This is a repository for gathering Environmental Gauge data, processing that data, and doing basic exploratory (spatial & temporal) data analysis.

The repository depends on several packages and functions developed by the USGS and NOAA to retreive data from U.S. government host servers. Significant processing is required to use some of these datasets for environmental modeling. This repository provides several functions to process datasets for this purpose.

A blog post describing the main functionality of this repository is available here

It is likely that others have made similar functions to process these and other environmental gauge data. If you have scripts to process these datasets that provide additional functionality, please write to the authors of this repository and ask to contribute or link code repositories!

1. USGS streamflow from the NWIS portal
2. EPA STORET, USGS, USDA and other water quality data via the water quality portal
3. NOAA ACIS, GHCN weather station data. This is a global dataset.

1. dataRetrieval - main package for streamflow and water quality gauge data retrieval functions from government servers
   Recommended resource - Package Readme: dataRetrieval.html (located somewhere on your computer after installing the R package)
   Function help
   Blog
   Slideshow

2. rnoaa - main package for NOAA weather station data retrieval.
   Documentation

hydroTSM vignette

hydroTSM boxplots and flow duration curves

1. Geothermal_ESDA - the ColorFunctions.R script is used for plotting.

Code Notes

This code was developed using R version 3.5.3, and all R packages were also downloaded for that version of R. You may try using other versions, but compatibility is not guaranteed.

This code was developed on Windows 10 OS. The compatibility on other OS is untested. File an issue on GitHub if you experience issues with your OS.

There are comment section and sub-section headers throughout this script that indicate blocks of code used for tasks and sub-tasks, respectively. Sub-headers are indented by one space more than the main header. In RStudio, these blocks can be opened and closed with arrows on the left side of the scripting window. This can be useful for navigating the script.

Because of the wide variety of needs for different projects, this main script serves as examples of how downloading and processing data could be completed; you may want to modify for your project (e.g., if you do not want to use DEM processing, or have no water quality data needs).

Input data for running this script are provided in the DataForExamples folder. Note that DEM data are in zipped folders and you will have to unzip them after downloading. The 2 DEM tiles used for the examples were from The National Map and correspond to "1 arc-second n40w078 1 x 1 degree ArcGrid" and "1 arc-second n40w077 1 x 1 degree ArcGrid". You can also run the script without running the DEM sections, which all have "DEM" in their section header.

All examples in this script run independently of one another after specifying directory and file information, and code options.

• Run the lines of code through the section entitled "Make Region of Interest (ROI) buffer"
• Streamflow Example: Downloading and processing streamflow data using two methods.
• Water Quality Example: Downloading and processing Total Nitrogen and Total Phosphorus water quality data using two methods. Outliers are not checked in this example because for this area the temporal data length is not great, and it would be difficult to detect and/or define an outlier.
• NOAA Weather Station Example: Downloading weather station gauges and selecting sites with precipitation, maximum temperature, and minimum temperature. Duplicates, negatives, and zeros are not checked in this example, but they could be using the functions employed for streamflow and water quality. Aggreation of timeseries to monthly and annual is also not shown, but could be implemented.

0. Preparing to Use the Code

Install R Libraries

Users may need to install several R libraries if using this code for the first time. These are listed in the "Load libraries and functions" heading within the script.

Prepare Input Data

For each acquisition of streamflow, water quality, and weather station data, users should have either:

1. Method 1 (recommended, applies to streamflow, water quality, and weather stations): Scenario is that you know your region of interest but do not yet have data. Supply a polygon shapefile of the region of interest (ROI). The code will use this shapefile and an optional radial buffer around this shapefile to acquire data. There is an option to specify a different buffer for weather stations because it's common to need a larger region for these information. OR
2. Method 2 (streamflow and water quality only): Scenario is that you already have data (e.g. a gauge number). Supply a csv file of streamflow gauges (water quality gauges). The streamflow file must contain columns called Source, and GaugeNum. It may contain other columns. The water quality file must be the same as downloaded from the section below entitled "Water Quality Retrieval Method 2".

Set directory and file names

The directories of input and output files, and the names of the files are specified at the top of the script.

Set the Project Coordinate System

Users will need the EPSG Code for their coordinate system of choice for their project. All data will be projected into and saved in this coordinate system.

