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 •
We introduce a case-study agnostic framework for the application of citizen science in a sustainable development context. This framework is tested against an activity in two secondary schools in western Nepal. While the purpose of this activity is to generate locally relevant knowledge on the physical processes behind natural hazards, we concentrate here on its implementation, i.e., to obtain a better understanding of the dynamic of the activity and to learn how it should be implemented. We determined the social capital of secondary schools as a gateway to the local community: they provide a unique setting to bring different stakeholders together. We find that co-designing a teaching programme is an effective means of both complementing local curricula and ensuring continued buy-in of local stakeholders (i.e., teachers). Student engagement depends on the local relevance of teaching materials, with more holistic or global concepts, such as climate change of lesser importance. Our activity focused on rainfall, including student-led data collection. These rainfall data provide a very good fit to co-located rain gauge data, with an average difference on weekly readings of 11.8%, reducing to 8.3% when averaged over all student readings. The autonomous development of student-organized science clubs suggested that our original framework underestimated students' capacity to apply knowledge elsewhere creatively. These clubs may be used to obtain participant feedback to improve and tailor future activities. Quantitative assessment of long-term sustainability remains challenging, due in part to high levels of student turnover. We suggest that integrating scientists wherever possible within a school or local community has a direct and positive result on participant retention.
<p>Long series of 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. Recently developed electromagnetic and ultrasonic methods can be used for automated (or direct) river discharge measurements; however, they are not widely used because they are expensive and are prone to damage during high flows.</p><p>At most gauging sites around the world, a rating curve is used to convert the measured stage into discharge. However, 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 to change in hydraulic resistance and channel geometry. More recently, methods have been developed for dynamic river discharge estimation by solving governing equations of river flow i.e., shallow water equations (SWE). However, these methods (a) solve SWE in its conservative form, (b) are most suitable for prismatic channels with no lateral flow, (c) require one flow value, and (d) assume channel roughness or calibrate it by using observed stage data from two or three gauging locations. Although, stage data from two or 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.</p><p>Therefore, motivated by a need to surmount the limitations in existing methods, we have developed a non-contact, robust, and cost-effective approach for dynamic river discharge estimation. We use an array of bespoke sensors to monitor the river stage at high resolutions and use these stage data to estimate river discharge. We present a methodology to calibrate a hydraulic model of a river reach by only using stage data from a network of such sensors. We use freely available HEC-RAS software as the solver for SWE. We have developed python scripts to control and automate HEC-RAS simulations and estimate river discharge dynamically.</p>
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