Low-cost Lidar Sensor Network for River Stage Monitoring and Discharge Estimation

Abstract:

River discharge data are essential for developing improved river and water management strategies and for coping with water-related hazards such as floods. However, continuous direct measurement of river discharge is practically infeasible. At most gauging sites, a rating-curve is used to convert measured stage into discharge. Using rating curves is fraught with difficulties, including (a) hysteresis effect during unsteady flow, (b) extrapolation error during high flows, (c) need for regular updating due change in hydraulic resistance and channel geometry. More recently, methods have been developed to account for the variable energy slope and dynamic river discharge estimation by solving shallow water equations (SWE). However, these methods (a) solve SWE in its conservative form, (b) are mostly suitable for prismatic channels with no lateral flow, and (c) assume channel roughness or calibrate it by using observed stage data from two or three gauging locations. Although, stage data from three gauging locations are theoretically sufficient to calibrate channel roughness, in practice error margins are still high due to sub-optimal positioning of gauging stations, and coarse temporal resolution of existing measurement networks.

Therefore, motivated by a need to surmount the limitations in existing methods, we have developed a novel, robust, and cost-effective approach for dynamic river discharge estimation. We use bespoke lidar sensors to monitor river stage at potentially high (a minute or even second) resolutions. We show a methodology to calibrate a hydraulic model of a river reach by using stage data from a network of such sensors. We solve SWE in its full form in HEC-RAS. A python script is developed to control and automate HEC-RAS simulations and estimate river discharge dynamically.