PROJECT
Water availability for cannabis in northern California: intersections of climate, policy, and public discourse 


CITATION
Morgan, B., K. Spangler, J. Stuivenvolt Allen, C. Morrisett, M. Brunson, S. S. Wang, N. Huntly (2020). 
	Water availability for cannabis in northern California: intersections of climate, policy, and public discourse, 
	HydroShare, http://www.hydroshare.org/resource/699227b982f5498281709e3d23a8cfce

	
PROJECT DESCRIPTION
This repository contains the data and code needed to reproduce results from Morgan et al. (2021), 
which is available at https://doi.org/10.3390/w13010005


PREREQUISITES
A basic working knowledge of R and Python programming is needed to replicate the results of this study. 

To use the data and code available in this project, please download and install: 
	>R for windows (https://cran.r-project.org/bin/windows/base/ ) or R for Mac (https://cran.r-project.org/bin/macosx/ )
	and Rstudio (https://rstudio.com/products/rstudio/download/#download ).
	License not required

	>Python (Version 3.7.9). Available at https://www.anaconda.com/products/individual , License not required. 
	Ensure the installation is specific to your machine and that you follow the installation instructions at 
	https://docs.anaconda.com/anaconda/install/ .ET_CA is a virtual anaconda environment necessary to run the 
	python scripts. Once anaconda is installed, the user can download the environment needed for this project 
	from their command line. Enter, “conda env create -f ET_CA.yml” in the directory where the ET_CA.yml file 
	is located. Activate the environment with “conda activate ET_CA” before running any python script.

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CONTENT DESCRIPTION
This repository is organized according to the three main analyses included in this study, as well as miscellaneous 
files that support the main analyses. Each folder contains the necessary input data and code required to reproduce 
the results, as well as the expected outputs.  

The Climate_Analysis folder contains 1 folder and the following files:
	Note: Currently, the scripts and data required to reproduce supplemental figure 1 and the atmospheric 
	river tracking are not in this repository because this analysis requires a volume of climate data that 
	cannot be shared in this repository. Please reach out to jacob.stu.allen@gmail.com if you are interested 
	in reproducing the atmospheric river tracking or adapting it for your own purposes. All scripts were 
	made to run through the command line using the Bourne again shell (BASH). 

>ET_CA_mac.yml is an anaconda environment that must be active on mac or linux machines to run the scripts 
that create figure 3, figure 6, supplemental figure 1 and supplemental figure 3.
>ET_CA_pc.yml is an anaconda environment that must be active on PC machines to run the scripts that create 
figure 3, figure 6, supplemental figure 1 and supplemental figure 3. 
>WATER.figure.3.power.spectra.py will produce figure 3 using existing data. None of the preliminary data 
processing scripts are necessary to reproduce the figure. 
>WATER.figure.6.dday.thresholds.py will produce figure 6 using existing data.
>WATER.figure.S1.ddays.py will produce a time series of diversion days from each USGS gauge along with a 
correlation matrix showing the relationship between each gauge.
>WATER.figure.S3.time.series.py will produce the time series of moisture variables along with their linear trends. 

>Data
	>>ET.wet.season.ar.frequency.txt is an output file with the wet-season frequency of atmospheric rivers from 
	1948 to 2019. 
	>>ET.wet.season.ddays.1954-2019.txt is the number of legal water diversion days for cannabis cultivation from 
	1954 to 2019. 
	>>ar_spectrum_90percentCI.csv, precip_spectrum.csv, soilw_spectrum.csv and streamflow_spectrum.csv are the 
	results of power spectra analysis for these moisture variables. 
	>>soilw.mon.mean.v2.nc comes from the Climate Prediction center. It is a NETCDF4 file that can be 
	downloaded at https://psl.noaa.gov/data/gridded/data.cpcsoil.html
	>>precip.comb.v2018to2016-v6monitorafter.total.nc is gridded precipitation data from the Global Precipitation 
	Climatology Centre and can be downloaded at https://psl.noaa.gov/data/gridded/data.gpcc.html
	>>flow.data.1954.csv is streamflow data from USGS gauges within the Emerald triangle. 
	>>DD_thresh_cm.csv is data describing the number of diversion days required to meet certain water 
	right allotments. 
	>>divert_days_1_23_2020.csv is a file with the number of diversion days for the USGS streamflow gauges 
	used in this study. 

