Woodson, D., Rajagopalan, B., Baker, S., Smith, R., Prairie, J., Towler, E., Ge, M. & Zagona, E. (2021). "Stochastic Decadal Projections of Colorado River Streamflow and Reservoir Pool Elevations Conditioned on Temperature Projections," Water Resources Research, 57 (12), https://doi.org/10.1029/2021WR030936


Decadal (~10-year)-scale flow projections in the Colorado River Basin (CRB) are increasingly important for water resources management and planning of its reservoir system. Physical models, ensemble streamflow prediction (ESP), do not have skill beyond interannual time scales. However, Global Climate Models have good skill in projecting decadal temperatures. This, combined with the sensitivity of CRB flows to temperature from recent studies, motivate the research question, can skill in decadal temperature projections be translated to operationally skillful flow projections and consequently, water resources management? To explore this, we used temperature projections from the Community Earth System Model-Decadal Prediction Large Ensemble (CESM-DPLE) along with past basin runoff efficiency as covariates in a Random Forest (RF) method to project ensembles of multiyear mean flow at the key aggregate gauge of Lees Ferry, Arizona. RF streamflow projections outperformed both ESP and climatology in a 1982–2017 hindcast, as measured by ranked probability skill score. The projections were disaggregated to monthly and subbasin scales to drive the Colorado River Mid-term Modeling System (CRMMS) to generate ensembles of water management variables. The projections of pool elevations in Lakes Powell and Mead, the two largest U.S. reservoirs that are critical for water resources management in the basin, were found to reduce the hindcast median root mean square error by up to −20% and −30% at lead times of 48 and 60 months, respectively, relative to projections generated from ESP. This suggests opportunities for enhancing water resources management in the CRB and potentially elsewhere.

Keywords: water resources, streamflow forecasting, Colorado River, climate projections, machine learning, Random Forest