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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #312235
Title: Improving the soil moisture data record of the U.S. Climate Reference Network (USCRN) and Soil Climate Analysis Network (SCAN)

Author
item Coopersmith, Evan
item BELL, JESSE - National Oceanic & Atmospheric Administration (NOAA)
item Cosh, Michael

Submitted to: Advances in Water Resources
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/23/2015
Publication Date: 5/1/2015
Citation: Coopersmith, E.J., Bell, J., Cosh, M.H. 2015. Improving the soil moisture data record of the U.S. Climate Reference Network (USCRN) and Soil Climate Analysis Network (SCAN). Advances in Water Resources. 79:80-90. DOI:10.1016/j.advwatres.2015.02.006.

Interpretive Summary: In situ soil moisture monitoring sensors have been added to existing weather and climate networks as the technology becomes available. This is beneficial to climate and weather monitoring and modeling community. It is proposed that the soil moisture record can be extended backward in time if a reliable precipitation measurement is available for the weather station. Using a simple soil moisture model, the NOAA Climate Reference Network (CRN), which deployed sensors in 2007, had its soil moisture record extended backward in time to include the time period for which precipitation gages were recording data. This technique is proven using the USDA Soil Climate Analysis Network, which has a similar deployment to CRN, but has a much longer time period, extending back to 2002. Low root mean square errors were achieved via this technique and this work will impact longer term scientific monitoring studies which need soil moisture as a parameter. Climatologists and remote sensing scientists will find this method useful for the extension of historical data records for model development and validation programs. In addition, watershed scientists can use this technique to fill in data gaps from temporarily malfunctioning sensors.

Technical Abstract: Soil moisture estimates are valuable for hydrologic modeling, drought prediction and management, climate change analysis, and agricultural decision support. However, in situ measurements of soil moisture have only become available within the past few decades with additional sensors being installed each year. Comparing newer in situ resources with older resources, previously required a period of cross-calibration, often requiring several years of data collection. One new technique to improve this issue is to develop a methodology to extend the in situ record backwards in time using a soil moisture model and ancillary available data sets. This study will extend the soil moisture record of the U.S. Climate Reference Network (USCRN) by calibrating a precipitation-driven model during the most recent few years when soil moisture data are available and applying that model backwards temporally in years where precipitation data are available and soil moisture data are not. This approach is validated by applying the technique to the Soil Climate Analysis Network (SCAN) where the same model is calibrated in recent years and validated during preceding years at locations with a sufficiently long soil moisture record. Results suggest that if two or three years of concurrent precipitation and soil moisture time series data are available, the calibrated model’s parameters can be applied historically to produce RMSE values less than 0.033 m3/m3. With this approach, in locations characterized by in situ sensors with short or intermittent data records, a model can now be used to fill the relevant gaps and improve the historical record as well.