Projected changes in precipitation and air temperature over the Volga River Basin from bias-corrected CMIP6 outputs

Document Type : Research

Authors

1 Civil Engineering Department, K. N. Toosi University of Technology, Tehran, IRAN,

2 Civil Engineering Department, K. N. Toosi University of Technology, Tehran, IRAN

Abstract

This paper investigates future changes in annual mean precipitation and air temperature across the Volga River basin, which serve as significant drivers of climate-induced changes in the Volga River's discharge, the primary input to the Caspian Sea. The thirteen Global Climate Models (GCMs) outputs under four Shared Socioeconomic Pathways (SSPs) scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5) from the sixth phase of Coupled Model Intercomparison Project (CMIP6) are used for this study. In the historical period (1950-2014), using comprehensive rating metrics and Taylor diagram, the GCMs are ranked according to their ability to capture the temporal and spatial variability of precipitation and air temperature. The Multi-Model Ensemble (MME) is generated, and bias-correction techniques are utilized to reduce the uncertainties and correct the biases in CMIP6 outputs. Bias-correction techniques are assessed in the historical period and the average of proper methods utilized for future projections (2015-2100). In the 21st century, future projections show that the Volga River basin could mainly experience a temperature increase of 0.4°C to 7.5°C, alongside a precipitation rise of 0.7% to 37%, depending on the scenarios considered. Comparison of future projections with an observational dataset from 2015 to 2017 indicates that the SSP2–4.5 is more likely scenario to represent the future climate of the Volga River basin.

