Abstract
Hydropower emits less carbon dioxide than fossil fuels but the lower albedo of hydropower reservoirs compared to terrestrial landscapes results in a positive radiative forcing, offsetting some of the negative radiative forcing of hydroelectricity generat ion. The cumulative effect of this lower albedo has not been quantified. Here we show, by quantifying the difference in remotely sensed albedo between globally distributed hydropower reservoirs and their surrounding landscape, that 19% of all investigated hydropower plants required 40 years or more for the negative radiative forcing from the fossil fuel displacement to offset the albedo effect. The length of these break-even times depends on the specific combination of climatic and environmental constraints, power plant design characteristics and country-specific electricity carbon intensities. We conclude that future hydropower plants need to minimize the albedo penalty to make a meaningful contribution towards limiting global warming.
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Data availability
The data underlying this analysis are freely available from the following sources: satellite remote sensing: https://lpdaacsvc.cr.usgs.gov/appeears/api/; global shortwave radiation at 5-km resolution (BESS_Rad): http://environment.snu.ac.kr/bess_rad/; albedo change radiative kernel (CACK v.1.0): https://portal.edirepository.org/nis/mapbrowse?packageid=edi.396.1; GRanD database (v.1.01): http://sedac.ciesin.columbia.edu/pfs/grand.html; GPPD database (v.1.1.0): http://datasets.wri.org/dataset/globalpowerplantdatabase. A complete example dataset for one selected hydropower reservoir is freely available under the following https://doi.org/10.5281/zenodo.4432576.
Code availability
The Matlab (MathWorks) and R62 scripts used to analyse data are freely available under the following https://doi.org/10.5281/zenodo.4432576.
References
Millar, R. J. et al. Emission budgets and pathways consistent with limiting warming to 1.5 °C. Nat. Geosci. 10, 741–747 (2017).
Goodwin, P. et al. Pathways to 1.5 °C and 2 °C warming based on observational and geological constraints. Nat. Geosci. 11, 102–107 (2018).
Chu, S., Cui, Y. & Liu, N. The path towards sustainable energy. Nat. Mater. 16, 16–22 (2016).
Hoffert, M. I. et al. Advanced technology paths to global climate stability: energy for a greenhouse planet. Science 298, 981–987 (2002).
Yang, W. et al. Burden on hydropower units for short-term balancing of renewable power systems. Nat. Commun. 9, 2633 (2018).
Jacobson, M. Z. 100% Clean, Renewable Energy and Storage for Everything (Cambridge Univ. Press, 2020).
van Vliet, M. T. H., Wiberg, D., Leduc, S. & Riahi, K. Power-generation system vulnerability and adaptation to changes in climate and water resources. Nat. Clim. Change 6, 375–380 (2016).
Turner, S. W. D., Ng, J. Y. & Galelli, S. Examining global electricity supply vulnerability to climate change using a high-fidelity hydropower dam model. Sci. Total Environ. 590–591, 663–675 (2017).
Zarfl, C., Lumsdon, A. E., Berlekamp, J., Tydecks, L. & Tockner, K. A global boom in hydropower dam construction. Aquat. Sci. 77, 161–170 (2014).
Farinotti, D., Round, V., Huss, M., Compagno, L. & Zekollari, H. Large hydropower and water-storage potential in future glacier-free basins. Nature 575, 341–344 (2019).
Wehrli, B. Climate science: renewable but not carbon-free. Nat. Geosci. 4, 585–586 (2011).
Scherer, L. & Pfister, S. Hydropower’s biogenic carbon footprint. PLoS ONE 11, e0161947 (2016).
Ocko, I. B. & Hamburg, S. P. Climate impacts of hydropower: enormous differences among facilities and over time. Environ. Sci. Technol. 53, 14070–14082 (2019).
Barros, N. et al. Carbon emission from hydroelectric reservoirs linked to reservoir age and latitude. Nat. Geosci. 4, 593–596 (2011).
Cogley, J. G. The albedo of water as a function of latitude. Mon. Weather Rev. 107, 775–781 (1979).
Cescatti, A. et al. Intercomparison of MODIS albedo retrievals and in situ measurements across the global FLUXNET network. Remote Sens. Environ. 121, 323–334 (2012).
Bright, R. M., Bogren, W., Bernier, P. & Astrup, R. Carbon-equivalent metrics for albedo changes in land management contexts: relevance of the time dimension. Ecol. Appl. 26, 1868–1880 (2016).
