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

  knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width=6,
  fig.height=4
)
options("rgdal_show_exportToProj4_warnings"="none",
        rmarkdown.html_vignette.check_title = FALSE)
library(magrittr)
sf::sf_use_s2(FALSE)
fdr_path <- "../../gfv2/workspace/data/fdrfac/NHDPlusCA/NHDPlus18/NHDPlusFdrFac18c/fdr/"
fac_path <- "../../gfv2/workspace/data/fdrfac/NHDPlusCA/NHDPlus18/NHDPlusFdrFac18c/fac/"

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)

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)

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)

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)
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)
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)
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)
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)
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)
nhdplusTools::plot_nhdplus(bbox = bbox, nhdplus_data = temp_gpkg, 
                           plot_config = list(
                             flowline = list(lwd = 0.5, col = "lightblue")), 
                           flowline_only = TRUE)
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.