The purpose of this vignette is to provide an outline of the steps needed to build a dynamic TOPMODEL implementation using the dynatopGIS package.
The dynatopGIS package implements a structured, object orientated,
data flow. The steps outlined below create a
catchment object to which actions are then applied to generate a
dynatopGIS package is written using the object
orientated framework provided by the
R6 package. This means
that some aspects of working with the objects may appear idiosyncratic
for some R users. In using the package as outlined in this vignette
these problems are largely obscured, except for the call structure.
However, before adapting the code, or doing more complex analysis users
should read about
R6 class objects (e.g. in the
R6 package vignettes or in the Advanced R book). One
particular gotcha is when copying an object. Using
creates a pointer, that is altering
my_object. To create a new independent copy of
The dynatopGIS packages works through a number of steps to generate a
Dynamic TOPMODEL object suitable for use in with the
dynatop package. Each step generates one or more layers
which are saved as raster or shape files into the projects working
directory (which is not necessarily the R working directory). A record
of these layers is kept in the json format meta data file.
This vignette demonstrates the use of the
package using data from the Swindale catchment in the UK.
To start first load the library
For this vignette we will store the data into a temporary directory
<- tempfile("dygis") demo_dir dir.create(demo_dir)
and initialise the analysis by creating a new object specifying the location of the meta data file, which will be created if it doesn’t exist.
<- dynatopGIS$new(file.path(demo_dir,"meta.json")) ctch #> Warning in initialize(...): Creating meta file at/tmp/RtmpeP6Ekc/ #> dygis39a84618b682/meta.json #> Warning in private$check_meta(verbose): No checks on the meta are currently #> performed
The basis of the analysis is a rasterised Digital Elevation Model
(DEM) of the catchment and a vectorised representation of the river
network with attributes. Currently these can be in any format supported
However, within the calculations used for sink filling, flow routing and topographic index calculations the raster DEM is presumed to be projected so that is has square cells such that the difference between the cell centres (in meters) does not alter.
For Swindale the suitable DEM and channel files can be found using:
<- system.file("extdata", "SwindaleDTM40m.tif", package="dynatopGIS", mustWork = TRUE) dem_file <- system.file("extdata", "SwindaleRiverNetwork.shp", package="dynatopGIS", mustWork = TRUE)channel_file
Either the DEM or channel files can be added to the project first. In this case we add the DEM with
<- terra::rast(dem_file) dem $add_dem(dem)ctch
Note: An additional row and column of
added to each edge of the DEM. All raster layers in the project have the
same projection, resolution and extent of the extended DEM.
Adding river channel data is more complex. The
add_channel method requires a
SpatialPolygonsDataFrame as generated by the
sp package. Each entry in the data.frame is treated as a
length of river channel which requires the following properties
Additional properties are currently kept but ignored with two
exceptions: - id - this is copied to
with a warning since
id is used internally - width
- if the channel is specified with a line sections then the
width property is used to buffer the lines to create channel
Since it is possible that these properties are present in a data file
under different names the
add_channel method allows for
renaming. To illustrate this let us examine the river network for
<- terra::vect(channel_file) sp_lines #> Warning: [vect] Z coordinates ignored head(sp_lines) #> class : SpatVector #> geometry : lines #> dimensions : 6, 11 (geometries, attributes) #> extent : 349884.5, 351675.7, 508614.5, 513074.7 (xmin, xmax, ymin, ymax) #> coord. ref. : OSGB36 / British National Grid (EPSG:27700) #> names : name1 identifier startNode endNode #> type : <chr> <chr> <chr> <chr> #> values : Hawthorn Gill 16D0AC09-E0B6-~ 730F01D2-0F4F-~ 72B1A40B-E106-~ #> Little Mosedal~ 643017BB-01BC-~ 9B06188C-F4C7-~ A19E0D3A-2FA1-~ #> Mosedale Beck D2C2703E-A32F-~ B03DB3BD-0E33-~ D649B60C-E631-~ #> form flow fictitious length name2 sinkdepth Shape_Leng #> <chr> <chr> <chr> <int> <chr> <num> <num> #> inlandRiver in direction false 431 NA -1 431.3 #> inlandRiver in direction false 513 NA -1 513.1 #> inlandRiver in direction false 740 NA -1 739.8
The main properties are present under appropriate names and a call to
add_channel method with no options would be successful.
However if we want to carry over the additional information in the
“identifier” as “channel_id” a named vector giving the variable names to
be used could be provided. In this case:
<- c(channel_id="identifier", property_names endNode="endNode", startNode="startNode", length="length")
The river network can then be created in the correct format by
$add_channel(sp_lines,property_names) ctch#> Warning in private$apply_add_channel(channel, property_names, default_width): #> Modifying to spatial polygons using default width
Since the data set for Swindale does not contain a channel width the default width of 2m is used.
So far, the DEM and channel data exist in isolation. Next, we compute some basic summaries for each square in the DEM, specifically: - land area - the area in the DEM cell covered by land - channel area - the area in the DEM cell covered by channel - channel id - the id of the channel within the cell, corresponding to the id in the channel object.
If multiple river lengths intersect a DEM cell the properties of the channel length with the largest area of intersection are used.
This computation is done by calling
dynatopGIS class has methods for returning and
plotting the GIS data in the project. The names of all the different GIS
layers stored is returned by
$get_layer() ctch#>  "dem" "channel" "land_area" "channel_area" "channel_id"
These can be plotted (with or without the channel), for example
or returned, for example
$get_layer("dem") ctch#> class : SpatRaster #> dimensions : 163, 124, 1 (nrow, ncol, nlyr) #> resolution : 40, 40 (x, y) #> extent : 347734, 352694, 507244, 513764 (xmin, xmax, ymin, ymax) #> coord. ref. : +proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +ellps=airy +units=m +no_defs #> source : dem.tif #> name : SwindaleDTM40m #> min value : 262.8004 #> max value : 710.7533
All layers are returned as
RasterLayer objects with the
exception of the
channel layer which is returned as a
SpatialPolygonsDataFrame object. The complete meta data
file can be retrieved with
For the hill slope to be connected to the river network all DEM cells must drain to those that intersect with the river network.
