SWOT Data Assimilation for Operational Reservoir Management on the Upper Niger River Basin

Abstract:

The future Surface Water and Ocean Topography (SWOT) satellite mission will provide two-dimensional maps of water elevation for rivers with width greater than 100 m globally. We describe a modeling framework and an automatic control algorithm that prescribe optimal releases from the Selingue dam in the Upper Niger River Basin, with the objective of understanding how SWOT data might be used to the benefit of operational water management. The modeling framework was used in a twin experiment to simulate the "true" system state and an ensemble of model states derived using corrupted meteorological forcings. Virtual SWOT observations of reservoir and river levels were assimilated into the model with a repeat cycle of 21 days. The updated state was used to initialize a Model Predictive Control (MPC) algorithm that computed the optimal reservoir release that meets a minimum flow requirement 300 km downstream of the dam at the entrance of the environmentally sensitive Niger Inner Delta. The data assimilation results indicate that the model updates had a positive effect on estimates of both water level and discharge. The "persistence", which describes the duration of the assimilation effect, was clearly improved by integrating a smoother into the assimilation procedure. We compared performances of the MPC with SWOT data assimilation to an open-loop MPC simulation. Results show that the assimilation of SWOT data resulted in substantial improvements in the performances of the Selingue Dam management with a greater ability to meet environmental requirements and a lower volume of water released from the dam.