From raw measurements to indicators: workflows for quality-controlled soil moisture monitoring in Austria

Understanding soil water dynamics is crucial for hydrological assessments in Austria’s intensively used landscapes. Reliable soil moisture observations support the understanding of vadose zone processes and can be used to assess infiltration capacity during heavy rainfall events, as well as to evaluate water availability during dry periods. However, sensor-related uncertainties and data quality issues limit the application of soil moisture monitoring networks in hydrological modelling, despite their long-term operation and broad relevance.

The Austrian Hydrological Service operates a nationwide monitoring network measuring soil water content, matric potential, and soil temperature at multiple depths across diverse climatic and land-use conditions. These long-term observations provide an important basis for climate trend analysis and the development of water management strategies. The sustainable use of such datasets depends on robust data management and quality assurance procedures.

This study focuses on establishing a standardized and reliable workflow for transforming raw soil water measurements into publicly accessible indicators. This includes the development of quality control and data processing procedures for Austria’s soil moisture monitoring network. Automated and semi-automated routines are used to identify measurement errors related to sensor problems, signal drift, and implausible temporal behaviour. These routines are complemented by systematic data correction procedures. The resulting quality-controlled time series form the basis for deriving soil water indicators (e.g. the Soil Water Index) and enable near-real-time visualization within the national hydrological portal eHYD.

The presented workflow improves the consistency, reliability, and accessibility of long-term soil moisture observations by providing a framework for quality control and data processing. This approach is transferable to other soil moisture monitoring systems with similar challenges regarding data quality, long-term maintenance, and operational use.