AWE Data Repositories
| Datasets/Assumptions | Description | Relevance for the methodology |
|---|---|---|
| Topographic data | The topographic data provided by Landfire is recommended to derive project-relevant topographic data. A maximum pixel size of 30 m x 30 m is required. | Topographic data is a relevant input in establishing the project area (methodology section 6.1.2) and for fire behavior modeling. Growth and yield modeling can be done aspatially and does not require topographic input. |
| Tree inventory | The latest iteration of the TreeMap dataset provided by USFS should be used as the underlying tree list. | TreeMap provides a tree-level model of conterminous U.S. forests at a 30m resolution. TreeMap data has to be updated by the project proponent to reflect the project’s starting year. Any disturbances that occured on the project area between the TreeMap vintage year and the project start date has to be fully captured (harvests, wildfire, fuel treatments, etc.). |
| Surface fuel models | Updated pre-treatment surface fuel models based on LANDFIRE 2016 have to be obtained and used from the First First Street Foundation. The process to obtain these data is as follows: 1) Send an email to the First Street Foundation Research Lab at [email protected] with AWE Surface Fuels in your subject line. 2) Include the AOI shapefile and a binding statement that these data will only be used for calculating avoided wildfire emissions for your project. Data requests come in on a rolling basis. A small team at First Street fields these requests so, please have patience with request turnaround times because there is often a data queue. Post fuel-treatment fuel model and load has to be evidence-based; backed up by local data and/or literature such as Scott and Burgan (2015) [https://www.fs.usda.gov/treesearch/pubs/9521] | Surface fuel models and loads drive wildfire behavior and emissions. A realistic choice of surface fuel models and loads pre and post fuel treatments for both treated and untreated stands is a key driver for AWE results. |
| Weather | Weather data should be derived from RAWS. | Weather data is needed to identify the project area and simulate stand-level wildfire impacts and wildfire behavior/spread. The project proponent has to use at least a 10-year average besides the requirements specified in the methodology section 6.2.2. |
| Ignition points | Ignition points should be derived from USFS Spatial wildfire occurrence data for the United States. | Historical ignition points are required to model wildfire behavior and determine the size of the project area depending on average wildfire size. See section methodology section 6.1.4 and 6.2.3. |
| Background forest management assumptions | Forest management scenarios under both the baseline and project scenario on areas not receiving fuel treatments have to represent reality for the entire project area. This includes management of forests not actively participating in fuel treatment implementation. Defensible assumptions need to be backed by local evidence and/or literature (e.g., Table 6-6 in Eve et al. 2014). | The choice of realistic scenarios that are representative of background management conditions is crucial for defensible growth and yield modeling under both the baseline and project activity to simulate changes to forest conditions. |
| Regeneration assumptions | Adjustments and a pulse of regeneration must be applied at every growth and yield modeling time step, along with a small-tree growth rate multiplier. These assumptions must be evidence-based and backed up by local documentation and/or literature. For CA, we recomment Collins et al. (2011) as a source. | CertainFVS variants lack a forest regeneration model, leaving the user to input this information. This shortcoming can distort forest stand conditions as they are projected into the future based on user inputs which may be inconsistent or subjective. Depending on the understory conditions, projected canopy base height can increase rapidly, thereby greatly reducing the potential for crown fire initiation (Moody et al. 2016). |
| Delayed regeneration data | Assumptions on risk for delayed reforestation as well as carbon pools and fluxes for affected areas have to be evidence based and backed up by local data and/or literature. For California, see Buchholz et al. 2019 as a data source. | Proponents must quantify the area and emissions associated with project land that is projected to be temporarily or permanently converted from forestland to grass or shrubland following high severity fire over the crediting period. |
| List of approved fire behavior models | GridFire FlamMap ElmFire FSim |
The wildfire behavior model is used to calculate wildfire spread and the probability of a stand to burn. |
| Fire probability map | TBA | TBA |