Harnessing Mid-Term Temperature Skill to Improve Streamflow Management in the Colorado River Basin

Abstract:

Mid-term climate prediction is an evolving field that aims to provide society with climate outlooks years to a decade in advance. Skillful mid-term temperature predictions offer a potential opportunity for water managers, especially in the Colorado River Basin (CRB), where streamflows are sensitive to temperature and the water system is highly managed. The goal of this project is to explore how mid-term temperature prediction skill can be harnessed to generate streamflow ensembles that can be used by the Bureau of Reclamation (Reclamation) to improve water management in the CRB. Temperature skill was assessed for the CRB using mid-term retroactive forecasts, or hindcasts, from two large ensemble datasets: NCAR’s Community Earth System Model large ensemble (CESM-LE), along with the corresponding initialized CESM large ensemble of decadal predictions (CESM-DPLE). Next, a temperature-conditioned block bootstrap resampling approach was developed to ingest the temperature ensembles to generate streamflow ensembles. Specifically, the approach resampled the Ensemble Streamflow Prediction (ESP) hindcasts to reflect the temperature hindcasts from the CESM large ensembles. Finally, the generated streamflow hindcasts were run through a Reclamation operational model, the Mid-Term Operations Model (MTOM), to assess operational performance in the CRB at a 2-5 year outlook. Results demonstrate the translation of temperature skill to streamflow projections, and to decision-relevant metrics. This presentation will provide an overview of the project and results, along with the overview of the iterative and collaborative process with CRB stakeholders that helped to align climate skill with water management needs.