SST preparation

Introduction

Building on the work performed in the Polygon preparation vignette, we will now create grouped SST time series for the regions in our study area based on the 50 and 200 m isobaths within each. This will not be done by using shape files to define the area, as previously planned. Rather we will find the mean depth for each SST pixel and assign it to one of the three depth classes within each region. How this looks is shown below.

# Packages used in this vignette
library(jsonlite, lib.loc = "../R-packages/")
library(tidyverse) # Base suite of functions
library(heatwaveR, lib.loc = "../R-packages/") # For detecting MHWs
# cat(paste0("heatwaveR version = ", packageDescription("heatwaveR")$Version))
library(FNN) # For fastest nearest neighbour searches
library(ncdf4) # For opening and working with NetCDF files
# library(tidync) # For a more tidy approach to managing NetCDF data
library(SDMTools) # For finding points within polygons
library(lubridate) # For convenient date manipulation

# Set number of cores
doMC::registerDoMC(cores = 50)

# Disable scientific notation for numeric values
  # I just find it annoying
options(scipen = 999)

Before we access the SST data from the NAPA (3-Oceans) model, let’s load all of the polygons and bathymetry data we will need to create our grouped time series.

# Corners of the study area
NWA_corners <- readRDS("data/NWA_corners.Rda")

# Individual regions
NWA_coords <- readRDS("data/NWA_coords_cabot.Rda")

# Lowres bathymetry
NWA_bathy <- readRDS("data/NWA_bathy_lowres.Rda")

# The base map
map_base <- ggplot2::fortify(maps::map(fill = TRUE, col = "grey80", plot = FALSE)) %>%
  dplyr::rename(lon = long) %>%
  mutate(group = ifelse(lon > 180, group+9999, group),
         lon = ifelse(lon > 180, lon-360, lon)) %>% 
  select(-region, -subregion)

Pixel prep

# Open bathymetry NetCDF file
nc_bathy <- nc_open("../../data/NAPA025/mesh_grid/bathy_creg025_extended_5m.nc")

# Extract and combine lon/lat/bathy
lon <- as.data.frame(ncvar_get(nc_bathy, varid = "nav_lon")) %>% 
  setNames(., as.numeric(nc_bathy$dim$y$vals)) %>%
  mutate(lon = as.numeric(nc_bathy$dim$x$vals)) %>% 
  gather(-lon, key = lat, value = nav_lon) %>% 
  mutate(lat = as.numeric(lat))
lat <- as.data.frame(ncvar_get(nc_bathy, varid = "nav_lat")) %>% 
  setNames(., as.numeric(nc_bathy$dim$y$vals)) %>%
  mutate(lon = as.numeric(nc_bathy$dim$x$vals)) %>% 
  gather(-lon, key = lat, value = nav_lat) %>% 
  mutate(lat = as.numeric(lat)) 
bathy <- as.data.frame(ncvar_get(nc_bathy, varid = "Bathymetry")) %>% 
  mutate(lon = as.numeric(nc$dim$x$vals)) %>% 
  gather(-lon, key = lat, value = bathy) %>% 
  mutate(lat = rep(as.numeric(nc_bathy$dim$y$vals), each = 528),
         bathy = ifelse(bathy == 0, NA, bathy),
         bathy = round(bathy, 2)) %>%
  na.omit() %>% 
  left_join(lon, by = c("lon", "lat")) %>% 
  left_join(lat, by = c("lon", "lat")) %>% 
  dplyr::rename(lon_index = lon, lat_index = lat,
                lon = nav_lon, lat = nav_lat) %>% 
  mutate(lon = round(lon, 4),
         lat = round(lat, 4))
nc_close(nc_bathy)

# Save bathymetry
# saveRDS(bathy, "data/NAPA_bathy.Rda")

# Save base lon/lat values
NAPA_coords <- left_join(lon, lat, by = c("lon", "lat")) %>% 
    dplyr::rename(lon_index = lon, lat_index = lat,
                lon = nav_lon, lat = nav_lat) %>% 
  mutate(lon = round(lon, 4),
         lat = round(lat, 4))
# saveRDS(NAPA_coords, "data/NAPA_coords.Rda")

# Save bathymetry subsetted to study area
  # This functions as an effective mask for water only pixels


# Clean up
rm(nc_bathy, bathy, lon, lat)

# Load NAPA bathymetry
NAPA_bathy <- readRDS("data/NAPA_bathy.Rda")# %>% 
  # mutate(index = paste0(lon, lat))

# Function for finding and cleaning up points within a given region polygon
pnts_in_region <- function(region_in){
  region_sub <- NWA_coords %>% 
    filter(region == region_in)
  coords_in <- pnt.in.poly(pnts = NAPA_bathy[4:5], poly.pnts = region_sub[2:3]) %>% 
    filter(pip == 1) %>% 
    # mutate(index = paste(lon, lat)) %>% 
    left_join(NAPA_bathy, by = c("lon", "lat")) %>% 
    dplyr::select(-pip) %>% 
    mutate(region = region_in)
  return(coords_in)
}

