# Chapter 10 Mersey IV - Surface derivatives

We know from the lecture that some of the factors which influence river hydrochemistry include land cover, soil type and bedrock geology. Information on these characteristics for the Mersey region under study is contained in the categorical files mersey_LC (based on LCM2000 data), mersey_HOST (Hydrology of Soil Types) and mersey_bedrock respectively. These datasets contain many different detailed classes, some of which are not applicable to the study region. Therefore, the datasets need to be simplified by aggregating some classes and omitting unnecessary classes.

We’ll illustrate the process for the land cover raster, which you can then repeat for the soil type and bedrock rasters.

### 10.1.1 Land cover

There are 26 classes in LCM2000 data, each with a unique numeric identifier. We are going to simplify these into the following five macro-classes, alongside their numeric identifiers:

• Arable = 41, 42, 43;
• Heath = 91, 101, 102;
• Grassland = 51, 52, 61, 71, 81;
• Urban = 171, 172;
• Wetland = 111, 121.

To do so, we’re going to use the fct_collapse function from the forcats package.

First, load the land cover raster into R as normal:

# Loads land cover raster
land_cover <- raster(here("data", "practical_2", "mersey_LC.tif"))

Because our raster is categorical (rather than continuous), it makes sense to convert the data format to a factor. In R, these are used to represent categorical variables.

Convert the land cover raster to a factor as follows:

# Converts the land cover raster to a factor, overwriting the original variable
land_cover <- as.factor(land_cover)

To assess the values stored in the land_cover raster, use the unique function, which should produce the following:

##  [1] -9999     0    11    21    41    42    43    51    52    61    71    81
## [13]    91   101   102   111   121   131   161   171   172   191   211   212
## [25]   221

As not all categories are applicable to our study area, we are next going to create a data frame of the land cover categories of interest.

Inspect the code below. Many of the code elements should be familiar to you. We are creating a vector called categories using the c() function, which contains all the classes of interest c(41, 42, 43, ...)). Next, we have converted that to a data frame using as.data.frame() and assigned a column name (ID). When you understand what is happening, add to your script and run.

# Categories of interest
categories <- as.data.frame(c(41, 42, 43, 91, 101, 102, 51, 52, 61, 71, 81, 171, 172, 111, 121))
colnames(categories) <- "ID"

If you want to inspect the output, you can use head(categories) to print out the first 6 rows:

##    ID
## 1  41
## 2  42
## 3  43
## 4  91
## 5 101
## 6 102

Using this new data frame, we are going to create a new column called name, which corresponds to the name of the land cover class (e.g. $$Arable = 41$$)

# Collapse categories into groups based on ID
categories$name <- fct_collapse(as.factor(categories$ID),
"Arable" = c("41", "42", "43"),
"Heath" = c("91", "101", "102"),
"Grassland" = c("51", "52", "61", "71", "81"),
"Urban" = c("171", "172"),
"Wetland" = c("111", "121"))

Inspect the above code. The syntax is reasonably complex, but you should understand what is happening if you inspect the output:

# Prints categories data frame
categories
##     ID      name
## 1   41    Arable
## 2   42    Arable
## 3   43    Arable
## 4   91     Heath
## 5  101     Heath
## 6  102     Heath
## 7   51 Grassland
## 8   52 Grassland
## 9   61 Grassland
## 10  71 Grassland
## 11  81 Grassland
## 12 171     Urban
## 13 172     Urban
## 14 111   Wetland
## 15 121   Wetland

Finally, we can use this updated data frame to replace (or substitute) values in the land cover raster (i.e. $$41, 91, ...$$) with the land cover class it represents. In this case, values are stored numerically (i.e. $$Arable =1,Heath =2, ...$$). One way to achieve this is using the subs function from the raster package.

Inspect the following code, which substitutes (reclassifies) the raster layer and saves to a new raster (.tif) using the writeRaster function.

