Please wait for the process to complete.
Checking for non-preferred file/folder path names (may take a long time depending on the number of files/folders) ...
This resource contains some files/folders that have non-preferred characters in their name. Show non-conforming files/folders.
||This resource does not have an owner who is an active HydroShare user. Contact CUAHSI (firstname.lastname@example.org) for information on this resource.|
|Storage:||The size of this resource is 109.6 MB|
|Created:||Feb 24, 2021 at 2:41 a.m.|
|Last updated:|| Mar 03, 2021 at 7 p.m.
|Citation:||See how to cite this resource|
|Content types:||Geographic Feature Content Geographic Raster Content|
|+1 Votes:||1 other +1 this|
|Comments:||No comments (yet)|
Data comes originally from the United States Geological Survey (USGS) but is organized in a .JSON format containing x,y location, original ground, and time-series observations. The purpose of this data is to be used in a Python based, machine learning algorithm where gaps within well data are imputed using remote Earth observations.
In an update made on March 3, 2021. The two files have been created from the original .JSON file: Well Locations.h5 and Well_Data.h5. These data represent the location of the wells within the aquifer being looked at, and the associated time series of them in a more friendly to work with file format.
In addition to this, the well locations have also been made available as a .shp file. A NED 10m model was used to create the .tiff file also made available. Combined, these files will allow users to view data as surface depth to water if they desire.
Web Map Service
Web Feature Service
Web Coverage Service
|This resource is described by||https://pubs.er.usgs.gov/publication/sir20195139|
|The content of this resource is derived from||https://maps.waterdata.usgs.gov/mapper/index.html|
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.http://creativecommons.org/licenses/by/4.0/