Predictive soil property map: Erodability
These data were compiled to demonstrate new predictive mapping approaches and provide comprehensive gridded 30-meter resolution soil property maps for the Colorado River Basin above Hoover Dam. Random forest models related environmental raster layers representing soil forming factors with field samples to render predictive maps that interpolate between sample locations. Maps represented soil pH, texture fractions (sand, silt clay, fine sand, very fine sand), rock, electrical conductivity (ec), gypsum, CaCO3, sodium adsorption ratio (sar), available water capacity (awc), bulk density (dbovendry), erodibility (kwfact), and organic matter (om) at 7 depths (0, 5, 15, 30, 60, 100, and 200 cm) as well as depth to restrictive layer (resdept) and surface rock size and cover. Accuracy and error estimated using a 10-fold cross validation indicated a range of model performances with coefficient of variation (R2) for models ranging from 0.20 to 0.76 with mean of 0.52 and a standard deviation of 0.12. Models of pH, om and ec had the best accuracy (R2 > 0.6). Most texture fractions, CaCO3, and SAR models had R2 values from 0.5-0.6. Models of kwfact, dbovendry, resdept, rock models, gypsum and awc had R2 values from 0.4-0.5 excepting near surface models which tended to perform better. Very fine sands and 200 cm estimates for other models generally performed poorly (R2 from 0.2-0.4), and sample size for the 200 cm models was too low for reliable model building. More than 90% of the soils data used was sampled since 2000, but some older samples are included. Uncertainty estimates were also developed by creating relative prediction intervals, which allow end users to evaluate uncertainty easily.
Complete Metadata
| accessLevel | public |
|---|---|
| bureauCode |
[
"010:12"
]
|
| contactPoint |
{
"fn": "Travis W Nauman",
"@type": "vcard:Contact",
"hasEmail": "mailto:tnauman@usgs.gov"
}
|
| description | These data were compiled to demonstrate new predictive mapping approaches and provide comprehensive gridded 30-meter resolution soil property maps for the Colorado River Basin above Hoover Dam. Random forest models related environmental raster layers representing soil forming factors with field samples to render predictive maps that interpolate between sample locations. Maps represented soil pH, texture fractions (sand, silt clay, fine sand, very fine sand), rock, electrical conductivity (ec), gypsum, CaCO3, sodium adsorption ratio (sar), available water capacity (awc), bulk density (dbovendry), erodibility (kwfact), and organic matter (om) at 7 depths (0, 5, 15, 30, 60, 100, and 200 cm) as well as depth to restrictive layer (resdept) and surface rock size and cover. Accuracy and error estimated using a 10-fold cross validation indicated a range of model performances with coefficient of variation (R2) for models ranging from 0.20 to 0.76 with mean of 0.52 and a standard deviation of 0.12. Models of pH, om and ec had the best accuracy (R2 > 0.6). Most texture fractions, CaCO3, and SAR models had R2 values from 0.5-0.6. Models of kwfact, dbovendry, resdept, rock models, gypsum and awc had R2 values from 0.4-0.5 excepting near surface models which tended to perform better. Very fine sands and 200 cm estimates for other models generally performed poorly (R2 from 0.2-0.4), and sample size for the 200 cm models was too low for reliable model building. More than 90% of the soils data used was sampled since 2000, but some older samples are included. Uncertainty estimates were also developed by creating relative prediction intervals, which allow end users to evaluate uncertainty easily. |
| distribution |
[
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| identifier | http://datainventory.doi.gov/id/dataset/USGS_5e90b27d82ce172707ed6c87 |
| keyword |
[
"Arizona",
"Colorado",
"Colorado River",
"Colorado River Basin",
"Colorado River Basin above Hoover Dam",
"Hoover Dam",
"Nevada",
"New Mexico",
"USGS:5e90b27d82ce172707ed6c87",
"Utah",
"Wyoming",
"accuracy and error estimated",
"available water capacity",
"bulk density",
"calcium carbonate",
"digital soil mapping",
"electrical conductivity",
"environmental conditions",
"environmental raster layers",
"erodibility",
"geoscientificInformation",
"gypsum",
"interpolate",
"machine learning",
"maps and atlases",
"organic matter",
"predicitve modeling",
"predictive mapping",
"predictive maps",
"random forest models",
"random forests",
"restrictive layer",
"rock",
"sodium adsorption ratio",
"soil conductivity",
"soil density",
"soil forming factors",
"soil pH",
"soil properties",
"soil property maps",
"soil sciences",
"soil texture",
"soils",
"surface rock cover",
"surface rock size",
"texture fractions",
"uncertainty"
]
|
| modified | 2020-08-27T00:00:00Z |
| publisher |
{
"name": "U.S. Geological Survey",
"@type": "org:Organization"
}
|
| spatial | -116.0000, 33.3000, -105.2000, 44.0000 |
| theme |
[
"Geospatial"
]
|
| title | Predictive soil property map: Erodability |
