# U.S. Data and Package Basics

#### dblodgett@usgs.gov

## TL;DR

Pick an outlet location and download some data.

    # Uncomment to install!
    # install.packages("nhdplusTools")

    library(nhdplusTools)
    library(sf)

    start_point <- st_sfc(st_point(c(-89.362239, 43.090266)), crs = 4269)
    start_comid <- discover_nhdplus_id(start_point)

    flowline <- navigate_nldi(list(featureSource = "comid",
                                   featureID = start_comid),
                              mode = "upstreamTributaries",
                              distance_km = 1000)

    subset_file <- tempfile(fileext = ".gpkg")
    subset <- subset_nhdplus(comids = as.integer(flowline$UT$nhdplus_comid),
                             output_file = subset_file,
                             nhdplus_data = "download",
                             flowline_only = FALSE,
                             return_data = TRUE, overwrite = TRUE)
    #> All intersections performed in latitude/longitude.
    #> Reading NHDFlowline_Network
    #> Writing NHDFlowline_Network
    #> Reading CatchmentSP
    #> Writing CatchmentSP

    flowline <- subset$NHDFlowline_Network
    catchment <- subset$CatchmentSP
    waterbody <- subset$NHDWaterbody

    ## Or using a file:

    flowline <- sf::read_sf(subset_file, "NHDFlowline_Network")
    catchment <- sf::read_sf(subset_file, "CatchmentSP")
    waterbody <- sf::read_sf(subset_file, "NHDWaterbody")

    plot(sf::st_geometry(flowline), col = "blue")
    plot(start_point, cex = 1.5, lwd = 2, col = "red", add = TRUE)
    plot(sf::st_geometry(catchment), add = TRUE)
    plot(sf::st_geometry(waterbody), col = rgb(0, 0, 1, alpha = 0.5), add = TRUE)

Or fetch NWIS an site as the starting point and generate a plot. Data is returned and/or stored in a local file for later use.

    # ?plot_nhdplus for more
    plot_data <- plot_nhdplus(
      outlets = list(featureSource = "nwissite", featureID = "USGS-05428500"),
      gpkg = subset_file, overwrite = TRUE)
    #> Zoom set to: 11

This vignette covers a range of utilities the nhdplusTools package offers for working with data in a U.S. context.

The first thing you are going to need to do is go get some data to work with. `nhdplusTools` provides the ability to download small subsets of the NHDPlus directly from web services. For large subsets, greater than a few thousand square kilometers, you can use `download_nhdplusv2()`.

If you are working with the whole National Seamless database, `nhdplusTools` has some convenience functions you should be aware of. Once you have it downloaded and extracted, you can tell the nhdplusTools package where it is with the `nhdplus_path()` function.

    nhdplus_path(file.path(work_dir, "natseamless.gpkg"))

    basename(nhdplus_path())
    #> [1] "natseamless.gpkg"

If you don’t need or want all the geometry for the flowlines, consider using `get_vaa()`. It allows you to retrieve specific NHDPlus attributes with very little overhead. It also supports access to an updated set of network attributes that incorporate numerous network updates from national hydrologic modeling projects.

    vaa <- get_vaa()
    names(vaa)
    #>  [1] "comid"      "streamleve" "streamorde" "streamcalc" "fromnode"
    #>  [6] "tonode"     "hydroseq"   "levelpathi" "pathlength" "terminalpa"
    #> [11] "arbolatesu" "divergence" "startflag"  "terminalfl" "dnlevel"
    #> [16] "thinnercod" "uplevelpat" "uphydroseq" "dnlevelpat" "dnminorhyd"
    #> [21] "dndraincou" "dnhydroseq" "frommeas"   "tomeas"     "reachcode"
    #> [26] "lengthkm"   "fcode"      "vpuin"      "vpuout"     "areasqkm"
    #> [31] "totdasqkm"  "divdasqkm"  "totma"      "wbareatype" "pathtimema"
    #> [36] "slope"      "slopelenkm" "ftype"      "gnis_name"  "gnis_id"
    #> [41] "wbareacomi" "hwnodesqkm" "rpuid"      "vpuid"      "roughness"
    nrow(vaa)
    #> [1] 2691339

### NHDPlus HiRes

NHDPlus HiRes is an in-development dataset that introduces much more dense flowlines and catchments. In the long run, `nhdplusTools` will have complete support for NHDPlus HiRes. So far, `nhdplusTools` will help download and interface NHDPlus HiRes data with existing `nhdplusTools` functionality. It’s important to note that `nhdplusTools` was primarily implemented using NHDPlusV2 and any use of HiRes should be subject to scrutiny.

