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caland's Introduction

CAlifornia natural and working LANDs carbon and greenhouse gas model (CALAND)

Version 3

Authors

Alan Di Vittorio
Maegen Simmonds
Lawrence Berkeley National Laboratory

Citing CALAND

Please cite the appropriate version using the corresponding Digital Object Identifier (DOI) and our peer-reviewed manuscripts, the citations for which will be made available in the most current readme file on CALAND's Github repository as they are published.

Manuscripts:

Di Vittorio, A.V., M.B. Simmonds, P. Nico. In review. Quantifying the effects of multiple land management practices, land cover change, and wildfire on the California landscape carbon budget with an empirical model. PLOS ONE.

Simmonds, M.B., A.V. Di Vittorio, C. Jahns, E. Johnston, A. Jones, and P.S. Nico. 2021. Impacts of California's climate-relevant land use policy scenarios on terrestrial carbon emissions (CO2 and CH4) and wildfire risk. Environ. Res. Lett. 16 014044 DOI: 10.1088/1748-9326/abcc8d

The citation for CALAND v3.0.0 technical documentation is:

Di Vittorio, A., and M. Simmonds (2019) California Natural and Working Lands Carbon and Greenhouse Gas Model (CALAND), Version 3, Technical Documentation. DOI: 10.5281/zenodo.3256727

The citation for CALAND v3.0.0 code is:

Di Vittorio, A., and M. Simmonds (2019) California Natural and Working Lands Carbon and Greenhouse Gas Model (CALAND), Version 3. DOI: 10.5281/zenodo.3256727

Repository updates

  • February 2021:
    • Version 3.0.1 includes a minor CALAND bug fix, updated raw climate scalar files, the county scaling functions (as added in Oct 2019), and tutorial materials
    • Bug fix:
      • In the case where Cultivated soil conservation flipped the sign of the baseline flux (in the Delta with the +SD mangement benefit), the climate scalar was not adjusted appropriately.
      • This has been corrected, and this case did not arise in the NWL study or in the two initial manuscripts.
    • Updated raw climate scalar files for RCP4.5 and RCP8.5:
      • The raw climate scalar files had incorrect values for the Delta Cultivated land. These have been updated by accounting for the negative baseline carbon accumulation in this land category.
      • This affects only 5% of the statewide cultivated land, and 0.5% of the total land in the state, and thus has a negligible effect on the results of the NWL study and the corresponding paper.
      • This has no affect on the multiple practice study as climate change effects were not applied.
    • The tutorial materials from October 2019 have been added to the repository in the ancillary folder. These materials have been separated into five zip files.
  • July 2020:
    • Updated Copyright and License. See below for details.
  • October 2019:
    • Added two new function files to estimate county-level management effects:
      • write_scaled_raw_scenarios.r: scales county-level raw scenarios to the region level for input to write_caland_inputs()
      • write_scaled_outputs.r: scales the CALAND outputs back to the county level
    • These functions are used together to effectively apply CALAND at the county level in place of the region levels, so that county-level effects of county-level management can be estimated
    • Caveats of county-level scaling
      • Increase in estimation uncertainty that is difficult to quantify
      • The scaled outputs approximate what CALAND would return for a single county if the region boundaries were counties instead of the current nine regions
      • County-level management has limitations compared to regular CALAND operation:
        • No land use/cover change (except what may be prescribed in a restoration scenario)
        • No land protection management (because there is no urban growth)
        • Always full Forest regeneration
        • Annual practices must be in a separate scenario from restoration practices
        • No fire with restoration practices (fire can be used with annual practices)
        • Land categories with zero county area cannot be restored at the county-levels (except for Delta Fresh_marsh)
        • Some county restoration targets may not be possible to limitations in scaled source area
    • Alternatively, one could simply simulate county-level targets without scaling and obtain the region-level effects of county-level management

Version History

  • CALAND version 3.0.1: February 2021; DOI: forthcoming
  • CALAND version 3.0.0: 25 June 2019; DOI: 10.5281/zenodo.3256727
  • CALND concept DOI for all versions, 25 June 2019: 10.5281/zenodo.3256726
  • CALAND version 2.0.0: 23 October 2017; not an official release
  • CALAND version 1.0.0: 25 January 2017; not an official release

License and Copyright

CALAND is licensed as open source software under a modified BSD license.

California Natural and Working Lands Carbon and Greenhouse Gas Model (CALAND) Copyright (c) 2020, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved.

If you have questions about your rights to use or distribute this software, please contact Berkeley Lab's Intellectual Property Office at [email protected].

Funding

The California Natural Resources Agency

NOTICE. This Software was developed under additional funding from the U.S. Department of Energy and the U.S. Government consequently retains certain rights. As such, the U.S. Government has been granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, distribute copies to the public, prepare derivative works, and perform publicly and display publicly, and to permit others to do so.

Table of Contents

Overview

CALAND is a system of algorithms (.r files) and data developed to quantify the impacts of various suites of California State-supported land use and land management strategies on landscape carbon and greenhouse gas emissions (CO2, CH4, and optional black carbon) relative to a baseline scenario for the Draft California 2030 Natural and Working Lands Climate Change Implementation Plan (2019). CALAND consists of the model itself (CALAND.r), a data pre-processing algorithm (write_caland_inputs.r) for generating model input files, and three post-processing algorithms (plot_caland.r, plot_scen_types.r, plot_uncertainty.r) for diagnosing, visualizing, and summarizing model ouputs.

Installation

Get the CALAND files and tools needed to use them by following the instructions below for downloading R, RStudio, and CALAND. CALAND can be run using R, RStudio, or command line. Note that using RStudio will require downloading both R and RStudio. Here are instructions for downloading R, RStudio, and CALAND.

Downloading R

Mac Users

  1. Go to www.r-project.org.
  2. Click the "download R" link in the middle of the page under "Getting Started."
  3. Select a CRAN location (a mirror site) nearest you and click the corresponding link.
  4. Click on the "Download R for (Mac) OS X" link at the top of the page.
  5. Click on the file containing the latest version of R under "Latest Release".
  6. Save the .pkg file, double-click it to open, and follow the installation instructions.
  7. Now that R is installed, you can download and install RStudio.

Windows Users

  1. Go to www.r-project.org.
  2. Click the "download R" link in the middle of the page under "Getting Started."
  3. Select a CRAN location (a mirror site) nearest you and click the corresponding link.
  4. Click on the "Download R for Windows" link at the top of the page
  5. Click on the "install R for the first time" link at the top of the page.
  6. Click "Download R for Windows" and save the executable file (.exe) somewhere on your computer. Run the .exe file and follow the installation instructions.
  7. Now that R is installed, you can download and install RStudio.

Downloading RStudio

  1. Go to www.rstudio.com and click on "Download RStudio".
  2. Click on "Download" under RStudio Desktop.
  3. Click on the version recommended for your system under Installers, and save the .dmg file (Mac User) or .exe (Windows User) on your computer, double-click it to open, and then drag and drop it to your applications folder.
  4. Now RStudio is installed.

Three options for downloading CALAND:

(1) Downloading CALAND releases from the Zenodo digital archive

  1. Go to Zenodo.org.
  2. Search for CALAND.
  3. Find the CAlifornia natural and working LANDs carbon and greenhouse gas model (CALAND) in the search results and click on it (e.g., selecting CALAND v3.0.0 should direct to http://zenodo.org/record/3256727).
  4. Download the .zip file for the desired version of CALAND (e.g., caland-v3.0.0.zip) by clicking on the 'Download' button. Now CALAND is on your local computer. The 'GitHub' button will take you to the CALAND GitHub repository (see below).
  5. Unzip the file and and save the resulting caland folder where you want the directory to be located.

(2) Downloading CALAND releases from California Natural Resources Agency (CNRA) Open Data Portal

  1. Go to the CNRA Natural and Working Lands Open Data Portal.
  2. Search for CALAND.
  3. Find the desired CALAND version in the search results and click on it. For example, CALAND version 3 should go to https://data.cnra.ca.gov/dataset/caland-version-3.
  4. You can click on the 'Download' button next to the desired version to go to the Zenodo download page or see more info via the 'More Info' button or by clicking on the name of the desired version (e.g., CALAND V.3.0.0). The 'More Info' page also includes a 'Download' button and a link to the Zenodo download page. Once at the download page click on the 'Download' button to get the .zip file for the desired version of CALAND (e.g., caland-v3.0.0.zip). Now CALAND is on your local computer.
  5. Unzip the file and and save the resulting caland folder where you want it to be located.

(3) Downloading the full CALAND repository from github

  1. Sign up for a free github account.
  2. Go to the CALAND github repository.
  3. Under repository name, click Clone or download.
  4. In the Clone with HTTPs section, click the clipboard icon to copy the clone URL for the repository.
  5. Open Terminal (MAC and Linux), or Git Bash (Windows).
  6. Change the current working directory to the location where you want the cloned directory to be located.
  7. Type git clone and then paste the URL you copied in Step 4.
  8. Press Enter. Your local clone will be created.

Ancillary Data not Included with CALAND

Downloading the initial 2010 land category raster data:

  1. Go to the CNRA Natural and Working Lands Open Data Portal.
  2. Search for CALAND.
  3. Find the desired CALAND version in the search results and click on it. For example, CALAND version 3 should go to https://data.cnra.ca.gov/dataset/caland-version-3.
  4. Download the .zip file for the desired land category raster data (e.g., calandv3_2010_land_category_raster_data.zip). Now it is on your local computer.
  5. Unzip the file and and save the raster data folder where you want it to be located.

