TerraCount Tool Manual - California...This manual provides an overview for using TerraCount - a...

Merced Tool Documentation 1

TerraCount Tool Manual

August 28, 2018 Tukman Geospatial LLC


Table of Contents 1. Introduction ................................................................................................................................................ 4

1.1. Overview ............................................................................................................................................. 4

1.2. Study Area .......................................................................................................................................... 4

1.3. Related Documents ............................................................................................................................ 4

2. Background, Installation, and Running the Tool ........................................................................................ 4

2.1. TerraCount .......................................................................................................................................... 4

2.2. Installation Instructions for TerraCount ............................................................................................. 8

2.2.1. Downloading Data & TerraCount.................................................................................................. 8

2.2.2. System Requirements ................................................................................................................... 8

2.2.3. Installing Tool in ArcMap .............................................................................................................. 8

2.2.4 Installing Tool in ArcPro ................................................................................................................ 9

2.2.5 Expected Run Times and Batch Running TerraCount ................................................................. 10

3. Glossary of Terms ..................................................................................................................................... 11

3.1. TerraCount Input Data and User Inputs ........................................................................................... 12

3.1.1. TerraCount Inputs (built-in) ........................................................................................................ 12

3.1.2. TerraCount User Inputs .............................................................................................................. 13

3.2. Activities ........................................................................................................................................... 15

3.2.1. Riparian Restoration ................................................................................................................... 16

3.2.2. Oak Woodland Restoration ........................................................................................................ 16

3.2.3. Cover Crops ................................................................................................................................. 16

3.2.4. Mulching ..................................................................................................................................... 16

3.2.5. Improved Nitrogen Fertilizer Management ................................................................................ 16

3.2.6. Hedgerow Planting ..................................................................................................................... 16

3.2.7. Urban Tree Planting .................................................................................................................... 17

3.2.8. Native Grassland Restoration ..................................................................................................... 17

3.2.9. Replacing Synthetic Nitrogen Fertilizer with Soil Amendments ................................................. 17

3.2.10. Compost Application to Non-Irrigated Grasslands ................................................................. 17

3.2.11. Avoided Conversion ................................................................................................................ 17

3.3. Activity Adoption Caps ..................................................................................................................... 21

3.4. Activity Sheets .................................................................................................................................. 21

3.5. User Input Interactions and Hierarchies ........................................................................................... 22


3.6. Python Script Summary by Module .................................................................................................. 22

4. Tool Outputs ............................................................................................................................................. 23

4.1. File Structure .................................................................................................................................... 23

4.1.1. Carbon Summary Reporting Tables ............................................................................................ 25

4.1.2. Log File ........................................................................................................................................ 26

5. Additional Information ............................................................................................................................. 37

A. Summary of python modules used in TerraCount .......................................................................... 38


1. INTRODUCTION

1.1. Overview This manual provides an overview for using TerraCount - a geoprocessing and analysis tool designed to aid Merced county in understanding the impacts of land-use/ land-cover (LULC) change on carbon storage, and to locate where conservation goals are closely aligned with emissions reduction potential. The initial Conservation Carbon Accounting Tool (C-CAT) was developed for Sonoma County in 2016 – the Merced County tool adapts and improves the code developed for Sonoma County. Ultimately the framework has the capability of being widely applied at a county level throughout the Unites States. The conceptual framework is implemented via a geospatial processing tool that produces tabular data to help answer the following questions:

• What are the impacts of land conversion? • How do different land use change scenarios affect carbon stocks? • How do different land use change scenarios affect other environmental benefits? • How do improved agricultural practices effect carbon stocks? • How do improved agricultural practices effect other environmental benefits?

1.2. Study Area This tool was prepared for Merced County, California. The outputs of the tool are intended to help the County account for planning-related GHG reductions that relate to positive benefits as identified in California’s regulatory framework (AB32).

1.3. Related Documents Useful related documents to this tool manual are as follows:

• The Project Guide and Final Report – an in-depth report that on the Merced Project’s methods and results. https://maps.conservation.ca.gov/TerraCount/downloads/ResilientCountiesGuide.pdf

• The Merced County TerraCount Data Processing Overview – a guide to the data processing required to create the datasets used in TerraCount. https://maps.conservation.ca.gov/TerraCount/downloads/Appendix_G_TerraCount_tool_data_processing.pdf

• The TerraCount Web App – A web application for viewing and exploring example (pre-run) scenarios from TerraCount. https://maps.conservation.ca.gov/TerraCount/

2. BACKGROUND, INSTALLATION, AND RUNNING THE TOOL

2.1. TerraCount Tukman Geospatial created a spatially explicit toolbox called TerraCount. The toolbox accepts a diverse set of inputs and is thereby capable of producing a variety of land conversion/ conservation scenarios. The tool, which runs within ESRI’s ArcMap or ArcGIS Pro software, produces tabular and graphic outputs

https://maps.conservation.ca.gov/TerraCount/downloads/ResilientCountiesGuide.pdf

https://maps.conservation.ca.gov/TerraCount/downloads/Appendix_G_TerraCount_tool_data_processing.pdf

https://maps.conservation.ca.gov/TerraCount/downloads/Appendix_G_TerraCount_tool_data_processing.pdf

https://maps.conservation.ca.gov/TerraCount/


characterizing projected change in carbon and a suite of environmental benefits between 2014 and 2030 based on user defined scenarios.

Planners can use the tool to run scenarios for the entire county or for any defined sub-area within the county. Because the tool is highly configurable, it can be used to model the effects of a wide range of land-use policies, conservation and land-management strategies, and restoration programs.

Figure 1 illustrates the tool framework and provides the components of the tool. Figure 2 shows a screen capture of the tool’s Graphical User Interface in ArcGIS Pro.

The toolbox was developed using Python and relies heavily on the Python Data Analysis Library (PANDAS).


