J7.1A Using the US Climate Reference Network to Improve Gridded Soil Moisture Products over the Conterminous US

Thursday, 10 January 2019: 1:30 PM
North 126BC (Phoenix Convention Center - West and North Buildings)
Michael Buban, ARL, Oak Ridge, TN; and C. B. Baker, T. P. Meyers, and C. R. Hain

Since regions of drought and excessive rainfall can have important socio-economic impacts, more accurate analysis and prediction of these extreme conditions will help mitigate adverse effects to the public and private communities. The US Climate Reference Network (USCRN) consists of 114 weather stations located across the coterminous United States. All stations use highly-accurate instruments to measure air temperature, surface temperature, solar radiation, relative humidity, precipitation, wind, soil temperature, and soil moisture. In this study, we develop a technique to use the USCRN stations as “anchor points” to characterize soil moisture conditions as a function of soil and surface properties and atmospheric conditions. These relationships are then used along with gridded datasets to create an improved and spatially expanded soil moisture product.

To produce the improve soil moisture product, the 4-km Parameter-elevation Relationships on Independent Slopes Model (PRISM) precipitation and Atmosphere-Land Exchange Inverse model (ALEXI) evapotranspiration grids are used as forcing functions. Next the high-resolution 90 m Soil Survey Geographic database (SSURGO) and United States General Soil Map (STATSGO) soil property datasets and National Land Cover Database (NLCD) vegetation type are aggregated to obtain a distribution of soil properties and vegetation type around each 4-km grid point. A weighting function is applied to this distribution to determine a soil and vegetation type at each grid point. The grid points are then matched to a USCRN station with the same soil characteristics and vegetation type, which determines a transfer function that is used to modify the grid. The resulting soil moisture product will be used to improve drought analysis and forecasts.

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