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Part III-Plotting and export.Rmd
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---
title: "Part III: Plotting, supplementation, and export of stats dataset"
author: "Mike Vlah"
date: "Last updated on `r format(Sys.time(), '%d %B, %Y')`"
output: html_document
editor_options:
chunk_output_type: console
knit: (function(inputFile, encoding) {
rmarkdown::render(inputFile, encoding = encoding, output_dir = "documentation") })
---
# Description
<font size="5">Summary of major workflow components</font>
This workflow is part of a series that describes the preparation of data used in Bernhardt et al. (). The creation of this dataset consists of several sub workflows as described below.
* **Part I: Standardized dataset creation:** Part I is the preparation of standardized datasets of stream metabolism from the [StreamPULSE data portal](https://data.streampulse.org/) and terrestrial ecosystem fluxes from the [FLUXNET2015 dataset](https://fluxnet.org/data/fluxnet2015-dataset/). Everything generated in Part I is included in `output_data`, so you may start with Part II.
* **Part II. Filtering, gap-filling, and calculating metrics:** In Part II the standardized dataset of lotic and terrestrial metabolism data was filtered down to the subset of sites used in Bernhardt et al. (). After filtering, additional descriptive metrics were calculated to use in analysis.
* **Part III. Plotting and export of stats dataset:** Datasets from part II are minimally subsetted and recast for plotting convenience. Figures 1-6 are generated. An analysis-ready dataset is compiled.
# Setup
See code for details
```{r setup, include = FALSE}
library(plyr)
library(tidyverse)
library(RColorBrewer)
library(plotrix)
library(nhdR)
library(here)
```
```{r setup2, include = FALSE}
dir.create(here('figures'),
showWarnings = FALSE)
dir.create(here('export_datasets'),
showWarnings = FALSE)
dir.create(here('output_data', 'spatial'),
showWarnings = FALSE)
#choose colors for plot categories
fnetcolor = 'sienna4'
spcolor = 'cadetblue4'
gppcolor = 'forestgreen'
ercolor = 'sienna'
source(here('plot_helpers.R'))
```
# load site data from Part II
Columns Stream_PAR_sum, Disch_ar1, MOD_ann_NPP will be used in plots.
```{r load site data, include = FALSE, eval = FALSE}
site_data_1 = readRDS(here('output_data', 'lotic_site_info_filtered.rds')) %>%
as_tibble() %>%
select(sitecode = Site_ID,
Stream_PAR_sum, Disch_ar1, Disch_skew, MOD_ann_NPP, nyears, ndays, coverage)
```
# load and prepare fluxnet data
We're beginning from two previously generated datasets here. The first is `fluxnet_filtered_metabolism.rds`, a collection of data.frames for each terrestrial site, organized in a list. We'll cast that to one big tibble, select and rename a few columns, calculate NEP from GPP and ER, and determine a mean value for each of those metrics for each DOY, across years. The resulting dataset (summarized by site and DOY) is the basis for the "lips plots" that show median GPP, ER, and NEP (and GPP and ER ranges) over the course of an average year. For every other plot, we use a tibble that summarizes terrestrial data by site, called `fnet_site`.
```{r load site data fnet, include = FALSE, eval = FALSE}
restricted_use_sites = c("RU-Sam", "RU-SkP", "RU-Tks", "RU-Vrk", "SE-St1", "ZA-Kru")
#summarize by site, starting with a dataset that's summarized by site-year
fnet_ann = readRDS(here('output_data', 'fluxnet_filtered_metabolism.rds'))
fnet_names = names(fnet_ann)
for(i in 1:length(fnet_ann)){
fnet_ann[[i]]$sitecode = fnet_names[i]
}
fnet_ann = fnet_ann[! fnet_names %in% restricted_use_sites]
fnet_full = Reduce(bind_rows, fnet_ann) %>%
as_tibble() %>%
filter(sitecode != 'CZ-BK2')
fnet_site = readRDS(here('output_data', 'fluxnet_site_info_filtered.rds')) %>%
as_tibble() %>%
select(sitecode = Site_ID,
GPP_site_mean = ann_GPP,
ER_site_mean = ann_ER) %>%
filter(! is.na(GPP_site_mean), #CZ-BK2 has NaN
! is.na(ER_site_mean)) %>%
mutate(NEP_site_mean = GPP_site_mean + ER_site_mean) %>%
arrange(sitecode)
#summarize by DOY for lips plots
fnet_lips = fnet_full %>%
select(sitecode, GPP, ER, DOY, Year) %>%
group_by(sitecode, DOY) %>%
summarize(GPP = mean(GPP, na.rm=TRUE), #average metab by day across years
ER = mean(ER, na.rm=TRUE)) %>%
ungroup() %>%
arrange(sitecode, DOY) %>%
rename(GPP_C_filled = GPP, ER_C_filled = ER) %>%
mutate(NEP_C_filled = GPP_C_filled + ER_C_filled)
```
# load and prepare streampulse data
Same procedures as for FLUXNET (terrestrial) data, here applied to StreamPULSE (aquatic) data.
