docs/coastal_units.md

In dblodgett-usgs/hyRefactor: Hydrologic Network Refactoring Tools Based on HY_Features

This article demonstrates how to work with coastal catchments and small coastal basins in an hyRefactor workflow.

First, we use nhdplusTools to download and plot some data along the California coast.

library(hyRefactor)
#> Registered S3 method overwritten by 'geojsonlint':
#>   method         from 
#>   print.location dplyr

temp_gpkg <- tempfile(fileext = ".gpkg")

bbox <- sf::st_bbox(c(xmin = -124.4, ymin = 39.8, xmax = -123.7, ymax = 40.3), 
                    crs = sf::st_crs(4326))
nhd_data <- nhdplusTools::plot_nhdplus(bbox = bbox, gpkg = temp_gpkg, overwrite = TRUE)
#> although coordinates are longitude/latitude, st_intersects assumes that they are planar
#> although coordinates are longitude/latitude, st_intersects assumes that they are planar
#> although coordinates are longitude/latitude, st_intersects assumes that they are planar
#> although coordinates are longitude/latitude, st_intersects assumes that they are planar
#> although coordinates are longitude/latitude, st_intersects assumes that they are planar
#> Zoom: 10
#> Map tiles by Carto, under CC BY 3.0. Data by OpenStreetMap, under ODbL.
#> Audotdetect projection: assuming Google Mercator (epsg 3857)

Now that we have some data to work with, we need to subset it so we have a few complete basins to work with.

Note that both basins that terminate at the coast AND the coastal flowlines are included in out subset below.

First we use the nhdplusTools::navigate_nldi for each terminal flowline in our subset to find complete basins then we use the nhdplusTools::subset_nhdplus to get the data we need.

terminals <- dplyr::filter(nhd_data$flowline, TerminalFl == 1)

coastal <- dplyr::filter(nhd_data$flowline, FTYPE == "Coastline")

flowline <- dplyr::bind_rows(
  lapply(terminals$COMID, function(x) {
    nhdplusTools::navigate_nldi(list(featureSource = "comid", featureID = x), 
                                mode = "UT", distance_km = 999)$UT_flowlines
}))

sub <- lapply(nhdplusTools::subset_nhdplus(c(as.integer(flowline$nhdplus_comid), coastal$COMID), 
                                    nhdplus_data = "download", flowline_only = FALSE),
              nhdplusTools::align_nhdplus_names)
#> All intersections performed in latitude/longitude.
#> Reading NHDFlowline_Network
#> Writing NHDFlowline_Network
#> Reading CatchmentSP
#> Writing CatchmentSP
#> although coordinates are longitude/latitude, st_intersects assumes that they are planar
#> although coordinates are longitude/latitude, st_intersects assumes that they are planar
#> although coordinates are longitude/latitude, st_intersects assumes that they are planar

plt <- function(x) sf::st_geometry(sf::st_transform(x, 3857))

net <- sub$NHDFlowline_Network
cats <- sub$CatchmentSP

nhdplusTools::plot_nhdplus(bbox = bbox, nhdplus_data = temp_gpkg, 
                           plot_config = list(
                             flowline = list(lwd = 0.5, col = "lightblue")), 
                           flowline_only = TRUE)
#> Zoom: 10
#> Map tiles by Carto, under CC BY 3.0. Data by OpenStreetMap, under ODbL.
#> Audotdetect projection: assuming Google Mercator (epsg 3857)
plot(plt(cats), add = TRUE, col = "tan")
plot(plt(net), add = TRUE, col = "blue")

Now that we have our subset ready to go, we’ll need to get some flow direction and flow accumulation data to use with hyRefactor later on.

fdr_dir = file.path(tempdir(check = TRUE), "fdr_fac")
dir.create(fdr_dir, showWarnings = FALSE)

if(!dir.exists(file.path(fdr_dir, "NHDPlusCA/"))) {
  hyRefactor::download_fdr_fac(fdr_dir, regions = "18")
}

fdr_path <- file.path(fdr_dir, "NHDPlusCA/NHDPlus18/NHDPlusFdrFac18c/fdr/")
fac_path <- file.path(fdr_dir, "NHDPlusCA/NHDPlus18/NHDPlusFdrFac18c/fac/")

library(terra)
#> terra 1.6.7
#> 
#> Attaching package: 'terra'
#> The following objects are masked from 'package:magrittr':
#> 
#>     extract, inset
fdr <- terra::rast(fdr_path)
fac <- terra::rast(fac_path)
crs <- terra::crs(fac)
fdr <- terra::crop(fdr, sf::st_transform(cats, crs))
fac <- terra::crop(fac, sf::st_transform(cats, crs))
plot(fdr)

Now that we have all our data ready to go, we can get our networks that we want to refactor pulled out from our coastal catchments and small coastal basins.

coastal <- net[net$FTYPE == "Coastline", ]
coastal_cats <- cats[cats$FEATUREID %in% coastal$COMID, ]

net <- dplyr::filter(net, !COMID %in% coastal$COMID)
cats <- cats[cats$FEATUREID %in% net$COMID, ]

nhd_outlets <- dplyr::filter(net, TerminalFl == 1)

sf::st_geometry(nhd_outlets) <- sf::st_geometry(nhdplusTools::get_node(nhd_outlets, "end"))

nhdplusTools::plot_nhdplus(bbox = bbox, nhdplus_data = temp_gpkg, 
                           plot_config = list(
                             flowline = list(lwd = 0.5, col = "lightblue")), 
                           flowline_only = TRUE)
#> Zoom: 10
#> Map tiles by Carto, under CC BY 3.0. Data by OpenStreetMap, under ODbL.
#> Audotdetect projection: assuming Google Mercator (epsg 3857)
plot(plt(cats), add = TRUE, col = "tan")
plot(plt(net), add = TRUE, col = "lightblue")
plot(plt(coastal_cats), add = TRUE, col = "grey")
plot(plt(coastal), add = TRUE, col = "blue")
plot(plt(nhd_outlets), add = TRUE, col = "black")

