Quantifying Streamflow Forecast Skill Elasticity to Initial Condition and Climate Prediction Skill

Abstract

ater resources management decisions commonly depend on monthly to seasonal streamflow forecasts, among other kinds of information. The skill of such predictions derives from the ability to estimate a watershed?s initial moisture and energy conditions and to forecast future weather and climate. These sources of predictability are investigated in an idealized (i.e., perfect model) experiment using calibrated hydrologic simulation models for 424 watersheds that span the continental United States. Prior work in this area also followed an ensemble-based strategy for attributing streamflow forecast uncertainty, but focused only on two end points representing zero and perfect information about future forcings and initial conditions. This study extends the prior approach to characterize the influence of varying levels of uncertainty in each area on streamflow prediction uncertainty. The sensitivities enable the calculation of flow forecast skill elasticities (i.e., derivatives) relative to skill in either predictability source, which are used 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 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. The results also underscore the importance of advancing hydrologic modeling, expanding watershed observations, and leveraging data assimilation, all of which help capture initial hydrologic conditions that are often the dominant influence on hydrologic predictions.