Summary & Purpose
Soil thickness is a fundamental variable in many earth science disciplines but difficult to predict. We find a strong inverse linear relationship between soil depth and hillslope curvature (r2=0.89, RMSE=0.17 m) at a field site in Idaho. Similar relationships are present across a diverse data set, although the slopes and y-intercepts vary widely. We show that the slopes of these functions vary with the standard deviations (SD) in catchment curvatures and that the catchment curvature distributions are centered on zero. Our simple empirical model predicts the spatial distribution of soil depth in a variety of catchments based only on high-resolution elevation data and a few soil depths. Spatially continuous soil depth datasets enable improved models for soil carbon, hydrology, weathering and landscape evolution.
Date of Publication or Submission
This study was conducted in collaboration and cooperation with the USDA Agriculture Research Service, Northwest Watershed Research Center, Boise, Idaho, and landowners within the Reynolds Creek Critical Zone Observatory (RC CZO). Support for this research was provided by NSF for RC CZO Cooperative agreement NSF EAR-1331872 (Kathleen Lohse, Principal Investigator; Nancy Glenn, Co-Principal Investigator; Alejandro Flores, Co-Principal Investigator; Shawn Benner, Co-Principal Investigator; Mark Seyfried, Co-Principal Investigator). Data are available at the criticalzone.org data portal. Every sample at RC CZO is registered with an International Geo Sample Number through System for Earth Sample Registration (SESAR). Gordon Gulch data collection was funded by the Keck Geology Consortium, the National Science Foundation (NSF EAR-1062720), University of Connecticut Research Foundation, and NSF Boulder Creek Critical Zone Observatory (NSF EAR-072496). The authors declare no financial conflicts.
Single Dataset or Series?
*.zip, *.mpk, *.jpg, *.xlsx, *.csv
The dataset consists of a zip file containing an ArcGIS map package, a set of *.jpg images of each of the raster layers in the map package, and a data dictionary in both excel and csv format explaining the terms and attributes of the data.
June 2010-June 2015
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Patton, Nicholas R.; Lohse, Kathleen A.; Godsey, Sarah E.; Seyfried, Mark S.; and Crosby, Benjamin T.. (2017). Dataset for Predicting Soil Thickness on Soil Mantled Hillslopes [Data set]. Retrieved from https://doi.org/10.18122/B2PM69
Available for download on Saturday, October 13, 2018