Options for Improving Climate Modeling to Assist Water Utility ...

Water Utility Climate Alliance White Paper
Options for Improving Climate Modeling to Assist Water Utility Planning for Climate Change
combination of variables. For example, does the observed dataset capture persistent warming
combined with decreases or increases in precipitation?
One of the concerns with statistical models is whether the quantitative relationships are credible
when extrapolated beyond the dataset used to build the models. Climate change, for example,
may create climates not observed or rarely observed. It is not clear if the statistical relationship
based on observed climate in the statistical model will be maintained under a changed climate.
The model may assume a linear relationship, which in reality could become nonlinear under
climate change. In addition, factors such as land use are likely to change in the future resulting in
change in the statistical relationships. Models based on current statistical relationships may not
capture such changes. In theory, if physical models accurately simulate physical properties
governing the system that is being modeled, then they should accurately simulate changes in the
system even if conditions are outside of observations. In practice, this can be difficult to verify.
There are counterarguments. Physical models contain quantitative relationships that are based on
observations. Thus, knowledge of how the physical system will behave under unobserved
conditions may be limited. Statistical models tend to be easier to build and less expensive to run
than physical models. Observations may contain a wide range of conditions and thus capture
many potential changes in climate.
For utilities, the choice of model to estimate change in the quantity or quality of water resources
may be complex. Producing more reliable results through the application of hydrologic models
may be more expensive than using statistical models. The only choice available may be the use
of statistical models. One hybrid approach is to use physical models to simulate a broad range of
climate conditions that may be consistent with a range of climate model projections. Such
information can be used to derive statistical relationships that can be used for further analysis.
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