Ancillary Data Included with CALAND

The CALAND release includes some ancillary data that can be used for visualizing outputs. There are four files included in caland-3.0.0/ancillary/:

  • A .csv file containing a table defining the initial 2010 land categories and their areas in square meters
  • A .zip file containing a shapefile of the CALAND ownership boundaries
  • A .zip file containing a shapefile of the CALAND region boundaries
  • A .zip file containing a shapefile of the CALAND region-ownership boundaries

Usage

The five R files (.r) located in the caland-3.0.0/ directory include (1) write_caland_inputs.r, (2) CALAND.r, (3) plot_caland.r, (4) plot_scen_types.r, and (5) plot_uncertainty.r, the first three of which are designed to be run in sequential order. However, for your first test runs you can skip write_caland_inputs.r, as there are example input files in caland-3.0.0/inputs directory that were created for the Draft California 2030 Natural and Working Lands Climate Change Implementation Plan. Each scenario that you want to model must be run individually using the functions in CALAND.r before proceeding to using plot_caland.r to compare individual scenarios and compute changes in carbon dynamics (increase or decrease) due to the effects of one scenario compared to another. Using plot_caland.r is essential for obtaining valid results; the outputs from CALAND.r are carbon emissions for individual scenarios, which are not reliable estimates of the carbon budget due to high uncertainty in the input data. Thus, CALAND is not intended to provide insight into whether the landscape is a net source or sink of carbon under a given scenario, but it is intended to quantify the change in carbon dynamics of a given scenario compared to a reference baseline scenario. The plot_caland.r file will produce graphics (.pdf) and data tables (.csv) for both individual scenario values and differences between scenarios. The individual scenario outputs are for diagnosing issues and understanding the model; while the differences between scenarios are the valid model results and the main purpose of CALAND. After using plot_caland.r, you can proceed to creating more detailed graphs using plot_scen_types.r or plot_uncertainty.r.

Each R file contains scripts (commands) that comprise the essential functions of CALAND. To run any of them you must make sure that the working directory is caland-3.0.0/ by typing the following:

setwd("<your_path>/caland-3.0.0/")

Here you will learn about the main functions in each of the R files and the settings (arguments) you must choose when running each of them. Note that some of the arguments are vectors, or a collection of two or more elements. To assign multiple elements to a vector we use c() which concatenates (i.e., links) elements into a vector. For example, to assign "Coastal_marsh", "Fresh_marsh", and "Cultivated" to the landtype argument lt, you would type the following:

lt = c("Coastal_marsh", "Fresh_marsh", "Cultivated")

R files and their functions

(1) write_caland_inputs.r

Overview of write_caland_inputs()

The write_caland_inputs() function is defined in the write_caland_inputs.r file. Its purpose is to read the raw data files found in the caland-3.0.0/raw_data directory, and organize them into the detailed input files needed to run the CALAND() model (i.e., one carbon input file (.xls) and an individual scenario input file (.xls) for each defined scenario). Each of the input files that write_caland_inputs() creates are Excel workbooks, which are comprised of individual worksheets, one for each data table. The raw data files define the management scenarios; initial 2010 areas and carbon densities; annual area changes; ecosystem carbon fluxes; management parameters; parameters for land conversion to cultivated or developed lands; wildfire parameters; wildfire areas; mortality fractions; and climate change scalars. There is a suite of settings (arguments) with various options that you choose when running write_caland_inputs(), such as climate (historical, RCP 4.5, or RCP 8.5) and whether you are controlling for wildire and land use land cover change (LULCC) for sensitivity testing of individual practices.

The CALAND() model operates on 940 land categories, but some of the raw data are not land category-specific. Thus, one of the main purposes of write_caland_inputs() is to disaggregate all non-land-category-specific raw data into individual land categories, and to save these expanded data tables in the carbon and scenario CALAND() input files. On the other hand, some of the raw data are land category-specific, including the initial 2010 areas and carbon densities, forest vegetation carbon fluxes, forest management carbon parameters, and some management areas.

CAUTION: Creating new raw data files and using this function to create new input files for CALAND() is a complicated task. All of the raw data have to be carefully assembled to ensure it is processed as intended. Therefore, creating new input files is not recommended unless you have a good understanding of the required data and how the model works.

Raw data input files to write_caland_inputs()

The inputs to write_caland_inputs()are referred to as raw data, as they are generally not region- and ownership-specific and must be disaggregated to all the 940 individual land categories simulated by CALAND(). The raw data include both .xls files and .csv files, which are in caland-3.0.0/raw_data/. The raw data used to generate the CALAND results reported in the Draft California 2030 Natural and Working Lands Implementation Plan (2019) are in parentheses next to each input file below.

  • Carbon parameter raw data file (e.g., lc_params.xls)

    • The carbon parameter raw data file is an Excel workbook comprised of 8 individual worksheets with the following names: vegc_uptake, soilc_accum, conversion2ag_urban, forest_manage, dev_manage, grass_manage, ag_manage, and wildfire. They represent vegetation carbon fluxes (historic climate); soil carbon fluxes (historic climate); land conversion C parameters; management C parameters for forest, developed lands, rangelands, and cultivated lands; and carbon parameters for low, med, and high wildfire severity, respectively.
    • Column headers are on row 12
    • Only non-zero values are included (missing values are zero)
    • More rows can be added that define new management practices as appropriate

  • Scenarios raw data file (e.g., nwl_scenarios_v6_ac.xls)

    • The scenarios raw data file is an Excel file comprised of at least one scenario of management practices and corresponding areas or fractions (one scenario per worksheet). Note the practice determines whether an area or fraction is used; it is not arbitrary.
    • 10 columns each: Region, Land_Type, Ownership, Management, start_year, end_year, start_area, end_area, start_frac, end_frac.
    • Either area (start_area and end_area) or fractional (start_frac, end_frac) values are used depending on the practice; the other two columns are NA. If area is required, the units can be in ha or ac, which must be specified by the units_scenario argument.
    • Region and Ownership can be "All" or a specific name
    • Each record defines management for a specific time period; outside of the defined time periods the management values are zero
    • Each scenario worksheet will result in the creation of a single input scenario file (.xls), which will be comprised of additional worksheets of data tables derived from the other raw data files described below.
    • The worksheet name for each scenario in the scenarios raw data file will determine the scenario input file name that is created.
    • Notes on restoration practices of the following land types:
      • Fresh marsh: comes out of only private and state land in the Delta, so make sure that these ownerships are designated.
      • Coastal marsh comes out of only private and state land in the coastal regions, so make sure these are available, from cultivated
      • Meadow restoration is only in the sierra cascades, and in private, state, and usfs nonwilderness, from shrub, grass, savanna, and woodland
      • Woodland restoration comes from cultivated and grassland
      • Afforestation is forest area expansion and comes from shrubland and grassland
      • Reforestation is forest area expansion that comes from shrubland only to match non-regeneration of forest

  • Climate raw data file (e.g., climate_c_scalars_iesm_rcp85.csv)

    • The climate raw data file is a .csv file of annual climate scalars (fraction) for vegetation and soil carbon fluxes.
    • The .csv file consits of four ID columns: Region, Land_Type, Ownership, and Component (Soil or Vegetation); and one column for each year, starting in year 2010.
    • These fractions will directly scale the vegetation and soil C flux values under a historic climate in CALAND()
    • There must be a valid climate file, even if historic climate is designated (CLIMATE = HIST), in which case all climate scalars will be changed to 1.

  • Wildfire raw data file (e.g., fire_area_canESM2_85_bau_2001_2100.csv)

    • The wildfire raw data file is a .csv file of annual wildfire areas (units = ha).
    • The .csv file consists of two ID columns (Region and Ownership), and a fire area column for each year, starting in year 2001. All possible Region-Ownership combinations are included (81 total), even if they don't exist.
    • The wildfire areas from 2001 to 2015 are averaged by write_caland_inputs() to compute the initial 2010 wildfire areas written to the scenario input file. If historic climate is designated, (CLIMATE = HIST), the average 2001 to 2015 burn areas in the RCP 8.5 wildfire raw data are used for initial 2010 burn areas and throughout the entire simulation (same value, no trend).
    • Wildfire severity fractions of the annual wildfire areas are hard-wired in write_caland_inputs(); and are not dependent on the selected climate. The high, medium, and low severity fractions are uniform across the State of California, starting with high = 0.26, medium = 0.29, and low = 0.45. The high severity fraction increases annually at a historical rate (i.e., 0.0027) and the low and medium fractions increase proportionally in order to account for the total wildfire area each year.
    • Non-regeneration areas are parameterized as a function of the high severity fraction and a threshold distance from burn edge; however this is handled in CALAND() with the NR_Dist argument.

  • Mortality raw data file (e.g., mortality_annual_july_2018.csv)

    • The mortality raw data file is a .csv file of mortality fractions (units = fraction), representing the annual mortality rate as a fraction of live biomass carbon (above-ground main canopy and roots).
    • The .csv file consits of three ID columns (Region, Land_Type, and Ownership) and one data column (Mortality_frac).
      • Land type must be specific but region and/or ownership can be "All".
      • Valid land types include any woody land type that have C accumulation in vegetation (i.e., Forest, Shrubland, Savanna, Woodland, and Developed). However, if rows do not exist for all valid land types, write_caland_inputs() will assign it a default annual mortality fraction of 0.01.
    • Unique treatment of Forest mortality: A doubled forest mortality trend is computed by write_caland_inputs() for 10 years (2015-2024) to emulate the recent and expected forest mortality due to insects and drought. This is the default setting, but can be changed in the arguments.
    • Unique treatment of Developed mortality: Mortality in Developed lands is processed differently from others in CALAND() because the urban system is highly managed, and allows for more control of what happens to the dead biomass; the mortality is transferred to the above-ground harvest for dead removal management.

  • New area GIS raw data file (e.g., CALAND_Area_Changes_2010_to_2101.csv)

    • The new area GIS raw data file is a .csv file of annual area changes (sq m) for each land category, starting in year 2010.
    • The .csv file consists of one ID column (Landcat) and one annual area change column for each year.
    • With the exception of Seagrass, Fresh marsh is the only land type that does not initially exist in this GIS raw data files. The Fresh marsh land categories are added by write_caland_inputs() with initial areas of 0, which only increases by restoration.

