Datasets for soils and controls on soil formation (available for download):

Raster datasets for the following variables have been constructed on precisely co-registered 1 km-resolution grids for the conterminous U.S.:

These data were constructed as part of NSF EAR grant #0086513, SGER: Climate Calibration of Soils for Application to Paleoclimates by PIs Jon Pelletier and Judy Parrish at the Department of Geosciences, University of Arizona. The purpose of this project was to quantify the controls on soil-forming factors to assess the extent to which modern soil types are correlated with modern climates. The impetus for the project was the observation that geologists are increasingly using paleosols as paleoclimate indicators. However, the linkage between soil types and certain climates has never been quantitatively established. The final report for our project can be found here. As a added benefit, we have constructed a dataset that may be of use to a wide variety of additional studies in soils and surficial processes. More information about the data contained below can be obtained from Jon Pelletier (jon@geo.arizona.edu) and will be available in a forthcoming paper. A brief dataset summary is contained here. Although the authors believe the data to be a high-quality product, no guarantees are expressed or implied.

Three of the datasets below were taken, with little modification, from the CONUS-Soil Database, which provides a remarkably valuable database for soils in the conterminus United States. Precipitation data were obtained from the PRISM project. Their data is not provided here - a link to the source (which is in a different projection) is provided instead to protect their copyright.

All these raster datasets are in Lambert Azimuthal projection, contain 4587 columns and 2889 rows, and are given in both ASCII and Arc/INFO bil format. The projection info are identical to those of the CONUS-Soil Database, given here. Non-numerical datasets, such as soil type and vegetation type, require the accompanying file which lists the class that each numerical value encodes in order for the data to be interpreted.

Soil type (by suborder):
IMAGE: (in these images, values increase from black (lowest) to red, blue,  yellow, and white (highest).

DATA: ASCII, BIL, hdr,description of numerical codes

Soil depth:
IMAGE:

DATA: ASCII, BIL, hdr

Dominant soil texture:
IMAGE:

DATA: ASCII, BIL, hdr, description of numerical codes

Mean atmospheric temperature:
IMAGE:

DATA: ASCII, BIL, hdr

Seasonality of temperature:
The temperature seasonality index is the mean temperature of the hottest month minus the mean temperature of the coldest month.
IMAGE:

DATA: ASCII, BIL, hdr

Mean precipitation:
IMAGE:

DATA available here.

Seasonality of precipitation:
Seasonality index of precipitation is the ratio of the average total precipitation for the three wettest consecutive months divided by the average total precipitation for the three driest consecutive months.
IMAGE:

DATA: ASCII, BIL, hdr

Parent material (surficial geology):
IMAGE:

DATA: ASCII, BIL, hdr, description of numerical codes

Vegetation type:
IMAGE:

DATA: ASCII, BIL, hdr, description of numerical codes

Vegetation density:
IMAGE:

DATA: ASCII, BIL, hdr

Elevation:
IMAGE:

DATA: ASCII, BIL, hdr

Topographic slope:
IMAGE:

DATA: ASCII, BIL, hdr