Abstract

Plausible future long-term streamflow time series are essential for evaluating different policies and management strategies in river basins and testing the operation of water resource systems. Relying solely on stationary historical data may not be sufficient in a changing climate. Alternatively, uncertainty in the wide range of streamflow projections from General Circulation Models calls into question their direct use in water resources planning. There is thus a need for an intermediate approach to identify ensembles of streamflow time series based on assumptions that provide a rationale for plausible future hydrologic conditions. We developed storylines of plausible future conditions that describe such a rationale by quantitatively defining the associated assumptions and then identifying matching streamflow ensembles. These representative ensembles provide inputs needed for running models to support planning that may need a scenario with specific characteristics or studies that rely on a wide range of scenarios, such as in Decision Making under Deep Uncertainty. Applying this approach in the Colorado River Basin, we worked to identify representative ensembles for each storyline. While three storylines were well matched among existing ensembles there was not a good match for the plausible storyline of warming-driven declining streamflow with increasing variability. To address this gap, we developed a general approach to create new streamflow ensembles using a stochastic nonparametric approach that combines observed and paleo-reconstructed flows and adjusts the marginal distribution of the streamflow time series to incorporate the estimated decline and increasing variability in future flow.