Optimal Reservoir Operation Under Internal Climate Variability for Improving Water Supply Reliability
Abstract
Uncertainties and variabilities associated with hydroclimatic variables pose a challenge for the planning, operation and management of water resources. Incorporating streamflow projections and associated uncertainties into reservoir operations can provide efficient optimal reservoir operation policies. Significant increase in climate variability and uncertainties in projections needs to be considered and verify the assumptions used for defining conventional rule curves to design and operate dams and reservoirs, which does not explicitly consider non-stationarity. Several studies analyze uncertainties associated with the streamflow projections. However, exploring its translation into reservoir operation for Internal Climate Variability (ICV) is lacking, which can be handled by considering Multiple Initial Condition Ensembles (MICE). Here, we estimate the streamflow projections for 50 MICE using the Variable Infiltration Capacity (VIC) hydrological model for four time periods, historical (2001-2015), near term (2016-2045), mid-term (2046-2075), and end term (2076-2100), to capture the effect of changing climate. We derive optimal operating policies based on Sampling Stochastic Dynamic Programming (SSDP), which can handle seasonal inflow variability and climate non-stationarity. SSDP addresses the uncertainties by considering the streamflow ensembles through the transition in the Bellman equation between different hydrologic scenarios. Here, we analyze the role of ICV for the Sardar Sarovar dam, India. We consider reliability, resiliency and vulnerability of the reservoir as performance indicators. Initial results show that ICV in streamflow is increasing with time and improves the performance of reservoir operation compared with actual rule curves. Our study shows potential to help stakeholders maximize the power generation and water supply reliability through improved optimal policies.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2021
- Bibcode:
- 2021AGUFM.H45L1319U