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Created: Feb 16, 2022 at 3:09 a.m.
Last updated: Feb 16, 2022 at 4:24 a.m.
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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

This resource belongs to the following collections:
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


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

Nozari, S., R. Bailey (2022). RF_GWL_projections_climate, HydroShare, http://www.hydroshare.org/resource/8f78dd6515ba418aa183e3cf67a895b0

This resource is shared under the Creative Commons Attribution CC BY.



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