Investigating the Effects of Climate Change on Temperature and Precipitation Using Neural Network and CMIP6 (Case Study: Aleshtar and Khorramabad Stations)

Document Type : Research Paper


1 Department of Water Science and Engineering, Faculty of Agriculture, Bu Ali Sina University, Hamadan, Iran.

2 Planning Deputy of the Regional Water Company of Lorestan Province, Iran.

3 Department of Water Resources Engineering, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran,

4 Department of Environmental Engineering, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran.


The purpose of this research is to evaluate the effects of climate change on temperature, precipitation, and future droughts in Al-Shatar and Khorramabad stations, for this purpose, the output of the general circulation model MRI-ESM2 according to the latest IPCC report (sixth report) and the emission scenarios SSP 1.2.6, SSP 2.4 5, SSP 3.7.0 and SSP 5.8.5 were used in these study areas. Using the decision tree model (M5 Tree), the most dominant predictor variables of the MRI-ESM2 model were selected. Next, the predictor variables were included as input in the advanced artificial neural network (FFNN) statistical microscale model and with the firefly optimization algorithm (FFA), the process The exponential micro-scale of precipitation parameters, maximum temperature, and minimum temperature for the base period (1970-2014) was carried out with favorable results in the studied stations. After proving the capability of the neural network model, forecasting the average temperature and monthly precipitation changes during the near future periods (2062 2023-2023) and the distant future (2063-2100) were carried out under the scenarios of the joint socio-economic trajectories (SSP) related to the coupled model of the sixth phase (CMIP6). In general, the results showed that these variables in both future periods On a monthly scale will have several fluctuations, so that in the two stations of Aleshtar and Khorramabad, during the periods of the near future and the distant future, the maximum temperature, and the minimum temperature will have an increasing trend compared to the observation period in all SSP scenarios, and the minimum temperature changes compared to The maximum temperature will be higher in the near future The annual rainfall of Elshtar station will decrease between 0.3 Percent and 16 Percent and Khorramabad station between Seven percent and 12 Percent under SSP scenarios. In the distant future, the average annual precipitation of Aleshatar station will decrease between 10-20 Percent and Khorramabad station between 12-24% under SSP scenarios.


