Abstract
The present study has assessed the possible water stress scenarios over six nuclear power plant locations (inland plants at Kakrapar, Kota, and Narora and coastal plants at Kodankulam, Kalpakkam, and Tarapur) of India based on downscaled climatic products from 12 coupled model inter-comparison project 5 (CMIP5) simulations. Firstly, statistically downscaled scenarios over power plant locations for water temperature, precipitation, evapotranspiration, and sea surface temperature (SST) have been developed using various statistical downscaling methods. Secondly, the water stress has been quantified by formulating a multivariate standardized water stress index (MSWSI) based on water temperature and freshwater availability (precipitation minus evapotranspiration) over inland plants and a univariate index from the SST for coastal plants. Results have indicated that three inland power plants are not expecting any scarcity of freshwater availability. However, they have been projected to face high to severe water stress from middle to end of the century due to a higher warming rate of water temperature under global warming conditions. Similarly, three coastal plants have also been projected to prevail high to severe water stress through enhanced SST warming. Therefore, the efficiency and productivity of the nuclear plants may reduce under changing climatic conditions.
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References
Akhter J, Das L, Deb A (2017) CMIP5 ensemble-based spatial rainfall projection over homogeneous zones of India. Clim Dyn 49(5-6):1885–1916. https://doi.org/10.1007/s00382-016-3409-8
Akhter J, Das L, Deb A (2018a) Possible challenges of nuclear power plants under climate change scenarios. J Clim Chang 4(1):63–69. https://doi.org/10.3233/jcc-180007
Akhter J, Das L, Meher JK, Deb A (2018b) Uncertainties and time of emergence of multi-model precipitation projection over homogeneous rainfall zones of India. Clim Dyn 50(9-10):3813–3831. https://doi.org/10.1007/s00382-017-3847-y
Akhter J, Das L, Meher JK, Deb A (2019) Evaluation of different large-scale predictor-based statistical downscaling models in simulating zone-wise monsoon precipitation over India. Int J Climatol 39(1):465–482. https://doi.org/10.1002/joc.5822
Benestad RE, Førland EJ, Hanssen-Bauer I (2002) Empirically downscaled temperature scenarios for Svalbard. Atmos Sci Lett 3(2-4):71–93. https://doi.org/10.1006/asle.2002.0051
Choudhary A, Dimri AP, Maharana P (2018) Assessment of CORDEX SA experiments in representing precipitation climatology of summer monsoon over India. Theor Appl Climatol 134(1-2):283–307. https://doi.org/10.1007/s00704-017-2274-7
Das L, Akhter J (2019) How well are the downscaled CMIP5 models able to reproduce the monsoon precipitation over seven homogeneous zones of India? Int J Climatol 39(7):3323–3333. https://doi.org/10.1002/joc.6022
Das L, Lohar D (2005) Construction of climate change scenarios for a tropical monsoon region. Clim Res 30:39–52 https://doi.org/10.3354/cr030039
Dimri AP, Kumar D, Chopra S, Choudhary A (2019) Indus River Basin: future climate and water budget. Int J Climatol 39(1):395–406. https://doi.org/10.1002/joc.5816
Durmayaz A, Sogut OS (2006) Influence of cooling water temperature on the efficiency of a pressurized-water reactor nuclear-power plant. Int J Energy Res 30(10):799–810. https://doi.org/10.1002/er.1186
Easterling DR (1999) Development of regional climate scenarios using a downscaling approach. Clim Chang 41(3-4):615–634. https://doi.org/10.1023/A:1005425613593
Förster H, Lilliestam J (2010) Modeling thermoelectric power generation in view of climate change: a reply. Reg Environ Chang 11(1):211–212. https://doi.org/10.1007/s10113-010-0117-5
Ganguli P, Kumar D, Ganguly AR (2017) US power production at risk from water stress in a changing climate. Sci Rep 7(1). https://doi.org/10.1038/s41598-017-12133-9
Ganguly AR, Kumar D, Ganguli P, Short G, Klausner J (2015) Climate adaptation informatics: water stress on power production. Comput Sci Eng 17(6):53–60. https://doi.org/10.1109/mcse.2015.106
Ghimire S, Choudhary A, Dimri AP (2018) Assessment of the performance of CORDEX-South Asia experiments for monsoonal precipitation over the Himalayan region during present climate: part I. Clim Dyn 50:2311–2334. https://doi.org/10.1007/s00382-015-2747-2
Gudmundsson L, Bremnes JB, Haugen JE, Engen Skaugen T (2012) Technical note: downscaling RCM precipitation to the station scale using quantile mapping – a comparison of methods. Hydrol Earth Syst Sci Discuss 9(5):6185–6201. https://doi.org/10.5194/hessd-9-6185-2012
Haensler A, Saeed F, Jacob D (2013) Assessing the robustness of projected precipitation changes over central Africa on the basis of a multitude of global and regional climate projections. Clim Chang 121(2):349–363. https://doi.org/10.1007/s10584-013-0863-8
Hay LE, Clark MP (2003) Use of statistically and dynamically downscaled atmospheric model output for hydrologic simulations in three mountainous basins in the western United States. J Hydrol 282(1-4):56–75. https://doi.org/10.1016/s0022-1694(03)00252-x
Hoffmann B, Häfele S, Karl U (2013) Analysis of performance losses of thermal power plants in Germany – a system dynamics model approach using data from regional climate modelling. Energy 49:193–203. https://doi.org/10.1016/j.energy.2012.10.034
