2019 年 11 巻 p. 25-37
River environmental management contains controlling physical and biological processes. Both processes are different phenomena with each other but are in common continuous-time. In general, the physical processes related to river hydraulics can be measured frequently and the continuous-time nature is captured. On the other hand, the biological processes like growth of population and organisms cannot be observed so frequently. Their observation is thus actually discrete-time. Many models for environmental management assume observation of the biological processes to be continuous-time, which is inconsistent with the reality. The objective of this paper is to develop a new model that can harmonize the continuous and discrete natures in management of the biological processes. This is achieved through employing a recent stochastic process formalism. Focusing on algae population management in river environment, we derive the optimality equation to determine the most cost-effective harvesting policy (observation timing and workload) based on discrete observations. A computational example with practical implications is presented focusing on a problem of benthic algae population management in Hii River, Japan.