The potential benefits of seasonal streamflow forecasts for the hydropower sector have been evaluated for several basins across the world but with contrasting conclusions on the expected benefits. This raises the prospect of a complex relationship between reservoir characteristics, forecast skill, and value. Here, we unfold the nature of this relationship by studying time series of simulated power production for 735 headwater dams worldwide. The time series are generated by running a detailed dam model over the period 1958-2000 with three operating schemes: basic control rules, perfect forecast-informed operations, and realistic forecast-informed operations. The realistic forecasts are issued by tailored statistical prediction models - based on lagged global and local hydroclimatic variables - predicting seasonal monthly dam inflows. As expected, results show that most dams (94 %) could benefit from perfect forecasts. Yet, the benefits for each dam vary greatly and are primarily controlled by the time-to-fill value and the ratio between reservoir depth and hydraulic head. When realistic forecasts are adopted, 25 % of dams demonstrate improvements with respect to basic control rules. In this case, the likelihood of observing improvements is controlled not only by design specifications but also by forecast skill. We conclude our analysis by identifying two groups of dams of particular interest: dams that fall in regions expressing strong forecast accuracy and having the potential to reap benefits from forecast-informed operations and dams with a strong potential to benefit from forecast-informed operations but falling in regions lacking forecast accuracy. Overall, these results represent a first qualitative step toward informing site-specific hydropower studies.
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Columbia Univ, Dept Appl Phys & Appl Math, New York, NY 10027 USAColumbia Univ, Dept Appl Phys & Appl Math, New York, NY 10027 USA
Orenstein, Patrick
Sobel, Adam h.
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Columbia Univ, Dept Appl Phys & Appl Math, New York, NY 10027 USA
Columbia Univ, Dept Earth & Environm Sci, New York, NY USAColumbia Univ, Dept Appl Phys & Appl Math, New York, NY 10027 USA
Sobel, Adam h.
Camargo, Suzana j.
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Columbia Univ, Lamont Doherty Earth Observ, Palisades, NY USAColumbia Univ, Dept Appl Phys & Appl Math, New York, NY 10027 USA
Camargo, Suzana j.
Elsaesser, Gregory s.
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Columbia Univ, Dept Appl Phys & Appl Math, New York, NY 10027 USA
NASA, Goddard Inst Space Studies, New York, NY USAColumbia Univ, Dept Appl Phys & Appl Math, New York, NY 10027 USA
Elsaesser, Gregory s.
Garg, Piyush
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Argonne Natl Lab, Lemont, IL USAColumbia Univ, Dept Appl Phys & Appl Math, New York, NY 10027 USA
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Agr Res Council, Inst Soil Climate & Water, Belvedere St 600, ZA-0001 Pretoria, South Africa
Univ Pretoria, Dept Geog Geoinformat & Meteorol, Pretoria, South AfricaAgr Res Council, Inst Soil Climate & Water, Belvedere St 600, ZA-0001 Pretoria, South Africa
Engelbrecht, Christien J.
Landman, Willem A.
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Univ Pretoria, Dept Geog Geoinformat & Meteorol, Pretoria, South Africa
CSIR Nat Resources & Environm, Climate Studies Modelling & Environm Hlth, Pretoria, South AfricaAgr Res Council, Inst Soil Climate & Water, Belvedere St 600, ZA-0001 Pretoria, South Africa
Landman, Willem A.
Graham, Richard
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Met Off Hadley Ctr, Exeter, Devon, EnglandAgr Res Council, Inst Soil Climate & Water, Belvedere St 600, ZA-0001 Pretoria, South Africa
Graham, Richard
McLean, Peter
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Met Off Hadley Ctr, Exeter, Devon, EnglandAgr Res Council, Inst Soil Climate & Water, Belvedere St 600, ZA-0001 Pretoria, South Africa
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School of Meteorology, University of OklahomaSchool of Meteorology, University of Oklahoma
Jie FENG
Jianping LI
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College of Global Change and Earth System Science (GCESS), Beijing Normal University
Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and TechnologySchool of Meteorology, University of Oklahoma