NDFS Synthesis Manuscript: Chlorophyll analysis
Explore relationship with daily average percent flow pulse water
Dave Bosworth
February 28, 2024
Purpose
Explore the relationship between continuous chlorophyll data and the modelled percent flow pulse water to see if should be included in the NDFS synthesis manuscript.
Global code and functions
# Load packages
library(tidyverse)
library(scales)
library(knitr)
library(here)
library(conflicted)
# Declare package conflict preferences
conflicts_prefer(dplyr::filter())Display current versions of R and packages used for this analysis:
devtools::session_info()## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
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## os Windows 10 x64 (build 19045)
## system x86_64, mingw32
## ui RTerm
## language (EN)
## collate English_United States.utf8
## ctype English_United States.utf8
## tz America/Los_Angeles
## date 2024-02-28
## pandoc 3.1.1 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
##
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## [2] C:/Program Files/R/R-4.2.3/library
##
## ──────────────────────────────────────────────────────────────────────────────Import Data
# Define file path for processed data
fp_data <- here("manuscript_synthesis/data/processed")
# Import daily average water quality data
df_wq <- readRDS(file.path(fp_data, "wq_daily_avg_2013-2019.rds"))
# Import daily average percent flow pulse water data
df_pfp <- readRDS(file.path(fp_data, "perc_flow_pulse_daily_avg_2016-2019.rds"))Prepare Data
# Create a vector for the factor order of StationCode
sta_order <- c("LIS", "STTD", "LIB", "RYI", "RVB")
# Prepare chlorophyll and percent flow pulse water data for exploration and analysis
df_chla_c <- df_wq %>%
select(StationCode, Date, Chla) %>%
drop_na(Chla) %>%
# Join flow data to chlorophyll data
left_join(df_pfp, by = join_by(StationCode, Date)) %>%
# Remove all NA flow values
drop_na(PercFlowPulseAvg) %>%
mutate(
# Add Year variable
Year = year(Date),
# Apply factor order to StationCode
StationCode = factor(StationCode, levels = sta_order),
# Convert percent flow pulse water values to proportions so they scale
# correctly with scales::label_percent()
PercFlowPulseAvg = PercFlowPulseAvg / 100
) %>%
arrange(StationCode, Date)Explore sample counts by Station
df_chla_c %>%
summarize(
min_date = min(Date),
max_date = max(Date),
num_samples = n(),
.by = c(StationCode, Year)
) %>%
arrange(StationCode, Year) %>%
kable()| StationCode | Year | min_date | max_date | num_samples |
|---|---|---|---|---|
| LIS | 2016 | 2016-07-15 | 2016-09-16 | 64 |
| LIS | 2017 | 2017-09-09 | 2017-11-03 | 47 |
| LIS | 2018 | 2018-08-28 | 2018-11-11 | 76 |
| LIS | 2019 | 2019-08-27 | 2019-11-06 | 72 |
| STTD | 2016 | 2016-07-17 | 2016-09-16 | 62 |
| STTD | 2017 | 2017-09-20 | 2017-09-25 | 6 |
| STTD | 2018 | 2018-08-30 | 2018-10-15 | 47 |
| STTD | 2019 | 2019-08-29 | 2019-11-06 | 70 |
| LIB | 2016 | 2016-07-22 | 2016-09-16 | 57 |
| LIB | 2017 | 2017-10-01 | 2017-10-23 | 23 |
| LIB | 2018 | 2018-09-04 | 2018-11-11 | 69 |
| LIB | 2019 | 2019-09-05 | 2019-11-06 | 56 |
| RYI | 2016 | 2016-07-22 | 2016-09-16 | 57 |
| RYI | 2018 | 2018-09-06 | 2018-11-11 | 67 |
| RYI | 2019 | 2019-09-02 | 2019-11-06 | 66 |
| RVB | 2016 | 2016-07-23 | 2016-09-16 | 47 |
| RVB | 2017 | 2017-10-10 | 2017-10-18 | 9 |
| RVB | 2018 | 2018-09-07 | 2018-11-11 | 66 |
| RVB | 2019 | 2019-09-03 | 2019-11-06 | 65 |
Except for RYI missing data for 2017 and RVB having limited data for 2017, it looks like we pretty good data coverage.
Plots
Let’s explore the data with some plots. First, lets plot the data in scatterplots of chlorophyll and percent flow pulse water facetted by Station and grouping all years together.
df_chla_c %>%
ggplot(aes(x = PercFlowPulseAvg, y = Chla)) +
geom_point() +
geom_smooth(formula = "y ~ x") +
facet_wrap(vars(StationCode), scales = "free") +
theme_bw() +
ylab("Chlorophyll (ug/L)") +
scale_x_continuous(name = "Percent Flow Pulse Water", labels = label_percent())
There’s some strange patterns in the data. Let’s break these scatterplots apart by year to see if they can be explained by annual differences.
df_chla_c %>%
ggplot(aes(x = PercFlowPulseAvg, y = Chla)) +
geom_point() +
geom_smooth(formula = "y ~ x") +
facet_wrap(
vars(StationCode, Year),
ncol = 4,
scales = "free",
labeller = labeller(.multi_line = FALSE)
) +
theme_bw() +
ylab("Chlorophyll (ug/L)") +
scale_x_continuous(name = "Percent Flow Pulse Water", labels = label_percent()) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Hmmm, a lot of these relationships look complicated. I’m not sure we’ll try using the daily average percent flow pulse water as a continuous predictor to model chlorophyll concentrations.