physical Geography, University of Mohaghegh Ardabili, Iran
2 PhD. Candidate in Climatology, Department of Physical Geography, University of Mohaghegh Ardabili, Iran
3 PhD Candidate in Climatology, Climate Research Center, Meteorological Organization, Mashhad, Iran
چکیده [English]

Introduction
Linking resolution global climate models to local scale as a micro climatic process is a significant issue. Recently, attempts have been made by the climatology scientists to develop dynamics and statistical downscaling methods to express climate change at a local and regional scale. Two general techniques are been used for downscaling of the output of general circulation models (GCM). The further is by statistical methods in which the output of a statistical model (MOS) and a planned approach to weather short-term numerical prediction is presented. The later is regional climate model (RCM), same as limited GCM model in a subnet of network global model by dynamic method that uses climatic conditions temporal changes according to GCM model. Both methods play an important role to determine the potential effects of the climate change caused by increased greenhouse gas emissions. Much work is done to use this method for downscaling of the global model output in different areas in which the performance of the model is assessed. Uncertainty analysis has been done on these methods or compared by other statistical methods.
 
Materials and Methods
In this study for more accurate validation of the two methods, uncertainty analysis is done on input data of daily temperature and precipitation. In uncertainty analysis of daily temperature data because of similarity of the statistical distribution to normal distribution, the monthly mean of downscaled data are statistically compared with observed data. In this case, parametric or non-parametric tests can be used to compare means. However, in uncertainty analysis of daily rainfall data because of lack of normality, comparison of the averages of downscaled data and observed data are not sufficient and should be compared with the dry and wet periods. In this method, the statistical distribution of downscaled dry and wet period durations is compared with the observed ones.
Before the uncertainty analysis, first, an exploratory analysis is performed on the data in the data statistical condition and the approach to data analysis to be ascertained. This analysis is based on the study of statistical assumptions of the model. If these assumptions are not established, the statistical analysis parametric methods and respective tests lose their credibility and nonparametric methods must be used. These assumptions are:
1. Data come from normal or near to normal distribution.
2. Data standard average is close to zero or there is no outlier.
3. Data have little temporary correlation.
Daily rainfall varies because of the skewness of the right (frequency of daily precipitation amounts toward zero), the normal assumption is questionable. Therefore, power transformations are used for normality of rainfall data in statistical analysis and modeling. On the other hand, the daily temperature data are normal in nature and there is not outlier. But, because of temporary correlation of daily temperature, the third assumption did not establish for them. Therefore, parametric methods are used for statistical analysis and comparative tests.
 
Results and Discussion
According to the three basic assumptions of the model, daily rainfall data are far from a normal distribution and the data have a lot of outlier points. But, there is not significant correlation period. Unlike the daily maximum and minimum temperature data of relatively normal distribution, they have not had many outlier points. But, there is a significant correlation of the data with time. Therefore, it can be concluded that none of the data of temperature and precipitation have conditions of the three basic assumptions. Therefore, non-parametric methods must be used for statistical analysis and modeling. Or alternative parameters are used such as the number of days wet or dry.
For the uncertainty analysis and comparison of the two models SDSM and LARS-WG we have used graphical and statistical methods. In this study, the absolute values of the differences between downscaled values and observed values in the 1961-1990 statistical periods are used as an indicator in the graphical analysis. Results of graphical analysis show that values of the absolute differences in different months of the SDSM model parameters are the minimum and maximum daily temperature LARS-WG is better than the model. The daily rainfall amounts during the months of absolute difference of the two models are relatively close to each other. Of course, the results of stations in Tabriz and Urmia are slightly different but are not significant.
To evaluate the significance difference between observation and downscaled values in two models, we have used Mann-Whitney test. The results show that for the minimum temperature, in both models SDSM and LARS-WG almost in half of the months, the model error is significant, although the SDSM model is better. However, the tendency to work and choose the most appropriate model for large-scale predictor variables from the NCEP-NCAR data were obtained from the appropriate geographic region and it is possible to achieve better performance. Parameter maximum daily temperature for the SDSM model has better performance than the LARS-WG, which confirmed the results of the chart. The downscaled maximum daily temperature has less error than the minimum daily temperature. Especially in SDSM model in Tabriz station only January has a significant error in the model. It has good performance for daily precipitation models. Especially in Tabriz station the values of the model error is not significant in any of the months. In accordance with the similar results, the performance of the two models is similar for the daily rainfall.
 
Conclusion
The results of this study indicates that in accordance with the results of the statistical downscaling SDSM and LARS-WG on stations of Tabriz and Urmia for daily minimum and maximum parameters, SDSM model has better performance than the LARS-WG. For daily precipitation performance, the two models are similar in the two stations. However, as the statistical distribution of daily rainfall data is not normal, the results of the models cannot be trusted. It is suggested that instead of precipitation, in the analysis we used the number of dry and wet days.

