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Soil moisture is the key state variable from both climate and hydrologic cycle assessment perspectives. Automated measurements of soil moisture were not possible in the past decades. Sensors deployed in the field with real-time monitoring networks such as the Automated Weather Data Network (AWDN) in Nebraska have not only become affordable but enhanced the monitoring capability of the network with valuable soil moisture data added to the existing stream of hourly and daily weather data for precipitation, air temperature, humidity, solar radiation, wind speed, and soil temperature. However, to assure the quality of the data, quality control (QC) tools are needed. Earlier studies lacked the QC of soil water data in general as they were not part of a network that routinely collected soil water measurements. This paper presents a systematic QC analysis and methodology to evaluate the performance of candidate QC techniques using spatiallyextenstive soil water dataset available from the AWDN network. 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.

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This is an author-produced, peer-reviewed version of this article. The final, definitive version of this document can be found online at Journal of Hydrologic Engineering, published by American Society of Civil Engineers. Copyright restrictions may apply. DOI: 10.1061/(asce)he.1943-5584.0000174