For the demo below, a small sample of HiRes data that has been loaded into `nhdplusTools` is used. The first line shows how you can download additional data (just change `download_files` to `TRUE`).

    download_nhdplushr(nhd_dir = "download_dir",
                       hu_list = c("0101"), # can mix hu02 and hu04 codes.
                       download_files = FALSE) # TRUE will download files.
    #> [1] "https://prd-tnm.s3.amazonaws.com/StagedProducts/Hydrography/NHDPlusHR/VPU/Current/GDB/NHDPLUS_H_0101_HU4_20220901_GDB.zip"

    out_gpkg <- file.path(work_dir, "nhd_hr.gpkg")
    hr_data <- get_nhdplushr(work_dir,
                             out_gpkg = out_gpkg)
    (layers <- st_layers(out_gpkg))
    #> Driver: GPKG
    #> Available layers:
    #>         layer_name geometry_type features fields             crs_name
    #> 1      NHDFlowline   Line String     2691     57 GRS 1980(IUGG, 1980)
    #> 2 NHDPlusCatchment Multi Polygon     2603      7 GRS 1980(IUGG, 1980)
    #> 3     NHDWaterbody       Polygon     1044     15 GRS 1980(IUGG, 1980)
    #> 4          NHDArea       Polygon       10     14 GRS 1980(IUGG, 1980)
    #> 5          NHDLine   Line String      142     12 GRS 1980(IUGG, 1980)
    #> 6      NHDPlusSink         Point        1     10 GRS 1980(IUGG, 1980)
    #> 7         NHDPoint      3D Point        7     10 GRS 1980(IUGG, 1980)
    names(hr_data)
    #> [1] "NHDFlowline"      "NHDPlusCatchment"
    unlink(out_gpkg)

    hr_data <- get_nhdplushr(work_dir,
                             out_gpkg = out_gpkg,
                             layers = NULL)
    (layers <- st_layers(out_gpkg))
    #> Driver: GPKG
    #> Available layers:
    #>         layer_name geometry_type features fields             crs_name
    #> 1      NHDFlowline   Line String     2691     57 GRS 1980(IUGG, 1980)
    #> 2 NHDPlusCatchment Multi Polygon     2603      7 GRS 1980(IUGG, 1980)
    #> 3     NHDWaterbody       Polygon     1044     15 GRS 1980(IUGG, 1980)
    #> 4          NHDArea       Polygon       10     14 GRS 1980(IUGG, 1980)
    #> 5          NHDLine   Line String      142     12 GRS 1980(IUGG, 1980)
    #> 6      NHDPlusSink         Point        1     10 GRS 1980(IUGG, 1980)
    #> 7         NHDPoint      3D Point        7     10 GRS 1980(IUGG, 1980)
    names(hr_data)
    #> [1] "NHDFlowline"      "NHDPlusCatchment" "NHDWaterbody"     "NHDArea"
    #> [5] "NHDLine"          "NHDPlusSink"      "NHDPoint"

## Discovery and Subsetting

One of the primary workflows `nhdplusTools` is designed to accomplish can be described in three steps:

1. what NHDPlus catchment is at the outlet of a watershed,
2. figure out what catchments are up or downstream of that catchment, and
3. create a stand alone subset for that collection of catchments.

