This is an abridged version of the full available-data vignette. The vignette can be viewed in its entirety at https://github.com/earthlab/cft-CRAN/blob/master/full_vignettes/available-data.md
This vignette provides a walk-through of a common use case of the Available Data function and the cft package: understanding climate futures for a region of interest. We’ll use Hot Springs National Park, located in Arkansas, USA and Yellowstone National Park, located in Wyoming, USA, as case studies.
Note that the available_data function is best used for downloading MACA climate model data for several climate variables from several climate models for any number of emission scenarios over a relatively small spatial region and over a relatively short time period. If you would like to download MACA climate model data for several climate variables from several climate models and any number of emission scenarios in their entirety for a single lat/long location, you should use the single_point_firehose function in the cft pacakge. A vignette on how to use the firehose function in the cft package is available at https://github.com/earthlab/cft-CRAN/blob/master/full_vignettes/firehose.md.
This vignette will show you how to:
data.frame containing climate dataTo get the most out of this vignette, we assume you have:
filter(),
group_by(),
and summarise())Global Circulation Models (GCMs) provide estimates of historical and future climate conditions. The complexity of the climate system has lead to a large number GCMs and it is common practice to examine outputs from many different models, treating each as one plausible future.
Most GCMs are spatially coarse (often 1 degree), but downscaling provides finer scale estimates. The cft package uses one downscaled climate model called MACA (Multivariate Adaptive Climate Analog) Version 2 (details here).
Load the cft package and other libraries required for vignette. If you need to install cft, install it from CRAN.
library(cft)## Loading required package: plyr## Loading required package: dplyr##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union## Loading required package: osmdata## Data (c) OpenStreetMap contributors, ODbL 1.0. https://www.openstreetmap.org/copyright## Loading required package: magrittrlibrary(sf)## Linking to GEOS 3.11.0, GDAL 3.5.2, PROJ 9.1.0; sf_use_s2() is TRUElibrary(tidync)
ls(pos="package:cft")## [1] "available_data" "single_point_firehose"inputs <- cft::available_data()## Trying to connect to the USGS.gov API## not a file:
## ' https://cida.usgs.gov/thredds/dodsC/macav2metdata_daily_future '
##
## ... attempting remote connection## Connection succeeded.## Reading results## Converting into an R data.table?available_data
?single_point_firehoseLook at the variables, emission scenarios, and models for which data are available
levels(as.factor(inputs$variable_names$Variable))## [1] "Eastward Wind" "Maximum Relative Humidity"
## [3] "Maximum Temperature" "Minimum Relative Humidity"
## [5] "Minimum Temperature" "Northward Wind"
## [7] "Precipitation" "Specific Humidity"
## [9] "Surface Downswelling Shortwave Flux"levels(as.factor(inputs$variable_names$`Variable abbreviation`))## [1] "huss" "pr" "rhsmax" "rhsmin" "rsds" "tasmax" "tasmin" "uas"
## [9] "vas"levels(as.factor(inputs$variable_names$Scenario))## [1] "RCP 4.5" "RCP 8.5"levels(as.factor(inputs$variable_names$`Scenario abbreviation`))## [1] "rcp45" "rcp85"levels(as.factor(inputs$variable_names$Model))## [1] "Beijing Climate Center - Climate System Model 1.1"
## [2] "Beijing Normal University - Earth System Model"
## [3] "Canadian Earth System Model 2"
## [4] "Centre National de Recherches Météorologiques - Climate Model 5"
## [5] "Commonwealth Scientific and Industrial Research Organisation - Mk3.6.0"
## [6] "Community Climate System Model 4"
## [7] "Geophysical Fluid Dynamics Laboratory - Earth System Model 2 Generalized Ocean Layer Dynamics"
## [8] "Geophysical Fluid Dynamics Laboratory - Earth System Model 2 Modular Ocean"
## [9] "Hadley Global Environment Model 2 - Climate Chemistry 365 (day) "
## [10] "Hadley Global Environment Model 2 - Earth System 365 (day)"
## [11] "Institut Pierre Simon Laplace (IPSL) - Climate Model 5A - Low Resolution"
## [12] "Institut Pierre Simon Laplace (IPSL) - Climate Model 5A - Medium Resolution"
## [13] "Institut Pierre Simon Laplace (IPSL) - Climate Model 5B - Low Resolution"
## [14] "Institute of Numerical Mathematics Climate Model 4"
## [15] "Meteorological Research Institute - Coupled Global Climate Model 3"
## [16] "Model for Interdisciplinary Research On Climate - Earth System Model"
## [17] "Model for Interdisciplinary Research On Climate - Earth System Model - Chemistry"
## [18] "Model for Interdisciplinary Research On Climate 5"
## [19] "Norwegian Earth System Model 1 - Medium Resolution"levels(as.factor(inputs$variable_names$`Model abbreviation`))## [1] "bcc-csm1-1" "bcc-csm1-1-m" "BNU-ESM" "CanESM2"
## [5] "CCSM4" "CNRM-CM5" "CSIRO-Mk3-6-0" "GFDL-ESM2G"
## [9] "GFDL-ESM2M" "HadGEM2-CC365" "HadGEM2-ES365" "inmcm4"
## [13] "IPSL-CM5A-LR" "IPSL-CM5A-MR" "IPSL-CM5B-LR" "MIROC-ESM"
## [17] "MIROC-ESM-CHEM" "MIROC5" "MRI-CGCM3" "NorESM1-M"This code downloads data for one model, one emission scenario, and 1 climate variable.