The algorithm of implemented in the
ensures this is the case. Since the algorithm is iterative the execution
time of the function is limited by capping the maximum number of
iterations. If this limit is reached without completion the method can
call again with the “hot start” option to continue from where it
For Swindale, where the example DEM is already partially filled the algorithm only alters a small area near the foot of the catchment.
$sink_fill() ctch ::plot( ctch$get_layer('filled_dem') - ctch$get_layer('dem'), terramain="Changes to height")
Two sets of properties are required for Dynamic TOPMODEL. The first set is those required within the evaluation of the model; gradient and contour length. The second set are those used for dividing the catchment up into Hydrological Response Units (HRUs). Traditionally the summary used for the separation of the HRUs is the topographic index, which is the natural logarithm of the upslope area divided by gradient.
These are computed using the formulae in Quinn et al. 1991.
The upstream area is computed by routing down slope with the fraction of the area being routed to the next downstream pixel being proportional to the gradient times the contour length.
The local value of the gradient is computed using the average of a subset of between pixel gradients. For a normal ‘hill slope’ cell these are the gradients to downslope pixels weighted by contour length. In the case of pixels which contain river channels the average of the gradients from upslope pixels weighted by contour length us used.
These properties are computed in an algorithm that passes over the data once in descending height. It is called as follows
The plot of the topographic index shows a pattern of increasing values closer to the river channels
## plot of topographic index (log(a/tan b)) $plot_layer('atb')ctch
Properties may come in additional GIS layers. To demonstrate the addition of an additional layer we will extract the filled dem
then separate it into a layers representing land above and below 500m.
<- terra::classify( tmp, tmp matrix(c(0,500,NA, 500,1000,-999), ncol=3, byrow=TRUE))
The resulting raster object can now be written to a file
which is added to the meta data with
$add_layer("greater_500",file.path(demo_dir,"greater_500.tif")) ctch$get_layer() ctch#>  "dem" "channel" "filled_dem" "land_area" "channel_area" #>  "channel_id" "gradient" "upslope_area" "atb" "greater_500"
dynatop simulations make use of ordered HRUs to
work downslope, a metric is required to order the downslope sequencing.
The calculation of four such metrics is supported
The computation is initiated with
The additional layers can be examined as expected
$get_layer() ctch#>  "dem" "channel" "filled_dem" #>  "land_area" "channel_area" "channel_id" #>  "gradient" "upslope_area" "atb" #>  "band" "shortest_flow_length" "dominant_flow_length" #>  "expected_flow_length" "greater_500" $plot_layer("band")ctch
Methods are provided for the classification of the catchment areas of
similar hydrological response. The classifications generated in this
process are augmented with a further distance based separation when
dynatop model (see following section).
By definition each channel length is treated as belonging to a single class.
To classify the hillslope two methods can be used.
classify method of a
a landscape property to be cut into classes.
For example to cut the topographic index for Swindale into 21 classes:
Providing a single value to the cuts argument determines the number of classes. The values used to cut the variable can be extracted from the meta data with
$get_class_method("atb_20") ctch#> $layer #>  "atb" #> #> $cuts #>  8.636791 9.266296 9.895801 10.525305 11.154810 11.784315 12.413819 #>  13.043324 13.672829 14.302333 14.931838 15.561343 16.190847 16.820352 #>  17.449857 18.079361 18.708866 19.338371 19.967875 20.597380 21.226885
combine_classes method of a
allows classes to be combined in two ways, which are applied in the
To demonstrate a pairing combination consider combining the atb classes generated above with the classification provided by the distance band
Additionally the land greater then 500 in altitude can be burnt in with
The each class in the combined classification the values of the classes used in the computations can be returned
head( ctch$get_class_method("atb_20_band_500") ) #> atb_20_band_500 atb_20 band greater_500 #> 1 1 1 1 NA #> 2 2 2 1 NA #> 3 3 3 1 NA #> 4 4 4 1 NA #> 5 5 5 1 NA #> 6 6 4 2 NA
Note that by giving the area to be burnt in a negative value when it was generated above we have ensured that the values do not clash with those generated by the cuts which (except potentially when a cut is NA) will always be positive.
A Dynamic TOPMODEL suitable for use with the
package can be generated using the
This uses an existing classification to generate the model. The required
model structure is given in the vignettes of
package and is not described here in details.
dynatop simulations make use of ordered HRUs to
work downslope, a classification which used a distance layer (see
earlier section) which represents the ordered downslope sequencing of
the pixels is recommended. It is strongly recommended that the
‘band’ distance metric is used directly as shown below.
Even if a distance layer is not used in the classification one must
be given to the
create_model method, so the resulting HRUs
can be ordered.
For example, in the case of the division of Swindale by topographic index into 21 classes and the bands directly the resulting model can be generated by
$create_model("new_model","atb_20_band","band") ctch#> The following Channel HRUs are outflows: 1
Looking at the files within the
list.files(demo_dir,pattern="new_model*") #>  "new_model.rds" "new_model.tif"
shows that an addition raster map of the HRUs has been created in
new_model.tif along with a file
containing a model suitable for
The map of HRUs can be plotted as with any layer
The values on the map correspond to the
ìd column of the
hillslope table in the