Say we want to get a subset of the NHDPlus upstream of a given location. We can start with `discover_nhdplus_id()` First, let’s look at a given point location. Then see where it is relative to our flowlines.

    lon <- -89.36
    lat <- 43.09

    start_point <- sf::st_sfc(sf::st_point(c(lon, lat)),
                              crs = 4269)

    plot(sf::st_geometry(flowline))
    plot(start_point, cex = 1.5, lwd = 2, col = "red", add = TRUE)

OK, so we have a point location near a river and we want to figure out what catchment is at its outlet. We can use the `discover_nhdplus_id()` function which calls out to a web service and returns an NHDPlus catchment identifier, typically called a COMID.

If we set `raindrop = TRUE`, we can also get a elevation derived downslope trace to the nearest flowline and some additional info. See `get_raindrop_trace()` for more on this functionality.

    start_comid <- discover_nhdplus_id(start_point, raindrop = TRUE)
    start_comid
    #> Simple feature collection with 2 features and 7 fields
    #> Geometry type: LINESTRING
    #> Dimension:     XY
    #> Bounding box:  xmin: -89.37037 ymin: 43.08522 xmax: -89.35393 ymax: 43.09491
    #> Geodetic CRS:  WGS 84
    #> # A tibble: 2 × 8
    #>   id    gnis_name   comid reachcode raindrop_pathDist measure intersection_point
    #>   <chr> <chr>       <int> <chr>                 <dbl>   <dbl> <list>
    #> 1 nhdF… Yahara R…  1.33e7 07090002…              91.8    42.4 <dbl [2]>
    #> 2 rain… <NA>      NA      <NA>                   NA      NA   <dbl [0]>
    #> # ℹ 1 more variable: geometry <LINESTRING [°]>

    plot(sf::st_geometry(start_comid))
    plot(sf::st_geometry(flowline), add = TRUE, col = "blue", lwd = 2)
    plot(start_point, cex = 1.5, lwd = 2, col = "red", add = TRUE)

If you have the whole National Seamless database and want to work at regional to national scales, skip down the the Local Data Subsetting section.

### Web Service Data Subsetting

`nhdplusTools` supports discovery and data subsetting using web services made available through the Network Linked Data Index (NLDI) and the Water Mission Area pygeoapir. The code below shows how to use the NLDI functions to build a dataset upstream of our `start_comid` that we found above.

The NLDI can be queried with any set of watershed outlet locations that it has in its index. We call these “featureSources”. We can query the NLDI for an identifier of a given feature from any of its “featureSources” and find out what our navigation options are as shown below.

    dataRetrieval::get_nldi_sources()$source
    #>  [1] "ca_gages"          "census2020-nhdpv2" "epa_nrsa"
    #>  [4] "geoconnex-demo"    "gfv11_pois"        "GRAND"
    #>  [7] "HILARRI"           "huc12pp"           "huc12pp_102020"
    #> [10] "nmwdi-st"          "npdes"             "nwisgw"
    #> [13] "nwissite"          "ref_dams"          "ref_gage"
    #> [16] "vigil"             "wade"              "wade_rights"
    #> [19] "wade_timeseries"   "WQP"               "comid"

    nldi_feature <- list(featureSource = "comid",
                         featureID = as.integer(start_comid$comid)[1])

    get_nldi_feature(nldi_feature)
    #> Simple feature collection with 1 feature and 3 fields
    #> Geometry type: LINESTRING
    #> Dimension:     XY
    #> Bounding box:  xmin: -89.37037 ymin: 43.08521 xmax: -89.35393 ymax: 43.09491
    #> Geodetic CRS:  WGS 84
    #> # A tibble: 1 × 4
    #>   sourceName    identifier comid                                        geometry
    #>   <chr>         <chr>      <chr>                                <LINESTRING [°]>
    #> 1 NHDPlus comid 13293750   13293750 (-89.37037 43.09491, -89.36997 43.09475, -8…

We can use `get_nldi_feature()` as a way to make sure the featureID is available for the chosen “featureSource”. Now that we know the NLDI has our comid, we can use the “upstreamTributaries” navigation option to get all the flowlines upstream or all the features from any of the “featureSources” as shown below.

