Impacts of climate-related water stress and temperature changes can cascade through energy systems, though models have yet to capture this compounding of effects. Here, we employ a coupled water-power-economy model to capture these important interactions in a study of the exceedance of water temperature thresholds for power generation in the Western U.S. Our analysis of the impacts of a range of climate forcing patterns on the coupled water-power-economic system has demonstrated that higher water temperatures can lead to a causal chain of events from electric power generators offline because of cooling water intake temperature limits to higher electricity costs and unmet electricity demand to economic adjustment to productivity reductions in electricity using sectors.
We find that not all reductions in reserve electricity generation capacity result in impacts and that when they occur, intermittent interruptions in electricity supply at critical times of the day, week, and year account for much of the economic impacts. Lastly, we find that impacts may be in different locations from the original water stress. We estimate that the consumption loss can be up to 0.3% annually and the drivers identified in coupled modeling can increase the average cost of electricity up to 3%. The key insights are that many climate patterns that result in generator outages from higher water temperatures do not result in any significant impacts (thresholds), and that most of the economic impacts result from electricity demand that cannot be met at specific times and locations (timing), and that these unmet demand events may occur at geographically distant locations from the generator outages (teleconnections).
The results underscore the importance of accounting for feedbacks between overlapping and interacting system networks. Importantly, this type of coupled model approach allows investigators to retain the spatial, temporal, and sectoral richness represented in each of these individual models that would be unachievable in one comprehensive model where detail is usually sacrificed for computational tractability. In particular, the chronological hourly resolution of the power system is critical to be able to represent discrete events of intermittent power disruptions, the largest factor affecting economic cost. Similarly, sectoral detail allows us to differentiate between those industries that are hardest hit by these disruptions and those that are not, providing further evidence that impacts are not likely to be uniform across space, time, and sector.