Main Subjects

  1. Ali, N. M. S., Güven, A., & Al-Juboori, A. M. (2018). Statistical Downscaling of Precipitation and Temperature Using Gene Expression Programming. Journal of Advanced Physics, 7(4), 518-521.
  2. Alizadeh Jabehdar, A., Asadi E., & Ghorbani, M. A. (2021). Selection of the most appropriate GCM models of IPCC's fourth, fifth and sixth assessment reports (Case Study: Ardabil synoptic station). Second International Conference and Fifth National Conference on Natural Resources and Environment.
  3. Alizadeh Jabehdar, A. (2021). Simulation of the inlet runoff to Yamchi Dam in Ardabil under the influence of climate change scenarios. Master dissertation, Tabriz University, Iran.
  4. Almazroui, M., Saeed, F., Saeed, S., Islam, M.N., Ismail, M., Klutse, N.A.B., & Siddiqui, M.H. (2020). Projected change in temperature and precipitation over Africa from Earth Systems and Environment, 4(3), 455-475.
  5. Anh, Q. T., & Taniguchi, K. (2018). Coupling dynamical and statistical downscaling for high-resolution rainfall forecasting: Case study of the Red River Delta, Vietnam. Progress in Earth and Planetary Science, 5(1), 1-18.
  6. Ansari, S., Dehban, H., Zareian, M., & Farokhnia, A. (2022). Investigation of temperature and precipitation changes in the Iran's basins in the next 20 years based on the output of CMIP6 model. Iranian Water Researches Journal, 16(1), 11-24. (In Persion).
  7. Asakereh, H., & Gholami, A. (2021). 'Simulating maximum temperature recorded in Qazvin Synoptic Station Using Statistical Downscaling of CanESM2 Output', Scientific- Research Quarterly of Geographical Data (SEPEHR), 30(118), 25-41. (In Persion).
  8. Aryal, A., Shrestha, S., & Babel, M.S. (2019). Quantifying the sources of uncertainty in an ensemble of hydrological climate-impact projections. Theoretical and Applied Climatology, 135(1/2), 193-209.
  9. Bates, B., Kundzewicz, Z., & Wu, S. (2008). Climate change and water Intergovernmental Panel on Climate Change Secretariat.
  10. Bhattacharya, B., & Solomatine, D. P. (2006). Machine learning in sedimentation Neural Networks, 19(2), 208-214.
  11. Bowden, G. J., Dandy, G. C., & Maier, H. R. (2005). Input determination for neural network models in water resources applications. Part 1-background and methodology. Journal of Hydrology, 301(1-4), 75-92.
  12. Chen, C., Kalra, A., & Ahmad, S. (2019). Hydrologic responses to climate change using downscaled GCM data on a watershed scale. Journal of Water and Climate Change, 10(1), 63-77.
  13. Campozano, L., Tenelanda, D., Sanchez, E., Samaniego, E., & Feyen, J. (2016). Comparison of Statistical Downscaling Methods for Monthly Total Precipitation: Case Study for the Paute River Basin in Southern Ecuador. Advances in Meteorology, 13pp.
  14. Danandeh Mehr, A., Sorman, A. U., Kahya, E., & Hesami Afshar, M. (2020). Climate change impacts on meteorological drought using SPI and SPEI: case study of Ankara, Turkey. Hydrological Sciences Journal, 65(2), 254-268.
  15. Dibike, B.Y., & Coulibaly, P. (2006). Temporal neural networks for downscaling climate variability and extremes. Neural Networks, 19, 135-144.
  16. Fischer, G., Tubiello, F. N., Van Velthuizen, H., & Wiberg, D. A. (2007). Climate change impacts on irrigation water requirements: Effects of mitigation, 1990–2080. Technological Forecasting and Social Change, 74(7), 1083-1107.
  17. Fowler, H. J., Blenkinsop, S., & Tebaldi, C. (2007). Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. International Journal of Climatology: A Journal of the Royal Meteorological Society, 27(12), 1547-1578.
  18. García-García, A., Cuesta-Valero, F. J., Beltrami, H., & Smerdon, J. E. (2019). Characterization of air and ground temperature relationships within the CMIP5 historical and future climate simulations: Journal of Geophysical Research: Atmospheres, 124(7), 3903-3929.
  19. Gidden, M. J., Riahi, K., Smith, S. J., Fujimori, S., Luderer, G., Kriegler, E., & Takahashi, K. (2019). Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century. Geoscientific Modeldevelopment, 12(4), 1443-1475.
