Climate Change Assessment by using LARS-WG Model in Gilan Province (Iran)

جغرافیا و مخاطرات محیطی

Article 3, Volume 4, Issue 4 - Serial Number 16, January 2016, Pages 55-74   

Document Type: Research Article

DOI: 10.22067/geo.v4i4.38892

Authors

ladan kazemi rad^{* 1}; Hosein Mohammadi²

¹Science and Research Branch, Islamic Azad University

²Tehran University

Abstract

. Introduction
Most of the warming over the last 50 years has been caused by emissions of carbon dioxide (CO2) and other greenhouse gases due to human activities. Observed changes in the climate due to increasing greenhouse-gas concentrations have made it essential to investigate these changes. The General Circulation Model (GCM) is the most current method of investigating climate change studies. Although they are imperfect and uncertain, these models are a key to understanding climate change.
However, it still has serious difficulties in reproducing daily precipitation and temperature despite an increasing ability of GCMs to successfully model the present-day climate. Even when daily GCMs output is available, the coarse spatial resolution of GCMs and large uncertainty in their output on a daily scale, particularly for precipitation, indicates that the output is not appropriate for direct use with process-based models and the analysis of extreme events. Output of GCMs requires application of various downscaling techniques. One of the downscaling techniques to create daily site-specific climate scenarios is to makes use of a stochastic weather generator. Recently, weather generators have been used in climate change studies to produce daily site-specific scenarios of future climate.
Two important reasons for using LARS-WG model include the provision of a means of simulating synthetic weather time-series with certain statistical properties which are long enough to be used in an assessment of risk in hydrological or agricultural applications and in providing the means of extending the simulation of weather time-series to unobserved locations. In fact, LARS-WG has been used in various studies, including the assessment of the impacts of climate change which can be divided into three distinct steps: calibration model, validation model, and the generation of synthetic weather data.
Changes in the climate variables are studied in Gilan Province located in the north of Iran. The output of two GCM models was compared with a stochastic weather generator. In this study LARS-WG and suitable GCM model were used to produce a climate change scenario.


2. Material and Methods
The study area is Gilan Province, which is situated in the north of Iran and located in the South of the Caspian Sea and has about extent areas of approximately14600 kilometers. The performance of the LARS-WG stochastic weather generator model was statistically evaluated by comparing the synthesized data with climatology period at 8 selected synoptic stations, based on 2 GCMs models (MPEH5, HADCM3) and 2 scenarios (A2, B1). In this study, it has shown the period of base data, including precipitation, minimum and maximum temperatures and solar radiation from 1992 to 2010. Firstly, LARS-WG model was performed based on the historical climate data obtained from 1992-2010 to verify the model. The model was performed after assessing the model ability in each station for all 4 states (2 GCMs models based on 2 scenarios). Then, the results were compared and the best model was chosen to evaluate the climate change in the study area.

3. Results and Discussion
Model validation is one of the most important steps of the entire process. The objective was to assess the performance of the model in simulating the climate at the chosen site to determinate whether or not it is suitable for using. Firstly, LARS-WG model was performed based on the historical climate data obtained from 1992-2010 to verify the model. A large number of years of simulated daily weather data were generated and were compared with the observed data through t-test. The monthly mean correlation of the precipitation, minimum and maximum temperature, and solar radiation was accepted at the 0.05 confidence level.
Then, to select a suitable GCM model, the LARS-WG stochastic weather generator model considered for MPEH5 and HADCM3 models in A2 and B1 scenarios was compared with the mean of all the models. Of all these 4 states, MPEH5 model based on A2 scenario, which has the least difference with the mean of the models, was selected and used to predict the future climate.
Finally, the produced data based on the selected model within the period of 2011-2030 was compared with the observed data within the period of 1992-2010 to evaluate the trend of changes between the two periods.

4. Conclusion
Research results have shown that the mean of precipitation in Gilan Province has decreased during 2011-2030. The mean of precipitation was estimated 15.2 mm. Likwise, precipitation has decreased in the most parts of the study area. The maximum decrease in precipitation has been related to Astara. In the south and west of Gilan Province, including Langerood, Amlash, Ramsar, Roodbar, and parts of Siahkal, Precipitation has increased, whereas the maximum increase has been in the boundary of Gilan with Mazandaran. Precipitation in other months has decreased except in February, March, August and November while the maximum decrease has been in September. The minimum temperature of the study area will increase in Anzali station most with 0.5 ºC. Generally, the mean of minimum temperature in study area has decreased within the period of 2011- 2030 having 0.4 ºC. Most changes have been in winter and spring when the minimum temperature has increased in these periods. The greatest increase happened in May with 1.9 ºC while the maximum temperature of Gilan Province was 0.4 ºC and the most changes have been in the east of the study area. Most changes have happened in April and May when the maximum temperature has increased in these months about 1.2 ºC. This has decreased wet day length (days with precipitation more than 0.1 mm) while the greatest decrease has happened in Rasht. This has been the minimum decrease in the mountainous areas, whereas it has been decreased the wet day length in the plain of Gilan more than mountainous areas. The amount of decrease in wet day length has been 11 days. The greatest decrease has happened in October with 4 days. This has increased the mean of the dry day length (days with precipitation less than 0.1 mm) that the amount of it has been 12 days. Most changes have happened in Rasht and the difference between the two periods has decreased toward the areas. This has also increased dry day length except in April, whereas the greatest increase is in October for 4 days. It has increased the mean of hot day length (days with maximum temperature more than 30 ºC), three days when the greatest increase has happened in Rasht. Except Anzali and Astara, hot day length has decreased in other places. The greatest increase has been in July for 2 days. This will decrease within the period of 2011- 2030; the mean of frost day length of Gilan Province (days with minimum temperature equal or less than 0 ºC) will decrease for 5 days as compared with the base period. The greatest decrease has happened in Astara, Rasht, Roodbar and the south of Talesh, whereas the fewest changes have been in Anzali. Despite the decrease in the average number of frost days in the whole province, in November and December the number of hot days increases. The number of frost days in other months will decrease while the greatest decrease will be in October for 3 days.