    flowline_nldi <- navigate_nldi(nldi_feature,
                                   mode = "upstreamTributaries",
                                   distance_km = 1000)

    plot(sf::st_geometry(flowline), lwd = 3, col = "black")
    plot(sf::st_geometry(flowline_nldi$origin), lwd = 3, col = "red", add = TRUE)
    plot(sf::st_geometry(flowline_nldi$UT), lwd = 1, col = "red", add = TRUE)

The NLDI only provides geometry and a comid for each of the flowlines. The `subset_nhdplus()` function has a “download” option that allows us to download four layers and all attributes as shown below. There is also a `navigate_network()` function that will replace `navigate_nldi()` and `subset_nhdplus()` for many use cases.

    output_file_download <- file.path(work_dir, "subset_download.gpkg")

    output_file_download <-subset_nhdplus(comids = as.integer(flowline_nldi$UT$nhdplus_comid),
                                          output_file = output_file_download,
                                          nhdplus_data = "download", return_data = FALSE,
                                          overwrite = TRUE)
    #> All intersections performed in latitude/longitude.
    #> Reading NHDFlowline_Network
    #> Writing NHDFlowline_Network

    sf::st_layers(output_file_download)
    #> Driver: GPKG
    #> Available layers:
    #>            layer_name geometry_type features fields crs_name
    #> 1 NHDFlowline_Network   Line String      168    138    NAD83

    flowline_download <- sf::read_sf(file.path(work_dir, "subset_download.gpkg"),
                                     "NHDFlowline_Network")

    plot(sf::st_geometry(dplyr::filter(flowline_download,
                                       streamorde > 2)),
         lwd = 7, col = "darkgrey")
    plot(sf::st_geometry(flowline_nldi$UT),
         lwd = 3, col = "red", add = TRUE)

This plot illustrates the kind of thing that’s possible (filtering to specific stream orders) using the attributes that are downloaded.

Before moving on, one more demonstration of what can be done using the NLDI. Say we knew the USGS gage ID that we want NHDPlus data upstream of. We can use the NLDI to navigate from the gage the same as we did for our comid. We can also get back all the nwis sites the NLDI knows about upstream of the one we chose!

    nldi_feature <- list(featureSource = "nwissite",
                         featureID = "USGS-05428500")

    flowline_nldi <- navigate_nldi(nldi_feature,
                                   mode = "upstreamTributaries",
                                   distance_km = 1000)

    output_file_nwis <- file.path(work_dir, "subset_download_nwis.gpkg")

    output_file_nwis <-subset_nhdplus(comids = as.integer(flowline_nldi$UT$nhdplus_comid),
                                      output_file = output_file_nwis,
                                      nhdplus_data = "download",
                                      return_data = FALSE, overwrite = TRUE)
    #> All intersections performed in latitude/longitude.
    #> Reading NHDFlowline_Network
    #> Writing NHDFlowline_Network

    sf::st_layers(output_file_download)
    #> Driver: GPKG
    #> Available layers:
    #>            layer_name geometry_type features fields crs_name
    #> 1 NHDFlowline_Network   Line String      168    138    NAD83

    flowline_nwis <- sf::read_sf(output_file_nwis,
                                     "NHDFlowline_Network")

    upstream_nwis <- navigate_nldi(nldi_feature,
                                   mode = "upstreamTributaries",
                                   data_source = "nwissite",
                                   distance_km = 1000)

    plot(sf::st_geometry(flowline_nwis),
         lwd = 3, col = "blue")
    plot(sf::st_geometry(upstream_nwis$UT_nwissite),
         cex = 1, lwd = 2, col = "red", add = TRUE)

### Local Data Subsetting

While web service data access is very convenient, some use cases make working with web services impossible or cumbersome such that working with local data is preferable. nhdplusTools supports such workflows with hybrid, web-service and local, workflows.

With the starting COMID we found with `discover_nhdplus_id()` above, we can use one of the network navigation functions, `get_UM()`, `get_UT()`, `get_DM()`, or `get_DD()` to retrieve a collection of comids along the upstream mainstem, upstream with tributaries, downstream mainstem, or downstream with diversions network paths. Here we’ll use upstream with tributaries.

