پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 161 |
| تعداد شماره‌ها | 6,479 |
| تعداد مقالات | 70,032 |
| تعداد مشاهده مقاله | 123,002,391 |
| تعداد دریافت فایل اصل مقاله | 96,234,762 |
ارزیابی دقت داده‌های CFSR و مدل LARS-WG در شبیه‌سازی پارامترهای اقلیمی استان چهارمحال و بختیاری | ||
| پژوهش های جغرافیای طبیعی | ||
| مقاله 10، دوره 48، شماره 2، تیر 1395، صفحه 321-334 اصل مقاله (1.05 M) | ||
| نوع مقاله: مقاله کامل | ||
| شناسه دیجیتال (DOI): 10.22059/jphgr.2016.59373 | ||
| نویسندگان | ||
| سمیرا اخوان* 1؛ نسرین دلاور2 | ||
| 1استادیار گروه علوم و مهندسی آب، دانشکدة کشاورزی، دانشگاه بوعلی سینا | ||
| 2دانشجوی کارشناسی‌ارشد آبیاری و زهکشی، گروه علوم و مهندسی آب، دانشکدة کشاورزی، دانشگاه بوعلی سینا | ||
| چکیده | ||
| هدف پژوهش حاضر، ارزیابی دقت مولد آب‌وهوایی LARS-WG و داده‌های CFSR در شبیه‌سازی پارامترهای اقلیمی (دمای کمینه و بیشینه و بارش) استان چهارمحال و بختیاری است. بدین‌منظور، از مقایسةشاخص‌های آماری RMSE، MBE، MAEو R2استفاده شد. در ایستگاه شهرکرد مقادیر RMSE و MAE برای بارش ماهانة داده‌های CFSR به ترتیب 49/20 و 19/11 میلی‌متر و برای بارش سالانه 88/92 و 51/72 میلی‌متر است. این مقادیر بارش، در مورد مدل LARS-WG در مقیاس ماهانه به ترتیب 45/41 و 75/24 میلی‌متر و در مقیاس سالانه 75/164 و 43/123 میلی‌متر است. در مجموع، داده‌های CFSRدر بازة زمانی کوتاه‌تر (ماهانه و سالانه) دارای آماره‌های خطاسنجی کمتری نسبت به مدل LARS-WGاست و همبستگی بیشتری با داده‌های مشاهداتی دارد. بنابراین، در تخمین پارامترهای اقلیمی کوتاه‌مدت، دقت بالاتری دارد. همچنین، نتایج بیانگر توان‌مندی مدل LARS-WG در شبیه‌سازی پارامترهای اقلیمی در بازة زمانی طولانی‌مدت (دهه) است. به‌همین دلیل، مقادیر آماره‌های مذکور در مقیاس‌های زمانی کوتاه‌تر، چندان مناسب نیست. بدین‌ترتیب، باتوجه به اهداف هر تحقیق، می‌توان از نتایج هر دو روش استفاده کرد. همچنین داده‌های CFSRدر نقاط فاقد ایستگاه هواشناسی گزینة ارزش‌مندی محسوب می‌شود. | ||
| کلیدواژه‌ها | ||
| بارش؛ دمای بیشینه؛ دمای کمینه؛ LARS-WG؛ CFSR | ||
| عنوان مقاله [English] | ||
| Assessment of accuracy in CFSR data and LARS-WG model in simulation of climate parameters, Chaharmahal and Bakhtiari province | ||
| نویسندگان [English] | ||
| Samira Akhavan1؛ Nasrin Delavar2 | ||
| 1Assistant Professor, Department of Water Engineering, College of Agriculture, Bu-Ali Sina University, Hamedan, Iran | ||
| 2MSc. in Irrigation and Drainage, Department of Water Engineering, College of Agriculture, Bu-Ali Sina University, Hamedan, Iran | ||
| چکیده [English] | ||
| Introduction Daily weather information is currently available for about 40000 stations across the world. But, distribution of these stations is relatively uneven in some parts of the world. Moreover, there are often large amounts of missing values (Schuol andAbbaspour, 2007: 301). Using generated data can help fill missing or even to correct erroneously measured data (Fodor et al., 2010: 91). LARS-WG is a stochastic weather generator which can simulate weather data under both current and future climate conditions at a single site (Semenov and Barrow, 2002: 3). There is another watershed modeling problem, which weather stations are often outside of/or at a long distance from the watersheds. Thus, the recorded data may not meaningfully indicate the weather taking place over a watershed. Therefore, some researchers have developed radar data to supply precipitation inputs in watershed modeling (Fuka et al., 2013: 1). But, these data are only available in small parts of the world. therefore, considering additional methods to generate weather conditions over watersheds is necessary. Using reanalysis dataset (CFSR) is one option (Fuka et al., 2013: 1). Dile and Srinivasan (2013) investigated CFSR climate data in the Lake Tana basin in the Nile basin. The results showed simulations with CFSR and conventional weather gave trivial differences in the water balance components in all except one watershed. In the four zones, both weather simulations indicated similar annual crop