Figure 1. Merced tool framework


Figure 2. Merced tool user interface in ArcGIS Pro


2.2. Installation Instructions for TerraCount

2.2.1. Downloading Data & TerraCount The TerraCount and its supporting data are available at:

TerraCount Python Scripts and Toolbox:

https://github.com/mtukman/mercedtool

TerraCount Supporting Data (Zipped ‘Master_Data’ Folder):

http://carb.press/master_data

2.2.2. System Requirements To use the tool on your local computer, first make sure that you meet the following system requirements:

• ArcMap, version 10.3 or greater OR ArcPro • 16 GB Memory • 64-bit background geoprocessing must be installed and enabled and the tool must be

run in the background (this requirement applies to ArcMap only, ArcGIS Pro will run the tool by default)

• 10 GB hard drive Space

2.2.3. Installing Tool in ArcMap Installation steps are as follows:

1. Download the Github Repository and the supporting data from https://github.com/mtukman/mercedtool.

2. Extract the contents of “mercedtool-master.zip” (which contains a folder called mercedtool-master) to a folder on your hard drive. The extracted folder contains a number of python modules and an ArcGIS toolbox called TerraCount. Download and extract the Master_Data folder from https://carb.press/master_data to anywhere on your hard drive.

3. Add the Merced Conservation Carbon Accounting Tool to the ArcMap Toolbox window within ArcMap. To do this, right-click the Arc Toolbox folder (see Figure 3 below) and click ‘Add Toolbox’. Browse to the location containing the toolbox that you extracted above in step 1 and select the “TerraCount ArcMap 10_5” toolbox. After adding the toolbox, navigate to the tool’s properties and make sure the “Always run in foreground” box is NOT checked.


http://carb.press/master_data


https://carb.press/master_data


Figure 3. Adding the tool to ArcMap

4. To have the tool automatically create charts and plots, you will need to have the Plotly package installed and to have a valid Plotly API key. Install Plotly using your preferred package manager (or see step 5 below if you are unfamiliar with installing packages). Obtain a Plotly API key by setting up a free Plotly account and generating a key in your user settings.

5. To install Plotly in ArcMap, try using pip from the command line. From a command prompt, navigate to the ‘Scripts’ folder of the 64-bit python that ArcMap uses. Then install Plotly (see below for an example of what this command looked like on our machine).

C:\Python27\ArcGISx6410.5\Scripts> pip.exe install plotly

6. Once your .mxd is saved, the contents of the Arc Toolbox window are also saved within the map document. The next time you open the document, the Toolbox window will be the same as when you saved the document. Remember that you can change and re-execute runs of the tool saved in your map’s geoprocessing history.

2.2.4 Installing Tool in ArcPro 1. Download the Github Repository and the supporting data from

https://github.com/mtukman/mercedtool. 2. Extract the contents of “mercedtool-master.zip” (which contains a folder called

mercedtool-master) to a folder on your hard drive. The extracted folder contains a number of python modules and an ArcGIS toolbox called TerraCount. Download and extract the Master_Data folder from https://carb.press/master_data anywhere on your hard drive.

3. To add the toolbox to ArcPro, open an ArcPro project and navigate to the catalog tab. Find the project tab in the catalog, and under it right click on the Toolboxes label. Choose ‘Add Toolbox’ and select the “TerraCount Pro” toolbox to add (see figure 4). Save the ArcPro project and the toolbox will stay in the map.


https://carb.press/master_data


Figure 4. Adding the tool to ArcGIS Pro

4. To have the tool automatically create charts and plots, you will need to have the Plotly package installed and to have a valid Plotly API key. Install Plotly using the python package manager in ArcGIS Pro. Obtain a Plotly API key by setting up a free Plotly account and generating a key in your user settings.

5. Once your .aprx is saved, the contents of the Arc Toolbox window are also saved within the map document. The next time you open the document, the Toolbox window will be the same as when you saved the document. Remember that you can change and re-execute runs of the tool saved in your map’s geoprocessing history.

2.2.5 Expected Run Times and Batch Running TerraCount If small user-defined areas of interest are specified as a custom processing area masks, the tool runs in as little as 10 minutes on a mid-range GIS workstation. For countywide runs, the tool takes between 30 minutes and 3 hours to run on a high-quality GIS workstation (depending on the tool parameters selected).

For an analyst who is familiar with python, the tool can be batch-run for multiple scenarios as shown in the example code below. After running the tool once, the user can go to the geoprocessing history tab in ArcMap or ArcPro, right click and ‘copy as python snippet’. This snippet can then be pasted into a python script and the input arguments edited to create numerous tool scenarios. Instead of running and re-running the tool by hand for each scenario, one can run the python script to execute all scenarios in sequence (for example, over a weekend) with different inputs for each scenario coded. See the sample python code below, which would run the tool three times with different inputs/scenarios.


3. GLOSSARY OF TERMS These are terms used in the tool and in the Guide that may cause confusion.

Reference Developed Footprint: The footprint of 2030 development based on 2001-2014 trends.

Merced Preferred Developed Footprint: The footprint of 2030 development created by the Merced Association of Governments. It is Merced County’s preferred spatial depiction of 2030 development.

Max Infill Developed Footprint: The footprint of 2030 development that represents ‘urban infill’ style development.

Reference Scenario: The 2030 simulated landcover from ST-SIM for natural lands, with the Reference Developed Footprint included for developed lands.

Merced Preferred Scenario: The 2030 simulated landcover from ST-SIM for natural lands, with the Merced Preferred Development Footprint included for developed lands.

Max Infill Scenario: The 2030 simulated landcover from ST-SIM for natural lands, with the Maximum Infill Developed Footprint included for developed lands.

Custom Developed Footprint: This refers to a custom development footprint for 2030 provided by the User.

Activities: Land use changing activities (such as oak restoration and riparian restoration) and agricultural activities such as cover cropping, hedgerow planting, etc.

cobenefits: Benefits besides greenhouse gas reductions achieved from implementing an activity. For example, water quality, air quality, or terrestrial biodiversity.

Scenario: A run of the tool with its unique combination of activities, user-defined masks, etc.


3.1. TerraCount Input Data and User Inputs Currently the model incorporates a combination of built-in inputs and user inputs. Some datasets are currently built-in to the model. Conservation masks, development masks, treatment masks and activities are optionally selected by the user and will influence where, how, and to what degree activities are applied across the landscape.

Geoprocessing in the toolbox requires a Teale Albers coordinate system. This coordinate system was chosen since Teale Albers can be used effectively across the state of California. User provided layers (such as user defined AOIs for use as processing masks) that are not already in Teale Albers will be projected to Teale Albers internally by the tool.