```{r load site data sp, include = FALSE, eval = FALSE}
sp_list = readRDS(here('output_data', 'lotic_gap_filled.rds'))
#summarize by site
sp_names = names(sp_list)
for(i in 1:length(sp_list)){
sp_list[[i]]$sitecode = sp_names[i]
}
sp_full = Reduce(bind_rows, sp_list) %>%
as_tibble() %>%
select(sitecode, Date, Year, DOY, GPP_C, ER_C, GPP_C_filled, ER_C_filled) %>%
arrange(sitecode, Date)
sp_site = sp_full %>%
group_by(sitecode, Year) %>%
summarize(GPP_ann_sum = sum(GPP_C_filled, na.rm = TRUE),
ER_ann_sum = sum(ER_C_filled, na.rm = TRUE)) %>%
ungroup() %>%
group_by(sitecode) %>%
summarize(GPP_site_mean = mean(GPP_ann_sum, na.rm = TRUE),
ER_site_mean = mean(ER_ann_sum, na.rm = TRUE)) %>%
ungroup() %>%
arrange(sitecode) %>%
mutate(NEP_site_mean = GPP_site_mean + ER_site_mean) %>%
left_join(site_data_1, by = 'sitecode')
#summarize by DOY for lips plots
sp_lips = sp_full %>%
select(sitecode, Year, DOY, GPP_C_filled, ER_C_filled) %>%
group_by(sitecode, DOY) %>%
summarize(GPP_C_filled = mean(GPP_C_filled, na.rm=TRUE), #average metab by day across years
ER_C_filled = mean(ER_C_filled, na.rm=TRUE)) %>%
ungroup() %>%
arrange(sitecode, DOY) %>%
mutate(NEP_C_filled = GPP_C_filled + ER_C_filled) %>%
left_join(site_data_1, by = 'sitecode') #include site data
```
# split sites by coverage
For some plots, we use different markers to indicate sites with high coverage for metabolism estimates (>= 80% of days across years) and those with low coverage (< 80% of days). Here we create vectors of sitecodes that fit each of these criteria, and boolean vectors representing the same, to be used for subsetting later.
```{r split by coverage, include = FALSE, eval = FALSE}
coverage_sp = site_data_1 %>%
select(sitecode, ndays, nyears, coverage)
coverage_fnet = readRDS(here('output_data', 'fluxnet_site_info_filtered.rds')) %>%
as_tibble() %>%
select(sitecode = Site_ID,
nyears, ndays, coverage) %>%
filter(sitecode != 'CZ-BK2')
sp_high_cov_sites = coverage_sp %>%
filter(coverage >= 0.8) %>%
pull(sitecode)
sp_high_cov_bool = sp_site$sitecode %in% sp_high_cov_sites
fnet_high_cov_sites = coverage_fnet %>%
filter(coverage >= 0.8) %>%
pull(sitecode)
fnet_high_cov_bool = fnet_site$sitecode %in% fnet_high_cov_sites
```
# (skip if only plotting) generate additional site data to be used in SEM
Here we acquire areas and average slopes for the watersheds of each site, and the Strahler order of each stream at the site location. These data are retrieved from NHDPlusV2. Because NHD watershed areas apply to NHD reaches rather than our exact study reaches, we multiply each NHD watershed area by the distance of each site along its NHD reach (as a proportion). The subdatasets generated in this section are saved to disk, and subsequent runs will read them from disk rather than regenerating them. Functions `calc_reach_prop`, `nhdplusv2_bulk`, and `nhdplusv2_from_comid` are defined in `plot_helpers.R`.
**NOTE: we ended up using watershed areas provided by USGS for most sites (see PART-I),
so this section became obsolete. It's still here in case somebody wants it for future
investigations.**
```{r addtl site data, include = FALSE, eval = FALSE}
site_data_2 = readRDS(here('output_data', 'lotic_site_info_filtered.rds')) %>%
as_tibble() %>%
select(sitecode = Site_ID,
lat = Lat,
lon = Lon,
epsg_crs, COMID, VPU)
#get reach proportional distance for as many sites as possible
got_reach_proportions <- file.exists(here('output_data', 'spatial', 'reach_proportion_reference.rds'))
if(got_reach_proportions){
site_data_2$reach_proportion = readRDS(here('output_data', 'spatial',
'reach_proportion_reference.rds'))
} else {
site_data_2$reach_proportion = NA
remake_reach_proportion_column = FALSE
errflag = FALSE
reach_proportions_needed = which(is.na(site_data_2$reach_proportion))
for(i in reach_proportions_needed){
print(paste(i, length(reach_proportions_needed), sep = '/'))
if(is.na(site_data_2$reach_proportion[i]) || remake_reach_proportion_column){
tryCatch({
rp = calc_reach_prop(VPU = site_data_2$VPU[i],
COMID = site_data_2$COMID[i],
lat = site_data_2$lat[i],
long = site_data_2$lon[i],
CRS = site_data_2$epsg_crs[i],
quiet = TRUE,
force_redownload = FALSE)
}, error = function(e) {print('error'); errflag <<- TRUE} )
if(errflag){
errflag = FALSE
site_data_2$reach_proportion[i] = NA
next
}
} else {
message('already got this one. set remake_reach_proportion_column = TRUE to get it again.')
next
}
site_data_2$reach_proportion[i] = rp
}
saveRDS(site_data_2$reach_proportion, here('output_data', 'spatial',
'reach_proportion_reference.rds'))
}
#retrieve NHDPlusV2 data (will be skipped if output_data/spatial/nhdplusv2_data.rds exists)
if(! file.exists(here('output_data', 'spatial' ,'nhdplusv2_data.rds'))){
#construct list of DSN=component pairs to acquire. see NHDPlus docs for more.
setlist = list('NHDPlusAttributes'='PlusFlowlineVAA',
'NHDPlusAttributes'='ElevSlope')
#retrieve NHDPlusV2 data
nhdplusv2_data = nhdplusv2_bulk(site_data_2, setlist, quiet=TRUE)
#nhd variable names do not have consistent naming conventions. sometimes they're
#all caps; other times camel case. here's a crude way to deal with that.
colnames(nhdplusv2_data) = toupper(colnames(nhdplusv2_data))
nhdplusv2_data = nhdplusv2_data[, ! duplicated(colnames(nhdplusv2_data))]
#choose variables to join; filter dupes
nhdplusv2_data = nhdplusv2_data %>%
as_tibble() %>%
select(COMID, STREAMORDE, FROMMEAS, TOMEAS,
SLOPE, REACHCODE, AREASQKM, TOTDASQKM, MAXELEVSMO,
MINELEVSMO) %>%
group_by(COMID) %>%
summarize_all(first) %>%
ungroup()
site_data_2 = left_join(site_data_2, nhdplusv2_data, by='COMID')
#correct catchment area (AREASQKM) based on where each site falls within its reach.
