Assessing the Assessment Methods: Climate Change and Hydrologic Impacts

Thursday, 18 December 2014
Levi D Brekke1, Martyn P Clark2, Ethan D Gutmann3, Naoki Mizukami3, Pablo A Mendoza4, Roy Rasmussen5, Kyoko Ikeda5, Tommy Pruitt1, J R Arnold6 and Balaji Rajagopalan7, (1)U.S. Bureau of Reclamation, Denver, CO, United States, (2)NCAR, Boulder, CO, United States, (3)National Center for Atmospheric Research, Boulder, CO, United States, (4)University of Colorado at Boulder, Boulder, CO, United States, (5)NCAR/RAL, Boulder, CO, United States, (6)U.S. Army Corps of Engineers, Seattle, WA, United States, (7)Univ Colorado, Civil, Environmental, and Architectural Engineering and Cooperative Institute for Research in Environmental Sciences, Boulder, CO, United States
The Bureau of Reclamation, the U.S. Army Corps of Engineers, and other water management agencies have an interest in developing reliable, science-based methods for incorporating climate change information into longer-term water resources planning. Such assessments must quantify projections of future climate and hydrology, typically relying on some form of spatial downscaling and bias correction to produce watershed-scale weather information that subsequently drives hydrology and other water resource management analyses (e.g., water demands, water quality, and environmental habitat). Water agencies continue to face challenging method decisions in these endeavors: (1) which downscaling method should be applied and at what resolution; (2) what observational dataset should be used to drive downscaling and hydrologic analysis; (3) what hydrologic model(s) should be used and how should these models be configured and calibrated? There is a critical need to understand the ramification of these method decisions, as they affect the signal and uncertainties produced by climate change assessments and, thus, adaptation planning.

This presentation summarizes results from a three-year effort to identify strengths and weaknesses of widely applied methods for downscaling climate projections and assessing hydrologic conditions. Methods were evaluated from two perspectives: historical fidelity, and tendency to modulate a global climate model’s climate change signal. On downscaling, four methods were applied at multiple resolutions: statistically using Bias Correction Spatial Disaggregation, Bias Correction Constructed Analogs, and Asynchronous Regression; dynamically using the Weather Research and Forecasting model. Downscaling results were then used to drive hydrologic analyses over the contiguous U.S. using multiple models (VIC, CLM, PRMS), with added focus placed on case study basins within the Colorado Headwaters. The presentation will identify which types of climate changes are expressed robustly across methods versus those that are sensitive to method choice; which method choices seem relatively more important; and where strategic investments in research and development can substantially improve guidance on climate change provided to water managers.