James Prairie

Water supply risk on the Colorado River: Can management mitigate?

[1] Population growth and a changing climate will tax the future reliability of the Colorado River water supply. Using a heuristic model, we assess the annual risk to the Colorado River water supply for 2008―2057. Projected demand growth superimposed upon historical climate variability results in only a small probability of annual reservoir depletion through 2057. In contrast, a scenario of 20% reduction in the annual Colorado River flow due to climate change by 2057 results in a near tenfold increase in the probability of annual reservoir depletion by 2057. However, our analysis suggests that flexibility in current management practices could mitigate some of the increased risk due to climate change― induced reductions in flows.

A nonparametric stochastic approach for multisite disaggregation of annual to daily streamflow

[1] Streamflow disaggregation techniques are used to distribute a single aggregate flow value to multiple sites in both space and time while preserving distributional statistics (i.e., mean, variance, skewness, and maximum and minimum values) from observed data. A number of techniques exist for accomplishing this task through a variety of parametric and nonparametric approaches. However, most of these methods do not perform well for disaggregation to daily time scales. This is generally due to a mismatch between the parametric distributions appropriate for daily flows versus monthly or annual flows, the high dimension of the disaggregation problem, compounded uncertainty in parameter estimation for multistage approaches, and the inability to maintain flow continuity across disaggregation time period boundaries. We present a method that directly simulates daily data at multiple locations from a single annual flow value via K-nearest neighbor (K-NN) resampling of daily flow proportion vectors. The procedure is simple and data driven and captures observed statistics quite well. Furthermore, the generated daily data are continuous and display lag correlation structure consistent with that of the observed data. The utility and effectiveness of this approach is demonstrated for selected sites in the San Juan River Basin, located in southwestern Colorado, and later compared with the disaggregation technique of Prairie et al. (2007) for several locations in the Colorado River Basin.

Mid-21st century projections in temperature extremes in the southern Colorado Rocky Mountains from regional climate models

This study analyzes mid-21st century projections of daily surface air minimum (Tmin) and maximum (Tmax) temperatures, by season and elevation, over the southern range of the Colorado Rocky Mountains. The projections are from four regional climate models (RCMs) that are part of the North American Regional Climate Change Assessment Program (NARCCAP). All four RCMs project 2°C or higher increases in Tmin and Tmax for all seasons. However, there are much greater (>3°C) increases in Tmax during summer at higher elevations and in Tmin during winter at lower elevations. Tmax increases during summer are associated with drying conditions. The models simulate large reductions in latent heat fluxes and increases in sensible heat fluxes that are, in part, caused by decreases in precipitation and soil moisture. Tmin increases during winter are found to be associated with decreases in surface snow cover, and increases in soil moisture and atmospheric water vapor. The increased moistening of the soil and atmosphere facilitates a greater diurnal retention of the daytime solar energy in the land surface and amplifies the longwave heating of the land surface at night. We hypothesize that the presence of significant surface moisture fluxes can modify the effects of snow-albedo feedback and results in greater wintertime warming at night than during the day.