input_variables <- inputs$variable_names %>%
filter(Variable %in% c("Maximum Relative Humidity")) %>%
filter(Scenario %in% c( "RCP 4.5")) %>%
filter(Model %in% c(
"Beijing Climate Center - Climate System Model 1.1")) %>%
pull("Available variable")
input_variables## [1] "rhsmax_bcc-csm1-1_r1i1p1_rcp45"You will need to specify an appropriate area of interest for downloading spatial data. We utilize the functionality of open street map to make it fast and easy to download shapefiles for any area of interest. Here I am using the open street map (OSM) protocol to retrieve a shapefile for Hot Springs National Park. I choice this park because it is the smallest national park.
This chunk of code is first using the getbb() function to retrieve a bounding box matching a plain language search request. That bounding box is then fed into the API call that retrieves the data and converts it into an r-specific spatial data format called sf.
OSM uses a ‘key’ and ‘value’ system for organizing requests. You may want to spend some time familiarizing yourself with the immense library of data you can access with this system. https://wiki.openstreetmap.org/wiki/Tags
bb <- getbb("Hot Springs")
my_boundary <- opq(bb, timeout=300) %>%
add_osm_feature(key = "boundary", value = "national_park") %>%
osmdata_sf()
my_boundary## Object of class 'osmdata' with:
## $bbox : 34.4264361,-93.1290543,34.5594048,-92.9850972
## $overpass_call : The call submitted to the overpass API
## $meta : metadata including timestamp and version numbers
## $osm_points : 'sf' Simple Features Collection with 611 points
## $osm_lines : NULL
## $osm_polygons : 'sf' Simple Features Collection with 2 polygons
## $osm_multilines : NULL
## $osm_multipolygons : 'sf' Simple Features Collection with 1 multipolygonsHere you re-calibrate the bounding box relative to the actual shapefiles you downloaded. The bounding box above was pulled from the osm database, this bounding box is for the polygons you actually used. These two boxes may be the same or they may be different but the one derived from your downloaded shapefile is reproducible.
Notice that we specify osm_multipolygons instead of osm_polygons. This is a case-specific choice. When you download a shapefile from OSM, if will include a number of different spatial object types and you can choose several of those options to move forward. We chose multipolygons because they best matched our area of interest. Use the quick plot below to visualize your area of interest before continuing.
boundaries <- my_boundary$osm_multipolygons
pulled_bb <- st_bbox(boundaries)
pulled_bb## xmin ymin xmax ymax
## -93.11393 34.49884 -93.01857 34.55948It’s time to download. Here is a simple and fast example of data from a single point (the centroid of our polygon) for one year (2099).
start_time <- Sys.time()
center_point <- st_centroid(boundaries) %>% st_bbox(center_point)
times <- inputs$available_times
Pulled_data_single_space_single_timepoint <- inputs$src %>%
hyper_filter(lat = lat <= c(center_point[4]+0.05) & lat >= c(center_point[2]-0.05)) %>%
hyper_filter(lon = lon <= c(center_point[3]+0.05) & lon >= c(center_point[1]-0.05)) %>%
hyper_filter(time = times$`Available times` == 44558) %>%
hyper_tibble(select_var = input_variables[1:38]) %>%
st_as_sf(coords = c("lon", "lat"), crs = 4326, agr = "constant")
end_time <- Sys.time()
print(end_time - start_time)## Time difference of 1.617556 secshead(Pulled_data_single_space_single_timepoint)## Simple feature collection with 6 features and 2 fields
## Attribute-geometry relationship: 2 constant, 0 aggregate, 0 identity
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -93.10602 ymin: 34.4796 xmax: -93.02267 ymax: 34.52126
## Geodetic CRS: WGS 84
## # A tibble: 6 × 3
## `rhsmax_bcc-csm1-1_r1i1p1_rcp45` time geometry
## <int> <dbl> <POINT [°]>
## 1 99 44558 (-93.10602 34.4796)
## 2 100 44558 (-93.06433 34.4796)
## 3 100 44558 (-93.02267 34.4796)
## 4 100 44558 (-93.10602 34.52126)
## 5 98 44558 (-93.06433 34.52126)
## 6 99 44558 (-93.02267 34.52126)