Microclimate measurement and modeling for Sky Island project
This repository holds data for the decagon soil moisture and precipitation sensors (decagon
folder) and for the iButton temperature/humidity sensors and associated soil moisture measurements (microclimate
)
These analyses require R and the following packages: lubridate, xts, dplyr, ggplot2, raster, sp, pcaMethods, tidyr
Note that running on windows may require an up-to-date version of lubridate. To obtain this, use the devtools package and install lubridate from github:
library(devtools)
install_github("hadley/lubridate")
Not that this will require installing Rtools for windows and installing the devtools package.
Requires a working python installation
See the microclimate README for more information.
See the methods document for more information.
See random-forest.R
reconstruct-climate.R
See github issue #8: https://github.com/schwilklab/skyisland-climate/issues/8
Steps for modeling microclimate across three west Texas mountain ranges:
The goal: A 60-year daily predicted time series for Tmin and one for Tmax for each point on the landscapes (and the same thing for the future under ESM projections).
Modified goal: summarize each landscape point to a set of annual climate summary variables rather than having 365 tmin and tmax values for each landscape point.
To achieve this goal we are will produce functions (will take multiple steps) that predict daily tmin and tmax as a function of topographic variables AND single daily weather station time series. This is a completely separate process for each mtn range. To do this we decompose our iButton data into temporal and spatial components.
PCAs. For each mtn range, use PCA to reduce the iButton time series to a set of loadings and scores. See "Decomposing iButton ...." above. Save these PCA models because as we will use the "loadings" (PC axes) to create the topographical models, and then expand these across a full raster map (not just the actual iButton locations) then transform back to scores in order to fit the time model.
NOTE: I originally considered splitting the time series seasonally because the topographic effects on tmin and tmax seem to vary seasonally. But that is currently not impllemented and would add considerable complexity. It does not seem necessary in my current tests
Some details to record our decisions re PCA: