Thursday, 27 January 2011
4E (Washington State Convention Center)
Rosalind Bark, University of Arizona, Tucson, AZ; and K. Morino
For long-term water resource planning and management, water managers need information on the risks of future drought as well as system vulnerabilities exposed by these risks. Projected hydrological data at regional and local scales are fundamental to these types of analyses. One approach to generating these data is utilizing downscaled climate output from global-scale general circulation models as input to locally-calibrated hydrological models. Alternatively, the analog approach can be deployed; here, we use reconstructed streamflow as a source of future hydrology scenarios. Tree-ring reconstructed streamflows are gaining broad acceptance amongst water resource managers in the western United States. These reconstructions provide a more complete representation of the variability in mean streamflow and a greater number of possible streamflow sequences as a consequence of their length (multiple centuries). For this research, we use the most recent and longest Colorado River streamflow reconstruction (Meko et al 2007) as a data source for input to Reclamation's long-term management model (Colorado River Simulation System; CRSS). The reconstructed streamflow records were sampled into average annual flow bins that increase in 0.5 million acre-feet (MAF) increments from 13 MAF to 16.5 MAF. The binned paleo reconstructed flows are run through CRSS and the output allows us to not only project the risk of Tier 1-, Tier 2- and Tier 3-shortages in the Lower Basin, but also to identify any step changes in shortage risk. In anticipation of reduced flows on the Colorado River, we are particularly interested in the drier periods of the paleo-record to provide insights into potential future conditions. For instance, a ten percent decline in average runoff would reduce the paleo average annual flow to 13.19 MAF and the instrumental average annual flow to 13.51 MAF. Thus those sequences with an average annual flow of 13 MAF and 13.5 MAF, over the 50-year period, might provide a good proxy for future flows.
The risk of shortage (drought) of different magnitudes and frequencies is essential information for water managers planning for future supply reliability. Using the paleo record it is possible to drill down to individual traces to investigate how the actual sequence of dry, normal, and wet years, combined with initial conditions, matters to system management. Using individual traces we can ask what set of conditions push the system into shortage, or deeper into shortage? What sequence of years will likely maintain shortage at a certain level? Or enable system recovery? Answers to these questions may provide water managers with an indication of the likely future water supply path they are on and therefore the suite of demand management and adaptation options they are likely to need to deploy. In time, the actual sequence may become more useful to water managers as annual and decadal forecasts of streamflow improve with enhanced inter-annual and decadal climate prediction skill.
To investigate the policy implications of climate change scenarios, we use the projections of shortage risk from CRSS under various flow scenarios as input into a water manager's decision tree: to do nothing; utilize alternative supplies; invest in alternative water sources/system efficiency; invest in demand management strategies; or combinations of these strategies. Using ensemble analysis we identify options for Arizonan water managers given the severity and frequency matrix of drought. Specifically, we investigate the decision sets of the Central Arizona Water Conservation District and generalized decision sets for tribal, and city water resource managers. We find that water management strategies evolve both over time and with the profile of future droughts. We also find that there are decision windows in which: proactive investment in demand management and alternative water sources; and cooperative action, are robust management options.
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