# Testing an \"IoT\" Tide Gauge Network for Coastal Monitoring
## 4 Field Experiment Results
### Comparison of the IoT Device and AWAC--Sea Levels  
Figure 7 compares sea-level data (with waves minimized by averaging) at Crosby from the IoT device and the AWAC for near tides between 6 February 18:00 and 7 February 15:00, 2020, and during Storm Ciara on 9 February 05:00-23:59, 2020. The two instruments generally agree during the earlier period (see Figure 7a), but there are obvious differences close to high water during the storm when large waves were present (See Figure 7b). Differences in the last tidal level recorded before drying out and the first tidal level recorded on the flood are an artifact of the rolling 15-min sleep time (sleeps for 15 min, does 40 s measurement and goes to sleep again) and the exact 10-min AWAC data, and combined with the 27-cm vertical separation of the pressure sensors. The Van de Casteele Test [22] can be applied to determine tide gauge performance. For example, [PERSON] et al. [23] tested a variety of tide gauges and demonstrated the test's ability to highlight different types of tide gauge error, while Andre  
Figure 7: Crosby tidal elevations (AWAC—red line, IoT tide gauge—black dashed line), (a) neap tides: 6 February 18:00–7 February 15:00, 2020, (b) Storm Ciara: 9 February 05:00–23:59, 2020.  
compare data from three different GNSS buoys for measuring sea level against a nearby radar tide gauge. The method compares a reference tide gauge data set (data known to be an accurate measurement of sea level; in this case, the AWAC sea-level data are taken as the reference) to contemporaneous data from the tide gauge being tested (\(H^{\prime}\)): the reference sea-level data (\(H\)) are plotted on the ordinate against the tide gauge difference (\(\Delta H\)) on the abscissa (\(x\)-axis), where \(\Delta H=H-H^{\prime}\). The resulting diagram would produce a straight vertical line centered on \(x=0\) for a perfect match between the two tide gauges. Applying the Van de Casteele Test to the IoT tide gauge, using the AWAC data as the reference, the near-vertical alignment of the differences in the two data sets (see Figure 8a) between low water and high water, although not perfectly vertical, indicates that there are no timing offsets (which would result in an ellipsoidal pattern) and no obvious systematic errors (such as scale errors, which would introduce a large gradient from low water to high water). The mean difference between the IoT device and the AWAC data was \(-0.005\) m, the standard deviation was \(0.040\) m, and the root-mean-square difference was \(0.040\) m (using all the recorded data and ignoring periods when the pressure sensor is exposed to air, i.e., towards low water). Although there is a slight gradient from low water to high water with larger offsets towards low water, it is unlikely to be due to density differences since both gauges use the same seawater density of \(1025\) kg m\({}^{-3}\) for the conversion to meters of water. However, this small gradient may be a result of turbulence around the channel marker during the lower levels of flood and ebb. Another possible explanation could be air bubbles trapped on the pressure sensor membrane during drying out (this could occur with either pressure sensor); higher pressures towards high water may compress the bubble resulting in smaller pressure differences between them. One way to eliminate this would be to deploy the IoT device horizontally so that the pressure sensor is not facing downwards. During Storm Ciara, large waves were present, and this can be seen in Figure 7b, Figure 8a,b and Figure 9. The pressure differences (seen within the corresponding sea  
Figure 8: Results of the Van de Casteele test: (**a**) for the IoT tide gauge using the AWAC tide gauge as a reference. (**b**) Time-series of tide gauge differences. levels) become larger with increasing wave heights, and the largest offsets occur between 09:30 and 14:30 on 9 February.