3 fireSense_IgnitionFit Module
3.1 Module Overview
This module fits a statistical model to estimate the contributions of climate and fuel to fire ignition. Estimate fire ignition (TODO: fill this out) - (Marchal et al. 2017b, a; 2019)
3.1.2 Module inputs and parameters
Describe input data required by the module and how to obtain it (e.g., directly from online sources or supplied by other modules) .
Table 3.1 shows the full list of module inputs.
| objectName | objectClass | desc | sourceURL |
|---|---|---|---|
| climateVariablesForFire | list |
The column name(s) in the fireSense_ignitionCovariates that is climate, in a named list, .e.g. climateVariablesForFire = list('ignition' = 'MDC')
|
NA |
| fireSense_ignitionCovariates | data.frame | table of aggregated ignition covariates with annual ignitions | NA |
| flammableRTM | SpatRaster | RTM without ice/rocks/urban/water. Flammable map with 0 and 1. | NA |
| ignitionFitRTM | SpatRaster |
A (template) raster with information with regards to the spatial resolution and geographical extent of fireSense_ignitionCovariates. Used to pass this information onto fireSense_ignitionFitted Needs to have number of non-NA cells as attribute: (ignitionFitRTM@data@attributes$nonNAs), and optionally, ignitionFitRTM@data@attributes$meanForestB
|
NA |
| fireSense_ignitionFormula | character | formula - as a character - describing the model to be fitted. | NA |
Summary of user-visible parameters (Table 3.3)
| paramName | paramClass | default | min | max | paramDesc |
|---|---|---|---|---|---|
| family | function…. | poisson,…. | NA | NA |
a family function (must be wrapped with quote()) or a character string naming a family function. Only the negative binomial has been implemented For additional details see ?family. This was formerly quote(MASS::negative.binomial(theta = 1, link = 'identity')).
|
| plot_fuelBiomassPerPrediction | numeric | 1 | 10 |
when generating plots of climate x fuel class, the log of biomass (g/m2) for which to generate predictions across a gradient of climate values. If supplied, it will override any values in sim$ignitionFitRTM$meanForestB
|
|
| rescaleVars | logical | TRUE | NA | NA |
Attempt to rescale variables? If rescalers is defined, use it to rescale variables as var / rescalers['var']. Otherwise, scale() will be used to rescale variables to [0,1], if they are not already within this range.
|
| .plots | character | screen | NA | NA | See ?Plots. There are a few plots that are made within this module, if set. |
| .plotInitialTime | numeric | NA | NA | when to do plot | |
| .runInitialTime | numeric | 0 | NA | NA | when to start this module? By default, the start time of the simulation. |
| .runInterval | numeric | NA | NA | NA | optional. Interval between two runs of this module, expressed in units of simulation time. By default, NA, which means that this module only runs once per simulation. |
| .saveInitialTime | numeric | NA | NA | NA | optional. When to start saving output to a file. |
| .saveInterval | numeric | NA | NA | NA | optional. Interval between save events. |
| .seed | list | NA | NA |
Named list of seeds to use for each event (names). E.g., list('init' = 123) will set.seed(123) at the start of the init event and unset it at the end. Defaults to NULL, meaning that no seeds will be set.
|
|
| .studyAreaName | character | NA | NA | NA |
Human-readable name for the study area used. If NA, a hash of studyAreaLarge will be used.
|
| .useCache | logical | FALSE | NA | NA | Should this entire module be run with caching activated? This is generally intended for data-type modules, where stochasticity and time are not relevant. |
3.1.6 Module outputs
Description of the module outputs (Table 3.5).
| objectName | objectClass | desc |
|---|---|---|
| covMinMax_ignition | data.table | Table of the original ranges (min and max) of covariates |
| fireSense_IgnitionFitted | fireSense_IgnitionFit |
A fitted model object of class fireSense_IgnitionFit.
|
| ignitionRescalers | integer | scaling vector if rescaling variables to 0-10 range |
3.1.7 Links to other modules
This model can be used to parameterize the fire ignition component of landscape fire models such as fireSense.