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| Type: | Resource | |
| Storage: | The size of this resource is 168.4 MB | |
| Created: | Feb 16, 2022 at 3:09 a.m. | |
| Last updated: | Feb 16, 2022 at 4:24 a.m. | |
| Citation: | See how to cite this resource |
| Sharing Status: | Public |
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| Views: | 560 |
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Abstract
This repository includes all the Python programming language scripts developed for long-term groundwater level projections using the random forests (RF) method in combination with ordinary kriging in Finney County in southwest Kansas under various climate scenarios. The Scikit-learn library is used to construct the RF model and the ArcPy package is utilized for all geospatial and geostatistical analyses.
The climate scenarios are developed based on the downscaled climatic data of 20 GCMs for the RCPs of 4.5 and 8.5. The repository also includes the required data for running the scripts. All the scripts and data are uploaded as a single 7z file.
To project future GWLs, initially change the home folder pathname in all 3 included python scripts, namely "all_calculations_in_arcpy.py", "projecting_water_level_variations_2017_2099_using_RF.py", and "removing_redundant_rasters.py". Then, run the "projecting_water_level_variations_2017_2099_using_RF.py" file.
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Related Resources
| Title | Owners | Sharing Status | My Permission |
|---|---|---|---|
| RF_Model_Finney_County | Soheil Nozari | Public & Shareable | Open Access |
| RF_Model_Finney_County | Soheil Nozari | Private & Shareable | None |
| RF_Model_Finney_County | Nafyad Kawo | Private & Shareable | None |
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
|---|---|---|
| National Institute of Food and Agriculture, U.S. Department of Agriculture | Sustaining agriculture through adaptive management to preserve the Ogallala aquifer under a changing climate | 2016-68007-25066 |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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