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Abstract: Soil moisture is a key state variable from both climate and hydrologic cycle assessment perspectives. Recently, automated measurements of soil moisture with sensors deployed at sites in a real-time monitoring network have provided valuable new data to monitor the soil water resource. However, to assure the quality of the data, quality control QC tools are needed. Earlier studies left little literature on the QC of soil water data as measurements were generally not part of a network that routinely collected measurements. This paper presents a systematic QC analysis and methodology to evaluate the performance of candidate QC techniques using a spatially extensive soil water data set. The six tests included are based on the general behavior of soil moisture, the statistical characteristics of the measurements, the soil properties, and the precipitation measurements. The threshold, step change, and spatial regression test proved most effective in identifying data problems. The results demonstrate that these methods will lead to early identification of potential instrument failures and other disturbances to the soil water measurements.