Data for "Soil Hydraulic Property Maps for the Contiguous USA at 100 Meter Resolution and Seven Depths: Code Design and Preliminary Results"

Dataset

Description

Estimates of the van Genuchten (1980, abbreviated as VG) parameters and saturated hydraulic conductivity (Ks) were made for the contiguous USA at a resolution of 100 meters and seven soil depths by combining the SoilGrids+ (SG+) soil property maps of Ramcharan et al., (2018) with the R3H3 member of the Rosetta3 hierarchical pedotransfer functions (PTFs) of Zhang et al. (2017). To this end, we developed multi-threaded code that significantly speeds up computation (up to a factor 25) depending on the level of parallelism. We verified estimates first by calculating simple summary statistics of estimated basic properties of SG+ with actual measured soil properties for 14,113 pedons in the National Cooperative Soil Survey (2023) labsample database (NCSS). Next, we computed summary statistics of PTF-estimated moisture contents for NCSS and SG+ data. The results show estimation errors are dominated by intrinsic errors of the PTF, and that (potentially correctable) systematic errors are present in SG+ soil properties and PTF estimates. The resulting hydraulic property maps contain well over 750 million points for each of the seven layers and show considerable horizontal and depth variation for each VG parameter and Ks, except the VG ā€œnā€ parameter, which is dominated by values between 1.25 and 1.6. The hydraulic property maps are 99.9% complete and we demonstrate that plausible profiles and uncertainty information can be generated for virtually each point. The maps are available as two multi-channel GeoTIFF maps per SG+ layer: one with the five hydraulic parameters and one with the corresponding covariances. For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to [email protected]
Date made available2024
PublisherUniversity of Arizona Research Data Repository

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