Analyze Catchment Area Characteristics

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Setup

Load HYPEtools Package and Import Files

# Load Package
library(HYPEtools)

# Get Path to HYPEtools Model Example Files
model_path <- system.file("demo_model", package = "HYPEtools")

# Import HYPE Model Files
gd <- ReadGeoData(file.path(model_path, "GeoData.txt"))
gcl <- ReadGeoClass(file.path(model_path, "GeoClass.txt"))

Import Checks

Look at names of Soil and Land Use Classes

# Read Comment in GeoClass.txt
comment(gcl)
#> [1] "!Landuses: 1=Water; 2=Coniferous forest; 3=Agricultural land\t\t\t\t\t\t\t\t\t\t\t\t\t"                                                                      
#> [2] "!Soils: 1=Fine soil; 2=Coarse soil\t\t\t\t\t\t\t\t\t\t\t\t\t"                                                                                                
#> [3] "!Combination\tLanduse\tSoil\tCropid-main\tCropid-2nd\tRotation\tVegetationstyp\tSpecial-class\tTile-depth\tDrain-depth\t#Soil-layers\tDepth1\tDepth2\tDepth3"

Summarize Upstream Soil Fractions for a Gauge

Summarize fractions for the whole drainage area upstream of the gauge.


# Select SUBID for which you want to summarize the upstream characteristics
Qobs.sbd <- 3587

# Upstream soil fractions in percent
sof <- as.numeric(UpstreamGroupSLCClasses(subid = Qobs.sbd, gd = gd, gc = gcl, type = "soil", progbar = FALSE)[, -1] * 100)

# Show how many classes are in each soil category
length(sof)
#> [1] 2

# Calculate how many soil classes have a value greater than 0
sum(sof != 0)
#> [1] 2

# Add names for soil categories
names(sof) <- c("Fine soil", "Coarse soil")

# Display the upstream soil percentages
sof
#>   Fine soil Coarse soil 
#>       95.30        4.74

Summarize Upstream Land Use Fractions for a Gauge

Summarize fractions for the whole drainage area upstream of the gauge.


# Select SUBID for which you want to summarize the upstream characteristics
Qobs.sbd <- 3587

# Upstream land use fractions in percent
luf <- as.numeric(UpstreamGroupSLCClasses(subid = Qobs.sbd, gd = gd, gc = gcl, type = "landuse", progbar = FALSE)[, -1] * 100)

# Show how many land use classes there are
length(luf)
#> [1] 3

# Calculate how many land use classes have a value greater than 0
sum(luf != 0)
#> [1] 3

# Add names for land use categories
names(luf) <- c("Water", "Coniferous", "Agriculture")

# Display the upstream land use percentages
luf
#>       Water  Coniferous Agriculture 
#>        7.13       81.90       10.90

Plot Upstream Soil and Land Use Fractions

Plot fractions for the whole drainage area upstream of the gauge.

# Plot Upstream Soil Fractions
barplot(sof, ylab = "Area (%)", names.arg = "", col = "red", ylim = c(0, 100), xlab = "Soil fraction")
mtext(text = names(sof), side = 3, at = seq(.7, by = 1.2, length.out = 5), line = -.1, padj = .5, cex = .9, las = 3, adj = 1)
mtext(Qobs.sbd, side = 1, adj = 0, font = 3)
box()


# Plot Upstream Land Use Fractions
barplot(luf, ylab = "Area (%)", names.arg = "", col = "green", ylim = c(0, 100), xlab = "Land use fraction")
mtext(text = names(luf), side = 3, at = seq(.7, by = 1.2, length.out = 8), line = -.1, padj = .5, cex = .9, las = 3, adj = 1)
mtext(Qobs.sbd, side = 1, adj = 0, font = 3)
box()

Summarize Upstream Soil and Land Use Fractions for All Subbasins

This summarizes the soil and land use fractions for the total upstream area for each subbasin.

# Get a vector of all subids in the model from GeoData
sbd <- gd$SUBID

# Summarize upstream soils
sof.all <- UpstreamGroupSLCClasses(gd = gd, gcl = gcl, type = "soil", progbar = FALSE)[, -1] * 100
sof.all <- cbind("SUBID" = sbd, sof.all)
sof.all
#>    SUBID soil_1  soil_2
#> 1   3344   84.8 15.3000
#> 2   3396   99.9  0.0623
#> 3   3407   99.8  0.1610
#> 4   3466   90.3  9.6900
#> 5   3558   99.9  0.1090
#> 6   3555   99.4  0.5550
#> 7   3607  100.0  0.0000
#> 8  63804   92.4  7.6200
#> 9  63931   96.7  3.3200
#> 10  3564   96.6  3.4200
#> 11  3581   93.6  6.3600
#> 12  3547   95.0  4.9800
#> 13  3594   95.3  4.7300
#> 14 40556   94.0  6.0400
#> 15 40541   94.4  5.6400
#> 16  3486   94.6  5.4000
#> 17  3361   94.7  5.3000
#> 18  3427   96.2  3.8200
#> 19  3435   95.7  4.3500
#> 20  3432   95.2  4.8000
#> 21 63794   94.9  5.1300
#> 22 63938   98.3  1.7500
#> 23 63937   95.2  4.8300
#> 24  3532   95.3  4.7000
#> 25  3587   95.3  4.7400