  • Original area GIS raw data files

    • CALAND v1 and v2 used two remote-sensing based area GIS files (i.e., area_lab_sp9_own9_2001lt15_sqm_stats.csv and area_lab_sp9_own9_2010lt15_sqm_stats.csv), but these files were not used in the Draft California 2030 Natural and Working Lands Climate Change Implementation Plan.
    • The original area GIS files include two .csv files of areas (sq m) for each land category in 2001 and 2010, respectively.
    • The .csv files consist of six ID columns (no headers, but rows 1 through 6 are Region integer code, Region, Land_type integer code, Land_type, Ownership integer code, and Ownership, respectively), and one area change column.
    • The land category (Land_cat) integer codes will be calculated and then matched with the integer codes in the carbon GIS raw data files.
    • The year has to be in the name of the file; currently write_caland_inputs() assumes that the years are 2001 and 2010.

  • Carbon GIS raw data files (e.g., gss_soc_tpha_sp9_own9_2010lt15_stats.csv, lfc_agc_se_tpha_sp9_own9_2010lt15_stats.csv, lfc_agc_tpha_sp9_own9_2010lt15_stats.csv, lfc_bgc_se_tpha_sp9_own9_2010lt15_stats.csv, lfc_bgc_tpha_sp9_own9_2010lt15_stats.csv, lfc_ddc_se_tpha_sp9_own9_2010lt15_stats.csv, lfc_ddc_tpha_sp9_own9_2010lt15_stats.csv, lfc_dsc_se_tpha_sp9_own9_2010lt15_stats.csv, lfc_dsc_tpha_sp9_own9_2010lt15_stats.csv, lfc_ltc_se_tpha_sp9_own9_2010lt15_stats.csv, lfc_ltc_tpha_sp9_own9_2010lt15_stats.csv, lfc_usc_se_tpha_sp9_own9_2010lt15_stats.csv, lfc_usc_tpha_sp9_own9_2010lt15_stats.csv)

    • The carbon GIS raw data files are .csv files (13 total) of univariate statistics of the initial C densities (units = Mg C per ha) of the seven carbon pools (i.e., soil, above-ground biomass, below-ground biomass (roots), down dead biomass, standing dead biomass, litter biomass, and understory biomass). These files were created by processing GIS raster data with a 30 m2 resolution. Thus, the statisitics are based on carbon density data associated with 30m2 cells within each 2010 land category boundary.
    • Each .csv file consists of 14 columns in the following order: zone (i.e., land category code), label (no data), valid cell count (i.e., count of non-missing cells), null cell count (count of missing cells), min, max, range, mean, mean of absolute values (mean_of_abs), stddev (standard deviation), variance, coefficient of variation (coeff_var), sum, absolute sum (sum_abs).
    • Only the min, max, mean, and standard deviation are used.
    • Vegetation C densities are from the California Air Resources Board inventory, one value per carbon pool for each land category.
    • Soil organic C densities are from gSSURGO; any zero values in the original data set were assumed not to be valid, and were therefore omitted in calculating the land category averages.
    • In the soil C density raster file (i.e., the file from which the soil C raw data file is summarizing) there were 17601 ha (mostly rivers) with zeros, and 12236544 ha (mostly desert) with missing data. The missing values will be converted to zero by write_caland_inputs(). However, all of these zero values are excluded from calculating the averages.

  • Controller for Wildfire and LULCC raw data file

    • The wildfire and LULCC raw data file is a .csv file designed for sensitivity testing of individual practices or processes in CALAND(); it turns on/off wildfire and/or LULCC for each scenario with the purpose of isolating an effect.
    • The .csv file consists of three columns, in order: Scenario, Wildfire, LULCC
      • Scenario column: matches the worksheet names in the scenarios raw data file; these scenario names MUST MATCH between these two files
      • Wildfire column: 1 = wildfire is on; 0 = wildfire is off
      • LULCC column: 1 = LULCC is on; 0 = LULCC is off

Arguments in write_caland_inputs()

  1. c_file: Assign a name to the carbon input file that write_caland_inputs() will create for CALAND() (needs to be .xls); default is c_file = "carbon_input_nwl.xls".
  2. inputs_dir: The directory within "./inputs" to put the generated input files; default is c_file = "" so they go into "./inputs".
  3. parameter_file: Carbon parameter raw data file (needs to be .xls file); default is parameter_file = "lc_params.xls".
  4. scenarios_file: Scenarios raw data file with region- and/or ownership-generic management scenarios that will be expanded upon in the scenario files created for CALAND() (needs to be .xls file with one scenario per worksheet); default is scenarios_file = "nwl_scenarios_v6_ac.xls".
  5. units_scenario: Specify units for input areas in scenarios_file (must be "ac" or "ha"); default is units_scenario = "ac".
  6. climate_c_file: Climate raw data file containing either RCP 4.5 or RCP 8.5 climate scalars for vegetation and soil carbon fluxes (needs to be .csv); the RCP must match the RCP associated with the wildfire raw data file. If historic climate is desired (CLIMATE = "HIST"), assign either RCP 4.5 or RCP 8.5 climate raw data file, as there needs to be a valid file from which write_caland_inputs() can convert all values to 1 (i.e., no future climate effect). Default is climate_c_file = "climate_c_scalars_iesm_rcp85.csv".
  7. fire_area_file: The wildfire raw data file containing annual burned area by region-ownership (needs to be .csv); the RCP must match the climate raw data file; needs to be RCP 8.5 for historic climate (CLIMATE = "HIST"). Default is fire_area_file = "fire_area_canESM2_85_bau_2001_2100.csv".
  8. mortality_file: Mortality raw data file containing mortality rates as annual fraction of above-ground biomass; includes values for woody land types that have C accumulation in vegetation; any missing valid land types will be assigned a default annual mortality fraction of 0.01. Default is mortality_file = "mortality_annual_july_2018.csv".
  9. area_gis_files_new: New area GIS raw data file of GIS statistics of annual area change by land category (sq m) (needs to be a .csv); default is area_gis_files_new = "CALAND_Area_Changes_2010_to_2101.csv".
  10. area_gis_files_orig: Original area GIS raw data files of GIS statistics of areas by land category (sq m) that are compared to compute annual area changes (concatenate two .csv file names, one for each year); default is area_gis_files_orig = c("area_lab_sp9_own9_2001lt15_sqm_stats.csv", "area_lab_sp9_own9_2010lt15_sqm_stats.csv").
  11. land_change_method: Assigning "Landcover" will use the original method of remote sensing landcover change from 2001 to 2010 (i.e., files assigned to area_gis_files_orig), or assigning "Landuse_Avg_Annual" will use the average LUCAS-modeled annual area changes from 2010 to 2050 based on projected change in cultivated and developed areas (i.e., file assigned to area_gis_files_new); default is land_change_method = "Landuse_Avg_Annual".
  12. carbon_gis_files: Carbon GIS raw data files of GIS statistics of carbon density by carbon pool and land category (Mg C per ha) (concatentate 13 .csv file names); default is carbon_gis_files = c("gss_soc_tpha_sp9_own9_2010lt15_stats.csv", "lfc_agc_se_tpha_sp9_own9_2010lt15_stats.csv", "lfc_agc_tpha_sp9_own9_2010lt15_stats.csv", "lfc_bgc_se_tpha_sp9_own9_2010lt15_stats.csv", "lfc_bgc_tpha_sp9_own9_2010lt15_stats.csv", "lfc_ddc_se_tpha_sp9_own9_2010lt15_stats.csv", "lfc_ddc_tpha_sp9_own9_2010lt15_stats.csv", "lfc_dsc_se_tpha_sp9_own9_2010lt15_stats.csv", "lfc_dsc_tpha_sp9_own9_2010lt15_stats.csv", "lfc_ltc_se_tpha_sp9_own9_2010lt15_stats.csv", "lfc_ltc_tpha_sp9_own9_2010lt15_stats.csv", "lfc_usc_se_tpha_sp9_own9_2010lt15_stats.csv", "lfc_usc_tpha_sp9_own9_2010lt15_stats.csv").
  13. scen_tag: (optional) A scenario file name tag that will be appended to the scenario names in the worksheets in the scenarios raw data file (scenarios_file); default is scen_tag = "default".
  14. start_year: The initial year of the simulation, which must match the year of the carbon GIS raw data files. Additionally, the new area file should include this year, and one of the original area GIS files needs to be for this year. Default is start_year = 2010.
  15. end_year: this is the final year output desired from the CALAND() simulation (matches the CALAND() end_year argument); default is end_year = 2101.
  16. CLIMATE: projected climate (RCP 4.5 or RCP 8.5) or historical climate ("PROJ" or "HIST", respectively); affects wildfire areas and vegetation and soil carbon flux values; the projected climate scenario is determined by the fire and climate input files; "HIST" uses RCP 8.5 wildfire average areas from 2001 to 2015 for historical average burn area, and sets all climate scalars to 1. Deafult is CLIMATE = "HIST".
  17. forest_mort_fact: Assign a number that will be used to adjust the forest mortality during the period specified by forest_mort_adj_first and forest_mort_adj_last; default is forest_mort_fact = 2.
  18. forest_mort_adj_first: the first year to adjust forest mortality; default is forest_mort_adj_first = 2015.
  19. forest_mort_adj_last: the last year to adjust forest mortality; default is forest_mort_adj_last = 2024.
  20. control_wildfire_lulcc_file: .csv file with flags (1 or 0) for each scenario in scenarios_file to control wildfire and LULCC. This is for sensitivity testing of individual practices or processes in CALAND(). Default is control_wildfire_lulcc_file = "individual_proposed_sims_control_lulcc_wildfire_aug2018.csv". Note this will not be used unless control_wildfire_lulcc = TRUE.
  21. control_wildfire_lulcc: if TRUE, read control_wildfire_lulcc_file to control wildfire and LULCC; default is control_wildfire_lulcc = FALSE.