3.1.1. TerraCount Inputs (built-in) The primary land use data driving the tool are the 2001 LANDFIRE, 2014 LANDFIRE, and a simulation of 2030 land cover produced using ST-SIM and Envision Tomorrow. 30-meter resolution Landsat satellite imagery is the foundation for LANDFIRE. For the Merced project, the LANDFIRE datasets were modified to improve accuracy the of Urban and Agricultural classes. LANDFIRE landcover type (EVT), canopy cover (EVC), and stand height (EVH) within the LANDFIRE dataset, combined with soils data, are used to relate land cover to carbon inventory values in the tool. More information about methodology behind the carbon inventory for this project can be found in the tool guide.

The tool models 2014-2030 land cover changes and changes in management practices. These changes are driven by activities that the tool user selects each time the tool is run. The tool reports back on the effect of the activities both on GHG emissions and on a suite of cobenefits. The list of activities and cobenefits are shown in Table 1.

Table 1. Merced tool activities and cobenefits

Activities cobenefits Improved Nitrogen Fertilizer Management Ag Land Quality Replacing Synthetic Nitrogen Fertilizer with Soil Amendments

Crop Value

Oak Woodland Restoration Ag/Urban Water Conservation Cover Crops Groundwater Recharge Mulching Water Quality Riparian Restoration Watershed Integrity Urban Tree Planting Flood Risk Reduction Hedgerow Planting Air Quality Avoided Conversion to Croplands Scenic Value Avoided Conversion to Urban Terrestrial Connectivity Compost Application to Non-Irrigated Grasslands Natural Habitat Area Native grassland Restoration Priority Conservation Areas Terrestrial Habitat Value Aquatic Biodiversity Value/Richness Flood Risk Attenuation Groundwater Banking Potential Habitat Stability Climate Connectivity


In addition to the user-driven land cover and management changes from selecting activities, there are 3 versions of the 2030 land cover that depict different 2030 development footprints. The tool provides full reporting for all three footprints each time the tool is run.

Each of these footprints represents a different pattern of urban and suburban development, a major driver of the conversion of natural and working landscapes to residential, business and industrial uses. The incorporation of these footprints into the tool allows the user to see the effect of different development patterns on landscape carbon storage over time.

The three footprints are:

• A reference footprint, which continues the development and land-conversion patterns observed in the 2001-2014 baseline period.

• The Merced County preferred footprint, created by the Merced Association of Governments, which represents the county's official preferred vision for land development through 2030.

• A maximum infill footprint, which focuses on redeveloping the county's core urban areas.

The three footprints were ‘painted’ using Envision Tomorrow, an urban planning tool. All three footprints assume the same rate of growth for jobs and the residential population.

3.1.2. TerraCount User Inputs There are several customizable parameters for user scenario creation. For example, the user can choose to include a variety of spatial layers to influence the tool. A ‘custom processing area’ mask may be specified which forces the tool to run only within that area. A ‘conservation mask’ may be specified which forces the area it encompasses to maintain 2014 landcover in the 2030 scenarios. A ‘development mask’ may be used that affects the tool in one of two ways: it can be used to add additional development to the 2030 reference developed footprint, or it can be used to create a “custom” 2030 developed footprint. Finally, a treatment mask may be applied. The treatment mask will let the tool run for the entire county, but any activities applied will be constrained to the treatment mask area. These masks are discussed below.

The tool has help information and parameter descriptions for each user input; please refer to this information for specific parameters when running the tool from within ArcMap and ArcGIS Pro. The following discussion provides additional background information on the user-defined inputs.

3.1.2.1. Custom Processing Area (Optional) The default processing area is the entire extent of Merced County. However, a user may be interested in only executing the tool on a specific area or region. In this case, the user may select a customized polygon mask as the processing area for analysis. For example, this polygon may be a single watershed or park boundary. When a processing area is selected, the tool will only be run within the extent of the selected polygon.

3.1.2.2. Conservation Mask (Optional) As discussed above, the tool uses a simulation of land cover to represent the state of the landscape in 2030. If a conservation mask is applied, all the land inside of the mask will retain its 2014 land cover despite what


the 2030 simulated landcover is. This will affect the 2030 projections of carbon and GHGs as well as the associated cobenefits. Figure 5 illustrates the concept of the conservation mask as it’s implemented in the Merced Tool.

Figure 5. The concept of the ‘conservation mask’ in the Merced Tool

3.1.2.3. Development Mask (Optional) As discussed above, there are three hard-coded 2030 development footprints with varying amounts of development. There are two options in the tool for further customization of the development footprints. Under the ‘Custom Development Options’ drop-down menu there are three choices:

• None – this is the default • Custom (Creates Custom Scenario) • Custom (Adds on to Reference Developed Footprint)

‘Custom (Creates Custom Scenario)’ will create a 4th development scenario called custom. It will add a new developed footprint that reflects the 2030 development as defined by the user provided mask (feature class). ‘Custom (Adds on to Reference Developed Footprint)’ will add additional development to the 2030 reference footprint as defined by the user provided mask.

3.1.2.4. Treatment Mask (Optional) The treatment mask option will limit the implementation of user defined activities to the areas within a user provided mask.

3.1.2.5. Terrestrial Habitat Reporting (Optional) A check-box near the bottom of the tool interface gives the user the option to report on Terrestrial Habitat. The terrestrial habitat reporting function takes a long time to run (about 2-2.5 hours for the entire county).

3.1.2.6. Watershed Integrity Reporting (Optional) A check-box near the bottom of the tool interface gives the user the option to report on Watershed Integrity. The watershed integrity reporting function takes a long time to run (about 2-2.5 hours for the entire county).


3.1.2.7. Plotly Plots (Optional) There is an option to have the tool output charts and plots using the Plotly plotting engine. These charts are very helpful in interpreting the results of the tool. If this option is selected, a Plotly API Key and Plotly username must also be supplied for the plotting outputs to be created. These can be obtained by creating a free account at www.plotly.com. It is recommended that if using this option, the tool should be run in ArcPro.

To use this option, the Plotly python package must be installed. For ArcPro the package can be installed easily from ArcPro’s package manager. For ArcMap users, the package can be installed into the user’s local python installation with a package manager like pip.

If the Plotly option is chosen, the tool will create two additional folders in the tool’s output folder. One will be called plots and the other will be called plot_tables. The tool will create simplified versions of the reporting tables with columns and rows formatted for the plotting package. Plotly will then use the simplified tables to create standardized plots from the tool outputs. These plots show the 2014 cobenefit values and the 2014-2030 MBA changes for the reference scenario and the treatment scenario.

3.1.2.8. Reporting Units This option allows the user to define what units the cobenefits are reported in, either Acres or Hectares. These units will be used in the reporting tables and the plots (if plots are created).