# Substitutes raster values with new categories
land_cover_classified <- subs(land_cover, categories)

# Write to new raster
writeRaster(land_cover_classified, here("output", "practical_2", "mersey_LC_reclass.tif"))

When you understand it, run the code, load the new raster into R and plot. Use the code below to visualise the reclassified raster, taking note of the updated fill aesthetic (scale_fill_distiller):

# Loads land cover raster using the raster and here packages
mersey_land_cover <- raster(here("output", "practical_2", "mersey_LC_reclass.tif"))

# Plots using ggplot
p <- ggplot() +
layer_spatial(mersey_land_cover, aes(fill = stat(band1))) + # Adds raster layer
theme_classic() +
labs(fill = "Land cover class", x = "Easting", y = "Northing") +
scale_fill_distiller(palette = "RdYlBu", na.value = NA) + # Updated fill aesthetic
theme(legend.position = "top")
p 

In the above image, the land cover classes are shown as follows:

• Arable = 1 (dark blue)
• Heath = 2 (light blue)
• Grassland = 3 (yellow)
• Urban = 4 (orange)
• Wetland = 5 (red)

First, save your script before continuing.

Next, using the methodology outlined above, repeat this process for the soil type (mersey_HOST) and bedrock geology rasters (mersey_bedrock). Make sure to use a consistent approach to file naming e.g. mersey_HOST_reclass.tif and mersey_bedrock_reclass.tif.

### 10.1.2 Hydrology of Soil Types (HOST)

There are 29 classes in HOST, each with a unique numeric identifier. Reclassify these into the following four new classes:

• Permeable = 1, 3, 4, 5, 6, 7, 15, 16;
• Impermeable = 12, 17, 18, 19, 21, 22;
• Gleyed = 8, 9, 13, 24;
• Peats = 10, 11, 14, 25, 26, 27, 29.

Utilising the order above, the values of the output raster should be as follows:

• Permeable = 1
• Impermeable = 2
• Gleyed = 3
• Peats = 4

### 10.1.3 Bedrock geology

There are 34 bedrock geology classes in Mersey Basin region, each with a unique numeric identifier. Reclassify into the following three new classes listed below:

• Sands_and_Muds (sands and muds) = 5, 16, 18, 24, 28, 34;
• Limestone = 10, 11, 17, 19;
• Coal = 9, 15, 22.

Utilising the order above, the values of the output raster should be as follows:

• Sands_and_Muds = 1
• Limestone = 2
• Coal = 3

When complete, save your script before continuing.

## 10.2 Task 5: Calculating surface derivatives

As well as the factors outlined above, other catchment characteristics may affect river hydrochemistry. Here, we are interested in the effects of elevation (the raw data from mersey_DEM_fill) and rainfall (mersey_rainfall), as well as topographic slope and aspect. These are known as surface derivatives as they are calculated (derived) from the DEM.

To calculate slope and aspect rasters, use the wbt_slope and wbt_aspect functions, using the original filled DEM as the input data (mersey_dem_fill.tif) and using appropriate output names (e.g. mersey_dem_slope and mersey_dem_aspect). Your outputs should resemble the following:

## 10.3 Task 6: Extracting surface derivatives

At this stage of the analysis we have all the relevant spatial datasets compiled. The next step is to derive the characteristics for each of the 70 catchments in the mersey_watersheds file (.shp), so we can relate these to the water quality data collected at each of the 70 monitoring sites.

For each catchment we want to extract the:

• The area (km2);
• The number of raster cells;
• Average elevation;
• Average slope;
• Average aspect;
• Average rainfall;
• Percentage of the each of the five land cover classes present;
• Percentage of the each of the four soil types present;
• Percentage of the each of the three bedrock geology types present.

Overall, we will calculate the average of the continuous datasets (elevation, slope, aspect, rainfall), and percentages of the categorical datasets (land cover, soil types, geology).

First, however, we need to link our mersey_watersheds file with the measurements of water quality, currently stored in a comma-separated file (mersey_EA_chemisty.csv).

### 10.3.1 Water quality measurements

To begin:

Load the mersey_watersheds.shp file into R using the st_read function, storing in a variable called watersheds.

Next, print out attribute names for the shapefile as follows:

colnames(watersheds)
## [1] "FID"      "VALUE"    "geometry"

For our analysis, the attribute of interest is VALUE, which contains the unique Environment Agency ID for each watershed. Importantly, this is also found in the mersey_EA_chemisty.csv file. This will enable us to join the two datasets, populating the attribute table of the watersheds variable with the water quality measurements stored in the csv.