Outputs from write_caland_inputs()

Output files are written to caland-3.0.0/inputs/ (unless a sub-directory is specified differently from the default inputs_dir = ""). There is one carbon output file and one scenario output file for each scenario in the scenarios_file:

  • Carbon .xls file: c_file
    • Carbon densities are in Mg C per ha (t C per ha)
    • Factors (or scalars) are fractions
  • Scenario .xls file(s): <scenario_name>_scen_tag_<climate>.xls
    • One for each scenario in the scenarios_file
    • <scenario_name> will be named based on the worksheet names in the scenarios_file
    • <climate> will be named based on the climate associated with the climate_c_file unless historical climate is chosen (CLIMATE = "HIST"), in which case "HIST" will be used.
    • areas are in ha
Example of write_caland_inputs() arguments used to generate the CALAND() input files for the Draft California 2030 Natural and Working Lands Climate Change Implementation Plan:

write_caland_inputs(c_file = "carbon_input_nwl.xls", inputs_dir = "", parameter_file = "lc_params.xls", scenarios_file = "nwl_scenarios_v6_ac.xls", units_scenario = "ac", climate_c_file = "climate_c_scalars_iesm_rcp85.csv", fire_area_file = "fire_area_canESM2_85_bau_2001_2100.csv", mortality_file = "mortality_annual_july_2018.csv", area_gis_files_new = "CALAND_Area_Changes_2010_to_2051.csv", area_gis_files_orig = c("area_lab_sp9_own9_2001lt15_sqm_stats.csv", "area_lab_sp9_own9_2010lt15_sqm_stats.csv"), land_change_method = "Landuse_Avg_Annual", carbon_gis_files = c("gss_soc_tpha_sp9_own9_2010lt15_stats.csv", "lfc_agc_se_tpha_sp9_own9_2010lt15_stats.csv", "lfc_agc_tpha_sp9_own9_2010lt15_stats.csv", "lfc_bgc_se_tpha_sp9_own9_2010lt15_stats.csv", "lfc_bgc_tpha_sp9_own9_2010lt15_stats.csv", "lfc_ddc_se_tpha_sp9_own9_2010lt15_stats.csv", "lfc_ddc_tpha_sp9_own9_2010lt15_stats.csv", "lfc_dsc_se_tpha_sp9_own9_2010lt15_stats.csv", "lfc_dsc_tpha_sp9_own9_2010lt15_stats.csv", "lfc_ltc_se_tpha_sp9_own9_2010lt15_stats.csv", "lfc_ltc_tpha_sp9_own9_2010lt15_stats.csv", "lfc_usc_se_tpha_sp9_own9_2010lt15_stats.csv", "lfc_usc_tpha_sp9_own9_2010lt15_stats.csv"), scen_tag = "default", start_year = 2010, end_year = 2101, CLIMATE = "PROJ", forest_mort_fact = 2, forest_mort_adj_first = 2015, forest_mort_adj_last = 2024, control_wildfire_lulcc_file = "individual_proposed_sims_control_lulcc_wildfire_aug2018.csv", control_wildfire_lulcc = FALSE)

(2) CALAND.r

Overview of CALAND()

The CALAND() function is the carbon and greenhouse gas accounting model, which is defined in the CALAND.r file. It uses the input files generated by write_caland_inputs(). A single scenario file is simulated each time CALAND() is run, producing a single main output .xls file that summarizes outputs for 214 variables including annual and cumulative metrics, each in an individual worksheet. There is a suite of settings (arguments) with various options that you choose when running CALAND(), such as which carbon values to use from the carbon inputs file (i.e., mean, mean+sd, mean-sd, min, or max) and which level of forest non-regeneration to assume following high severity wildfire.

Model structure & order of operations:

  • This model follows basic density (stock) and flow guidelines similar to IPCC Tier 3 protocols by integrating observed historical carbon flows (fluxes) and initial carbon densities with wildfire, climate effects, and land cover change derived from external models.
  • Resolution is at the land category level (region-landtype-ownership combination), which only has a spatial boundary for the intial simulation year. Beyond that, processes within each land category are implemented on non-spatially explicit areas within static region-ownership boundaries. In other words, there are no grid-cell level computations. This highlights how CALAND() is not a stand-level model.
  • Carbon calculations occur in start_year up to end_year - 1.
  • end_year denotes the final area after the changes in end_year - 1 .
  • Initial carbon density input values are the univariate statistics of the total pixel population within each land category (i.e., land type-region-ownership combination).
  • Carbon accumulation (flux) input values are univariate statistics of literature values for a given land type (coarse resolution) or land category (fine resolution), depending on the availability of data.
  • The initial land category area data are used for carbon operations in the first year.
  • Annual ecosystem carbon fluxes are calculated for each land category using inputs for historic carbon fluxes and mortality rates, scaled up to the annual input area data with adjustments made for (in order of operation) climate, management, and wildfire.
  • Land conversion carbon fluxes for each land category are calculated after ecosystem carbon fluxes within the same year.
  • Each subsequent step uses the updated carbon values from the previous step.
  • The new carbon density and area for each land category are assigned to the beginning of the next year.
  • All wood products are lumped together and labeled as "wood".
  • Wood product emissions are based on landfill decay of discarded products.
  • Bioenergy emissions are based on combustion of biomass feedstock by current California bioenergy plants.

Input files to CALAND():

The input .xls files for CALAND() are in caland-3.0.0/inputs/ (unless a sub-directory indir is specified differently from the default of no subdirectory (indir = "") (see Arguments section below). The following Excel input files have matching number of header rows (rows preceding the first row of data):

  • Carbon input file: The initial carbon state (carbon densities) of the seven carbon pools, and all the carbon flow parameters (fluxes and scalars) are in a .xls file in the inputs/ directory. This file is created by write_caland_inputs, and it does not change unless the raw land carbon input file lc_params.xls is modified.

    • carbon_input_nwl.xls Default filename, containing the initial carbon densities (mean, min, max, SD) in seven carbon pools and the historical soil and vegetation carbon fluxes (mean, min, max, SD) for each land category, the carbon adjustment parameters for wildfire, conversion of any land type to Cultivated or Developed lands, and Forest, Developed, and Rangeland (Grassland, Savanna, Woodland) management, and the soil carbon fluxes (mean, min, max, SD) in Cultivated lands under the 'soil conservation' practice.

  • Scenario input file: The scenario that will be simulated.

    • <scenario_name>.xls Contains the initial areas of each land type per region-ownership combination (i.e., land category), annual net area changes per land category; annual wildfire area per region-ownership combination; annual mortality per land category; annual managed area per land category; and climate change scalars for vegetation and soil carbon fluxes per land category.

    • Example scenario input files: There are five scenario input xls files in the caland-3.0.0/inputs/ directory that were created using write_caland_inputs() for the Draft California 2030 Natural and Working Lands Climate Change Implementation Plan (2019). These scenarios incorporate RCP8.5 climate effects on carbon exchange and wildfire area. 'Default' in the filename means that the scenarios include doubled forest mortality from 2015 to 2024 to emulate recent and ongoing die-off due to insects and drought.

      • Historical Baseline Scenario: The NWL_Historical_v6_default_RCP85.xls file is the reference scenario used to compare the changes in management that are prescribed in the following alternative scenarios. It does not include any California State-funded management or increases in the forest fraction of urban lands. Note that this scenario was intended to run in CALAND() with the setting for maximum non-regeneration (i.e., forest conversion to shrubland) in forest areas burned by high-severity wildfire.
      • Alternative A Scenario: The NWL_Alt_A_v6_default_RCP85.xls file adds desired, low levels of California State-funded management to the Historical Baseline Scenario. Note that agricultural management is limited in scope, and only represents California Natural Resources Agency-funded areas for soil conservation in cultivated lands. Note that this scenario was intended to run in CALAND() with the setting for full regeneration post-wildfire to represent maximum reforestation of forest areas that would not otherwise recover fully following high-severity wildfire.
      • Alternative B Scenario: The NWL_Alt_B_v6_default_RCP85.xls file adds desired, high levels of California State-funded management to the Historical Baseline Scenario. Note that agricultural management is limited in scope, and only represents California Natural Resources Agency-funded areas for soil conservation in cultivated lands. Note that this scenario was intended to run in CALAND() with the setting for full regeneration post-wildfire to represent maximum reforestation of forest areas that would not otherwise recover fully following high-severity wildfire.
      • Alternative A Scenario without Avoided Conversion: The NWL_Alt_A_v6_NoAC_default_RCP85.xls file mimics the low levels of state-funded management in Alternative A, but without avoided conversion.
      • Alternative B Scenario without Avoided Conversion: The NWL_Alt_B_v6_NoAC_default_RCP85.xls file mimics the high levels of state-funded management in Alternative B, but without avoided conversion.

Arguments in CALAND():

CALAND() has 15 arguments that control which input files and values are used, and how the model will operate for each run. A single scenario is simulated at a time. At a minimum, you must specify the scenario filename each time you run CALAND() and the other arguments will automatically be assigned default values explained here:

