A gridded global data set of soil, intact regolith, and sedimentary deposit thicknesses for regional and global land surface modeling

Abstract

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.

The evolution of process-based hydrologic models: historical challenges and the collective quest for physical realism

“…The challenge is to estimate spatial variations in the storage and transmission properties of the landscape. Advances are possible through the development of new data sources on geophysical attributes (Simard et al, 2011;Gleason and Smith, 2014;Fan et al, 2015;Chaney et al, 2016b;Pelletier et al, 2016;De Graaf et al, 2017), new approaches to link geophysical attributes to model parameters (Samaniego et al, 2010;Kumar et al, 2013;Rakovec et al, 2015), and new diagnostics to infer model parameters Yilmaz et al, 2008;Pokhrel et al, 2012). Such focus will give the parameter estimation problem the scientific attention that it deserves, rather than the far-too-common approach where parameter estimation is relegated to a "tuning exercise" in model applications.…”

“…These include maps of global permeability and porosity for the upper 50 m of the world's aquifers (Gleeson et al, 2014), soil characteristics and regolith thickness (Pelletier et al, 2016;Shangguan et al, 2016;Hengl et al, 2017), and global thickness of the upper aquifers (de Graaf et al, 2015;Fan et al, 2015;Fan, 2016). However, these datasets have been globally extrapolated from locally established empirical relationships between subsurface properties and surface lithology (Hartmann and Moosdorf, 2012).…”

“…We see three specific paths forward. First, there are numerous opportunities to improve information on geophysical properties, including estimates of vegetation structure (Simard et al, 2011), soil depth (Pelletier et al, 2016), soil properties (Chaney et al, 2016b), bedrock depth and permeability (Fan et al, 2015), and the physical characteristics of rivers (Gleason and Smith, 2014).…”

Estimating the thickness of unconsolidated coastal aquifers along the global coastline

“…We collected state of the art open source global datasets (Table 1) that provide information on topography and bathymetry (Weatherall et al 2015), thickness estimation of the surface sediments (Pelletier et al 2016), aquifer thickness estimation from a global hydrological model (de Graaf et al 2015), lithology (Hartmann & Moosdorf 2012) and coastline position (Natural Earth 2017). The core of our aquifer thickness estimation (ATE) method is to combine topographical and lithological information.…”

“…Our focus is limited to aquifer systems 10 formed by unconsolidated sediments and uses only available open source global datasets during the estimation process. The dataset thus created is different from previously created global datasets (Table S1 in the Supplementary Information) that either focused only on estimating the thickness of the soil (or regolith) layer, (Pelletier et al 2016;Shangguan et al 2017), or estimate the total thickness of porous media and do not distinguish between unconsolidated and consolidated sediments or rocks (Whittaker et al 2013;de Graaf et al 2015). To illustrate the use of the new dataset in a regional groundwater 15 modelling, we will show the results of variable-density groundwater flow and coupled salt transport models for three distinctly different coastal cross-sections.…”