Year Established: 2014 Start Date: 2014-03-01 End Date: 2015-02-28
Total Federal Funds: $13,933 Total Non-Federal Funds: $27,866
Principal Investigators: Levan Elbakidze, Garth Taylor
Abstract: Using three global circulation models from IPCC (Intergovernmental Panel on Climate Change) a study by USBOR (2008) found that in the Boise River the most significant impact of climate change will be an increase in flooding especially during the months of January through March. This finding is consistent with the general concerns expressed in the IPCC (2007) pertaining to the impacts of increasing temperatures in mountain west of North America. The report by USBOR (2008) concludes that current flood control regulations for the Boise River Reservoirs, developed based on observed runoff/inflows from 1967 to 1981, are not adequate to manage a more voluminous and earlier spring runoff due to warming temperatures. The objective of this project is to revisit reservoir flood control regulations. The general objective of reservoir management is to minimize flood risks while maximizing benefits from irrigation water use and hydropower production. There is a trade-off between maintaining a full reservoir for power production and irrigation and maintaining an empty reservoir to reduce flood risk. A tradeoff involves balancing of benefits, associated with each water use (ie. flood prevention versus irrigation and hydropower) and a dynamic economic framework must be developed. Specifically, we propose to examine the sensitivity of optimal rule curves used for balancing flood control needs with demand for water for irrigation and hydropower production when reservoir inflow is stochastic in terms of timing as well as quantity. In contrast to previous research (for example Chatterjee et al. 1998; Ahmad et al. 2000) which assumed perfect weather forecast, this study will focus on the effect of stochastic inflows on optimal management of reservoir discharge during a water year using rule curves. While dynamic multiuse management of reservoirs has been addressed in previous economic literature, the effect of stochastic inflows has not been incorporated into dynamic economic models for reservoir management when irrigation demand, flood control, and hydropower production are all considered. In this project we will fill this gap in scientific literature. The analytical framework will follow the well-established optimal control methodology (Kamien and Schwartz, 1991). Itos stochastic calculus (Malliaris and Brock, 1982) will be used formulate the model and examine the properties of optimal solutions with respect to stochasticity and timing of inflows as well as risk characteristics and preferences associated with flood management. For empirical analysis stochastic dynamic programming will be used (Miranda and Fackler, 2002).