Water and Irrigation Management

Water and Irrigation Management

Forecasting the effects of climate change scenarios on temperature & precipitation based on CMIP6 models (Case study: Sari station)

Document Type : Research Paper

Authors
Adib Roshani 1
Mahdi Hamidi 2
1 MSc Student, Department of Water and Hydraulic Structures, Factually of Civil Engineering, Babol Noshirvani University of Technology, Babol, Iran.
2 Associate Professor, Department of Water and Hydraulic Structures, Factually of Civil Engineering, Babol Noshirvani University of Technology, Babol, Iran.
10.22059/jwim.2022.330603.920
Abstract
Climate change has many impacts on all environmental processes and society. In this study, three models selected from Coupled Model Intercomparison Project Phase 6 (CMIP6) including ACCESS-CM2, HadGEM3-GC31-LL, and NESM3 are validated. The best model (i.e. ACCESS-CM2) is selected to simulate the climatic parameters of the Sari Station using the latest emission scenarios called “shared socioeconomic pathways (SSP).” The LARS-WG is adopted for downscaling, and two emission scenarios SSP2-4.5 and SSP5-8.5 are used for two periods 2041-2060 and 2081-2100, respectively. Several statistical tests are conducted including F-test, T-student, Kolomogrov-Smirnov, coefficient of determination (R2), and root mean square error (RMSE) to validate the LARS-WG model. The verification results indicate the efficiency of the LARS-WG model. The Man-Kendal and Sen’s slope tests are adopted to determine the trend of climatic observational parameters. In general, the results show that the average temperature change increases in the range of 1.16-4.09 °C and also the average annual rainfall increases by 24-36 percent. The Sen’s slope results in terms of maximum and minimum temperatures show an ascending trend in this parameter, but it is descending in the rainfall. Since long-term climate change is one of the factors affecting groundwater and surface resources, it is necessary to develop proper management strategies for the future, preserving ecosystems, and adapting humans to these changes.
Keywords
Downscaling
Emission Scenario
LARS-WG
SSP
Subjects
  1. Ansarifar, M. M., Salarijazi, M., Ghorbani, KH., & Kaboli, A.R. (2019). Simulation of groundwater level in a coastal aquifer. Marine Georesources and Geotechnology, 38(3), 257-265.
  2. Bagherpour, M., Seyed, M., Fathabadi, A., & Mohamadi, A. (2017). Study of Mann-Kendall Test Performance in Detecting the Series of Autocorrelation. Journal of Watershed Management Science, 11(36), 11-21. (In Persian).
  3. Carneiro, J. F., Boughriba, M., Correia, A., Zarhloule, Y., Rimi, A., & Houadi, B. E. (2010). Evaluation of climate change effects in a coastal aquifer in Morocco using a density-dependent numerical model. Environmental Earth Sciences, 61(1), 241-252.
  4. Goodarzi, E., Massah Bavani, A., Dastorani, M., & Talebi, A. (2014). Evaluating effect of downscaling methods; change-factor and LARS-WG on surface runoff (A case study of Azam-Harat River basin, Iran). Desert, 19(2), 99-109.
  5. Gupta, V., Singh, V., & Jain, M.K. (2020). Assessment of precipitation extremes in India during the 21st century under SSP1-1.9 mitigation scenarios of CMIP6 GCMs. Journal of Hydrology, 590(1), 125422.
  6. Hajjarpour, A., Yousefi, M., & Kamkar, B. (2014). Precision test of simulators LARS-WG WeatherMan and CLIMGEN in three different climates simulated (Gorgan, Gonbad & Mashhad). Geography and Development, 12(35), 201-216. (In Persian).
  7. Hamidi, M., & Sabbagh-Yazdi, S. R. (2008) Modeling of 2D density-dependent flow and transport in porous media using finite volume method. Computers and Fluids, 37(8), 1047-1055.
  8. Hamidi, M., Reza, S., & Yazdi, S. (2006). Numerical modeling of seawater intrusion in coastal aquifer using finite volume unstructured mesh method. WSEAS Transactions on Mathematics, 5(6), 648.
  9. Maddah, H.A. (2016). Modeling the Relation between Carbon Dioxide Emissions and Sea Level Rise for the Determination of Future (2100) Sea Level. American Journal of Environmental Engineering, 6(2), 52-61.
  10. Majumder, M., & Roy, U. (2016) Vulnerability of Watersheds to Climate Change Assessed by Neural Network and Analytical Hierarchy Process (1th ed.). Singapore: Springer
  11. Mann, H. B. (1945). Nonparametric Tests Against Trend. Econometrica, 13(3), 245-259.
