caladaptR

Features

caladaptr is an API client that makes it easier to work with data from Cal-Adapt.org in R. The niche of caladaptr is to bring data into R after which you can use other packages for analysis and visualization:

caladaptr allows you to:

  • query any of Cal-Adapt’s ~950 raster data layers
  • retrieve values by point, a preset area-of-interest (e.g., census tract), or a user-provided polygon
  • cache large queries in a local SQLite database
  • download cropped rasters as TIFs and import them into R as stars objects

caladaptr functions have been designed to:

  • be pipe friendly
  • return tibbles for compatibility with tidyverse packages
  • return values with encoded units (managed by the units package)
  • accept and return sf data frames where spatial objects are needed
  • return rasters as spatiotemporal arrays (stars objects)

caladaptr does not support downloading the original NetCDF rasters nor station data (e.g., sea level rise, stream gauges). For these data, see the Data Download tool or FTP server on Cal-Adapt.org.

Installation

caladaptr is available on r-universe. To install it, you can run:

options(repos = c(ajlyons = 'https://ajlyons.r-universe.dev',
                  CRAN = 'https://cloud.r-project.org'))
install.packages('caladaptr')

Alternately, you can install it directly from GitHub. (This requires the remotes package plus RTools for Windows users.)

remotes::install_github("ucanr-igis/caladaptr")

If you are trying (and failing) to install caladaptr on RStudio Cloud (or another Linux machine), try to install sf by itself, and then caladaptr.

if (!require(sf)) install.packages("sf")
options(repos = c(ajlyons = 'https://ajlyons.r-universe.dev',
                  CRAN = 'https://cloud.r-project.org'))
install.packages('caladaptr')

General Workflow

In general, there are three steps to retrieving Cal-Adapt data with caladaptr:

  1. Create an ‘API request object’

  2. Feed the API request into a function that fetches data (either values or rasters)

  3. Wrangle the data that comes back into the format required for your analysis

Example: Projected Annual Temperature at a Point Location

In this example, we’ll get the projected maximum daily temperature for a point location near Sacramento, averaged by calendar year from 2040-2070, for 10 GCMs and 2 emissions scenarios. Then we’ll plot them.

1. Create an API request object

Creating an API request object is a bit like filling in an order form. The request is essentially a description of the data you want.

There are a number of constructor functions you can mix and match to create an API request object. Below we create a request for 30 years of projected annual average maximum daily temperature from the LOCA downscaled CMIP5 climate projections from Scripps, for a single point location.

library(caladaptr)
#> caladaptr (version 0.6.8)
#> URL: https://ucanr-igis.github.io/caladaptr
#> Bug reports: https://github.com/ucanr-igis/caladaptr/issues

sac_tasmax_cap <- ca_loc_pt(coords = c(-121.4687, 38.5938)) %>%     ## specify a location
  ca_gcm(c("HadGEM2-ES", "CNRM-CM5", "CanESM2","MIROC5",            ## select GCM(s)
           "ACCESS1-0", "CCSM4", "CESM1-BGC", 
           "CMCC-CMS", "GFDL-CM3", "HadGEM2-CC")) %>%     
  ca_scenario(c("rcp45","rcp85")) %>%                               ## select emission scenarios(s)
  ca_cvar(c("tasmax")) %>%                                          ## select climate variables
  ca_period("year") %>%                                             ## select a temporal aggregation period
  ca_years(start = 2040, end = 2070)                                ## select start and end dates

sac_tasmax_cap
#> Cal-Adapt API Request
#> Location(s): 
#>   x: -121.469
#>   y: 38.594
#> Variable(s): tasmax
#> Temporal aggregration period(s): year
#> GCM(s): HadGEM2-ES, CNRM-CM5, CanESM2, MIROC5, ACCESS1-0, CCSM4, CESM1-BGC, CMCC-CMS, GFDL-CM3, HadGEM2-CC
#> Scenario(s): rcp45, rcp85
#> Dates: 2040-01-01 to 2070-12-31
#>

2. Fetch data

Functions that fetch data from Cal-Adapt include ca_getvals_tbl(), ca_getvals_db() (see Large Queries vignette), and ca_getrst_stars() (see Rasters Part I vignette). Below we’ll fetch data into a tibble by passing the API request to ca_getvals_tbl():

sac_tasmax_tbl <- sac_tasmax_cap %>% ca_getvals_tbl(quiet = TRUE)
head(sac_tasmax_tbl)
#> # A tibble: 6 × 8
#>      id cvar   period gcm        scenario spag  dt          val
#>   <int> <fct>  <fct>  <fct>      <fct>    <fct> <chr>       [K]
#> 1     1 tasmax year   HadGEM2-ES rcp45    none  2040-12-31 299.
#> 2     1 tasmax year   HadGEM2-ES rcp45    none  2041-12-31 299.
#> 3     1 tasmax year   HadGEM2-ES rcp45    none  2042-12-31 299.
#> 4     1 tasmax year   HadGEM2-ES rcp45    none  2043-12-31 300.
#> 5     1 tasmax year   HadGEM2-ES rcp45    none  2044-12-31 300.
#> 6     1 tasmax year   HadGEM2-ES rcp45    none  2045-12-31 298.
dim(sac_tasmax_tbl)
#> [1] 620   8

3. Wrangle Data

Depending what your goal it, you may need to add or delete columns, reshape the data, group rows, etc. Below we’ll add a column for Fahrenheit using set_units() from the units package.

