Abstract

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.