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RCCZO -- GIS / Map Data, Regolith Survey, Geomorphology -- Predicting Soil Thickness -- Reynolds Creek Experimental Watershed -- (2014-2017)
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|Created:||Feb 25, 2020 at 5:13 p.m.|
|Last updated:|| Apr 24, 2020 at 5:25 p.m.
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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.
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|Recommended Citation||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|
|BSU ScholarWorks Link||https://scholarworks.boisestate.edu/reynoldscreek/3/|
This resource was created using funding from the following sources:
|Agency Name||Award Title||Award Number|
|National Science Foundation||Reynolds Creek Critical Zone Observatory||EAR-1331872|
People or Organizations that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.
|USDA-ARS Northwest Watershed Research Center||Reynolds Creek Experimental Watershed|
|Idaho State University|
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This resource is shared under the Creative Commons Attribution CC BY.http://creativecommons.org/licenses/by/4.0/