Size: 342 MB
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The Policy_Analysis folder contains two folders and the following files
>Data
	>>Emerald_gages.csv is an input file for the script “Divert_day_code_V2.R” and contains the metadata for 
	the U.S. Geological Survey (USGS) gauges and monthly instream flow criteria (cubic feet per second, cfs) 
	for each gauge used in this study.
	>>divert_days_full.csv is an input file for the script “RRVS_V2.R” and is also an output from 
	“Divert_day_code_V2.R”. It contains the number of legal diversion days at 19 USGS gauges throughout the 
	Emerald Triangle for water years 1981-2019. Water years are defined as October 1 to September 30.
	>>ET_CannabisRegistration_eWRIMS_9.2.2020.xlsx is a supporting file and contains metadata for registered cannabis 
	water rights downloaded from the Electronic Water Rights Information Management System (eWRIMS public water rights 
	database, https://www.waterboards.ca.gov/waterrights/water_issues/programs/ewrims/ ) and meeting the following 
	criteria: type = registration cannabis, location = Humboldt/ Trinity/ Mendocino counties.
	>>Storage_data.csv is an input file for the script “RRVS_V2.R” and includes water rights from the eWRIMS spreadsheet 
	that meet the following criteria: beneficial use = irrigation, diversion rate of 10 gal/min, and diversion and 
	storage dates of 1 November-31 March.  

>Divert_day_code_V2.R is the script used to calculate the total number of diversion days (water years 1981-2019) 
for the 19 USGS gauges included in our study.
> RRVS_V2.R is the script used to calculate the performance metrics (Reliability, Resiliency, Vulnerability)
for legal cannabis water access and generates Figure 2b and Figure 5.

>Output
	>>Data
		>>>DD_thresh_cm.csv is an output file from the script “RRVS_V2.R” and contains the number of days 
		needed to secure a full permitted water right (cubic meters) under 8, 12, and 24 hr/day diversion 
		operation. This is used as input data to generate Figure 6. 
		>>>divert_days_full.csv is an output file from the script “Divert_day_code_V2.R” and is used as an 
		input to the script “RRVS_V2.R.”
		>>>flow.data.csv is an output file from the script “Divert_day_code_V2.R” and contains daily average 
		streamflow data (cubic feet per second, cfs) for the 19 USGS gauges in this study. This is used as 
		an input to the climate analysis. 
	>>Figures
		>>>RRV_heatmap.png (Figure 5) is an output figure from the script “RRVS_V2.R.”
		>>>Water_storage_hist.png (Figure 2b) is an output figure from the script “RRVS_V2.R.” 

Size: 2.34 MB
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The Content_Analysis folder contains three folders and the following files:
>Data
	>>Codebook_Master.xlsx is a supporting file that contains the code definitions used in the content analysis 
	of news media. 
	>>ET.monthly.precip.Jan2015-Dec2019.csv is an input file to the script “Figure4.R” and contains monthly 
	average precipitation (mm) for the Emerald Triangle between January 2015 and December 2019. The data 
	comes from the Global Precipitation Climatology Centre and it's free to access.
	>>Newspaper-Articles_Final.csv is an input file to the script “Figure4.R” and contains metadata for news 
	media articles included in the content analysis. 
	>>Sankey_watdisc.csv is an input file to the script “Figure7.Rmd” and contains the frequency of occurrence 
	between perspective and discussion codes.

>Figure4.R is the script used to generate the time series of article publication and precipitation. 
>Figure7.Rmd is the script used to generate a Sankey diagram for perspective and discussion cooccurrences. 

>Output
	>>Figures
		>>>Figure4.png (Figure 4) is an output figure from the script “Figure4.R.”
		>>>disc&persp.jpg (Figure 7) is an output figure from the script “Figure7.Rmd.”

Size: 703 KB

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REPLICATION INSTRUCTIONS
Download and unzip the repository. 

>To reproduce the supporting data used in the policy and climate analyses:  
	>>Navigate to Policy_Analysis/Divert_day_code_V2.R  and double click to open in R studio. The script 
	automatically sets a working directory and installs all necessary packages for the code.
	>>Run the code line-by-line using control-enter or run the entire code by highlighting all code and pressing 
	control-enter. This code will take around ~3 minutes to run. 
	>>Data outputs from this code will automatically be placed in Policy_Analysis/Output/Data. This includes 
	divert_days_full.csv, which is used as a data input to the RRVS_V2.R script and in Figure 3, and flow.data.csv, 
	which is also used in Figure 3.  
	>>Note: These data are already placed in their respective input data folders, so this step is unnecessary 
	to reproduce the remaining results. 

