Incorporating Delayed Forest Reestablishment into Forest Restoration Quantification Methodologies

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I. Objective

The California Air Resources Board (CARB) is responsible for developing quantification methodologies (QM) for estimating greenhouse gas (GHG) emission reductions and other benefits from projects funded by the Greenhouse Gas Reduction Fund (GGRF). The goal of this project is to develop a methodology for estimating the carbon impacts of forest fuel treatments on delayed forest reestablishment (DFR) following high-severity wildfire in California and to incorporate that methodology into CARB’s Quantification Methodology (QM) for Forest Restoration & Management. This will allow for a more comprehensive estimate of the greenhouse gas benefits of fuel reduction treatments under California’s evolving fire regime.

II. Background

Under California’s Cap-and-Trade program, the State’s portion of the proceeds from Cap-and-Trade auctions is deposited in the GGRF. The Legislature and Governor enact budget appropriations from the GGRF for State agencies to invest in projects that help achieve the State’s climate goals. These investments are collectively called California Climate Investments. Senate Bill (SB) 862 (Committee on Budget and Fiscal Review, Chapter 36, Statutes of 2014) requires CARB to develop guidance on reporting and quantification methods (QM) for all State agencies that receive appropriations from the GGRF. These QMs are designed to estimate the greenhouse gas benefits of projects using information available at the time of project selection.

One of California Climate Investments major investment areas is nature-based solutions to accelerate climate smart management of California’s landscapes. This is implemented by a suite of California Department of Forestry and Fire Protection’s (CAL FIRE) programs, which funds restoration and fuel reduction treatments in California forests.

Due to the complexity and variability of forest management, the QM for Forest Restoration & Management is among the most sophisticated of the California Climate Investments QMs. It includes methods for modeling the short-term carbon emissions associated with multiple forms of forest fuel reduction, as well as the expected value of avoided greenhouse gas emissions from the dampening effect of fuel reduction on wildfire emissions, determined probabilistically based on the likelihood that a wildfire will occur.

The scope of the QM for Forest Restoration & Management does not currently consider the effect of DFR following a severe wildfire. While many California forests are well-adapted to regenerate and regrow following frequent, mixed-severity fire, they are facing delays under the modern fire regime. Modern-type wildfires burn larger forest patches at high severity than was typical under the historical fire regime, which was dominated by frequent fire caused by lightning and tribal land stewardship. Since most native California tree species are not adapted to re-seed and grow in large, high-severity burn patches, those areas can convert to shrubs like chaparral for decades or longer – a phenomenon called DFR for the purposes of this work. Since shrubs store less carbon than mature forest, DFR can lead to long-term reductions of total ecosystem carbon. Fuel treatments can help prevent this carbon loss by decreasing the probability of large high-severity fire patches and thereby lowering the chances of a long-term conversion to shrubs. DFR has been incorporated into similar accounting methods for voluntary carbon markets.12

This project will synthesize the best available science, perform original data collection, and develop methodologies to incorporate the impacts of DFR into the QM for Forest Restoration & Management. Adding a DFR component will improve the accuracy of greenhouse gas benefit estimations to better reflect the long-term benefits of fuel reduction in frequent-fire forests.

III. Scope of Work

Task 1 - Estimate the Probability of Delayed Forest Reestablishment

The contractor will combine historical satellite data and original data collection to develop a model of DFR probability. The following is a suggested methodology for model development. Modifications or improvements are welcome throughout project proposals.

First, the contractor will calculate the prevalence of DFR in California in recent decades, measured as the proportion of severely burned forest that contained <10% tree cover 20 or more years after fire. Past high-severity wildfire patches will be identified using wildfire records and satellite data. The subset of those areas that experienced DFR spanning more than 20 years will be identified using field visits and/or manual interpretation of satellite or aerial imagery. Data collection may be facilitated by Collect Earth Online or a similar tool for expediting manual imagery interpretation. Uncertainty will be estimated using statistical analyses.

The contractor will combine the above data sets with other available models/datasets on post-fire regeneration (e.g., the Postfire Conifer Reforestation Planning Tool) to create a spatially explicit map of the probability of DFR that adjusts probability based on local characteristics such as slope, aspect, and precipitation. This map will show the probability of DFR given a hypothetical occurrence of high-severity wildfire in each location across California forests. Map resolution will be determined in consultation with CARB staff and should be less than one hectare pixel size.

Task 1 deliverables will include the map of DFR probability, reproducible code to recreate said map as new data arise, and documentation outlining the datasets and methodology as well as instructions for re-creating the results.