Kirschbaum, M. U. F. et al. Implications of albedo changes following afforestation on the benefits of forests as carbon sinks. Biogeosciences 8, 3687–3696 (2011).
Bright, R. M. & Kvalevåg, M. M. Technical note: evaluating a simple parameterization of radiative shortwave forcing from surface albedo change. Atmos. Chem. Phys. 13, 11169–11174 (2013).
Bright, R. M. & O’Halloran, T. L. Developing a monthly radiative kernel for surface albedo change from satellite climatologies of Earth’s shortwave radiation budget: CACK v1.0. Geosci. Model Dev. 12, 3975–3990 (2019).
Projected Costs of Generating Electricity (International Energy Agency and Nuclear Energy Agency, 2010).
Bala, G. et al. Combined climate and carbon-cycle effects of large-scale deforestation. Proc. Natl Acad. Sci. USA 104, 6550–6555 (2007).
Bonan, G. B. Forests and climate change: climate benefits of forests. Science 320, 1444–1449 (2008).
Myhre, G., Kvalevåg, M. M. & Schaaf, C. B. Radiative forcing due to anthropogenic vegetation change based on MODIS surface albedo data. Geophys. Res. Lett. 32, L21410 (2005).
Seneviratne, S. I. et al. Land radiative management as contributor to regional-scale climate adaptation and mitigation. Nat. Geosci. 11, 88–96 (2018).
Riahi, K. et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change 42, 153–168 (2017).
Rogelj, J. et al. Scenarios towards limiting global mean temperature increase below 1.5 °C. Nat. Clim. Change 8, 325–332 (2018).
Couto, T. B. A. & Olden, J. D. Global proliferation of small hydropower plants – science and policy. Front. Ecol. Environ. 16, 91–100 (2018).
Ardizzon, G., Cavazzini, G. & Pavesi, G. A new generation of small hydro and pumped-hydro power plants: advances and future challenges. Renew. Sustain. Energy Rev. 31, 746–761 (2014).
CO2 Emissions from Fuel Combustion (International Energy Agency, 2016).
Ang, B. W. & Su, B. Carbon emission intensity in electricity production: a global analysis. Energy Policy 94, 56–63 (2016).
Gibson, L., Wilman, E. N. & Laurance, W. F. How green is ‘green’ energy? Trends Ecol. Evol. 32, 922–935 (2017).
Grill, G. et al. Mapping the world’s free-flowing rivers. Nature 569, 215–221 (2019).
Moran, E. F., Lopez, M. C., Moore, N., Muller, N. & Hyndman, D. W. Sustainable hydropower in the 21st century. Proc. Natl Acad. Sci. USA 115, 11891–11898 (2018).
Zarfl, C. et al. Future large hydropower dams impact global freshwater megafauna. Sci. Rep. 9, 18531 (2019).
Winemiller, K. O. et al. Balancing hydropower and biodiversity in the Amazon, Congo, and Mekong. Science 351, 128–129 (2016).
Reid, A. J. et al. Emerging threats and persistent conservation challenges for freshwater biodiversity. Biol. Rev. 94, 849–873 (2019).
Barbarossa, V. et al. Impacts of current and future large dams on the geographic range connectivity of freshwater fish worldwide. Proc. Natl Acad. Sci. USA 117, 3648–3655 (2020).
Nilsson, C., Reidy, C. A., Dynesius, M. & Revenga, C. Fragmentation and flow regulation of the world’s large river systems. Science 308, 405–408 (2005).
Johnson, P. T. J., Olden, J. D. & Vander Zanden, M. J. Dam invaders: impoundments facilitate biological invasions into freshwaters. Front. Ecol. Environ. 6, 357–363 (2008).
Maavara, T. et al. River dam impacts on biogeochemical cycling. Nat. Rev. Earth Environ. 1, 103–116 (2020).
Constantine, J. A., Dunne, T., Ahmed, J., Legleiter, C. & Lazarus, E. D. Sediment supply as a driver of river meandering and floodplain evolution in the Amazon Basin. Nat. Geosci. 7, 899–903 (2014).
Richter, B. D. et al. Lost in development’s shadow: the downstream human consequences of dams. Water Alter. 3, 14–42 (2010).
