# Using nasapower with large geographic areas

### An example with two states in Brazil, Rio Grande do Sul and Paraná

Fetching POWER data using nasapower for small, single queries is easy and straightforward. However, if you wish to have daily data for a larger area it can be trickier to implement.

Here I demonstrate fetching multiple seasons of rainfall data for two states in Brazil and two growing seasons using nasapower. An example of this use case can be found in Emerson Del Ponte’s slide deck from the International Congress of Plant Pathology, 2019, “Can rainfall be a useful predictor of epidemic risk across temporal and spatial scales?”

## Load libraries

To get the rainfall data for the states in Brazil, we will use nasapower (Sparks 2019), rnaturalearth (South 2017), raster (Hijmans 2019) packages to fetch the data, dplyr (Wickham et al. 2019) for data manipulation and ggplot2 (Wickham 2016) to visualise the final data.

library(rnaturalearth)
library(raster)
library(dplyr)
library(nasapower)
library(ggplot2)
library(purrr)

## Data import

To get the state data we will use rnaturalearth to download simple features data for Brazil and subset the states Rio Grande do Sul and Paraná into separate objects.

BRA <- ne_states(country = "Brazil",
returnclass = "sf")

# subset spatial objects of only the states of interest
RS <- BRA[BRA$name_en == "Rio Grande do Sul", ] PR <- BRA[BRA$name_en == "Paraná", ]

## Get the rainfall data

Now that we have objects for the states we can create a raster grid to represent the 0.5 x 0.5 degree grid that is the NASA-POWER data and select only cells that fall within the two states of interest.

### POWER source

Create a grid of 0.5 x 0.5 arc degrees and extract the x, y values from it for each state to use the coordinates to query the POWER data.

# create a global 0.5 x 0.5 degree raster object
r <- raster(xmn = -180,
xmx = 180,
ymn = -90,
ymx = 90,
resolution = 0.5)

values(r) <- 1:ncell(r)

plot(r, main = "Full global raster at 0.5 x 0.5 degrees")


# Extract the two states, first crop by bounding box, then mask the raster
PR_coords <- crop(r, PR)
RS_coords <- crop(r, RS)

PR_coords <- mask(PR_coords, PR)
plot(PR_coords, main = "Paraná")
# add the sf object, note the use of indexing to plot only the first column
# to avoid warning messages
plot(PR[, 1], col = NA, add = TRUE)


RS_coords <- mask(RS_coords, RS)
plot(RS_coords, main = "Rio Grande do Sul")
plot(RS[, 1], col = NA, add = TRUE)


# extract the centroid values of the cells to use querying the POWER data
PR_coords <- as.data.frame(xyFromCell(PR_coords, 1:ncell(PR_coords)))
RS_coords <- as.data.frame(xyFromCell(RS_coords, 1:ncell(RS_coords)))
names(PR_coords) <- names(RS_coords) <- c("LON", "LAT")
coords <- rbind(PR_coords, RS_coords)

WARNING This step is time intensive. WARNING

Using nested for() loops, query the NASA-POWER database to gather precipitation data for the states where rust was reported and save a CSV file of the rainfall.


power <- vector(mode = "list", 2) # hold two growing seasons
precip <- vector(mode = "list", nrow(coords)) # hold the cells

seasons <- list(
c("2014-11-01", "2015-03-31"),
c("2015-11-01", "2016-03-31")
)

for (i in seq_along(seasons)) {
# two "seasons" (outer loop 2x)
season <- seasons[[i]]

# inner loop for each pair coords
for (j in seq_along(1:nrow(coords))) {
NA_df <-
data.frame(
LON = coords[1, 1],
LAT = coords[1, 2],
YEAR = NA,
MM = NA,
DD = NA,
DOY = NA,
YYYYMMDD = NA,
PRECTOT = NA
)

p_get_power <- possibly(get_power, otherwise = NA_df)

# 312 coordinate pairs (inner loop 312x)
site <- as.numeric(coords[j,])
power_precip <- p_get_power(
community = "AG",
lonlat = site,
pars = "PRECTOT",
dates = season,
temporal_average = "DAILY"
)
precip[[j]] <- power_precip
Sys.sleep(5) # wait 5 seconds between requests so we don't hammer the server
}
precip_df <- bind_rows(precip)
power[[i]] <- precip_df
}
power_df <- bind_rows(power)

# adds states to rows
power_df <- data.frame(STATE = c(rep("PR", nrow(PR_coords)),
rep("RS", nrow(RS_coords))),
power_df)

## Acknowledgements

These data were obtained from the NASA Langley Research Center POWER Project funded through the NASA Earth Science Directorate Applied Science Program.

## References

Lionel Henry and Hadley Wickham (2019). purrr: Functional Programming Tools. R package version 0.3.2. https://CRAN.R-project.org/package=purrr

Robert J. Hijmans (2019). raster: Geographic Data Analysis and Modeling. R package version 3.0-2. https://CRAN.R-project.org/package=raster

Andy South (2017). rnaturalearth: World Map Data from Natural Earth. R package version 0.1.0. https://CRAN.R-project.org/package=rnaturalearth

Adam Sparks (2018). nasapower: A NASA POWER Global Meteorology, Surface Solar Energy and Climatology Data Client for R. Journal of Open Source Software, 3(30), 1035, https://doi.org/10.21105/joss.01035

Adam Sparks (2019). nasapower: NASA-POWER Data from R. R package version 1.1.2, <URL: https://CRAN.R-project.org/package=nasapower>.

Hadley Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.

Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2019). dplyr: A Grammar of Data Manipulation. R package version 0.8.3. https://CRAN.R-project.org/package=dplyr

## Session information

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