The purpose of this vignette is to provide an outline of the steps needed to build a dynamic TOPMODEL implementation using the dynatopGIS package.
Implementation notes
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_objectcreates 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$clone()Getting started
The dynatopGIS packages works through a number of steps to generate a
Dynamic TOPMODEL object suitable for use in with the
dynatop package. Each step generates one or more layers
which are saved as raster or shape files into the projects working
directory (which is not necessarily the R working directory). A record
of these layers is kept in the json format meta data file.
This vignette demonstrates the use of the dynatopGIS
package using data from the Swindale catchment in the UK.
To start first load the library
For this vignette we will store the data into a temporary directory
demo_dir <- tempfile("dygis")
dir.create(demo_dir)and initialise the analysis by creating a new object specifying the location of the meta data file, which will be created if it doesn’t exist.
ctch <- dynatopGIS$new(file.path(demo_dir,"demo"))
#> Creating new folder
#> Starting new project at /tmp/RtmpFb8DWA/dygis1eee4e2b5813/demoAdding catchment data
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 raster and sp libraries
respectively.
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)Either the DEM or channel files can be added to the project first. In this case we add the DEM with
dem <- terra::rast(dem_file)
ctch$add_dem(dem)Note: An additional row and column of NA is
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
- name - a label for the channel length
- endNode - a label for the downstream end of the river length
- startNode - a label for the upstream end of the river length
- length - the length in meters
- area - surface area in square meters
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)
#> 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 -1 739.8Some 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="identifier",
endNode="endNode",
startNode="startNode",
length="length")
chn <- convert_channel(sp_lines,property_names)
#> Warning in convert_channel(sp_lines, property_names): Modifying to spatial
#> polygons using default widthSince the data set for Swindale does not contain a channel width the default width of 2m is used for buffering.
The river network can then be added to the project by
ctch$add_channel(chn)Getting and plotting catchment information
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$get_layer()
#> layer source
#> 1 dem /tmp/RtmpFb8DWA/dygis1eee4e2b5813/demo/dem.tif
#> 2 channel /tmp/RtmpFb8DWA/dygis1eee4e2b5813/demo/channel.tifThese can be plotted (with or without the channel), for example
ctch$plot_layer("dem", add_channel=TRUE)
or returned, for example
ctch$get_layer("dem")
#> class : SpatRaster
#> dimensions : 163, 124, 1 (nrow, ncol, nlyr)
#> resolution : 40, 40 (x, y)
#> extent : 347734, 352694, 507244, 513764 (xmin, xmax, ymin, ymax)
#> coord. ref. : +proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +ellps=airy +units=m +no_defs
#> source : dem.tif
#> name : dem
#> min value : 262.8004
#> max value : 710.7533All layers are returned as RasterLayer objects with the
exception of the channel layer which is returned as a
SpatialPolygonsDataFrame object.
Filling sinks
For the hill slope to be connected to the river network all DEM cells must drain to those that intersect with the river network.
The algorithm of implemented in the sink_fill method
ensures this is the case. Since the algorithm is iterative the execution
time of the function is limited by capping the maximum number of
iterations. If this limit is reached without completion the method can
call again with the “hot start” option to continue from where it
finished.
For Swindale, where the example DEM is already partially filled the algorithm only alters a small area near the foot of the catchment.
ctch$sink_fill()
terra::plot( ctch$get_layer('filled_dem') - ctch$get_layer('dem'),
main="Changes to height")
Computing properties
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')
Adding additional layer
Properties may come in additional GIS layers. To demonstrate the addition of an additional layer we will extract the filled dem
tmp <- ctch$get_layer("filled_dem")then separate it into a layers representing land above and below 500m.
The resulting raster object can now be added to the project with
ctch$add_layer(tmp, "greater_500")
ctch$get_layer()
#> layer source
#> 1 dem /tmp/RtmpFb8DWA/dygis1eee4e2b5813/demo/dem.tif
#> 2 channel /tmp/RtmpFb8DWA/dygis1eee4e2b5813/demo/channel.tif
#> 3 filled_dem /tmp/RtmpFb8DWA/dygis1eee4e2b5813/demo/filled_dem.tif
#> 4 gradient /tmp/RtmpFb8DWA/dygis1eee4e2b5813/demo/gradient.tif
#> 5 upslope_area /tmp/RtmpFb8DWA/dygis1eee4e2b5813/demo/upslope_area.tif
#> 6 atb /tmp/RtmpFb8DWA/dygis1eee4e2b5813/demo/atb.tif
#> 7 greater_500 /tmp/RtmpFb8DWA/dygis1eee4e2b5813/demo/greater_500.tifFlow distances and ordering
Since 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
- shortest flow length - the shortest length based on the pixel flow paths to a channel
- Dominant flow length - the distance to a channel moving in the dominant (largest fraction) flow direction from any grid cell
- Expected flow length - the distance to the channel based on a weighted average of the downslope flow lengths. Weights are given by the fraction of flow in each direction.
- Band - A strict computational order, starting at the channel arranged such that if all pixels in band \(i+1,i+2,\ldots\) are evaluated the inflows to band \(i\) are known.