#use this to correct watershed area (TOTDASQKM) and to determine an areal
#correction factor that can be multiplied with any areal summary data.
site_data_2$AREASQKM_corr = round(site_data_2$AREASQKM * site_data_2$reach_proportion, 5)
site_data_2$TOTDASQKM_corr = site_data_2$TOTDASQKM - (site_data_2$AREASQKM - site_data_2$AREASQKM_corr)
site_data_2$areal_corr_factor = site_data_2$TOTDASQKM_corr / site_data_2$TOTDASQKM
#save progress
saveRDS(site_data_2, here('output_data', 'spatial/nhdplusv2_data.rds'))
}
```
# (Figure 1) GPP-ER biplot and dist plots
See code for details
```{r fig 1, include = FALSE, eval = FALSE}
axis_cex = 2.4 #applies to labels and tick values
jpeg(width=10, height=10, units='in', res=300, quality=100, type='cairo',
filename=here('figures', 'gpp_er_biplot_cumulAnnual.jpeg'))
# par(mar=c(4.5, 4.5, 2, 2))
par(mar=c(6.5, 6.5, 2, 2))
log_gpp_fnet = log(fnet_site$GPP_site_mean)
log_er_fnet = log(fnet_site$ER_site_mean * -1) * -1
log_gpp_sp = log(sp_site$GPP_site_mean)
log_er_sp = log(sp_site$ER_site_mean * -1) * -1
gpptck = c(1:9 %o% 10^(0:3))
ertck = rev(c(1:9 %o% 10^(0:3)))
# plot(log_gpp_fnet,
# log_er_fnet, col=alpha(fnetcolor, alpha=0.5),
plot(log_gpp_fnet[fnet_high_cov_bool],
log_er_fnet[fnet_high_cov_bool], col=alpha(fnetcolor, alpha=0.5),
xlab='', ylab='', bg=alpha(fnetcolor, alpha=0.5),
cex=1.5, cex.lab=axis_cex, cex.axis=axis_cex, ylim=-log(c(5000, 5)),
pch=21, yaxt='n', xaxt='n', xlim=log(c(20, 5000)), lwd=2)
mtext(expression(paste("Annual GPP (g C"~"m"^"-2"*" y"^"-1"*')')),
1, line=5, cex=axis_cex)
mtext(expression(paste("Annual ER (g C"~"m"^"-2"*" y"^"-1"*')')),
2, line=3.5, cex=axis_cex)
# points(log_gpp_sp, log_er_sp, lwd=2,
# col=alpha(spcolor, alpha=0.5), cex=1.5, pch=21, bg=alpha(spcolor, alpha=0.5))
points(log_gpp_sp[! sp_high_cov_bool], log_er_sp[! sp_high_cov_bool], lwd=2,
col=alpha(spcolor, alpha=0.5), cex=1.5, pch=21, bg='transparent')
points(log_gpp_fnet[! fnet_high_cov_bool], log_er_fnet[! fnet_high_cov_bool],
col=alpha(fnetcolor, alpha=0.5), cex=1.5, pch=21, bg='transparent', lwd=2)
points(log_gpp_sp[sp_high_cov_bool], log_er_sp[sp_high_cov_bool], lwd=2,
col=alpha(spcolor, alpha=0.5), cex=1.5, pch=21, bg=alpha(spcolor, alpha=0.5))
legend('topright', legend=c('FLUXNET', 'StreamPULSE'), pch=21, bty='n', pt.cex=1.5,
col=c(alpha(fnetcolor, alpha=0.5), alpha(spcolor, alpha=0.5)), pt.lwd=2,
pt.bg=c(alpha(fnetcolor, alpha=0.5), alpha(spcolor, alpha=0.5)), x.intersp=2)
legend('topright', legend=c('FLUXNET', 'StreamPULSE'), pch=21, bty='n', pt.cex=1.5,
col=c(alpha(fnetcolor, alpha=0.5), alpha(spcolor, alpha=0.5)),
pt.bg='transparent', pt.lwd=2)
all_gpp = c(fnet_site$GPP_site_mean, sp_site$GPP_site_mean)
all_gpp[all_gpp <= 0] = NA
gpprng = range(all_gpp, na.rm=TRUE)
all_er = c(fnet_site$ER_site_mean, sp_site$ER_site_mean)
all_er[all_er >= 0] = NA
errng = range(all_er, na.rm=TRUE)
gpptck_log = log(gpptck)
axis(1, at=gpptck_log, labels=rep('', length(gpptck_log)), cex.axis=axis_cex, padj=0.4, tcl=-0.4)
axis(1, at=log(c(20, 100, 1000, 5000)), labels=c(20, 100, 1000, 5000),
cex.axis=axis_cex, padj=0.4, lwd.ticks=2, tcl=-.6)
ertck_log = log(ertck) * -1
axis(2, at=ertck_log, labels=rep('', length(ertck_log)), cex.axis=axis_cex, padj=0.4, tcl=-0.4)
axis(2, at=log(c(5000, 1000, 100, 10, 1)) * -1, labels=c(5000, 1000, 100, 10, 1) * -1,
cex.axis=axis_cex, padj=0.2, lwd.ticks=2, tcl=-.6)
abline(a=0, b=-1, lty=2)
dev.off()
#--- distplots
jpeg(width=8, height=8, units='in', res=300, quality=100, type='cairo',
filename=here('figures', 'gpp_er_distplots.jpeg'))
par(mfrow=c(2, 1), mar=c(2,1,1,1), oma=c(0, 0, 0, 0))
#GPP
dens = density(na.omit(log_gpp_fnet))
# dens = density(na.omit(log_gpp_fnet[fnet_high_cov_bool]))
gpp_dens_fnet = tibble(x=dens$x, y=dens$y)
dens = density(na.omit(log_gpp_sp))
# dens = density(na.omit(log_gpp_sp[sp_high_cov_bool]))
gpp_dens_sp = tibble(x=dens$x, y=dens$y)
plot(gpp_dens_fnet$x, gpp_dens_fnet$y, type='n', ann=FALSE, xaxt='n', yaxt='n',
bty='n', xlim=c(-2,9.5))
axis(1, at=gpptck_log, labels=gpptck, padj=-1.3, tick = TRUE, line=-0.2, tcl=-0.2)
