Toward a unified stochastic framework for projection and prediction of streamflow under changing conditions

Stochastic methods for synthetic streamflow simulations have traditionally supported operational management and planning of surface water systems in both short- and long-term futures. Here we focus on a particular strain of stochastic streamflow generators that use copulas, a generic statistical framework for formulating interdependencies, to resample streamflow series at single and multiple sites. Such stochastic simulators are based on a series of conditional probabilities that are inferred from joint probabilities between streamflow series in time and space. We discuss the core algorithm behind such stochastic samplers and provide a set of practical guidelines across a range of timescales, flow regimes and catchment characteristics on when and how these schemes should and can be developed. We then provide a generalized framework for altering the parameters of these samplers so that streamflow series can be generated under changing conditions, whether such changes are initiated by climate or human interventions. By informing these samplers with weather and climate indices, we show how the quality of streamflow projections can be significantly improved across a range of temporal scales and highlight the potential of such climatic-informed streamflow samplers for short-term predictions, particularly during high flow seasons.