Keywords

Climate change; GCMs models; LARS-WG; Gilan province

References

ابراهیمی، حسین و دیگران؛ 1384. بررسی وجود تغییر دما در دشت مشهد به‌عنوان نمایه تغییر اقلیم در منطقه. فصلنامه تحقیقات جغرافیایی. دوره 20، شماره 79. صص 18-5.

اسماعیلی، رضا و دیگران؛ 1389. ارزیابی تغییرات طول دوره رشد و یخبندان ناشی از نوسانات اقلیمی مطالعه موردی خراسان رضوی، پژوهش‌های جغرافیای طبیعی. شماره 73. صص 69-82.

اسماعیلی، رضا و دیگران؛ 1390. پهنه‌بندی میزان تغییرات اقلیمی از دیدگاه کشاورزی در دوره اقلیمی آینده، مطالعه موردی استان خراسان رضوی. جغرافیا و برنامه‌ریزی محیطی. سال 22. شماره پیاپی 41. شماره 1. صص 52-35.

آبابایی، بهنام و دیگران؛ 1390. ارزیابی عملکرد مدل LARS-WG در ۱۲ ایستگاه هواشناسی ساحلی ایران. پژوهش آب ایران. سال 5، شماره 9.

بابائیان، ایمان و دیگران؛ 1385. معرفی و ارزیابی مدل LARS-WG برای مدلسازی پارامترهای هواشناسی استان خراسان در دوره 1961 تا 2003. مجله نیوار. شماره 62 و 63. صص 66-49.

بابائیان، ایمان و دیگران؛ 1388. ارزیابی تغییر اقلیم کشور در دوره 2039-2010 میلادی با استفاده از ریزمقیاس نمایی داده‌های مدل گردش عمومی جو ECHO-G. جغرافیا و توسعه. شماره 16. صص152-135.

بابائیان، ایمان؛ 1391. ارزیابی شاخص‌های اقلیم کشاورزی تحت سناریوهای تغییر اقلیم در ایستگاه‌های منتخب خراسان رضوی. نشریه آب‌وخاک (علوم و صنایع کشاورزی). جلد 26. شماره 4. صص 967-953.

خلیلی ‌اقدم، نبی و دیگران؛ 1391. ارزیابی توانایی مدل LARS-WG در پیش‌بینی برخی از پارامترهای جوی سنندج. پژوهش‌های حفاظت آب‌وخاک. جلد 19. شماره 4. صص 103-85.

روشن، غلامرضا و دیگران؛ 1391. آزمون مدل مناسب گردش عمومی جو برای پیش‌بینی مقادیر دما و بارش ایران تحت شرایط گرمایش جهانی. جغرافیا و توسعه. شماره 27. صص 36-19.

سادات آشفته، پریسا. مساح بوانی، علیرضا؛ 1389. تأثیر تغییر اقلیم بر دبی‌های حداکثر: مطالعه موردی، حوضه آیدوغموش، آذربایجان شرقی. مجله علوم و فنون کشاورزی و منابع طبیعی. علوم آب‌وخاک. سال 14، شماره 53.

سلطانی، افشین. قلی پور، منوچهر؛ 1385. شبیه‌سازی اثر تغییراقلیم بر رشد، عملکرد و مصرف آب نخود. مجله علوم کشاورزی و منابع طبیعی. سال سیزدهم، شماره2.

سیاری، نسرین و دیگران؛ 1390. مقایسه دو مدل گردش عمومی جو (HadCM3, CGCM2) در پیش‌بینی پارامترهای اقلیمی و نیاز آبی گیاهان تحت تغییراقلیم (مطالعه موردی: حوضه کشف رود). نشریه آب‌وخاک (علوم و صنایع کشاورزی). جلد 25، شماره 4.

