Background & Summary
Soils have a fundamental role in the global hydrologic cycle by governing rainfall infiltration and
groundwater recharge, which ultimately affects the lateral transport of water and subsequent runoff
potential. Knowledge of soil hydraulic properties is therefore of interest to ecologists, hydrologists, and
soil scientists, and is critical for parameterization of a variety of empirical and physically-based
hydrologic models, dynamic-vegetation models, and land-surface models
1–3
.
The U.S. Department of Agriculture (USDA) curve-number (CN) method provides a simplified
approach to the estimation of key hydrologic processes while being grounded in a physical understanding
of saturated flow and runoff processes
4–6
. The CN method avoids the problems inherent to
parameterizing and running more complex models due to its simplicity and relatively low data input
requirements, and has been implemented in a variety of hydrologic, erosion, and water-quality models
7–9
.
CN selection is derived from the hydrologic response of various combinations of soil types and land cover
classes
2,10
. Particularly relevant to the subject of this analysis, and the data product we make available, is
the classification and development of soil parameters for CN-based runoff modeling. The lack of globally
consistent data derived from contemporary soil information served as the overarching motivation for this
analysis.
CN-based runoff estimates require informati on regarding the minimum infiltration rate of rainfall
into the soil and the t ransmission rat e of groundwater through t he soil profile after prolonged wetting.
Runoff occurs when the rainfall rate exceeds the infiltration capacity of soils. The rate at which these
processes occur is prim arily affected by t he physical nature of soils (e .g., texture , compaction ), in
addition to land cover, antecedent moisture, and rainfall i ntensity. For example, coarse-texture d
sandy soils have larger pore spacing, allowing water t o infiltrate quickly relative to fine-tex tured
clay soils.
Soils are thus classified into four hydrologic soil groups (HSGs) to infer runoff potential (Table 1)
11
.
HSG-A has the lowest runoff potential (typically contains more than 90% sand and less than 10% clay),
HSG-B has moderately low runoff potential (typically contains between 10 to 20% clay and 50 to 90%
sand), HSG-C has moderately high runoff potential (typically contains between 20 to 40% clay and less
than 50% sand), and HSG-D has high runoff potential (typically contains more than 40% clay and less
than 50% sand). Classification is determined by the least transmissive soil layer—often measured as
saturated hydraulic conductivity (K
s
)—depth to water table or depth to an impermeable layer (e.g.,
duripan, bedrock). If K
s
is unknown or not available, infiltration and transmission rates can be inferred
from soil texture, with the underlying assumption that soils with similar content of sand, silt, and clay
have analogous hydraulic properties
12–14
. Wet soils have high runoff potential (regardless of texture) due
to the presence of a groundwater table within 60 cm of the surface. These soils are assigned dual HSGs, as
a less restrictive group can be assigned (according to texture or K
S
) if they can be adequately drained.
We derived HSGs from texture classes in accordance with USDA
11
specifications (Table 1). The
resulting data product—HYSOGs250m—represents typical soil runoff potential suitable for regional,
continental, and global scale analyses and is available in a gridded format at a spatial resolution of 250 m
(Fig. 1).
Our analysis indicates that soils with moderately high runoff potential dominate the global
distribution (57.4%), followed by soils with moderately low (HSG-B 12.2%), high (HSG-D 10.1%), and
low runoff potential (HSG-A 3.0%) (Table 2). Dual HSGs A/D, B/D, C/D, and D/D accounted for 0, 1.4,
13.5, and 2.4% of the global distribution, respectively. Some global trends were observed for soils with
high and low runoff potential. Low runoff potential soils are found predominantly in parts of the Sahara
and Arabian Deserts, which are characterized by very deep and well-drained sandy soils. High runoff
potential soils occur predominantly within tropical and sub-tropical zones (with notable additions
occurring in the Alaska-Yukon Arctic and Canadian Taiga and Boreal Shield) and are characterized by
soils with high clay content or shallow soils (o 50 cm to bedrock). No clear pattern could be discerned
for soils with moderately low runoff potential at the global scale, as these HSGs occur in arid and humid
environments and at both high and low elevations.
Methods
The process for producing HYSOGs250m consisted of five primary steps (Fig. 2). We classified HSGs
from USDA-based soil texture classes (Fig. 3), depth to bedrock (Fig. 4), and groundwater table depth
(Fig. 5) as specified by the USDA-Natural Resources Conservation Service (USDA-NRCS) National
Engineering Handbook (NEH)
11
. Soil texture classes and depth to bedrock were obtained from the
SoilGrids predictions (soilgrids.org)
15
. These data and associated meta-data are available for download as
GeoTiffs at ftp://ftp.soilgrids.org/data/recent. Groundwater table depth
16
and associated meta-data are
available for download as NetCDF at https://glowasis.deltares.nl/thredds/catalog/opendap/opendap/
Equilibrium_Water_Table/catalog.html. All computations were performed within the R open source
environment for statistical computing
17
and functions from the raster package
18
.
Soil texture to 1 m depth was represented with SoilGrids predictions (soilgrids.org) soilGrids250m
texture classes at six depths: 0, 5, 15, 30, 60, and 100 cm. The soilGrids were stacked into a multi-band
raster (textStack) using the raster::stack function (Fig. 2a). For the purpose of this analysis, we refer to
individual grid cells (~250 m × 250 m) in the raster stack (1 m depth) as soil pedons. Each grid cell in the
raster stack (or pedon) was re-classified into one of four HSGs (hsgStack) using the classification scheme
www.nature.com/sdata/
SCIENTIFIC DATA
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5:150091
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DOI: 10.1038/sdata.2018.91 2