To simplify this, use the following code to replace the column name VALUE with a new name Seed_Point_ID. The latter is used in the mersey_EA_chemisty.csv.

# Replaces column name 'VALUE' with 'SEED_Point_ID'
names(watersheds)[names(watersheds) == 'VALUE'] <- 'Seed_Point_ID'

You can re-use the colnames function to check it worked correctly:

With this updated:

We can now load the Environment Agency data using read.csv(), as shown in Chapter 3:

# Loads csv using read.csv
ea_data <- read.csv(here("data", "practical_2", "mersey_EA_chemistry.csv"))

and merge using the merge function:

# Merge based upon matching Seed_Point_IDs
watersheds_ea <- merge(watersheds, ea_data, by = "Seed_Point_ID")

Use the head() function to inspect the first few rows of our new data frame.

## Simple feature collection with 6 features and 14 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 343660.8 ymin: 382963.7 xmax: 353552.6 ymax: 394555.6
## Projected CRS: OSGB 1936 / British National Grid
##   Seed_Point_ID FID   EA_ID    Group   Ph   SSC     Ca    Mg  NH4  NO3  NO2
## 1             1  54 1940214 Training 7.79 21.37  60.82 11.12 0.24 2.64 0.08
## 2             2  42 1941025  Testing 7.79 33.52  75.45 17.52 4.46 3.24 0.11
## 3             3  40 1941017 Training 8.55 11.69  58.54 20.50 0.25 0.83 0.02
## 4             4  47 1941007 Training 7.71 34.06  96.83 46.33 0.24 3.65 0.07
## 5             5  45 1941002 Training 8.08 70.81 141.98 85.85 0.40 4.47 0.05
## 6             6  44 1941003 Training 8.12 34.00 174.49 86.27 0.21 2.69 0.06
##    TON  PO4    Zn                       geometry
## 1 2.73 0.34 50.00 MULTIPOLYGON (((345159.5 38...
## 2 3.35 0.99 20.51 MULTIPOLYGON (((348107.1 39...
## 3 0.84 0.07 35.23 MULTIPOLYGON (((346108.7 39...
## 4 3.73 0.21 74.26 MULTIPOLYGON (((350505.1 39...
## 5 4.52 0.14 20.16 MULTIPOLYGON (((351854 3906...
## 6 2.75 0.15 18.27 MULTIPOLYGON (((352903.1 39...

### 10.3.2 Spatial areas

With our datasets now linked, we may want to calculate the area of our watersheds, expressed as either km2 or as a count of raster cells.

To calculate the km2 area, we can use the st_area() function. At it’s most simple, it could be written as follows:

# Calculates area geometry using st_area()
watersheds_ea$area <- st_area(watersheds_ea) However, because our vector data is measured in metres (due to the British National Grid), our calculated area would also be in metres. Given the size of the watersheds, this could result in large, unwieldy values. To simplify, we’ll use the set_units function from the units package as follows, which will store our area in a more manageable km2 format: # Calculates area geometry using st_area(), converting to km^2 using the units package watersheds_ea$area <- set_units(st_area(watersheds_ea), km^2)

The procedure for calculating the count of raster cells is slightly more complicated, because this depends on the spatial resolution of the raster layer. Our raster layer contains cells of approximately ~50 m2.

Here we can use the extract function from the raster package, which is described here. We’ll be using this function on a number of occasions in the remainder of this practical, so it’s important that you understand what it’s doing.

Broadly, the function extracts values from a raster object at the locations of spatial vector data, where the value of interest is user-defined. For example, this could be the mean (e.g. the average elevation of a DEM within a vector polygon), the count (e.g. the number of cells within a vector polygon), or a minimum or maximum (e.g. the maximum elevation within a vector polygon).

To calculate the number of raster cells within each watershed, we first need to load the DEM into R:

# Load elevation raster
mersey_dem <- raster(here("data", "practical_2", "mersey_dem_fill.tif"))

before using the extract function as follows. This takes in both raster (mersey_dem) and vector input data (watersheds_ea), where the value of interest is determined by the fun parameter (i.e. a function). Normally, we can specify an existing base R function (e.g. mean) but here we are using a user-defined function fun=function(x, ...) length(x) to count the length (or number) of raster cells for each watershed.