    UT_comids <- get_UT(flowline, start_comid$comid[1])
    UT_comids
    #>   [1]  13293380  13293384  13293388  13293392  13293398  13293424  13293430
    #>   [8]  13293454  13293504  13293506  13293576  13293750  13294110  13294128
    #>  [15]  13294134  13294280  13294284  13294288  13294290  13294298  13294304
    #>  [22]  13294310  13294312  13294394  13293376  13293382  13293386  13293390
    #>  [29]  13293394  13293396  13293400  13293404  13293406  13293416  13293422
    #>  [36]  13293426  13293432  13293434  13293440  13293444  13293446  13293452
    #>  [43]  13293456  13293458  13293464  13293474  13293480  13293484  13293488
    #>  [50]  13293492  13293494  13293496  13293508  13293520  13293524  13293526
    #>  [57]  13293530  13293536  13293540  13293542  13293550  13293554  13293574
    #>  [64]  13293584  13293588  13293612  13293620  13293634  13293676  13293694
    #>  [71]  13293696  13294118  13294124  13294130  13294132  13294138  13294154
    #>  [78]  13294158  13294166  13294264  13294268  13294272  13294274  13294276
    #>  [85]  13294278  13294282  13294286  13294292  13294294  13294300  13294308
    #>  [92]  13294314  13294366  13294368  13294382  13294384  13294386 937090090
    #>  [99] 937090091  13293378  13293412  13293420  13293428  13293436  13293438
    #> [106]  13293442  13293450  13293460  13293462  13293468  13293472  13293476
    #> [113]  13293478  13293486  13293490  13293498  13293502  13293512  13293514
    #> [120]  13293516  13293518  13293522  13293532  13293548  13293552  13293556
    #> [127]  13293558  13293564  13293568  13293572  13293578  13293586  13293614
    #> [134]  13293624  13293672  13293674  13293678  13293690  13293692  13294112
    #> [141]  13294122  13294140  13294144  13294148  13294150  13294152  13294168
    #> [148]  13294176  13294184  13294270  13294296  13294306  13302588  13302590
    #> [155]  13293410  13293418  13293448  13293562  13293570  13293600  13293646
    #> [162]  13293688  13294114  13294116  13294120  13294174  13294178  13293590

If you are familiar with the NHDPlus, you will recognize that now that we have this list of COMIDs, we could go off and do all sorts of things with the various flowline attributes. For now, let’s just use the COMID list to filter our `fline` `sf` `data.frame` and plot it with our other layers.

    plot(sf::st_geometry(flowline))
    plot(start_point, cex = 1.5, lwd = 2, col = "red", add = TRUE)
    plot(sf::st_geometry(dplyr::filter(flowline, comid %in% UT_comids)),
         add=TRUE, col = "red", lwd = 2)

Say you want to save the network subset for later use in R or in some other GIS. The `subset_nhdplus()` function is your friend. If you have the whole national seamless database downloaded, you can pull out large subsets of it like shown below (this queries for data from the local geodatabase without loading the whole thing into memory). If you don’t have the whole national seamless, look at the second example in this section.

    output_file <- file.path(work_dir, "subset.gpkg")

    output_file <-subset_nhdplus(comids = UT_comids,
                                 output_file = output_file,
                                 nhdplus_data = nhdplus_path(),
                                 return_data = FALSE, overwrite = TRUE)
    #> All intersections performed in latitude/longitude.
    #> Reading NHDFlowline_Network
    #> 168 comids of 168
    #> Writing NHDFlowline_Network
    #> Reading CatchmentSP
    #> 168 comids of 168
    #> Writing CatchmentSP
    #> Reading NHDArea
    #> Writing NHDArea
    #> Reading NHDWaterbody
    #> Writing NHDWaterbody
    #> Reading NHDFlowline_NonNetwork
    #> Writing NHDFlowline_NonNetwork
    #> Reading Gage
    #> Writing Gage
    #> Reading Sink
    #> No features to write in Sink

