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The Austrian Hydrographic Service operates an extensive network of hydrographic measuring stations all over the country. The soil water measuring network comprises 62 stations where, among others, water content, matric potential, and soil temperature are measured. Only through the implementation of a consistent quality control system and data harmonization is it possible to provide expedient datasets that can be published or used for further purposes. Although automated quality control procedures for soil moisture are integrated in some national and international networks, there is currently no uniform quality control and harmonization of measurement data implemented in the Austrian network. Thus, the goal of this study was to evaluate existing options for the implementation of a system for the control of data quality with the highest possible degree of automatization. Technical implementation and structure of the stations vary greatly in the monitoring network in terms of measuring depths and installed sensors. Of the 62 measuring stations, 15 were set up as so-called type-2 measuring stations with a lower number of sensors and an IOT data transmitter. The aim of this type of station being reduced installation and maintenance effort as well as lower costs. On the other end of the complexity range, weighing lysimeters with field reference are maintained since decades. To establish the most appropriate procedure, established systems from international literature, like ISMN and SaQC or NASMD, were compiled and tested for their applicability in the Austrian monitoring network. For evaluation, the results of the automated quality check routines were compared to those of a visual expert check via an error matrix. The aim was to determine which quality control procedures are most effective and lead to the best results in terms of flagging different quality levels or likely errors. Leaning on the findings of the quality control procedures for soil moisture, a procedure for the matric potential shall be developed and implemented in the network, since this state variable of soils is crucial for understanding soil water processes.
Jagersberger et al. (Mon,) studied this question.