The Value of Hydrologic Information in Stochastic Dynamic Programming Models of a Multireservoir System

Reservoir operating policies can be derived using stochastic dynamic programming (SDP) with different hydrologic state variables. This paper considers several choices for such hydrologic state variables for SDP models of the Shasta‐Trinity system in northern California, for three different benefit functions. We compare how well SDP models predict their policies will perform, as well as how well these policies performed when simulated. For a benefit function stressing energy maximization, all policies did nearly as well, and the choice of the hydrologic state variable mattered very little. For a benefit function with larger water and firm power targets and severe penalties on corresponding shortages, predicted performance significantly overestimated simulated performance, and policies that employed more complete hydrologic information performed significantly better.