Bob Berwyn has a nice rundown on the process now underway at the Colorado Water Conservation Board to determine how much water is left in the Colorado River for the state of Colorado to develop. Here’s the nut of the issue:

After conducting what they say is one of the most rigorous studies ever of the Colorado River Basin, state officials concluded there may be some additional water available for development and use — or there may not, depending on what numbers are plugged into the computer models.

One of the models suggested there could be as much as 900,000 acre-feet of water in the Colorado River Basin available for consumptive use under the terms of the Colorado River Compact. Consumptive use permanently removes water from a watershed. Agricultural activities, irrigation and industrial cooling operations are example of consumptive uses. However another model suggested that, if climate impacts are more severe, Colorado may have no water left to develop.

There are continued efforts to refine the modeling work and come up with the “right” answer, but what we’ve really got here is a classic case of decision-making in the face of uncertainty that the models ultimately cannot resolve.

Mike Hulme, Roger Pielke Jr. and Suraje Dessai have been exploring this question, and offered this helpful analysis recently. It’s focused on climate modeling, which is only a part of the Colorado River problem, but I think it applies more broadly to the issues Berwyn is writing about:

Guaranteeing precision and accuracy over and above what science can credibly deliver risks contributing to flawed decisions. We are not suggesting that scientists abandon efforts to model the behaviour of the climate system. Far from it. Models as exploratory tools can help identify physically implausible outcomes and illuminate the boundaries where uncertain knowledge meets fundamental ignorance. But using models in this way will require a significant rethink on the role of predictive climate science in decision-making. In some cases the prudent course of action will be to let policymakers know the very real limitations of predictive science. For decision-makers, the lesson is to plan for a range of possible alternatives. Instead of seeking certainty, decision-makers need to ask questions of scientists such as ‘What physically could not happen?’ or ‘What is the worst that could happen?’