yields. Nevertheless, the conventional weather simulation results were better than the CFSR weather simulation, but they can be applied as important option for the regions where no weather stations exist such as remote subbasin of the Upper Nile basin. Soltani and Hoogenboom (2003) evaluated the weather generators WGEN and SIMMETEO for 5 Iranian locations. The results revealed that WGEN was successful to generate maximum and minimum temperatures and SIMMETEO was acceptable to reproduce minimum temperature and solar radiation. The objective of current study is to make an assessment of accuracy of weather generator of LARS-WG and CFSR data in simulation of climate parameters of Chaharmahal and Bakhtiari province. Materials and Methods The study was conducted in Chaharmahal and Bakhtiary province. This province, with an area of 16532 km2, is located between 31° 09' to 32° 48' north latitude and 49° 28' to 51° 25' East longitude and provides more than 10% of the water resources of Iran. 1. LARS-WG model LARS-WG model applies complex statistical distributions for simulation of meteorological variables. The basis of this model to simulate dry and wet periods is daily precipitation and radiation series of semi-empirical distribution. The temperature is estimated by Fourier series. The output of this model includes minimum temperature, maximum temperature, precipitation and solar radiation (Babaeian et al., 2007: 62). 2. Required data for LARS-WG model Required data for LARS-WG model includes daily maximum temperature, minimum temperature, precipitation and solar radiation (sunshine hours). These data were provided for four selected synoptic weather stations (Shahrekord, Koohrang, Boroojen and Lordegan). 3. CFSR data Reanalysis is a systematic approach to produce data sets for climate monitoring. Reanalysis data are created through a fixed data assimilation design and models which use all available observations every 6 hours over the period being analyzed. CFSR data has a global horizontal resolution of 38 km. The CFSR adjacent stations were determined for the four mentioned stations. Daily weather data of each station during 1991-2010 was implemented in the LARS-WG model. For assessment of both data, the comparison of statistical indices such as RMSE, MBE, MAE and R2 was used in daily, monthly, annual and decade scales. Results and Discussion The results showed that there is no correlation between the output of LARS-WG model and observed daily precipitation data in each of the four stations. The values of these coefficients for minimum and maximum temperatures increased in all stations. In general, due to high values of RMSE and MAE, this model was not successful in simulation of daily climate parameters. Performance of the model to simulate monthly and annual scale was better than daily. Ability of LARS-WG model in simulation of long-term period (decade) was satisfactory. The results indicated that monthly and annual climate parameters by CFSR data have been predicted by a more effective performance. Because statistical indices of CFSR data are lower than LARS-WG. These data underestimated the precipitation in Shahrekord station. RMSE and MAE values of monthly precipitation are 20.49 and 11.19, respectively in Shahrekord station, for CFSR data. These values for annual precipitation are 92.88 and 72.51. For LARS-WG model in monthly scale, RMSE and MAE values are 41.45and 24.75 and these values in annual scale are 164.75 and 123.43. Conclusion In recent years, it is necessary to get accurate and long-term meteorological data due to climate events and scarcity of meteorological stations across the country. Thus, it is a reasonable solution to use weather generators. The objective of current study was assessment of accuracy of weather generator LARS-WG and CFSR data in simulation of climate parameters of Chaharmahal and Bakhtiari province. In general, the