THE TOP TWO METERS: MOVING FROM 2D TO 3D MAPPING OF SOIL DATABASES

What happens in the soil horizons influences the movement of water and contaminants in both Surface and groundwater. In fact, the National resource conservation services soil databases provide valuable insights for groups working with the environment, including hydrologists, engineers, soil scientists, geologists, land managers, and environmental policymakers. The soil databases are freely available online from the United States Department of Agriculture, but they require an understanding of the database structures to fully use and visualize the information within them. Most users of soil data work only with the 2-dimensional maps of the soils at the surface or generalized maps of the horizons. This study demonstrates a Python and ArcGIS-based process of converting gNATSGO data from its database format into 3d sliceable format. gNATSGO database covers the entire United States using a 10 Meter grid cell which equates to a 1:20,000 scale dataset. The horizon data in the databases provides information to two meters in depth. A NetCDF file format was used to store soil data, to make such data easier to access. NetCDF is a common file format for multi-dimensional data and is compatible with many software packages and programming languages used for scientific computing.

The notebooks and tools developed allow users to easily visualize the soil data in ArcGIS Pro and Python in both 2d and 3d space encouraging users to draw insights about the soil in their areas of interest. The notebook re-samples of horizon information in the soil database into 1 cm depth layers to the full 2-meter depth resolution of the soil layers. The tools have the ability to include multiple variables from the horizon database (e.g., k-saturation, available water capacity, textures, sieve sizes, even the difficulty of working an excavation at depth). Users can create maps and cross-sections of soil data without the need to understand database management systems. A case study in Beaufort County, South Carolina shows the utility of the notebooks and tools for working with the soil databases. Although the use of NetCDF files for this application is not highly efficient with computer storage space, it is easier to use to visualize and slice than the original database format. This project demonstrates a successful collaboration between the data science and geology programs at the College of Charleston.