# Run the function
NWA_NAPA_info <- plyr::ldply(unique(NWA_coords$region), pnts_in_region)

# Visualise to ensure success
ggplot(NWA_coords, aes(x = lon, y = lat)) +
  geom_polygon(data = map_base, aes(group = group), show.legend = F) +
  geom_polygon(aes(fill = region), alpha = 0.2) +
  geom_point(data = NWA_NAPA_info, aes(colour = region)) +
    coord_cartesian(xlim = NWA_corners[1:2],
                  ylim = NWA_corners[3:4]) +
  labs(x = NULL, y = NULL)

# Cut the depth strata into sub-regions as desired
NWA_NAPA_info <- NWA_NAPA_info %>% 
  mutate(sub_region = cut(bathy, breaks = c(0, 50, 200, ceiling(max(bathy))), dig.lab = 4))
# saveRDS(NWA_NAPA_info, "data/NWA_NAPA_info.Rda")

# Visualise to ensure success
sub_region_map <- ggplot(NWA_coords, aes(x = lon, y = lat)) +
  geom_polygon(data = map_base, aes(group = group), show.legend = F) +
  # geom_polygon(aes(fill = region), alpha = 0.2) +
  geom_point(data = NWA_NAPA_info, aes(colour = region, alpha = sub_region), 
             shape = 15, size = 0.5) +
  guides(colour = guide_legend(override.aes = list(size = 5))) +
  scale_alpha_manual(values = c(0.4, 0.7, 1)) +
  coord_cartesian(xlim = NWA_corners[1:2],
                  ylim = NWA_corners[3:4]) +
  labs(x = NULL, y = NULL, colour = "Region", alpha = "Sub region")
# ggsave(plot = sub_region_map, filename = "output/sub_region_map.pdf", height = 5, width = 6)

# Visualise
sub_region_map

SST prep

With the NAPA pixels successfully assigned to regions based on their thermal properties, and sub-regions based on their depth, we now need to go about clumping these SST pixels into one average time series per sub/region.

# The NAPA data location
NAPA_files <- dir("../../data/NAPA025/1d_grid_T_2D", full.names = T)

# Function for loading the individual NAPA NetCDF files and subsetting SST accordingly 
load_NAPA_sst_sub <- function(file_name, coords = NWA_NAPA_info){
  
  nc <- nc_open(as.character(file_name))
  
  date_start <- ymd(str_sub(basename(as.character(file_name)), start = 29, end = 36))
  date_end <- ymd(str_sub(basename(as.character(file_name)), start = 38, end = 45))
  date_seq <- seq(date_start, date_end, by = "day")
  
  sst <- as.data.frame(ncvar_get(nc, varid = "sst")) %>% 
    mutate(lon = as.numeric(nc$dim$x$vals)) %>%
    gather(-lon, key = lat, value = temp) %>%
    mutate(t = rep(date_seq, each = 388080),
           lat = rep(rep(as.numeric(nc$dim$y$vals), each = 528), times = 5),
           temp = round(temp, 2)) %>%
    select(lon, lat, t, temp) %>%
    na.omit() %>% 
    dplyr::rename(lon_index = lon, lat_index = lat) %>% 
    right_join(coords, by = c("lon_index" , "lat_index")) %>% 
    group_by(region, sub_region, t) %>% 
    summarise(temp = mean(temp, na.rm = T))
  
  nc_close(nc)
  return(sst)
}

# Clomp'em 
system.time(
  NAPA_sst_sub <- plyr::ldply(NAPA_files,
                              .fun = load_NAPA_sst_sub, 
                              .parallel = TRUE)
) # 1.5 seconds for 1, 135 seconds for all

# For some reason the rounding didn't stick in the function above so we wack it again here
NAPA_sst_sub <- NAPA_sst_sub %>% 
  mutate(temp = round(temp, 2))

# Save
# saveRDS(NAPA_sst_sub, "data/NAPA_sst_sub.Rda")

MHW detection

With our clumped SST time series ready the last step in this vignette is to detect the MHWs within each.