  1. scen_file_arg: Assigns the scenario .xls file; assumed to be in caland-3.0.0/inptus/indir. This is the only argument that does not have a default value. Thus, it is required that you assign it. All other arguments will be assigned default values unless you change them.
  2. c_file_arg: Assigns the carbon parameter input file; assumed to be in caland-3.0.0/inputs/outdir. If nothing is specified, default is: c_file_arg = "carbon_input_nwl.xls".
  3. indir: Assigns the directory in caland-3.0.0/inputs/ that contains scen_file and c_file; do not include "/" character at the end. If nothing is specified, the default is: indir = "" meaning that it is located in caland-3.0.0/inputs/.
  4. outdir: Assigns the directory in caland-3.0.0/outputs/ to save CALAND() output files; do not include "/" character at the end. If nothing is specified, default is blank: outdir = "" meaning that output files will be saved in caland-3.0.0/outputs/.
  5. start_year: Simulation begins at the beginning of this year. If nothing is specified, default is: start_year = 2010.
  6. end_year: Simulation ends at the beginning of this year (so the simulation goes through the end of end_year - 1). If nothing is specified, default is: end_year = 2101.
  7. value_col_dens: Selects which carbon density values to use; 5 = min, 6 = max, 7 = mean, 8 = std dev. If nothing is specified, default is the mean: value_col_dens = 7.
  8. value_col_accum: Integer code identifying which soil and vegetation carbon flux values to use (i.e., 5 = min, 6 = max, 7 = mean, 8 = std dev). If nothing is specified, default is the mean: value_col_accum = 7.
  9. value_col_soilcon: Integer code identifying which soil carbon flux values to use for the cultivated soil conservation practice (i.e., 6 = min, 7 = max, 8 = mean, 9 = std dev). If nothing is specified, default is the mean: value_col_soilcon = 8.
  10. ADD_dens: For use with value_col_dens = 8; assign ADD_dens = TRUE to add the std dev to the mean carbon density values, or assign ADD_dens = FALSE to subtract the std dev from the mean carbon density values. If nothing is specified, default is addition: ADD_dens = TRUE.
  11. ADD_accum: For use with value_col_accum = 8; assign ADD_accum = TRUE to add the std dev to the mean carbon flux values, or assign ADD_accum = FALSE to subtract the std dev from the mean carbon flux values. If nothing is specified, default is addition: ADD_accum = TRUE.
  12. ADD_soilcon: For use with value_col_soilcon = 9; assign ADD_soilcon = TRUE to add the std dev to the mean carbon flux value of the cultivated soil conservation practice, or assign ADD_soilcon = FALSE to subtract the std dev from the mean carbon flux of the cultivated soil conservation practice. If nothing is specified, default is addition: ADD_soilcon = TRUE.
  13. NR_Dist: For adjusting the amount of non-regenerating forest after high severity wildfire, use -1 for full regeneration (i.e., no non-rengeration) or 120 for maximum non-regeneration, which is the threshold distance (m) to the edge of a burn patch, beyond which the forest will not regenerate and will convert to shrubland. The default is maximum non-regeneration: NR_Dist = 120. A shorter distance increases non-regenerated area, and a longer distance decreases non-regenerated area.
  14. WRITE_OUT_FILE: Chooses whether to save the output file; WRITE_OUT_FILE = TRUE saves the output file, and WRITE_OUT_FILE = FALSE does not save the output file. If nothing is specified, default is to save: WRITE_OUT_FILE = TRUE.
  15. blackC: Chooses how the global warming potential (GWP) of black carbon is computed; blackC = TRUE assigns a GWP of 900, and blackC = FALSE assigns a GWP of 1 (equivalent to CO2). If nothing is specified, default is to treat black C the same as CO2: blackC = FALSE, which is recommended as our current understanding is that black C does not behave like the main greenhouse gases.

Outputs from CALAND():

Output files are written to caland-3.0.0/outputs/ (unless a sub-directory is specified differently from the default outdir = ""). There are two outputs files:

  • Main output .xls file: <scenario_name>_output_<tags>.xls

    • Sign (+/-) of output carbon values: carbon emissions versus land carbon uptake depends on the sign and the varibale name:

      • Carbon variables in which a negative value indicates carbon emissions and positive value indicates land carbon uptake:
      • variable names containing "den": a positive value indicates land carbon uptake.
        • variable names containing "Gain_C_stock" but not "Atmos": a positive value indicates land carbon uptake.
        • all other stock variables except those containing "Loss_C_stock" or "Atmos"
      • Carbon variables in which a positive value indicates carbon emissions and negative value indicates land carbon uptake:
        • variable names containing "CumCO2", "CumCH4eq", CumBCeq", "AnnCO2", "AnnCH4eq", AnnBCeq"
        • variable names containing "Loss_C_stock"
        • variable names containing "Atmos"

    • Precision: to the integer for ha, Mg C, and Mg C/ha

    • Filename description: "_output_" is appended to the input scenario name, followed by a series of tags that denote (i) which type of input value was used (mean, mean+/-sd, min, or max) for carbon density, historical carbon fluxes, and the 'soil conservation' soil carbon flux on Cultivated lands; (ii) how black carbon was accounted for; and (iii) the level of forest non-regeneration following high-intensity wildfire.

    • Key for "" labeling of filename:

      • D = carbon density
      • A = historical carbon accumulation (flux)
      • S = soil carbon accumulation under 'soil conservation' management
      • No variable specified means that the same value type was used for the three variables
      • sd = standard deviation
      • mean, max, min are self explanatory
      • + = add (applied to sd only)
      • - = subtract (applied to sd only)
      • BC1 = black carbon treated like CO2
      • BC900 = black carbon segregated as a greenhouse gas with a global warming potential of 900
      • NR<number> = non-regeneration threshold distance [m] to edge of a high-severity burn patch, above which forest will not regenerate (i.e., converts to shrubland)

  • Log file output of soil carbon depletion: <scenario_name>_output_<tags>_land_cats_depleted_of_soil_c_&_sum_neg_cleared-<sum>.csv

    • Filename description: Same as above plus an aggregate sum of soil carbon depletion across all land categories at the end of the filename.

(3) plot_caland.r

Overview of plot_caland()

The plot_caland() function, defined in the plot_caland.r file, compares outputs from the CALAND() model. It is designed to compare the CALAND() output .xls file for the baseline scenario to any number of alternative scenarios (at least one required), and will output a suite of individual data tables (.csv files) and corresponding graphics (.pdf files) that summarize various variables at your desired level of spatial aggregation of region, land type, and ownership combination(s). The aggregation level can be zoomed out to the entire State of California (summing across all regions, land types, and ownerships) or as granular as a specific region, land type, and ownership combination (i.e., land category), such as North Coast, Forest on Private lands.

Sensitivity tests of individual practices

plot_caland() has the ability to generate per area outputs for sensitivity tests of individual practices by designating INDIVIDUAL = TRUE in plot_caland(). These diagnostics are designed to estimate effects of a practice in isolation. Thus, these outputs are only valid when the following conditions are met:

  • Comparison of a static run (i.e., baseline with only ecosystem exchange enabled, no LULCC, no management practices, no wildfire, except when evaluating effects on fire emissions) with a run with a single practice enabled. One exception is for evaluating avoided conversion effects, in which case LULCC has to be on in the baseline so that urban growth rate can be reduced in the scenario.
  • For any runs with restoration/reforestation/afforestation/LULCC, the land type must be "All_land" in order to capture the dynamics of LULCC and wildfire; any other land type will not give valid results.

These effects change over time because most practices have effects beyond the year(s) of implementation (e.g. rangeland compost application, forest harvest, forest fuel reduction practices). One consideration is that restoration activities have secondary effects on land use/cover change related to forcing conversion of other land types and the proportion of land types burned by wildfire.

Example CALAND() input scenario files designed for sensitivity testing can be found in /caland-3.0.0/inputs/ind_sims_27aug2018.

Input files to plot_caland()

The input .xls files for plot_caland() are the main .xls output files from CALAND(), located in caland-3.0.0/outputs/ (unless the sub-directory data_dir is specified differently from the default of no subdirectory (data_dir = "") (see Arguments section below). Two or more of these files are required, one of which will serve as the reference baseline scenario and the other as an alternative scenario that tests the impacts of change(s) to the baseline. Additional alternative scenarios can be compared to the baseline in a single run as well.

Arguments in plot_caland():

  1. scen_fnames is a vector of scenario output file names written by CALAND().
    • The first scenario file in scen_fnames must be the name of the baseline scenario to which the other scenarios will be compared. For example: scen_fnames = c("Baseline.xls", "A.xls", "B.xls", "C.xls", "D.xls").
    • The number of scenario files in scen_fnames must correspond directly to the labels in scen_snames (see argument #2).
  2. scen_snames vector of abbreviated scenario labels which will be printed in the plot legends (8 characters maximum for each label).
  3. data_dir path to the directory containing the CALAND() output files; do not include the "/" character at the end; default is data_dir = "./outputs".
  4. reg vector of individual regions to plot, including the entire State of California All_region; can be any number of available regions. Default is all of them: reg = c("All_region", "Central_Coast", "Central_Valley", "Delta", "Deserts", "Eastside", "Klamath", "North_Coast", "Sierra_Cascades", "South_Coast", "Ocean").
  5. lt vector of land types to plot; can be any number of available types. Default is all of them: lt = c("All_land", "Water", "Ice", "Barren", "Sparse", "Desert", "Shrubland", "Grassland", "Savanna", "Woodland", "Forest", "Meadow", "Coastal_marsh", "Fresh_marsh", "Cultivated", "Developed_all", "Seagrass").
  6. own array of ownerships to plot; can be any number of available types. Default is the aggregation of all ownerships (All_own): own = "All_own". However any number of them can be specified: own = c("All_own", "BLM", "DoD", "Easement", "Local_gov", "NPS", "Other_fed", "Private", "State_gov", "USFS_nonwild").
  7. figdir folder within data_dir to save the graphs (.pdf) and corresponding data tables (.csv); do not include the "/" character at the end. Default is: figdir = "figures".
  8. INDIVIDUAL Indicates whether a sensitivity test is being performed on scenarios that isolate the effects of individual practices. This test is only valid if the scenarios were configured for this purpose. The default is not to compute these outputs: INDIVIDUAL = FALSE.
  9. units_ha TRUE = units of plot_caland() outputs will be in "ha" (same as CALAND() outputs), FALSE = units of plot_caland() outputs will be converted to "ac". Default is units_ha = "FALSE".
  10. blackC TRUE = black GWP equal to 900, FALSE = black GWP equal to 1. Default is blackC = FALSE. Note: this needs to match the CALAND() blackC argument that was used.
  11. blackC_plot TRUE = plot BC, CO2, and CH4, FALSE = plot only CO2 and CH4 (BC added to CO2 which is only valid if black C is FALSE). Default is blackC_plot = FALSE. Note: this needs to match the CALAND() blackC argument that was used.
  12. last_year the last year to plot; this should be the final run year + 1 because cumulative and area outputs reflect previous years e.g., 2050 is the final run year, but everything is defined and output to 2051, so last_year = 2051. Default is last_year = 2051.

