The hydrological modelling of the
transfR package is based on a geomorphological analysis of
the studied catchments. In this vignette, we give some guidance on how
to perform this geomorphological analysis. More specifically, we extract
the catchment delineation and hydraulic length maps from a digital
elevation model (DEM). This analysis is one of the two inputs needed
(together with the time series of flow observations) to build a
transfR object and start using the transfR
package (see the Get started with transfR vignette).
Hydraulic length is defined as the distance within the river network along an identified flow path to the outlet. It can be extracted from a DEM in many different ways, such as with the GRASS toolkits (Jasiewicz and Metz 2011), Whitebox GAT (see J. B. Lindsay (2016) or WhiteboxTools), TauDEM (D. Tarboton, Utah State University) or online services (Squividant et al. (2015) for catchment delineation only). This vignette presents one possible workflow by making use of two main R packages:
elevatr
that provides elevation data worldwide from Various APIs.whitebox,
an R frontend for the ‘WhiteboxTools’ library, which is an advanced
geospatial data analysis platform.Functions of the whitebox package generally do not work
with objects in R memory, but with files written on the disk storage. It
is therefore advised to define the working directory where these files
will be written.
elevatrThe elevatr package allows retrieving grids (rasters) of
elevation data worldwide for various zoom levels and data sources. It
uses the Open Topography
Global Datasets API for accessing Shuttle Radar Topography Mission
(SRTM) data.
It is necessary to assign a geographic projection for the catchment
delineation and hydraulic length maps with the whitebox
package. We chose to use the Lambert93 projection, the official
projection for maps of metropolitan France, for which the EPSG code is 2154.
library(elevatr)
library(progress) # Needed by elevatr
# Set up a projection (French Lambert-93 projection)
EPSG <- 2154
# Define a bbox that will encompass the catchments of the study area
blavet_bbox <- st_bbox(c(xmin = -3.3, xmax = -2.7, ymax = 48.11, ymin = 47.77),
crs = st_crs(4326))
blavet_loc <- st_as_sfc(blavet_bbox) |> st_sf()
# Retrieve elevation data as raster
dem_raw <- elevatr::get_elev_raster(blavet_loc, z = 10) # ~76m resolution
# Project and define spatial resolution:
dem_100m <- st_warp(st_as_stars(dem_raw), cellsize = 100, crs = st_crs(EPSG))
names(dem_100m) <- "warp"
# Set negative values (ocean) to NA
dem_100m[dem_100m < 0] <- NA
# Write to file
write_stars(dem_100m["warp"], file.path(wbt_wd,"dem_100m.tif"))#> Warning:
#> Issue when downloading elevation data.
#> The vignette will not be fully built.The hydrological modelling distinguish the hillslope from the river
network. Both will have very different transfer dynamics, and the
transfR package aims to describe the transfer function of
the river network only. Defining where this river network begins and the
flow path it takes is a key, and non-trivial, issue for
hydrogeomorphologists. The easiest way to draw a drainage network from a
DEM is usually to define a minimum drainage area threshold at which the
drainage network is assumed to start. However, defining this threshold
can be difficult as it may vary spatially, especially with the geology
of the region. It may therefore be better to use a known river network
and force the flow paths to follow it. Here we will use the French TOPAGE river network as
a reference (see the description of the Blavet dataset to
download it from the Web Feature Service (WFS) “Sandre - Eau France”).
We will implement a stream burning technique into the DEM using the
function whitebox::wbt_burn_streams_at_roads()
and following John B. Lindsay (2016).
library(transfR)
library(whitebox)
# Get the French Topage river network from the Blavet dataset
data(Blavet)
CoursEau_Topage2019 <- Blavet$network
# Change projection and write files
network_topage <- st_transform(CoursEau_Topage2019, EPSG)
st_write(network_topage, file.path(wbt_wd, "network_topage.shp"),
delete_layer = TRUE, quiet = TRUE)
whitebox::wbt_rasterize_streams("network_topage.shp",
base = "dem_100m.tif",
output = "network_topage.tif",
nodata = 0,
wd = wbt_wd)
# Burn this river network on the DEM
# We will neglect the effect of the road embankments at this DEM resolution of 100m
# by creating an empty shapefile for roads
st_write(st_sfc(st_multilinestring(),crs = EPSG), file.path(wbt_wd,"roads.shp"),
delete_layer = TRUE, quiet = TRUE)
whitebox::wbt_burn_streams_at_roads(dem = "dem_100m.tif",
streams = "network_topage.shp",
roads = "roads.shp",
output = "dem_100m_burn.tif",
wd = wbt_wd)The whitebox package provides all the tools to perform a
usual catchment delineation workflow from a DEM and outlet’s
coordinates. It can be done in 6 different steps:
# Remove the depressions on the DEM
whitebox::wbt_fill_depressions(dem = "dem_100m_burn.tif",
output = "dem_fill.tif",
wd = wbt_wd)
# Flow direction raster
whitebox::wbt_d8_pointer(dem = "dem_fill.tif",
output = "d8.tif",
wd = wbt_wd)
# Compute flow accumulation
whitebox::wbt_d8_flow_accumulation(input = "d8.tif",
pntr = TRUE,
output ="facc.tif",
wd = wbt_wd)
# Extract a stream network (threshold = 1 km2) consistent with flow direction
whitebox::wbt_extract_streams(flow_accum = "facc.tif",
threshold = 100, # 100 cells for 1 km2
output = "network_1km2.tif",
zero_background = TRUE,
wd = wbt_wd)
whitebox::wbt_remove_short_streams(d8_pntr = "d8.tif",
streams = "network_1km2.tif",
output = "network_d8.tif",
min_length= 200,
wd = wbt_wd)Coordinates of the outlets are retrieved from hydro.eaufrance.fr and snapped to the pixel of the river network that is consistent with the previously defined flow directions.
