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 8.1 MB | |
Created: | Sep 11, 2017 at 9:08 p.m. | |
Last updated: | Nov 21, 2017 at 3:50 a.m. | |
Citation: | See how to cite this resource |
Sharing Status: | Public |
---|---|
Views: | 2243 |
Downloads: | 57 |
+1 Votes: | Be the first one to this. |
Comments: | No comments (yet) |
Abstract
Data exploration using GRACE remotely derived groundwater levels and well point datasets
The problem: If the well is dry, is the problem due to hydrology or humans? Kenya, Uganda, and Tanzania are three countries with an extensive well dataset. What are the spatial statistics of failures? Are functioning/non-functioning wells scattered randomly, or does failure follow a hydrologic pattern?
A common challenge in interpreting and validating remote sensing data is in comparing these data to direct observations on the ground. Often remotely sensed data will cover large regions and have different temporal and spatial sampling frequency than point observations derived in the field. This kind of analysis requires geospatial tools to enable resampling, assessment of spatial statistics and extrapolation of point data to broader regions. In Geohackweek 2016, our project team ('Oh Well') left code to explore this problem in this HydroShare resource.
Please see the attached project presentation slide show for an introduction to the team.
Source of the Notebook:
<a href="http://nbviewer.jupyter.org/github/amrhein/freshwaterhack/blob/master/grace_wells.ipynb" rel="nofollow">http://nbviewer.jupyter.org/github/amrhein/freshwaterhack/blob/master/grace_wells.ipynb</a>
Google Earth Engine Resources:
Here is a script that selects a single CHIRPS precipitation image from the collection:
<a href="https://code.earthengine.google.com/2870eedb36d247bc25d95c9cc2c4ac50" rel="nofollow">https://code.earthengine.google.com/2870eedb36d247bc25d95c9cc2c4ac50</a>
Here is a script to get mean CHIRPS data:
<a href="https://code.earthengine.google.com/3a09aaa437f327c392ac7798df1e2c09" rel="nofollow">https://code.earthengine.google.com/3a09aaa437f327c392ac7798df1e2c09</a>
Subject Keywords
Content
Related Resources
Title | Owners | Sharing Status | My Permission |
---|---|---|---|
Freshwaterhack Project: Groundwater Resources and GRACE | Christina Norton · Joe Cook | Discoverable & Shareable | Open Access |
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