3.3 Comparison of 1 (pond-only) versus 2 (paired pond-control) logger design
Under the wet state criteria defined above, 3-state HMMs for the pond-only model accurately predicted inundation states for 92% of site visits for the 14 ponds. The paired pond-control model, which used combined data from the loggers inside and outside of each pond, accurately predicted inundation states for 90% of sites visits. Most of the incorrect state predictions were likely due to sediment or additional debris accumulating within the rugged housing units, which was more likely to affect precision of drying dates rather than initial inundation timing.
Models using temperature data from pond loggers alone predict inundation timing that closely aligned with those using paired pond-control logger data, indicating that a single logger design may be sufficient to capture inundation timing of longer-duration events (Table S5, Figure 4). However, control logger data may help alleviate some of the wet state false-positives, particularly when the standard deviation of daily air temperature is relatively low or issues such as sediment in rugged housing units occur. For example, earlier inundation dates are predicted for several ponds by the pond-only model relative to the paired pond-control model. This may be a true wet state, or the coincident low temperature standard deviations measured by the control loggers may have simply resulted in lower variance in the temperature on those days. For site T13, the state was correctly predicted as wet by the paired pond-control model, but not by the pond-only model in April 2019. While we did observe water in the pond at this time, the water level was just at the base of the rock pile covering the logger housing, which may explain the discrepancies between the models. In cases such as this when shallow water is present, the 2-logger design may help to increase the probability of detecting inundation. Predicting pond drying may require an array of pond loggers situated at different heights within the pond to capture this fine-scale variation or a different type of sensor, such as pressure transducers. But considerations exist for the pond-control logger model as well. For example, pond-only models predicted wet states for most ponds in the winter months (between December and February) that were not predicted by the paired pond-control models. The relatively low tSDs of the loggers in the winter months may be due to snow accumulation on top of the control loggers.