# Localize the outlets of the studied catchments (with manual adjustments to help snapping)
outlets_coordinates <- data.frame(id = names(Blavet$hl),
X = c(254010.612,255940-100,255903,237201,273672,265550),
Y = c(6772515.474,6776418-200,6776495,6774304-200,6762681,6783313))
outlets <- st_as_sf(outlets_coordinates, coords = c("X", "Y"), crs=2154)
st_write(outlets, dsn = file.path(wbt_wd, "outlets.shp"),
delete_layer = TRUE, quiet = TRUE)
# Snap the outlets on the stream raster
whitebox::wbt_jenson_snap_pour_points(pour_pts = "outlets.shp",
streams = "network_d8.tif",
output = "outlets_snapped.shp",
snap_dist = 200,
wd = wbt_wd)
outlets_snapped <- st_read(file.path(wbt_wd, "outlets_snapped.shp"), quiet = TRUE)
# Delineate catchments
catchments <- st_sfc(crs = EPSG)
for(id in outlets_snapped$id){
st_write(outlets_snapped[outlets_snapped$id==id,],
file.path(wbt_wd, paste0(id, "_outlet.shp")), delete_layer = TRUE, quiet = TRUE)
whitebox::wbt_watershed(d8_pntr = "d8.tif",
pour_pts = paste0(id, "_outlet.shp"),
output = paste0(id, "_catchment.tif"),
wd = wbt_wd)
# Vectorize catchments
drainage <- read_stars(file.path(wbt_wd, paste0(id, "_catchment.tif")))
contours <- st_contour(drainage, breaks = 1) |> st_geometry() |> st_cast("POLYGON")
contours <- contours[which.max(st_area(contours))]
catchments <- rbind(catchments, st_sf(data.frame(id, geom = contours)))
}Resulting catchments delineation can be plotted and checked.
# Compare drainage areas to the dataset provided with transfR
compare_areas <- data.frame(
name = names(Blavet$hl),
expected_area = st_area(st_geometry(Blavet$obs)) |> units::set_units("km^2") |> round(1),
computed_area = st_area(catchments) |> units::set_units("km^2") |> round(1)
)
print(compare_areas)The whitebox package does not provide a function to
compute hydraulic length directly. However, it can compute the total downslope
flow path length to the outlet using
whitebox::wbt_downslope_flowpath_length() function and the
downslope
distance to the stream using
whitebox::wbt_downslope_distance_to_stream() function. The
hydraulic length does not take into account the length of the flow path
over the hillslopes, so it can be calculated simply by the difference of
these two flow distances.
Flow path lengths are computed once for all the study area. For each
catchment, this raster is then trimmed (i.e. removing NA values outside
the bounding box of the catchment) and the values of flow path lengths
are corrected such as the minimum flow path length is equal to half a
pixel width/height. The hydraulic lengths are finally gathered in a list
as expected by the as_transfr() function.
# Compute hydraulic length
whitebox::wbt_downslope_flowpath_length(d8_pntr = "d8.tif",
output = "fpl.tif",
wd = wbt_wd)
whitebox::wbt_downslope_distance_to_stream(dem = "dem_fill.tif",
streams = "network_topage.tif",
output = "d2s.tif",
wd = wbt_wd)
fpl <- read_stars(file.path(wbt_wd, "fpl.tif"))
d2s <- read_stars(file.path(wbt_wd, "d2s.tif"))
hl_region <- fpl-d2s
names(hl_region) <- "hl"
# Crop hydraulic length for each catchment
hl <- list()
for(id in catchments$id){
crop <- st_crop(hl_region, catchments[catchments$id==id,])
crop <- crop-min(crop$hl, na.rm = TRUE)
crop$hl <- units::set_units(crop$hl, "m")
hl[[id]] <- crop
}Resulting maps of hydraulic length can be plotted and checked.
Catchment delineations can be used as the spatial dimension of
stars objects to georeference the observed flow time series
of gauged catchments and locate ungauged catchments (see vignette
Preparation of input data: creation of a stars object for
details). Here we will create a stars object using the
observed discharge of the Blavet dataset and the
delineations that we just computed with whitebox.
obs_st <- st_as_stars(list(Qobs = Blavet$obs$Qobs),
dimensions = st_dimensions(
time = st_get_dimension_values(Blavet$obs,1),
space = st_geometry(catchments)))The maps of hydraulic length can finally be used to create an object
of class transfR by using the function
as_transfr() (argument hl) and perform
simulations.
A transfer of hydrograph from the gauged catchments to the ungauged
catchments can then quickly be implemented using the
quick_transfr() function.
The simulated time series will be available in its stars object as new attributes.