5. BC CEF Data Import

Import data from the BC-CEF Assessment Units

This example was developed to transform data outputs from the BC Provincial Cumulative Effects Framework into a stressor-magnitude and stressor-response file that can be readily imported into the Joe Model. The BCCEF is centered around risk and hazard potential for assessment units with readily available GIS metrics such as road density, total landcover alternation and various natural vulnerability indicators. By integrating the BCCEF with the Joe Model, we can adjust the risk level benchmarks as stressor-response curves to modify assumptions for variables of interest.

BC CEF: Cumulative Effects Framework https://www2.gov.bc.ca/gov/content/environment/natural-resource-stewardship/cumulative-effects-framework

BC CEF: Data Portal (download data here) https://experience.arcgis.com/experience/3aed6c004103495c85252051931a1b29/

To begin navigate to the BC CEF and area of interest. Launch the ERSI application and download the data for an area of interest. The downloaded file should be available as a gdb (file geodatabase). The following blocks of code will convert the data into a format compatible with the Joe Model.

# Install the CEMPRA (first-time users)
# devtools::install_github("essatech/CEMPRA")
library(CEMPRA)

# Use the sf library and dplyr
library(sf); library(dplyr)

# Update the path to your geodatabase file
path_to_gdb <- "C:/Users/mattj/Downloads/NicolaWSs.gdb"
# Load the data
bccef <- st_read(path_to_gdb, layer = "NicolaWSs")

# Ensure this looks right for your areas of interest
plot(st_geometry(bccef))

# Review the attribute
colnames(bccef)[1:10]

Isolate target variables of interest

The BC CEF dataset contains numerous indicators, vulnerability metrics, precursor variables and informational attributes. It’s best to isolate a target list of meaningful stressor magnitude variables (or proxies) that you will bring forward into the Joe Model as stressors.

In this example we will isolate our assessment to the following variables from the BC CEF framework for aquatic ecosystems:


# Ensure this looks right for your areas of interest
plot(st_geometry(bccef))

# Review the attribute
colnames(bccef)[1:10]

# Remove any duplicate ID object
bccef <- bccef[!(duplicated(bccef$ASSESS_UNI_NUM)), ]
bccef <- bccef[!(bccef$ASSESS_UNI_NAME == "Nicola Watershed"), ]
bccef <- bccef[bccef$AU_AREA_HA < 30000, ]


# Need to cut smaller polygons out of larger polygons
plot(st_geometry(bccef), col = "grey")
plot(st_geometry(bccef))

# ECA_PERCENT
head(bccef)
summary(bccef$ECA_PERCENT)

var1 <- conversion_bc_cef(
  data = bccef,
  Stressor = "ECA_PERCENT",
  Function = "continuous",
  Units = "%",
  Low_Limit = 0,
  Up_Limit = 50,
  value_vector = rev(c(0, 10, 15, 20, 30, 50)),
  msc_vector = rev(c(100, 90, 80, 60, 40, 0))
)

summary(bccef$RUNOFF_GEN_POT_SUM)
boxplot(bccef$RUNOFF_GEN_POT_SUM ~ bccef$RUNOFF_GEN_POT_RATING)
var2 <- conversion_bc_cef(
  data = bccef,
  Stressor = "RUNOFF_GEN_POT_SUM",
  Function = "continuous",
  Units = "",
  Low_Limit = 4,
  Up_Limit = 6,
  value_vector = c(6, 5, 4),
  msc_vector = c(30, 50, 100)
)
var2$stressor_response$sr_data


summary(bccef$RUNOFF_ATTENUATION_SUM)
boxplot(bccef$RUNOFF_ATTENUATION_SUM ~ bccef$RUNOFF_ATTENUATION_RATING)
var3 <- conversion_bc_cef(
  data = bccef,
  Stressor = "RUNOFF_ATTENUATION_SUM",
  Function = "continuous",
  Units = "",
  Low_Limit = 2,
  Up_Limit = 5,
  value_vector = c(2, 3, 4, 5),
  msc_vector = c(40, 60, 80, 100)
)
var3$stressor_response$sr_data


summary(bccef$HYDROLOGIC_RESP_POT_SUM)
boxplot(bccef$HYDROLOGIC_RESP_POT_SUM ~ bccef$HYDROLOGIC_RESP_POT_RATING)
var4 <- conversion_bc_cef(
  data = bccef,
  Stressor = "HYDROLOGIC_RESP_POT_SUM",
  Function = "continuous",
  Units = "",
  Low_Limit = 6,
  Up_Limit = 9,
  value_vector = c(9, 8, 7, 6),
  msc_vector = c(40, 60, 80, 100)
)
var4$stressor_response$sr_data


