IAHS2022-291, updated on 23 Sep 2022
IAHS-AISH Scientific Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

Ali Nazemi and Masoud Zaerpour
Ali Nazemi and Masoud Zaerpour
  • Concordia University, Building, Civil and Environmental Engineering, Canada (ali.nazemi@concordia.ca)

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.

How to cite: Nazemi, A. and Zaerpour, M.: Toward a unified stochastic framework for projection and prediction of streamflow under changing conditions , IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-291, https://doi.org/10.5194/iahs2022-291, 2022.