Channel cross section

#Packages needed for this project

library(tidyverse)
library(here)

Read in the data frame.

df <- read_csv(here('hydrology/src_classes_spring_2022/data/channel_df.csv'))

Let’s plot the raw data

df %>% 
  ggplot(aes(x = Width_m, y = Channel_height_m, color = Location)) +
  geom_point() +
  geom_line()

Now lets invert the channel height.

df %>% 
  mutate(invert_height = 1 - Channel_height_m) %>% 
  ggplot(aes(x = Width_m, y = invert_height, color = Location)) +
  geom_point() +
  geom_line()

That is starting to look OK. Now let’s add the water level.

df <- df %>% 
  mutate(invert_height = 1 - Channel_height_m, new_water = Water_level_m + invert_height) 

df %>% 
  ggplot() +
  geom_point(aes(x = Width_m, y = invert_height, color = Location), alpha = 0.5, size = 3) +
  geom_line(aes(x = Width_m, y = invert_height, color = Location)) +
  geom_line(aes(x = Width_m, y = new_water), color = "blue", size = 1.5) +
  geom_point(aes(x = Width_m, y = new_water), color = "blue", alpha = 0.5, size = 3)+ 
  ylab("Height (m)") +
  xlab("Width (m)") +
  theme_linedraw() +
  theme(legend.title = element_blank())

Figure 1. Cross-sectional data for the Poudre River at the Environmental Learning Center April 2022.

And to calculate the area of the channel. The first part is the linear interpolation.You will note that cor_d does have one negative value. You could remove that with a filter.

seq <- seq(from = 0.02, to = 0.17, length.out = 61)

df <- cbind(df, seq) %>% 
  mutate(cor_d = Channel_height_m - seq)

df <- df %>% 
  mutate(area_m2 = (lead(Width_m) - lag(Width_m)) / 2 *cor_d)

total_area <- sum(df$area_m2, na.rm = TRUE)

We could also calculate the wetted area, mean BF-depth, etc. I don’t have the notes on location of BF, so I’ll just do wetted area.

df <- df %>% 
  mutate(wetted_area_m2 = (lead(Width_m) - lag(Width_m)) / 2 *
  Water_level_m)

total_wetted_area <- sum(df$wetted_area_m2, na.rm = TRUE)

Here we can do some bankfull calculations. Left bankfull (LBF) was at 1.3 and RBF was at 28.5.

bf_df <- df %>% 
  filter(Width_m %in% c(1.5:28.5)) %>% 
  select(-Water_level_m, -new_water, -invert_height)

seq_bf <- seq(from = 0.14, to = 0.25, length.out = 28)

bf_df <- cbind(bf_df, seq_bf) %>% 
  mutate(cor_d_bf = Channel_height_m - seq_bf)
  
bf_df$cor_d_bf[bf_df$cor_d_bf < 0] <- 0

mean_bf_d <- mean(bf_df$cor_d_bf)

bf_df <- bf_df %>% 
  mutate(bf_area_m2 = (lead(Width_m) - lag(Width_m)) / 2 *cor_d_bf)  

tot_bf_area <- sum(bf_df$bf_area_m2, na.rm = TRUE)

bf_df$bf_area_m2[bf_df$bf_area_m2 < 0] <- 0