Huth R (1999) Statistical downscaling in central Europe: evaluation of methods and potential predictors. Clim Res 13(2):91–101. https://doi.org/10.3354/cr013091
Huth R (2004) Sensitivity of local daily temperature change estimates to the selection of downscaling models and predictors. J Clim 17(3):640–652. https://doi.org/10.1175/1520-0442(2004)017<0640:soldtc>2.0.co;2
Ibrahim SMA, Ibrahim MMA, Attia SI (2014) The impact of climate changes on the thermal performance of a proposed pressurized water reactor: nuclear-power plant. Int J Nucl Energy 2014:1–7. https://doi.org/10.1155/2014/793908
International Atomic Energy Agency (IAEA) (2016) Climate change and nuclear power 2016, Vienna. (http://www-pub.iaea.org/MTCD/Publications/PDF/CCANP16web-86692468.pdf)
Kalnay E et al (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77(3):437–471. https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2
Koch H, Vögele S (2009) Dynamic modelling of water demand, water availability and adaptation strategies for power plants to global change. Ecol Econ 68(7):2031–2039. https://doi.org/10.1016/j.ecolecon.2009.02.015
Linnerud K, Mideksa TK, Eskeland GS (2011) The impact of climate change on nuclear power supply. Energy J 32(1). https://doi.org/10.5547/issn0195-6574-ej-vol32-no1-6
Maraun D et al (2010) Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Rev Geophys 48(3). https://doi.org/10.1029/2009rg000314
Mastrandrea MD, Field CB, Stocker TF, Edenhofer O, Ebi KL, Frame DJ, Held H, Kriegler E, Mach KJ, Matschoss PR, Plattner G-K, Yohe GW, Zwiers FW (2010) Guidance note for lead authors of the IPCC fifth assessment report on consistent treatment of uncertainties. Intergovernmental Panel on Climate Change (IPCC) 14 Apr. 2016. [Available at http://www.ipcc.ch]
Meher JK, Das L, Akhter J, Benestad RE, Mezghani A (2017) Performance of CMIP3 and CMIP5 GCMs to simulate observed rainfall characteristics over the Western Himalayan region. J Clim 30(19):7777–7799. https://doi.org/10.1175/JCLI-D-16-0774.1
Mishra V, Kumar D, Ganguly A, Sanjay J, Mujumdar M, Krishanan R, Shah R (2014) Reliability of regional and global climate models to simulate precipitation extremes over India. J Geophys Res Atmos 119:9301–9323. https://doi.org/10.1002/2014JD021636
Pai DS, Sridhar L, Rajeevan M, Sreejith OP, Satbhai NS, Mukhopadhyay B (2014) Development of a new high spatial resolution (0.25× 0.25) long period (1901–2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region. Mausam 65(1):1–18
Parish ES, Kodra E, Steinhaeuser K, Ganguly AR (2012) Estimating future global per capita water availability based on changes in climate and population. Comput Geosci 42:79–86. https://doi.org/10.1016/j.cageo.2012.01.019
Pervez MS, Henebry GM (2014) Projections of the Ganges–Brahmaputra precipitation—downscaled from GCM predictors. J Hydrol 517:120–134. https://doi.org/10.1016/j.jhydrol.2014.05.016
Rai P, Choudhary A, Dimri AP (2019) Future precipitation extremes over India from the CORDEX-South Asia experiments. Theor Appl Climatol 137(3-4):2961–2975. https://doi.org/10.1007/s00704-019-02784-1
Rummukainen M (1997) Methods of statistical downscaling of GCM simulations. Reports Meteorology and Climatology 80, Tech. rep., Swedish Meteorological and Hydrological Institute, SE-601 76 Norrkping, Sweden.
Sachindra DA, Huang F, Barton A, Perera BJC (2014) Statistical downscaling of general circulation model outputs to precipitation—part 1: calibration and validation. Int J Climatol 34(11):3264–3281. https://doi.org/10.1002/joc.3914
Sannan MC, Nageswararao MM, Mohanty UC (2019) Performance evaluation of CORDEX-South Asia simulations for Future projections of Northeast Monsoon Rainfall over South Peninsular India. Meteorog Atmos Phys. https://doi.org/10.1007/s00703-019-00716-2
Vrontisi ZN (2013) Energy production, conversion, transmission and distribution, policy, planning, and mitigation processes – general considerations. Climate Vulnerability:207–215. https://doi.org/10.1016/b978-0-12-384703-4.00323-3
Wilby R, Hay L, Leavesley G (1999) A comparison of downscaled and raw GCM output: implications for climate change scenarios in the San Juan River basin, Colorado. J Hydrol 225:67–91. https://doi.org/10.1016/S0022-1694(99)00136-5
Acknowledgments
The authors would like to acknowledge INDIA-WRIS portal and CWC of India for stream temperature data. Authors are also grateful to IMD for gridded rainfall data, Goddard Earth Sciences Data, and Information Services Center for GLDAS gridded evapotranspiration data and CRU, University of East Anglia for gridded temperature data. Authors would like to thank NOAA/OAR/ESRL PSL for NCEP/NCAR reanalysis data, as well as for gridded SST data and climate modeling groups of CMIP5 under WCRP for GCM outputs. Authors are thankful to the developers of the packages “ESD” and “hydroGOF” in R.
Funding
This study has been financially supported by the Department of Science and Technology (DST), Government of India, through the INSPIRE fellowship (IF150304).
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Akhter, J., Das, L. & Deb, A. Assessing future water stress scenarios over six nuclear power plant locations of India through downscaled CMIP5 models. Theor Appl Climatol 142, 191–204 (2020). https://doi.org/10.1007/s00704-020-03278-1
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DOI: https://doi.org/10.1007/s00704-020-03278-1