شاه کرمی، نازنین و دیگران؛ 1386. تحلیل عدم قطعیت مدل‌های جفت شده اقیانوس -اتمسفر -گردش عمومی جو بر سناریوهای تغییر اقلیم دما و بارندگی در حوضه زاینده‌رود. کارگاه فنی اثرات تغییراقلیم در مدیریت منابع آب. کمیته ملی آبیاری و زهکشی ایران.

شهابفر، علیرضا. قیامی باجگیرانی، علی؛ 1380. پیش‌بینی درازمدت متغیرهای اقلیمی توسط مدل‌های رقومی گردش عمومی و روش‌های تبدیل مقیاس خروجی‌های این مدل‌ها از مقیاس جهانی به منطقه‌ای. بولتن علمی مرکز اقلیم‌شناسی. جلد 1، شماره 8. صص 22-10.

صبوری، غلامرضا؛ 1393. مطالعه تغییرات پهنه‌های اقلیمی ایران در دوره 2099-2010 تحت پدیده گرمایش جهانی با استفاده از ریزمقیاس نمایی مدل‌های گردش عمومی جو. پایان‌نامه، دانشکده کشاورزی، دانشگاه فردوسی مشهد.

عباسی، فاطمه و دیگران؛ 1389. پیش‌بینی تغییرات اقلیمی خراسان جنوبی در دوره 2039-2010 میلادی با استفاده از ریزمقیاس نمایی آماری خروجی مدل ECHO-G. نشریه آب‌وخاک (علوم و صنایع کشاورزی). جلد 24. شماره 2. صص 233-218.

غیور، حسنعلی. مسعودیان، ابوالفضل؛ 1375. درباره اقلیم و مدلسازی اقلیمی. شماره 32، صص 50-39.

گل محمدی، مریم. مساح بوانی، علیرضا؛ 1390. بررسی تغییرات شدت و دوره بازگشت خشک‌سالی حوضه قره‌سو در دوره‌های آتی تحت تأثیر تغییراقلیم. نشریه آب‌وخاک (علوم و صنایع کشاورزی). جلد 25، شماره 2.

Richter, G. M., & Semenov, M. A. (2004). Modeling impacts of climate change on wheat yields in England and Wales, assessing drought risks, Agriculture Systems, 84(1), 77-97.

Rodriguez Diaz , J. A., Weatherhead, E. K., Knox, J. W., & Camacho, E. (2007). Climate change impacts on irrigation water requirements in the Guadalquivir river basin in Spain. Regional Environmental Change, 7(3), 149-159.

Roshan, Gh., Khoshakhlagh, F., & Azizi, Gh. (2012). Assessment of suitable general atmosphere circulation models for forecasting temperature and precipitation amounts in Iran under condition of global warming. Geography and Development, 27, 19-36.

Roy, K., Rahman, M., & Kuman, U. (2009). Future climate change and moisture stress: Impact on crop agriculture in south-western Bangladesh. Climate Change and Development Perspective, 1(1), 1-8.

Saburi, Gh. (2014). The study zone climate changes under global warming Iran in the period 2099-2010 using the downscaling of general circulation models of the atmosphere. Unpublished thesis, Faculty of Agriculture, Ferdowsi University of Mashhad.

Sadat Ashofte, P., Massah Boani, A. (2010). Effects of climate change on maximum discharges: A case study of Aidoghmoush basin, East Azarbaijan. Science and Technology of Agriculture and Natural Resources, 14(53), 25-39.

SajjadKhan, M., Coulibaly, P., & Dibike, Y. (2006). Uncertainty analysis of stochastically downscaling methods. Journal of Hydrology, 319(1-4), 357-382.

Sayari, N., Alizadeh, A., Banayan, M., Farid, A., & Hesami, M. (2011). Comparison of two general circulation models (HadCM3, CGCM2) in the prediction of climatic parameters under climate change and the need for water (case study: Discover the river basin). Water and Soil (Agricultural Sciences and Technology), 25(4), 912-925.

Semenov, M. A. (2008). Simulation of extreme weather events by a stochastic weather generator. Climate Research, 35(3), 203-212.

Semenov, M. A., & Barrow, E. M. (1997). Use of a stochastic weather generator in the development of climate change scenarios. Climatic Change, 35(4), 397-414.

Semenov, M. A., & Brooks, R. J. (1999). Spatial interpolation of the LARS-WG stochastic weather generator in Great Britain. Climate Research, 11(2), 137-148.

Semenov, M. A., Brooks, R. J., Barrow, E. M., & Richardson, C. W. (1998). Comparison of the WGEN and LARS-WG stochastic weather generators in diver's climates. Climate Research, 10(2), 95-107.

Shahabfar, A., Ghiami, A. (2002). Predicting long-term climatic data by using digital general circulation models. Scientific Bulletin of Climatology Center, 1(8), 10-22.

Soltani, A., & Gholipoor, M. (2006). Simulating the effects of climate change on growth, yield and water use peas. Agricultural Sciences and Natural Resources, 13(2), 69-79.