Now we will identify coastal basins that we want to lump into coastal catchments as part of the refactor workflow.

min_da_km <- 10

little_terminal <- dplyr::filter(net, TerminalPa %in% 
                                   dplyr::filter(nhd_outlets, 
                                                 TotDASqKM <= min_da_km & 
                                                   TerminalFl == 1)$TerminalPa)

outlets <- dplyr::select(nhd_outlets, COMID) %>%
  dplyr::mutate(type = "terminal") %>%
  dplyr::filter(COMID %in% cats$FEATUREID) %>%
  dplyr::mutate(keep = ifelse(COMID %in% little_terminal$COMID, "temporary", "keep"))

nhdplusTools::plot_nhdplus(bbox = bbox, nhdplus_data = temp_gpkg, 
                           plot_config = list(
                             flowline = list(lwd = 0.5, col = "lightblue")), 
                           flowline_only = TRUE)
#> Zoom: 10
#> Map tiles by Carto, under CC BY 3.0. Data by OpenStreetMap, under ODbL.
#> Audotdetect projection: assuming Google Mercator (epsg 3857)
plot(plt(net), add = TRUE, col = "lightblue")
plot(plt(outlets[outlets$keep == "keep",]), add = TRUE, col = "red")
plot(plt(outlets[outlets$keep == "temporary",]), add = TRUE, col = "black")

We can now run refactor_nhdplus and reconcile_catchment_divides.

Note that the exclude_cats parameter is set to all outlet flowlines in small basins that were identified above.

Also note that the net variable no longer contains coastal flowlines.

tf <- file.path(tempfile(fileext = "tf.gpkg"))
tr <- file.path(tempfile(fileext = "tr.gpkg"))

refactor_nhdplus(nhdplus_flines = net, 
                 split_flines_meters = 100000, 
                 split_flines_cores = 1, 
                 collapse_flines_meters = 2000,
                 collapse_flines_main_meters = 2000,
                 out_refactored = tf, 
                 out_reconciled = tr, 
                 three_pass = TRUE, 
                 purge_non_dendritic = FALSE, 
                 exclude_cats = unique(c(outlets$COMID, little_terminal$COMID)),
                 warn = FALSE)

refactored <- sf::st_transform(sf::read_sf(tf), crs)
reconciled <- sf::st_transform(sf::read_sf(tr), crs)
cats <- sf::st_transform(cats, crs)
sf::st_precision(cats) <- 10

divides <- reconcile_catchment_divides(catchment = cats,
                                       fline_ref = refactored,
                                       fline_rec = reconciled,
                                       fdr = fdr_path,
                                       fac = fac_path,
                                       para = 1)

nhdplusTools::plot_nhdplus(bbox = bbox, nhdplus_data = temp_gpkg, 
                           plot_config = list(
                             flowline = list(lwd = 0.5, col = "lightblue")), 
                           flowline_only = TRUE)
#> Zoom: 10
#> Map tiles by Carto, under CC BY 3.0. Data by OpenStreetMap, under ODbL.
#> Audotdetect projection: assuming Google Mercator (epsg 3857)
plot(plt(divides), add = TRUE, col = "tan")
plot(plt(reconciled), add = TRUE, col = "blue")

Finally, we can identify the outlets

keep_outlets <- dplyr::filter(outlets, keep == "keep") %>%
  dplyr::select(COMID, type)

mapped_outlets <- map_outlet_ids(keep_outlets, reconciled) %>%
  dplyr::filter(COMID %in% keep_outlets$COMID)

zero_order <- list(basin = little_terminal$COMID, zero = coastal$COMID)

agg_cats <- aggregate_catchments(flowpath = reconciled, 
                                 divide = divides, 
                                 outlets = dplyr::select(mapped_outlets, ID, type),
                                 zero_order = zero_order,
                                 coastal_cats = sf::st_transform(coastal_cats, sf::st_crs(divides)),
                                 da_thresh = 1, 
                                 only_larger = TRUE)
#> removing 2 headwater/diversion flowlines without catchments.
#> Running 205 headwaters for 6 outlets.
nhdplusTools::plot_nhdplus(bbox = bbox, nhdplus_data = temp_gpkg, 
                           plot_config = list(
                             flowline = list(lwd = 0.5, col = "lightblue")), 
                           flowline_only = TRUE)
#> Zoom: 10
#> Map tiles by Carto, under CC BY 3.0. Data by OpenStreetMap, under ODbL.
#> Audotdetect projection: assuming Google Mercator (epsg 3857)
plot(plt(agg_cats$cat_sets), add = TRUE, col = "tan")
plot(plt(agg_cats$fline_sets), add = TRUE, col = "blue")
plot(plt(agg_cats$coastal_sets), add = TRUE, col = "grey")

dblodgett-usgs/hyRefactor documentation built on Aug. 25, 2023, 9:09 p.m.