Skip to main content

Advertisement

SpringerLink
Log in

A statistical framework to project wave climate and energy potential in the Caspian Sea: application of CMIP6 scenarios

  • Original Paper
  • Published:
International Journal of Environmental Science and Technology Aims and scope Submit manuscript

Abstract

Caspian Sea as the largest inland water body plays an important role in economy of its neighbor countries apart from its environmental significance. This study explores wave climate in three locations in the southern Caspian Sea considering the socioeconomic pathway scenarios. In this regard, significant wave height, peak wave period and potential wave energy have been projected for a 20-year period of 2081–2100. To achieve reliable forecasts of wave characteristics and also to manipulate different sources of uncertainty, a new statistical framework is employed. The proposed framework gain Weibull distribution based technique for regionalization of near surface wind speed obtained from the climate model to feed the ANN (artificial neural network) model used for wave modeling. Furthermore, a post processing approach is used to ascertain consistency of the distribution of the model forecasts with those of the reference data. Considering two climate change scenarios, it was found that significant wave height and peak wave period do not change remarkably in terms of annual and seasonal variability when compared to the present climate. Moreover, wave energy analysis for the historical and future periods demonstrated sustainability of this renewable energy resources which is promising since clean energies are attracting much attention to meet the increasing demand for electricity. In the southern Caspian Sea, the middle part of the sea is subject to stronger waves indicating its higher potential for energy extraction than the western and eastern parts.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Abbaspour M, Rahimi R (2011) Iran atlas of offshore renewable energies. Renew Energy 36:388–398

    Article  Google Scholar 

  • Alizadeh MJ, Kavianpour MR, Kamranzad B, Etemad-Shahidi A (2019) A weibull distribution based technique for downscaling of climatic wind field. Asia-Pacific J AtmosSci. https://doi.org/10.1007/s13143-019-00106-z

    Article  Google Scholar 

  • Alizadeh MJ, Kavianpour MR, Kamranzad B, Etemad-Shahidi A (2020) A distributed wind downscaling technique for wave climate modeling under future scenarios. Ocean Model 145:101513

    Article  Google Scholar 

  • Amirinia G, Kamranzad B, Mafi S (2017) Wind and wave energy potential in southern Caspian Sea using uncertainty analysis. Energy 120:332–345

    Article  Google Scholar 

  • Berbić J, Ocvirk E, Carević D, Lončar G (2017) Application of neural networks and support vector machine for significant wave height prediction. Oceanologia 59:331–349

    Article  Google Scholar 

  • Chang T-J, Wu Y-T, Hsu H-Y, Chu C-R, Liao C-M (2003) Assessment of wind characteristics and wind turbine characteristics in Taiwan. Renew Energy 28:851–871

    Article  Google Scholar 

  • Dastgheib A, Reyns J, Thammasittirong S, Weesakul S, Thatcher M, Ranasinghe R (2016) Variations in the wave climate and sediment transport due to climate change along the coast of Vietnam. J Mar SciEng 4:86

    Google Scholar 

  • Duan W, Han Y, Huang L, Zhao B, Wang M (2016) A hybrid EMD-SVR model for the short-term prediction of significant wave height. Ocean Eng 124:54–73

    Article  Google Scholar 

  • Etemad-Shahidi A, Mahjoobi J (2009) Comparison between M5′ model tree and neural networks for prediction of significant wave height in Lake Superior. Ocean Eng 36(15–16):1175–1181

    Article  Google Scholar 

  • Hadadpour S, Etemad-Shahidi A, Kamranzad B (2014) Wave energy forecasting using artificial neural networks in the Caspian Sea. In: Proceedings of the institution of civil engineers-maritime engineering, 1 Thomas Telford Ltd, pp 42–52

  • Ham FM, Kostanic I (2000) Principles of neurocomputing for science and engineering. McGraw-Hill Higher Education, Newyork

    Google Scholar 

  • Hemer MA, Trenham CE (2016) Evaluation of a CMIP5 derived dynamical global wind wave climate model ensemble. Ocean Modell. https://doi.org/10.1016/j.ocemod.2015.10.009

    Article  Google Scholar 

  • Hemer MA, Wang XL, Weisse R, Swail VR (2012) Advancing wind-waves climate science: the COWCLIP project. Bull Am Meteor Soc 93:791–796

    Article  Google Scholar 

  • Jeong DI, Kim YO (2005) Rainfall-runoff models using artificial neural networks for ensemble streamflow prediction. Hydrol Process Int J 19(19):3819–3835

    Article  Google Scholar 

  • Kamranzad B, Hadadpour S (2020) A multi-criteria approach for selection of wave energy converter/location. Energy. https://doi.org/10.1016/j.energy.2020.117924

