Data Activity: Visualize Stream Discharge Data in R to Better Understand the 2013 Colorado Floods
Last Updated: Apr 8, 2021
Several factors contributed to the extreme flooding that occurred in Boulder, Colorado in 2013. In this data activity, we explore and visualize the data for stream discharge data collected by the United States Geological Survey (USGS). The tutorial is part of the Data Activities that can be used with the Quantifying The Drivers and Impacts of Natural Disturbance Events Teaching Module.
After completing this tutorial, you will be able to:
- Download stream gauge data from USGS's National Water Information System.
- Plot precipitation data in R.
- Publish & share an interactive plot of the data using Plotly.
Things You'll Need To Complete This Lesson
Please be sure you have the most current version of R and, preferably, RStudio to write your code.
R Libraries to Install:
Data to Download
We include directions on how to directly find and access the data from USGS's National National Water Information System Database. However, depending on your learning objectives you may prefer to use the provided teaching data subset that can be downloaded from the NEON Data Skills account on FigShare.
Set Working Directory This lesson assumes that you have set your working directory to the location of the downloaded and unzipped data subsets.
An overview of setting the working directory in R can be found here.
R Script & Challenge Code: NEON data lessons often contain challenges that
reinforce learned skills. If available, the code for challenge solutions is found in the downloadable R script of the entire lesson, available in the footer of each lesson page.
What were the patterns of stream discharge prior to and during the 2013 flooding events in Colorado?
About the Data - USGS Stream Discharge Data
The USGS has a distributed network of aquatic sensors located in streams across the United States. This network monitors a suit of variables that are important to stream morphology and health. One of the metrics that this sensor network monitors is Stream Discharge, a metric which quantifies the volume of water moving down a stream. Discharge is an ideal metric to quantify flow, which increases significantly during a flood event.
As defined by USGS: Discharge is the volume of water moving down a stream or river per unit of time, commonly expressed in cubic feet per second or gallons per day. In general, river discharge is computed by multiplying the area of water in a channel cross section by the average velocity of the water in that cross section.
For more on stream discharge by USGS.
Obtain USGS Stream Gauge Data
This next section explains how to find and locate data through the USGS's National Water Information System portal. If you want to use the pre-compiled dataset downloaded above, you can skip this section and start again at the Work With Stream Gauge Data header.
Step 1: Search for the data
To search for stream gauge data in a particular area, we can use the interactive map of all USGS stations. By searching for locations around "Boulder, CO", we can find 3 gauges in the area.
For this lesson, we want data collected by USGS stream gauge 06730200 located on Boulder Creek at North 75th St. This gauge is one of the few the was able to continuously collect data throughout the 2013 Boulder floods.
You can directly access the data for this station through the "Access Data" link on the map icon or searching for this site on the National Water Information System portal .
On the Boulder Creek stream gauge 06730200 page , we can now see summary information about the types of data available for this station. We want to select Daily Data and then the following parameters:
- Available Parameters = 00060 Discharge (Mean)
- Output format = Tab-separated
- Begin Date = 1 October 1986
- End Date = 31 December 2013
Now click "Go".
Step 2: Save data to .txt
The output is a plain text page that you must copy into a spreadsheet of choice and save as a .csv. Note, you can also download the teaching dataset (above) or access the data through an API (see Additional Resources, below).
Work with Stream Gauge Data
We will use
ggplot2 to efficiently plot our data and
plotly to create interactive plots.
# load packages library(ggplot2) # create efficient, professional plots library(plotly) # create cool interactive plots ## Set your working directory to ensure R can find the file we wish to import and where we want to save our files. Be sure to move the downloaded files into your working directory! wd <- "C:/Users/fsanchez/Documents/data/" # This will depend on your local environment setwd(wd) ## Error in setwd(wd): cannot change working directory
Import USGS Stream Discharge Data Into R
Now that we better understand the data that we are working with, let's import it into R. First, open up the
discharge/06730200-discharge_daily_1986-2013.txt file in a text editor.
What do you notice about the structure of the file?