results showed the ability of LARS-WG model in simulation of long-term period (decade) data. Thus, values of statistical indicators are not satisfying in short-time periods. Statistical indices of CFSR data are lower than LARS-WG in simulation of short-time period (monthly and annual). They are highly correlated with the observations and they can simulate climate parameters in short- time. Therefore, with the purposes of any specific research, both LARS-WG model and CFSR dataset can be used. Moreover, CFSR data can be applied as valuable option for the regions where there are no weather stations. | ||
| کلیدواژه‌ها [English] | ||
| CFSR, LARS-WG, maximum temperature, Minimum Temperature, precipitation | ||
| مراجع | ||
|
بابائیان، ا.؛ نجفی نیک، ز.؛ زابلعباسی، ف.؛ حبیبی نوخندان، م.؛ ادب، ح.؛ و ملبوسی، ش. (1386). مدل‌سازی اقلیم ایران در دورة 2010- 2039 با استفاده از ریزمقیاس‌نمایی آماری خروجی مدل ECHO-G، کارگاه فنی اثراتتغییراقلیمدرمدیریتمنابعآب، بهمن، تهران: 62-72. حجارپور، ا.؛ یوسفی، م.؛ و کامکار، ب. (1393). آزمون دقت شبیه‌سازهای LARS-WG، WeatherMan و CLIMGEN در شبیه‌سازی پارامترهای اقلیمی سه اقلیم مختلف (گرگان، گنبد و مشهد)، جغرافیاوتوسعه، 12(35): 201-216. خلیلی اقدم، ن.؛ مساعدی، ا.؛ سلطانی، ا. و کامکار، ب. (1391). ارزیابی توانایی مدل LARS- WG در پیش‌بینی برخی از پارامترهای جوی سنندج، مجلةپژوهش‌هایحفاظتآبوخاک، 19(4): 85-102. سایت ادارة کل هواشناسی استان چهارمحال و بختیاری http://www.chaharmahalmet.ir Babaeian, A.; Najafi Nik, Z.; Zabol Abassi, F.; Habibi Nokhandan, M.; Adab, H. and Malbousi, Sh. (2007). Iran climate modeling using statistical downscaling output ECHO-G model in period 2039-2010, Technical Workshop on the Effects of Climate Change on Water Resources Management, Tehran, January 2008: 72-62.
Chaharmahal and Bahktiari Meteorological Organization website: http://www.chaharmahalmet.ir
Dile, Y.T. and Srinivasan R. (2013). Evaluation of CFSR climate data for hydrologic prediction in data scarce watersheds: An application in the blue Nile River basin, Journal of American Water Resources Association (JAWRA), 50(5): 1226–1241.
Fodor, N.; Dobi, I.; Mika, J. and Szeid, L. (2010). MV-WG: A new multi-variable weather generator, Meteorol Atmos Phys, 107: 91–101
Fuka, D.R.; Walter, T.M.; MacAlister, C.; Degaetano, A.T.; Steenhuis, T.S. and Easton, Z.M. (2013). Using the climate forecast system reanalysis as weather input data for watershed models, Hydrological Processes, DOI: 10.1002/hyp.10073.
Hajarpour, 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 climates (Gorgan, Gonbad and Mashhad), Iranian Journal of Geography and Development, 12(35): 201-216.
Khalili Aghdam, N.; Mosaedi, A.; Soltani, A. and Kamkar, B. (2012). Evaluation of ability of LARS-WG model for simulating some weather parameters in Sanandaj, Water and Soil Conversation, 19(4): 85-102.
Mavromatis, T. and Hansen, J.W. (2001). Interannual variability characteristics and simulated crop response of four stochastic weather generators, Agricultural and Forest Meteorology, 109: 283–296.
Saha, S.; Moorthi, S.; Pan, H.; Behringer, D.; Stokes, D. and Grumbine, R. (2010). The NCEP climate forecast system reanalysis, Bulletin of the American Meteorological Society, 91(8): 1015-1057.
Schuol, J. and Abbaspour, K.C. (2007). Using monthly weather statistics to generate daily data in a SWAT model application to West Africa, Ecological Modeling, 2 0 I: 301-311.
Semenov, M.A. (2008). Simulation of extreme weather events by a stochastic weather generator, Climate Research, 35: 203–212.
Semenov, M.A. and Barrow, E.M. (2002). LARS-WG, A Stochastic weather generator for use in climate impact studies (User Manual).
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.
Soltani, A. and Hoogenboom, G. (2003). A statistical comparison of the stochastic weather generators WGEN and SIMMETEO, Climate Research, 24: 215–230. | ||
|
آمار تعداد مشاهده مقاله: 1,739 تعداد دریافت فایل اصل مقاله: 1,204 |
||