Climate change impact on annual precipitation and temperature of Zanjan province with uncertainties investigation

Document Type : Research Article


1 Assistant Professor, Department of Civil Engineering, Payame Noor University, Iran

2 Assistant Professor, Department of Water Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran

3 Assistant Professor, Technical and Engineering Department, Tehran Science and Research Branch, Islamic Azad University, Tehran, Iran


In this paper, climate change impact on annual precipitation and temperature series for Zanjan province was assessed and uncertainties were investigated. Spatial average of annual time series of precipitation and temperature for Zanjan province were calculated and then modelled using ARMA model and 100 annual precipitation and temperature series of length 30 years were generated for spatial average of Zanjan province. Using the models, future scenarios of 6 GCMs under 3 emission scenarios were downscaled and 100 annual precipitation and temperature series of length 30 years were generated for each of the scenarios. 90% bounds of the variable statistics for the current condition were compared with the 90% bounds of the corresponding values for all of the future scenarios and the uncertainties were investigated. Model validation showed that the models are adequate for generation of the annual temperature and precipitation series. In confidence level of 90%, it is expected that average temperature of Zanjan province increase from 0.6 to 3.2 ºC and average precipitation change from -25% to +15% in 2035-64 period. So, the uncertainty due to the GCMs structure and the emission scenarios are considerable and should be taken into account.


Main Subjects

1. IPCC. Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; 2013.
2. IPCC. Climate change 2001. Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the third assessment report of the Intergovernmental Panel on Climate Change. UK: Cambridge University Press; 2001.
3. Devkota L.P. and D.R. Gyawali. Impacts of climate change on hydrological regime and water resources management of the Koshi River Basin, Nepal. Journal of Hydrology: Regional Studies, 2015;4: 502–515.
4. Rana AK,FosterT,Bosshard J, Olsson and Bengtsson L. Impact of climate change on rainfall over Mumbai using Distribution-based Scaling of Global Climate Model projections. Journal of Hydrology: Regional Studies, 2014;1: 107–128.
5. Khazaei MR, Zahabiyoun B, and Saghafian B. Assessment of climate change impact on floods using weather generator and continuous rainfall-runoff model. International Journal of Climatology, 2012;32: 1997-2006.
6. Ahmadvand Kahrizi M, Rouhani H. Assessing the conservation impacts of climate change based on temperature projected on 21 century (Case study: Arazkoseh and Nodeh stations). Ecohydrology, 2017;3(4): 597-609 (in Persian).
7. Khazaei MR. Climate change impact assessment on hydrological regimes of a mountainous river basin in Iran. Journal of Water and Soil Resources Conservation, 2016;5(3): 43-54 (in Persian).
8. Fowler HJ, Blenkinsop S, and Tebaldi C. Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. International Journal of Climatology, 2007;27: 1547-1578.
9. IPCC. General Guidelines on the use of Scenario Data for Climate Impact and Adaptation Assessment, version 2, 2007.
10. Kay AL, DaviesHN, Bell VA, and JonesRG. Comparison of uncertainty sources for climate change impacts: flood frequency in England. Climatic Change, 2009;92: 41-63.
11. Semenov MA, Brooks RJ, Barrow EM,and RichardsonCW. Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates. Climate Research, 1998;10: 95-107.
12. Chapman T. Stochastic modelling of daily rainfall: the impact of adjoining wet days on the distribution of rainfall amounts. Environmental Modelling & Software, 1998;13: 317-324.
13. Dubrovsky M, Buchtele J, and ZaludZ. High-frequency and low-frequency variability in stochastic daily weather generator and its effect on agricultural and hydrologic modeling. Climatic Change, 2004;63: 145-179.
14. Khazaei MR, Ahmadi S, Saghafian B, and Zahabiyoun B. A new daily weather generator to preserve extremes and low-frequency variability. Climatic Change, 2013;119:631–645.
15. Reaney SM, andFowler HJ. Uncertainty estimation of climate change impacts on river flow incorporating stochastic downscaling and hydrological model parameterisation error sources, BHS 10th National Hydrology Symposium, Exeter, 2008.
16. Minville M, Brissette F, and Leconte R. Uncertainty of the impact of climate change on the hydrology of a nordic watershed. Journal of Hydrology, 2008;358:70-83.
17. Semenov MA. Development of high-resolution UKCIP02-based climate change scenarios in the UK. Agricultural and Forest Meteorology, 2007;144: 127-138.
18. Wilby RL, DawsonCW, and BarrowEM. SDSM - a decision support tool for the assessment of regional climate change impacts. Environmental Modelling & Software, 2002;17: 147-159.
19. Mavromatis T. and HansenJW. Interannual variability characteristics and simulated crop response of four stochastic weather generators, Agricultural and Forest Meteorology, 2001;109: 283-296.
20. Hansen JWand MavromatisT. Correcting low-frequency variability bias in stochastic weather generators. Agricultural and Forest Meteorology,2001;109:297-310.
21. Salahi B, Goudarzi M, HosseiniSA. Predicting the temperature and precipitation changes during the 2050s in Urmia Lake Basin. Watershed Engineering and Management, 2017;8(4): 425-438 (in Persian).
22. Rezaee M, Nahtaj M, Moghadamniya A, Abkar A, Rezaee M. Comparison of Artificial Neural Network and SDSM Methods in the Downscaling of Annual Rainfall in the HadCM3 Modelling (Case study: Kerman, Ravar and Rabor). Water Engineering, 2015;8(24): 25-40 (in Persian).
23. Semiromi ST, Moradi H, Khodagholi M. Predicted changes in some of climate variables using downscale model LARS-WG and output of HADCM3 model under different scenarios. Watershed Engineering and Management, 2015;7(2): 145-156 (in Persian).
24. Liu Y, Wu J,Liu Y, Hu BX,Hao Y, Huo X, et al. Analyzing effects of climate change on streamflow in a glacier mountain catchment using an ARMA model. Quaternary International, 2015;358:137-145.
25. Salas JD, Delleur JW, Yevjevich V, Lane WL. Applied modeling of hydrologic time series. Water Resources Publications, Littleton, CO, p 484, 1980.
Volume 4, Issue 3
September 2017
Pages 847-860
  • Receive Date: 05 March 2017
  • Revise Date: 25 April 2017
  • Accept Date: 05 May 2017
  • First Publish Date: 23 September 2017