Ma et al. 2023 random forest water table depth and uncertainty
Data and Resources
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'ma_2023'HTML
Water table depth estimates provided by a random forest model trained on...
Additional Info
Field | Value |
---|---|
Last Updated | July 9, 2025, 08:00 (UTC) |
Created | July 9, 2025, 08:00 (UTC) |
accessRights | Users must create a HydroFrame API account and register their PIN before using the hf_hydrodata package. If it's your first time using this package you will need to sign up for a HydroFrame account on the HydroFrame Signup Page (https://hydrogen.princeton.edu/signup; note: this only needs to be done once). Visit our HydroFrame PIN Page (https://hydrogen.princeton.edu/pin) to create a 4-digit PIN. After creating your PIN, you must register that PIN on the machine that you intend to use. |
columnDataDict | Access column data dictionary at: https://hf-hydrodata.readthedocs.io/en/latest/gen_ma_2023.html |
creationMethod | Long-term mean water table depth estimates were obtained using the median of tree outputs from the trained random forest model. The uncertainty was assessed based on the coefficient of variation of the tree outputs from the random forest model, which was calculated as the standard deviation of the tree outputs divided by their mean. |
creatorEmail | hydrogen-support@princeton.edu |
creatorName | HydroFrame Team |
creatorWebsite | https://hydroframe.org/ |
dataAuthType | public |
dataBbox | [-121.47939483437318, 31.651836025255015, -76.09875469594509, 50.49802132270979] |
dataProvenance | [{"date":"2023-10-05","name":"Creation and publication of dataset"}] |
dataType | NumPy array |
datasetPageUrl | https://hf-hydrodata.readthedocs.io/en/latest/gen_ma_2023.html |
datasetVersion | v1.0 |
docsURL | https://hf-hydrodata.readthedocs.io/en/latest/gen_ma_2023.html |
doi | https://doi.org/10.1111/gwat.13362 |
issueDate | 2023-10-05 |
lastUpdateDate | 2023-10-05 |
license | other |
otherLicense | Copyright © 2024 The Trustees of Princeton University and The Arizona Board of Regents on behalf of The University of Arizona, College of Science Hydrology & Atmospheric Sciences. All rights reserved. hf_hydrodata was created by William M. Hasling, Laura Condon, Reed Maxwell, George Artavanis, Will Lytle, Amy M. Johnson, Amy C. Defnet. It is licensed under the terms of the MIT license. |
pocEmail | hydrogen-support@princeton.edu |
pocName | HydroFrame Team |
pocWebsite | https://hydroframe.org/ |
publisherEmail | hydrogen-support@princeton.edu |
publisherName | HydroFrame Team |
publisherWebsite | https://hydroframe.org/ |
purpose | Machine-learning-based water table depth estimates for ParFlow-CONUS1 domain. |
spatialCov | Grid: conus1 Spatial Resolution: 1000 meters XY Grid Spatial Extent: 3342 x 1888 LatLon Spatial Exent: -121.47939483437318, 31.651836025255015, -76.09875469594509, 50.49802132270979 Origin (meters): -1885055.4995, -604957.0654 Projection: +proj=lcc +lat_1=33 +lat_2=45 +lon_0=-96.0 +lat_0=39 +a=6378137.0 +b=6356752.31 |
spatialCovFormat | text |
spatialRes | 1000 meter |
status | submitted |
theme | ["groundwater"] |
uploadType | dataset |