ارزیابی عملکرد دو مدل LARS-WG و ClimGen در تولید سری های زمانی بارش و درجه حرارت در ایستگاه تحقیقات دیم سیساب، خراسان شمالی

نوع مقاله : مقالات پژوهشی

نویسندگان

• نجمه خلیلی ¹
• کامران داوری ¹
• امین علیزاده ¹
• حسین انصاری ¹
• حجت رضایی پژند ²
• محمد کافی ¹
• بیژن قهرمان ¹

¹ دانشگاه فردوسی مشهد

² دانشگاه آزاد اسلامی، واحد مشهد

https://doi.org/10.22067/jsw.v30i1.45058

چکیده

انجام مطالعات مربوط به ارزیابی ریسک و مدیریت ریسک منابع آب و خشکسالی نیازمند دسترسی به سری درازمدت داده های هواشناسی است. این در حالی است که در بسیاری از ایستگاه های هواشناسی داده های برداشت شده از طول دوره آماری کافی برخوردار نیستند. برای رفع این مشکل می توان از مدل های تولید داده (مولد وضع هوا) استفاده کرد. در این تحقیق، از دو مولد پرکاربرد LARS-WG و ClimGen برای تولید 500 سری زمانی داده های روزانه بارش و درجه حرارت حداقل و حداکثر در ایستگاه تحقیقات دیم سیساب واقع در خراسان شمالی استفاده شد. کارآیی مدل ها با استفاده از شاخص های خطای مجذور میانگین مربعات خطا RMSE، میانگین خطای مطلق MAE و ضریب تعیین CD ارزیابی شد. همچنین با استفاده از سه آزمون آماریt – استیودنت، F و2X، شباهت 16 مشخصه آماری بین داده های مشاهده شده و شبیه سازی شده توسط دو مدل LARS-WG مورد بررسی قرار گرفت. نتایج نشان داد که در تولید سری زمانی بارش، مقادیر RMSE و MAE برای مدل LARS-WG کمتر از مدلClimGen بوده و از طرفی مقدار CD در مدل LARS-WG به یک نزدیک تر بوده است. از نظر شبیه سازی درجه حرارت حداقل و حداکثر، نتایج بدست آمده نشان می دهد که مدل ClimGen در مدل سازی میانگین های روزانه و ماهانه درجه حرارت حداقل و حداکثر موفق تر از مدل LARS-WG عمل کرده است. بطوری که در مدل LARS-WG از بین آزمون های آماری انجام شده بر روی میانگین ماهانه درجه حرارت حداقل و حداکثر به ترتیب 2 و 3 آزمون در سطح معنی داری 95% رد شده اند. نتایج همچنین نشان داد که مدل ClimGen در مدل سازی دوره های یخبندان و گرمای شدید موفق تر از مدل LARS-WG بوده است.

کلیدواژه‌ها

• شبیه سازی داده های هواشناسی
• کارآیی مدل
• مولد وضع هوا

20.1001.1.20084757.1395.30.1.27.7

عنوان مقاله [English]

Evaluation of the Performance of ClimGen and LARS-WG models in generating rainfall and temperature time series in rainfed research station of Sisab, Northern Khorasan

نویسندگان [English]

• najmeh khalili ¹
• Kamran Davary ¹
• Amin Alizadeh ¹
• Hossein Ansari ¹
• Hojat Rezaee Pazhand ²
• Mohammad Kafi ¹
• Bijan Ghahraman ¹

¹ Ferdowsi University of Mashhad

² Islamic Azad University of Mashhad

چکیده [English]

