An assessment of LARS-WG’s model to forecast meteorological parameters for climatic zones of cotton-harvested areas over Iran

Document Type : Research Paper

Authors

1 Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

2 Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.

Abstract

Background and Objectives: Nowadays, climatologists and researchers are interested in the long-term forecast of climatic variables to be informed about the extent of their variations and to take measures to mitigate the adverse effects of climate change. Accordingly, general circulation models (GCMs) of atmosphere have been developed to predict climatic parameters. LARS-WG is a model to downscale the output of GCMs. It was used in the present research to generate data of daily precipitation, radiation, and minimum and maximum temperature across the cotton cultivation climatic zones (Meteorological stations) in Iran. The objective of this study is to estimate climatic factors (rainfall, radiation and minimum and maximum temperature) in the future period in major climatic cotton cultivation areas in Iran, which may be used for optimized water resource preservation and management in these regions. Also, the 5th IPCC report was used in this study, in contrast with the majority of investigations that use the 4th report.
Materials and Methods: In this research, the important areas under cotton cultivation in Iran are the target area. The data of 23 synoptic stations were used in these areas. The required meteorological data recorded in the stations were daily rainfall, minimum temperature, maximum temperature and sunshine. The present study assessed the performance of five different GCM models in simulating the data of precipitation, radiation, minimum temperature, and maximum temperature in nine synoptic stations (SABZEVAR, GHOOCHAN, GHOM, HASHMABAD, LAR, BILESOWAR, EDAREGORGAN, HASANABADEDARAB, and MASHHAD) from 2011 to 2016, firstly. Finally, two GCMs (MIROC5 and GFDL-CM3) were selected for the research purpose based on the results of t-test, F-test, and the Kolmogorov-Smirnov non-parametric test. Then, the parameters were predicted by the selected GCM models for 20 years (2041-2060) under the emission scenarios of RCP4.5 and RCP8.5.
Results: According to employing the scenarios of RCP4.5 and RCP8.5, the predicted solar radiation does not show a significant change in the future period versus the base period of 1981-2010 in all studied regions based on both GCM models. The highest change (0.249 MJ / m2 / day) is based on the MIROC5 model and the RCP8.5 scenario in climate zone 6102 (HASHMABAD station). The predictions revealed that the parameters of maximum and minimum temperature would be ascending for all the climatic regions over the future period. The highest variations in the average long-term annual minimum and maximum temperatures versus the base period would be 2.67 and 2.75°C happening in Climatic Region 6002 Includes stations (HASANABADEDARAB, KHAF, HAJI ABAD and GONBAD) over the 2041-2060 period under Scenario RCP8.5. For both scenarios RCP4.5 and RCP8.5, the highest temperature increase was observed in the climatic zones where located in south of Alborz and the east of Zagros mountain chains as well as the Central Iranian Dry Plateau and the lowest increasing was in the climatic regions located around the Caspian Sea, the Persian Gulf and the south of Iran. For the total annual precipitation over the future period (2041-2060), the highest amplification was predicted by MIROC5 to be 98.5 mm under Scenario RCP4.5 in Climatic Region 6202 (EDAREGORGAN station), and the greatest loss of precipitation was predicted by the same model to be -29.8 mm in Scenario RCP8.5 in Climatic Region 6102.
Conclusion: Given the prediction of rising average annual minimum and maximum temperature and the decline of precipitation over the future period (2041-2060) in the climatic regions leading to the arid plain of central and southern Iran, it can be concluded that the variations of meteorological parameters induced by climate change will be significant in the cotton-growing climatic regions of Iran over the future decades.

