Reliability, sensitivity, and uncertainty of reservoir performance under climate variability in basins with different hydrogeologic settings in Northwestern United States

ABSTRACT

This study investigated how reservoir performance varied across different hydrogeologic settings and under plausible future climate scenarios. Modelling was conducted for the Santiam River Basin, OR, USA, comparing the North Santiam Basin, with high permeability and extensive groundwater (GW) storage, and the South Santiam Basin, with low permeability, little GW storage, and rapid runoff response. The coupling of projections of future temperature and precipitation from global climate models, a surface water–GW hydrologic model, and a formal Bayesian uncertainty analysis produced synthetic hydrographs of reservoir inflows. Inflow hydrographs were summarized as median and extreme future flows for inputs to a reservoir operations model. The performance of reservoir operations was evaluated as the failure to meet flood management, spring and summer environmental flows, and hydropower generation objectives. Despite projected increases in winter flows and decreases in summer flows, results provided little evidence of a response in reservoir operation performance to a changing climate, with the exception of summer flow targets. Independent of climate impacts, historical prioritization of reservoir operations played an important role in the reliability of flood regulation, demonstrating the importance of reservoir operations relative to hydrologic responses to climate change in this basin. Results also highlight how hydrologic uncertainty is likely to complicate planning for climate change in basins with substantial GW interactions.

KEYWORDS:

• Uncertainty
• reliability
• sensitivity
• climate change
• reservoir operations
• rule curves

Acknowledgements

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The research was supported in part by the National Science Foundation [grant number 0846360] through TeraGrid resources provided by Purdue University [grant number TG-ECS100006]. Funding was also provided by Universidad San Francisco de Quito (USFQ), Ecuador and Secretaria de Educacion Superior, Ciencia, Tecnologia e Innovacion (SENESCYT) under [grant number 20110435].