Statistical downscaling of climate models projection of minimum temperature under RCP scenarios in Western of Iran

Document Type : Original Article

Authors

1 PhD student in Agrometeorology, Department of Earth Sciences, Islamic Azad University, Science and Research Branch, Tehran.Iran

2 - Associate Professor of Agrometeorology, Department of Earth Sciences, Islamic Azad University, Science and Research Branch

3 Associate Professor of Meteorology, Department of Earth Sciences, Islamic Azad University, Science and Research Branch, Tehran, Iran

4 Assistant Professor of Water Science and Engineering, Bu-Ali Sina University, Hamadan, Iran

Abstract

Considering the significant effect of chilling and frost phenomenon on agricultural production in Iran, the aim of this study is projection of minimum temperature in three provinces of Kurdistan, Kermanshah and Ilam, Western of Iran. For this purpose, the data of 17 meteorological stations during the baseline period of (1989-2018) were collected. Then, the HadGEM2 and CanESM2 climate models outputs were statistically downscaled using LARS-WG and SDSM weather generators under three climate change scenarios of RCP2.6, RCP4.5 and RCP8.5 during future period (2021–2050) and compared with observed data. The performance of the two weather generators, were compared using MSE, RMSE, MAE and R2 indices. The results indicated the good accuracy of both statistical models in simulating the minimum temperature in the study area; however the SDSM model performed better than the LARS-WG. The projected changes of minimum temperature compared to the baseline period revealed a significant increase varying between 0.6 – 1.5 oC in study stations. The most significant change was observed in northern parts of study region especially in the Saqhez and Zarineh stations. The findings of this study can be used in frost risk management and agroclimatic planning in the region.