Lehner, B. et al. High-resolution mapping of the world’s reservoirs and dams for sustainable river-flow management. Front. Ecol. Environ. 9, 494–502 (2011).
Global Power Plant Database (GPPD) (World Resources Institute, 2018); https://datasets.wri.org/dataset/globalpowerplantdatabase
Schaaf, C. & Wang, Z. MCD43A1 MODIS/Terra+Aqua BRDF/Albedo Model Parameters Daily L3 Global – 500m V006 (NASA EOSDIS Land Processes DAAC, 2019); https://lpdaac.usgs.gov/products/mcd43a1v006/
Friedl, M. & Sulla-Menashe, D. MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V006 (NASA EOSDIS Land Processes DAAC, 2019); https://lpdaac.usgs.gov/products/mcd12q1v006/
NASA JPL NASA Shuttle Radar Topography Mission Global 3 arc second (NASA EOSDIS Land Processes DAAC, 2013); https://lpdaac.usgs.gov/products/srtmgl3v003/
NASA/METI/AIST/Japan Spacesystems and Japan/US ASTER Science Team. ASTER Global Digital Elevation Model V003 (NASA EOSDIS Land Processes DAAC, 2018); https://lpdaac.usgs.gov/products/astgtmv003/
Liu, J. et al. Validation of moderate resolution imaging spectroradiometer (MODIS) albedo retrieval algorithm: dependence of albedo on solar zenith angle. J. Geophys. Res. Atmos. 114, D01106 (2009).
Chen, J. M., Liu, J., Cihlar, J. & Goulden, M. L. Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications. Ecol. Model. 124, 99–119 (1999).
Ham, J. M. in Micrometeorology in Agricultural Systems Vol. 47 (eds Hatfield, J. L. & Baker, J. M.) 533–560 (American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, 2005).
Ryu, Y., Jiang, C., Kobayashi, H. & Detto, M. MODIS-derived global land products of shortwave radiation and diffuse and total photosynthetically active radiation at 5 km resolution from 2000. Remote Sens. Environ. 204, 812–825 (2018).
Desai, A. R., Vesala, T. & Rantakari, M. Measurements, modeling, and scaling of inland water gas exchange. Eos 96 (2015).
Bright, R. M. & O’Halloran, T. L. A Monthly Shortwave Radiative Forcing Kernel for Surface Albedo Change Using CERES Satellite Data Version 1 (Environmental Data Initiative, 2019).
Joos, F. et al. Carbon dioxide and climate impulse response functions for the computation of greenhouse gas metrics: a multi-model analysis. Atmos. Chem. Phys. 13, 2793–2825 (2013).
Lehner, B. et al. Global Reservoir and Dam (GRanD) Database Technical documentation, Version 1.1 (SEDAC, 2011).
Mu, M., Tang, Q., Han, S., Liu, X. & Cui, H. Using GRanD database and surface water data to constrain area–storage curve of reservoirs. Water 12, 1242 (2020).
Pekel, J. F., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016).
Pianosi, F. & Wagener, T. A simple and efficient method for global sensitivity analysis based on cumulative distribution functions. Environ. Model. Softw. 67, 1–11 (2015).
Pianosi, F., Sarrazin, F. & Wagener, T. A Matlab toolbox for global sensitivity analysis. Environ. Model. Softw. 70, 80–85 (2015).
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).
RStudio. Integrated Development for R (RStudio, 2019).
Grömping, U. Relative importance for linear regression in R: the package relaimpo. J. Stat. Softw. 17, https://doi.org/10.18637/jss.v017.i01 (2006).
Acknowledgements
This work was partially funded by the Autonomous Province Bozen-Südtirol (ALCH4 project) and grants by the Austrian National Science Fund (FWF, grant numbers P31669-B22 and I03859). This publication incorporates data from the GRanD database which is a Global Water System Project (2011). We thank F. Kitz for help with statistics in R and H. Iwata and K. Scholz for providing measured albedos for Lake Suwa and Lakes Lunz and Mondsee, respectively.
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G.W. conceived the study. G.W., E.T. and A.H. analysed the data and wrote the manuscript together.
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Wohlfahrt, G., Tomelleri, E. & Hammerle, A. The albedo–climate penalty of hydropower reservoirs. Nat Energy 6, 372–377 (2021). https://doi.org/10.1038/s41560-021-00784-y
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DOI: https://doi.org/10.1038/s41560-021-00784-y
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