Regime-shifting streamflow processes: Implications for water supply reservoir operations

This paper examines the extent to which regime-like behavior in streamflow time series impacts reservoir operating policy performance. We begin by incorporating a regime state variable into a well-established stochastic dynamic programming model. We then simulate and compare optimized release policies—with and without the regime state variable—to understand how regime shifts affect operating performance in terms of meeting water delivery targets. Our optimization approach uses a Hidden Markov Model to partition the streamflow time series into a small number of separate regime states. The streamflow persistence structures associated with each state define separate month-to-month streamflow transition probability matrices for computing penalty cost expectations within the optimization procedure. The algorithm generates a four-dimensional array of release decisions conditioned on the within-year time period, reservoir storage state, inflow class, and underlying regime state. Our computational experiment is executed on 99 distinct, hypothetical water supply reservoirs fashioned from the Australian Bureau of Meteorology's Hydrologic Reference Stations. Results show that regime-like behavior is a major cause of suboptimal operations in water supply reservoirs; conventional techniques for optimal policy design may misguide the operator, particularly in regions susceptible to multiyear drought. Stationary streamflow models that allow for regime-like behavior can be incorporated into traditional stochastic optimization models to enhance the flexibility of operations.