Introduction: Colorado Riverflow and Climate Change

Climate change is one of the most pressing environmental challenges of our time, with far-reaching impacts that extend across ecosystems, economies, and communities. Among the many critical aspects affected by climate change are water resources, which are becoming increasingly strained as temperatures rise and precipitation patterns shift. In Colorado, where river systems are the lifeblood of both urban and rural communities, the consequences of these changes are particularly pronounced. It is estimated that approximately 83% of Colorado’s drinking water is sourced from river systems, which also supply water to several downstream states, including Arizona, Nevada, and California. The impacts of climate change on river systems in Colorado are already evident, manifesting in reduced water availability and increased competition for resources. Since 2000, streamflows across the state have decreased by an estimated 3% to 19% compared to the historical average from 1951 to 2000. This decline is attributed to a combination of factors, including diminished snowpack, earlier snowmelt, and altered precipitation patterns. These reductions in streamflow are expected to continue, further stressing the region’s water supplies.

Reduced snowpack, earlier snowmelt, and changes in precipitation patterns have direct impacts on water availability. These patterns are exacerbated by increasing global temperatures. As these factors worsen, they could have cascading effects on agriculture, industry, and residential water use. The implications of these changes are profound, not only for Colorado’s water supply but also for the millions of people and industries across the southwestern United States that depend on Colorado's many rivers.

The Colorado River, in particular, is a critical water source for the region, serving over 40 million people and supporting agriculture, industry, and recreation. However, the river’s flow is highly variable and influenced by a complex set of environmental factors. Key among these are temperature fluctuations, precipitation levels, and the amount of snowpack that accumulates in the river’s mountainous headwaters. The intricate relationship between these factors makes predicting future riverflow challenging but essential, especially in the context of climate change. Understanding these dynamics is crucial for developing effective water management strategies. This project aims to address this challenge by leveraging advanced machine learning techniques to predict future riverflow in the Colorado River Basin. The project will utilize a robust dataset that includes historical riverflow data, snowpack measurements, and meteorological data tracking precipitation and temperature over time. These data points provide a comprehensive view of the factors influencing riverflow. To account for potential future scenarios under climate change, the project will also incorporate two Representative Concentration Pathway (RCP) scenarios—a base case reflecting moderate climate changes, and a worst-case scenario representing severe changes in temperature and precipitation patterns. This dual approach allows for a more nuanced understanding of possible future conditions.

The primary goal of this project is to develop a predictive model that can provide accurate forecasts of riverflow in the Colorado River Basin. Such a model would be invaluable for water managers tasked with making critical decisions about water allocation, drought mitigation, and long-term resource planning in the face of increasing uncertainty. By integrating climate projections with historical data, this model aims to serve as a vital tool for ensuring the sustainable management of water resources in the region. Accurate predictions can help mitigate the risks of water shortages and ensure fair distribution among stakeholders. While significant research has been conducted on the effects of climate change in Colorado, there has been a notable gap in the application of machine learning techniques specifically focused on predicting riverflow in the Colorado River Basin. This project seeks to fill that gap by exploring the complex interactions between snowpack, snowmelt, precipitation, temperature, and their combined effects on riverflow, drinking water supply, and agriculture. The integration of these variables is expected to enhance the precision of the predictive model. The outcomes of this research could have significant implications not only for Colorado but also for the broader southwestern United States, where water scarcity is an ever-growing concern. As water resources become more constrained, the importance of accurate and timely predictions will only increase.

Colorado River

Key Questions

  • How does climate change affect riverflow?
  • How does snowpack influence soil moisture, and how does that translate to riverflow?
  • How can we quantify evapotranspiration?
  • What is the relationship between temperature and precipitation? (Will be important as RCP data consists of temperature and precipitation into 2100.)
  • How does riverflow (cubic ft/s) impact water management downstream?
  • What is considered a drought?
  • In times of drought, what are the effects on people?
  • Can accurate prediction give water managers meaningful actions to take?
  • What sites/stations are best for ML training data?
  • Do we consider upstream/headwater origination, or is it better to model downstream flows after dams, diversions, etc. due to human intervention?
  • References

    1. Bolinger, R.A., J.J. Lukas, R.S. Schumacher, and P.E. Goble, 2024: Climate Change in Colorado, 3rd edition. Colorado State University, https://doi.org/10.25675/10217/237323.

    2. Barnett, T. P., Malone, R., Pennell, W., Stammer, D., Semtner, B., & Washington, W. (2004). The effects of climate change on water resources in the west: Introduction and overview. Climatic Change, 62(1-3), 1-11.

    3. Udall, B., & Overpeck, J. (2017). The twenty-first century Colorado River hot drought and implications for the future. Water Resources Research, 53(3), 2404-2418.