  12. Mao, X., Enot, P., Barry, D.A., Li, L., Binley, A., & Jeng, D. Sh. (2006). Tidal Influence on Behaviour of a Coastal Aquifer Adjacent to a Low-Relief Estuary. Journal of Hydrology, 327(1–2), 110-127.
  13. Mekonnen, D. F., & Disse, M. (2018). Analyzing the future climate change of Upper Blue Nile River basin using statistical downscaling techniques. Hydrology and Earth System Sciences, 22(4), 2391-2408.
  14. Murphy, J. (1998). An Evaluation of Statistical and Dynamical Techniques for Downscaling Local Climate. Journal of Climate, 12(8), 2256–2284.
  15. Nasiri, M., Hamidi, M., & Kardan Moghadam, H. (2019). Evaluating the effect of supplying drinking water and agriculture water of Sari-Neka aquifer on the salinity movement with the utilization of Gelvard dam. Journal of Water and Soil Resources Conservation, 9(2), 71-88. (In Persian).
  16. Nasiri, M., Kardan Moghadam, H., & M., Hamidi. (2020). A hybrid approach with SWARA and COPRAS methods in ranking management strategies to control seawater intrusion in coastal aquifers. Water and Irrigation Management, 10(3) 365-379. (In Persian).
  17. O’Neill, B., Kriegler, E., Ebi, K., Kemp-Benedict, E., Riahi, K., Rothman, D., Van Ruijven, B., Van Vuuren, D., Birkmann, J., Kok, K., Levy., & Solecki, W. (2017). The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century. Global Environmental Change, 42(1), 169-180.
  18. Priestley, M. D., Ackerley, D. Catto, J. L., Hodges, K. I., McDonald, R. E., & Lee, R. W. (2020). An Overview of the Extratropical Storm Tracks in CMIP6 Historical Simulations. Journal of Climate, 33(15), 6315-6343.
  19. Ramezanipour, M. (2018). Predicting climate change for three decades case study: Maz&aran. Physical Geography Quarterly, 11(39), 115-130. (In Persian).
  20. Rasouli, A. A., Rezaeibanafsheh, M., Msah Bovany, AR., Khorshiddoust, A. M., & Qermzcheshmeh, B. (2014). Investigation Impact of Morpho-Climatic Parameters on Aaccuracy of LARS-WG Model. Journal of Science and Watershed Engineering, 8(24), 9-18. (In Persian).
  21. Razack, M., Jallidin, M., & Houmed-Gaba, A. (2019). Simulation of Climate Change Impact on A Coastal Aquifer under Arid Climate. The Tadjourah Aquifer (Republic of Djibouti, Horn of Africa). Water, 11(11), 2347.
  22. Salehnia, N. (2016). Developing a drought monitoring and predicting system under climate change scenarios by a combination of meteorological, gridded data set and numerical model outputs in the Kashafrood Basin. Ph. D. dissertation, University of Ferdowsi, Mashhad (In Persian).
  23. Semenov, M. A., & Barrow, E. M. (1997). Use of a stochastic weather generator in the development of climate change scenarios. Climatic Change, 34(1), 397-414.
  24. Shagega, F. P., Munishi, S. E., & Kongo, V. M. (2020). Assessment of potential impacts of climate change on water resources in Ngerengere catchment, Tanzania. Physics and Chemistry of the Earth, 116(1),
  25. Su, Buda., Huang, J., Mondal, S. K., Zhai, J., Wang, Yanjun., Wen, Shanshan., Gao, Miaoni., Yanran, Lv., Jiang, Shan., Jiang, Tong., & Aiwei, L. (2020). Insight from CMIP6 SSP-RCP scenarios for future drought characteristics in china. Atmospheric Research, 250(1)
  26. Su-Yuan, L., Li-Juan, M., Zhi-Hong, J., Guo-Jie, W., Raj, G. K., Jing, ZH., Hui, ZH., Ke, F., Yu, H., & Chun, L. (2020). Projected drought conditions in Northwest China with CMIP6 models under combined SSPs and RCPs for 2015-2099. Advances in Climate Change Research, 11(3), 210-217
  27. Tam, V. T., Batelaan, O., & Beyen, I. (2016). Impact assessment of climate change on a coastal groundwater system, Central Vietnam. Environmental Earth Sciences, 75(1), 908.
  28. Tayebiyan, A., Mohammad, T. A., Malakotian, M., Nasiri, A., Heidari, M. A., & Yazdanpanah, Gh. (2019). Potential impact of global warming on river runoff coming to Jor reservoir, Malaysia by integration of LARS-WG with artificial neural network. Environmental Health Engineering and Management Journal, 6(2), 130-149.
  29. Zoheyri, Z., ghazavi, R., omidvar, E., & Davudirad, A. (2020). Comparison of LARS-WG and SDSM Downscaling Models for Prediction Temperature and Precipitation Changes under RCP Scenarios. Arid Regions Geographic. Arid Regions Geographic Studies, 10(40), 39-52. (In Persian).
Water and Irrigation Management
Volume 11, Issue 4
Winter 2022
Pages 781-795