Climatic Change

, Volume 122, Issue 4, pp 621–634 | Cite as

A sensitivity-based approach to evaluating future changes in Colorado River discharge

  • Julie A. Vano
  • Dennis P. Lettenmaier


Projections of a drier, warmer climate in the U.S. Southwest would complicate management of the Colorado River system—yet these projections, often based on coarse resolution global climate models, are quite uncertain. We present an approach to understanding future Colorado River discharge based on land surface characterizations that map the Colorado River basin’s hydrologic sensitivities (e.g., changes in streamflow magnitude) to annual and seasonal temperature and precipitation changes. The approach uses a process-based macroscale land surface model (LSM; in this case, the Variable Infiltration Capacity hydrologic model, although methods are applicable to any LSM) to develop sensitivity maps (equivalent to a simple empirical model), and uses these maps to evaluate long-term annual streamflow responses to future precipitation and temperature change. We show that global climate model projections combined with estimates of hydrologic sensitivities, estimated for different seasons and at different change increments, can provide a basis for approximating cumulative distribution functions of streamflow changes similar to more common, computationally intensive full-simulation approaches that force the hydrologic model with downscaled future climate scenarios. For purposes of assessing risk, we argue that the sensitivity-based approach produces viable first-order estimates that can be easily applied to newly released climate information to assess underlying drivers of change and bound, at least approximately, the range of future streamflow uncertainties for water resource planners.


Streamflow Emission Scenario Couple Model Intercomparison Project Variable Infiltration Capacity Future Time Period 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors thank James Prairie (USBR) for his assistance in accessing USBR Colorado River Basin Water Supply and Demand Study data, Bart Nijssen (University of Washington) for his feedback on the sensitivity-based approach development, and three anonymous reviewers for their constructive comments on the manuscript. Support for this work was provided by NOAA’s Regional Integrated Science Assessment program, grant number NA10OAR4310218, to Oregon State University’s Climate Impacts Research Consortium.

Supplementary material

10584_2013_1023_MOESM1_ESM.pdf (722 kb)
ESM 1 (PDF 721 kb)


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.College of Earth, Ocean, and Atmospheric SciencesOregon State UniversityCorvallisUSA
  2. 2.Department of Civil and Environmental EngineeringUniversity of WashingtonSeattleUSA

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