Low-cost, non-contact sensor networks for river stage monitoring and dynamic discharge estimation

River monitoring and discharge estimation are crucial to developing mitigation measures for weather and climate extremes. This study demonstrates the potential of non-contact, low-cost, bespoke lidar sensors for monitoring river levels and proposes a methodology for estimating discharge using river stage data from such sensor networks. Firstly, using different laboratory and field experiments, this study evaluates the sensor performance as a function of measurement distance, surface roughness, air temperature, water turbidity, and measurement angle to monitor river levels. To enable the computational experiments that underpin my scientific enquiry and part of discharge estimation methodology development, I developed a Python application to calibrate hydraulic models under homogenous and heterogenous Manning’s n assumptions, perform uncertainty and sensitivity analysis of unsteady flow parameters, and perform probabilistic flood inundation analysis in HEC-RAS. Then, using synchronous measurements of stage data from a network of sensors, a novel method for estimating the dynamic river discharge has been developed. This methodology has been tested on idealised rivers with varying channels and flow conditions, as well as on the Wandle River in the UK. After testing the developed discharge estimation method, two approaches for optimising a sensor network, that is the sensor position, number, and spacing, have been developed and assessed for various case studies. The laboratory experiments demonstrate that the sensors can take measurements under all tested conditions, up to an incidence angle of ∼ 40° and within a relative error of 0.1%. The test results show that the developed discharge estimation method can be successfully applied to both prismatic and natural channels with or without lateral flow. Moreover, unlike previous studies, this method does not require an initial discharge value. The optimisation results show that, compared to three sensors, using four sensors placed closer to the downstream boundary improves parameter calibration and discharge estimation.Open Acces