# Summarize upstream land use
luf.all <- UpstreamGroupSLCClasses(gd = gd, gcl = gcl, type = "landuse", progbar = FALSE)[, -1] * 100
luf.all <- cbind("SUBID" = sbd, luf.all)
luf.all
#>    SUBID landuse_1 landuse_2 landuse_3
#> 1   3344      1.30      85.2    13.600
#> 2   3396     10.80      74.2    15.000
#> 3   3407     10.70      73.6    15.800
#> 4   3466      3.89      80.2    15.900
#> 5   3558     26.10      73.4     0.497
#> 6   3555     11.80      83.7     4.500
#> 7   3607      7.80      91.3     0.920
#> 8  63804      6.73      84.1     9.200
#> 9  63931     12.70      81.3     6.060
#> 10  3564     12.60      81.2     6.180
#> 11  3581      3.18      90.0     6.860
#> 12  3547      7.33      86.3     6.400
#> 13  3594      7.04      86.9     6.080
#> 14 40556      6.25      84.6     9.200
#> 15 40541      6.71      83.8     9.450
#> 16  3486      6.55      83.4    10.000
#> 17  3361     14.00      85.3     0.745
#> 18  3427     11.60      87.8     0.593
#> 19  3435     12.50      85.1     2.450
#> 20  3432      8.54      84.0     7.440
#> 21 63794      6.66      83.3    10.000
#> 22 63938      7.70      80.4    11.900
#> 23 63937      6.75      83.1    10.200
#> 24  3532      7.18      82.0    10.800
#> 25  3587      7.13      81.9    10.900

Summarize Soil and Land Use by Individual Subbasins

This summarizes the soil and land use fractions within just each subbasin area.

# Summarize subbasin soils
sof.sbd.all <- GroupSLCClasses(gd = gd, gcl = gcl, type = "soil", verbose = FALSE)[, -1] * 100
sof.sbd.all <- cbind("SUBID" = sbd, sof.sbd.all)
sof.sbd.all
#>    SUBID       AREA   soil_1  soil_2
#> 1   3344 2012608500  84.7500 15.2501
#> 2   3396 1112100200  99.9376  0.0623
#> 3   3407   10551800  89.4554 10.5444
#> 4   3466 2359837800  90.5252  9.4750
#> 5   3558  154308900  99.8914  0.1087
#> 6   3555  187596400  99.0778  0.9220
#> 7   3607 1563873200 100.0000  0.0000
#> 8  63804  853734600  92.3847  7.6151
#> 9  63931 1762503100  95.8143  4.1858
#> 10  3564   14564400  69.1240 30.8759
#> 11  3581 6261965800  93.6349  6.3648
#> 12  3547  615319700  98.4508  1.5492
#> 13  3594 1226245300  97.5376  2.4621
#> 14 40556 3592919600  94.5337  5.4660
#> 15 40541 1632334200  99.7367  0.2632
#> 16  3486 1073738400  99.6694  0.3307
#> 17  3361 1070852700  94.6982  5.3018
#> 18  3427  480763600  99.4852  0.5149
#> 19  3435 1216238400  94.9739  5.0261
#> 20  3432 1739028400  94.4824  5.5178
#> 21 63794 1259415200  98.8926  1.1073
#> 22 63938 2913067100  98.2519  1.7482
#> 23 63937    7209200  99.7633  0.2366
#> 24  3532 1098516200  99.5008  0.4992
#> 25  3587  231451000  88.2830 11.7169

# Summarize subbasin land use
luf.sbd.all <- GroupSLCClasses(gd = gd, gcl = gcl, type = "landuse", verbose = FALSE)[, -1] * 100
luf.sbd.all <- cbind("SUBID" = sbd, luf.sbd.all)
luf.sbd.all
#>    SUBID       AREA landuse_1 landuse_2 landuse_3
#> 1   3344 2012608500    1.2984   85.1508   13.5509
#> 2   3396 1112100200   10.7671   74.2386   14.9942
#> 3   3407   10551800    0.0000    2.7361   97.2637
#> 4   3466 2359837800    2.8875   79.2264   17.8863
#> 5   3558  154308900   26.0620   73.4415    0.4966
#> 6   3555  187596400    0.0000   92.2010    7.7988
#> 7   3607 1563873200    7.8028   91.2776    0.9196
#> 8  63804  853734600    6.7265   84.0698    9.2035
#> 9  63931 1762503100   19.8918   71.0023    9.1060
#> 10  3564   14564400    0.0000   59.9522   40.0477
#> 11  3581 6261965800    3.1752   89.9599    6.8646
#> 12  3547  615319700   13.4997   83.4072    3.0931
#> 13  3594 1226245300    4.3993   92.3861    3.2143
#> 14 40556 3592919600    6.6157   83.2779   10.1061
#> 15 40541 1632334200   12.8052   74.3974   12.7973
#> 16  3486 1073738400    3.2067   73.8250   22.9684
#> 17  3361 1070852700   13.9634   85.2920    0.7446
#> 18  3427  480763600    6.2788   93.4668    0.2545
#> 19  3435 1216238400   13.6581   81.5245    4.8174
#> 20  3432 1739028400    2.2440   82.3727   15.3835
#> 21 63794 1259415200    2.0353   79.5062   18.4584
#> 22 63938 2913067100    7.6999   80.3565   11.9437
#> 23 63937    7209200    0.0000   78.0271   21.9728
#> 24  3532 1098516200   20.0171   49.3721   30.6108
#> 25  3587  231451000    0.0000   74.7462   25.2537

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