Seasonal Changes of Precipitation and Temperature of Mountainous Watersheds in Future Periods with Approach of Fifth Report of Intergovernmental Panel on Climate Change (Case study: Kashafrood Watershed Basin)

Document Type : Research Article

Authors

• Amirhosein Aghakhani Afshar ¹
• Yousef Hassanzadeh ¹
• Ali Asghar Besalatpour
• Mohsen Pourreza Bilondi ²

¹ University of Tabriz

² University of Birjand

10.22067/jsw.v0i0.56154

Abstract

Introduction: Hydrology cycle of river basins and water resources availability in arid and semi-arid regions are highly affected by climate changes, so that recently the increase of temperature due to the increase of greenhouse gases have led to anomaly in the Earth’ climate system. At present, General Circulation Models (GCMs) are the most frequently used models for projection of different climatic change scenarios. Up to now, IPCC has released four different versions of GCM models, including First Assessment Report models (FAR) in 1990, Second Assessment Report models (SAR) in 1996, Third Assessment Report models (TAR) in 2001 and Fourth Assessment Report models (AR4) in 2007. In 2011, new generation of GCM, known as phase five of the Coupled Model Intercomparison Project (CMIP5) released which it has been actively participated in the preparation of Intergovernmental Panel on Climate Change (IPCC) fifth Assessment report (AR5). A set of experiments such as simulations of 20th and projections of 21st century climate under the new emission scenarios (so called Representative Concentration Pathways (RCPs)) are included in CMIP5. Iran is a country that located in arid and semi-arid climates mostly characterized by low rainfall and high temperature. Anomalies in precipitation and temperature in Iran play a significant role in this agricultural and quickly developing country. Growing population, extensive urbanization and rapid economic development shows that Iran faces intensive challenges in available water resources at present and especially in the future. The first purpose of this study is to analyze the seasonal trends of future climate components over the Kashafrood Watershed Basin (KWB) located in the northeastern part of Iran and in the Khorsan-e Razavi province using fifth report of Intergovernmental Panel on climate change (IPCC) under new emission scenarios with Mann-Kendall (MK) test. Mann-Kendall is one of the most commonly used nonparametric tests to detect climatic changes in time series and trend analysis. The second purpose of this study is to compare CMIP5 models with each other and determine the changes in rainfall and temperature in the future periods in compare with base period on seasonal scale in all parts of this basin.
Materials and Methods: In this research, keeping in view the importance of precipitation and temperature parameters, fourteen models obtained from the General Circulation Models (GCMs) of the newest generation in the Coupled Model Intercomparison Project Phase 5 (CMIP5) were used to forecast the future climate changes in the study area. In historical time (1992-2005), simulated data of these models were compared with observed data (34 rainfall and 12 temperature stations) using four evaluation criteria for goodness-of-fit including Nash-Sutcliffe (NS), Percent of Bias (PBIAS), coefficient of determination (R2) and the ratio of the root mean square error to the standard deviation of measured data (RSR). Furthermore, all models have a very good rating performance for all of the evaluation criteria and therefore investigation is done for precipitation data as an important component in survey of climate subject to select the optimum models in kashafrood watershed basin.
Results and Discussion: By comparing four evaluation criteria for fourteen models of CMIP5 during historical time, finally, four climate models, including GFDL-ESM2G, IPSL-CM5A-MR, MIROC-ESM and NorESM1-M which indicated more agreement with observed data according to the evaluation criteria were selected. Furthermore, four Representative Concentration Pathways (RCPs) of new emission scenario, namely RCP2.6, RCP4.5, RCP6.0 and RCP8.5 were extracted, interpolated and then under three future periods, including near-century (2006-2037), mid-century (2037-2070) and late-century (2070-2100) were investigated and compered.
Conclusions: The results of Mann-Kendall test which was applied to examine the trend, revealed that the precipitation have variable positive and negative trends which were statistically significant. In addition, mean temperature have a significant positive trend with 90, 99 and 99.9% confidence level. In seasonal scale, survey of climatic variable (rainfall and mean temperature) showed that the maximum and minimum of precipitations occur during spring and summer and mean temperature in all seasons is higher than historical baseline, respectively. Maximum and minimum of mean temperature occur in summer and winter, and the amount of seasonal precipitation in these seasons will be reduced. Finally, across all parts of kashafrood watershed basin, rainfall and mean temperature will be reduced and increased, respectively. In conclusion, models of CMIP5 can simulate the future climate change in this region and four models of CMIP5 can be used for this region.

Keywords

• Climate change
• Fifth assessment report
• Greenhouse Gases
• RCP scenario
• Mann-Kendall test

20.1001.1.20084757.1395.30.5.8.6

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References

1. Ahmed K.F., Wang G., Silander J., Wilson A.M., Allen J.M., Horton, R., and Anyah R. 2013. Statistical downscaling and bias correction of climate model outputs for climate change impact assessment in the U.S. northeast. Journal Global and Planetary Change, 100:320-332.

2. Ashraf Vaghefi S., Mousavi S.J., Abbaspour K.C., Srinivasan R. and Yang H. 2014. Analyses of the impact of climate change on water resources components, drought and wheat yield in semiarid regions: Karkheh River Basin in Iran. Journal of Hydrological Processes, 28(4):2018-2032.

