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 contains content types with files that need to be updated to match with metadata changes. Show content type files that need updating.
| Authors: |
|
|
|---|---|---|
| Owners: |
|
This resource does not have an owner who is an active HydroShare user. Contact CUAHSI (help@cuahsi.org) for information on this resource. |
| Type: | Resource | |
| Storage: | The size of this resource is 85.0 GB | |
| Created: | Sep 28, 2022 at 7:03 p.m. | |
| Last updated: | Nov 03, 2022 at 7:50 p.m. | |
| DOI: | 10.4211/hs.9462b23c5e1e46bdae6ef8abcdbed365 | |
| Citation: | See how to cite this resource | |
| Content types: | Multidimensional Content |
| Sharing Status: | Published |
|---|---|
| Views: | 710 |
| Downloads: | 820 |
| +1 Votes: | Be the first one to this. |
| Comments: | No comments (yet) |
Abstract
Groundwater (GW) impacts water, energy, and carbon cycles by providing additional moisture to the root zone. Although the interactions of shallow GW and the terrestrial land surface are widely recognized, incorporating shallow GW into the land surface, climate, and agroecosystem models as a lower boundary condition is not yet possible due to the lack of groundwater data.
Here, we provide global maps of the terrestrial land surface areas influenced by shallow GW at daily timesteps. We derived this data using spaceborne soil moisture observations from NASA's SMAP satellite. We used the Level-2 enhanced passive soil moisture (L2_SM_P_E) product to detect shallow GW signals. The presence of shallow GW is obtained using an ensemble machine learning model. The model is trained using results from global simulations. We published the details of our approach in a separate research paper (Soylu and Bras, 2022 - https://ieeexplore.ieee.org/document/9601254).
Our data covers the period from mid-2015 to 2021 (a separate NetCDF file for each year) with a 9 km spatial resolution, the same as the SMAP "Equal Area Scalable Earth" (EASE) grids.
Reference:
Soylu, M.E, and Bras, R.L. "Global Shallow Groundwater Patterns From Soil Moisture Satellite Retrievals." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15 (2022): 89-101
Subject Keywords
Coverage
Spatial
Temporal
| Start Date: | |
|---|---|
| End Date: |
Content
Data Services
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
|---|---|---|
| NASA | Detecting Shallow Groundwater and Irrigation Signals from SMAP Soil Moisture Retrieval | 80NSSC20K1795 |
Contributors
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.
| Name | Organization | Address | Phone | Author Identifiers |
|---|---|---|---|---|
| Morgan Johnstone | Georgia Institute of Technology | |||
| Kevin D. Beale | Georgia Institute of Technology |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
Comments
There are currently no comments
New Comment