# Calculates the number of raster cells per watershed
watersheds_ea$count <- extract(mersey_dem, watersheds_ea, fun=function(x, ...) length(x))  Run the above code, which should create a new attribute column called count. This can be previewed using head(), specifying the column of interest ($count):

### 10.3.3 Continuous derivatives

Before we move on to extract our continuous derivatives (average elevation, rainfall, slope and aspect), it is worth noting that R variables can be removed from the environment as follows:

# Removes object(s) from memory
rm(mersey_dem)

This can be useful if R is running slowly.

To extract continuous derivatives, we are going to use the extract function again.

First, ensure the DEM, rainfall, slope and aspect rasters are loaded into R and stored with sensible variable names (e.g. mersey_dem, mersey_rainfall, mersey_slope, mersey_aspect).

The code to extract the average elevation for each watershed is shown here, using the function mean:

# Extracts raster values for each watershed, calculates mean (fun=mean), and stores in attribute table ($average_elevation) watersheds_ea$average_elevation <- extract(mersey_dem, watersheds_ea, fun=mean, na.rm=TRUE)

When you’re happy you understand it, copy to your script and run, before repeating the process for the other continuous variables, and storing the data using sensible attribute names e.g. watersheds_ea$average_elevation, $average_rainfall, $average_slope, $average_aspect.

Use the head() function to inspect the output.

### 10.3.4 Calculating categorical derivatives

As we approach the end of this part of Practical 2, we are going to extract and normalise (convert to %) the categorical derivatives (land cover, soil types, bedrock).

First, ensure the reclassified land cover, soil type and bedrock rasters are loaded into R and stored with sensible variable names (e.g. land_cover, soils, bedrock).

Next, we are going to use the extract function again, but this time returning the count of each category (e.g. Arable, Heath, Grassland, Urban, Wetland) for each watershed area, as shown here:

# Extract land cover counts (5 classes so levels = 1:5)
land_cover_classes <- extract(land_cover, watersheds_ea, fun=function(i,...) table(factor(i, levels = 1:5)))

Rather than trying to decipher the code straight away, copy to your script, run and use head() to inspect the output, which should be as follows. This may take a little while (~30 seconds on a i7 computer with 16 Gb of RAM):

head(land_cover_classes)
##         1 2   3   4 5
## [1,]  379 0 340 785 0
## [2,] 1832 0 984 861 0
## [3,]   42 0 491 524 0
## [4,]  177 0  49 249 0
## [5,]  185 0  55  57 0
## [6,]  154 0  61 246 0

We have produced a data frame with 5 columns (representing the 5 land cover classes) and 70 rows (representing the 70 watersheds), where the row-column values represent the number of raster cells corresponding to each land cover class.

This is based upon a user-defined function, incorporating the table function from the data.table package. Important: as the land cover dataset contains 5 classes, the function splits the underlying data into five groups using levels = 1:5. This needs to be updated when applying to the soils and bedrock datasets.

To improve the readability of the data frame, update its column names as follows:

colnames(land_cover_classes) <- c("Arable", "Heath", "Grassland", "Urban", "Wetland")

Use head() to inspect the output:

head(land_cover_classes)
##      Arable Heath Grassland Urban Wetland
## [1,]    379     0       340   785       0
## [2,]   1832     0       984   861       0
## [3,]     42     0       491   524       0
## [4,]    177     0        49   249       0
## [5,]    185     0        55    57       0
## [6,]    154     0        61   246       0

Replicate this approach for the soil type and bedrock datasets, using sensible variable names (e.g. soils_classes and bedrock_classes), remembering to update the extract function used (i.e. levels = 1:4 for the soil data and levels = 1:3 for the bedrock data).

When complete, you should have the following data frames in your R environment:

• watersheds_ea:
• containing the water quality measurements, the spatial areas and the continuous derivatives;
• land_cover_classes:
• containing the count (number of cells) for each land cover class;
• soils_classes:
• containing the count (number of cells) for each soil type;
• bedrock_classes:
• containing the count (number of cells) for each bedrock type;

To simplify your R environment, remove all other variables using the rm() function.