summary(bccef$STREAMFLOW_HAZARD_SUM)
boxplot(bccef$STREAMFLOW_HAZARD_SUM ~ bccef$STREAMFLOW_HAZARD_RATING)
var5 <- conversion_bc_cef(
  data = bccef,
  Stressor = "STREAMFLOW_HAZARD_SUM",
  Function = "continuous",
  Units = "",
  Low_Limit = 2,
  Up_Limit = 6,
  value_vector = c(6, 5, 4, 3, 2),
  msc_vector = c(20, 40, 60, 80, 100)
)
var5$stressor_response$sr_data


summary(bccef$SEDIMENT_GEN_DELIV_POT_SUM)
boxplot(bccef$SEDIMENT_GEN_DELIV_POT_SUM ~ bccef$SEDIMENT_GEN_DELIV_POT_RATING)
var6 <- conversion_bc_cef(
  data = bccef,
  Stressor = "SEDIMENT_GEN_DELIV_POT_SUM",
  Function = "continuous",
  Units = "",
  Low_Limit = 3,
  Up_Limit = 6,
  value_vector = c(9, 8, 7, 6),
  msc_vector = c(40, 60, 80, 100)
)
var6$stressor_response$sr_data

table(bccef$SEDIMENT_HAZARD_RATING)
bccef$shr <- 1
bccef$shr <- ifelse(bccef$SEDIMENT_HAZARD_RATING == "H", 4, bccef$shr)
bccef$shr <- ifelse(bccef$SEDIMENT_HAZARD_RATING == "M", 3, bccef$shr)
bccef$shr <- ifelse(bccef$SEDIMENT_HAZARD_RATING == "L", 2, bccef$shr)
boxplot(bccef$shr ~ bccef$SEDIMENT_HAZARD_RATING)
bccef$SEDIMENT_HAZARD_RATING <- bccef$shr
var7 <- conversion_bc_cef(
  data = bccef,
  Stressor = "SEDIMENT_HAZARD_RATING",
  Function = "continuous",
  Units = "",
  Low_Limit = 1,
  Up_Limit = 4,
  value_vector = c(4, 3, 2, 1),
  msc_vector = c(40, 60, 80, 100)
)
var7$stressor_response$sr_data


summary(bccef$RIPARIAN_LANDUSE_SUM)
boxplot(bccef$RIPARIAN_LANDUSE_SUM ~ bccef$RIPARIAN_HAZARD_RATING)
var8 <- conversion_bc_cef(
  data = bccef,
  Stressor = "RIPARIAN_LANDUSE_SUM",
  Function = "continuous",
  Units = "",
  Low_Limit = 3,
  Up_Limit = 7,
  value_vector = c(3, 4, 5, 6, 7),
  msc_vector = c(20, 40, 60, 80, 100)
)
var8$stressor_response$sr_data

Compile objects together and export files


all <- list(var1, var2, var3, 
            var4, var5, var6,
            var7, var8)

sm_dat <- list()
sr_main <- list()
sr_dat <- list()
sheet_names <- c()

for(i in 1:length(all)) {
  
  this_set <- all[[i]]
  sm_dat[[i]] <- this_set$stressor_magnitude
  sr_main[[i]] <- this_set$stressor_response$sr_main
  sr_dat[[i]] <- this_set$stressor_response$sr_data
  sheet_names <- c(sheet_names, this_set$stressor_response$sr_name)

}

sm_dat <- do.call("rbind", sm_dat)
sr_main <- do.call("rbind", sr_main)

sr_out <- append(list(sr_main), sr_dat)
names(sr_out) <- c("Main", sheet_names)

writexl::write_xlsx(sm_dat, path = "stressor_magnitude.xlsx")
writexl::write_xlsx(sr_out, path = "stressor_response.xlsx")


# Export geometry
bccef_geom <- bccef[, c("ASSESS_UNI_NUM", "ASSESS_UNI_NAME")]
colnames(bccef_geom) <- c("HUC_ID", "NAME", "Shape")
sf::st_write(bccef_geom, dsn = "watersheds.gpkg", delete_dsn = TRUE)

2024-03-15

Import data from the BC-CEF Assessment Units

Isolate target variables of interest

Compile objects together and export files