Developing a soil property database for the Oklahoma Mesonet.

摘要

Scope and Method of Study: The objective of this study was to create a comprehensive database of soil hydraulic and physical properties of the Oklahoma Mesonet station soils. Replicate soil cores were collected at 117 Mesonet stations. The artificial neural network model Rosetta was used to estimate the van Genuchten water retention curve parameters, replacing the Arya and Paris estimated parameters.;Findings and Conclusions: The resulting database covers 13 environmental variables with 541 complete replicated sample sets that represent combinations of site and depth for 117 Mesonet Stations. The database contains the percent sand, silt, and clay; the bulk density, the volumetric water content at -33, and -1500 kPa; the van Genuchten parameters of residual volumetric water content, &thetas; r, saturated volumetric water content, &thetas;s (cm 3 cm-3), alpha, &agr; (kpa-1), and n (unitless); the saturated hydraulic conductivity, Ks (cm day -1), as well as the matching point parameter, Ko (cm day -1), and the empirical parameter, L (unitless). The performance of the Rosetta model was determined based on the root mean squared difference (RMSD) of the modeled data vs. that found through oven-drying and was found to be 0.053 cm3 cm-3, compared to the Arya and Paris method RMSD of 0.078 cm3 cm-3. The improved estimates of the soil hydraulic and physical properties of the Mesonet station soils has expanded the functionality of the monitoring system by providing increased accuracy for the Mesonet soil water content data. The estimation of soil water content by the Oklahoma Mesonet was improved by 32%. In addition, daily plant available water maps are currently available on the website, www.mesonet.org.