3.1.2.9. Ignore Adoption Caps The acreage adoption caps for each activity were calculated by analyzing how many acres of landcover each activity could be applied to, and how many acres of each landcover were already applying the activity in the county. Additionally, each activity was evaluated as to what a reasonable maximum adoption amount would be. By checking this option, all adoption caps are discarded, but the suitability requirements for each activity will remain in effect.

3.2. Activities The tool applies user-defined activities by identifying the appropriate geography for the activity based on a set of suitability rules (suitability rules for each activity are described later in this section), and then applying the activity to the areas that meet these rules. The user can select 0, 1 or more activities for each run of the tool. There is a total of 11 activities. In this section we provide information on how the user implements activities in the tool. This section also provides an overview of each activity, with a description of the geographic constraints that the tool uses to define suitable areas for the activity.

Each activity has four options in the tool that are required to be filled in by the user. These are:

• Yes/No flag - whether the activity will be applied. • Adoption Acres – the number of acres to apply the activity to (number is limited

to the max suitable acres; the tool will tell you if you exceed the maximum acreage).

• Year the treatment begins – This is the year when the activity begins to be applied, if 2014 is entered, the activity would begin in 2014.

http://www.plotly.com/


• Years to full activity adoption – This defines how long it takes from the specified beginning year to reach full adoption of the activity. For example, if the user specifies 2,000 acres of riparian restoration, to begin in 2014 and reach full adoption in 5 years, 400 acres of woody riparian would be added each year between 2014 and 2019, then the full 2,000 acres of activity would be applied until 2030.

The following several pages list and describe the activities available in the Merced County tool.

3.2.1. Riparian Restoration Description: Restoration of woody riparian vegetation near streams and rivers

Suitability: Applies to all Grassland, Irrigated Pasture, Annual Cropland, Vineyard, Rice, Orchard, Wetland and Barren lands in the riparian zone that do not have existing woody vegetation. The riparian zone is defined as being within 1000 ft. of a river centerline or 100 ft. of a stream centerline that is also classified as being able to support woody riparian.

3.2.2. Oak Woodland Restoration Description: Restoration of oak woodlands (across the ranges of blue oak and valley oak)

Suitability: Applied to grasslands and shrublands, irrigated pasture and barren within CWHR ranges identified for blue oak woodland and valley oak woodland.

3.2.3. Cover Crops Description: Grasses and forbs planted for seasonal vegetative cover

Suitability: Applied to orchards and some annual croplands (not silage crops, tomatoes, and sweet potatoes). Vineyards and sweet potatoes already heavily adopt cover cropping.

3.2.4. Mulching Description: Addition of crop or straw residues to bare soils

Suitability: Applies to annual croplands and rice.

3.2.5. Improved Nitrogen Fertilizer Management Description: Managing the amount (rate), source, placement (method of application), and timing of plant nutrients and soil amendments

Suitability: Applied to orchards, some annual croplands, rice and vineyard. Reduce rates of N fertilizer application on eligible properties by up to 33%.

3.2.6. Hedgerow Planting Description: Planting of hedgerows for insectaries or dust control in orchards and vineyards

Suitability: Applies to orchards and vineyards.


3.2.7. Urban Tree Planting Description: Planting of trees in urban areas, resulting in increased urban canopy cover

Suitability: Apply to urban areas as defined by Landfire. Increase urban canopy cover by up to 5% by 2030 (2014 canopy cover is 10.2 %).

3.2.8. Native Grassland Restoration Description: Restoration of native grasses

Suitability: Non-irrigated grassland.

3.2.9. Replacing Synthetic Nitrogen Fertilizer with Soil Amendments Description: Full or partial replacement of synthetic nitrogen fertilizer with soil organic matter amendments, such as manure or other organic by-products

Suitability: Applied to most annual croplands (except for cotton, alfalfa, silage crops) and some orchards.

3.2.10. Compost Application to Non-Irrigated Grasslands Description: Application of a half inch thick layer of compost on grassland and rangeland

Suitability: Non-Irrigated grassland, applies in flat areas with slope < 15%, does not apply in wetlands or near riparian areas (< 1000 feet from rivers/ < 100 feet from streams).

3.2.11. Avoided Conversion There are multiple ways to analyze avoided conversion in the tool – these are outlined in Table 2 and illustrated in Figure 6. In the tool GUI, the user can avoid conversion through a list of avoided conversions (see the fourth row in table 3).

Using the conversion list, there are 14 avoided conversion options. The user can choose up to 9 of the 14 avoided conversions. When an avoided conversion is selected, such as avoided conversion from orchard to urban, the user will need to define acres of conversion to avoid. Avoiding conversion from orchard to urban will find the areas that switched from orchard in 2014 to urban in the 2030 reference scenario and treat a selection of these areas (the acreage defined by the user) as if they hadn’t transitioned from orchard to urban.

These possible avoided conversion choices are:

• Avoided Conversion from Wetland to Annual Cropland • Avoided Conversion from Grassland to Annual Cropland • Avoided Conversion from Irrigated Pasture and Annual Cropland • Avoided Conversion from Orchard to Annual Cropland • Avoided Conversion from Annual Cropland to Urban • Avoided Conversion from Grassland to Urban • Avoided Conversion from Irrigated Pasture to Urban • Avoided Conversion from Orchard to Urban • Avoided Conversion from Annual Cropland to Orchard


• Avoided Conversion from Grassland to Orchard • Avoided Conversion from Irrigated Pasture to Orchard • Avoided Conversion from Vineyard to Orchard • Avoided Conversion from Annual Cropland to Irrigated Pasture • Avoided Conversion from Orchard to Irrigated Pasture


Table 2. Avoided Conversion in the tool

Method for Applying AC Mechanism Description

Type of Avoided Conversion Use Case

Avoided conversion using a user-defined Conservation Mask

User provides polygons in tool GUI

User uploads polygons where they don’t want any ’14-’30 conversion or land use changing activities to occur. All areas in the polygons will retain the land cover from 2014, and don’t transition to a new 2030 land cover.

All Tool user wants to ensure certain areas of the county aren’t changed to a new land cover in 2030 by an activity or by a LUCAS transition.

Avoided conversion based on Pre-Created Development Footprints

Pre-created development scenarios hard coded in tool

All runs of the tool will provide reporting for each of three pre-created 2030 development footprints – reference development, Merced preferred development, and Maximum Infill development. The scenarios were painted in EnvisionTomorrow.

