A Global High‐Resolution Data Set of Soil Hydraulic and Thermal Properties for Land Surface Modeling

Abstract

AbstractModeling land surface processes requires complete and reliable soil property information to understand soil hydraulic and heat dynamics and related processes, but currently, there is no data set of soil hydraulic and thermal parameters that can meet this demand for global use. In this study, we propose a fitting approach to obtain the optimal soil water retention parameters from ensemble pedotransfer functions (PTFs), which are evaluated using the global coverage National Cooperative Soil Survey Characterization Database and show better performance for global applications than our original ensemble estimations (median values of PTFs) as done in Dai et al. (2013, https://doi.org/10.1175/JHM‐D‐12‐0149.1). Soil hydraulic conductivities are still estimated as the median values of multiple PTFs, and the results are shown to perform comparably to the estimates from the existing precision‐advanced models. Soil thermal properties are estimated following the schemes identified by Dai et al. (2019a, http://arxiv.org/abs/1908.04579), which evaluated several highly recommended schemes based on their land modeling applications. Using these approaches, we develop two global high‐resolution data sets of soil hydraulic and thermal parameters based on Global Soil Dataset for Earth System Models (GSDE) and SoilGrids soil composition databases. The delivered variables include six basic soil properties, four soil hydraulic parameters in the Campbell (1974, https://doi.org/10.1097/00010694‐197406000‐00001) model, five soil hydraulic parameters in the van Genuchten (1980, https://doi.org/10.2136/sssaj1980.03615995004400050002x) model, and four soil thermal properties. The delivered data sets are available at a 30″ × 30″ geographical spatial resolution and provide four sets of vertical profiles following the resolutions of SoilGrids, Noah‐Land Surface Models (LSM), Joint UK Land Environment Simulator (JULES), and Common Land Model/Community Land Model (CoLM/CLM). The data sets can be used in both regional and global applications.