Keywords

References

1.Abasi, F., Malbosi, Sh., Babaian, E., Asmari, M., and Borhani, R. 2010. Climate change prediction of south Khorasan province during 2010-2039 by using statistical downscaling of ECHO-G Data. J. Water Soil. 24: 2. 218-233. (In Persian)
2.Babaian, A., Najafi Nik, Z., Zabol Abbasi, F., Nowkhandan, M., and Malbosi, Sh. 2009. Assessment of climate changing in 2010-2039 using downscaling data GCM (ECHO-G). Geograph. and Dev. J. 16: 34-41. (In Persian)
3.Bannayan, M., Lotfabadi, S., Sanjani, S., Mohammadian, A., and Agaalikhani, M. 2011. Effects of precipitation and temperature on cereal yield variability in northeast of Iran. Int. J. Biometeorol. 55: 387-401.
4.Chadwick, R., Boutle, I., and Martin, G. 2013. Spatial patterns of precipitation change in CMIP5: Why the rich do not get richer in the tropics. J. Clim. 26: 11. 3803-3822.
5.Cowden, J.R., Watkins, Jr.D.W., and Mihelcic, J.R. 2008. Stochastic rainfall modeling in west aafrica: Parsimonious approaches for domestic rainwater harvesting assessment. J. Hydrol. 361: 1-2. 64-77.
6.Deihimfard, R., Eyni Nargeseh, H., and Farshadi, Sh. 2017. Modeling the effects of climate change on irrigation requirement and water use efficiency of wheat fields of Khuzestan province. J. Water Soil. 31: 4. 1015-1030. (In Persian)
7.IPCC, 2007. International panel on climate change (IPCC). Exit EPA disclaimer contribution of working group II to the third assessment report of the intergovernmental panel on climate change. AR4.
8.IPCC, 2014. International panel on climate change (IPCC). Contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change. AR5.
9.Jiang, Z.H., Chen, W.L., Song, J., and Wang, J. 2009. Projection and evaluation of the precipitation extremes indices over China based on seven IPCC AR4 coupled climate models. Chine. J. Atm. Sci. 33: 1. 109-120.
10.Kharin, V.V., Zwiers, F.W., Zhang, X., and Wehner, M. 2013. Changes in temperature and precipitation extremes in the CMIP5 ensemble. J. Clim. Change. 119: 345-357.
11.Kumar bal, P., Ramachandran, A., Geetha, R., Bhaskaran, B., Thirumurugan, P., Indumathi, J., and Jayanthi, N. 2015. Climate change projections for Tamil Nadu, India: deriving high-resolution climate data by a downscaling approach using PRECIS. J. Theor Appl. Climatol. 123: 3-4. 523-535.
12.Luo Qanyan, M.A., Williams, J., Belloti, W., and Bryan, B. 2003. Quantative and visual assessments of climate change impacts on south Australian wheat production. Agric. Syst. 77: 3. 173-186.
13.Ma, C., Pan, S., Wang, G., Liao, Y., and Xu, Y.P. 2016. Changes in precipitation and temperature in Xiangjiang River Basin. China. J. Theor. Appl. Climatol. 123: 3-4. 859-871.
14.Massah Bavani, A.R., and Morid, S. 2006. Impact of climate change on the water resources of zayandeh rud basin. J. Sci. and Technol. Agric. and Natur. Resour. 9: 4. 28-34. (In Persian)
15.Moss, R.H., Edmonds, J.A., Hibbard, K.A., Manning, M.R., Rose, S.K., Van Vuuren, D.P., Carter, T.R., Emori, S., Kainuma, M., Kram, T., Meehl, G.A., Mitchell, J.F., Nalicenovic, N., Riahi, K., Smith, S.J., Stouffer, R.J., Thomson, A.M., Weyant, J.P., and Wilbanks, T.J. 2010. The next generation of scenarios for climate change research and assessment. Nature. 463: 7282. 747-756.
16.Pirmoradian, N., Hadinia, H., and Ashrafzadeh, A. 2016. Prediction of minimum and maximum temperature, radiation and precipitation in Rasht synoptic station under different climate change scenarios. J. Geogr. Plann. 20: 55. 29-44. (In Persian)
17.Reidsma, P., Ewert, F., Lansink, AO., and Leemans, R. 2010. Adaptation to climate change and climate variability in European agriculture: the importance of farm level responses. Eur. J. Agron. 32: 91–102.
18.Richter, G.M., and Semenov, M.A. 2004. Modeling impacts of climate change on wheat yields in England and wales: assessing drought risks. Agric. Syst. 84: 1. 77-97.
19.Sajjad Khan, M., Coulibaly, P., and Dibike, Y. 2006. Uncertainty analysis of stochastically downscaling methods. J. Hydrol. 319: 1-4. 357-382.
20.Saunders, M.A. 1999. Earth’s future climate. Philos. T. Roy. Soc. 357: 3459-3480.
21.Semenov, M.A. 2008. Simulation of extreme weather events by a stochastic weather generator. Clim. Res. 35: 203-212.
22.Terink, W., Immerzeel, W.W., and Droogers, P. 2013. Climate change projections of precipitation and reference evapotranspiration for the Middle East and Northern Africa until 2050. Int. J. Climatol. 33: 14. 3055-3072.
23.van Bussel, L.G.J., Grassini, P., Van Wart, J., Wolf, J., Claessens, L., Yang, H., Boogaard, H., de Groot, H., Saito, K., Cassman, K.G., and van Ittersum, M.K. 2015. From field to atlas: Upscaling of location-specific yield gap estimates. Field Crops Res. 177: 98-108.
24.Williams, A.G. 1991. Modeling future climates: From GCMs to statistical downscaling approaches, University of Toronto at Scarborough, 56p.
25.Zarghami, M., Abdi, A., Babaeian, I., Hassanzadeh, Y., and Kanani, R. 2011. Impacts of climate change on runoffs in East Azerbaijan, Iran. J. Global Planet Change. 78: 3-4. 137-146.
26.Zhao, Z.C., Luo, Y., Jiang, Y., and Xu, Y. 2008. Projections of surface air temperature
change in China for the next two decades. J. Meteorol. Environ. 24: 5. 1-5.
Journal of Crop Production
Volume 13, Issue 4
February 2021
Pages 27-40

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