  20. Ghorbani, M. A., Deo, R. C., Karimi, V., Yaseen, Z. M., & Terzi, O. (2018). Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir, Turkey. Stochastic Environmental Research and Risk Assessment, 32(6), 1683-1697.
  21. Haykin, S. (1996). Neural networks expand SP's horizons. IEEE Signal Processing Magazine, 13(2), 24-49.
  22. Jato-Espino, D., Sillanpää, N., Charlesworth, S. M., & Rodriguez-Hernandez, J. (2019). A simulation-optimization methodology to model urban catchments under non-stationary extreme rainfall events. Environmental Modelling & Software, 122, 103960.
  23. Kasiri, M., Goodarzi, M., Jnbaz Ghobadi, G. R., Motavali, S. (2020). FutureProjection of temperature and precipitation changes in the southern coast of Caspian sea. Physical Geography Quarterly, 13(47), 2020, 35-51.
  24. Kim, J. H., Sung, J. H., Chung, E. S., Kim, S. U., Son, M., & Shiru, M. S. (2021). Comparison of projection in meteorological and hydrological droughts in the Cheongmicheon Watershed for RCP4. 5 and SSP2-4.5. Sustainability, 13(4), 2066.
  25. Kisi, Ö. (2004). Multi-layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation/Prévision et estimation de la concentration en matières en suspension avec des perceptrons multi-couches et l’algorithme 156 d’apprentissage de Levenberg-Marquardt. Hydrological Sciences Journal, 49(6).
  26. Laddimath, R. S., & Patil, N. S. (2019). Artificial neural network technique for statistical downscaling of global climate model. MAPAN-Journal of Metrology Society of India, Springer, 34(1), 121-127.
  27. Mahdizadeh, S., Meftah halghi, M., Seyyed Ghasemi, S., & Mosaedi, A. (2011). Study of precipitation variation due to climate change (Case study: Golestan dam basin). Journal of Water and Soil Conservation, 18(3), 117-132. (In Persion)
  28. Montenegro-Murillo, D. D., Pérez-Ortiz, M. A., & Vargas-Franco, V. (2019). Using Artificial Neural Networks to predict monthly precipitation for the Cali river basin, Colombia. Dyna, 86(211), 122-130.
  29. Nengker, T., Choudhary, A., & Dimri, P. (2018). Assessment of the performance of CORDEX-SA experiments in simulating seasonal mean temperature over the Himalayan region for the present climate: part I: Climate Dynamics, 50, 2411-2441.
  30. Nie, S., Fu, S., Cao, W., & Jia, X. (2020). Comparison of monthly air and land surface temperature extremes simulated using CMIP5 and CMIP6 versions of the Beijing Climate Center climate model: Theoretical and Applied Climatology, 1-16.
  31. Nourani, V., Rouzegari, N., Molajou, A., & Baghanam, A. H. (2020). An integrated simulation-optimization framework to optimize the reservoir operation adapted to climate change scenarios. Journal of Hydrology, 587, 125018.
  32. Nourani, V., Razzaghzadeh, Z., Baghanam, A. H., & Molajou, A. (2019). ANNbased statistical downscaling of climatic parameters using decision tree predictor screening method. Theoretical and Applied Climatology, 137(3), 1729-1746
  33. Olsson, T., Kämäräinen, M., Santos, D., Seitola, T., Tuomenvirta, H., Haavisto, R., & Lavado-Casimiro, W. (2017). Downscaling climate projections for the Peruvian coastal Chancay-Huaral Basin to support river discharge modeling with WEAP. Journal of Hydrology: Regional Studies, 13, 26-42.
  34. Omidvar, E., Rezaei, M., & Pirnia, A. (2019). Performance Evaluation of Artificial Neural Network Models for Downscaling and Predicting of Climate Variables . Journal of Watershed Management Research, 9 (18), 80-90. (In Persion)
  35. O'Neill, B. C., Tebaldi, C., Vuuren, D. P. V., Eyring, V., Friedlingstein, P., Hurtt,, & Sanderson, B. M. (2016). The scenario model intercomparison project(ScenarioMIP) for CMIP6. Geoscientific Model Development, 9(9), 3461-3482.
  36. O’Neill, B. C., Kriegler, E., Ebi, K. L., Kemp-Benedict, E., Riahi, K., Rothman, S., & Solecki, W. (2017). The roads ahead: Narratives for sharedsocioeconomic pathways describing world futures in the 21st century. Global Environmental Change, 42, 169-180.
  37. Pal, M., Singh, N. K., & Tiwari, N. K. (2012). M5 model tree for pier scour prediction using field dataset. KSCE Journal of Civil Engineering, 16(6), 1079-1084.