## Add a column with Fahrenheit units
library(dplyr)
library(units)
sac_tasmax_tbl2 <- sac_tasmax_tbl %>% mutate(temp_f = set_units(val, degF))
head(sac_tasmax_tbl2)
#> # A tibble: 6 × 9
#>      id cvar   period gcm        scenario spag  dt          val temp_f
#>   <int> <fct>  <fct>  <fct>      <fct>    <fct> <chr>       [K] [degF]
#> 1     1 tasmax year   HadGEM2-ES rcp45    none  2040-12-31 299.   78.1
#> 2     1 tasmax year   HadGEM2-ES rcp45    none  2041-12-31 299.   78.0
#> 3     1 tasmax year   HadGEM2-ES rcp45    none  2042-12-31 299.   77.8
#> 4     1 tasmax year   HadGEM2-ES rcp45    none  2043-12-31 300.   79.7
#> 5     1 tasmax year   HadGEM2-ES rcp45    none  2044-12-31 300.   79.5
#> 6     1 tasmax year   HadGEM2-ES rcp45    none  2045-12-31 298.   77.2

Now we can plot it:

library(ggplot2)
ggplot(data = sac_tasmax_tbl2, 
       aes(x = as.Date(dt), y = as.numeric(temp_f))) +
  geom_line(aes(color=gcm)) +
  facet_grid(scenario ~ .) +
  labs(title = "Annual Average Maximum Daily Temperature for Sacramento", x = "year", y = "temp (F)")

Constructing API Requests

API request objects are constructed by stringing together functions that provide the key elements of the request, including the location(s), dataset(s), and time frame.

Location functions (pick one):

Function Location Type
ca_loc_pt() points in matrices
ca_loc_aoipreset() preset areas-of-interest
ca_loc_sf( ) user-supplied sf object (point or polygon)

Date functions (pick one):

Function Date Type
ca_dates() specific dates
ca_years() years

Dataset functions (pick one group):

Functions Dataset
ca_gcm() + ca_scenario() + ca_cvar() + ca_period() LOCA downscaled modeled climate data (including all Scripps and VIC)
ca_livneh() + ca_cvar() + ca_period() Livneh data (observed historical)
ca_slug() Everything else

ca_example_apireq() is a convenience function that returns sample API requests for testing. You can use ca_preflight() to check an API request for errors. Plotting an API request will show the location(s), with an option to overlay the loca grid cells:

samp_cap <- ca_example_apireq(3)
samp_cap %>% ca_preflight()
#> General issues
#>  - none found
#> Issues for querying values
#>  - none found
#> Issues for downloading rasters
#>  - none found
plot(samp_cap, locagrid = TRUE, static = TRUE)

For more examples, including retrieving data for a preset area-of-interest (i.e., census tracts), see the API Requests vignette and ‘R Notebooks’ on the website.

Constants

To help you pass arguments for the various constructor functions, caladaptr provides the following constants:

## Climate Variables 
cvars
#>  [1] "tasmax"     "tasmin"     "pr"         "swe"        "baseflow"  
#>  [6] "et"         "rainfall"   "runoff"     "snowfall"   "soilMoist1"
#> [11] "Tair"

## Global Climate Models
## Note: the first 4 are the 'priority' models recommended under California's 4th Climate Change Assessment.
gcms
#>  [1] "HadGEM2-ES" "CNRM-CM5"   "CanESM2"    "MIROC5"     "ACCESS1-0" 
#>  [6] "CCSM4"      "CESM1-BGC"  "CMCC-CMS"   "GFDL-CM3"   "HadGEM2-CC"
#> [11] "ens32avg"   "ens32max"   "ens32min"

## Emission scenarios
scenarios
#> [1] "rcp45"      "rcp85"      "historical"

## Temporal resolution periods
periods
#> [1] "day"    "month"  "year"   "30yavg"

Data Catalog

caladaptr can not tell you which climate data you need for your project. Nor will it teach you how to use climate data appropriately or wisely. However it does have a copy of the Cal-Adapt raster series data catalog, so you can see what’s available and specify the datasets you need.

To view the entire data catalog in a RStudio Viewer pane, run the following. You can then use the filter button to find datasets that match a key word.

View(ca_catalog_rs())

You can also search for datasets using ca_catalog_search(), and download a fresh copy of the catalog from Cal-Adapt using ca_catalog_fetch().

Most / all of the Cal-Adapt rasters series have been downscaled to approximately 6km (3.7 mi) using the LOCA downscaling method, and are available for the coverage area shown below. For a finer scale analysis, you can download the LOCA grid cells as a polygon layer using ca_locagrid_geom().