Comparison with literature

Libraries

library(regionaldrivers)

Data loading

SSCT_field_data is loaded with the package. Here is what 10 random rows of each looks like:

SSCT_field_data

region	SiteID	Lat	Long	ward.grp	SSCT.ward	Ac	bf.d	bf.w	bf.w.d	CV_bf.d	CV_bf.w	D50	D84	slope	vc.dist
NCC	NCC_CH_4_14992	38.523	-123.063	NCC03	4	83.084	0.53	14.62	35.31	0.58	0.27	32	90	0.006	30
SAC	SAC_PGR_32	38.822	-122.719	SAC06	4	5.000	0.90	6.05	6.70	0.38	0.19	64	200	0.001	303
K	K_PH_2_17814	41.806	-123.830	K07	10	4.000	1.32	16.93	13.80	0.27	0.39	90	128	0.034	247
SAC	SAC_LSR_47	38.772	-120.454	SAC03	1	633.000	1.11	19.06	17.10	0.21	0.31	1000	5000	0.008	36
NC	SFE_2017_63	39.671	-123.668	NC04	9	6.000	1.16	6.32	5.40	0.37	0.09	23	128	0.004	30
SC	SC_CH_2_10195	34.236	-117.818	SC05	4	228.652	0.74	9.93	17.19	0.39	0.43	32	190	0.005	50
NC	NC_CH_1_15014	40.342	-123.474	NC07	7	85.000	2.13	19.98	9.80	0.24	0.18	45	128	0.006	1
SAC	SAC_WS_20	38.655	-121.340	SAC09	2	69.000	1.18	7.09	6.00	0.22	0.22	2	2	0.000	4375
SCC	SCC_PL_2_42171	37.020	-121.905	SCC05	10	27.000	0.76	8.41	11.86	0.28	0.20	45	1000	0.009	103
SAC	SAC_RGW_504WE0527	40.107	-122.030	SAC02	5	68.000	1.74	17.82	10.35	0.09	0.10	250	2500	0.029	24

Drainage area versus slope plot

Slope versus relative roughness

UMAP

2D

library(umap)

SSCT_umap_data <- SSCT_field_data %>% 
    dplyr::select(Ac, bf.d, bf.w, bf.w.d, CV_bf.d, CV_bf.w, D50, D84, slope, vc.dist) %>%
    dplyr::mutate_all(scales::rescale)

UMAP_embedding <- umap::umap(SSCT_umap_data, metric = "euclidean", verbose = TRUE, knn.repeat = 10, n_components = 2)

SSCT_labels <- SSCT_field_data %>% dplyr::select(SSCT.ward, SiteID, type)

plot_df <- cbind(UMAP_embedding$layout, SSCT_labels)
colnames(plot_df) <- c("x", "y", "label", "SiteID", "type")

p_umap <- ggplot(plot_df, aes(x = x, y = y, color = label, group = SiteID, shape = type)) + 
    geom_point() +
    scale_shape_manual(labels = c("cascade", "plane-bed", "step-pool", "pool-riffle", "n/a"), values = c(0, 1, 15, 19, 2),
                     guide = guide_legend(override.aes = list(colour = 1))) +
    scale_color_manual(values = colors, 
                    guide = guide_legend(override.aes = list(shape = 18))) +
    theme_minimal()

    (p_umap)

3D

UMAP_embedding <- umap::umap(SSCT_umap_data, metric = "euclidean", verbose = TRUE, knn.repeat = 10, n_components = 3)
plotly::plot_ly(
    x=UMAP_embedding$layout[, 1],
    y=UMAP_embedding$layout[, 2],
    z=UMAP_embedding$layout[, 3],
    type="scatter3d",
    size = 2,
    mode="markers",
    color = SSCT_field_data$SSCT.ward, 
    colors=colors
)