mtext('GPP', 1, line=1)
polygon(x=c(gpp_dens_fnet$x, rev(gpp_dens_fnet$x)),
y=c(gpp_dens_fnet$y, rep(0, nrow(gpp_dens_fnet))),
col=alpha(fnetcolor, alpha=0.7),
border=alpha(fnetcolor, alpha=0.7))
polygon(x=c(gpp_dens_sp$x, rev(gpp_dens_sp$x)),
y=c(gpp_dens_sp$y, rep(0, nrow(gpp_dens_sp))),
col=alpha(spcolor, alpha=0.7),
border=alpha(spcolor, alpha=0.7))
#ER
dens = density(na.omit(log_er_fnet))
# dens = density(na.omit(log_er_fnet[fnet_high_cov_bool]))
er_dens_fnet = tibble(x=dens$x, y=dens$y)
dens = density(na.omit(log_er_sp))
# dens = density(na.omit(log_er_sp[sp_high_cov_bool]))
er_dens_sp = tibble(x=dens$x, y=dens$y)
plot(er_dens_fnet$x, er_dens_fnet$y, type='n', ann=FALSE, xaxt='n', yaxt='n',
bty='n', xlim=c(2, -9.5))
axis(1, at=ertck_log, labels=ertck, padj=-1.3, tick = TRUE, line=-0.2, tcl=-0.2)
mtext('ER', 1, line=1)
polygon(x=c(rev(er_dens_fnet$x), er_dens_fnet$x),
y=c(rev(er_dens_fnet$y), rep(0, nrow(er_dens_fnet))),
col=alpha(fnetcolor, alpha=0.7),
border=alpha(fnetcolor, alpha=0.7))
polygon(x=c(er_dens_sp$x, rev(er_dens_sp$x)),
y=c(rev(er_dens_sp$y), rep(0, nrow(er_dens_sp))),
col=alpha(spcolor, alpha=0.7),
border=alpha(spcolor, alpha=0.7))
dev.off()
#NEP
nep_fnet = fnet_site$NEP_site_mean
nep_sp = sp_site$NEP_site_mean
jpeg(width=8, height=8, units='in', res=300, quality=100, type='cairo',
filename=here('figures', 'nep_distplot.jpeg'))
par(mfrow=c(2, 1), mar=c(2,1,1,1), oma=c(0, 0, 0, 0))
dens = density(na.omit(nep_fnet))
# dens = density(na.omit(nep_fnet[fnet_high_cov_bool]))
nep_dens_fnet = tibble(x=dens$x, y=dens$y)
dens = density(na.omit(nep_sp))
# dens = density(na.omit(nep_sp[sp_high_cov_bool]))
nep_dens_sp = tibble(x=dens$x, y=dens$y)
plot(nep_dens_sp$x, nep_dens_sp$y, type='n', ann=FALSE, xaxt='n', yaxt='n',
bty='n', xlim=c(-1723, 2116))
axis(1, padj=-1.3, tick = TRUE, line=-0.2, tcl=-0.2)
mtext('NEP', 1, line=1)
polygon(x=c(rev(nep_dens_fnet$x), nep_dens_fnet$x),
y=c(rev(nep_dens_fnet$y), rep(0, nrow(nep_dens_fnet))),
col=alpha(fnetcolor, alpha=0.7),
border=alpha(fnetcolor, alpha=0.7))
polygon(x=c(nep_dens_sp$x, rev(nep_dens_sp$x)),
y=c(rev(nep_dens_sp$y), rep(0, nrow(nep_dens_sp))),
col=alpha(spcolor, alpha=0.7),
border=alpha(spcolor, alpha=0.7))
dev.off()
```
# (Figures 2 and 3) lips plots and probability densities
See code for details
```{r Figs 2 and 3, include = FALSE, eval = FALSE}
dir.create(here('figures' ,'lips'), showWarnings = FALSE)
dir.create(here('figures', 'lips', 'lips_cross'), showWarnings = FALSE)
dir.create(here('figures', 'probdens'), showWarnings = FALSE)
axis_cex = 2.4 #applies to labels and tick values
lips_plot = function(quant_filt=NULL, outfile, use_fnet=FALSE, ylims=NULL,
wee=FALSE){
dset = if(use_fnet) fnet_lips else sp_lips
dset_site = if(use_fnet) fnet_site else sp_site
var_quant_filt = NULL
if(! is.null(quant_filt)){
filter_expr <- c()
for(i in seq_along(quant_filt)){
quant_comp = strsplit(quant_filt[i], ' ')[[1]]
qf = quantile(dset_site[, quant_comp[1], drop=TRUE], na.rm=TRUE,
probs = as.numeric(quant_comp[3]))
filter_expr = c(filter_expr,
paste(quant_comp[1], quant_comp[2], qf))
}
if(length(filter_expr) > 1){
filter_expr = paste(filter_expr, collapse = ' & ')
}
filt_sites = dset_site %>%
filter_(filter_expr) %>%
pull(sitecode)
dset = filter(dset, sitecode %in% filt_sites)
}
nsites_included = length(unique(dset$sitecode))
smry = dset %>%
select(DOY, GPP_C_filled, ER_C_filled, NEP_C_filled) %>%
group_by(DOY) %>%
summarize_all(list(median=~median(., na.rm=TRUE),
quant25=~quantile(., na.rm=TRUE)[2],
quant75=~quantile(., na.rm=TRUE)[4])) %>%
ungroup()
jpeg(width=11, height=11, units='in', filename=outfile, type='cairo',
res=300, quality=100)
# pdf(file=outfile, width=10, height=10)
if(! is.null(ylims)){
# plot_dims = top_dims = dev.size('in')
# top_dims[2] = plot_dims[2] * abs(ylims[2]) / sum(abs(ylims))
gpplim = c(0, ylims[2])
erlim = c(ylims[1], 0)
} else {
# top_dims = dev.size('in')
gpplim=c(0, 5)
erlim=c(-5, 0)
if(use_fnet){
gpplim[2] = 11
erlim[1] = -7
}
}
if(wee){
axis_cex_ = axis_cex * 2
line_ = -1.5
