Alaska? #340
Comments
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That's correct. Sorry for the trouble. For Alaska applications, you will have to go to NHD data directly. Unfortunately, nhdplusTools support for Alaska (region 19) is pretty thin. I've recently been doing a little more work in region 19 (https://code.usgs.gov/wma/nhgf/mainstems/-/blob/master/reg19_prototype.R) but it's very early. If you want to dig in, you could work with my new work over in hydroloom https://github.com/DOI-USGS/hydroloom and make some hay, but there are a few things that would require fairly specific knowledge of the NHD and some of the stuff in hydroloom. What application are you trying to accomplish? Maybe I could use it as an excuse to advance parts of that work? |
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Thanks for the reply! I will take a look at hydroloom and see what I can do. I just looked at the region 19 code; it's nice to see clearly written tidyverse-style R. We are working on a project that's concerned with vehicle pollution entering the Chena River (and upstream tributaries) so my initial task was getting line layers for the Chena River and all the tributaries that drain into it. After that layer is intersected with a road layer, one important parameter will be the distance the crossing is from the Chena River, so some understanding of the network and direction of flow will matter for calculating the path and distance to the Chena. We have done this sort of work using the NHD directly, but it isn't easy and your package looked like a much simpler workflow that also requires no non-reproducible ArcMap/ArcGIS Pro mouse work. |
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Alright -- so a quick doodle testing out some of the stuff I've been working on was productive. I would like to:
With those caveats, the below shows how to index your point to an NHD flowline and do an upstream / downstream navigation. There's no "upmain" or "downmain" withouth more work -- which is what the prototype I linked above does, but this is probably a good start?
UPDATE: 4/19/23 -- this depends on
library(hydroloom)
library(dplyr)
options(timeout = 60000)
options(timeout = 60000)
dir <- nhdplusTools::download_nhd("../../../Data/ak", "1908", TRUE)
fs <- list.files(dir, pattern = ".*1908.*.gdb", full.names = TRUE)
fl <- sf::read_sf(fs, "NHDFlowline")
fl <- filter(fl, ftype != 566)
fl <- select(fl, id = permanent_identifier, gnis_id, gnis_name, reachcode,
flowdir, wbid = wbarea_permanent_identifier, ftype, fcode)
# measures will be incorrect but we can fix this later.
fl <- mutate(fl, aggregate_id_from_measure = 0, aggregate_id_to_measure = 100)
point <- sf::st_sfc(sf::st_point(c(-147.911472, 64.796633)),
crs = 4269)
i <- hydroloom::index_points_to_lines(fl, point)
sub <- fl[fl$id == i$id,]
mapview::mapview(list(sub, point))
flow_table <- sf::read_sf(fs, "NHDFlow")
# need to get the flow table into hydroloom ids
flow_table <- select(flow_table,
id = from_permanent_identifier,
toid = to_permanent_identifier)
# LOOPS!!!
flow_table <- flow_table |>
filter(id %in% fl$id) |>
mutate(toid = ifelse(!toid %in% id, "", toid)) |>
mutate(r = 1:n())
remove <- left_join(flow_table, flow_table,
by = c("toid" = "id")) |>
filter(id == toid.y) |>
pull(r.x)
flow_table <- filter(flow_table, !r %in% remove) |>
select(-r)
down <- navigate_network_dfs(flow_table, sub$id, direction = "down")
up <- navigate_network_dfs(flow_table, sub$id, direction = "up")
subdown <- fl[fl$id %in% unique(unlist(down)),]
subup <- fl[fl$id %in% unique(unlist(up)),]
mapview::mapview(list(subup, subdown, point))
``` |
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Excellent! Thanks so much! I will try your code and see where it takes me tomorrow morning. |
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FYI, here's what I came up with as a first cut for the analysis I need to do: library(tidyverse)
library(glue)
library(sf)
library(fs)
library(zip)
# devtools::install_github("DOI-USGS/hydroloom")
library(hydroloom)
options(timeout = 60000)
u <- glue(
"https://prd-tnm.s3.amazonaws.com/",
"StagedProducts/Hydrography/NHD/",
"HU4/GDB/NHD_H_1908_HU4_GDB.zip"
)
d <- "./data/ak"
if (!dir_exists(d)) {
dir_create(d)
}
f <- path(d, basename(u))
if (!file_exists(f)) {
download.file(u, f, mode = "wb")
zip::unzip(f, exdir = d)
}
fs <- dir_ls(d, regexp = ".*1908.*.gdb")
# Chena & Tanana River intersection
point <- sf::st_sfc(
sf::st_point(c(-147.911472, 64.796633)),
crs = 4269
)
i <- hydroloom::index_points_to_lines(fl, point)
# Map of NHD data model:
# https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/NHD%20v2.3%20Model%20Poster%2006012020.pdf
fc <- sf::read_sf(fs, "NHDFCode") |>
select(fcode, description) |>
rename(fcode_description = description)
# Get flowlines from FileGDB
fl <- sf::read_sf(fs, "NHDFlowline") |>
# Don't include "Coastline"
filter(ftype != 566) |>
select(
permanent_identifier, # 40-char unique identifier
gnis_id, # GNIS unique identifier
gnis_name, # Proper name of geographic entity
reachcode, # first 8 chars = subbasin code (FIPS 103), next 6 random
flowdir, # direction of flow relative to coordinate order. 1 = WithDigitized, 0 = Unintialized (?!)