Keywords

References

Aghashahi, M. Ardestani, M. Nicksokhan, MH. and Tahmasebi, B. 2012. Introduceand compare the LARS-WG and SDSM model in order to fine- scale environmental modeling studies of climate change, the 6thNational Conferenceand Exhibition of Environmental Engineering, Tehran, p 10. (In Farsi)
Azizi, Q. 2004. Climate Change, First Edition, Qomes Publication, Tehran. 284 p. (In Farsi)
Babaeian, I., Najafi Nik, Z. 2010. Climate Change Analysis of Khorasan Razavi Province in the Period of 2039-2010 Using GCM Model Output Rotation. Geography and Regional Development, 15: 2-19. (In Farsi)
Carter, T R., Parry, M L., Harasawa, H., Nishioka, S. 1994. IPCC technical guidelines for assessing climate change impacts and adaptions, IPCC Special Report to Working Group II of IPCC, London
Dibike, Y B., Coulibaly, P. 2005. Hydrologic impact of climate change in the Saguenay Watershed: Comparison of Ownscaling Methods and Hydrologic Models. Journal of Hydrology, 307: 145–163.
Dimri., A P., Kumar, D., Choudhary, A., Maharana, P. 2018. Future changes over the Himalayas: Maximum and minimum temperature, Global and Planetary Change, 162: 212-234.
Fallahghalhari, Gh A., Ahmadi, H. 2015. Spatio-temporal estimation of flowering date of fruit trees in West Azarbaijan province in order to reduce and prevent climatic hazards. Insurance and Agricultural Research Quarterly, 46: 104-81. (In Farsi)
Ghaderzadeh, A. 2015. Evaluation of Climate Change Consequences on Phenological Stages of Apple Trees in Urmia. M.Sc., Faculty of Literature   and   Humanities,    Department   of     Physical Geography, Mohaghegh Ardebili University. (In Farsi)
Goudarzi, M., Hosseini, S A., Masgari, I. 2015. Meteorological Models, First Edition, Azar Kelk Publications, Zanjan.Iran. (In Farsi)
Guan, Y., Zheng, F., Zhang, P., Qin, C. 2015. Spatial and temporal changes of meteorological disasters in china during 1950-2013. Natural Hazards. 75:2607-2623.
Hidalgo-Galvez, M. D., García-Mozo, H., Oteros, J., Mestre, A., Botey, R., Galán, C. 2017. Phenological behaviour of early spring flowering trees in Spain in response to recent climate changes. Theoretical and Applied Climatology, 132: 263-273..
Hosseini, S A., Ahmadi, H. 2016. Projection of temperature changes using statistical downscaling of HadCM3 outputs. Journal of Agricultural Meteorology, 1: 68-73. (In Farsi)
Houshyar, M., Sobhani, B., Hosseini, S A. 2018. Projection of Urmia Maximum Temperature Variation Using Statistical downscaling of CanESM2 Model. Geography and Planning, Volume 23(63): 305-325. (In Farsi)
Hu, T S., Lam, K.C., Ng, S T. 2001. River flow time series prediction with a range dependent neural network. Hydrological Science Journal, 46: 729-745.
IPCC. 2013. Summary for Policymakers. In: Climate Change 2013: The Physical Sceince Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovenmental Panel on Climate Change;
Karamouz, M., Ramezani, F., Razavi, S. 2007. Forecasting the long-term of rainfall through meteorological signals: Application of Artificial Neural Networks, 7th International Congress on Civil Engineering. Tehran; P. 11. (In Farsi)
Kay, AL., Davies, H N., Bell, VA., Jones, R G. 2009. Comparison of uncertainty sources for climate change impactsL flood frequency in England. Climate Change, 92: 41-63.
Khalili, A., Rahimi, J., Bazrafshan, J. 2016. Quantitative Forecasting of the Possible Impacts of Climate Change on the History  and                                 Abbasnia, M., Tavousi, T., Khosravi, M. 2017. Comprehensive Evaluation of the Future Seasonal Changes of the Maximum Temperature of Iran During the Warm Period Based on General Climate Circulation Models. Geographical arrangement of space, 7( 25): 134-121. (In Farsi)
Lin, J Y., Cheng, C T., Chau, K W. 2006. Using support vector machines for long-term discharge prediction. Hydrological Science Journal, 51: 599-612.
Modala, N R., Ale, S., Goldberg, D W., Olivares, M., Munster, C L., Rajan, N., Feagin, R A. 2016. Climate change projections for the Texas High Plains and Rolling Plains. Theoretical and Applied Climatology, 124:1-18.
Moonen, A. C., Ercoli, L., Mariotti, M., Masoni, A. 2002. Climate change in Italy indicated by agrometeorological indices over 122 years. Agricultural and Forest Meteorology, 111: 13-27.
Salahi, B., Goodarzi, M., Hosseini, S A. 2016. Forecasting Temperature and Precipitation Changes in the 2050s in Urmia Lake Basin, Journal of Watershed Engineering and Management, 4: 425-438. (In Farsi)
Shamsipour, A A. 2013. Climate Modeling Theory and Method, University of Tehran Publications, p 101. (In Farsi)
Sarkar, J., Chicholikar, J. R., Rathore, L. S. 2015. Predicting Future Changes in Temperature and Precipitation in Arid Climate of Kutch, Gujarat: Analyses Based on LARS-WG Model, research articl, Current science, 109(11): 2084-2093.
Sedaghatkerdar, A., Fattahi, E. 2008. Warning of drought indices in Iran,  Journal  of  Geography  and Development, University of Sistan and Baluchestan; 6 (11):76-59. (In Farsi)
Semonov, M. A., Stratonovitch, P. 2010. Use of multi-model ensembles from global climate models for assessment of climate change impacts. Climate Research. 41: 1-14.
Sharma, D., Gupta, A. D., Babel, M. S. 2007. Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping River Basin, Thailand. Hydrol. Earth Syst. Sci., 11: 1373-1390.
Tatsumi, K., Oizumi, T., Yamashiki, Y. 2013. Introduction of daily minimum and maximum temperature change signals in the Shikoku region using the statistical downscaling method by GCMs. Hydrological Research Letters, 7(3): 48-53.
Wilby R.L., Dawson C.W., Barrow, E.M. 2002. SDSM- a decision support tool for the assessment of regional climate change impacts, Environmental Modeling & Software, 17: 147-159.
Wilby, R.L., Dawson, W.C. 2007. SDSM 4.2- A decision support tool for the assessment of regional climate change impacts, SDSM manual version 4.2, Environment Agency of England and Wales:94p.
Wilks, D.S., Wilby, R.1999. The weather generation game: a review of stochastic weather models. Progress in Physical Geography, 23: 329-357.                              Risk of Late Glacial Occurrence during the 21st Century in Iran. Journal of Agricultural Meteorology, 2: 38-48. (In Farsi)

Files

Share

How to cite

Statistics