Avoided Conversion to Development

A planner wants to understand the implications of the pre-created 2030 development footprints.

Avoided conversion based on a User-Defined Development Footprint

User provides polygons in tool GUI

The user has the option to upload their own development footprint, which is than evaluated as a fourth scenario in addition to the three pre-created scenarios.

Avoided Conversion to Development

A planner wants to compare their own 2030 development scenario to the three pre-created 2030 scenarios.

Avoided conversion using a Conversion List that matches a type of conversion with the acreage to avoid

User provides acreages in tool GUI to avoid one or more conversions

User picks one or more conversion(s) to avoid and enters an acreage. An example would be ‘Avoided Conversion from Annual Grassland to Orchard.’ If a user enters 300 acres for this avoided conversion, then the tool will randomly select 300 acres of orchard (in the 2030 simulated Reference Scenario) that was annual cropland in 2014 and convert it back to annual cropland in the 2030 scenario.

All A planner is interested in altering the baseline trends in landcover change. For example, the baseline trend shows large increases in annual cropland -> orchard between ’14 and ’30. This method will allow the user to look at the implications of avoiding some or all of this conversion.


Figure 6. Comparing developed footprints to learn about avoided conversion


3.3. Activity Adoption Caps Each activity has an adoption cap that represents its maximum countywide acreage. The adoption caps were calculated by evaluating existing adoption and estimating a realistic upper limit for additional adoption. The tool implements an activity on suitable areas for the activity (see section above for activity suitability) up to the countywide adoption cap.

Table 3. Adoption caps by activity

Activity Adoption Cap (acres) Riparian Restoration 25,000 Oak Woodland Restoration 52,000 Cover Crops 55,000 Mulching 40,000 Improved Nitrogen Fertilizer Management 80,000 Hedgerow Planting 55,00 Native Grassland Restoration 17,000 Replacing Synthetic Nitrogen Fertilizer with Soil Amendments

20,000

Compost Application to Non-irrigated Grasslands 15,000

3.4. Activity Sheets The Merced tool is meant for use by a county planner to evaluate the implications on carbon and a suite of cobenefits of a variety of activities. It is outside the scope of this tool to analyze the effects of activities at the field scale. The tool is developed with regional datasets and is intended for use at a regional scale, not at a field scale. To provide information to land managers, farmers, and others in Merced County who want detailed, field level information about implementing activities, TNC partners have created activity sheets for each activity. These activity sheets are available as links inside of the tool and are listed in table 4. For a full primer on how to interpret the activity sheets and decipher their information, see this document.

Table 4. Links to activity sheets Activity Activity Sheet Link Riparian Restoration https://carb.press/riparian-restoration Oak Woodland Restoration https://carb.press/oak-restoration Urban Tree Planting https://carb.press/urban-tree-planting Avoided Conversion to Croplands https://carb.press/ac-cropland Avoided Conversion to Urban https://carb.press/ac-urban Cover Crops https://carb.press/covercrops Mulching https://carb.press/mulching Improved Nitrogen Fertilizer Management https://carb.press/fertmngmt Hedgerow Planting https://carb.press/hedgerow Native Grassland Restoration https://carb.press/grass-restoration Replacing Synthetic Nitrogen Fertilizer with Soil Amendments

https://carb.press/compost

Compost Application to Non-Irrigated Grasslands

https://carb.press/cag

http://carb.press/activity_sheets

https://carb.press/riparian-restoration

https://carb.press/oak-restoration

https://carb.press/urban-tree-planting

https://carb.press/ac-cropland

https://carb.press/ac-urban

https://carb.press/covercrops

https://carb.press/mulching

https://carb.press/fertmngmt

https://carb.press/hedgerow

https://carb.press/grass-restoration

https://carb.press/compost

https://carb.press/cag


3.5. User Input Interactions and Hierarchies This section reviews the ‘order of operations’ that the tool uses to apply activities, masks, and other user inputs. Operations proceed in the following order:

1. The custom processing area mask is applied first and restricts the rest of the tool’s functions to that area.

2. The custom development mask (if provided by the user) is applied. 3. The conservation mask is applied. 4. If a treatment mask is provided, it will flag each pixel inside of the treatment mask as

available for activities or avoided conversion. 5. The Riparian Restoration activity is applied. The user-defined number of acres of suitable

landcover is randomly selected and the landcover for the 2030 treatment scenarios (Reference Scenario, Merced Preferred and Maximum Infill) is replaced with young forest.

6. The Oak Woodland Restoration activity is applied. The user-defined number of acres of suitable landcover is randomly selected and the landcover for the 2030 treatment scenarios (Reference Scenario, Merced Preferred and Maximum Infill) is replaced with young forest.

7. The avoided conversion selections are applied. The avoided conversion options (e.g., ‘Avoid Conversion from Grassland to Urban’) are mutually exclusive and will not interfere or affect each other. The tool checks to make sure the 2030 Reference Scenario and 2030 Reference Scenario Treated have the same landcover, and that the 2014 landcover matches the avoided conversion option selected. If a conservation mask is used, anything inside of the conservation mask will be ineligible for the avoided conversion functions.

8. The remaining non-land cover changing activities are applied. The user-defined acreage of pixels for an activity is randomly selected and flagged from a pool of suitable pixels for the activity. Pixels are flagged for GHG and cobenefit calculations.

3.6. Python Script Summary by Module TerraCount runs on a series of interconnected Python modules. In order, the modules run as follows:

• The main_program.py module receives variable (“Parameters” in ESRI-speak) from the ArcMap tool and calls functions from the other modules. This is the main calling script.

• The generic.py module sets the paths & workspaces and defines several functions that will be used throughout the remaining modules.

• The helpers.py module contains functions that are called by the rest of scripts. • The Initial.py module imports the data for the tool, creates the baseline

development scenarios and applies the user-provided masks. • The ActivityApplication.py module takes the user-defined activities and

parameters and does random selections to apply the activities to the processing area.


• The ApplyActions.py module calculates the carbon reductions for each activity. • The Reporting.py module generates reporting tables for each cobenefit and for

Carbon Reductions. • The Create_Plots.py module takes the reports and creates new tables and Plotly

plots.

For more information on the python modules, see the Additional Information section at the end of this document.