Introduction:Many existing results on water and agriculture researches require long-term statistical climate data, while practically; the available collected data in synoptic stations are quite short. Therefore, the required daily climate data should be generated based on the limited available data. For this purpose, weather generators can be used to enlarge the data length. Among the common weather generators, two models are more common: LARS-WG and ClimGen. Different studies have shown that these two models have different results in different regions and climates. Therefore, the output results of these two methods should be validated based on the climate and weather conditions of the study region.
Materials and Methods:The Sisab station is 35 KM away from Bojnord city in Northern Khorasan. This station was established in 1366 and afterwards, the meteorological data including precipitation data are regularly collected. Geographical coordination of this station is 37º 25׳ N and 57º 38׳ E, and the elevation is 1359 meter. The climate in this region is dry and cold under Emberge and semi-dry under Demarton Methods. In this research, LARG-WG model, version 5.5, and ClimGen model, version 4.4, were used to generate 500 data sample for precipitation and temperature time series. The performance of these two models, were evaluated using RMSE, MAE, and CD over the 30 years collected data and their corresponding generated data. Also, to compare the statistical similarity of the generated data with the collected data, t-student, F, and X2 tests were used. With these tests, the similarity of 16 statistical characteristics of the generated data and the collected data has been investigated in the level of confidence 95%.
Results and Discussion:This study showed that LARS-WG model can better generate precipitation data in terms of statistical error criteria. RMSE and MAE for the generated data by LAR-WG were less than ClimGen model while the CD value of LARS-WG was close to one. For the minimum and maximum temperature data there was no significant difference between the RMSE and CD values for the generated and collected data by these two methods, but the ClimGen was slightly more successful in generating temperature data. The X2 test results over seasonal distributions for length of dry and wet series showed that LARS-WG was more accurate than ClimGen.The comparison of LARS-WG and ClimGen models showed that LARS-WG model has a better performance in generating daily rainfall data in terms of frequency distribution. For monthly precipitation, generated data with ClimGen model were acceptable in level of confidence 95%, but even for monthly precipitation data, the LARS-WG model was more accurate. In terms of variance of daily and monthly precipitation data, both models had a poor performance.In terms of generating minimum and maximum daily and monthly temperature data, ClimGen model showed a better performance compared to the LARS-WG model. Again, both models showed a poor performance in terms of variance of daily and monthly temperature data, though LAR-WG was slightly better than ClimGen. For lengths of hot and frost spells, ClimGen was a better choice compared to LARS-WG.
Conclusion:In this research, the performances of LARS-WG and ClimGen models were compared in terms of their capability of generating daily and monthly precipitation and temperature data for Sisab Station in Northern Khorasan. The results showed that for this station, LARS-WG model can better simulate precipitation data while ClimGen is a better choice for simulating temperature data. This research also showed that both models were not very successful in the sense of variances of the generated data compared to the other statistical characteristics such as the mean values, though the variance for monthly data was more acceptable than daily data.

کلیدواژه‌ها [English]

• Data Generating
• Rainfall time series
• Sisab
• Temperature time series
• Weather Generator

مراجع

1- Ababaei B., Sohrabi T. and Mirzaei F. 2010. Evaluation of a stochastic weather generator in different climates. Computer and Information Science. 3(3): 217-229.

2- Al Janabi F. 2013. Comparison between three stochastic weather generators (GEM6, ClimGen and LARS-WG) for a dry climate region in Babylon Governorate/Iraq. Conference of REGKLAM, Dresden, Germany.

3- Babaeian I., Kwon W. T. and Im E. S. 2004. Application of weather generator technique for climate change assessment over Korea. Korea Meteorological Research Institute, Climate Research lab.

4- Babaeian I. and Najafi Nik Z. 2006. Future climate change projection over North-East of Iran during 2010-2039, 6th Conference of Numerical Wether Prediction, Tehran. 20 Dec. 117-125. (in Persian).

5- Babaeian I., Najafi Nik Z., Zabol Abbasi F., Habibi Nokhandan M., Adab H. and Malbusi SH. 2009. Climate change assessment over Iran using statistical downscaling of ECHO-G outputs during 2010-2039. Geography and development, 7(16): 135-152. (in Persian with English abstract).

6- Bazrafshan J., Khalili A., Hoorfar A., Torabi S. and Hajjam S. Comparison of the performance of climGen and LARS-WG models in simulating the weather factors for diverse climates of Iran. Iran-Water Resources Research, 5(1): 44-57. (in Persian with English abstract).

7- Danuso F.2002. Climak: a stochastic model for weather data generation. Italian Journal ofAgronomy, 6(1): 57-71.

8- Fox D.G. 1981. Judging air quality model performance: a summary of the AMS workshop on dispersion models performance. Bulletin ofAmerican Meteorological Society. 62: 599-609.

9- Geng S., Auburn J.S., Brandstetter E., and Li B. 1988. A program to simulate meteorologicalvariables: documentation for SIMMETEO. Agronomy Progress Rep 204, Department of Agronomy and Range Science, University of California, Davis, CA.

