knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
The purpose of this vignette is to provide an outline of the steps needed to build a dynamic TOPMODEL implementation using the dynatopGIS package.
The dynatopGIS package implements a structured, object orientated, data
flow. The steps outlined below create a dynatopGIS
catchment object to which
actions are then applied to generate a model.
The dynatopGIS
package is written using the object orientated framework
provided by the R6
package. This means that some aspects of working with the
objects may appear idiosyncratic for some R users. In using the package as
outlined in this vignette these problems are largely obscured, except for the
call structure. However, before adapting the code, or doing more complex analysis
users should read about R6
class objects (e.g. in the R6
package vignettes
or in the Advanced R book). One particular gotcha is when copying an object. Using
my_new_object <- my_object
creates a pointer, that is altering my_new_object
also alters
my_object
. To create a new independent copy of my_object
use
my_new_object <- my_object$clone()
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 dynatopGIS
package using data from the Swindale
catchment in the UK.
To start first load the library
library("dynatopGIS")
For this vignette we will store the data into a temporary directory
demo_dir <- tempfile("dygis") 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.
ctch <- dynatopGIS$new(file.path(demo_dir))
The basis of the analysis is a rasterised Digital Elevation Model (DEM) of
the catchment and a vectorised representation of the river network with
attributes. Currently these can be in any format supported by the terra
library.
However, within the calculations used for sink filling, flow routing and topographic index calculations the raster DEM is presumed to be projected so that is has square cells such that the difference between the cell centres (in meters) does not alter.
For Swindale the suitable DEM and channel files can be found using:
dem_file <- system.file("extdata", "SwindaleDTM40m.tif", package="dynatopGIS", mustWork = TRUE) channel_file <- system.file("extdata", "SwindaleRiverNetwork.shp", package="dynatopGIS", mustWork = TRUE)
Before adding either the DEM or channel a raster map of the catchment outline must be provided. This defines not only the catchment boundaries but also, if required, subcatchments, each of which must be given a unique number. The projection and resolution of this map is used in all subsequent GIS processing
In this example the catchment map is generated from the DEM, which, by
convention must contain a edge rows and columns containing only NA
values.
dem <- terra::rast(dem_file) dem <- terra::extend(dem,1) ## pad with NA values catchment_outline <- terra::ifel(is.finite(dem),1,NA) ctch$add_catchment(catchment_outline)
Either the DEM or channel files can be added to the project first. In this case we add the DEM with
ctch$add_dem(dem)
Adding river channel data is more complex. The add_channel
method requires a
SpatVector
object (or a file name that can be loaded as a SpatVect
object).
Each vector object is treated as a length of river channel
which requires the following properties
Additional properties are currently kept but ignored with the exception of id which is overwritten.
Since it is possible that these properties are present in a data file
under different names some basic preprocessing may be required.
The convert_channel
function is designed to help with this.
To illustrate this let us examine the river network for Swindale
sp_lines <- terra::vect(channel_file) head(sp_lines)
Some of the main properties are present under appropriate names (startNode,
endNode, length) but the remainder are missing. Also the river network is
defined as a series of lines, rather then polygons. The convert_channel
function addresses these shortcomings by
- changing the names of the required properties
- buffering the line objects to create polygons
The convert_channel
function takes a named vector giving the variable names
to be use for the properties. If we want to carry over the identifier as the name we could call
convert_channel
with as follows:
property_names <- c(name="name1", endNode="endNode", startNode="startNode", length="length") chn <- convert_channel(sp_lines,property_names)
Since the data set for Swindale does not contain a channel width or slope default values are used and warnings issued.
The river network can then be added to the project by
ctch$add_channel(chn)
The 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
ctch$get_layer()
These can be plotted (with or without the channel), for example
ctch$plot_layer("dem", add_channel=TRUE)
or returned, for example
ctch$get_layer("dem")
All layers are returned as SpatRast
objects with the exception of the
channel_vect
layer which is returned as a SpatVect
object.
For the hill slope to be connected to the river network all DEM cells must
drain to those that intersect with the river network. The algorithm
implemented in the sink_fill
method ensures this is the case.
