A low-cost “Internet of Things” (IoT) tide gauge network was developed to provide real-time and “delayed mode” sea-level data to support monitoring of spatial and temporal coastal morphological changes. It is based on the Arduino Sigfox MKR 1200 micro-controller platform with a Measurement Specialties pressure sensor (MS5837). Experiments at two sites colocated with established tide gauges show that these inexpensive pressure sensors can make accurate sea-level measurements. While these pressure sensors are capable of ~1 cm accuracy, as with other comparable gauges, the effect of significant wave activity can distort the overall sea-level measurements. Various off-the-shelf hardware and software configurations were tested to provide complementary data as part of a localized network and to overcome operational constraints, such as lack of suitable infrastructure for mounting the tide gauges and for exposed beach locations.

Spatially explicit data on tidal and waves are required as part of coastal monitoring applications (e.g., radar monitoring of coastal change) for the design of interventions to mitigate the impacts of climate change. A deployment over two tidal cycles of a low-cost Global Navigation Satellite System (GNSS) buoy at Rossall (near Fleetwood), UK demonstrated the potential to record good quality sea level and wave data within the intertidal zone. During each slack water and the following ebb tide, the sea level data were of good quality and comparable with data from nearby tide gauges on the national tide gauge network. Moreover, the GNSS receiver was able to capture wave information and these compared well with data from a commercial wave buoy situated 9.5 km offshore. Discontinuities were observed in the elevation data during flood tide, coincident with high accelerations and losing satellite signal lock. These were probably due to strong tidal currents, which, combined with spilling waves, would put the mooring line under tension and allow white water to spill over the antenna resulting in the periodic loss of GNSS signals, hence degrading the vertical solutions.

Safe port operations require accurate information on vessel location, routine monitoring and maintenance of navigation channels, and accurate information on coastal hydrodynamics. Accurate bathymetric data enables port operators to have a high level of confidence in assuring sufficient water depth for vessels, and to effectively direct surveying and dredging operations to maintain navigation routes. However, this is not readily facilitated for nearshore approaches where migrating sandbanks and shoals pose a hazard to shipping. In this presentation, we present an innovative and novel data assimilation method of combining satellite data, hydrodynamic model (Delft3D) outputs and land-based radar data using machine learning and advanced statistical methods (Dynamic Mode Decomposition). To assimilate these data we use machine learning and statistical methods to detect "patterns" or "modes" in near- and far-field wave climate that are attributable to sub- and intertidal bathymetry and changes therein. We then combine the dominant modes into a low-order representation of the system, providing informed estimates of spatial resolutions and temporal scales where no measurements are physically performed. Satellite data and associated hydrodynamic model outputs are used to provide information on wave direction and height for the offshore-nearshore approaches while land-based marine radar located in the appropriate position provide wave data at higher temporal and more local spatial resolution. The data nexus we present in this presentation demonstrates significant improvements in capability above and beyond the use of a given technology in isolation.

A low-cost Global Navigation Satellite System (GNSS) buoy platform was developed for measuring coastal sea levels to provide information where logistical, physical and/or financial constraints prevent the application of established tide gauges and satellite altimetry, and where spatially and temporally discrete tidal data are required to support surveys for monitoring coastal morphodynamics. The buoy was constructed from scaffold tubing and four floats, with a central platform for the GNSS receiver and antenna. The overall cost was around 300 pound (buoy parts 100 pound, GPS/logger 200) pound. The single frequency GNSS receiver was set up to record GPS/GLONASS satellite data at 5Hz. Post-processing was carried out with RTKLIB software to derive solutions for sea level. GNSS buoy performance was evaluated against a reference tide gauge using a Van de Casteele test. The RMSE of 1.4 cm, computed from the differences between the GNSS-buoy and reference tide gauge, demonstrates that this low-cost GNSS buoy tide gauge compares well with previous, more expensive GNSS buoys and is suitable for obtaining local-scale coastal sea level data. In addition to providing coastal data at cm accuracy, the results also indicate the presence of high frequency oscillations in the sea level data caused by coastal geometry and wave-induced disturbance.