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LandR_BiomassGMCC.Rmd
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LandR_BiomassGMCC.Rmd
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---
title: "LBMR_GMCS"
author: ""
date: "03 March 2017"
output: pdf_document
---
# Overview
This module is to provide growth and mortality functions that sensitive to climate moisture index anomaly (a proxy index of climate change-associated water availability).
The module provides two algorithms: 1) growth and mortality responses to climate moisture index anomaly without depending on spatial availability (i.e., spatial drought gradient); 2) growth and mortality response to climate moisture index anomaly depending on spatial water availibity.
The first algorithm is using the results from Chen & Luo 2016 Ecology Letter paper;
The second algroritm is using the equations that Luo, Chen and McIntire's recent paper.
For both algorithms, the sensitivities of growth and mortality vary with stand age (ie., time since fire).
## R Markdown
R Markdown syntax allows R code, outputs, and figures to be rendered in the documentation.
For help writing in R Markdown, see http://rmarkdown.rstudio.com/.
# Usage
```{r module_usage}
# library(igraph)
# library(SpaDES)
#
# moduleDir <- file.path("C:/Users/yonluo/Documents/GitHub/LBMR_ClimateSensitive")
# inputDir <- file.path(moduleDir, "inputs") %>% checkPath(create = TRUE)
# outputDir <- file.path(moduleDir, "outputs")
# cacheDir <- file.path(outputDir, "cache")
# times <- list(start = 0, end = 10)
# parameters <- list(
# #.progress = list(type = "text", interval = 1), # for a progress bar
# ## If there are further modules, each can have its own set of parameters:
# #module1 = list(param1 = value1, param2 = value2),
# #module2 = list(param1 = value1, param2 = value2)
# )
# modules <- list("LBMR_GMCS")
# objects <- list()
# paths <- list(
# cachePath = cacheDir,
# modulePath = moduleDir,
# inputPath = inputDir,
# outputPath = outputDir
# )
#
# mySim <- simInit(times = times, params = parameters, modules = modules,
# objects = objects, paths = paths)
#
# spades(mySim)
```
# Events
1. Growth and Mortality responses to ACMIA (annual climate moisture index anomaly) without depending on spatial water availability (Chen, Luo 2016 Ecology Letters)
Growth: (Mg per ha per cm)
(ACMIA-0.935)*0.016+(log(SA)-4.40)*(ACMIA-0.934)*0.031
Mortality:
(ACMIA-0.935)*0.028 # does not significantly depending on Stand age
where 0.935 and 4.40 are the averages for Chen and Luo's dataset for ACMIA and log(SA).
2. Growth and Mortality responses to ACMIA (annual climate moisture index anomaly) depending on spatial water availability (Luo, Chen, McIntire recent paper)
Growth:
(ACMIA-0.935)*0.018+(SpCMI-8.043)*(-0.015)+(log(SA)-4.40)*(ACMIA-0.935)*0.039+(ACMIA-0.934)*(SpCMI-8.043)*(-0.002)
Mortality:
(ACMIA-0.935)*0.027+ (SpCMI-8.043)*(0.049)+(ACMIA-0.934)*(SpCMI-8.043)*(-0.002)
# does not significantly depending on Stand age
where 0.935, 4.40 and 8.043 are the averages for Chen and Luo's dataset for ACMIA, log(SA) and SpCMI, respectively.
## Plotting
Write what is plotted.
## Saving
Write what is saved.
# Data dependencies
## Input data
How to obtain input data, and a description of the data required by the module.
If `sourceURL` is specified, `downloadData("LBMR_GMCS", "path/to/modules/dir")` may be sufficient.
## Output data
Description of the module outputs.
# Links to other modules
Describe any anticipated linkages to other modules.