wbarea_permanent_identifier, # pid of waterbody through which this flows
ftype, # unique identifier of feature type
fcode # feature type, characteristics, values
) |>
rename(
id = permanent_identifier,
wbid = wbarea_permanent_identifier
) |>
# inner_join(i, by = "id") |>
left_join(fc, by = "fcode") |>
mutate(
# required for hydroloom funcs:
aggregate_id_from_measure = 0,
aggregate_id_to_measure = 100
)
st_geometry(fl) <- "wkb_geometry"
sub <- fl %>% inner_join(i, by = "id")
# to / from flow table, filtered to segments in fl
full_flow_table <- sf::read_sf(fs, "NHDFlow") |>
select(from_permanent_identifier, to_permanent_identifier) |>
rename(
id = from_permanent_identifier,
toid = to_permanent_identifier
)
filtered_flow_table <- full_flow_table |>
inner_join(
fl %>%
st_drop_geometry() %>%
select(id),
by = "id"
) |>
mutate(
toid = ifelse(!toid %in% id, "", toid),
r = row_number()
)
remove <- left_join(
filtered_flow_table, filtered_flow_table,
by = c("toid" = "id"),
relationship = "many-to-many"
) |>
filter(id == toid.y) |>
pull(r.x)
flow_table <- filter(filtered_flow_table, !r %in% remove) |>
select(-r)
# Traverse the flow network for all upstream segments
up <- navigate_network_dfs(flow_table, sub$id, direction = "up")
# Filter flowlines to these segments
subup <- fl[fl$id %in% unique(unlist(up)), ] |>
inner_join(flow_table, by = "id") |>
select(
id, toid,
gnis_id, gnis_name, reachcode, wbid,
ftype, fcode, fcode_description,
wkb_geometry
)
mapview::mapview(list(subup, point))
# At this point, I think we can use this sf data frame for getting distances
# to the Chena River. First, get the segment that the crossing is on:
crossing_point <- sf::st_sfc(
sf::st_point(c(-147.2204, 65.0092)),
crs = 4269
)
crossing_index <- hydroloom::index_points_to_lines(fl, crossing_point)
crossing_segment <- fl %>%
inner_join(crossing_index, by = "id")
# Now do the same thing as above, but go down
chena_flow_table <- subup |>
st_drop_geometry() |>
select(id, toid)
downstream_crossing <- navigate_network_dfs(
chena_flow_table,
crossing_segment$id,
direction = "down"
)
subdown <- subup[subup$id %in% unique(unlist(downstream_crossing)), ] |>
inner_join(chena_flow_table, by = c("id", "toid")) |>
select(
id, toid,
gnis_id, gnis_name, reachcode, wbid,
ftype, fcode, fcode_description,
wkb_geometry
)That's about 90% of where I wanted to get. The next steps for my particular analysis are to get the length of Thanks very much! |
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Very cool. There's plenty of other smarts that could be added if we do a little more network derivative calculation. The prototype workflow I linked previously is actually going to get plowed into the NHD in the long run. So better capabilities are coming. For the record, I did tick off a few of the things I mentioned doing up above. They are now on the latest release of hydroloom. https://github.com/DOI-USGS/hydroloom/blob/main/vignettes/flow-table.Rmd is the vignette I got out of the deal. Needs writing and some more description of what's happening but this was a great excuse to work that up. Thanks for the prompt. |
I just attempted to run the first few lines of the example from the U.S. Data and Package Basics vignette, but replacing the location with the intersection of the Chena and Tanana Rivers in Interior Alaska:
and I get an error:
The code works with
c(89.362, 43.090), so I'm guessing this is a case where the dataset/API/R library isn't applicable to Alaska.Suggestions? Thanks!
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