The computation is initiated with
ctch$compute_flow_lengths()
#> Warning in .getSpatDF(x@ptr$df): NAs introduced by coercion to integer range
#> Warning in .getSpatDF(x@ptr$df, ...): NAs introduced by coercion to integer
#> rangeThe additional layers can be examined as expected
ctch$get_layer()
#> layer
#> 1 dem
#> 2 channel
#> 3 filled_dem
#> 4 gradient
#> 5 upslope_area
#> 6 atb
#> 7 greater_500
#> 8 band
#> 9 shortest_flow_length
#> 10 dominant_flow_length
#> 11 expected_flow_length
#> source
#> 1 /tmp/RtmpFb8DWA/dygis1eee4e2b5813/demo/dem.tif
#> 2 /tmp/RtmpFb8DWA/dygis1eee4e2b5813/demo/channel.tif
#> 3 /tmp/RtmpFb8DWA/dygis1eee4e2b5813/demo/filled_dem.tif
#> 4 /tmp/RtmpFb8DWA/dygis1eee4e2b5813/demo/gradient.tif
#> 5 /tmp/RtmpFb8DWA/dygis1eee4e2b5813/demo/upslope_area.tif
#> 6 /tmp/RtmpFb8DWA/dygis1eee4e2b5813/demo/atb.tif
#> 7 /tmp/RtmpFb8DWA/dygis1eee4e2b5813/demo/greater_500.tif
#> 8 /tmp/RtmpFb8DWA/dygis1eee4e2b5813/demo/band.tif
#> 9 /tmp/RtmpFb8DWA/dygis1eee4e2b5813/demo/shortest_flow_length.tif
#> 10 /tmp/RtmpFb8DWA/dygis1eee4e2b5813/demo/dominant_flow_length.tif
#> 11 /tmp/RtmpFb8DWA/dygis1eee4e2b5813/demo/expected_flow_length.tif
ctch$plot_layer("band")
Classifying into Hydrological Response Units
Methods are provided for the classification of the catchment areas of
similar hydrological response. The classifications generated in this
process are augmented with a further distance based separation when
generating a dynatop model (see following section).
By definition each channel length is treated as belonging to a single class.
To classify the hillslope two methods can be used.
The classify method of a dynatopGIS allows
a landscape property to be cut into classes.
For example to cut the topographic index for Swindale into 21 classes:
ctch$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")
#> $type
#> [1] "classification"
#>
#> $layer
#> [1] "atb"
#>
#> $cuts
#> [1] 8.6361 9.3714 10.1066 10.8418 11.5771 12.3123 13.0476 13.7828 14.5181
#> [10] 15.2533 15.9885 16.7238 17.4590 18.1943 18.9295 19.6647 20.4000 21.1352
#> [19] 21.8705 22.6057 23.3410The combine_classes method of a dynatopGIS
allows classes to be combined in two ways, which are applied in the
order shown
- pairing - where unique combinations of classes create one new class
- burning - where a single class is imposed upon an area
To demonstrate a pairing combination consider combining the atb classes generated above with the classification provided by the distance band
ctch$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") )
#> $type
#> [1] "combination"
#>
#> $groups
#> atb_20_band_500 atb_20 band burns
#> 1 1 3 194 TRUE
#> 2 2 4 3 FALSE
#> 3 3 6 2 FALSE
#> 4 4 5 3 FALSE
#> 5 5 6 3 FALSE
#> 6 6 5 4 FALSE
#> 7 7 3 6 FALSE
#> 8 8 1 8 FALSE
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#> 1209 1209 4 151 FALSE
#> 1210 1210 3 152 FALSE
#> 1211 1211 2 153 FALSE
#> 1212 1212 4 152 FALSE
#> 1213 1213 3 153 FALSE
#> 1214 1214 2 154 FALSE
#> 1215 1215 4 153 FALSE
#> 1216 1216 3 154 FALSE
#> 1217 1217 3 155 FALSE
#> 1218 1218 2 156 FALSE
#> 1219 1219 1 157 FALSE
#> 1220 1220 5 154 FALSE
#> 1221 1221 4 155 FALSE
#> 1222 1222 3 156 FALSE
#> 1223 1223 2 157 FALSE
#> 1224 1224 1 158 FALSE
#> 1225 1225 4 156 FALSE
#> 1226 1226 3 157 FALSE
#> 1227 1227 1 159 FALSE
#> 1228 1228 4 157 FALSE
#> 1229 1229 3 158 FALSE
#> 1230 1230 2 159 FALSE
#> 1231 1231 6 156 FALSE
#> 1232 1232 4 158 FALSE
#> 1233 1233 2 160 FALSE
#> 1234 1234 4 159 FALSE
#> 1235 1235 4 160 FALSE
#> 1236 1236 5 160 FALSE
#> 1237 1237 6 161 FALSE
#> 1238 1238 6 162 FALSENote 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.
Generating a dynamic TOPMODEL
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_band","band")Looking at the files within the demo_dir folder
list.files(demo_dir,pattern="new_model*")
#> [1] "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 new_model.rds
containing a model suitable for dynatop. The values on the
map correspond to the ìd column of the hru table in the
dynatop model.