The first 24 lines are descriptive text and not actual data. Also notice that this file is separated by tabs, not commas. We will need to specify the
tab delimiter when we import our data.We will use the
read.csv() function to import it into an R object.
When we use
read.csv(), we need to define several attributes of the file
- The data are tab delimited. We will this tell R to use the
"/t"separator, which defines a tab delimited separation.
- The first group of 24 lines in the file are not data; we will tell R to skip
those lines when it imports the data using
- Our data have a header, which is similar to column names in a spreadsheet. We
will tell R to set
header=TRUEto ensure the headers are imported as column names rather than data values.
- Finally we will set
stringsAsFactors = FALSEto ensure our data come in as individual values.
Let's import our data.
(Note: you can use the
and read it in directly using
read.csv() function and then skip to the View
Data Structure section).
#import data discharge <- read.csv(paste0(wd,"disturb-events-co13/discharge/06730200-discharge_daily_1986-2013.txt"), sep= "\t",skip=24, header=TRUE,stringsAsFactors = FALSE) ## Warning in file(file, "rt"): cannot open file 'C:/Users/ ## fsanchez/Documents/data/disturb-events-co13/discharge/06730200- ## discharge_daily_1986-2013.txt': No such file or directory ## Error in file(file, "rt"): cannot open the connection #view first few lines head(discharge) ## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'head': object 'discharge' not found
When we import these data, we can see that the first row of data is a second
header row rather than actual data. We can remove the second row of header
values by selecting all data beginning at row 2 and ending at the
total number or rows in the file and re-assigning it to the variable
nrow function will count the total
number of rows in the object.
# nrow: how many rows are in the R object nrow(discharge) ## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'nrow': object 'discharge' not found # remove the first line from the data frame (which is a second list of headers) # the code below selects all rows beginning at row 2 and ending at the total # number of rows. discharge <- discharge[2:nrow(discharge),] ## Error in eval(expr, envir, enclos): object 'discharge' not found
We now have an R object that includes only rows containing data values. Each
column also has a unique column name. However the column names may not be
descriptive enough to be useful - what is
discharge/06730200-discharge_daily_1986-2013.txt file in a text editor or browser. The text at
the top provides useful metadata about our data. On rows 10-12, we see that the
values in the 5th column of data are "Discharge, cubic feed per second (Mean)". Rows 14-16 tell us more about the 6th column of data,
the quality flags.
Now that we know what the columns are, let's rename column 5, which contains the discharge value, as disValue and column 6 as qualFlag so it is more "human readable" as we work with it in R.
#view names names(discharge) ## Error in eval(expr, envir, enclos): object 'discharge' not found #rename the fifth column to disValue representing discharge value names(discharge) <- "disValue" ## Error in names(discharge) <- "disValue": object 'discharge' not found names(discharge) <- "qualCode" ## Error in names(discharge) <- "qualCode": object 'discharge' not found #view names names(discharge) ## Error in eval(expr, envir, enclos): object 'discharge' not found
View Data Structure
Let's have a look at the structure of our data.
#view structure of data str(discharge) ## Error in str(discharge): object 'discharge' not found
It appears as if the discharge value is a
chr) class. This is
likely because we had an additional row in our data. Let's convert the discharge
column to a
numeric class. In this case, we can reassign that column to be of
integer given there are no decimal places.
# view class of the disValue column class(discharge$disValue) ## Error in eval(expr, envir, enclos): object 'discharge' not found # convert column to integer discharge$disValue <- as.integer(discharge$disValue) ## Error in eval(expr, envir, enclos): object 'discharge' not found str(discharge) ## Error in str(discharge): object 'discharge' not found
Converting Time Stamps
We have converted our discharge data to an
integer class. However, the time
datetime is still a
To work with and efficiently plot time series data, it is best to convert date and/or time data to a date/time class. As we have both date and time date, we will use the class POSIXct.
To learn more about different date/time classes, see the Dealing With Dates & Times in R - as.Date, POSIXct, POSIXlt tutorial.