Task 2 - Estimate the Carbon Storage of Post-fire Shrubs

In order to understand the greenhouse gas benefits of avoiding DFR, the carbon storage potential of post-fire shrubs must be modeled. Task 2 includes original data collection, potentially involving destructive sampling paired with observational field studies, as empirical data on shrub carbon storage in the literature is limited. Data collection should be geographically distributed throughout forested California and should span a range of years since fire.

Using newly collected data in conjunction with available raw data from previous studies, the contractor will develop a model of per-acre shrub carbon storage that occurs in severely burned landscapes. Shrub carbon will be modeled as it relates to ecoregion and time since high-severity wildfire in 5-year increments. In addition to ecoregion and years since fire, the contractor may include additional independent variables in the model that are shown to be important drivers of shrub carbon density. However, these additional variables must be information that project proponents can reasonably expect to have available at the time of grant application. For example, post-wildfire shrub species composition may not be included as a model driver because it would not be known at the time when fuel treatments are performed.

Task 2 deliverables will include a statistical model of post-fire shrub carbon density per acre by ecoregion, the raw data used to generate it, and documentation of the methods used to develop said model.

To accommodate data collection, there is an opportunity to extend the contract timeline beyond the two-year proposed period.

Task 3 - Incorporate Tasks 1 & 2 Into the QM for Forest Restoration and Management

The contractor will work with CARB to incorporate DFR into existing workflows and to automate new QM components as much as possible. For example, the contractor will provide code that ingests project application information, applies the shrub carbon model described in Task 2, and outputs project-specific estimates of shrub carbon density in 5-year increments that are formatted appropriately for use as inputs into the QM.

In the current QM, wildfire is simulated once for the baseline (counterfactual) scenario and once for the treatment scenario at the midpoint of the effective period for the proposed fuel reduction treatment. Wildfire is simulated by modifying key files used to run the Fire and Fuels Extension of the Forest Vegetation Simulator (FFE-FVS). Currently, CARB is in the process of working with scientists on a separate contract to migrate the FFE-FVS model runs to a cloud platform. This migration will allow for increased volume of model runs for each proposed project. As part of Task 3, the contractor for this work will create a script that 1) automatically performs wildfire simulations using FFE-FVS, with each model run simulating a wildfire at a different 5-year timestep throughout the project period, for both baseline and treatment scenarios, 2) calculates the area vulnerable to DFR for each pair of model runs, along with the local DFR probability, and 3) calculates the carbon storage effects of DFR for each run using the shrub carbon model developed in Task 2.

The land area vulnerable to DFR for each model run, or “DFR footprint,” will be the total area in which the baseline scenario wildfire modeling predicts high-severity fire and the treatment scenario wildfire modeling predicts low- or moderate-severity wildfire according to the same definition of fire severity used in Task 1. This footprint will be used for calculations of the greenhouse gas benefits due to DFR.

The current QM defines project lifespan as 50-80 years, depending on site class. Due to limitations in the age of satellite data, it is infeasible for contractors to estimate the probability of DFR for a period greater than 20-30 years. To conservatively account for potential tree growth that may occur between 20-30 years and 50-80 years, the contractors will write a script to calculate potential maximum carbon in regenerating trees in the DFR footprint at 50-80 years that could go undetected in Task 1. See Buchholz et al. 2019 for an example of this step.3

The DFR greenhouse gas benefit will be defined as the difference between end-year project-scenario forest carbon and baseline-scenario shrub carbon within the DFR footprint, averaged across wildfire simulation runs. Equation 1 shows an example of how this could be calculated for a project with an 80-year lifespan:

Equation 1:

Equation showing difference between end-year project-scenario forest carbon and baseline-scenario shrub carbon within the DFR footprint, averaged across wildfire simulation runs.

Where:

  • GHG Benefit is the DFR-related greenhouse gas benefit of the project.
  • n is the number of FFE-FVS wildfire simulation runs, with each model run simulating a wildfire at a different 5-year timestep throughout the project period. For a project lifespan of 80 years, n = 16.

Equation representing the number of FFE-FVS wildfire simulation runs, with each model run simulating a wildfire at a different 5-year timestep throughout the project period.

  • Cdfrforesti is the carbon in the DFR footprint according to FFE-FVS modeling using the project scenario, at 80 years post-treatment, for simulation i,

  • Cdfrshrubi is the carbon in post-fire shrub in the DFR footprint of simulation i, according to Task 2, at 80 years post-treatment
  • Cdfrregeni is the carbon in post-fire tree regeneration that may arise in the DFR footprint for simulation i after the satellite detection period, modeled at 30-80 years using FVS
  • Pdf is the probability of DFR given a wildfire occurrence, specific to the project location, according to Task 1
  • Pfire is the probability of wildfire occurring in the project area at any point during the project lifespan.45

Task 3 deliverables will include revised formulas, user guide, and documentation for the QM, as well as computer code to add the DFR module to existing automated QM workflows as described above.