In calling the sink_fill
method a flow direction algorithm
is specified and the resulting flow paths recorded. If subcatchments are
present in the catchment map then only flow paths within the subcatchment are
considered.
The algorithm of used by the sink_fill
method is iterative and the execution time of the
function is limited by capping the maximum number of iterations. If
this limit is reached without completion the method can call again with the
"hot start" option to continue from where it finished.
For Swindale, where the example DEM is already partially filled the algorithm only alters a small area near the foot of the catchment.
ctch$sink_fill() terra::plot( ctch$get_layer('filled_dem') - ctch$get_layer('dem'), main="Changes to height")
The computational scheme in the dynatop
package works with an ordered
sequence of HRUs constructed such that the sequence moves downslope to
catchment outlet. This is achieved by banding the channel reaches and
hillslope cells such that the catchment outlet(s) are in band 1, those cells
or reaches draining only into band 1 are in band 2 and so forth. banding is
achieved by the following call
ctch$compute_band() ctch$plot_layer("band")
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
ctch$compute_properties()
The plot of the topographic index shows a pattern of increasing values closer to the river channels
## plot of topographic index (log(a/tan b)) ctch$plot_layer('atb')
Although not used in ordering the HRUs dynatopGIS
also provides the ability to compute flow distances for the hill slope cells.
The calculation of three distances is supported
The computation, in this example for the shortest flow length, is initiated with
ctch$compute_flow_lengths(flow_routing="shortest")
The additional layers can be examined as expected
ctch$get_layer() ctch$plot_layer("shortest_flow_length")
Properties may come in additional GIS layers. To demonstrate the addition of an additional layer we will extract the filled dem
tmp <- ctch$get_layer("filled_dem")
then separate it into a layers representing land above and below 500m.
## T tmp <- terra::ifel(tmp<=500,NA,-999)
The resulting raster object can now be added to the project with
ctch$add_layer(tmp, "greater_500") ctch$get_layer()
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 generating a
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.
The classify
method of a dynatopGIS
allows a landscape property to be
cut into classes.
For example to cut the topographic index for Swindale into 21 classes:
ctch$classify("atb_20","atb",cuts=20) ctch$plot_layer("atb_20")
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
ctch$get_method("atb_20")
The combine_classes
method of a dynatopGIS
allows classes to be combined
in two ways, which are applied in the order shown
To demonstrate a pairing combination consider combining the atb classes generated above with the classification provided by the distance band
ctch$combine_classes("atb_20_band",c("atb_20","band")) ctch$plot_layer("atb_20_band")
Additionally the land greater then 500 in altitude can be burnt in with
ctch$combine_classes("atb_20_band_500",pairs=c("atb_20","band"),burns="greater_500") ctch$plot_layer("atb_20_band_500")
The each class in the combined classification the values of the classes used in the computations can be returned
head( ctch$get_method("atb_20_band_500")$groups )
Note that by giving the area to be burnt in a negative value when it was generated above we have ensured that the values do not clash with those generated by the cuts which (except potentially when a cut is NA) which will always be positive.
A Dynamic TOPMODEL suitable for use with the dynatop
package can be
generated using the create_model
method. This uses an existing
classification
to generate the model. The required model structure is
given in the vignettes of dynatop
package and is not described here in
details.
Since dynatop
simulations make use of ordered HRUs to work downslope, a
classification which used a distance layer (see earlier section) which represents the ordered downslope
sequencing of the pixels is recommended.
Even if a distance layer is
not used in the classification one must be given to the create_model
method, so the resulting HRUs can be ordered.
Currently only the 'band' distance metric as used below will produce valid model.
For example, in the case of the division of Swindale by topographic index into 21 classes and the bands directly the resulting model can be generated by
ctch$create_model(file.path(demo_dir,"new_model"),"atb_20")
Looking at the files within the demo_dir
folder
list.files(demo_dir,pattern="new_model*")
shows that an addition raster map of the HRUs has been created in
new_model.tif
along with a file new_model.rds
containing a model
suitable for dynatop
. The values on the map correspond to the ìd
of the HRUs in
the dynatop
model.
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