March 19, 2017

Course resources:

Data sources

Overview: https://github.com/ClimateActionUCI/datasets

Domain Source Short description Folder Data type Temporal scope Spatial scope Source URL
Climate PRISM Current & historical climate PRISM raster 1895-present; annual, monthly, daily, norms USA; 4km or 800m http://prism.oregonstate.edu/
Climate WorldClim CMIP5/IPCC current & future climate projections CMIP5 raster 2050, 2070; monthly World; 10, 5, 2.5 min(~4km), 30secs http://worldclim.org/version1
Climate adaptwest Ensemble CMIP5 projections CMIP5_NA raster 2020, 2050, 2080; monthly North America; 1 km https://adaptwest.databasin.org/pages/adaptwest-climatena
Eco GBIF Species occurrence data ENV coordinates Day World; lat/long http://www.gbif.org/
Eco USFWS Endangered species critival habitats ENV shapefiles current https://ecos.fws.gov/ecp/report/table/critical-habitat.html
Political boundaries US census state & county boundaries BOUNDARY shapefile USA ftp://ftp2.census.gov/geo/tiger/TIGER2016/

Climate data

PRISM

Current and historical climate for the USA (spatial & temporal)
Coordinate system: +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs

Parameter name Descrption
tmean Mean temperature
tmax Maximum temperature
tmin Minimum temperature
ppt Total precipitation (Rain and snow)
vpdmin Daily minimum vapor pressure deficit [normal data only]
vpdmax Daily maximum vapor pressure deficit [normal data only]

Description: http://www.prism.oregonstate.edu/documents/PRISM_datasets.pdf
Getting PRISM data: http://rpubs.com/collnell/get_prism

PRISM

R package:

library(devtools)
install_github(repo = "prism", username = "ropensci")
library(prism)#r package: https://github.com/ropensci/prism

Temporal resolution:

get_prism_annual()
get_prism_dailys()
get_prism_monthlys()
get_prism_normals() ##30 year averages for baseline

Spatial resolution either 4km or 800m:

get_prism_normals(type = 'ppt', resolution = '4km', mon = 1)

January 2017 precipitation

rOpenSci

Files

CMIP5 data

Future world climate projections
Coordinate system: +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m
source: http://worldclim.org/CMIP5v1

GCMs: 19 global climate models
4 RCPs: climate scenarios adopted by IPCC

CMIP5 data

Temporal resolution: 2050 (avg 2041-2060), 2070 (avg 2061-2080)
Spatial resolution: 2.5 minutes (~4km)

Parameter name Descrption
tn Monthly min temperature
tx Monthly max temperature
pr Monthly precipitation (Rain and snow)
bi 18 bioclimatic variables (temp seasonality, temp range, hottest temp, coldest temp ++ )

Drive: https://drive.google.com/drive/folders/0B-TN-iTwt6b3ZTdGR2ZiOHJ2bzA?usp=sharing
Files: All models & GCMs are in the climate/CMIP5 folder
Filename: cn45tn70.grd (GCM+RCP+variable+time period)

Climate NA

Climate NA

Files Source Short description Data type Temporal scope Spatial scope Source URL
all models for all variables WorldClim Projected climate & bioclimatic variables raster monthly for 2050, 2070 World; 2.5min http://worldclim.org/cmip5_2.5m
ensemble model AdaptWest Projected NA ensemble model raster monthly for 2050, 2070 World; 2.5min http://worldclim.org/cmip5_2.5m

Ensemble model
- average of all CMIP5 models
- Just North America, smaller files

Drive: https://drive.google.com/drive/folders/0B-TN-iTwt6b3RGNWeFJqODI0UG8?usp=sharing
Files: NA_ENSEMBLE_rcp45_year_type_ASCII

Shapefiles

File Short description Data type Projection Files Data generation Temporal scope Spatial scope Source URL
USA_state USA state boundaries shapefile '+proj=longlat +ellps=WGS84' ————— 2016 ————— USA ——————
CA_state CA state boundary shapefile '+proj=longlat +ellps=WGS84' ————— 2016 ————— CA ——————
USA_county USA county boundaries shapefile '+proj=longlat +ellps=WGS84' ————— 2016 ————— USA ——————
CA_county CA county boundaries shapefile '+proj=longlat +ellps=WGS84' ————— 2016 ————— CA ——————
CAPD CA protected areas shapefile '+proj=longlat +ellps=WGS84' holdings, superunit, units 2016 ————— CA ——————
library(rgdal)
ca_county<-readOGR('data/shapefiles/CA_county')
plot(ca_county)

Ecological data

Vegetation

FedData

Package: https://github.com/bocinsky/FedData

  • National Elevation Dataset (NED) digital elevation models (1 and 1/3 arc-second; USGS)
  • National Hydrography Dataset (NHD) (USGS)
  • Soil Survey Geographic (SSURGO) database from the National Cooperative Soil Survey (NCSS), USDA
  • Global Historical Climatology Network (GHCN), coordinated by National Climatic Data Center at NOAA,
  • International Tree Ring Data Bank (ITRDB), coordinated by National Climatic Data Center at NOAA.

Other
https://github.com/ClimateActionUCI/Resources

Questions

https://github.com/ClimateActionUCI/Resources/blob/master/Questions.md

  1. Which areas in California have the most to gain from ecological conservation efforts? How can conservation be prioritized?

  2. How well do protected areas protect species? How do species fare over time and with changing climate?

  3. How will (species distributions/ecosregions/vegetation zones/Important plant areas) be affected under future climate scenarios?

  4. How does urban development and growth affect native species? Where is the human/ecological conflict? What ecosystems and species will be affected? To conserve biodiversity and/or native ecosystems, where should major development (eg wind farms) ideally occur?

  5. Does protecting umbrella species (eg California gnatcatcher in Coastal Sage Scrub or Tule Elk in grasslands) protect ecosystem-associated flora and fauna? Are these species found in protected areas?

  6. How have rare plant & their natural communities that have a very restricted range (e.g., Hesperocyparis forbesii, Pseudotsuga macrocarpa) changed over the last 20 years?

  7. What does future land use in California look like? How will it be different from the past?

Milestones

Milestone Description
1 – Select question & datasets Choose a [project question][quest] and identify the [relevant datasets][data]
2 – Plan & assign tasks Identify component tasks to answer question, plan analyses, and assign roles to group members
3 – Preliminary analysis & reflection Complete preliminary analyses (e.g. summary statistics) and evaluate data suitability
4 – Background research Relate analyses to previous research on the topic (e.g. what has been found, how has it been communicated?)
5 – Revisit question Revisit suitability of datasets for the question and reframe the question as needed
6 – Team check-in Evaluate the remaining tasks, assignments to team members, and question
7 – Start Shiny app Craft the message, layout, and code for Shiny app (it's ok to use dummy data if the analyses aren't done)
8 – Minimum viable app Finish a minimum viable Shiny app that addresses the selected question and collect team member feedback
9 – Limitations Explicitly identify the assumptions, caveats, and limitations of the data and analyses

Teams