#view class class(discharge$datetime) ## Error in eval(expr, envir, enclos): object 'discharge' not found #convert to date/time class - POSIX discharge$datetime <- as.POSIXct(discharge$datetime, tz ="America/Denver") ## Error in as.POSIXct(discharge$datetime, tz = "America/Denver"): object 'discharge' not found #recheck data structure str(discharge) ## Error in str(discharge): object 'discharge' not found
No Data Values
Next, let's query our data to check whether there are no data values in
it. The metadata associated with the data doesn't specify what the values would
-9999 are common values
# check total number of NA values sum(is.na(discharge$datetime)) ## Error in eval(expr, envir, enclos): object 'discharge' not found # check for "strange" values that could be an NA indicator hist(discharge$disValue) ## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'hist': object 'discharge' not found
Excellent! The data contains no NoData values.
Plot The Data
Finally, we are ready to plot our data. We will use
ggplot from the
package to create our plot.
ggplot(discharge, aes(datetime, disValue)) + geom_point() + ggtitle("Stream Discharge (CFS) for Boulder Creek") + xlab("Date") + ylab("Discharge (Cubic Feet per Second)") ## Error in ggplot(discharge, aes(datetime, disValue)): object 'discharge' not found
- What patterns do you see in the data?
- Why might there be an increase in discharge during that time of year?
Plot Data Time Subsets With ggplot
We can plot a subset of our data within
ggplot() by specifying the start and
end times (in a
limits object) for the x-axis with
plot data for the months directly around the Boulder flood: August 15 2013 -
October 15 2013.
# Define Start and end times for the subset as R objects that are the time class start.end <- as.POSIXct(c("2013-08-15 00:00:00","2013-10-15 00:00:00"),tz= "America/Denver") # plot the data - Aug 15-October 15 ggplot(discharge, aes(datetime,disValue)) + geom_point() + scale_x_datetime(limits=start.end) + xlab("Date") + ylab("Discharge (Cubic Feet per Second)") + ggtitle("Stream Discharge (CFS) for Boulder Creek\nAugust 15 - October 15, 2013") ## Error in ggplot(discharge, aes(datetime, disValue)): object 'discharge' not found
We get a warning message because we are "ignoring" lots of the data in the dataset.
Plotly - Interactive (and Online) Plots
We have now successfully created a plot. We can turn that plot into an interactive plot using Plotly. Plotly allows you to create interactive plots that can also be shared online. If you are new to Plotly, view the companion mini-lesson Interactive Data Vizualization with R and Plotly to learn how to set up an account and access your username and API key.
Time subsets in plotly
To plot a subset of the total data we have to manually subset the data as the Plotly
package doesn't (yet?) recognize the
limits method of subsetting.
Here we create a new R object with entries corresponding to just the dates we want and then plot that data.
# subset out some of the data - Aug 15 - October 15 discharge.aug.oct2013 <- subset(discharge, datetime >= as.POSIXct('2013-08-15 00:00', tz = "America/Denver") & datetime <= as.POSIXct('2013-10-15 23:59', tz = "America/Denver")) # plot the data disPlot.plotly <- ggplot(data=discharge.aug.oct2013, aes(datetime,disValue)) + geom_point(size=3) # makes the points larger than default disPlot.plotly # add title and labels disPlot.plotly <- disPlot.plotly + theme(axis.title.x = element_blank()) + xlab("Time") + ylab("Stream Discharge (CFS)") + ggtitle("Stream Discharge - Boulder Creek 2013") disPlot.plotly
You can now display your interactive plot in R using the following command:
# view plotly plot in R ggplotly(disPlot.plotly)
If you are satisfied with your plot you can now publish it to your Plotly account.
# set username Sys.setenv("plotly_username"="yourUserNameHere") # set user key Sys.setenv("plotly_api_key"="yourUserKeyHere") # publish plotly plot to your plotly online account if you want. api_create(disPlot.plotly)
Additional information on USGS streamflow measurements and data:
- Find peak streamflow for other locations
- USGS: How streamflow is measured
- USGS: How streamflow is measured, Part II
- USGS National Streamflow Information Program Fact Sheet
API Data Access
USGS data can be downloaded via an API using a command line interface. This is particularly useful if you want to request data from multiple sites or build the data request into a script. Read more here about API downloads of USGS data.
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