Example of plot_caland() arguments used to compute the 2010 through 2050 effects of Alternative A scenario relative to the Baseline scenario in the Draft California 2030 Natural and Working Lands Climate Change Implementation Plan:

plot_caland(scen_fnames = c(“NWL_Historical_v6_default_RCP85_output_mean_BC1_NR120.xls”, “NWL_Alt_A_v6_default_RCP85_output_mean_BC1.xls”), scen_lnames = c(“Baseline”, “Alt_A”), scen_snames = c(“BASE”, “AltA”), data_dir = "./outputs", reg = c("All_region", "Central_Coast", "Central_Valley", "Delta", "Deserts", "Eastside", "Klamath", "North_Coast", "Sierra_Cascades", "South_Coast", "Ocean"), lt = c("All_land", "Water", "Ice", "Barren", "Sparse", "Desert", "Shrubland", "Grassland", "Savanna", "Woodland", "Forest", "Meadow", "Coastal_marsh", "Fresh_marsh", "Cultivated", "Developed_all", "Seagrass"), own = c("All_own"), figdir = "figures", INDIVIDUAL = FALSE, units_ha=FALSE, blackC = FALSE, blackC_plot = FALSE, last_year = 2051)

Optimizing processing time

It will take several hours to run plot_caland() for five scenarios in all individual land type and region combinations (including "All_land" and "All_region") with all ownerships aggregated ("All_own") in R or RStudio. Assuming you have at least four cores on your computer, the processing time can be reduced by about half by splitting up plot_caland() into separate instances and running them in command line (not in R or RStudio). Specifically, you can split up each scenario comparison (five scenarios = four comparisons; one baseline compared to four alternative scenarios) into four instances each. Then split up the regions into the four instances for each scenario comparison (11 regions total, including "Ocean", and all regions ("All_region"). You will run each separate instance simultaneously via command line. Thus, to optimize processing time for 5 scenarios including the baseline, you will create 16 individual instances in command line (4 scenario comparisons x 4 region groupings). Here are 16 individual instances you could run in command line if you had caland() .xls output files for five scenarios (e.g., "Baseline.xls", "A.xls", "B.xls", "C.xls", "D.xls"):

Baseline x A

plot_caland(scen_fnames = c("Baseline.xls", "A.xls"), scen_snames = c("Baseline","A"), reg = c("All_region", "Ocean"), lt = c("All_land", "Water", "Ice", "Barren", "Sparse", "Desert", "Shrubland", "Grassland", "Savanna", "Woodland", "Forest", "Meadow", "Coastal_marsh", "Fresh_marsh", "Cultivated", "Developed_all", "Seagrass"), own=c("All_own"))

plot_caland(scen_fnames = c("Baseline.xls", "A.xls"), scen_snames = c("Baseline","A"), reg = c("Central_Coast", "Central_Valley", "Delta"), lt = c("All_land", "Water", "Ice", "Barren", "Sparse", "Desert", "Shrubland", "Grassland", "Savanna", "Woodland", "Forest", "Meadow", "Coastal_marsh", "Fresh_marsh", "Cultivated", "Developed_all", "Seagrass"), own=c("All_own"))

plot_caland(scen_fnames = c("Baseline.xls", "A.xls"), scen_snames = c("Baseline","A"), reg = c("Deserts", "Eastside", "Klamath"), lt = c("All_land", "Water", "Ice", "Barren", "Sparse", "Desert", "Shrubland", "Grassland", "Savanna", "Woodland", "Forest", "Meadow", "Coastal_marsh", "Fresh_marsh", "Cultivated", "Developed_all", "Seagrass"), own=c("All_own"))

plot_caland(scen_fnames = c("Baseline.xls", "A.xls"), scen_snames = c("Baseline","A"), reg = c("North_Coast", "Sierra_Cascades", "South_Coast"), lt = c("All_land", "Water", "Ice", "Barren", "Sparse", "Desert", "Shrubland", "Grassland", "Savanna", "Woodland", "Forest", "Meadow", "Coastal_marsh", "Fresh_marsh", "Cultivated", "Developed_all", "Seagrass"), own=c("All_own"))

Baseline x B

plot_caland(scen_fnames = c("Baseline.xls", "B.xls"), scen_snames = c("Baseline","B"), reg = c("All_region", "Ocean"), lt = c("All_land", "Water", "Ice", "Barren", "Sparse", "Desert", "Shrubland", "Grassland", "Savanna", "Woodland", "Forest", "Meadow", "Coastal_marsh", "Fresh_marsh", "Cultivated", "Developed_all", "Seagrass"), own=c("All_own"))

plot_caland(scen_fnames = c("Baseline.xls", "B.xls"), scen_snames = c("Baseline","B"), reg = c("Central_Coast", "Central_Valley", "Delta"), lt = c("All_land", "Water", "Ice", "Barren", "Sparse", "Desert", "Shrubland", "Grassland", "Savanna", "Woodland", "Forest", "Meadow", "Coastal_marsh", "Fresh_marsh", "Cultivated", "Developed_all", "Seagrass"), own=c("All_own"))

plot_caland(scen_fnames = c("Baseline.xls", "B.xls"), scen_snames = c("Baseline","B"), reg = c("Deserts", "Eastside", "Klamath"), lt = c("All_land", "Water", "Ice", "Barren", "Sparse", "Desert", "Shrubland", "Grassland", "Savanna", "Woodland", "Forest", "Meadow", "Coastal_marsh", "Fresh_marsh", "Cultivated", "Developed_all", "Seagrass"), own=c("All_own"))

plot_caland(scen_fnames = c("Baseline.xls", "B.xls"), scen_snames = c("Baseline","B"), reg = c("North_Coast", "Sierra_Cascades", "South_Coast"), lt = c("All_land", "Water", "Ice", "Barren", "Sparse", "Desert", "Shrubland", "Grassland", "Savanna", "Woodland", "Forest", "Meadow", "Coastal_marsh", "Fresh_marsh", "Cultivated", "Developed_all", "Seagrass"), own=c("All_own"))

Baseline x C

plot_caland(scen_fnames = c("Baseline.xls", "C.xls"), scen_snames = c("Baseline","C"), reg = c("All_region", "Ocean"), data_dir = "", figdir = "", lt = c("All_land", "Water", "Ice", "Barren", "Sparse", "Desert", "Shrubland", "Grassland", "Savanna", "Woodland", "Forest", "Meadow", "Coastal_marsh", "Fresh_marsh", "Cultivated", "Developed_all", "Seagrass"), own=c("All_own"), INDIVIDUAL = FALSE))

plot_caland(scen_fnames = c("Baseline.xls", "C.xls"), scen_snames = c("Baseline","C"), reg = c("Central_Coast", "Central_Valley", "Delta"), lt = c("All_land", "Water", "Ice", "Barren", "Sparse", "Desert", "Shrubland", "Grassland", "Savanna", "Woodland", "Forest", "Meadow", "Coastal_marsh", "Fresh_marsh", "Cultivated", "Developed_all", "Seagrass"), own=c("All_own"))

plot_caland(scen_fnames = c("Baseline.xls", "C.xls"), scen_snames = c("Baseline","C"), reg = c("Deserts", "Eastside", "Klamath"), lt = c("All_land", "Water", "Ice", "Barren", "Sparse", "Desert", "Shrubland", "Grassland", "Savanna", "Woodland", "Forest", "Meadow", "Coastal_marsh", "Fresh_marsh", "Cultivated", "Developed_all", "Seagrass"), own=c("All_own"))

plot_caland(scen_fnames = c("Baseline.xls", "C.xls"), scen_snames = c("Baseline","C"), reg = c("North_Coast", "Sierra_Cascades", "South_Coast"), lt = c("All_land", "Water", "Ice", "Barren", "Sparse", "Desert", "Shrubland", "Grassland", "Savanna", "Woodland", "Forest", "Meadow", "Coastal_marsh", "Fresh_marsh", "Cultivated", "Developed_all", "Seagrass"), own=c("All_own"))

Baseline x D

plot_caland(scen_fnames = c("Baseline.xls", "D.xls"), scen_snames = c("Baseline","D"), reg = c("All_region", "Ocean"), lt = c("All_land", "Water", "Ice", "Barren", "Sparse", "Desert", "Shrubland", "Grassland", "Savanna", "Woodland", "Forest", "Meadow", "Coastal_marsh", "Fresh_marsh", "Cultivated", "Developed_all", "Seagrass"), own=c("All_own"))

plot_caland(scen_fnames = c("Baseline.xls", "D.xls"), scen_snames = c("Baseline","D"), reg = c("Central_Coast", "Central_Valley", "Delta"), lt = c("All_land", "Water", "Ice", "Barren", "Sparse", "Desert", "Shrubland", "Grassland", "Savanna", "Woodland", "Forest", "Meadow", "Coastal_marsh", "Fresh_marsh", "Cultivated", "Developed_all", "Seagrass"), own=c("All_own"))

plot_caland(scen_fnames = c("Baseline.xls", "D.xls"), scen_snames = c("Baseline","D"), reg = c("Deserts", "Eastside", "Klamath"), lt = c("All_land", "Water", "Ice", "Barren", "Sparse", "Desert", "Shrubland", "Grassland", "Savanna", "Woodland", "Forest", "Meadow", "Coastal_marsh", "Fresh_marsh", "Cultivated", "Developed_all", "Seagrass"), own=c("All_own"))

plot_caland(scen_fnames = c("Baseline.xls", "D.xls"), scen_snames = c("Baseline","D"), reg = c("North_Coast", "Sierra_Cascades", "South_Coast"), lt = c("All_land", "Water", "Ice", "Barren", "Sparse", "Desert", "Shrubland", "Grassland", "Savanna", "Woodland", "Forest", "Meadow", "Coastal_marsh", "Fresh_marsh", "Cultivated", "Developed_all", "Seagrass"), own=c("All_own"))

Outputs from plot_caland()

Output files consist of a suite of graphs (.pdf) and corresponding data tables (.csv), which are written to caland-3.0.0/data_dir/figdir/ within each region, land type, and ownership combination directory, where data_dir and figdir are arguments to plot_caland(). Naming of the .pdf and .csv filenames is automatic and determined from the caland() .xls outout filenames that are assigned to scen_fnames. Specifically, the text preceding "_output_....xls" is used as the .pdf and .csv filenames.

Notes on outputs

  • Seagrass carbon is represented in totality by the soil carbon pool, which means that all other pools, except total organic carbon, will have zero values.

