This is a short introduction to demonstrate how to access water data (flow, stage, water chemistry, etc) via the USGS
dataRetrieval package. It will follow the same pipeline of downloading data, tidying, and then plotting and mapping that data for communication or reporting purposes. We’ll demonstrate how to download some data for a parameter, or a set of stations, or a specific region.
Let’s load the libraries we’re going to need first.
library(dataRetrieval) library(here) library(dplyr) library(viridis) library(ggplot2) library(lubridate) library(leaflet) library(sf) library(mapview) library(USAboundaries) library(DT) library(plotly)
This package has lots of functionality for many different data types. Let’s first load the library, and then look up the USGS parameter codes that measure nitrate. We’re using a
grep() function here, which is a common way to look up text for any particular string, or part of a string.
# here we create an object of all the parameters that relate to nitrate <- parameterCdFile[grep("nitrate", nitrCds $parameter_nm, parameterCdFileignore.case=TRUE),]
# how many rows nrow(nitrCds)
##  119
# how many different measurement units are there? unique(nitrCds$parameter_units)
##  "count" "None" "mg/l as N" "%" "mg/l" "mg/kg" "mg/kg as N" "% by wt" "ug/L as N" "mg/l asNO3" "mg/l NO3" "mg/m2 as N" ##  "ueq/L" "mg/m2" "mg/m2 NO3" "ton/d as N" "ug/l" "tons/day" "lb/d as N" "lb/day" "umol/L" "MPN/g" "ug/kg" "per mil"
# try looking up flow <- parameterCdFile[grepl("flow", flowCds $srsname, parameterCdFileignore.case = TRUE),] # look in rows
##  11
How many parameter metrics exist for flow?
Now that we know to look up the codes for different parameters (see the
parameter_cd column), we can pick a few and search within given state or region.
For example, let’s stick with nitrate for a little bit, and say we get data for nitrate using the parameter code
00618, then specify we only want stations that collect that data in
Bear in mind, if you run the following code chunk it may take a few minutes, as it’s retrieving all the stations that collect nitrate data with the
00618, and all the records for those stations (in California!). That’s because we use the
seriesCatalogueOutput=TRUE here…later we’ll show an alternative. I’ve saved this rather large file here if you just want to grab it and avoid running this code chunk.
# select a code of interest <- c("00618") # nitrate cd_code # now run function: note with seriesCatalogOutput=TRUE, this can take a bit! <- readNWISdata(stateCd="CA", parameterCd=cd_code, niCA service="site", seriesCatalogOutput=TRUE) nrow(niCA) # OVER 1.2 million records! # save(niCA, file = "data/nitrate_00618_CA_NWIS_2018.rda")
Then we filter down this massive dataset to sites that have more than 300 records, and have Tahoe in the station name. Then make a leaflet map! Leaflet is a nice interactive open-source map that we can generate using any spatial data in R.
# take a look at how many distinct stations are available %>% dplyr::distinct(station_nm) %>% tally # that's a lot!niCA
## n ## 1 9249
# filter to stations with TAHOE in name and number of records > 300 <- niCA %>% dplyr::filter(grepl("TAHOE", station_nm), count_nu > 300) niCA.Tahoe # make the data spatial with the sf <- st_as_sf(niCA.Tahoe, coords = c("dec_long_va","dec_lat_va"), remove = FALSE, crs=4326) niCA.Tahoe.sf # Mapview: make an interactive map quickly: mapview(niCA.Tahoe.sf, layer.name="USGS Stations <br> collecting nitrate data")
# Leaflet::Similar map as above, but more code/complexity # leaflet(data=niCA.1) %>% # start with the data # addProviderTiles("CartoDB.Positron") %>% # select a map # addCircleMarkers(~dec_long_va,~dec_lat_va, # add the coordinates # color = "red", radius=4, stroke=FALSE, # fillOpacity = 0.8, opacity = 0.8, # popup=~station_nm)
We can try the same command but with
seriesCatalogOutput=FALSE. This means it doesn’t pull the actual records, but rather the sites where a given parameter is available. That means we won’t be able to filter by the number of records available for a given site, but we can see where sites are located. This example filters to a specific Hydrologic Unit Code (HUC), and makes another leaflet map.
# filter for phosporous <- c("00662","00665") # phosphorous ph_code <- readNWISdata(stateCd="CA", parameterCd=ph_code, phCA service="site", seriesCatalogOutput=FALSE)
%>% dplyr::distinct(huc_cd) %>% tallyphCA
## n ## 1 104
# do some tidying! .1 <- phCA %>% phCA::filter(huc_cd == "16050101") # filter by HUC dplyr # make the data spatial with the sf <- st_as_sf(phCA.1, coords = c("dec_long_va","dec_lat_va"), remove = FALSE, crs=4326) phCA.sf # Mapview: make an interactive map quickly: mapview(phCA.sf, layer.name="USGS Stations collecting <br> phosphorous data")
Let’s now download data for a specific site number and a specific date range. In this case, let’s look for flow at a site near Meeks Bay in Lake Tahoe.
<- "10336645"# GENERAL C NR MEEKS BAY CA siteNo <- "00060" # 00060 flow, pCode <- "2016-10-01" start.date <- "2019-03-01" end.date # get NWIS data <- readNWISuv(siteNumbers = siteNo, tahoe parameterCd = pCode, startDate = start.date, endDate = end.date)
Now we have a nice dataset, but let’s tidy it up a bit. There are some built in functions that are part of the
dataRetrieval library, let’s use them to add a Water Year column, and fix up the column names. Then we’ll filter to our Meeks Bay site, and plot.
# add the water year <- addWaterYear(tahoe) tahoe # rename the columns to something easier to understand (i.e., not X00060_00000) <- renameNWISColumns(tahoe) tahoe # here we'll calculate and add approximate day of the WATER YEAR (doesn't take leap year into account) $DOWY <- yday(tahoe$dateTime) + ifelse(month(tahoe$dateTime) > 9, -273, 92) tahoe # filter to Meeks site only (we currently have all tahoe) <- filter(phCA, site_no=="10336645")phCA.meeks
# plot flow with facets to see different years <- ggplot() + geom_line(data=tahoe, aes(x=DOWY, y=Flow_Inst), color="dodgerblue") + (plot1 facet_grid(waterYear~., scales = "free_y") + labs(y="Hourly Flow (cfs)", x= "Day of Water Year", title="Hourly Discharge for General Creek Near Meeks Bay, Tahoe"))
Let’s work with some spatial data now. We’ll use the HUC boundaries for Squaw Creek, and crop our data by that watershed. These are the HUCs for the Squaw/Tahoe Basin of interest:
For more info and training see the USGS page.
By default when we read in a shapefile, the
sf package will print out a bunch of metadata about the projection, dimension, type of data, etc. We can turn that on and off with
quiet=FALSE. Let’s read in a HUC8 for Tahoe, and centroids for every single HUC12 in California.
<- st_read("data/h8_tahoe.shp", quiet=FALSE) h8_tahoe #mapview(h8_tahoe) # load huc centroids from a zipped directory! Here we have to wrap things in unzip() unzip("data/huc12_centroids.zip", exdir = "data/huc12s") <- read_sf("data/huc12s/huc12_centroids.shp", quiet = TRUE) %>% h12_centroids st_transform(crs=4326) #%>% # then remove raw files since file is added in memory ::dir_delete(path="data/huc12s/")fs
# a map mapview(h12_centroids) + mapview(h8_tahoe)