10- Hajarpoor A., Yousefi M., and Kamkar B. 2014. Accuracy assessment of weather assimilators of CLIMGEN, LARS-WG and Weather Man in assimilation of three different climatic parameters of three different climate (Gorgan, Gonbad and Mashhad), Geography and Development. 35: 201-216. (in Persian with English abstract).

11- Hamilton J .D. 1994. Time Series Analysis , Princeton University Press, NJ. pp 799.

12- Http://www.Irimet.Net

13- Jones P.G., and Thornton P.K. 2000. MarkSim: Software to generate daily weather data for Latin America and Africa. Agronomy Journal. 92: 445-453.

14- Johnson G.L., Hanson C. L., Hardegree S. P. and Ballard E. B. 1996. Stochastic weather simulation: overview and analysis of two commonly used models. Journal of AppliedMeteorology. 35: 1878-1896.

15- Loague K., and Green R.E. 1991. Statistical and graphical methods for evaluating solute transport models: overview and application. Journal ofContamination Hydrology. 7: 51-73.

16- McCaskill M.R., 1990. TAMSIM-A program for preparing meteorological records for weather driven models. Trop. Agron. Tech. Memo. No. 65,CSIRO, Division of Tropical Crops and Pastures,Brisbane.

17- McCuen, R.H., (2002), Modeling hydrologic change: statistical methods, Lewis Publishers, Dept. of Civiland Environmental Engineering, University ofMaryland, 433p.

18- Meshkati A. H., Kordjazi m. and Babaeian I. 2011. Evaluation of LARS-WG model in simulation of some observed meteorological parameters in Golestan province (1993-2007). Journal of Geographical Science, 16 (19):81-96. (in Persian with English abstract).

19- Moradi I. and Nosrati K. 2002. Evaluation of stoshastic simulation methods for generating meteorological data. Procceding of 3th International Iran and Russia Conference Agricalture and Natural Resources, Moscow. 246-251.

20- Nicks A. D., Lane L. J. and Gander G. A. 1995. USDA-Water erosion prediction project hillslope profile and watershed model documentation. NSERL Report No. 10. Eds. D. C. Flanagan and M. A. Nearing. W. Lafayette IN: USDA-ARS National Soil Erosion Research Laboratory. 2: 1-2.22.

21- Nosrati K., Zehtabian Gh. R., Moradi E. and Shahbazi A. 2008. Evaluation of stochastic simulation method for generating meteorological data. Geographical Research Quarterly. 39 (62): 1-9. (in Persian with English abstract).

22- Pickering N.B., Hansen J.W., Jones J.W., Wells C.M., Chan V.K. and Godwin D.C. 1994. WeatherMan: A utility for managing and creating daily weather data. Agronomy Journal. 86: 332-337.

23- Racsko P., Szeidl L. and Semenov M. 1991. A serial approach to local stochastic weather models”, Ecological Modelling. 57: 27-41.

24- Richardson, C.W. (1981), “Stochastic simulation of daily precipitation, temperature, and solar radiation”, Water Resources Research, 17, pp. 182-190.

25- Richardson C.W. and Wright D.A. 1984. WGEN: a model for generating daily weather variables,Report, United States Department of Agriculture, Agriculture Research Service, ARS-8. pp83.

26- Semenov M.A., Brooks R.J., Barrow E.M. and Richardson C.W. 1998. Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates. Climate Research. 10: 95-107.

27- Sharply A.N., and Williams J.R., Eds. 1990. EPIC-Erosion Productivity Impact Calculator, 1. Model documentation, U.S. Department of Agriculture, Agricultural Research Service, Tech. Bull. 1978.

28- Soltani A., and Hoogenboom G. 2003. A statistical comparison of the stochastic weather generators WGEN and SIMMETEO. Climate Research, 24: 215-230.

29- Stöckle C.O., Campbell G.S., and Nelson R. 1999. ClimGen manual, biological systems engineering department, Washington State University, Pullman, WA, pp 28.

30- Tingem M., Rivington M., Azam-Ali S., and Colls J. 2007. Assessment of the ClimGen stochastic weather generator at Cameroon sites. African Journal of Environmental Science and Technology. 1(4): 86-92.

31-Wilby R.L., Dawson C.W., and Barrow E.M. 2002. SDSM – a decision support tool for the assessment of regional climate change impacts. Environmental Modeling and Software, 17: 147-159.