>To reproduce Figure 3 depicting the time series of moisture variables along with the power spectra of each 
variable (all instructions are for command line users): 
	>>Ensure anaconda3 has been installed and be sure to select the option to add anaconda3 to your machine path.
	>>Within your command line, navigate to the Climate_Analysis directory within your unzipped repository. 
	>>Create an anaconda environment from the provided .yml files. 
		>>>Enter, “conda env create -f ET_CA_pc.yml” for PC users and “conda env create –f ET_CA_mac.yml” for mac users.
	>>Activate the provided anaconda environment, ET_CA_pc/mac, by using “conda activate ET_CA_pc/mac” on your 
	command line. Conda may ask you to run “conda init ” to initialize the conda command. Follow the 
	instructions output by your shell. 
	>>After your conda environment is activated, you will be able to run the scripts without further modifications. 
	>>Execute the script by typing, “python WATER.figure.3.power.spectra.py”.
		>>>A runtime warning will display in your shell because sea surface temperature data has NANs over all land mass 
		and two parts of the script require taking averages of the sea surface temperature array. 
	>>Figure outputs from this code will be placed in the Climate_Analysis/Output folder.
	
>To reproduce the monthly news article and precipitation timeseries plot (Figure 4): 
	>>Navigate to Content_Analysis/Figure4.R and double click to open in R studio. The script automatically 
	sets a working directory and installs all necessary packages for the code.
	>>Run the code line-by-line using control-enter or run the entire code by highlighting 
	all code and pressing control-enter. 
	>>Figure outputs from this code will automatically be placed in Content_Analysis/Output/Figures and 
	include Figure4.png (Figure 4).
	
>To reproduce the Reliability, Resiliency, and Vulnerability plot (Figure 5) and the storage histogram (Figure 2b): 
	>>Navigate to Policy_Analysis/RRVS_V2.R and double click to open in R studio. The script automatically sets a 
	working directory and installs all necessary packages for the code.
	>>Run the code line-by-line using control-enter or run the entire code by highlighting all code 
	and pressing control-enter. 
	>>Figure outputs from this code will automatically be placed in Policy_Analysis/Output/Figures. These outputs 
	include RRV_heatmap.png (Figure 5) and Water_storage_hist.png (Figure 2b). 
	>>Data outputs from this code will automatically be placed in Policy_Analysis/Output/Data. This includes 
	DD_thresh_cm.csv, which is used to create Figure 6. 

>To reproduce the diversion day thresholds plot (Figure 6):
	>>Navigate to the Climate_Analysis directory in your local path. 
	>>Activate your ET_CA environment with “conda activate ET_CA” (see Figure 3 directions for pc/mac)
	>>Execute the script with “python WATER.figure.6.dday.thresholds.py”
	>>Figure outputs will be automatically placed in the Climate_Analysis/Output directory. 

>To reproduce the Sankey plot for perspective and discussion codes (Figure 7): 
	>>Navigate to Content_Analysis/Figure7.Rmd and double click to open in R studio. This is an Rmarkdown document. 
	The script automatically sets a working directory and installs all necessary packages for the code.
	>>Run the code as chunks by placing cursor within the code chunk and clicking Run>Run Current Chunk 
	(upper right hand corner) or run the entire code by clicking Run>Run All.
	>>Figure outputs from this code will automatically be placed in Content_Analysis/Output/Figures and include 
	disc&persp.jpg (Figure 7). 
	
>To reproduce the timeseries of diversion days and correlation matrix (Figure S1):
	>>Navigate to the Climate_Analysis directory in your local path. 
	>>Activate your ET_CA environment with “conda activate ET_CA” (see Figure 3 directions for pc/mac)
	>>Execute the script with “python WATER.figure.S1.ddays.py”
	>>Figure outputs will be automatically placed in the Climate_Analysis/Output directory and include 
	WATER.figure.S1.png.

>To reproduce the timeseries of climate variables (Figure S2):
	>>Navigate to the Climate_Analysis directory in your local path. 
	>>Activate your ET_CA environment with “conda activate ET_CA” (see Figure 3 directions for pc/mac)
	>>Execute the script with “python WATER.figure.S3.time.series.py”
	>>Figure outputs will be automatically placed in the Climate_Analysis/Output directory and include 
	WATER.figure.S3.png.