Set plot x- and y-axis limits

If you do not want to use a particular limit, set equal to NULL. Other plot modifications will require editing of the script.

Set the quantile that defines a high outlier

The default is 0.99, for the 99th percentile.

Obtain DEM Tiles (optional)

Users who want to use the DEM tile merging/mosaicking functionality, processDEM, will have to download DEM raster tiles manually. These must have the same raster pixel resolution, be in the same coordinate system, and be spatially adjacent to each other.

1a. Streamflow Gauge Retrieval

There are two methods implemented to download streamflow gauge data, depending on what information you're starting with. Throughout the code there are labels for Method 1 only and Method 2 only processing steps.

Streamflow Gauge Retrieval Method 1:

NOTE: This method seems more reliable than Method 2, and returned more gauges in several test regions.

The function whatNWISsites() will return gauge numbers with coordinates for a specified state (county, or various other criteria)
Search Criteria for whatNWISsites(): Table 1 on waterqualitydata website

• References to "domain service" in Table 1 indicate to look at Table 2 on the same website.
• Likely the most common filtering method: characteristicName options
  Example: whatNWISsites(statecode="MD", characteristicName="Phosphorus")
• Note: this function must use something like grep when characteristicName is specified. Searching for Phosphorus returned all instances of Phosphorus in the table. If you want a specific type of metric only, specify the parameterCd (parameter code) instead of the characteristicName.
• This function does not return full site information. For full site information, use readNWISsite() with the vector of gauge numbers that is returned using whatNWISsites().

Streamflow Gauge Retrieval Method 2:

You can find USGS gauges of any type in your region of interest here

1. Record the coordinates of the bounding box for your region of interest! They could be important for the next step. Select the data you want and copy the resulting table of values into a txt file. txt is important to retain leading zeros on gauge numbers. If you inhereted a csv or txt file without leading zeros, a script below can add them to NWIS gauges. Other gauge datasets seem to not require leading zeros.
2. Coordinates of gauges are not reported in that download :(
   The USGS function readNWISsite() can look up the coordinates and all site info, given the gauge numbers :)
   You can also find coordinates for your gauges if you provide a bounding box of coordinates here

1b. Streamflow Data Coordinate Reprojection

The downloaded gauges may be in several different coordinate systems. An automatic reprojection and merging is in development. For now, a manual method is implemented that requires users to look up EPSG codes for the projections of their downloaded datasets. The reprojections are found under the headers "Make a spatial dataframe: Method X" where X is 1 or 2.

2a. Water Quality Portal Data Retrieval

There are two methods implemented to download water quality data. Throughout the code there are labels for Method 1 only and Method 2 only processing steps.

There are other water quality data that are not contained within the water quality portal. These datasets may also be processed by some functions in this repository, but may require the user to modify their dataset to be compatible with the functions.

Water Quality Portal Retrieval Method 1:

You can use the whatWQPsites(), using the same query criteria as the whatNWISsites() function, described in 1a. above. The whatWQPsites() function returns all site information, same as the waterqualitydata website described in Method 2 below. You will have to modify the function choices to meet your specific needs. These functions are below the header "Method 1: Read water quality station data using the USGS function"

Water Quality Portal Retrieval Method 2:

You can find water quality sites here
Download "site data only" to receive a csv file with gauge/site information with coordinates. The MonitoringLocationIdentifier field is used to download data for each gauge/site. Note that the example in this script shows loading and processing data from a file, but makes plots only for the Method 1 approach. The sites selected may be different between the two methods, as shown on the maps that are plotted for each method's water quality sites.

2b. Water Quality Data Coordinate Reprojection

Similarly to 1b above, the downloaded information may have several coordinate systems. The places where manual reprojections can be found are under the headers "Make a spatial dataframe: Method X" where X is 1 or 2.

3. Weather Data Retrieval from NOAA

The ghcnd_stations() funuction grabs all available weather stations. The region of interest shapefile with radial buffer is used to select gauges.

4. Comparing altitude of gauge vs. DEM

Sections for DEM processing are located at the end of the Streamflow and Weather Gauge sections under the headers "DEM for Streamflow" and "DEM for Weather Gauges" respectively.