par(mfrow=c(2, 1), oma=c(1, 1, 0, 0), mar=c(0, 5, 3, 1), lend=2)
} else {
axis_cex_ = axis_cex
line_ = -2
par(mfrow=c(2, 1), oma=c(1, 1, 0, 0), mar=c(0, 5, 3, 1), lend=2)
}
# fin=top_dims, new=TRUE)
plot(smry$DOY, smry$GPP_C_filled_median, ylab='', yaxs='i', type='l',
bty='n', lwd=4, xlab='', ylim=gpplim, xaxs='i', xaxt='n', yaxt='n',
col='gray30')
polygon(x=c(smry$DOY, rev(smry$DOY)),
y=c(smry$GPP_C_filled_quant25, rev(smry$GPP_C_filled_quant75)),
border=NA, col=alpha('forestgreen', alpha=0.6))
axislocs = if(max(ylims) >= 10) seq(0, 10, 2) else c(0, 2, 4)
axis(2, las=0, line=0, xpd=NA, tck=-.02, labels=FALSE,
at=axislocs, cex.axis=axis_cex_, tcl=-0.3)
# at=round(seq(0, gpplim[2], length.out=5), 1))
axis(2, las=0, line=-0.5, xpd=NA, tcl=0, col='transparent',
at=axislocs, cex.axis=axis_cex_)
# at=round(seq(0, gpplim[2], length.out=5), 1))
abline(h=0, lty=2, lwd=2, col='gray60')
medsums = round(colSums(select(smry, contains('median'))), 1)
if(! wee){
mtext(expression(paste(bold("g C") ~ bold("m") ^ bold("-2") *
bold(" d") ^ bold("-1"))), side=2,
line=line_, outer=TRUE, cex=axis_cex_)
}
# if(filter_label){
# legend('topright', title='Filters', bty='n', title.col='gray30',
# lty=1, seg.len=0.2, lwd=2, legend=c(..., var_quant_filt))
# }
legend('right', title='Cumulative\nMedian Sums', bty='n',
legend=c(paste('GPP:', medsums[1]), paste('ER:', medsums[2]),
paste('NEP:', medsums[3])), title.col='gray30')
legend('left', paste('Sites included:', nsites_included), bty='n')
# if(! is.null(ylims)){
# plot_dims = bottom_dims = dev.size('in')
# bottom_dims[2] = plot_dims[2] * abs(ylims[1]) / sum(abs(ylims))
# } else {
# bottom_dims = dev.size('in')
# }
par(mar=c(4, 5, 0, 1))#, fin=bottom_dims)
if(wee){
padj_ = 0.8
DOY1 = ''
} else {
padj_ = 0.5
DOY1 = 1
}
plot(smry$DOY, smry$ER_C_filled_median, ylab='', yaxs='i', type='l',
bty='n', lwd=4, xlab='', ylim=erlim, xaxs='i', xaxt='n', yaxt='n')
polygon(x=c(smry$DOY, rev(smry$DOY)),
y=c(smry$ER_C_filled_quant25, rev(smry$ER_C_filled_quant75)),
border=NA, col=alpha('sienna', alpha=0.6))
if(max(ylims) >= 10){
axislocs = seq(0, -10, -2)
axisextra = -11
} else {
axislocs = c(0, -2, -4)
axisextra = NULL
}
axis(2, las=0, line=0, xpd=NA, tck=-.02, labels=FALSE,
# at=round(seq(0, erlim[1], length.out=5), 1))
at=c(axislocs, axisextra), cex.axis=axis_cex_, tcl=-0.3)
axis(2, las=0, line=-0.5, xpd=NA, tcl=0, col='transparent',
# at=round(seq(0, erlim[1], length.out=5), 1))
at=axislocs, cex.axis=axis_cex_)
axis(1, line=0, tck=-.02, labels=FALSE, at=c(1, seq(60, max(smry$DOY), 60)),
cex.axis=axis_cex_, tcl=-0.3)
axis(1, line=-0.5, tcl=0, col='transparent', at=c(1, seq(60, max(smry$DOY), 60)),
cex.axis=axis_cex_, padj = padj_, tcl=-0.3,
labels = c(DOY1, seq(60, max(smry$DOY), 60)))
lines(smry$DOY, smry$NEP_C_filled_median, col='black', lwd=4, xpd=NA, lend=1)
if(! wee){
mtext('DOY', side=1, line=3.5, font=2, cex=axis_cex_)
}
dev.off()
}
pdf_plot = function(var, outfile){
jpeg(width=7, height=4, units='in', filename=outfile, type='cairo',
res=300, quality=100)
# pdf(outfile, height=4, width=7)
vv = na.omit(sort(sp_site[[var]]))
dens = density(vv)
vq = quantile(vv, probs=c(0.25, 0.75))
densdf = tibble(x=dens$x, y=dens$y)
dens25 = dens75 = densdf
dens25 = dens25[densdf$x <= vq[1], ]
dens75 = dens75[densdf$x >= vq[2], ]
plot(densdf$x, densdf$y, type='l', xlab='width', ylab='density', bty='l',
col='gray50', lwd=2)
polygon(x=c(dens25$x, rev(dens25$x)),
y=c(dens25$y, rep(0, nrow(dens25))), col='gray50', border='gray50')
polygon(x=c(dens75$x, rev(dens75$x)),
y=c(dens75$y, rep(0, nrow(dens75))), col='gray50', border='gray50')
dev.off()
}
#overall
lips_ylim = c(-11, 11)
lips_plot(quant_filt=NULL, outfile=here('figures', 'lips', 'lips_overall_sp.jpeg'),
ylims=lips_ylim)
lips_plot(quant_filt=NULL, outfile=here('figures', 'lips', 'lips_overall_fnet.jpeg'),
use_fnet=TRUE, ylims=lips_ylim)
pdf_plot('Disch_ar1', here('figures', 'probdens', 'probdens_Qar1.jpeg'))
pdf_plot('MOD_ann_NPP', here('figures', 'probdens', 'probdens_MODIS.jpeg'))
pdf_plot('Stream_PAR_sum', here('figures', 'probdens', 'probdens_PAR.jpeg'))