Keywords

Main Subjects

References

  1. Yue Y, Yan D, Yue Q, Ji G, Wang Z. Future changes in precipitation and temperature over the Yangtze River Basin in China based on CMIP6 GCMs. Atmos Res [Internet]. 2021;264(July):105828. Available from: https://doi.org/10.1016/j.atmosres.2021.105828
  2. Mondal SK, Tao H, Huang J, Wang Y, Su B, Zhai J, et al. Projected changes in temperature, precipitation and potential evapotranspiration across Indus River Basin at 1.5–3.0 °C warming levels using CMIP6-GCMs. Sci Total Environ [Internet]. 2021;789:147867. Available from: https://doi.org/10.1016/j.scitotenv.2021.147867
  3. Yazdandoost F, Moradian S, Izadi A, Aghakouchak A. Evaluation of CMIP6 precipitation simulations across different climatic zones: Uncertainty and model intercomparison. Atmos Res [Internet]. 2020;(September):105369. Available from: https://doi.org/10.1016/j.atmosres.2020.105369
  4. Shrestha AB, Wagle N, Rajbhandari R. A review on the projected changes in climate over the Indus Basin. Indus River Basin. 2019;145–58.
  5. Gusain A, Ghosh S, Karmakar S. Added value of CMIP6 over CMIP5 models in simulating Indian summer monsoon rainfall. Atmos Res [Internet]. 2020;232(September 2019):104680. Available from: https://doi.org/10.1016/j.atmosres.2019.104680
  6. Ahmed K, Sachindra DA, Shahid S, Iqbal Z, Nawaz N, Khan N. Multi-model ensemble predictions of precipitation and temperature using machine learning algorithms. Atmos Res [Internet]. 2020;236:104806. Available from: https://www.sciencedirect.com/science/article/pii/S0169809519309858
  7. Khan N, Shahid S, Ahmed K, Ismail T, Nawaz N, Son M. Performance assessment of general circulation model in simulating daily precipitation and temperature using multiple gridded datasets. Water (Switzerland). 2018;10(12).
  8. Mishra V, Bhatia U, Tiwari AD. Bias-corrected climate projections for South Asia from Coupled Model Intercomparison Project-6. Sci Data [Internet]. 2020;7(1):1–13. Available from: http://dx.doi.org/10.1038/s41597-020-00681-1
  9. Raju KS, Kumar DN. Review of approaches for selection and ensembling of GCMS. J Water Clim Chang. 2020;11(3):577–99.
  10. Hoseini SM, Zolfaghari MR, Soltanpour M. Probabilistic estimates of future changes in evaporation from the Caspian Sea based on multimodel ensembles of CMIP6 projections. Int J Climatol. 2023;(March):5830–44.
  11. Kattsov VM, Akentieva E, Aleksandrov E, Alekseev G, Anisimov O, Balonishnikova Z, et al. Report on climate risks in the Russian Federation. St Petersbg. 2017;106.
  12. Rodionov SN. Global and regional climate interaction: the Caspian Sea experience. Global and regional climate interaction: the Caspian Sea experience. 1994.
  13. Nesterenko YM, Solomatin N V., Nikiforov SE, Yu Nesterenko M, Kapustina OA. Climate changes analysis in the Volga Region and the Urals. IOP Conf Ser Earth Environ Sci. 2023;1154(1).
  14. Georgiadi AG, Kashutina EA, Milyukova IP. Long Periods of Increased/Decreased Runoffs of Large Russian Rivers. IOP Conf Ser Earth Environ Sci. 2019;386(1).
  15. Gelfan AN, Gusev EM, Kalugin AS, Krylenko IN, Motovilov YG, Nasonova ON, et al. Runoff of Russian Rivers under Current and Projected Climate Change: a Review 2. Climate Change Impact on the Water Regime of Russian Rivers in the XXI Century. Water Resour. 2022;49(3):351–65.
  16. Kalugin A. Hydrological and Meteorological Variability in the Volga River Basin under Global Warming by 1.5 and 2 Degrees. Climate. 2022;10(7):1–23.
  17. Beck HE, Zimmermann NE, McVicar TR, Vergopolan N, Berg A, Wood EF. Present and future köppen-geiger climate classification maps at 1-km resolution. Sci Data. 2018;5:1–12.
  18. Eyring V, Bony S, Meehl GA, Senior CA, Stevens B, Stouffer RJ, et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci Model Dev. 2016;9(5):1937–58.
  19. O’Neill BC, Tebaldi C, Van Vuuren DP, Eyring V, Friedlingstein P, Hurtt G, et al. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci Model Dev. 2016;9(9):3461–82.
  20. Alexander LV, Zhang X, Peterson TC, Caesar J, Gleason B, Klein Tank AMG, et al. Global observed changes in daily climate extremes of temperature and precipitation. J Geophys Res Atmos. 2006;111(D5).
  21. Willmott CJ, Matsuura K. Terrestrial Air Temperature and Precipitation: Monthly and Annual Time Series (1900 - 2017). http://climate.geog.udel.edu/~climate/html_pages/README.ghcn_ts2.html. 2018.
