Model parameter uncertainty and climate change effects in hydropower vulnerability projections
Hydropower is a major source of renewable energy in the Western United States and could be a particularly important source of both firm power and ancillary services in a decarbonizing grid. However, hydropower availability is vulnerable to drought conditions exacerbated by climate change, with reservoir levels falling below minimum power pool thresholds. An under-examined component of this vulnerability is how model parameter uncertainty affects simulated reservoir levels. Here, I investigate the extent to which large-scale hydrologic models can capture the effect of climate change on reservoir levels for three reservoirs in California, and how parameter uncertainty affects conclusions derived from these models. Climate data from Daymet is processed through pywatershed, a Python-based implementation of the Precipitation-Runoff Modeling System (PRMS) used in the National Hydrologic Model. The resulting runoff estimates serve as input for mosartwmpy, which simulates water management, including reservoir storage and release. Climate changes are simulated using a delta approach on historical climate data and the Generalized Likelihood Uncertainty Estimation (GLUE) method is used to address parameter uncertainty. I find that, though on average reservoir inflows decrease, center of timing shifts earlier, and low storage frequency increases in a warming climate, the effects of parameter choice are significant enough to overcome the effects of simulated warming. However, the changes in some metrics due to incremental temperature increases are better constrained despite parameter uncertainty. These findings underscore the importance of considering a range of model outcomes and metrics when making water management decisions.
climate change, hydrologic modeling, hydropwer, parameter uncertainty
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Colorado School of Mines. Arthur Lakes Library