Last Updated: 1/24/2025
Contact: Bhuwan Ghimire (bhuwan.ghimire@usu.edu)

This resource contains data on water budget over land cover classes, used in the MS Thesis:
Ghimire, B. (2025). "Investigating Changes in Hydroclimate, Land Cover, and Evapotranspiration across The Great Salt Lake Basin and its Major Subbasins," Civil and Environmental Engineering,
Utah State University.

Overview
It contains data, representative points, and Python scripts (jupyter notebooks) used to retrieve and analyze water yield and evapotranspiration for each land cover class in 
the Great Salt Lake basin. Precipitation, evapotranspiration, daily mean air temperature, and potential evapotranspiration over each land cover class were retrieved from ClimateEngine API
 using representative points, rather than generally used polygon boundaries, and averaged over water years (2004-2021). Land cover maps were used to define the boundary of the 
land cover class. Retrieving these variables using polygon boundaries was not feasible due to the large number of vertices created when converting the raster map into polygons. 
These data were used to estimate water yield and total evapotranspiration, providing insights into water availability and usage across different land cover classes.


Data Sources:
ClimateEngine (https://climateengine.com/) - open-access climate cloud computing platform to obtain timeseries over the points.
The point having its coordinate were the input into the ClimateEngine API.

The following data sources were used:
#1 Precipitation: PRISM (Units: mm)
#2 Daily Mean Air Temperature: PRISM (Units: °C)
#3 Monthly Evapotranspiration: MODIS-ET SSEBop (Units: mm)
#4 Monthly Potential Evapotranspiration based on Hargreaves : PRISM (Units: mm)

For consistency, all data were aggregated over the water year.

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Folder Structure
#1. PythonCodes
"ClimateEngineAPI_Points.ipynb": Customized script to retrieve timeseries data over points from ClimateEngine API (https://support.climateengine.org/article/42-api tutorials). 
Executing this script requires an API key from ClimateEngine. This requires an API key from ClimateEngine for functionality.The customization enables easy selection of predefined
 variables using the input Excel file "Variable_inputs.xlsx" within the folder.

"SelectRandomRepresentativePoints.ipynb": Python code to randomly select points from the complete set of grid cell center points for each land cover class.

"ElevationVsClimaticVariables.ipynb": Python code for the analysis and visualization of relationship between precipitation, evapotranspiration, potential evapotranspiration and air
 temperature with elevation of the land cover classes.

Other python scripts were used to analyze change in water yield and change in evapotranspiration due to land cover change.


#2. RepresentativePoints
This folder contain Excel files with the coordinates of representative points of each land cover class, obtained by random sampling of points from the complete set of grid cell center 
points for each land cover class.


#3. WaterYearAverages
This folder contains subfolders for the Great Salt Lake subbasins, each with an Excel file of water-year averaged data for the following variables:
	Precipitation : WY_Annual_Precip.csv
	Air Temperature	: WY_Annual_Temp.csv
	Potential Evapotranspiration : WY_Annual_PET.csv
	Evapotranspiration : WY_Annual_Evapo.csv
Note: The header in these files represents the land cover classes.