  22. Muche ME, Sinnathamby S, Parmar R, Knightes CD, Johnston JM, Wolfe K, et al. Comparison and evaluation of gridded precipitation datasets in a Kansas agricultural watershed using SWAT. JAWRA J Am Water Resour Assoc. 2020;56(3):486–506.
  23. Wang S, Li H, Zhang M, Duan L, Zhu X, Che Y. Assessing Gridded Precipitation and Air Temperature Products in the Ayakkum Lake, Central Asia. Sustain. 2022;14(17).
  24. Liang-Liang L, Jian L, Ru-Cong Y. Evaluation of CMIP6 HighResMIP models in simulating precipitation over Central Asia. Adv Clim Chang Res. 2022;13(1):1–13.
  25. Jiang Z, Li W, Xu J, Li L. Extreme precipitation indices over China in CMIP5 models. Part I: Model evaluation. J Clim. 2015;28(21):8603–19.
  26. Taylor KE. Summarizing multiple aspects of model performance in a single diagram. J Geophys Res Atmos. 2001;106(D7).
  27. Wu T, Lu Y, Fang Y, Xin X, Li L, Li W, et al. The Beijing Climate Center climate system model (BCC-CSM): The main progress from CMIP5 to CMIP6. Geosci Model Dev. 2019;12(4):1573–600.
  28. Swart NC, Cole JNS, Kharin V V., Lazare M, Scinocca JF, Gillett NP, et al. The Canadian Earth System Model version 5 (CanESM5.0.3). Geosci Model Dev. 2019;12(11):4823–73.
  29. Lauritzen PH, Nair RD, Herrington AR, Callaghan P, Goldhaber S, Dennis JM, et al. NCAR release of CAM‐SE in CESM2. 0: A reformulation of the spectral element dynamical core in dry‐mass vertical coordinates with comprehensive treatment of condensates and energy. J Adv Model Earth Syst. 2018;10(7):1537–70.
  30. Voldoire A, Saint‐Martin D, Sénési S, Decharme B, Alias A, Chevallier M, et al. Evaluation of CMIP6 deck experiments with CNRM‐CM6‐1. J Adv Model Earth Syst. 2019;11(7):2177–213.
  31. Séférian R, Nabat P, Michou M, Saint‐Martin D, Voldoire A, Colin J, et al. Evaluation of CNRM Earth System Model, CNRM‐ESM2‐1: Role of Earth system processes in present‐day and future climate. J Adv Model Earth Syst. 2019;11(12):4182–227.
  32. Li L, Yu Y, Tang Y, Lin P, Xie J, Song M, et al. The flexible global ocean‐atmosphere‐land system model grid‐point version 3 (FGOALS‐g3): description and evaluation. J Adv Model Earth Syst. 2020;12(9):e2019MS002012.
  33. Kelley M, Schmidt GA, Nazarenko LS, Bauer SE, Ruedy R, Russell GL, et al. GISS‐E2. 1: Configurations and climatology. J Adv Model Earth Syst. 2020;12(8):e2019MS002025.
  34. Roberts MJ, Baker A, Blockley EW, Calvert D, Coward A, Hewitt HT, et al. Description of the resolution hierarchy of the global coupled HadGEM3-GC3. 1 model as used in CMIP6 HighResMIP experiments. Geosci Model Dev. 2019;12(12):4999–5028.
  35. Boucher O, Servonnat J, Albright AL, Aumont O, Balkanski Y, Bastrikov V, et al. Presentation and evaluation of the IPSL‐CM6A‐LR climate model. J Adv Model Earth Syst. 2020;12(7):e2019MS002010.
  36. Tatebe H, Ogura T, Nitta T, Komuro Y, Ogochi K, Takemura T, et al. Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6. Geosci Model Dev. 2019;12(7):2727–65.
  37. Hajima T, Watanabe M, Yamamoto A, Tatebe H, Noguchi MA, Abe M, et al. Development of the MIROC-ES2L Earth system model and the evaluation of biogeochemical processes and feedbacks. Geosci Model Dev. 2020;13(5):2197–244.
  38. Yukimoto S, Kawai H, Koshiro T, Oshima N, Yoshida K, Urakawa S, et al. The Meteorological Research Institute Earth System Model version 2.0, MRI-ESM2. 0: Description and basic evaluation of the physical component. J Meteorol Soc Japan Ser II. 2019;97(5):931–65.
  39. Seland Ø, Bentsen M, Seland Graff L, Olivié D, Toniazzo T, Gjermundsen A, et al. The Norwegian earth system model, noresm2–Evaluation of thecmip6 deck and historical simulations. Geosci Model Dev Discuss. 2020;1–68.
  40. Gondim R, Silveira C, de Souza Filho F, Vasconcelos F, Cid D. Climate change impacts on water demand and availability using CMIP5 models in the Jaguaribe basin, semi-arid Brazil. Environ Earth Sci. 2018;77(15).
  41. Amengual A, Homar V, Romero R, Alonso S, Ramis C. A statistical adjustment of regional climate model outputs to local scales: application to Platja de Palma, Spain. J Clim. 2012;25(3):939–57.
  42. Hempel S, Frieler K, Warszawski L, Schewe J, Piontek F. A trend-preserving bias correction–the ISI-MIP approach. Earth Syst Dyn. 2013;4(2):219–36.
Numerical Methods in Civil Engineering
Volume 8, Issue 2 - Serial Number 2
December 2023
Pages 36-47

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History

  • Receive Date: 22 October 2023
  • Revise Date: 15 November 2023
  • Accept Date: 03 January 2024

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