(4) plot_scen_types.r

Overview of plot_scen_types()

The plot_scen_types() function, defined in the plot_scen_types.r file, is designed to use the .csv outputs from plot_caland() to plot individual land types for a designated variable and all available scenarios in the .csv file. You also specify which land types, region (or all regions aggregated), and ownership (or all ownerships aggregated) to plot for each scenario.

Input files to plot_scen_types()

The .csv input files, created by plot_caland(), are assumed to be in caland-3.0.0/data_dir/figdir, in the appropriate land type and ownership directories. The plot_scen_types() output files will go directly into caland-3.0.0/data_dir/figdir/reg/own, which should be the same as used in plot_caland().

Arguments in plot_scen_types()

  1. varname: name of variable to plot. See the outputs from plot_caland(); the name is between the ownership and "_output.csv" in these file names; do not include the surrounding "_" characters.
  2. ylabel: label for y-axis corresponding to the units of varname, and whether they are changes from baseline (i.e., varname ending in "_diff") or absolute values. Note that this function does not convert units so the output units of your desired plotting variable must be correctly matched with the .csv file.
  3. data_dir: The path to the directory containing the CALAND() output files, which also contains figdir; do not include the "/" character at the end; default is data_dir = "./outputs".
  4. file_tag: Additional tag to file name to note what regions, landtypes, and/or ownerships are included; default is file_tag = "" (nothing added).
  5. reg: Vector of region names to plot; default is: reg = c("All_region", "Central_Coast", "Central_Valley", "Delta", "Deserts", "Eastside", "Klamath", "North_Coast", "Sierra_Cascades", "South_Coast").
  6. lt: Vector of land types to plot; can be any number of available land types; default is: lt = c("Water", "Ice", "Barren", "Sparse", "Desert", "Shrubland", "Grassland", "Savanna", "Woodland", "Forest", "Meadow", "Coastal_marsh", "Fresh_marsh", "Cultivated", "Developed_all").
  7. own: Vector of ownerships to plot; can be any number of available ownerships; default is: own = c("All_own")
  8. figdir: The directory within data_dir containing the .csv data to plot, and where to save the figures that plot_scen_types() creates; do not include the "/" character at the end.

Notes on plot_scen_types():

  • Plotting the Ocean region does not provide any comparison with land types because only Seagrass exists in the Ocean.
  • Seagrass has only a subset of the plot_caland() output files, so if including All_region make sure that the desired varname is available for Seagrass.
  • Seagrass is not in the default land type list.

Example of plot_scen_types() arguments that will create a single figure comparing the total organic C stock in land types over time, aggregated across all regions and ownerships, for each scenario (Alternative A and Baseline):

plot_scen_types(varname = "All_orgC_stock", ylabel = "MMTC", data_dir = "./outputs", file_tag = "", reg = c("Central_Coast", "Central_Valley", "Delta", "Deserts", "Eastside", "Klamath", "North_Coast", "Sierra_Cascades", "South_Coast", "All_region"), lt = c(Water", "Ice", "Barren", "Sparse", "Desert", "Shrubland", "Grassland", "Savanna", "Woodland", "Forest", "Meadow", "Coastal_marsh", "Fresh_marsh", "Cultivated", "Developed_all"), own = c("All_own"), figdir = "figures")

Outputs from plot_scen_types()

Output files consist of a suite of graphs (.pdf) and corresponding data tables (.csv), which are written to caland-3.0.0/data_dir/figdir/ within each region directory, where data_dir and figdir are arguments to plot_scen_types(). Naming of the .pdf and .csv filename is automatic and determined from the region, ownership, scenario name in the input .csv file, the varname argument, and an optional file_tag argument.

(5) plot_uncertainty.r

Overview of plot_uncertainty()

The plot_uncertainty() function, defined in the plot_uncertainty.r file, is designed to plot shaded uncertainty bounds around average values for a single variable over time for each scenario using .csv outputs from plot_caland(). This requires having run CALAND() and plot_caland() three times each for each desired scenario; once for the mean inputs, and the other two times for lower and upper uncertainty bounds. For example, using the following combination of inputs to CALAND() will generate minimum and maximum emissions:

  • Low emissions: low initial carbon density (i.e., mean-SD) and high carbon fluxes (i.e., mean+SD)

  • High emissions: high initial carbon density (i.e., mean+SD) and low carbon fluxes (i.e., mean-SD)

Input files to plot_uncertainty()

Inputs to plot_uncertainty() are .csv output files from plot_caland(). Within each .csv file, there can be any number of scenarios. Each group of scenarios must have a corresponding .csv file for the mean, low, and high emissions. It is possible to plot up to three groupings (a, b, c) with plot_uncertainty(). Three scenario groups amounts to a total of nine .csv input files to plot_uncertainty(): three input .csv files (mean, low, and high emissions) for each group. All .csv files have matching formats. For a single group ("group a"), they are assumed to be in caland-3.0.0/outputs/mean, caland-3.0.0/outputs/low, caland-3.0.0/outputs/high, respectively, unless figdir is specified differently than the default: figdir = c("mean","low","high").

Arguments in plot_uncertainty()

  1. start_year: year to start plotting; default is start_year = 2010.
  2. end_year: year to end plotting; default is end_year = 2051.
  3. varname: name of a single variable to plot (see the .csv output filenames from plot_caland()); the variable name is between the ownership and "_output.csv" in the filename. However, do not include the surrounding "_" characters.
  4. ylabel: label for y-axis corresponding to the units of varname, and whether they are changes from baseline (i.e., varname ending in "diff") or absolute values. Note that this function does not convert units so the output units of your desired plotting variable must be correctly matched with the .csv file.
    • Here are some examples:
      • ylabel = "Change from Baseline (MMT CO2eq)"
      • ylabel = "Change from Baseline (MMT CO2eq)"
      • ylabel = "MMT CO2eq"
      • ylabel = "Change from Baseline (MMT C)"
      • ylabel = "MMT C"
      • ylabel = "Change from Baseline (Mg C/ha)"
      • ylabel = "Mg C/ha"
      • ylabel = "Change from Baseline (Mg C/ac)"
      • ylabel = "Mg C/ac"
  5. file_tag: tag to add to end of the new file names created by plot_uncertainty() (e.g., to note what time period is plotted); default is file_tag = "" (nothing added).
  6. data_dir_a: the path to the directory containing the three folders of plot_caland() outputs (mean, low, and high emissions) for scenario group a; do not include the "/" character at the end; default is data_dir_a = "./outputs".
  7. figdirs_a: a vector of three folder names within data_dir_a containing the .csv data files to plot. The folder names must be assigned in order of mean, low, and high; do not include the "/" character at the end of each folder name. The default is figdirs_a = c("mean", "low", "high"); thus, the .csv files for the mean and lower and upper uncertainty bounds are assumed to be in data_dir_a/mean, data_dir_a/low, and data_dir_a/high, respectively, and in the appropriate region, land type, and ownership directories. The figures will be written to the folder representing the mean.
  8. scenarios_a: a vector of one or more scenario names for group 'a' that are listed in the Scenario column in the .csv file containing varname for the mean.
  9. scen_labs_a: a vector of one or more scenario labels; must match the same number of elements in scenarios_a, and correspond directly to the order of elements in scenarios_a.
  10. data_dir_b: the path to the directory containing the three folders of plot_caland() outputs (mean, low, and high emissions) for scenario group b; do not include the "/" character at the end; default is data_dir_b = NA (i.e., no group b), which will plot only scenario group a.
  11. figdirs_b: a vector of three folder names within data_dir_b containing the .csv data files to plot. The folder names must be assigned in order of mean, low, and high; do not include the "/" character at the end of each folder name. The default is figdirs_b = NA (i.e., no group b). However if you choose to plot a group b, the location of the .csv files for the mean and lower and upper uncertainty bounds should follow the same logic as group a. The figures will be written to the folder representing the mean.
  12. scenarios_b: a vector of one or more scenario names for group 'b' that are listed in the Scenario column in the .csv file containing varname for the mean.
  13. scen_labs_b: a vector of one or more scenario labels; must match the same number of elements in scenarios_b, and correspond directly to the order of elements in scenarios_b.
  14. data_dir_c: the path to the directory containing the three folders of plot_caland() outputs (mean, low, and high emissions) for scenario group c; do not include the "/" character at the end; default is data_dir_c = NA (i.e., no group c), which will only plot scenario group a, or group a and b.
  15. figdirs_c: a vector of three folder names within data_dir_c containing the .csv data files to plot. The folder names must be assigned in order of mean, low, and high; do not include the "/" character at the end of each folder name. The default is figdirs_c = NA (i.e., no group c). However if you choose to plot a group c, the location of the .csv files for the mean and lower and upper uncertainty bounds should follow the same logic as group a. The figures will be written to the folder representing the mean.
  16. scenarios_c: a vector of one or more scenario names for group 'c' that are listed in the Scenario column in the .csv file containing varname for the mean.
  17. scen_labs_c: a vector of one or more scenario labels; must match the same number of elements in scenarios_c, and correspond directly to the order of elements in scenarios_c.
  18. reg: a vector of one or more region names to plot; default is reg = "All_region" (i.e., aggregated across all regions), but can be any number of available regions: "All_region", "Central_Coast", "Central_Valley", "Delta", "Deserts", "Eastside", "Klamath", "North_Coast", "Sierra_Cascades", "South_Coast".
  19. lt: a vector of one or more land types to plot; default is lt = "All_land" (i.e., aggregated across all land types), but can be any number of available land types
  20. own: a vector of one or more ownerships to plot; default is own = "All_own" (i.e., aggregated across all ownerships), but can be any number of available ownerships.

Notes on plot_uncertainty():

  • Plotting the ocean region does not provide any comparison with land types because only seagrass exists in the ocean.
  • Seagrass has only a subset of all the .csv files output from plot_caland(); thus, if including Seagrass make sure that the desired varname is available.

Outputs from plot_uncertainty()

Output plots (.pdf) and corresponding data (.csv) will be saved to the mean folder for scenario group a.

