nhdplusTools offers a few indexing functions and some
supporting functions that are worth being aware of. Most of these
functions wrap similar but more general functions from hydroloom.
The core functions are: get_flowline_index() and
get_waterbody_index() – they do the heavy lifting of
finding flowlines and waterbodies near point locations.
For flowline indexes, there are a number of useful utilities: -
disambiguate_flowline_indexes() uses numeric or character
attributes to attempt to determine the best flowline match when many
near by matches exist. This is especially useful for mainstem /
tributary disambiguation. - get_hydro_location() retrieves
the point location of an index along a flowline. -
rescale_measures() converts 0:100 reachcode measures to
0:100 flowline measures. - get_partial_length() retrieves a
partial length (upstream and downstream) of an index location. -
get_path_lengths() retrieves the distance between the
outlets of pairs of flowlines. Use with
get_partial_length() to determine network distance between
indexes.
For waterbody indexes, the get_wb_outlet() function is
helpful too determine which flowline is the outlet of a waterbody.
First we’ll load up some data. In this case, we use flowlines from the NHDPlus subset that’s included in the package and a set of points to index. We’ll use the NHDPlus Gages layer for this example. The data in this example is big. The R session needs a lot of memory to hold the whole NHDPlus flowline layer and run the calculations.
library(nhdplusTools)
nhdplus_path(file.path(work_dir, "natseamless.gpkg"))
flowlines <- sf::read_sf(nhdplus_path(), "NHDFlowline_Network") |>
sf::st_zm()
gages <- sf::read_sf(nhdplus_path(), "Gage")Now we can call get_flowline_index() on the data we just
loaded. The get_flowline_index() function has an input,
search_radius which should correspond to the units of the
points input. Projection and unit conversion is attempted.
See the function documentation for details. See the documentation of the
nn2 function from the
RANN package for more information on how the search works.
NOTE: If you have a small area in which you need flowline indexes,
get_flowline_index() has an option to download flowlines in
the bounding box of your input points.
indexes <- get_flowline_index(sf::st_transform(flowlines, 5070), # albers
sf::st_transform(sf::st_geometry(gages), 5070),
search_radius = units::set_units(200, "meters"),
max_matches = 1)
indexes <- left_join(sf::st_sf(id = c(1:nrow(gages)),
geom = sf::st_geometry(gages)),
indexes, by = "id")
plot(sf::st_geometry(sf::st_zm(flowlines)))
plot(sf::st_geometry(indexes), add = TRUE)Now let’s look at the results and see how the
get_flowline_index() did. The below shows the percent of
COMIDs and REACHCODEs that match and shows a histogram of the measure
differences for the REACHCODEs that were matched.
p_match <- 100 * length(which(indexes$COMID %in% gages$FLComID)) / nrow(gages)
paste0(round(p_match, digits = 1),
"% were found to match the COMID in the NHDPlus gages layer")
p_match <- 100 * length(which(indexes$REACHCODE %in% gages$REACHCODE)) / nrow(gages)
paste0(round(p_match, digits = 1),
"% were found to match the REACHCODE in the NHDPlus gages layer")
matched <- cbind(indexes,
dplyr::select(sf::st_drop_geometry(gages),
REACHCODE_ref = REACHCODE,
COMID_ref = FLComID,
REACH_meas_ref = Measure)) %>%
dplyr::filter(REACHCODE == REACHCODE_ref) %>%
dplyr::mutate(REACH_meas_diff = REACH_meas - REACH_meas_ref)
hist(matched$REACH_meas_diff, breaks = 100,
main = "Difference in measure for gages matched to the same reach.")
round(quantile(matched$REACH_meas_diff,
probs = c(0, 0.1, 0.25, 0.5, 0.75, 0.9, 1)),
digits = 2)The above example used the native nodes of the NHDPlus as the
potential measure snap locations. The get_flowline_index()
function has the ability to refine these by segmentizing the line to
some given resolution. Let’s try the same thing using a resolution of
10m and see if we can do any better.
Note that the sf::st_segmentize function takes care of
the distance conversion and segmentizes our lon/lat lines to 10m on the
fly. Also note, we are working in units of degrees for this sample.
(this is probably not a good way to do things, but is being shown here
for the sake of demonstration)
indexes <- get_flowline_index(flowlines,
sf::st_geometry(gages),
search_radius = units::set_units(0.1, "degrees"),
precision = 10)
indexes <- left_join(data.frame(id = seq_len(nrow(gages))), indexes, by = "id")Now lets look at out comparison again.
p_match <- 100 * length(which(indexes$COMID %in% gages$FLComID)) / nrow(gages)
paste0(round(p_match, digits = 1),
"% were found to match the COMID in the NHDPlus gages layer")
p_match <- 100 * length(which(indexes$REACHCODE %in% gages$REACHCODE)) / nrow(gages)
paste0(round(p_match, digits = 1),
"% were found to match the REACHCODE in the NHDPlus gages layer")
matched <- cbind(indexes,
dplyr::select(sf::st_set_geometry(gages, NULL),
REACHCODE_ref = REACHCODE,
COMID_ref = FLComID,
REACH_meas_ref = Measure)) %>%
dplyr::filter(REACHCODE == REACHCODE_ref) %>%
dplyr::mutate(REACH_meas_diff = REACH_meas - REACH_meas_ref)
hist(matched$REACH_meas_diff, breaks = 100,
main = "Difference in measure for gages matched to the same reach.")
round(quantile(matched$REACH_meas_diff,
probs = c(0, 0.1, 0.25, 0.5, 0.75, 0.9, 1)), digits = 2)get_flowline_index() has a parameter
max_matches that controls how many indexed flowlines are
returned per point. This is useful for points that are near many
flowlines and some further disambiguation is needed to determine exactly
which flowline the point should be indexed to.