# Load the time series data
NAPA_sst_sub <- readRDS("data/NAPA_sst_sub.Rda")

# Calculate base results
NAPA_MHW_sub <- NAPA_sst_sub %>%
  group_by(region, sub_region) %>%
  nest() %>%
  mutate(clims = map(data, ts2clm,
                     # NB: I've chosen here to use as much of the 2015 data as exists,
                     # rather than to use none of it as I think it will create a better climatology
                     # even though the last two days of the year are missing
                     climatologyPeriod = c("1994-01-01", "2015-12-29")),
         events = map(clims, detect_event),
         cats = map(events, category)) %>%
  select(-data)
# saveRDS(NAPA_MHW_sub, "data/NAPA_MHW_sub.Rda")

With the MHWs detected, let’s visualise the results to ensure everything worked as expected.

# Load MHW results
NAPA_MHW_sub <- readRDS("data/NAPA_MHW_sub.Rda")

# Events
NAPA_MHW_event <- NAPA_MHW_sub %>%
  select(-clims, -cats) %>%
  unnest(events) %>%
  filter(row_number() %% 2 == 0) %>%
  unnest(events)

event_lolli_plot <- ggplot(data = NAPA_MHW_event , aes(x = date_peak, y = intensity_max)) + 
        geom_lolli(colour = "salmon", colour_n = "red", n = 3) + 
  labs(x = "Peak Date", y = "Max. Intensity (°C)") +
  # scale_y_continuous(expand = c(0, 0))+
  facet_grid(region~sub_region)
# ggsave(plot = event_lolli_plot, filename = "output/event_lolli_plot.pdf", height = 7, width = 13)

# Visualise
event_lolli_plot

Everything appears to check out. Up next in the Variable preparation vignette we will go through the steps necessary to build the data that will be fed into our self-organising maps as seen in the Self-organising map (SOM) analysis vignette.

One last point however is that according to Richaud et al. (2016) the different slopebreaks for the different regions occur at different depths, from 50 m to 400 m depending. They do however note that using a static definition of 200 m for the break does not produce significantly different results. Therefore, for the time being we will maintain the spatial breaks used above.

Session information

sessionInfo()

R version 3.6.0 (2019-04-26)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/openblas-base/libblas.so.3
LAPACK: /usr/lib/libopenblasp-r0.2.18.so

locale:
 [1] LC_CTYPE=en_CA.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_CA.UTF-8        LC_COLLATE=en_CA.UTF-8    
 [5] LC_MONETARY=en_CA.UTF-8    LC_MESSAGES=en_CA.UTF-8   
 [7] LC_PAPER=en_CA.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_CA.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] bindrcpp_0.2.2       lubridate_1.7.4      SDMTools_1.1-221    
 [4] ncdf4_1.16           FNN_1.1.2.1          heatwaveR_0.3.6.9004
 [7] data.table_1.11.6    forcats_0.3.0        stringr_1.3.1       
[10] dplyr_0.7.6          purrr_0.2.5          readr_1.1.1         
[13] tidyr_0.8.1          tibble_1.4.2         ggplot2_3.0.0       
[16] tidyverse_1.2.1      jsonlite_1.6        

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.18      lattice_0.20-35   assertthat_0.2.0 
 [4] rprojroot_1.3-2   digest_0.6.16     foreach_1.4.4    
 [7] R6_2.2.2          cellranger_1.1.0  plyr_1.8.4       
[10] backports_1.1.2   evaluate_0.11     httr_1.3.1       
[13] pillar_1.3.0      rlang_0.2.2       lazyeval_0.2.1   
[16] readxl_1.1.0      rstudioapi_0.7    whisker_0.3-2    
[19] R.utils_2.7.0     R.oo_1.22.0       rmarkdown_1.10   
[22] labeling_0.3      htmlwidgets_1.3   munsell_0.5.0    
[25] broom_0.5.0       compiler_3.6.0    modelr_0.1.2     
[28] pkgconfig_2.0.2   htmltools_0.3.6   tidyselect_0.2.4 
[31] workflowr_1.1.1   codetools_0.2-15  doMC_1.3.5       
[34] viridisLite_0.3.0 crayon_1.3.4      withr_2.1.2      
[37] R.methodsS3_1.7.1 grid_3.6.0        nlme_3.1-137     
[40] gtable_0.2.0      git2r_0.23.0      magrittr_1.5     
[43] scales_1.0.0      cli_1.0.0         stringi_1.2.4    
[46] reshape2_1.4.3    xml2_1.2.0        iterators_1.0.10 
[49] tools_3.6.0       glue_1.3.0        maps_3.3.0       
[52] hms_0.4.2         parallel_3.6.0    yaml_2.2.0       
[55] colorspace_1.3-2  rvest_0.3.2       plotly_4.9.0     
[58] knitr_1.20        bindr_0.1.1       haven_1.1.2