While more hydrological data is being generated than ever before, the
power of modelling this collected information is not fully realized
unless it is of high quality, especially considering hydrological data
from sensor networks, which is often errant due to the possibility of
malfunction or non-conducive environmental conditions. Fluctuations or
errors are difficult to predict, identify, and interpret. Manual models
of quality assurance are not designed for managing datasets with
continuous timeseries or spatially extensive coverage, resulting in
time- consuming models that rely on humanmade decision making and lack
statistical inference. This research hypothesizes that the stochasticity
of rainfall and deterministic properties of flow can be used in concert
to create a more characteristic quality assurance model for
high-resolution environmental data. An automated implementation of this
model is presented herein with the application of two use-cases, which
maintains statistical integrity and circumvents biases and potential for
user error of manual frameworks.