Low-cost instrumentation for monitoring wadi discharge: A Raspberry Shake and time-lapse camera system

In recent years, Oman has faced increasing challenges with flash floods, driven by climate change and rapid urbanization. Climate change has intensified the water cycle, causing more frequent and severe precipitation in this arid region. Urban expansion into wadi floodplains, which historically acted as natural flood channels, has worsened the situation. Oman's flood preparedness is critically hindered by the lack of effective early warning systems. While sensor networks could monitor rainfall and wadi flow to provide flood alerts and water management data, their implementation is limited by the country's vast territory, complex terrain, and high infrastructure costs.

The existing wadi monitoring infrastructure in Oman relies on two primary types of measurement devices: pressure gauges and radar sensors. However, each technology presents distinct operational challenges in the dynamic wadi environment. Pressure gauges, which must be installed directly within the wadi bed to measure water levels, are vulnerable to damage or complete loss during powerful flood events. Radar gauges, while avoiding direct water contact, face different limitations. These devices are typically mounted on structures along the wadi banks to measure water levels from above. However, this positioning becomes problematic due to the naturally shifting nature of wadi channels, which can migrate significantly over time through erosion and sediment deposition.

Image-based monitoring systems offer a promising solution to the challenges of wadi measurement. Cameras can be safely installed outside the channel while maintaining visibility across the entire river cross-section. Different studies have shown that cameras can accurately measure water levels, even with low-cost equipment. Moreover, these systems can measure flow velocities by analysing short video sequences, enabling discharge estimation. However, image-based methods have a significant limitation: they perform poorly in challenging lighting conditions, e.g. at night, during heavy rain or dust events.

Recently, seismic observations were utilized to infer river level and bedload flux, using low cost sensors (e.g. Raspberry Shake) installed at safe distance to the hazardous flood corridor. These studies employed physical models, which predict the seismic frequency spectra created by bedload transport and turbulent flow. Those models rely on a large number of parameters to be set, including water level. Therefore, Monte Carlo approaches are used to randomly sample parameters for synthetic spectra calculation to be compared against the empirical one, ultimately leading to the water level.

The integration of cameras and seismic sensors can allow for a robust and synergetic measurement system. Optical measurements of water level and surface velocities can effectively constrain the parameters used in seismic signal analysis, significantly improving water level estimation accuracy when image-based methods are not available, particulary during night time operations. With the increasing availability of low-cost seismometers, we have developed and implemented a combined low-cost seismo-optical monitoring system. To evaluate this approach, the setup was installed at two reaches of Wadi Al-Hawasinah in Oman. Our study examines initial results from flow events of varying magnitudes and assesses the practical applicability of this integrated monitoring solution.