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Dan Amrhein

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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>

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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>

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