  38. Pearson, C. J., Bucknell, D., & Laughlin, G. P. (2008). Modelling crop productivity and variability for policy and impacts of climate change in eastern Canada. Environmental Modelling & Software, 23(12), 1345-1355.
  39. Quinlan, J. R. (1992). Learning with continuous classes. In 5th Australian joint conference on artificial intelligence, Vol. 92, 343-348.
  40. Rahimi, R., & Rahimi, M. (2018). 'Spatial and Temporal Analysis of Climate Change in the Future and Comparison of SDSM, LARS-WG and Artificial Neural Network Downscaling Methods (Case Study: Khuzestan Province)', Iranian journal of Ecohydrology, 5(4), 1161-1174. (In Persion)
  41. Rogelj, J., Popp, A., Calvin, K. V., Luderer, G., Emmerling, J., Gernaat, D.,&Tavoni, M. (2018). Scenarios towards limiting global mean temperatureincrease below 1.5 C. Nature Climate Change, 8(4), 325-332.
  42. Sarzaeim, P., Bozorg-Haddad, O., Bozorgi, A., & Loáiciga, H. A. (2017). Runoff projection under climate change conditions with data-mining methods. Journal of Irrigation and Drainage Engineering, 143(8), 04017026.
  43. Sharafti, A., & Khazaei, M. (2017). Exploration of Randomness Characteristic of Rainfall Pattern Using RDP Model in Symareh Catchment., Journal of Environmental Science and Technology, 19(1), 1-14. (In Persion)
  44. Tabari, H., Shadmani, M., Sabziparvar, A., & Marofi, S. (2008). Comparison of empirical methods, nonlinear regression and artificial neural network in estimating daily evaporation from class A evaporation pan in a dry region.3rd Iran Water Resources Management Conference, Tabriz . (In Persion)
  45. Kawagoe, S., & Sarukkalige, R. (2019). Estimation of probable maximum precipitation at three provinces in Northeast Vietnam using historical data and future climate change scenarios. Journal of Hydrology: Regional Studies, 23, 100599.
  46. Teegavarapu, R. S., & Goly, A. (2018). Optimal selection of predictor variables in statistical downscaling models of precipitation. Water Resources Management, 32(6), 1969-1992.
  47. Tripathi, S., Srinivas, V., & Nanjundiah, R.S. (2006). Downscaling of precipitation forclimate change scenarios: A support vector machine approach. Journal of Hydrology, Pp: 621-640.
  48. Valipour, E., Ghorbani, M., & Asadi, E. (2019). Evaluation and Optimization of Rain Gauge Network Based on the Geostatistic Methods and Firefly Algorithm. (Case study: Eastern Basin of Urmia Lake). Irrigation Sciences and Engineering, 42(4), 153-166
  49. Wilby, R. L., Dawson, C.W., & Barrow, E.M. (2002). SDSM- A Decision Suport Tool for the Assessment of Regional Climate Change Impacts. Journal of Environmental Modeling and Software, 17, 147-159
  50. Xu, C. Y. (1999). From GCMs to river flow: a review of downscaling methods and hydrologic modelling approaches. Progress in physical Geography, 23(2), 229- 249.
  51. Witten, I. H., & Frank, E. (2006). Data mining: Practical machine learning tools and techniques 2nd edition.
  52. Yang, X. S. (2010). Firefly algorithm, stochastic test functions and design optimisation. International Journal of Bio-Inspired Computation, 2(2), 78-84.
  53. Yousefi, H., Pirbazari, S., Moridi, A., Khajehpour, H., Karbasi, H., & Fathi, T. (2021). 'Investigating Temperature Variation due to Climate Change in Iran', Water and Irrigation Management, 11(2), 237-248 .(In Persion)
  54. Zamani, R., Ali, A. M. A., & Roozbahani, A. (2020). Evaluation of adaptation scenarios for climate change impacts on agricultural water allocation using Fuzzy MCDM Methods. Water Resources Management, 34(3), 1093-1110.
  55. Zarrin, A., & Dadashi-Roudbari, A. (2022). Evaluation of CMIP6 models in estimating the temperature in Iran with emphasis on Equilibrium Climate Sensitivity (ECS) and Transient Climate Response (TCR). Iranian Journal of Geophysics. (In Persion)
  56. Zhu, X., Dong, W., Wei, Z., Guo, Y.,Gao, X., Wen, X., & Chen, J. (2018). Multi-decadal evolution characteristicsof global surface temperature anomalydata shown by observation and CMIP5models: International Journal of Climatology, 38, 1533-1542.