For this example, we’ll just look at one point but keep all the sites for disambiguation down below.
all_indexes <- get_flowline_index(flowlines,
sf::st_geometry(gages),
search_radius = units::set_units(0.01, "degrees"),
max_matches = 10)
indexes <- left_join(sf::st_sf(id = 42,
geom = sf::st_geometry(gages)[42]),
all_indexes[all_indexes$id == 42, ], by = "id")
plot(sf::st_geometry(sf::st_buffer(indexes, 500)), border = NA)
plot(sf::st_geometry(indexes), add = TRUE)
plot(sf::st_geometry(sf::st_zm(flowlines)), col = "blue", add = TRUE)
indexesNow that we have multiple matches, we can use the function
disambiguate_flowline_indexes() to figure out which is the
“best” match. “best” is in scare quotes here because there are many
potential sources of ambiguity here and we are really just narrowing
down based on the information we have at hand. (read below for a case in
point)
Below, we run disambiguate_flowline_indexes() on all the
indexes we found then pull out the one we looked at just above as an
example (gage 42 in our list).
unique_indexes <- disambiguate_flowline_indexes(
all_indexes,
flowlines[, c("COMID", "TotDASqKM"), drop = TRUE],
data.frame(ID = seq_len(nrow(gages)),
area = gages$DASqKm))
unique_index <- left_join(sf::st_sf(id = 42,
geom = sf::st_geometry(gages)[42]),
unique_indexes[unique_indexes$id == 42, ], by = "id")
plot(sf::st_geometry(sf::st_buffer(indexes, 500)), border = NA)
plot(sf::st_geometry(indexes), add = TRUE)
plot(sf::st_geometry(sf::st_zm(flowlines[flowlines$COMID %in% indexes$COMID,])),
col = "grey", lwd = 3, add = TRUE)
plot(sf::st_geometry(sf::st_zm(flowlines[flowlines$COMID %in% unique_index$COMID,])),
col = "blue", add = TRUE)
unique_indexAs can be seen in this example, a drainage area disambiguation resulted in an unexpected result. Further inspection of this particular gage and the network data used, shows that the main path through this small diversion is coded counter to the main path in the real world. So in this case, if our interest is in the best match to the hydrographic network data we have, this is the best match, as the closest spatial match is incorrectly modeled by the hydrographic data set. As always, buyer beware!
The get_flowline_index() function estimates a
hydrographic address as a linear reference to a flowline. For points
near bodies of water, these could be an inappropriate kind of index.
This is because where flowlines run through a waterbody. they are
“artificial paths” and do not represent the waterbody. The
get_waterbody_index() function is intended to address
points that are in or near the shore of a waterbody.
This next block of code loads the NHDPlus Waterbody layer and creates
an interactive map. Of interest on gages that are near the short of
bodies of water but far away from flowlines. Note that we drop the
NHDPlus geometry and use the source LonSite and
LatSite attributes for geometry.
waterbody <- sf::read_sf(nhdplus_path(), "NHDWaterbody")
gages <- sf::st_drop_geometry(gages) %>%
dplyr::filter(!is.na(LonSite)) %>%
sf::st_as_sf(coords = c("LonSite", "LatSite"), crs = 4326)
plot(sf::st_geometry(sf::st_zm(flowlines)))
plot(sf::st_geometry(waterbody), add = TRUE)
plot(sf::st_geometry(gages), add = TRUE)This next block shows how to call get_flowline_index()
and get_waterbody_index() and what the output looks
like.
flowline_indexes <- left_join(data.frame(id = seq_len(nrow(gages))),
get_flowline_index(
sf::st_transform(flowlines, 5070),
sf::st_geometry(sf::st_transform(gages, 5070)),
search_radius = units::set_units(200, "m")), by = "id")
indexed_gages <- cbind(dplyr::select(gages,
orig_REACHCODE = REACHCODE,
orig_Measure = Measure,
FLComID,
STATION_NM),
flowline_indexes,
get_waterbody_index(
st_transform(waterbody, 5070),
st_transform(gages, 5070),
st_drop_geometry(flowlines),
search_radius = units::set_units(200, "m")))
plot(sf::st_geometry(sf::st_zm(flowlines)))
plot(sf::st_geometry(waterbody), add = TRUE)
plot(sf::st_geometry(indexed_gages), add = TRUE)
dplyr::select(sf::st_drop_geometry(indexed_gages), near_wb_COMID, near_wb_dist, in_wb_COMID, outlet_fline_COMID)