3. Brekke L., Thrasher B.L., Maurer E.P. and Pruitt T. 2013. Downscaled CMIP3 and CMIP5 Climate Projections: Release of Downscaled CMIP5 Climate Projections, Comparison with Preceding Information, and Summary of User Needs. U.S. Department of the Interior, Bureau of Reclamation, Technical Service Center, Denver, Colorado, p116.

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. Journal of Climate, 26(11):3803–3822.

5. Cuo L., Zhang Y., Zhu F. and Liang L. 2015. Characteristics and changes of streamflow on the Tibetan Plateau: a review. Journal of Hydrology: Regional Studies, 2:49–68.

6. IPCC. 1990. Climate Change: The IPCC Scientific Assessment (1990). Cambridge Univ. Press: Cambridge, UK.

7. IPCC. 2014. Climate Change 2014 Synthesis Report. Summary for Policymakers. Intergovernmental Panel on Climate Change (IPCC). Retrieved December 18, 2014, from www.ipcc.ch/pdf/assessment report/AR5/syr/SYR_AR5_SPMcorr1.pdf.

8. IPCC. 2007. Climate Change 2007: Impacts, Adaptation, and Vulnerability. Exit EPA Disclaimer Contribution of Working Group II to the Third Assessment Report of the Intergovernmental Panel on Climate Change. (ed.) by ML. Parry et al. Cambridge Univ. Press, Cambridge, UK, 365pp.

9. Javan K., Nasirisaleh F. and TaheriShahraiyni H. 2013. The influences of climate change on the runoff Gharesoo River Watershed. American Journal of Climate Change, 2(4):296-305.

10. Jiang Z.H., Chen W.L. and Song J. et al. 2009. Projection and evaluation of the precipitation extremes indices over China based on seven IPCC AR4 coupled climate models. Chinese Journal of Atmospheric Sciences, 33(1):109-120.

11. Kharin V.V., Zwiers F.W., Zhang X. and Wehner M. 2013. Changes in temperature and precipitation extremes in the CMIP5 ensemble. Journal of Climatic Change, 119:345–357.

12. Kendall M.G. 1955. Rank Correlation Methods. Griffin. London.

13. Kumar bal P., Ramachandran A., Geetha R., Bhaskaran B., Thirumurugan P., Indumathi J., and Jayanthi N. 2016. Climate change projections for Tamil Nadu, India: deriving high-resolution climate data by a downscaling approach using PRECIS. Journal ofTheoretical and Applied Climatology, 123(3–4):523-535.

14. Ma C., Pan S., Wang G., Liao Y., and Xu Y.P. 2016. Changes in precipitation and temperature in Xiangjiang River Basin, China. Journal ofTheoretical and Applied Climatology, 123(3–4):859-871.

15. Mann H.B. 1945. Nonparametric tests against trend. Journal ofEconometrica, 13(3):245-259.

16. Miao C.Y., Duan Q.Y., Sun Q.H. and Li J.D. 2013. Evaluation and application of Bayesian multi-model estimation in temperature simulations. Journal of Progress in Physical Geography, 37:727–744.

17. Moriasi D.N., Arnold J.G., Van Liew M.W., Bingner R.L., Harmel R.D. and Veith T.L. 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Journal ofAmerican Society of Agricultural and Biological Engineers, 50(3):885–900.

18. Moss R., Edmonds J. and Hibbard K. et al. 2010. The next generation of scenarios for climate change research and assessment. Journal of Nature, 463(7282):747–756.

19. Najafi M.R. and Moazami S. 2015. Trends in total precipitation and magnitude–frequency of extreme precipitation in Iran, 1969–2009. International Journal of Climatology,Doi: 10.1002/joc.4465.

20. Najafi M.R., Zwiers F.P. and Gillett N.P. 2015. Attribution of Arctic temperature change to greenhouse-gas and aerosol influences. Journal of‎Nature Climate Change, 5(3):246–249.

21. Pervez M.S. and Henebry G.M. 2015. Assessing the impacts of climate and land use and land cover change on the freshwater availability in the Brahmaputra River basin. Journal of Hydrology: Regional Studies,3:285–311.

22. Schubert S.D. and Lim Y.K. 2013. Climate variability and weather extremes: Model-simulated and historical data. Journal of Extremes in a Changing Climate, P.239–285. Springer. Netherlands. Doi: 10.1007/978-94-007-4479-0_9.

23. 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. International Journal of Climatology,33(14):3055-3072.

24. Van Vuuren D.P., Edmonds J. and Kainuma M. et al 2011. The representative concentration pathways: an overview. Journal of Climatic Change, 109:5–31.

25. Wan H., Zhang X., Zwiers F. and Min S.K. 2014. Attributing northern high-latitude precipitation change over the period 1966–2005 to human influence. Journal of Climate Dynamics, 45:1713-1726.

26. Xu C.H. and Xu Y. 2012. The Projection of Temperature and Precipitation over China under RCP Scenarios using a CMIP5 Multi-Model Ensemble. Atmospheric and Oceanic Science Letters, 5(6):527−533.

27. Zarghami M., Abdi A., Babaeian I., Hassanzadeh Y. and Kanani R. 2011. Impacts of Climate Change on Runoffs in East Azerbaijan, Iran. Journal Global and Planetary Change. 78(3-4):137-146.

28. Zhao Z.C., Luo Y. and Jiang Y. et al. 2008. Projections of surface air temperature change in China for the next two decades. Journal of Meteorology and Environment (in Chinese), 24(5):1-5.