Supporting data and tools for "Toward automating post processing of aquatic sensor data"

This resource contains the supporting data and code files for the analyses presented in "Toward automating post processing of aquatic sensor data," an article published in the journal Environmental Modelling and Software. This paper describes pyhydroqc, a Python package developed to identify and correct anomalous values in time series data collected by in situ aquatic sensors. For more information on pyhydroqc, see the code repository (https://github.com/AmberSJones/pyhydroqc) and the documentation (https://ambersjones.github.io/pyhydroqc/). The package may be installed from the Python Package Index (more info: https://packaging.python.org/tutorials/installing-packages/).

Included in this resource are input data, Python scripts to run the package on the input data (anomaly detection and correction), results from running the algorithm, and Python scripts for generating the figures in the manuscript. The organization and structure of the files are described in detail in the readme file. The input data were collected as part of the Logan River Observatory (LRO). The data in this resource represent a subset of data available for the LRO and were compiled by querying the LRO’s operational database. All available data for the LRO can be sourced at http://lrodata.usu.edu/tsa/ or on HydroShare: https://www.hydroshare.org/search/?q=logan%20river%20observatory.

There are two sets of scripts in this resource: 1.) Scripts that reproduce plots for the paper using saved results, and 2.) Code used to generate the complete results for the series in the case study. While all figures can be reproduced, there are challenges to running the code for the complete results (it is computationally intensive, different results will be generated due to the stochastic nature of the models, and the code was developed with an early version of the package), which is why the saved results are included in this resource. For a simple example of running pyhydroqc functions for anomaly detection and correction on a subset of data, see this resource: https://www.hydroshare.org/resource/92f393cbd06b47c398bdd2bbb86887ac/.