#subsets
lips_ylim = c(-5, 5)
lips_plot(quant_filt='Disch_skew > 0.75', outfile=here('figures', 'lips', 'lips_Qskew_75.jpeg'),
ylims=lips_ylim, wee=TRUE)
lips_plot(quant_filt='Disch_skew < 0.25', outfile=here('figures', 'lips', 'lips_Qskew_25.jpeg'),
ylims=lips_ylim, wee=TRUE)
lips_plot(quant_filt='MOD_ann_NPP > 0.75', outfile=here('figures', 'lips', 'lips_MODIS_75.jpeg'),
ylims=lips_ylim, wee=TRUE)
lips_plot(quant_filt='MOD_ann_NPP < 0.25', outfile=here('figures', 'lips', 'lips_MODIS_25.jpeg'),
ylims=lips_ylim, wee=TRUE)
lips_plot(quant_filt='Stream_PAR_sum > 0.75', outfile=here('figures', 'lips', 'lips_PAR_75.jpeg'),
ylims=lips_ylim, wee=TRUE)
lips_plot(quant_filt='Stream_PAR_sum < 0.25', outfile=here('figures', 'lips', 'lips_PAR_25.jpeg'),
ylims=lips_ylim, wee=TRUE)
#cross-subsets
lips_plot(quant_filt = c('Stream_PAR_sum < 0.25', 'Disch_skew < 0.25'),
outfile = 'figures/lips/lips_cross/lips_PAR25-Qskew25.jpeg',
ylims = lips_ylim,
wee = TRUE)
lips_plot(quant_filt = c('Stream_PAR_sum < 0.25', 'Disch_skew > 0.75'),
outfile = 'figures/lips/lips_cross/lips_PAR25-Qskew75.jpeg',
ylims = lips_ylim,
wee = TRUE)
lips_plot(quant_filt = c('Stream_PAR_sum > 0.75', 'Disch_skew > 0.75'),
outfile = 'figures/lips/lips_cross/lips_PAR75-Qskew75.jpeg',
ylims = lips_ylim,
wee = TRUE)
lips_plot(quant_filt = c('Stream_PAR_sum > 0.75', 'Disch_skew < 0.25'),
outfile = 'figures/lips/lips_cross/lips_PAR75-Qskew25.jpeg',
ylims = c(-5, 5.6),
wee = TRUE)
```
# (Figure 4) bubble plots
See code for details
```{r fig 4, include = FALSE, eval = FALSE}
dir.create(here('figures', 'bubble_plots'), showWarnings = FALSE)
bubble_plot = function(xvar, comp, logx=FALSE, outfile){
jpeg(width=8, height=8, units='in', filename=outfile, type='cairo',
res=300, quality=100)
# pdf(outfile, height=8, width=8)
par(mar=c(5, 5, 4, 6))
plotcol = case_when(comp == 'GPP_site_mean' ~ gppcolor,
comp == 'ER_site_mean' ~ ercolor,
comp == 'NEP_site_mean' ~ 'black')
if(xvar == 'Stream_PAR_sum'){
xxlab = 'Light Availability (Mean Annual Surface PAR)'
} else {
xxlab = 'MODIS NPP'
}
gpprng = range(sp_site$GPP_site_mean, na.rm=TRUE)
if(comp == 'ER_site_mean'){
xx = abs(sp_site[[comp]])
} else {
xx = sp_site[[comp]]
}
if(comp != 'NEP_site_mean'){
rescaled = ((xx - gpprng[1]) /
(gpprng[2] - gpprng[1])) * (5 - 1) + 1
} else {
xxrng = range(xx, na.rm=TRUE)
# rescaled = ((xx - xxrng[1]) /
# (xxrng[2] - xxrng[1])) * (3) + 0.2
rescaled = ((xx - -2000) /
(1000 - -2000)) * (4) + 0.5
}
xxvar = sp_site[[xvar]]
if(logx){
xxvar = log(xxvar)
if(xvar == 'Stream_PAR_sum'){
xlm = c(.5, 2.8)
} else {
xlm = c(3.95, 7.2)
}
} else {
if(xvar == 'Stream_PAR_sum'){
xlm = c(2, 15.5)
} else {
xlm = range(xxvar, na.rm=TRUE)
}
}
plot(xxvar,
sp_site$Disch_ar1, pch=21,
# plot(xxvar[sp_high_cov_bool],
# sp_site$Disch_ar1[sp_high_cov_bool], pch=21,
xlab=xxlab, xaxt='n',
ylab='Predictability of Flow (Discharge AR-1 Coeff.)',
col=alpha(plotcol, alpha=0.5), bty='o',
ylim=c(0.1, 1), xlim=xlm,
xpd=NA, main='', cex=rescaled, font.lab=2,
# xpd=NA, main='', cex=rescaled[sp_high_cov_bool], font.lab=2,
bg=alpha(plotcol, alpha=0.5))
# points(xxvar[! sp_high_cov_bool],
# sp_site$Disch_ar1[! sp_high_cov_bool], pch=21,
# col=alpha(plotcol, alpha=0.5), bg='transparent',
# cex=rescaled[! sp_high_cov_bool])
if(comp != 'NEP_site_mean'){
legend('right', legend=c(expression(paste(0.01)), '', '',
expression(paste(4000))),
bty='n', pch=21, pt.bg='transparent', x.intersp=1.7,
pt.cex=c(1, 2, 3, 5), col='gray30', xpd=NA,
y.intersp=c(1, 2, 1.2, 1.6),
inset=c(-0.16, 0), title='')
} else {
legend('right', legend=c(expression(paste(-2000)), '', '',
expression(paste(1000))),
bty='n', pch=21, pt.bg='transparent', x.intersp=1.7,
pt.cex=seq(0.5, 4, length.out=4), col='gray30', xpd=NA,
y.intersp=c(1, 2, 1.2, 1.6),
inset=c(-0.18, 0), title='')
}
if(comp == 'GPP_site_mean'){
legend('right', legend=c('','','',''),
bty='n', pch=21, pt.bg='transparent', x.intersp=1.5,
pt.cex=c(1, 2, 3, 5), col='transparent', xpd=NA,
inset=c(-0.15, 0),
title=expression(paste(bold('Cumul.\nAnnual\nGPP'))))
} else if(comp == 'ER_site_mean'){