    Article  Google Scholar 

  • Kamranzad B, Etemad-Shahidi A, Kazeminezhad M (2011) Wave height forecasting in Dayyer, the Persian Gulf. Ocean Eng. https://doi.org/10.1016/j.oceaneng.2010.10.004

    Article  Google Scholar 

  • Komijani F, Chegini V, Siadatmousavi S (2019) Seasonal variability of circulation and air-sea interaction in the Caspian Sea based on a high resolution circulation model. J Great Lakes Res 45:1113–1129

    Article  Google Scholar 

  • Lavidas G, Agarwal A, Venugopal V (2018) Availability and accessibility for offshore operations in the mediterranean sea. J Waterway Port Coastal Ocean Eng 144:05018006

    Article  Google Scholar 

  • Lemos G, Semedo A, Dobrynin M, Behrens A, Staneva J, Bidlot J-R, Miranda PM (2019) Mid-twenty-first century global wave climate projections: results from a dynamic CMIP5 based ensemble. Glob Planet Change. https://doi.org/10.1016/j.gloplacha.2018.09.011

    Article  Google Scholar 

  • Lesani S, Niksokhan MH (2019) Climate change impact on Caspian Sea wave conditions in the Noshahr Port. Ocean Dyn 69:1287–1310

    Article  Google Scholar 

  • Moeini M, Etemad-Shahidi A (2007) Application of two numerical models for wave hindcasting in Lake Erie. Appl Ocean Res 29:137–145

    Article  Google Scholar 

  • Morim J, Hemer M, Andutta F, Shimura T, Cartwright N (2019) Skill and uncertainty of surface wind fields from general circulation models: intercomparison of bias between AGCM AOGCM and ESM global simulations. Int J Climatol. https://doi.org/10.1002/joc.6357

    Article  Google Scholar 

  • Morim J et al (2020) A global ensemble of ocean wave climate projections from CMIP5-driven models. Sci data. https://doi.org/10.1038/s41597-020-0446-2

    Article  Google Scholar 

  • Oh SG, Suh MS (2018) Changes in seasonal and diurnal precipitation types during summer over South Korea in the late twenty-first century (2081–2100) projected by the RegCM4. 0 based on four RCP scenarios. Clim Dyn 51(7):3041–3060

    Article  Google Scholar 

  • O’Neill BC, Tebaldi C, van Vuuren DP, Eyring V, Friedlingstein P, Hurtt G, Knutti R et al (2016) The scenario model intercomparison project (ScenarioMIP) for CMIP6. Geosci Model Dev 9(9):3461–3482

    Article  Google Scholar 

  • Rao A, Sinha M, Basu S (2013) Bay of Bengal wave forecast based on genetic algorithm: a comparison of univariate and multivariate approaches. Appl Math Model 37:4232–4244

    Article  Google Scholar 

  • Wandres M, Pattiaratchi C, Hemer MA (2017) Projected changes of the southwest Australian wave climate under two atmospheric greenhouse gas concentration pathways. Ocean Model 117:70–87

    Article  Google Scholar 

  • Wang XL, Swail VR (2006) Climate change signal and uncertainty in projections of ocean wave heights. Clim Dyn 26:109–126

    Article  Google Scholar 

  • Wang XL, Feng Y, Swail V (2012) North Atlantic wave height trends as reconstructed from the 20th century reanalysis. Geophys Res Lett. https://doi.org/10.1029/2012GL053381

    Article  Google Scholar 

  • Zamani A, Azimian A, Heemink A, Solomatine D (2010) Non-linear wave data assimilation with an ANN-type wind-wave model and Ensemble Kalman Filter (EnKF). Appl Math Modell. https://doi.org/10.1016/j.apm.2009.10.013

    Article  Google Scholar 

Download references

Acknowledgment

This project has been supported by a research grant of the University of Tabriz (number 1807).

Author information

Authors and Affiliations

  1. Center of Excellence in Hydroinforamtics and Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran

    M. J. Alizadeh & V. Nourani

  2. Faculty of Civil and Environmental Engineering, Near East University, TRNC, via Mersin 10, 99138, Nicosia, Turkey

    V. Nourani

  3. Faculty of Civil Engineering, K.N.Toosi University of Technology, Tehran, Iran

    M. R. Kavianpour

Authors
  1. M. J. Alizadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. Nourani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. R. Kavianpour
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. J. Alizadeh.

Ethics declarations

Conflict of interest

The author declared that they have no conflict of interest.

Additional information

Editorial responsibility: Samareh Mirkia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alizadeh, M.J., Nourani, V. & Kavianpour, M.R. A statistical framework to project wave climate and energy potential in the Caspian Sea: application of CMIP6 scenarios. Int. J. Environ. Sci. Technol. 19, 2323–2336 (2022). https://doi.org/10.1007/s13762-021-03314-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13762-021-03314-1

Keywords

Search

Navigation