Climatic change impact assessment on temperature, precipitation, and runoff of Droudzan catchment area of Fars province using multi-model ensemble mean approach.

Subject Areas : Subjects

Abolghasem Sayadi ¹ (Factualy memeber of Islamic Azazd University, Marvadasht barnch)
naser Taleb Beydokhti ² (Full professor, Faculty of Civil and Environmental Engineering, Shiraz University, Shiraz, Iran.)
Mohsen najarchi ³ (Assistant Professor, Department of Civil Engineering, Department of Civil Engineering, Arak Branch, Islamic Azad University, Arak, Iran.)
Mohammad Mahdi NajafiZadeh ⁴ (Department of Mechanical Engineering, Arak Branch, Islamic Azad University, Arak, Iran)

Received: 2018-12-19 Accepted : 2020-01-26 Published : 2020-02-20

Keywords: climatic changes, rainfall-runoff ANN- statistical downscaling, ensemble model,

Abstract :

This study investigated the effects of climatic changes on temperature, rainfall, and runoff in the Doroudzan catchment in the northeast of Fars province, Iran. Temperature and rainfall changes in two future middle and far period downscaled and studied using 15 CMIP3 climatic models, under emission scenarios A2, B1and A1B, from the database of the LARS WG5.5 model. The difference in the amount of variations in temperature and rainfall in the periods and the observational amounts under the 15 models indicated the uncertainty of the changes values. To reduce this uncertainty and limit the results to the management and planning of water resources, an ensemble approach was considered. For the preparation of the ensemble approach, the parameters from the files of the 15- models file scenarios were averaged so that a climatic ensemble model could be obtained for each period. Then, the runoffs of the next two periods were produced using the FEEDFORWARD neural network. The results indicated an increase in the average monthly maximum temperature and the minimum temperature. The results also showed a decrease in the rainfall in the early months of the year as well as an increase in the rainfall in the spring in most scenarios. Generally, results showed a reduction in the average annual rainfall. The maximum amount of reduction was in far future far period. Besides, a reduction occurred in the average runoff of the catchment in the periods, values in the most years.

References:

1)       بحری, م., م. دستورانی, و م. گودرزی, بررسی خشکسالی‌های دهه 2030-2011 تحت اثر تغییر اقلیم، مطالعه موردی: حوزه حوضه اسکندری، استان اصفهان. مهندسی و مدیریت حوضه, 2015. 7(2). 157-171.

2)       Al-Safi, H.I.J. and P.R. Sarukkalige, Assessment of  future climate change impacts on hydrological behavior of Richmond                   River Catchment. Water Science and Engineering, 2017. 10(3): p. 197-208.

3)       Asadi S, Shahrabi J, Abbaszadeh P, Tabanmehr S. A new hybrid artificial neural networks for rainfall–runoff process modeling. Neurocomputing. 2013 Dec 9;121:470-80.

4)       Bishop, C. and C.M. Bishop, Neural networks for pattern recognition. 1995: Oxford university press.

5)       Change, C., The Physical Science Basis. Summary for Policymakers. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Jelektronnyj resurs] URL: http://www. ipcc. ch, 2007.

6)       Comer, A., A. Fenech, and W. Gough, Selecting a global climate model for understanding future scenarios of climate change. Linking climate models to policy and decision-making, 2007: p. 133-145.

7)       Farajzadeh, J., A.F. Fard, and S. Lotfi, Modeling of monthly rainfall and runoff of Urmia lake basin using “feed-forward neural network” and “time series analysis” model. Water Resources and Industry, 2014. 7: p. 38-48.

8)        Kashani MH, Ghorbani MA, Dinpashoh Y, Shahmorad S. Integration of Volterra model with artificial neural networks for rainfall-runoff simulation in forested catchment of northern Iran. Journal of Hydrology. 2016 Sep 1;540:340-54.

9)       Li Z, Huang G, Wang X, Han J, Fan Y. Impacts of future climate change on river discharge based on hydrological inference: A case study of the Grand River Watershed in Ontario, Canada. Science of the Total Environment. 2016 Apr 1;548:198-210.

10)   Michie, M., Introduction, in Working Cross-culturally. 2014, Springer. p. 1-12.

11)   Mislan H, Hardwinarto S, Sumaryono MA. Rainfall monthly prediction based on artificial neural network: a case study in Tenggarong Station, East Kalimantan-Indonesia. Procedia Computer Science. 2015;59:142-51.

12)   Patel, A.B. and G.S. Joshi, Modeling of Rainfall-Runoff Correlations Using Artificial Neural Network-A Case Study of Dharoi Watershed of a Sabarmati River Basin, India. Civil Engineering Journal, 2017. 3(2): p. 78-87

13)   Semenov, M.A. and P. Stratonovitch, Use of multi-model ensembles from global climate models for assessment of climate change impacts. Climate research, 2010. 41(1):41 p. 1-14.

14)   Semenov, M.A., E.M. Barrow, and A. Lars-Wg, A stochastic weather generator for use in climate impact studies. User Man Herts UK, 2002.

15)   Solomon S, Manning M, Marquis M, Qin D. Climate change 2007-the physical science basis: Working group I contribution to the fourth assessment report of the IPCC. Cambridge university press; 2007 Sep 10.

16)   Salajegheh, A., A. Fathabadi, and M. Mahdavi, Investigation on the efficiency of neuro-fuzzy method and statistical models in simulation of rainfall-runoff process. Journal of Range and Watershed Management, 2009. 62(1): p. 65-79.

17)   Sinha, J., A Comparison of Network Types in Artificial Neural Network-Based Rainfall-Runoff Modelling. International Journal of Applied Research on Information Technology and Computing, 2017. 8(1): p. 41-50.

18)   Solaimani, K., Rainfall-runoff prediction based on artificial neural network (a case study: Jarahi Watershed). American-Eurasian J. Agric. & Environ. Sci, 2009. 5(6): p. 856-865.

19)   Wang, J., W. Wu, and J.M. Zurada, Deterministic convergence of conjugate gradient method for feedforward neural networks. Neurocomputing, 2011. 74(14): p. 2368-2376.

20)   Wang J, Zhang B, Sun Z, Hao W, Sun Q. A novel conjugate gradient method with generalized Armijo search for efficient training of feedforward neural networks. Neurocomputing. 2018 Jan 31;275:308-16.

21)   Wood AW, Leung LR, Sridhar V, Lettenmaier DP. Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Climatic change. 2004 Jan 1;62(1-3):189-216.

22)   Zarghami M, Abdi A, Babaeian I, Hassanzadeh Y, Kanani R. Impacts of climate change on runoffs in East Azerbaijan, Iran. Global and Planetary Change. 2011 Aug 1;78(3-4):137-46.