Integrated streamflow, water presence, and meteorological drivers for stream network modeling in the HJ Andrews Experimental Forest
This data release provides integrated datasets for modeling streamflow and water presence across the HJ Andrews Experimental Forest stream network in western Oregon. The data include daily meteorological drivers from GRIDMET (precipitation, temperature, humidity, radiation, wind speed, evapotranspiration, vapor pressure deficit), observations of stream discharge and wet/dry status from gaging stations, sensors, and manual observations, static catchment characteristics (elevation, slope, aspect, curvature, drainage area), and National Hydrography Dataset High Resolution stream network topology. These datasets support development of machine learning models that integrate categorical water presence observations with continuous streamflow measurements to improve hydrologic predictions in headwater streams, where traditional streamflow gaging is sparse.
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[
"010:12"
]
|
| contactPoint |
{
"fn": "Jacob A Zwart",
"@type": "vcard:Contact",
"hasEmail": "mailto:jzwart@usgs.gov"
}
|
| description | This data release provides integrated datasets for modeling streamflow and water presence across the HJ Andrews Experimental Forest stream network in western Oregon. The data include daily meteorological drivers from GRIDMET (precipitation, temperature, humidity, radiation, wind speed, evapotranspiration, vapor pressure deficit), observations of stream discharge and wet/dry status from gaging stations, sensors, and manual observations, static catchment characteristics (elevation, slope, aspect, curvature, drainage area), and National Hydrography Dataset High Resolution stream network topology. These datasets support development of machine learning models that integrate categorical water presence observations with continuous streamflow measurements to improve hydrologic predictions in headwater streams, where traditional streamflow gaging is sparse. |
| distribution |
[
{
"@type": "dcat:Distribution",
"title": "Digital Data",
"format": "XML",
"accessURL": "https://doi.org/10.5066/P19R5TXW",
"mediaType": "application/http",
"description": "Landing page for access to the data"
},
{
"@type": "dcat:Distribution",
"title": "Original Metadata",
"format": "XML",
"mediaType": "text/xml",
"description": "The metadata original format",
"downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.6977e36dd4be02609dd04095.xml"
}
]
|
| identifier | http://datainventory.doi.gov/id/dataset/USGS_6977e36dd4be02609dd04095 |
| keyword |
[
"GRIDMET",
"HJ Andrews Experimental Forest",
"Headwater",
"Intermittent Streams",
"Machine Learning",
"NHDPlus",
"OR",
"Oregon",
"River",
"Streamflow",
"US",
"USGS:6977e36dd4be02609dd04095",
"United States",
"Water Presence",
"environment",
"inlandWaters",
"water resources"
]
|
| modified | 2026-04-27T00:00:00Z |
| publisher |
{
"name": "U.S. Geological Survey",
"@type": "org:Organization"
}
|
| spatial | -122.261305329, 44.2011353574327, -122.10639966049, 44.2794807687694 |
| theme |
[
"geospatial"
]
|
| title | Integrated streamflow, water presence, and meteorological drivers for stream network modeling in the HJ Andrews Experimental Forest |
