Daily soil temperature modeling improved by integrating observed snow cover and estimated soil moisture in the USA Great Plains

Soil temperature (Ts) plays a critical role in land–surface hydrological processes and agricultural ecosystems. However, soil temperature data are limited in both temporal and spatial scales due to the configuration of early weather station networks in the USA Great Plains. Here, we examined an empirical model (EM02) for predicting daily soil temperature (Ts) at the 10 cm depth across Nebraska, Kansas, Oklahoma, and parts of Texas that comprise the USA winter wheat belt. An improved empirical model (iEM02) was developed and calibrated using available historical climate data prior to 2015 from 87 weather stations. The calibrated models were then evaluated independently, using the latest 5-year observations from 2015 to 2019. Our results suggested that the iEM02 had, on average, an improved root mean square error (RMSE) of 0.6 ∘C for 87 stations when compared to the original EM02 model. Specifically, after incorporating the changes in soil moisture and daily snow depth, the improved model was 50 % more accurate, as demonstrated by the decrease in RMSE. We conclude that, in the USA Great Plains, the iEM02 model can better estimate soil temperature at the surface soil layer where most hydrological and biological processes occur. Both seasonal and spatial improvements made in the improved model suggest that it can provide a daily soil temperature modeling tool that overcomes the deficiencies of soil temperature data used in assessments of climatic changes, hydrological modeling, and winter wheat production in the USA Great Plains.