IV. Deliverables

The project pre-proposal must include but is not limited to the following deliverables:

During Active Contract Period

  • Work with CARB staff at the beginning of the project to create a 1-page plain-language outreach deliverable for the public describing the project’s goals, process, and planned deliverables (available in multiple languages, template will be provided).
  • Quarterly Progress Reports.
  • Monthly progress update meetings with CARB contract manager (if applicable).

Prior to Contract Close

Task 1

  • Raster map covering all California forested land depicting the probability of DFR given a hypothetical high-severity wildfire, at one hectare resolution or finer.
  • Reproducible code, including annotation, documentation, README files, and underlying data, sufficient for CARB staff to recreate deliverable (a) as new data arise.
  • Brief summary report (less than two pages) written for a general audience that describes the methods and results of Task 1.

Task 2

  • Statistical model of post-fire shrub carbon density per acre by ecoregion and time since fire, representing all forested lands in California.
  • The raw data used to generate deliverable (d)
  • Reproducible code, including annotation, documentation, README files, and underlying data, sufficient for CARB staff to recreate deliverable (d) as new data arise.
  • Brief summary report (less than two pages) written for a general audience that describes the methods and results of Task 2.

Task 3 

  • Computer code that ingests project application information, applies the shrub carbon model described in Task 2, and outputs project-specific estimates of shrub carbon density in 5-year increments that are formatted appropriately for use as inputs into the QM.
  • Computer code that amends CARB’s existing QM cloud workflow.
  • User guide and documentation describing deliverables (h) and (i) sufficient for CARB staff to reproduce the results independently.
  • Brief summary report (less than two pages) written for a general audience that describes the accomplishments of Task 3.

Overall

  • Presentation summarizing findings at a seminar directed to staff at CARB and other relevant state agencies.
  • Final Report synthesizing the methods and results of all three tasks.

NOTE: For deliverables that will be publicly posted on the web, the contractor will be responsible for ensuring their documents comply with the accessibility standards in the Web Content Accessibility Guidelines 2.0 or subsequent version. CARB will consult with the contractor to identify which deliverables will be publicly posted.

V. Timeline

It is anticipated this project will be completed 48 months from the start date (start date is estimated to be in Spring 2025). The estimated budget for this project is up to $700,000.

Scoring Criteria

1. RESPONSIVENESS TO THE GOALS AND OBJECTIVES OUTLINED IN THE PROPOSAL SOLICITATION (10 POINTS)

The proposal should explain—in adequate detail and clear, understandable language—how the proposed project satisfies the project objectives. The proposal should demonstrate an understanding of the QM for Forest Restoration & Management and how DFR would be incorporated into it.

2. WORK EXPERIENCE AND SUBJECT MATTER EXPERTISE (20 POINTS)

The proposal should demonstrate that the proposers have the work experience and subject matter expertise required to successfully carry out the proposed project as described. Additionally, the proposal should describe how the project will build upon previous relevant work.

3. EXPANDING EXPERTISE (10 POINTS)

The proposal should explain how the project team expands expertise such as by incorporating multidisciplinary expertise or perspectives, including members from various public universities, non-academic institutions, or community-based organizations, or providing opportunities to build skills and expertise for individuals from underrepresented groups. Reviewers will consider if key personnel contributing significantly to the project (i.e., a principal investigator, co-principal investigator or co-investigator, contributing 25 percent or more of their time to the project) are a member of an underrepresented group.

4. EXPLANATION OF TECHNICAL OR METHODOLOGICAL APPROACH (30 POINTS)

The proposal should clearly explain the logic and feasibility of the project’s methodology, spell out the sequence and relationships of major tasks, and explain methods for performing the work. The proposal should include a clear description and plan for how each task will be completed. This should include a detailed methodology for all three tasks, including plans for study design, data collection, field work, statistical analysis, uncertainty analysis, and code development. A timeline for competing each task and subtask should be provided.  

5. LEVEL AND QUALITY OF EFFORT AND COST EFFECTIVENESS (15 POINTS)

The proposal should describe how time and resources will be allocated, how the team organization, work plan, and project management plan will ensure timely completion of tasks, and how the proposer will ensure the project’s success. Proposal reviewers will evaluate, for example: if the objectives of the project can be met given this allocation, if there is adequate supervision and oversight to ensure that the project will remain on schedule, if time and cost are appropriately divvied up across different project tasks and stages.