    sf::st_layers(output_file)
    #> Driver: GPKG
    #> Available layers:
    #>               layer_name geometry_type features fields             crs_name
    #> 1    NHDFlowline_Network   Line String      168    136 GRS 1980(IUGG, 1980)
    #> 2            CatchmentSP Multi Polygon      167      6 GRS 1980(IUGG, 1980)
    #> 3                NHDArea       Polygon        1     14 GRS 1980(IUGG, 1980)
    #> 4           NHDWaterbody       Polygon       90     21 GRS 1980(IUGG, 1980)
    #> 5 NHDFlowline_NonNetwork   Line String       45     12 GRS 1980(IUGG, 1980)
    #> 6                   Gage         Point       33     19 GRS 1980(IUGG, 1980)

Now we have an output geopackage that can be used later. It contains the network subset of catchments and flowlines as well as a spatial subset of other layers as shown in the status output above. To complete the demonstration, here are a couple more layers plotted up.

    catchment <- sf::read_sf(output_file, "CatchmentSP")
    waterbody <- sf::read_sf(output_file, "NHDWaterbody")

    plot(sf::st_geometry(flowline))
    plot(start_point, cex = 1.5, lwd = 2, col = "red", add = TRUE)
    plot(sf::st_geometry(dplyr::filter(flowline, comid %in% UT_comids)),
         add=TRUE, col = "red", lwd = 2)
    plot(sf::st_geometry(catchment), add = TRUE)
    plot(sf::st_geometry(waterbody), col = rgb(0, 0, 1, alpha = 0.5), add = TRUE)

## Indexing

nhdplusTools supports a number of indexing use cases. See the function index for specifics.

Using the data above, we can use the `get_flowline_index()` function to get the comid, reachcode, and measure of our starting point like this.

    get_flowline_index(flowline, start_point)
    #>   id    COMID      REACHCODE REACH_meas       offset
    #> 1  1 13293750 07090002007373    41.8003 0.0009619034

`get_flowline_index()` will work with a list of points too. For demonstration purposes, we can use the gages in our subset from above.

    gage <- sf::read_sf(output_file, "Gage")

    get_flowline_index(flowline, sf::st_geometry(gage), precision = 10)
    #>    id    COMID      REACHCODE REACH_meas         offset
    #> 1   2 13294276 07090002008387    14.8355 0.000020891030
    #> 2   3 13294264 07090002007650    56.4439 0.000031104771
    #> 3   4 13293750 07090002007373    42.5089 0.000016222605
    #> 4   5 13294312 07090002008383     1.2211 0.000013118677
    #> 5   6 13294264 07090002007650    41.0519 0.000040136119
    #> 6   7 13294264 07090002007650     2.0872 0.000009600705
    #> 7   9 13294300 07090002008379    85.4441 0.000024421783
    #> 8  10 13293690 07090002007648     0.7642 0.000022907103
    #> 9  11 13294264 07090002007650    71.4042 0.008212854054
    #> 10 13 13294176 07090002007664     6.1174 0.000037883863
    #> 11 15 13294290 07090002008374    88.0213 0.000037222413
    #> 12 16 13294138 07090002007709    73.3717 0.000010914584
    #> 13 18 13293486 07090002007724     8.8530 0.000019633805
    #> 14 19 13293512 07090002007723     5.9868 0.000003962819
    #> 15 20 13294176 07090002007664    16.9958 0.000011728694
    #> 16 22 13293474 07090002007713    72.0814 0.000009896451
    #> 17 23 13293520 07090002007676    18.1942 0.000038997161
    #> 18 26 13293512 07090002007723    68.1716 0.000019188910
    #> 19 27 13294264 07090002007650    97.1673 0.000022821372
    #> 20 29 13293456 07090002007738    90.8364 0.000027257370
    #> 21 31 13293512 07090002007723    67.6223 0.000030945071
    #> 22 33 13293576 07090002008236     2.7112 0.000016468104

For more info about `get_flowline_index()` and other indexing functions, see the article `vignette("indexing")` about it or the reference page that describes it.