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Hydraulic scenarios

library(hydromeso)
h <- mesohabitat_example_rasters()
d2 <- h$depth * 1.25
v2 <- h$velocity * 1.15
depth <- list(low = h$depth, high = d2)
velocity <- list(low = h$velocity, high = v2)

Unique names pair dates or discharges safely. Unmatched or duplicated names are rejected. Positional pairing is available only when explicitly requested.

scenarios <- classify_mesohabitat_series(depth, velocity)
summarize_mesohabitat(scenarios)
##    scenario class_id                 label cell_count    area_m2   hectares
## 1       low        1          Shallow Pool        470  14148.535  1.4148535
## 2       low        2           Medium Pool        205   6171.170  0.6171170
## 3       low        3             Deep Pool        130   3913.425  0.3913425
## 4       low        4           Slow Riffle        823  24774.986  2.4774986
## 5       low        5           Fast Riffle        115   3461.876  0.3461876
## 6       low        6               Raceway       1036  31186.982  3.1186982
## 7       low        7   Faster than Raceway       3951 118937.996 11.8937996
## 8       low        8 Faster than Deep Pool      14604 439628.086 43.9628086
## 9      high        1          Shallow Pool        363  10927.485  1.0927485
## 10     high        2           Medium Pool        164   4936.936  0.4936936
## 11     high        3             Deep Pool        119   3582.289  0.3582289
## 12     high        4           Slow Riffle        617  18573.714  1.8573714
## 13     high        5           Fast Riffle        128   3853.218  0.3853218
## 14     high        6               Raceway        475  14299.051  1.4299051
## 15     high        7   Faster than Raceway       1884  56714.550  5.6714550
## 16     high        8 Faster than Deep Pool      17584 529335.813 52.9335813
##    square_kilometres       acres percentage
## 1        0.014148535   3.4961791  2.2030562
## 2        0.006171170   1.5249293  0.9609076
## 3        0.003913425   0.9670284  0.6093560
## 4        0.024774986   6.1220325  3.8576918
## 5        0.003461876   0.8554481  0.5390457
## 6        0.031186982   7.7064710  4.8560981
## 7        0.118937996  29.3902190 18.5197332
## 8        0.439628086 108.6344659 68.4541114
## 9        0.010927485   2.7002405  1.7015094
## 10       0.004936936   1.2199435  0.7687261
## 11       0.003582289   0.8852029  0.5577951
## 12       0.018573714   4.5896647  2.8920970
## 13       0.003853218   0.9521509  0.5999812
## 14       0.014299051   3.5333724  2.2264929
## 15       0.056714550  14.0144706  8.8309739
## 16       0.529335813 130.8017279 82.4224244
plot_mesohabitat(scenarios)

The following operation takes median depth and median velocity first and then classifies those two surfaces:

median_result <- mesohabitat_from_median_hydraulics(depth, velocity)
names(median_result)
## [1] "median_depth"                       "median_velocity"                   
## [3] "mesohabitat_from_median_hydraulics"
plot_mesohabitat(median_result$mesohabitat_from_median_hydraulics)

This is mesohabitat derived from median hydraulics, not “median mesohabitat.” The modal nominal class is a different operation. Ties can return NA or the lowest class identifier as a deterministic identifier-only convention.

modal_mesohabitat(scenarios, ties = "NA")
## class       : SpatRaster
## size        : 230, 445, 1  (nrow, ncol, nlyr)
## resolution  : 18, 18  (x, y)
## extent      : 1725550, 1733560, 312049.5, 316189.5  (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=lcc +lat_0=38.3333333333333 +lon_0=-98 +lat_1=38.7166666666667 +lat_2=39.7833333333333 +x_0=400000 +y_0=0 +ellps=GRS80 +units=us-ft +no_defs
## source(s)   : memory
## categories  : mesohabitat
## name        :     modal_mesohabitat
## min value   :          Shallow Pool
## max value   : Faster than Deep Pool
compare_mesohabitat(scenarios[[1]], scenarios[[2]])
## $transitions
##    from_class to_class cell_count     area_m2
## 1           1        1        363  10927.4855
## 2           1        2         41   1234.2340
## 3           1        4         60   1806.1959
## 4           1        6          6    180.6196
## 5           2        2        123   3702.7020
## 6           2        3         32    963.3046
## 7           2        6         33    993.4078
## 8           2        8         17    511.7556
## 9           3        3         87   2618.9844
## 10          3        8         43   1294.4406
## 11          4        4        557  16767.5182
## 12          4        5         60   1806.1959
## 13          4        6        114   3431.7721
## 14          4        7         92   2769.5003
## 15          5        5         68   2047.0220
## 16          5        7         47   1414.8535
## 17          6        6        322   9693.2511
## 18          6        7        260   7826.8487
## 19          6        8        454  13666.8818
## 20          7        7       1485  44703.3478
## 21          7        8       2466  74234.6486
## 22          8        8      14604 439628.0860
## 
## $gains
##   class_id    area_m2
## 1        1     0.0000
## 2        2  1234.2340
## 3        3   963.3046
## 4        4  1806.1959
## 5        5  1806.1959
## 6        6  4605.7995
## 7        7 12011.2025
## 8        8 89707.7265
## 
## $losses
##   class_id   area_m2
## 1        1  3221.049
## 2        2  2468.468
## 3        3  1294.441
## 4        4  8007.468
## 5        5  1414.853
## 6        6 21493.730
## 7        7 74234.649
## 8        8     0.000
## 
## $unchanged_area_m2
## [1] 530088.4
## 
## $percentage_changed
## [1] 17.46039

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