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

1 دانشگاه فردوسی مشهد

2 دانشگاه آزاد اسلامی، واحد مشهد

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


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
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