کلیدواژه‌ها [English]

  • climate change model
  • Downscaling
  • LARS-WG
  • SDSM
  • uncertainty
  1. اشرف، ب.، موسوی بایگی، م.، کمالی، غ. و داوری، ک. (1390). «پیش‌بینی تغییرات فصلی پارامترهای اقلیمی در بیست سال آتی با استفاده از ریزمقیاس نمایی آماری داده‌های مدل HADCM3 )مطالعة موردی: استان خراسان رضوی)». نشریةآبوخاک (علوم و صنایع کشاورزی)، ج25. ش4. مهر- آبان: 957-945.
  2. باباییان، ا.، نجفی نیک، ز.، حبیبی نوخندان، م.، زابل عباسی، ف.، ادب، ح. و ملبوسی، ش. (1386). «مدل‌سازی اقلیم ایران در دورة 2039-2010 با استفاده از ریزمقیاس نمایی آماری خروجی مدل ECHO-G ». کارگاهفنی اثراتتغییراقلیمدرمدیریتمنابعآب. 24 بهمن 1386.
  3. باباییان، ا. و کوهی، م. (1391). «ارزیابی شاخص‌های اقلیم کشاورزی تحت سناریوهای تغییر اقلیم در ایستگاه‌های منتخب خراسان رضوی». نشریةآبوخاک (علوم و صنایع کشاورزی). ج26 .ش4. مهر- آبان: 967-953.
  4. دهقانی‌پور، ا.ح.، حسن‌زاده، م.ج.، عطاری، ج. و عراقی‌نژاد، ش. (1390). «ارزیاتی توانمندی مدل SDSM در ریزمقیاس نمایی بارش، دما و تبخیر )مطالعة موردی:ایستگاه سینوپتیک تبریز(». یازدهمین سمینار سراسری آبیاری و کاهش تبخیر. 20-18 بهمن 1390.
  5. گل‌محمدی، م. و مساح بوانی، ع.ر. (1390). «بررسی تغییرات شدت و دورة بازگشت خشکسالی حوضة قره‌سو در دوره‌های آتی تحت تأثیر تغییر اقلیم». نشریةآبوخاک (علوم و صنایع کشاورزی). ج25. ش2. خرداد- تیر: 326-315.
  1. Ashraf, B., Mousavibaygi, M., Kamali, G. and Davari, K. (2011). "Predict seasonal changes of climatology parameters in next 20 years by using statistical downscaling of HADCM3 model output(case study: Korasan Razavi Province)". Journal of Water and Soil (Science and Industrial Agriculture). Vol. 25. No. 4. Nov 2011: 945-957. (In Persian).
  2. Babaeian, A., Najafinik, Z., Habibinokhandan, M., Zabolabbasi, F., Adab, H. and Malbousi, S. (2007). "The modeling of Iran climate in the period 2010-2039, by using statistical downscaling of ECHO-G model output". Technical workshop on climate change impacts on water resources management. 13 Feb 2007. (In Persian).
  3. Babaeian, A. and Kouhi, M. (2012). "Indexes evaluation of agriculture climate under climate change scenarios at selected stations in Khorasan Razavi". Journal of Water and Soil (Science and Industrial Agriculture). Vol. 26. No. 4. Nov 2012: 953-967. (In Persian).
  4. Dehghanipoor, A., Hassanzadeh, M., Attari, J. and Eraghinejad, S. (2011) "Evaluation of empowerment of SDSM model in downscaling of precipitation, temperature and evaporation (Case study: Tabriz station)". Eleventh Seminar irrigation and evaporation. 18-20 February 2011. (In Persian).
  5. Golmohammadi, M. and Masahboani, A. (2011). "Evaluation of drought severity and recurrence in future periods affected by climate change in the basin Gharehsou". Journal of Water and Soil (Science and Industrial Agriculture). Vol. 25. No. 2. Jun 2011: 315-326. (In Persian).
  6. Cheema, S.B., Rasul, G., Ali, G., Kazmi, D.H. (2013). "A Comparison of Minimum Temperature Trends with Model Projections". Pakistan Journal of Meteorology. Vol. 8. Issue 15.
  7. Karamouz, M., Fallahi, M., Nazif, S. and Rahimi Farahan, M. (2009). "Long Lead Rainfall Prediction Using Statistical Downscaling and Arti_cial Neural Network Modeling". Transaction A: Civil Engineering. Vol. 16. No. 2: 165-172.
  8. Kazmi, D.H., Rasul, G., Li, J. and Cheema, S.B. (2014). "Comparative Study for ECHAM5 and SDSM in Downscaling Temperature for a Geo-Climatically Diversified Region, Pakistan". Applied Mathematics. Vol. 5: 137-143.
  9. Meenu, R., Rehana, S. and Mujumdar, P.P. (2012) "Assessment of hydrologic impacts of climate change in Tunga–Bhadra river basin, India with HEC-HMS and SDSM". Hydrological Processes. Published online in Wiley Online Library. DOI: 10.1002/hyp.9220.
  10. Nury, A.H. and Alam, M.J.B. (2014) "Performance Study of Global Circulation Model HADCM3 Using SDSM for Temperature and Rainfall in North-Eastern Bangladesh". Journal Of Scientific Research. 6 (1): 87-96.
  11. Rajabi, A. and Shabanlou, S. (2012) "Climate Index Changes In Future By Using SDSM In Kermanshah, Iran". Journal of Environmental Research And Development. Vol. 7. No. 1. July-September 2012.
  12. Resko, P., Szeidl, L. and Semenov, M.A. (1991) "A serial approach to local stochastic models". J. Ecological Modeling. 57: 27-41.
  13. Sajjad Khan, M., Coulibaly, P. and Dibike, Y. (2006) "Uncertainty analysis of statistical downscaling methods". Journal of Hydrology. 319: 357–382.
  14. Wilby, R.L., Dawson, C.W. and Barrow, E.M. (2002). "A decision support tool for the assessment of regional climate change impacts". Environmental Modelling & Software. 17: 147–159.
  15. Zhaofei, Liu, Zongxue, Xu, Charles, S.P., Guobin, Fu and Liu, L. (2011). "Evaluation of two statistical downscaling models for daily precipitation over an arid basin in China". Int. J. Climatol. 31: 2006–2020.