Stochastic Decadal Projections of Colorado River Streamflow and Reservoir Pool Elevations Conditioned on Temperature Projections. Issue 12 (17th December 2021)

• Record Type: Journal Article
• Authors: Woodson, David; Rajagopalan, Balaji; Baker, Sarah; Smith, Rebecca; Prairie, James; Towler, Erin; Ge, Ming; Zagona, Edith
• Is Part Of: Water resources research. Volume 57:Issue 12(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-17
• Subjects: water resources -- streamflow forecasting -- Colorado River -- climate projections -- machine learning -- Random Forest; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2021WR030936
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: 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. Plain Language Summary: The Colorado River Basin (CRB) is a critical resource for tens of millions in the North American southwest but does not provide consistent streamflow year to year and is susceptible to multiyear droughts. While many good forecasts for Colorado River flow exist at seasonal time scales, projections beyond 1 year into the future perform little better than simply using a long‐term average, despite the importance of these so‐called "decadal" projections for water resource planning. As past studies have found that CRB streamflow is moderately influenced by air temperature, we seek to leverage relatively accurate climate model projections of temperature to improve CRB flow forecasts at decadal time scales using a statistical model. We find modest improvements in decadal projections of pool elevation at Lakes Powell and Mead compared to a standard method, supporting the potential for use of climate model projections in flow forecasting. Key Points: A Random Forest method for decadal (2–10 years) ensemble flow projections conditioned on temperature projections from climate models Midterm (1–5 years) ensemble flow projection of water resources management variables including reservoir pool elevations Encouraging prospects for translating skillful temperature projections to multiyear flow and water management at CRB and other basins