RF_GWL_projections_climate


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.
Resource type: Composite 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
Views: 143
Downloads: 13
+1 Votes: Be the first one to 
 this.
Comments: No comments (yet)

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.

Subject Keywords

Deleting all keywords will set the resource sharing status to private.

Content

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

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

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.

http://creativecommons.org/licenses/by/4.0/
CC-BY

Comments

There are currently no comments

New Comment

required