4. TOOL OUTPUTS

4.1. File Structure The model outputs consist of reporting tables in a Comma Separated Value (CSV) format. These tables report on the effects of model parameters on cobenefits. In this section, the tables will be described, as well as the fields within the tables. The tool also produces a suite of optional charts that are very useful for interpreting the tool’s results. These are described in section 3.5.

Figure 7 shows the file and folder structure for the tool outputs. Reporting csvs are placed in the top of the output folder, along with the logfile and any spatial polygon masks that were used in the run of the tool. If ‘Generate Plotly Plots’ is turned to ‘Yes,’ in the tool GUI, then two subfolders are created – one that holds simplified tables used in chart creation and one that holds the plots themselves. Figure 8 shows a screen capture of an output folder.


Figure 7. Output file and folder structure – Merced Tool


Figure 8. An example of the output folder from TerraCount

4.1.1. Carbon Summary Reporting Tables The tool creates three summary tables that describe how the run of the tool effects emissions and carbon reductions. These tables provide simple, high level reporting and may contain all the necessary information that the user is interested in. Table 5 provides a description of each of these tables.

Table 5. Description of Summary Tables

Table Name Description Total_report.csv The total_report.csv table is a general overview of the

results of the run of the tool. It includes the acreage selected, the year the activity was started, the years to full adoption of the activity and the total CO2e reductions between 2014 and 2030 by activity for CO2, CH4 and N2O.

Ann_emissions_and_CStock_Change.csv The annual_emissions.csv tables shows the N2O and CH4 annual emission rates and annual CO2e stock growth for the 2030 Reference scenario with and without activities applied.

Total_reductions.csv The total_reductions.csv table shows the total reductions for CO2e, CH4 and N2O for the Reference scenario with activities applied compared to the Reference scenario with no activities applied.


4.1.2. Log File The logfile is a text file written during the running of the tool and saved to the user-defined output folder and named ‘logfile.txt.’ The logfile contains useful information about the parameters used in the run of the model, and the outputs created during the run of the tool. Included in the logfile:

• Time and Date the tool was started • Folder paths • Paths to user-defined masks (specified shapefiles or feature classes) • Avoided conversion options selected and user specified acres • Activities chosen, and user specified acres, adoption year and years to full adoption for

the activity • Actual acreage selected for each activity during the model run • Name and file path for CSVs created by tool

4.1.2.1. Output Tables and Charts The Merced Tool produces many reporting tables that summarize GHG reductions and cobenefits for each scenario or run of the tool. Table 5 shows the list of the output tables from the Merced tool. An output table is produced for each cobenefit metric that the tool reports on. In addition, a carbon table is produced that reports out on GHG reductions for the scenario. If the user of the tool selects ‘Generate Plotly Plots’ from the tool’s GUI, a number of charts are automatically produced that summarize the information contained in the reporting tables. The Plotly charts are a visual and intuitive way to quickly make sense of a tool run. The reporting tables provide more complete reporting but are less accessible and will require more time to interpret than the plots.

Table 6. Description of output tables from TerraCount

Table Name (.csv) Cobenefit Reporting Units

act_acres Acres selected for each activity in model run Acres

aquatic

Hectares of general landcover in watersheds with important aquatic habitat Hectares

carbon Tons of CO2 equivalents sequestered (existing and reduced) Tons

co_val_airpollute Tons of Carbon Monoxide removed from the atmosphere Tons

countymovement Hectares of low, medium and high movement resistance landcover Hectares

cropvalue US Dollar value of agricultural crops US Dollars

ecamovement

Hectares of low, medium and high movement resistance landcover in Essential Connectivity Areas Hectares

eco_resil Hectares of general landcover in areas important for Natural Resilience Hectares



flood100 Hectares of general landcover in 100-year floodplain Hectares

flood500 Hectares of general landcover in 500-year floodplain Hectares

fmmp Hectares of important farmland lost to development Hectares

groundwater Acre feet of annual groundwater recharge lost due to development Acre Feet

lcchange Hectares of landcover Hectares leach_nitrates Tons of nitrate leaching Tons

no2_val_airpollute Tons of Nitrogen Dioxide removed from the atmosphere Tons

o3_val_airpollute Tons of Ozone removed from the atmosphere Tons

pca_cover_change Hectares of landcover in Priority Conservation Areas Tons

pm2_5_val_airpollute Tons of PM 2.5 removed from the atmosphere Tons

pm10_val_airpollute Tons of PM 10 removed from the atmosphere Tons

runoff_nitrates Tons of nitrate runoff Tons

scenic Hectares of general landcover in highly visible areas Hectares

so2_val_airpollute Tons of Sulfur Dioxide removed from the atmosphere Tons

soc_res Hectares of general landcover in area important for Social Resilience Hectares

terrhab

Hectares of habitat quality degraded and improved for selected species guilds Hectares

watcon Acre feet of annual water demand Acre Feet

ann_emissions_and_CStock_Change

The annual_emissions.csv tables shows the N2O and CH4 annual emission rates and annual CO2e stock growth for the 2030 Reference scenario with and without activities applied. Tons

annual_emissions_aggregate

Annual emissions for 2030 Reference and 2030 Treatment aggregated into CO2e Tons

emissions

Tons of annual CH4 and N2O emissions based on land cover for development scenarios and activities Tons

emissions_reductions N2O emissions reductions from activities Tons



total_reductions

The total_reductions.csv table shows the total reductions for CO2e, CH4 and N2O for the Reference scenario with activities applied compared to the Reference scenario with no activities applied. Tons

total_report

The total_report.csv table is a general overview of the results of the run of the tool. It includes the acreage selected, the year the activity was started, the years to full adoption of the activity and the total CO2e reductions between 2014 and 2030 by activity for CO2, CH4 and N2O. Tons

4.1.2.2. Fields in Reporting Tables The fields, field names, and content vary between reporting tables based on differences in cobenefit reporting metrics. For example, ‘fmmp.csv’ only reports on important farmland lost to development - land that converted from agricultural or natural landcover in 2014 to a developed landcover in 2030. The reporting table ‘lcchange.csv’ reports on the all landcover in each scenario whether there was a change in landcover between 2014 and a 2030 scenario.

4.1.2.2.1. Key to Reporting Table Field Name Abbreviations These following abbreviations and phrases are used often in the reporting tables. Their definitions will help to understand and decode the tables.

• bau: Acronym for the Reference Scenario (which was previously called ‘business as usual’).