To merge the remaining files, we can use cbind(), which binds data frames together based on their columns:

# Combines watersheds data frame with categorical counts
watersheds_ea <- cbind(watersheds_ea, land_cover_classes, soils_classes, bedrock_classes)

### 10.3.5 Normalising categorical derivatives

In the final step of the practical, we are going to normalise our categorical derivatives i.e. to establish the percentage cover of each category, rather than a raw count of raster cells. The former is more informative as it allows us to compare watersheds of differing sizes.

This is a relatively simple calculation and involves dividing the total number of raster cells in each watershed (stored in $count) by the number corresponding to each category (stored in $Arable, $Heath, $Grassland, …), before multiplying by 100.

However, we have 12 categorical variables to normalise. We could type out each calculation manually e.g.

# Normalising categorical variables
watersheds_ea$Arable_percent <- watersheds_ea$Arable/watersheds_ea$count*100 watersheds_ea$Heath_percent <- watersheds_ea$Heath/watersheds_ea$count*100
atersheds_ea$Grassland_percent <- watersheds_ea$Grassland/watersheds_ea$count*100 ... However, we don’t want to waste time or effort if we could perform these calculations iteratively. To that end, we’ll start by: Creating a vector of the column names we want to normalise: # Creates vector of categorical variables categorical_names <- c("Arable", "Heath", "Grassland", "Urban", "Wetland", "Permeable", "Impermeable", "Gleyed", "Peats", "Sands_and_Muds", "Limestone", "Coal") # Prints vector categorical_names ## [1] "Arable" "Heath" "Grassland" "Urban" ## [5] "Wetland" "Permeable" "Impermeable" "Gleyed" ## [9] "Peats" "Sands_and_Muds" "Limestone" "Coal" Next, we are going to iterate through this vector using a for loop. This was illustrated briefly in Chapter 3 here, but we’ll explain it more fully now: Copy the following code to your script and run: # Loops through each element of categorical_names and stores it in variable "i" for (i in categorical_names){ # Prints element stored in i print(i) } ## [1] "Arable" ## [1] "Heath" ## [1] "Grassland" ## [1] "Urban" ## [1] "Wetland" ## [1] "Permeable" ## [1] "Impermeable" ## [1] "Gleyed" ## [1] "Peats" ## [1] "Sands_and_Muds" ## [1] "Limestone" ## [1] "Coal" The code works by looping (or iterating) through each element of the categorical_names vector. On every iteration, the variable i (short for iterator) is updated with the next element of categorical_names. On the first iteration, i = "Arable", on the second iteration i = "Heath", and so on. Now copy and run the following updated version: # Loops through each element of categorical_names and stores it in variable "i" for (i in categorical_names){ # Creates a new column name using the variable "i" and the string "percent", separated by an underscore. col <- paste(i, "percent", sep="_") # Prints new column name print(col) } ## [1] "Arable_percent" ## [1] "Heath_percent" ## [1] "Grassland_percent" ## [1] "Urban_percent" ## [1] "Wetland_percent" ## [1] "Permeable_percent" ## [1] "Impermeable_percent" ## [1] "Gleyed_percent" ## [1] "Peats_percent" ## [1] "Sands_and_Muds_percent" ## [1] "Limestone_percent" ## [1] "Coal_percent" Here we are iteratively creating a column name based on the element stored in the variable i i.e. "Arable_percent", "Heath_percent", "Grassland_percent". In this final version of the for loop, we are creating new columns in the watersheds_ea data frame based on the column name (col) and the normalisation approach described above (i.e. Arable/count*100). However, rather than having to specify the input and output columns manually, this is handled for us iteratively using the col and i variables: # Loops through each element of categorical_names and stores it in variable "i" for (i in categorical_names){ # Creates a new column name using the variable "i" and the string "percent", separated by an underscore. col <- paste(i, "percent", sep="_") # Updates watersheds_ea with the percentage cover of each category watersheds_ea[col] <- as.numeric(watersheds_ea[[i]]/watersheds_ea$count*100)
}

