A Variational Ensemble Streamflow Prediction Assessment Approach for Quantifying Streamflow Forecast Skill Elasticity
Thursday, 18 December 2014: 11:20 AM
Water resources decision-making commonly depends on monthlytoseasonal streamflow forecasts, among other kinds of information. The skill of suchpredictions derives from the ability to estimate a watershed’s initial moisture and energy conditions and to forecast future weather and climate. We investigate the first two sources of predictability in an idealized experiment using calibrated hydrologic simulation models for 424-watersheds that span the continental US. Earlier work in this area outlined an ensemble-based strategy for attributing streamflow forecast uncertainty between two endpoints representing zero and perfect information about future forcings (ie, the National Weather Service ensemble streamflow prediction, or ESP approach) and initial conditions. This study expands this approach to characterize the influence of varying levels of uncertainty in each area on streamflow prediction uncertainty, and, importantly, we calculate derivatives in predictability throughout the initial conditions and future forcing space to illustrate how science investments can improve streamflow forecasts. Ensemble hindcasts are initialized on a monthly basis for each basin’s periods of record, and forecasts of streamflow for the 1, 3 and 6 month periods following the initialization are evaluated. Regional and seasonal variations in watershed hydroclimatology largely determine the relative importance of initial hydrologic conditions and seasonal climate forecasts, leading to striking differences between rainfall driven and snowmelt driven watersheds. We use the flow forecast skill derivatives – elasticities relative to skill in either predictability source – to characterize the regional, seasonal and predictand variations in flow forecast skill dependencies. The resulting analysis provides insights on the relative benefits of investments toward improving watershed monitoring (through modeling and measurement) versus improved climate forecasting. Among other key findings, the results suggest that climate forecast and initial condition skill improvements can be amplified in streamflow prediction skill, which means that climate forecasts may have greater benefit for monthly-to-seasonal flow forecasting than is apparent from climate forecast skill considerations alone.