Open AccessA gridded global data set of soil, intact regolith, and sedimentary deposit thicknesses for regional and global land surface modeling
Author(s) -
Pelletier Jon D.,
Broxton Patrick D.,
Hazenberg Pieter,
Zeng Xubin,
Troch Peter A.,
Niu GuoYue,
Williams Zachary,
Brunke Michael A.,
Gochis David
Publication year - 2016
Publication title -
journal of advances in modeling earth systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.03
H-Index - 58
ISSN - 1942-2466
DOI - 10.1002/2015ms000526
Subject(s) - regolith , bedrock , geology , biogeochemical cycle , landform , sedimentary rock , earth science , soil science , geomorphology , hydrology (agriculture) , geochemistry , physics , astrobiology , chemistry , geotechnical engineering , environmental chemistry
Earth's terrestrial near‐subsurface environment can be divided into relatively porous layers of soil, intact regolith, and sedimentary deposits above unweathered bedrock. Variations in the thicknesses of these layers control the hydrologic and biogeochemical responses of landscapes. Currently, Earth System Models approximate the thickness of these relatively permeable layers above bedrock as uniform globally, despite the fact that their thicknesses vary systematically with topography, climate, and geology. To meet the need for more realistic input data for models, we developed a high‐resolution gridded global data set of the average thicknesses of soil, intact regolith, and sedimentary deposits within each 30 arcsec (∼1 km) pixel using the best available data for topography, climate, and geology as input. Our data set partitions the global land surface into upland hillslope, upland valley bottom, and lowland landscape components and uses models optimized for each landform type to estimate the thicknesses of each subsurface layer. On hillslopes, the data set is calibrated and validated using independent data sets of measured soil thicknesses from the U.S. and Europe and on lowlands using depth to bedrock observations from groundwater wells in the U.S. We anticipate that the data set will prove useful as an input to regional and global hydrological and ecosystems models.