When you’re happy you understand the above code, run it. To finish the practical, we’re going to remove the geometry stored in the data frame (not required for subsequent analysis) before saving as a comma-separated file:

# Drops geometry attribute from watersheds_ea
watersheds_ea <- st_drop_geometry(watersheds_ea)

# Writes data frame to comma-separated file
write.csv(x = watersheds_ea, here("output", "practical_2", "mersey_watersheds_ea.csv"), row.names=FALSE)

Use head() to inspect the final output, which should resemble the following:

##   Seed_Point_ID FID   EA_ID    Group   Ph   SSC     Ca    Mg  NH4  NO3  NO2
## 1             1  54 1940214 Training 7.79 21.37  60.82 11.12 0.24 2.64 0.08
## 2             2  42 1941025  Testing 7.79 33.52  75.45 17.52 4.46 3.24 0.11
## 3             3  40 1941017 Training 8.55 11.69  58.54 20.50 0.25 0.83 0.02
## 4             4  47 1941007 Training 7.71 34.06  96.83 46.33 0.24 3.65 0.07
## 5             5  45 1941002 Training 8.08 70.81 141.98 85.85 0.40 4.47 0.05
## 6             6  44 1941003 Training 8.12 34.00 174.49 86.27 0.21 2.69 0.06
##    TON  PO4    Zn              area count average_elevation average_rainfall
## 1 2.73 0.34 50.00  3.9614467 [km^2]  1587          21.51344         539.8778
## 2 3.35 0.99 20.51 10.2618193 [km^2]  4111          39.95542         581.1832
## 3 0.84 0.07 35.23  3.6619044 [km^2]  1467          66.75006         591.3545
## 4 3.73 0.21 74.26  1.2131462 [km^2]   486          44.82042         549.0123
## 5 4.52 0.14 20.16  0.7413671 [km^2]   297          43.72755         557.1751
## 6 2.75 0.15 18.27  1.4028564 [km^2]   562          49.89805         562.9039
##   average_slope average_aspect Arable Heath Grassland Urban Wetland Permeable
## 1     0.4837142       93.89182    379     0       340   785       0         0
## 2     1.2318246      206.14489   1832     0       984   861       0       271
## 3     1.9288961      196.52557     42     0       491   524       0       292
## 4     0.5148268      139.09166    177     0        49   249       0         0
## 5     1.6296281      120.98029    185     0        55    57       0         0
## 6     1.0673156      118.31011    154     0        61   246       0         0
##   Impermeable Gleyed Peats Sands_and_Muds Limestone Coal Arable_percent
## 1           0   1030   557           1414         0    0      23.881537
## 2           0   3463   377           4111         0    0      44.563367
## 3           0   1175     0           1467         0    0       2.862986
## 4           0    486     0            486         0    0      36.419753
## 5           0    297     0            297         0    0      62.289562
## 6           0    562     0            562         0    0      27.402135
##   Heath_percent Grassland_percent Urban_percent Wetland_percent
## 1             0          21.42407      49.46440               0
## 2             0          23.93578      20.94381               0
## 3             0          33.46967      35.71915               0
## 4             0          10.08230      51.23457               0
## 5             0          18.51852      19.19192               0
## 6             0          10.85409      43.77224               0
##   Permeable_percent Impermeable_percent Gleyed_percent Peats_percent
## 1           0.00000                   0       64.90233     35.097669
## 2           6.59207                   0       84.23741      9.170518
## 3          19.90457                   0       80.09543      0.000000
## 4           0.00000                   0      100.00000      0.000000
## 5           0.00000                   0      100.00000      0.000000
## 6           0.00000                   0      100.00000      0.000000
##   Sands_and_Muds_percent Limestone_percent Coal_percent
## 1               89.09893                 0            0
## 2              100.00000                 0            0
## 3              100.00000                 0            0
## 4              100.00000                 0            0
## 5              100.00000                 0            0
## 6              100.00000                 0            0

In the final week of Environmental Modelling and Monitoring Concepts (Week 12, Mersey V), we’ll use this data frame to evaluate the environmental controls on water quality.