(6) write_scaled_raw_scenarios.r

Overview of write_scaled_scenarios()

The write_scaled_raw_scenarios() function, defined in the write_scaled_raw_scenarios.r file, is designed to scale a raw county-level scenario to the region level for input to write_caland_inputs(). This function is used in conjunction with write_scaled_outputs() to estimate county-level effects of county-level management. See below for caveats of this scaling.

Input files to write_scaled_raw_scenarios()

There is one main scenario input file to write_scaled_raw_scenarios() and one supporting area input file. The scenario input file (scen_file) follows the same format as the input file to write_caland_inputs(), and it should contain county-specific values and all of the relevant scenarios for comparison (i.e., baseline and all alternatives). View the county area files (area_lab_sp9_own9_2010lt15_cnty_ac.csv for acres and area_lab_sp9_own9_2010lt15_cnty_ha.csv for hectares) in caland/ancillary to see the available land category area within the specified county. Designations of "All" region or ownership for non-urban land types are expanded to only those regions or ownerships present in the specified county. Ownership must be "All" for Developed_all land type. Only EITHER "Reforestation" OR "Afforestation" can be defined in a scenario. It is recommended to use "Reforestation" for all forest area expansion as its only source is Shrubland. A single county-level scenario can contain only annual practices OR restoration practices, although urban forest expansion may accompany either of these sets of practices. write_scaled_raw_scenarios() includes checks on area availability, including the effects of interactions between restoration practices on sources. A county land category cannot be restored if it does not exist or its initial area is zero (except for Delta Fresh_marsh). Note that the three urban area (Developed_all) practices (Dead_removal, Urban_forest, Growth) must be completely defined in space and time. Growth must always be defined as 1 because land use/cover change is not allowed (must use a lulcc/wildfire control file for write_caland_inputs()) in county scaling (except for prescribed restoration). Also note that Dead_removal must always be defined as 1 in order to fully implement mortality in urban forest. All relevant years across scenarios must be defined in each scenario. The relevant years are start_year, end_year, start_year - 1 (except for the first year specified), and end_year + 1 (except for the last year specified). Furthermore, Forest non-regeneration must be off (NR_Dist =-1 in CALAND() for county scaled scenarios, and wildfire must be off for county scaled restoration scenarios. The supporting area input file provides the land category areas within each county, and is different than the ancillary files listed above.

Arguments in write_scaled_raw_scenarios()

Only the first three of the four arguments to write_scaled_raw_scenarios() should be specified by the user.

  1. scen_file: The name of the raw scenarios file for a specific county. This file is an Excel file must be in <your_caland_folder>/raw_data. If you want this file and the output files to be in a folder within raw_data, prefix this file name with the existing folder. The default is "amador_example.xls".
  2. county: The name of the county. This must match the county name in the county area file. The default is "Amador".
  3. units: The area units for the raw input scenario file. This can be "ac", or "ha". The default is "ac".
  4. county_category_areas_file: The name of the file containing the breakdown of county areas for the land categories. The default is area_lab_sp9_own9_2010lt15_cnty_sqm_stats.csv and this should not be changed.

Outputs from write_scaled_raw_scenarios()

There are two output files created by write_scaled_raw_scenarios(), and they both will go into the same raw_data folder as scen_file.

  1. The scaled raw scenario file is an Excel file and will be named by appending the county name and the units to scen_file.
  2. The scalar file contains the values to scale the CALAND outputs down to the county level. It is an excel file and will be named by appending the county name and the units and "scalars" to scen_file.

(7) write_scaled_outputs.r

Overview of write_scaled_outputs()

The write_scaled_outputs() function, defined in the write_scaled_outputs.r file, is designed to scale CALAND outputs to the county level for comparison and diagnoses via plot_caland(). This function is used in conjunction with write_scaled_raw_scenarios() to estimate county-level effects of county-level management. See below for caveats of this scaling.

Input files to write_scaled_outputs()

There should be two or more CALAND() output files and an output scaling file that are input to this function. The CALAND() output files need to correspond with the scenarios scaled by write_scaled_raw_scenarios(), as does the scaling file that is generated by write_scaled_raw_scenarios().

Arguments in write_scaled_outputs()

  1. scen_fnames: A vector of CALAND() output file names. These need to correspond with the scenarios scaled by write_scaled_raw_scenarios().
  2. data_dir: The path and directory containing the files listed in scen_fnames (do not include the final "/"). The scaled output files will be written to this directory. The default is "./outputs/amador".
  3. scalar_file: The name of the output scalar file generated by write_scaled_raw_scenarios(). This file must be in <your_caland_folder>/raw_data. If this file is in a folder within raw_data, prefix this file name with the existing folder (for related details see the description above for the scen_file argument to write_scaled_raw_scenarios()).

Outputs from write_scaled_outputs()

Scaled, county-level output files corresponding to scen_fnames will be generated and named by appending the county name and "scaled" to the unscaled file names. Non-county land categories will have values equal to zero. These scaled output files are in the exact same format as regular CALAND() output files and can be used directly by plot_caland().

Caveats of county-level scaling

  • Increase in estimation uncertainty that is difficult to quantify
  • The scaled outputs approximate what CALAND would return for a single county if the region boundaries were counties instead of the current nine regions
  • County-level management has limitations compared to regular CALAND operation:
    • No land use/cover change (except what may be prescribed in a restoration scenario)
    • No land protection management (because there is no urban growth)
    • Always full Forest regeneration
    • Annual practices must be in a separate scenario from restoration practices
    • No fire with restoration practices (fire can be used with annual practices)
    • Land categories with zero county area cannot be restored at the county-levels (except for Delta Fresh_marsh)
    • Some county restoration targets may not be possible to limitations in scaled source area

Instructions for your first test runs with CALAND

Once you have completed installing R or R and RStudio, and downloading CALAND, you will have a local copy of all the R scripts, four example input scenario files, a carbon input file, all the raw files for creating new input files on your local computer, and the tools to use them. Now you can use the carbon and scenario input files that were already created with write_caland_inputs() to do a test run with CALAND() and two of the plotting functions: plot_caland() and plot_scen_types().

Load the CALAND(), plot_caland(), and plot_scen_types() functions in R

  1. Open the following R files containing the functions (located in your caland-3.0.0/ directory): CALAND.r, plot_caland.r, and plot_scen_types.r.

  2. In R or RStudio, find the console in the bottom-left corner. This is where you will interact with R and run the the functions.

  3. Set the working directory to caland-3.0.0/ by typing the following into the console:
    setwd("<your_path>/caland-3.0.0/")

  4. Load the functions in each file by clicking anywhere in your script window, and then choose Edit→Run all (in R), or click the Source button in the top-right corner of the editor window (in RStudio). Alternatively, you can highlight the entire code, and then press Ctrl+R (in R) or Ctrl+Enter (in RStudio).

Run the functions in R
  1. Read the CALAND.r section.
  2. Run CALAND() twice using two example sets of arguments:

  • Run the historical baseline scenario using RCP8.5 climate change effects on wildfire and ecosystem carbon fluxes, mean initial carbon density, mean carbon accumulation inputs, maximum non-regeneration, and with black carbon counted as CO2. Type (or copy and paste) the following into the R console: CALAND(scen_file="NWL_Historical_v6_default_RCP85.xls", c_file_arg = "carbon_input_nwl.xls", indir = "", outdir = "", start_year = 2010, end_year = 2101, value_col_dens = 7, ADD_dens = TRUE, value_col_accum = 7, ADD_accum = TRUE, value_col_soilcon=8, ADD_soilcon = TRUE, NR_Dist = 120, WRITE_OUT_FILE = TRUE, blackC = FALSE)

  • Next, run the Alternative A scenario using the same arguments except for the scenario filename scen_file and the non-regeneration distance NR_Dist. Here, non-regeneration is eliminated to emulate 100% reforestation of non-regenerating Forest patches. Type (or copy and paste) the following into the R console: CALAND(scen_file="NWL_Alt_A_v6_default_RCP85.xls", c_file_arg = "carbon_input_nwl.xls", indir = "", outdir = "", start_year = 2010, end_year = 2101, value_col_dens = 7, ADD_dens = TRUE, value_col_accum = 7, ADD_accum = TRUE, value_col_soilcon=8, ADD_soilcon = TRUE, NR_Dist = -1, WRITE_OUT_FILE = TRUE, blackC = FALSE)

  1. Read the plot_caland() and plot_scen_types() sections above and make some plots by running the following examples in order. Note that CALAND() must be finished running before starting plot_caland(), and plot_caland() must be finished running before starting plot_scen_types(), as each function uses output files from the previous function as input files to the next function.

  • plot_caland() example. The following will compare CALAND() outputs for Alternative Scenario A to the Baseline Scenario at the Statewide level (All_region, All_land, and All_own), as well as each land type aggregated across all regions (All_region) and all ownerships (All_own): plot_caland(scen_fnames = c("NWL_Historical_v6_default_RCP85_output_mean_BC1_NR120.xls", "NWL_Alt_A_v6_default_RCP85_output_mean_BC1.xls"), scen_snames = c("Baseline","A"), reg = c("All_region"), lt = c("All_land", "Water", "Ice", "Barren", "Sparse", "Desert", "Shrubland", "Grassland", "Savanna", "Woodland", "Forest", "Meadow", "Coastal_marsh", "Fresh_marsh", "Cultivated", "Developed_all", "Seagrass"), own=c("All_own"))

  • plot_scen_types() example. The following will plot the change in total organic C stock of all the individual land types in Scenario A relative to the Baseline Scenario, aggregated across all regions and ownerships: plot_scen_types(varname = "All_orgC_stock_diff", ylabel = "Change from Baseline (MMTC)", data_dir = "./outputs", file_tag = "", reg = c("All_region"), lt = c(Water", "Ice", "Barren", "Sparse", "Desert", "Shrubland", "Grassland", "Savanna", "Woodland", "Forest", "Meadow", "Coastal_marsh", "Fresh_marsh", "Cultivated", "Developed_all"), own = c("All_own"), figdir = "figures")

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