Downloaded gauge/site coordinates may include the altitude of the gauge, which can be important vs. DEM elevation (e.g. if there's a cliff at the gauge vs. DEM mean elevation of the pixel).

• Ensure that the units and the vertical datum are the same for your DEM and all gauge altitudes. A common error in gauge altitudes is USGS data reported in m instead of in ft.
• DEMs in the US tend to be NAVD88 in m, whereas gauges tend to be referenced to NGVD29 in ft. Differences after unit conversion tend to be minor in the US, except in the West
• Altitude datum codes
• Altitude collection method codes

5. Check Data Quality Codes

You can find data quality codes for USGS datasets here

• You should always look at these quality codes, and process your data accordingly. This script colors streamflow timeseries by error code, but doesn't process further than that.
• codes for streamflow 1
• codes for streamflow 2
  Yes, there are 2 separate reference schemes for streamflow data.

6. Process Data

Functions in the code do the following checks and actions for downloaded products: i. Check for duplicate observations ii. Check for zeros and negative values iii. Fill date gaps (add NA values to dates that are missing from timeseries) iv. Aggregate to daily, monthly, and annual timesteps v. Spatio-temporal outlier detection (simple at the moment)

7. Make Plots

Plots for each downloaded dataset include: Maps of data locations Timeseries Boxplots Histograms Hypsometric curves for DEMs

1. addZerosToGaugeNames.R - for adding leading zeros to NWIS streamflow gauge numbers.
2. extractWQdata.R - for extracting separate timeseries for each variable collected at sites downloaded from the water quality portal database. Also places timeseries in chronological order.
3. missingDates.R - for filling in missing dates in downloaded timeseries. Missing dates are assigned NA values. Also places timeseries in chronological order.
4. processDEM.R - for mosaicking DEM files into one DEM file.
5. checkDuplicates.R - checks timeseries for duplicate records, and keeps only one of them.
6. checkZerosNegs.R - several functions to check timeseries for zero and negative values, and replace with user-specified values.
7. fdcDefaultModification.R - a modified flow duration curve script from the hydroTSM R package
8. formatMonthlyMatrix.R - transforms a monthly timeseries into a matrix with 12 columns for the months and n rows for the years.
9. matplotDates.R - modified matplot() function that allows dates to be plotted on the x-axis in date format instead of numeric format.
10. aggregateTimeseries.R - function to summarize timeseries data by day, month, or year from its original format.
11. scatterHistCols.R - modified scatterHist() function from the psych package to accept colors, and provide density smoothing by color.

1. Method1Example_GFBR.R - example employing only the Method 1 streamflow and water chemistry (TN and TP) approaches to the Baltimore Ecosystem Study Long Term Ecological Research Site in Gwynns Falls and Baisman Run (GFBR). Estimated runtime is less than 1 hour. Estimated RAM is < 1 GB.
2. BES_GF_BR_SL.R - Lines 1 to 2152 provide examples of loading the processed data output from Method1Example_GFBR.R for further analysis, processing site water quality data (instead of Water Quality Portal Data) and site precipitation data (instead of NOAA data) using the repository functions, and creating timeseries from the processed datasets for use in model runs. Lines 2153 - end provide examples of using the USGS WRTDS regression method to estimate daily average nutrient concentrations, and using export coefficient models to estimate non-point source pollution loads from ungauged sub-catchments. Note that the WRTDS modified functions script is currently not in this repository because I'm waiting on a reply from the USGS about how is best to host these functions (see EGRET GitHub issues). A text file containing my modified WRTDS functions is available to download from that issue, and you can paste that into an R script and save it to run this example. Estimated runtime is less than 2 hours. Estimated RAM is about 25 GB.

Citation: Smith, J.D. et al. (2019). EnGauge. Online Github Repository. https://github.com/jds485/EnGauge

Contact Information:
Jared D. Smith (js4yd@virginia.edu)
Jonathan R. Lamontagne (Jonathan.Lamontagne@tufts.edu)
Caitline A. Barber (Caitline.Barber@tufts.edu)
Julianne D. Quinn (jdq6nn@virginia.edu)

Please see the LICENSE file. You must cite this repository if you use it for your work.