A Global High‐Resolution Data Set of Soil Hydraulic and Thermal Properties for Land Surface Modeling. (13th September 2019)

• Record Type: Journal Article
• Authors: Dai, Yongjiu; Xin, Qinchuan; Wei, Nan; Zhang, Yonggen; Shangguan, Wei; Yuan, Hua; Zhang, Shupeng; Liu, Shaofeng; Lu, Xingjie
• Is Part Of: Journal of advances in modeling earth systems. Volume 11:Number 9(2019)
• Page Start: 2996
• Page End: 3023
• Publication Date: 2019-09-13
• Subjects: global land surface modeling -- soil thermal properties -- soil hydraulic properties -- high‐resolution global soil data sets; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1029/2019MS001784
• Languages: English
• ISSNs: 1942-2466

Abstract

Abstract: Modeling 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. Plain Language Summary: The parameters of soil hydraulic and thermal properties are essential to the modeling of land surface processes, but no data set is currently available for global use. Using some newly proposed and existing widely used schemes, we developed two global high‐resolution data sets of soil hydraulic and thermal parameters based on two commonly used soil composition databases (Global Soil Dataset for Earth System Models [GSDE] and SoilGrids). The delivered variables include multiple basic soil properties and all the required parameters for solving soil hydraulic and heat dynamics and related processes. The data sets follow the vertical profiles of SoilGrids data set and four land surface models (i.e., Noah‐Land Surface Models [LSM], Joint UK Land Environment Simulator [JULES], Common Land Model [CoLM], and Community Land Model [CLM]). The data sets can be used in both regional and global applications. Key Points: A new fitting approach is proposed to obtain the optimal soil water retention parameters from ensemble pedotransfer functions The adopted approaches for soil hydraulic parameter estimations are evaluated to be superior or comparative to other approaches Global maps of soil hydraulic and thermal parameters for use in land models are produced based on two soil composition data sets