legend('right', legend=c('','','',''),
bty='n', pch=21, pt.bg='transparent', x.intersp=1.5,
pt.cex=c(1, 2, 3, 5), col='transparent', xpd=NA,
inset=c(-0.15, 0),
title=expression(paste(bold('Cumul.\nAnnual\nER'))))
} else {
legend('right', legend=c('','','',''),
bty='n', pch=21, pt.bg='transparent', x.intersp=1.5,
pt.cex=c(1, 2, 3, 5), col='transparent', xpd=NA,
inset=c(-0.15, 0),
title=expression(paste(bold('Cumul.\nAnnual\nNEP'))))
}
if(logx){
if(xvar == 'Stream_PAR_sum'){
tcks = c(1, 2, 4, 8, 16)
} else {
tcks = c(200, 400, 800, 1600, 3200)
}
tcks_log = log(tcks)
axis(1, at=tcks_log, labels=tcks)
} else {
axis(1)
}
dev.off()
}
bubble_plot(xvar='Stream_PAR_sum', comp='GPP_site_mean', logx=FALSE,
outfile=here('figures', 'bubble_plots', 'PAR_GPP_linear.jpeg'))
bubble_plot(xvar='Stream_PAR_sum', comp='GPP_site_mean', logx=TRUE,
outfile=here('figures', 'bubble_plots', 'PAR_GPP_log.jpeg'))
bubble_plot(xvar='Stream_PAR_sum', comp='ER_site_mean', logx=FALSE,
outfile=here('figures', 'bubble_plots', 'PAR_ER_linear.jpeg'))
bubble_plot(xvar='Stream_PAR_sum', comp='ER_site_mean', logx=TRUE,
outfile=here('figures', 'bubble_plots', 'PAR_ER_log.jpeg'))
bubble_plot(xvar='Stream_PAR_sum', comp='NEP_site_mean', logx=FALSE,
outfile=here('figures', 'bubble_plots', 'PAR_NEP_linear.jpeg'))
bubble_plot(xvar='Stream_PAR_sum', comp='NEP_site_mean', logx=TRUE,
outfile=here('figures', 'bubble_plots', 'PAR_NEP_log.jpeg'))
bubble_plot(xvar='MOD_ann_NPP', comp='GPP_site_mean', logx=FALSE,
outfile=here('figures', 'bubble_plots', 'MODNPP_GPP_linear.jpeg'))
bubble_plot(xvar='MOD_ann_NPP', comp='GPP_site_mean', logx=TRUE,
outfile=here('figures', 'bubble_plots', 'MODNPP_GPP_log.jpeg'))
bubble_plot(xvar='MOD_ann_NPP', comp='ER_site_mean', logx=FALSE,
outfile=here('figures', 'bubble_plots', 'MODNPP_ER_linear.jpeg'))
bubble_plot(xvar='MOD_ann_NPP', comp='ER_site_mean', logx=TRUE,
outfile=here('figures', 'bubble_plots', 'MODNPP_ER_log.jpeg'))
bubble_plot(xvar='MOD_ann_NPP', comp='NEP_site_mean', logx=FALSE,
outfile=here('figures', 'bubble_plots', 'MODNPP_NEP_linear.jpeg'))
bubble_plot(xvar='MOD_ann_NPP', comp='NEP_site_mean', logx=TRUE,
outfile=here('figures', 'bubble_plots', 'MODNPP_NEP_log.jpeg'))
```
# export regression/SEM dataset
Combine site data compiled in parts II and III with coverage metrics, export for further analyses (See section IV).
```{r export statset, include = FALSE, eval = FALSE}
site_data = readRDS(here('output_data', 'lotic_site_info_filtered.rds')) %>%
as_tibble() %>%
rename(sitecode = Site_ID)
# site_data_B = readRDS(here('output_data' ,'spatial' ,'nhdplusv2_data.rds')) %>%
# select(sitecode, lat, lon, stream_order = STREAMORDE, slope = SLOPE,
# ws_area_km2 = TOTDASQKM_corr) %>%
# filter(sitecode != 'nwis_05406457') #duplicate of WI_BEC
# site_data = full_join(site_data_A, site_data_B)
stats_set = site_data %>%
right_join(coverage_sp) %>%
arrange(sitecode)
write.csv(stats_set, here('export_datasets' ,'streampulse_synthesis_statset.csv'),
row.names = FALSE)
```
# (Figure X) interannual CVs
This figure may end up in the main text, an appendix, or nowhere. (jotting this here in case I forget to update after we decide what to do with figure X)
```{r Figure X, include = FALSE, eval = FALSE}
fnet_cvs = fnet_full %>%
# mutate(NEP = GPP + ER) %>%
group_by(sitecode, Year) %>%
summarize(across(c(GPP, ER), sum, na.rm = TRUE)) %>%
filter(n() > 3) %>%
ungroup() %>%
group_by(sitecode) %>%
summarize(across(c(GPP, ER),
~sd(., na.rm = TRUE) / mean(., na.rm = TRUE) * 100),
.groups = 'drop')
sp_cvs = sp_full %>%
rename(GPP = GPP_C_filled,
ER = ER_C_filled) %>%
# mutate(NEP = GPP + ER) %>%
group_by(sitecode, Year) %>%
summarize(across(c(GPP, ER), sum, na.rm = TRUE)) %>%
filter(n() > 3) %>%
ungroup() %>%
group_by(sitecode) %>%
summarize(across(c(GPP, ER),
~sd(., na.rm = TRUE) / mean(., na.rm = TRUE) * 100),
.groups = 'drop')
probdens2 <- function(d_sp, d_fnet, v){
dens_sp = density(na.omit(d_sp[[v]]))
dens_sp = tibble(x=dens_sp$x, y=dens_sp$y)
dens_fnet = density(na.omit(d_fnet[[v]]))
