Preset Areas-of-Interest

The Cal-Adapt API server has 11 preset areas of interest (aka boundary layers).

You can query these features without passing a spatial object!

Example:

norcal_cap <- ca_loc_aoipreset(type = "counties", 
                         idfld = "fips", 
                         idval = c("06105", "06049", "06089", "06015")) %>% 
  ca_gcm(gcms[1:4]) %>%
  ca_scenario(scenarios[1:2]) %>%
  ca_period("year") %>%
  ca_years(start = 2040, end = 2060) %>%
  ca_cvar("pr") %>% 
  ca_options(spatial_ag = "max")

Plot an API request to verify the location:

plot(norcal_cap)

User-Defined Geometries

An API request can use a simple feature data frame as the query location (point, polygon, and multipolygon).

Use sf::st_read() to import Shapefiles, geojson, KML, geopackage, ESRI geodatabases, etc.

library(sf)
pinnacles_bnd_sf <- st_read("./notebooks/data/pinnacles_bnd.geojson")

## Reading layer `pinnacles_bnd' from data source 
##   `D:\GitHub\cal-adapt\caladaptr-res\docs\workshops\ca_intro_apr22\notebooks\data\pinnacles_bnd.geojson' 
##   using driver `GeoJSON'
## Simple feature collection with 1 feature and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -121.2455 ymin: 36.4084 xmax: -121.1012 ymax: 36.56416
## Geodetic CRS:  WGS 84

pinnacles_bnd_sf

Crate an API request:

pin_cap <- ca_loc_sf(loc = pinnacles_bnd_sf, idfld = "UNIT_CODE") %>% 
  ca_slug("et_month_MIROC5_rcp45") %>%
  ca_years(start = 2040, end = 2060) %>% 
  ca_options(spatial_ag = "mean")

plot(pin_cap, locagrid = TRUE)

Fetch data:

pin_tbl <- pin_cap %>% ca_getvals_tbl()
head(pin_tbl)

Finding Data: Raster Series Catalog

caladaptR comes with a copy of the Cal-Adapt raster series “data catalog”.

For each raster series you can see the:

The catalog can be retrieved using ca_catalog_rs() (returns a tibble).

ca_catalog_rs() %>% head()

##  - using raster series catalog from cache

ca_catalog_rs() %>% View()

To search the catalog using a keyword, you can use ca_catalog_search()

all_snow_layers_df <- ca_catalog_search("snow", quiet = TRUE)

all_snow_layers_df %>% select(slug, name) %>% DT::datatable()

To view the properties of a specific dataset (e.g., to see the units or start/end dates), search on the slug:

ca_catalog_search("snowfall_day_livneh_vic")

## 
## snowfall_day_livneh_vic

##   name: Livneh VIC daily snowfall

##   url: https://api.cal-adapt.org/api/series/snowfall_day_livneh_vic/

##   tres: daily

##   begin: 1950-01-01T00:00:00Z

##   end: 2013-12-31T00:00:00Z

##   units: mm/day

##   num_rast: 1

##   id: 519

##   xmin: -124.5625

##   xmax: -113.375

##   ymin: 31.5625

##   ymax: 43.75

Using Climate Data Wisely

Is this a wise or unwise use of climate data?

library(caladaptr)
library(dplyr)
library(ggplot2)
library(units)

slo_cap <- ca_loc_pt(coords = c(-120.6276, 35.2130)) %>%
  ca_cvar("pr") %>%
  ca_gcm(gcms[1:10]) %>%
  ca_scenario("rcp45") %>%
  ca_period("day") %>%
  ca_dates(start = "2070-02-01", end = "2070-02-28")

slo_mmday_tbl <- slo_cap %>% 
  ca_getvals_tbl(quiet = TRUE) %>% 
  mutate(pr_mmday = set_units(as.numeric(val) * 86400, mm/day))

feb2070_weekends_df <- data.frame(dt = as.Date("2070-02-01") + c(0,1,7,8,14,15,21,22), y = 50)

ggplot(data = slo_mmday_tbl, aes(x = as.Date(dt), y = as.numeric(pr_mmday))) +
  geom_col(data = feb2070_weekends_df, aes(x = dt, y = y), fill = "red", alpha = 0.2) +
  geom_point(aes(color=gcm)) + 
  labs(title = "Chance of Rain at the San Luis Obispo Country Club, February 2070", 
       subtitle = "Pink bars represent the weekends. Emissions scenario: RCP 4.5.",
       x = "date", y = "mm") +
  theme(plot.caption = element_text(hjust = 0)) 

Best Practices Analyzing Modeled Climate Data

1) Examine 20-30 Year Time Periods

2) Look at multiple models & emissions scenarios

3) Look at both central tendency and the range of variability

4) Aggregate, aggregate, aggregate

5) Compare, compare, compare

6) Consider climate analogues

Comparing the Past to the Future

What is the appropriate historic baseline for modeled future climate scenarios?

Observed historic record and modeled future - different animals

Model historic climate and modeled future - comparable

The Dilemma of Multiple Futures

Example: How will heat spells in Madera County change under climate change compared to historic conditions?

Source Data (example):

That’s 365 * 30 * 10 * 3 = 328,500 values of tasmax, for each pixel in the county!

In what order should we

1. identify heat days
2. find consecutive heat days
3. find the mean number of heat spells per i) GCP, ii) RCP, iii) county

General Order of Operations

Data Wrangling

Cal-Adapt data always comes down in a “long” format:

ca_loc_pt(coords = c(-117.0, 33.1)) %>%
  ca_cvar(c("tasmax", "tasmin")) %>%
  ca_gcm(gcms[1:4]) %>%
  ca_scenario(scenarios[1:2]) %>%
  ca_period("year") %>%
  ca_years(start = 2040, end = 2060) %>% 
  ca_getvals_tbl(quiet = TRUE) %>% 
  head()

To make useful plots, maps, and summaries, you often have to ‘munge’ or ‘wrangle’ the data, which may include:

Fortunately you have a very robust toolbox:

[cheatsheet]

[cheatsheet]

[cheatsheet]

[cheatsheet]

Top-Seven Data Wrangling Functions

Notebook 2. Find and Wrangle Data

In Notebook 2, you will:

Notebook 2. Finding Data | solutions

END PART II!