Extended Abstract (1.2M)10.1
Multiobjective, manifoldly constrained Monte Carlo optimization and uncertainty estimation for an operational hydrologic forecast model
Sean W. Fleming, BC Hydro, Burnaby, BC, Canada; and F. A. Weber and S. Weston
River forecasts have two broad uncertainty classes: errors associated with meteorological forecasts, and those associated with the hydrologic model. We developed a technology (dubbed Absynthe) to address the latter error class in a practical and defensible way. The technique merges the proven, Monte Carlo-based Generalized Likelihood Uncertainty Estimation (GLUE) concept for model parameter identification with: (i) multiple performance goals defined by operational and physical considerations, including matching daily, seasonal, and annual flows as well as snowpack, as expressed via individual behavioural criteria and a net likelihood function; (ii) several moving (rank-based) constraints to assure non-pathological parameter sets, containing values that are physically plausible not only for each parameter individually but also collectively; and (iii) a hard constraint on snow-free elevation bands to force the surface meteorological component of the watershed model toward correct solutions. The result is an ensemble of parameter sets reflecting model uncertainty as captured in a loosely Bayesian framework. BC Hydro will combine these with ensemble NWP weather forecasts to generate uncertainty estimates for operational hydroelectric reservoir inflow forecasts.
Session 10, Water Resources and Forecasting Applications
Wednesday, 20 January 2010, 4:00 PM-5:00 PM, B304
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