H34F-04
Robustness Analysis of Regional Water Supply Portfolios using Synthetic Inflow Scenarios with Variable Drought Frequency

Wednesday, 16 December 2015: 16:45
3011 (Moscone West)
Jonathan D Herman1, Harrison Bray Zeff2, Jonathan Richard Lamontagne3, Patrick M Reed3 and Gregory W Characklis2, (1)University of California Davis, Civil & Environmental Engineering, Davis, CA, United States, (2)University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, (3)Cornell University, Ithaca, NY, United States
Abstract:
Robustness analyses of water supply systems have moved toward exploratory simulation to discover scenarios in which existing or planned policies may fail to meet stakeholder objectives. Such assessments rely heavily on the choice of plausible future scenarios, which, in the case of drought management, requires sampling or generating a broad ensemble of reservoir inflows which do not necessarily reflect the historical record. Here we adapt a widely used synthetic streamflow generation method to adjust the frequency of low-flow periods, which can be related to impactful historical events from the perspective of decision makers. Specifically, the modified generation procedure allows the user to specify parameters n, p such that events with observed weekly non-exceedance frequency p appear in the synthetic scenario with approximate frequency np (i.e., the pth percentile flow occurs n times more frequently). Additionally, the generator preserves the historical autocorrelation of streamflow and its seasonality, as well as approximate multi-site correlation. Using model simulations from recent work in multi-objective urban drought portfolio planning in North Carolina, a region whose water supply faces both climate and population pressures, we illustrate the decision-relevant consequences caused by raising the frequency of low flows associated with the 2007-2008 drought. This method explores system performance under extreme events of increasing frequency prior to reconciling these findings with climate model projections, and thus can be used to support bottom-up robustness methods in water systems planning.