Abstract
Climate change is causing widespread glacier retreat1, and much attention is devoted to negative impacts such as diminishing water resources2, shifts in runoff seasonality3, and increases in cryosphere-related hazards4. Here we focus on a different aspect, and explore the water-storage and hydropower potential of areas that are expected to become ice-free during the course of this century. For roughly 185,000 sites that are glacierized at present, we predict the potentially emerging reservoir storage volume and hydropower potential. Using a climate-driven glacier-evolution model5 and topographical analysis6, we estimate a theoretical maximal total storage and hydropower potential of 875 ± 260 cubic kilometres and 1,355 ± 515 terawatt-hours per year, respectively (95% confidence intervals). A first-order suitability assessment that takes into account environmental, technical and economic factors identifies roughly 40 per cent of this potential (355 ± 105 cubic kilometres and 533 ± 200 terawatt-hours per year) as possibly being suitable for realization. Three quarters of the potential storage volume is expected to become ice-free by 2050, and the storage volume would be enough to retain about half of the annual runoff leaving the investigated sites. Although local impacts would need to be assessed on a case-by-case basis, the results indicate that deglacierizing basins could make important contributions to national energy supplies in several countries, particularly in High Mountain Asia.
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Data availability
The data generated herein are available at http://doi.org/10.3929/ethz-b-000353109. The reference list provides information on data obtained from third parties.
Code availability
The computer codes used for evaluations are available from the corresponding author upon request.
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Acknowledgements
This study was supported by the Swiss National Science Foundation (SNSF), grant number PZENP2_154290. We thank all those who made freely available any data used here; D. Gernaat for distance-to-powerline information used in the economic analysis; and D. Felix for punctual advice regarding hydropower infrastructure design.
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Contributions
D.F. and V.R. conceived the study; M.H. produced the glacier runoff projections and provided the DEMs necessary for the analyses; D.F. conceived and implemented the code for the dam-construction analysis; V.R. conceived the suitability score system with the help of D.F. and H.Z.; V.R. performed global-scale calculations, and designed most figures; H.Z. and L.C. implemented and performed the economic analyses with the help of D.F., and assisted in figure design and production. The manuscript was drafted by V.R. and D.F., with contributions from M.H., H.Z. and L.C.
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Extended data figures and tables
Extended Data Fig. 1 Visualization of virtually installed dams and reservoirs.
a–f, An arbitrary subset of dams and reservoirs for locations in: a, Alaska; b, Scandinavia; c, North Asia; d, Central Asia; e, Low Latitudes; and f, New Zealand (see Extended Data Fig. 7 for the nomenclature of regions). The approximate locations of the sites are shown in the bottom right corners. Background images are from GoogleEarth (providers: a–f, DigitalGlobe; b, c, e, Landsat/Copernicus; b, d, e, CNES/Aribus; a, f, Maxar Technologies; f, Planet.com).
Extended Data Fig. 2 Temporal evolution of the total hydropower potential from all considered basins within individual regions.
Regional definitions for panels a–r are in Extended Data Fig. 7 and follow the Randolph Glacier Inventory20. Projections refer to three climate scenarios (green, blue and red) based on selected representative concentration pathways23. Temporal variations reflect variations in runoff. Note that different vertical scales are used for each row. Grey shading indicates 95% confidence intervals.
Extended Data Fig. 3 Cumulative hydropower potential and potential reservoir volume for a given suitability score.
a, b, Hydropower potential (a) and reservoir volume (b). The ‘steps’ in the suitability score are caused by some of the suitability indicators only taking discrete values, and the final suitability score being an average of three categories (see Methods). The inflection point at score 50 is marked, as are other features worth noticing. The suitability score is dimensionless.
Extended Data Fig. 4 Globally aggregated hydropower potential and potential storage volume as a function of suitability.
a, b, Hydropower potential (a) and reservoir volume (b). The score for every suitability indicator (environmental, technical or economic) is given individually. The last row of each panel shows the final suitability score (see Methods).
Extended Data Fig. 5 Hydropower potential as a function of suitability, aggregated per country.
a–o, The 15 countries with the highest cumulative hydropower potentials. Scores for individual indicators are given separately.
Extended Data Fig. 6 Cumulative hydropower energy potential as a function of production cost in different regions of the world.
a–r, Regions are defined in Extended Data Fig. 7, and are sorted according to the maximal potential for which the estimated production cost is below 0.5 USD kWh–1. Different colours depict individual cost components (see Methods and Extended Data Table 1 for calculations). Note that the panels have different scales (the grey bars in the upper right corners are always equivalent to 3 TWh yr–1).
Extended Data Fig. 7 Global distribution of the hydropower potential from deglacierizing basins, compared with present renewable electricity production.
The total maximal potential per country (blue shading) is shown as a percentage of the present renewable electricity production. Data for the latter are taken from the International Renewable Energy Agency16 for the year 2017. The red boxes indicate regions as defined in the Randolph Glacier Inventory20 and are used for aggregating data in Extended Data Figs. 2, 6. The basemap was generated using Matplotlib30.
Extended Data Fig. 8 Global distribution of the top 1,000 sites in terms of hydropower potential.
Circle sizes and colours depict the hydropower potential and the suitability score, respectively. The basemap was generated using Matplotlib30.
Extended Data Fig. 9 Conceptual representation of the procedure used for determining the design discharge QD.
Qavg is the average monthly discharge and Vres (orange area) is the volume of the reservoir. Small and large reservoirs are discerned on the basis of annual discharge: a reservoir is considered to be large if it can store half of the annual discharge or more. In this case, turbining the annual discharge every year requires QD to equal Qavg (a smaller QD would imply water accumulation from year to year, thus leading to reservoir overspill and production loss over time). Smaller reservoirs require a larger QD, because less water can temporarily be stored. Letters on the x-axis denote months.
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Farinotti, D., Round, V., Huss, M. et al. Large hydropower and water-storage potential in future glacier-free basins. Nature 575, 341–344 (2019). https://doi.org/10.1038/s41586-019-1740-z
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DOI: https://doi.org/10.1038/s41586-019-1740-z
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