A Technical Evaluation of Lidar‐Based Measurement of River Water Levels. Issue 4 (6th April 2020) - Paul, Jonathan D.; Buytaert, Wouter; Sah, Neeraj
Abstract: Measuring river water level (stage) is key for a variety of applications including discharge estimation and flood prediction. Although a variety of in situ and noncontact methods are available, there is an urgent need for new and more cost‐efficient methods. Rapid technological progress has accelerated the use of noncontact methods and development of time‐of‐flight distance sensors. Among available techniques, the use of lidar for distance measurement is promising because of its low cost, high energy efficiency, and small measurement footprint. However, lidar has rarely been used to measure water levels. Here we test a near‐infrared (905 nm) lidar sensor to determine its suitability for stage measurements under a range of environmental conditions. Using different laboratory and field setups, we assess sensor performance as a function of measurement distance, surface roughness, air temperature, water turbidity, and measurement angle. Despite the low reflectivity of water for infrared radiation, we find that the tested sensor is able to take measurements under all tested conditions, up to an incidence angle of ∼40°. The accuracy of the sensor is within the technical specifications of the device and is characterized by a relative error of around 0.1%. We find a strong dependence of the accuracy on sensor temperature, which we attribute to suboptimal internal compensation of the electronics. The precision of the sensor decreases with increasing measured distance and increases with surface roughness of the water body. We did not find any significant impact of water turbidity on the measurements. Key Points: We describe a new noncontact lidar prototype to measure river water level The prototype can take measurements under a range of environmental conditions, to ∼35 m range, and to inclinations of ∼40° Lidar has the potential to enrich the observational database in data‐scarce regions globally