Incorporating Multi-Year Temperature Predictions for Water Resources Planning

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Abstract

Multi-year climate predictions provide climate outlooks years to a decade in advance. As multi-year temperature predictions become more mainstream and skillful, guidance is needed to assist practitioners who wish to explore this maturing field. This paper demonstrates the process and considerations of incorporating multi-year temperature predictions into water resources planning. Multi-year temperature predictions from the Community Earth System Model Decadal Prediction Large Ensemble are presented as discrete and probabilistic products, and used to force two common hydrologic modeling approaches, conceptual and empirical. The approaches are demonstrated to simulate streamflow in the Upper Colorado River Basin watershed in Colorado, US, where diagnostics show that increasing temperatures are associated with decreasing streamflows. Using temperature information for lead years 2-6, two analyses are performed: (i) a retrospective hindcast for the climatological period (1981-2010), and (ii) a blind forecast for 2011-2015. For the retrospective hindcast, including temperature information improved the percent error as compared to climatology. For the blind forecast, the multi-year temperature prediction for warming was skillful, but the corresponding multi-year average streamflow predictions from both approaches were counterintuitive: with the predicted warming, the multi-year average streamflow was predicted to be lower than the climatological mean, however the observed multi-year average streamflow was higher than the climatological mean. This was due to above average precipitation during the prediction time frame, particularly one of the years. Removing that year, the multi-year streamflow average became lower than the climatological mean. Temperature provides a marginal source of streamflow predictability, but there will be substantial uncertainty until prediction skill for year-to-year climate variability, especially for precipitation, increases.

Corresponding author: towler@ucar.edu

Abstract

Multi-year climate predictions provide climate outlooks years to a decade in advance. As multi-year temperature predictions become more mainstream and skillful, guidance is needed to assist practitioners who wish to explore this maturing field. This paper demonstrates the process and considerations of incorporating multi-year temperature predictions into water resources planning. Multi-year temperature predictions from the Community Earth System Model Decadal Prediction Large Ensemble are presented as discrete and probabilistic products, and used to force two common hydrologic modeling approaches, conceptual and empirical. The approaches are demonstrated to simulate streamflow in the Upper Colorado River Basin watershed in Colorado, US, where diagnostics show that increasing temperatures are associated with decreasing streamflows. Using temperature information for lead years 2-6, two analyses are performed: (i) a retrospective hindcast for the climatological period (1981-2010), and (ii) a blind forecast for 2011-2015. For the retrospective hindcast, including temperature information improved the percent error as compared to climatology. For the blind forecast, the multi-year temperature prediction for warming was skillful, but the corresponding multi-year average streamflow predictions from both approaches were counterintuitive: with the predicted warming, the multi-year average streamflow was predicted to be lower than the climatological mean, however the observed multi-year average streamflow was higher than the climatological mean. This was due to above average precipitation during the prediction time frame, particularly one of the years. Removing that year, the multi-year streamflow average became lower than the climatological mean. Temperature provides a marginal source of streamflow predictability, but there will be substantial uncertainty until prediction skill for year-to-year climate variability, especially for precipitation, increases.

Corresponding author: towler@ucar.edu
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