Exploring turbidity fluctuations"

In loadflux: Explore Intra-Event Suspended Sediment Dynamics

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

This is an example workflow of how to perform basic turbidity analysis with loadflux package and tidyverse ecosystem.

library(dplyr)
library(purrr)
library(tidyr)
library(tsibble)
library(loadflux)

Hydrological Events

First, we need to demarcate hydrological events

data(djanturb)
df <- hydro_events(dataframe = djanturb,
                   q = discharge,
                   datetime = time,
                   window = 21)

df %>% 
  event_plot(q = discharge,
             datetime = time,
             he = he,
             ssc = ntu,
             y2label = "Turbidity")

Turbidity Index

Then we can calculate Turbidity Index TI for every hydrological event

TI_index <- df %>% 
  group_by(he) %>% 
  nest() %>% 
  mutate(TI = map_dbl(data, ~TI(.x, ntu, time))) %>% 
  select(-data) %>% 
  ungroup()

TI_index

Hydrological events parameters

To summarize the hydrological events parameters an approach from features package can be used. For this purpose we need to transform our dataframe into tsibble object:

library(tsibble)

df_ts <- df %>% 
  as_tsibble(key = he,
             index = time)

df_ts

Then we can calculate start, end and length of the every hydrological event:

library(feasts)

df_ts %>% 
  features(time,
           feat_event)

Or with the help of brolgar and feasts packages we can calculate turbidity statistics, autocorrelation and spectral functions:

library(brolgar)
library(feasts)

df_ts %>% 
  features(ntu, feat_five_num)

df_ts %>% 
  features(ntu, c(feat_spectral,
                  feat_acf))

loadflux documentation built on Nov. 5, 2021, 5:07 p.m.