Reconstructing missing and anomalous data collected from high-frequency in-situ sensors in fresh waters

In-situ sensors that collect high-frequency data are used increasingly to monitor aquatic environments. These sensors are prone to technical errors, resulting in unrecorded observations and/or anomalous values that are subsequently removed and create gaps in time series data. We present a framework based on generalized additive and auto-regressive models to recover these missing data. To mimic sporadically missing (i) single observations and (ii) periods of contiguous observations, we randomly removed (i) point data and (ii) day and week-long sequences of data from a two-year time series of nitrate-concentration data collected from Arikaree River, USA, where synoptically collected water temperature, turbidity, conductance, elevation and dissolved oxygen data were available.