• med: This references the Merced County Preferred Scenario. • max: This references the Maximum Infill Scenario. • ha: Hectares • base: Refers to ‘Baseline’ - This indicates that the development scenario is one of the

three baseline scenarios developed for 2030 (Reference, Merced Preferred, and Max Infill) with no activities applied to it.

• trt: Refers to ‘Treatment’ - This is the baseline with the activities defined by the user applied included.

4.1.2.2.2. Field Naming Conventions In the tool’s output tables, each field will have a prefix that describes units (tons, ha, ac ft, etc..) that will vary for different cobenefits (see table 5 for a description of the tables and their reporting units), but the end of the field name (what is shown below in the bullets below) will not change. The following is a summary of the naming conventions for the fields in the reporting tables:

• Reporting Field: The classes that are being reported on. Usually the first (far left) field in the reporting table. Each table has one of these fields:

o landcover: Custom landcover class


o gen_class: General landcover class (Custom landcover class generalized into Agriculture, Natural and Developed)

o resistance_class: Low, Medium and High Resistance to terrestrial animal movement

o guild: Group of organisms for which habitat quality change is reported. Mammals, birds, amphibians and threatened/endangered species

o fmmp_class: Farmland class (Local Importance, State Importance, Unique and Prime)

• …_change_base_bau: The change in cobenefit between 2014 and the 2030 Reference Scenario with no activities included.

• …_base_bau: The amount of cobenefit in the 2030 Reference Scenario with no activities included.

• …_change_base_med: The change in cobenefit between 2014 and the 2030 Merced Preferred Scenario with no activities included.

• …_base_med: The amount of cobenefit in the 2030 Merced Preferred Scenario with no activities included.

• …_change_base_max: The change in cobenefit between 2014 and the 2030 Max Infill Scenario with no activities included.

• …_base_max: The amount of cobenefit in the 2030 Max Infill Scenario with no activities included.

• …_change_trt_bau: The change in cobenefit between 2014 and the 2030 Reference Scenario with all activities defined in the scenario included.

• …_trt_bau: The amount of cobenefit in the 2030 Reference Scenario with all activities defined in the scenario included.

• …_change_trt_med: The change in cobenefit between 2014 and 2030 Merced Preferred Scenario with all activities defined in the scenario included.

• …_trt_med: The amount of cobenefit in the 2030 Merced Preferred Scenario with all activities defined in the scenario included.

• …_change_trt_max: The change in cobenefit between 2014 and the 2030 Max Infill Scenario with all activities defined in the scenario included.

• …_trt_max: The amount of cobenefit in the 2030 Max Infill Scenario with all activities defined in the scenario included.

• ..._change_eda: The change in cobenefit in disadvantaged communities between the 2030 Reference Scenario with no activities included and the 2030 Reference Scenario with all activities included (not applicable for all cobenefits).

• ..._change_pca: The change in cobenefit in priority conservation areas between the 2030 Reference Scenario with no activities included and the 2030 Reference Scenario with all activities included (not applicable for all cobenefits).


• ..._change_vp: The change in cobenefit in vernal pool areas between the 2030 Reference Scenario with no activities included and the 2030 Reference Scenario with all activities included (not applicable for all cobenefits).

• …_2014: The amount of the cobenefit in 2014, if the cobenefit is reportable for 2014.

4.1.2.2.3. Other Fields in Reporting Tables The tool will add additional fields to the reporting tables if the user applies a custom development mask or a conservation mask. Also, fields will be added for land cover changing activities such as riparian restoration and oak woodland restoration. The following additional fields may appear in reporting tables, depending on the combination of masks and activities selected by the User.

• …_change_trt_cust: The change in cobenefit between 2014 and the 2030 Reference Scenario with all activities defined in the scenario included (if custom development was selected).

• …_trt_ cust: The amount of cobenefit in the 2030 Custom Development Scenario (if custom development was selected).

• ..._change_rre: The change in a cobenefit from the 2030 Reference Scenario without riparian restoration applied to the 2030 Reference Scenario with riparian restoration applied.

• ..._change_oak: The change in a cobenefit from the 2030 Reference Scenario without oak woodland restoration applied to the 2030 Reference Scenario with oak woodland restoration applied.

• ..._change_con: The change in a cobenefit from the 2030 Reference Scenario without a conservation mask applied to the 2030 Reference Scenario with the user-defined conservation mask applied.

• ..._change_dev: The change in a cobenefit from the 2030 Reference Scenario without a development mask applied to the 2030 Reference Scenario with the user-defined development mask applied.

The user of the tool can specify a number of avoided conversions that alter the predicted baseline trends in land cover. These avoided conversions are specified in the tool GUI. If the user enters avoided conversions, fields will be created in the reporting tables that show the changes that these avoided conversions will have versus the reference scenario. Added fields are listed below:

• ..._change_ac_wet_arc: The change in a cobenefit from avoiding converting land from wetland to annual cropland between 2014 and 2030.

• ..._change_ac_gra_arc: The change in a cobenefit from avoiding converting land from grassland to annual cropland between 2014 and 2030.

• ..._change_ac_irr_arc: The change in a cobenefit from avoiding converting land from irrigated pasture to annual cropland between 2014 and 2030.

• ..._change_ac_orc_arc:The change in a cobenefit from avoiding converting land from orchard to annual cropland between 2014 and 2030.


• ..._change_ac_arc_urb: The change in a cobenefit from avoiding converting land from annual cropland to urban between 2014 and 2030.

• ..._change_ac_gra_urb: The change in a cobenefit from avoiding converting land from grassland to urban between 2014 and 2030.

• ..._change_ac_irr_urb: The change in a cobenefit from avoiding converting land from irrigated pasture to urban between 2014 and 2030.

• ..._change_ac_orc_urb: The change in a cobenefit from avoiding converting land from orchard to urban between 2014 and 2030.

• ..._change_ac_arc_orc: The change in a cobenefit from avoiding converting land from annual cropland to orchard between 2014 and 2030.

• ..._change_ac_gra_orc: The change in a cobenefit from avoiding converting land from grassland to orchard between 2014 and 2030.

• ..._change_ac_irr_orc: The change in a cobenefit from avoiding converting land from irrigated pasture to orchard between 2014 and 2030.

• ..._change_ac_vin_orc: The change in a cobenefit from avoiding converting land from vineyard to orchard between 2014 and 2030.