dens_fnet = tibble(x=dens_fnet$x, y=dens_fnet$y)
xlims = range(c(range(dens_sp$x), range(dens_fnet$x)))
plot(dens_sp$x, dens_sp$y, type='n', ann=FALSE, yaxt='n',# xaxt='n',
bty='n', xlim=xlims)
# tck = c(0.1, 1, 10, 100, 1000, 10000, 100000)
# # ertck = rev(c(10000, 1000, 100, 10, 1, ))
# tck_log = log(tck)
# # axis(1, at=tck_log, labels=tck, cex.axis=2.4, padj=0.4)
# # ertck_log = log(ertck) * -1
# # axis(2, at=ertck_log, labels=ertck * -1, cex.axis=2.4, padj=0.2)
# axis(1, at=tck_log, labels=tck, padj=-1.3, tick = TRUE, line=-0.2, tcl=-0.2)
mtext(paste(v), 1, line=-15)
polygon(x=c(dens_fnet$x, rev(dens_fnet$x)),
y=c(dens_fnet$y, rep(0, nrow(dens_fnet))),
col=alpha(fnetcolor, alpha=0.7),
border=alpha(fnetcolor, alpha=0.7))
polygon(x=c(dens_sp$x, rev(dens_sp$x)),
y=c(dens_sp$y, rep(0, nrow(dens_sp))),
col=alpha(spcolor, alpha=0.7),
border=alpha(spcolor, alpha=0.7))
}
jpeg(width=8, height=8, units='in', res=300, quality=100, type='cairo',
filename=here('figures', 'interannual_CVs.jpg'))
par(mfrow=c(2, 1), mar=c(2,1,1,1), oma=c(0, 0, 0, 0))
probdens2(sp_cvs, fnet_cvs, 'GPP')
# jpeg(width=8, height=8, units='in', res=300, quality=100, type='cairo',
# filename='figures/interannual_CVs/ER.jpg')
# par(mfrow=c(2, 1), mar=c(2,1,1,1), oma=c(0, 0, 0, 0))
probdens2(sp_cvs, fnet_cvs, 'ER')
dev.off()
```
# (Figure Y) peak months
This figure may end up in the main text, an appendix, or nowhere. (jotting this here in case I forget to update after we decide what to do with figure Y)
```{r Figure Y, include = FALSE, eval = FALSE}
dir.create(here('figures', 'peak_months'), showWarnings = FALSE)
library(lubridate)
error.bars <- function(x, y, upper, lower=upper, cap.length=0.1, horiz=F,...){
if(length(x) != length(y) | length(y) !=length(lower) | length(lower) != length(upper))
stop("One or more vectors is not the same length")
if(horiz==F) {
arrows(x,y+upper, x, y-lower, angle=90, code=3, length=cap.length, ...)
} else if (horiz==T) {
arrows(x+upper,y, x-lower, y, angle=90, code=3, length=cap.length, ...)
}
}
sitemonths_sp = sp_full %>%
rename(GPP = GPP_C_filled,
ER = ER_C_filled) %>%
mutate(month = month(Date)) %>%
group_by(sitecode, Year, month) %>%
summarize(across(c(GPP, ER), mean, na.rm = TRUE),
.groups = 'drop') %>%
group_by(sitecode, month) %>%
summarize(across(c(GPP, ER), #mean, na.rm = TRUE),
list(mean = ~mean(., na.rm = TRUE),
sd = ~sd(., na.rm = TRUE))),
n = n(),
.groups = 'drop')
sitemonths_fnet = fnet_full %>%
mutate(month = month(Date)) %>%
group_by(sitecode, Year, month) %>%
summarize(across(c(GPP, ER), mean, na.rm = TRUE),
.groups = 'drop') %>%
group_by(sitecode, month) %>%
summarize(across(c(GPP, ER), #mean, na.rm = TRUE),
list(mean = ~mean(., na.rm = TRUE),
sd = ~sd(., na.rm = TRUE))),
n = n(),
.groups = 'drop')
#GPP
maxmonths_etc_sp_gpp = sitemonths_sp %>%
group_by(sitecode) %>%
filter(GPP_mean == max(GPP_mean),
n == max(n)) %>%
summarize(GPP_mean = first(GPP_mean),
GPP_sd = first(GPP_sd),
n = first(n),
month = first(month),
.groups = 'drop') %>%
mutate(SE = GPP_sd / sqrt(n))
maxmonths_etc_fnet_gpp = sitemonths_fnet %>%
group_by(sitecode) %>%
filter(GPP_mean == max(GPP_mean),
n == max(n)) %>%
summarize(GPP_mean = first(GPP_mean),
GPP_sd = first(GPP_sd),
n = first(n),
month = first(month),
.groups = 'drop') %>%
mutate(SE = GPP_sd / sqrt(n))
maxmonths_sp_gpp = c('1' = 0, table(maxmonths_etc_sp_gpp$month), '12' = 0)
maxmonths_fnet_gpp = table(maxmonths_etc_fnet_gpp$month)
maxmonths_gpp = matrix(c(maxmonths_fnet_gpp, maxmonths_sp_gpp),
nrow = 2, byrow = TRUE,
dimnames = list(c('FLUXNET', 'StreamPULSE'),
month.abb))
jpeg(width=8, height=8, units='in', res=300, quality=100, type='cairo',
filename=here('figures', 'peak_months/GPP.jpg'))
barplot(maxmonths_gpp, border = NA, beside = TRUE,
col = c(fnetcolor, spcolor),
ylab = 'n', main = 'Highest GPP months')
legend(x=25, y=60, legend=c('FLUXNET', 'StreamPULSE'),
fill = c(fnetcolor, spcolor), bty = 'n', border = NA)
text(x=25, y=52, adj = 0, paste('n FLUXNET =', sum(maxmonths_fnet_gpp)))
text(x=25, y=49, adj = 0, paste('n StreamPULSE =', sum(maxmonths_sp_gpp)))