Predicting prolonged drought in the Colorado River Basin

The Colorado River water supply is predictable for several years in advance by utilizing a state-of-the-art climate model, long-term ocean memories, atmospheric teleconnections, and land filtering effect.
Predicting prolonged drought in the Colorado River Basin
Photo by Scott Markewitz

“Should I buy a season pass for a Utah ski resort for next year?” That question marked the first moment that I started to think about research to predict the Colorado River water supply years ahead. Skiing is a blessing for Utahns, and many ski resorts begin selling season passes at discounted prices the year before the next ski season. The price gradually increases until the ski season starts, and it seems like a bargain to buy early. However, some winters just don’t have good snow—crucial to the ski experience—especially during drought years. In those years, purchasing the season pass would seem expensive compared to buying a few day-passes. To make my decision wisely, I decided to develop a multi-year drought forecast for Utah.

Through doing the work, I realized there are many demands for a multi-year drought forecasting. Water is an essential resource in the Intermountain West, including Utah. One of the primary water supplies in this region is the Colorado River. A shortage in the Colorado River water supply severely stresses regional water resources and affects agriculture, wildfires, hydropower, recreation, and ecologic services. From the perspective of proactive management, water resource managers all look forward to having a reliable forecast of the Colorado River water supply that goes beyond just a season. Earlier Utah Climate Center research has revealed that the Colorado River water supply demonstrates a wet-dry cycle that repeats every 10-20 years, but its mechanism remains a mystery. 

Photo from GettyImages.

By utilizing a state-of-the-art climate prediction system based on physics, we achieved some useful prediction results in estimating the Colorado River water supply several years in advance. This prediction system combines a comprehensive “Earth system model” that integrates all climate systems, armed with an advanced assimilation technique that ingests real ocean data. The Earth system model consists of many computer programs to simulate physical and biogeochemical processes in the atmosphere, land, ocean, and sea ice, which require substantial computational resources and knowledge to conduct the simulations. It took almost one year to perform all the model simulations using our approach to establish the statistics to derive water supply. We spent another year analyzing the data to determine sources of the Colorado River’s periodic water shortages and its impacts on area crop yield and wildfire intensity. Through collaboration among an agronomist, a fire ecologist, and climate scientists, we finally identified the source of multi-year predictability for the Colorado River water supply, originating from the tropical Pacific, the North Pacific, and the tropical Atlantic oceans.

Our research is an initial step toward achieving Earth system forecasting. The current Earth system model enables numerical climate prediction across a broad spectrum of timescales from recent weather to future climate. Climate scientists continuously work to understand many processes crucial to climate modeling, such as cloud physics, ocean waves, land hydrology, biogeochemical cycles, and marine ecosystems, and to develop tools that translate into Earth system predictability. In the future, we could further enhance our predictive skills of the Colorado River water supply by advancing mechanistic components of an Earth system model and prediction systems. My journey never ends, and I am one step closer to achieving my most advantageous decision about buying an early-season ski pass.

Chikamoto, Y., S.-Y. Simon Wang, M. Yost, L. Yocom, and R. R. Gillies: Colorado River water supply is predictable on multi-year timescales owing to long-term ocean memory, Communications Earth & Environment, 1, 26, doi:10.1038/s43247-020-00027-0

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