• ..._change_ac_arc_irr: The change in a cobenefit from avoiding converting land from annual cropland to irrigated pasture between 2014 and 2030.

• ..._change_ac_orc_irr: The change in a cobenefit from avoiding converting land from orchard to irrigated pasture between 2014 and 2030.

4.1.2.3. Example Reporting Tables In this section, we will look at two reporting tables (carbon and crop value) for a single run of the tool to provide a deeper dive into the structure and detailed information provided in the reporting tables. For this sample tool run, we used the following user-defined settings for our scenario:

• A user-defined processing area that limits the tool’s analysis to about 10% of the county (in and around the city of Merced)

• We included the following activities: o Riparian Restoration (1000 acres) o Hedgerow Planting (1000 acres) o 1000 Acres of avoided conversion from Annual Cropland to Orchard

Figures 9 and 10 on the following pages provide a detailed look at a GHG reporting table (carbon.csv) and a crop value cobenefit reporting table. To accompany the information in the figures, it is useful to review the actual csvs output by the tool, which are available here:

• GHG Reporting Table (https://carb.press/csv_ghgs) • Crop Value Reporting Table (https://carb.press/cropval_csv) • Total Reductions Table (https://carb.press/total_reductions) • Total Report Table (https:// carb.press/total_report)

https://carb.press/csv_ghgs

https://carb.press/cropval_csv

https://carb.press/total_reductions

https://carb.press/total_report



Figure 9. Example reporting table fields – CO2 Equivalents


Figure 10. Example reporting table fields – Crop Value cobenefit


Figure 11. Example reporting table fields - Emissions


4.1.2.4. Plotly Plots If the user chooses the ‘Generate Plotly Plots’ option (see installation instructions to install and configure this in section 2), then a ‘Plots’ folder is added to the tool’s output folder and a number of charts are created as .png files. These charts summarize the cobenefits for the 2014 landscape and compare the 2014 – 2030 effects of the activities chosen by the user with the reference scenario, both for GHGs and cobenefits. Table 6 show the names of the charts created by Plotly.

Table 7. Charts created by Plotly 2014 Charts 2030 Charts

2014 Ag and Urban Water Conservation.png 2030 Ag and Urban Water Conservation.png 2014 Air Quality.png 2030 Air Quality.png 2014 Aquatic Biodiversity.png 2030 Aquatic Biodiversity.png 2014 Crop Value.png 2030 Carbon Reductions Compare.png 2014 Farmland.png 2030 Carbon Reduction.png 2014 Flood Risk Reduction.png 2030 Crop Value.png 2014 Natural Habitat Area.png 2030 ECA Terrestrial Connectivity.png 2014 Natural Resilience.png 2030 Farmland Loss.png 2014 Nitrate Leaching.png 2030 Flood Risk Reduction.png 2014 Nitrate Runoff.png 2030 Groundwater Recharge.png 2014 Priority Conservation Areas.png 2030 Natural Habitat Area.png 2014 Scenic Value.png 2030 Priority Conservation Areas.png 2014 Social Reslience.png 2030 Scenic Value.png 2014 Terrestrial Connectivity ECA.png 2030 Social Resilience.png 2014 Terrestrial Connectivity.png 2030 Terrestrial Connectivity.png 2030 Water Quality – Nitrate Leaching.png 2030 Water Quality – Nitrate Runoff.png Activity Acres Selected.png 2030 Annual Flux 2030 Aggregate Flux Total Reductions

4.1.2.5. Spatial Data The tool has a handful of options to provide spatial data for various masks. When a mask is specified, the tool will project the spatial data into the coordinate system that the base data uses. The projected spatial dataset is then exported to the output folder specified in the tool.


5. ADDITIONAL INFORMATION


A. Summary of python modules used in TerraCount Module Module Description Main Function(s) Main Function Description

main_program.py

Module receives variable (“Parameters” in ESRI-speak) from the ArcMap tool and calls functions from the other modules. This is the main calling script.

No main functions The module does write out all of the tool's inputs and parameters to the log-file and sets a number of variables based on the user's parameters.

generic.py Sets paths & workspaces, defines several functions that will be used throughout the remaining modules

set_paths_and_workspaces() Sets the paths to data and folders. Creates global variables.

helpers.py Contains a number of small functions that are using in the major modules. Many small functions Write to logfile, do random selection, print

messages etc.

intial.py Module imports the parameters from the tool GUI, create baseline development scenarios and applies the user-provided masks.

DoInitial(procmask, cs, cd, devmask, c1,c14,c30,joins,nears,points, tempgdb, scratch, cm = 0, conmask = 'None', treatmask = 'None')

Takes the user-defined masks, tabular data and carbon look up tables, imports them as Pandas dataframes, cleans up the data and merges the dataframes.

activity_application.py Module does random selection and flagging of areas for the user-defined activities.

DoActivities(df,activitylist, dictact,acdict,logfile, treatmask = 'None',customdev = 0, ug = 0, sflag = 0)

Takes the merged dataframe, list of chosen activities, avoided conversions and flags for provided masks. Randomly select the desired acreages for each activity and change landcovers as necessary.

applyactivities.py Module applies carbon reductions to areas selected in the activities and avoided conversions.

ApplyGHG(df,activitylist, dictact, trt, ug = 0, logfile = 'None')

Takes the dataframe, activities and avoidances lists, urban tree planting % and activity carbon reductions look up table path.

reporting.py Module that calculates the cobenefits and Carbon Reduction tables.

report(df, outpath, glu, wlu, rlu, clu, nlu, alu, cov14, cov30, lupath, acdict = 'None', oak = 0, rre = 0, cd = 0 , cm = 0, gra = 0, cproc = 0, terflag = 0, ug = 0, ucc = 0, logfile = 'none'); carbreport(df, outpath,activitylist,carb14, carb30,acdict = 'None', cd = 0 , cm = 0, ug = 0, logfile = 'None')

The report function takes the dataframe, 2014 and 2030 cover %s, a number of look-up tables and all of the mask flags, calculates 2014-2030 change and 2014 baseline for each cobenefit. The carbon reporting function uses the carbon look-up tables and sums up carbon for each landcover in 2014 and 2030.

create_plots.py Module that creates plots and simplified tables from the tool outputs.

Plots(folder, aclist, actlist, thabflag,cproc, apikey, username)

Takes the tool's output folder, activity list, a couple mask flags and Plotly API Key and Username and creates tables and charts in new folders.

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