This exercise is an extension from the other two exercises. We will focus on how to manage unique data sets from logger, in which record the hydro-logical data with specific time intervals required. The data set used is taken from half-hourly flow and rainfall records of the River Hirnant, Wales-UK from November to December 1972 here (Andrews & Herzberg,1985). In this exercise, we will discuss the threshold analysis in hydrology. Threshold is often used as a starting point to analyze hydro-logical behavior of an event. For example, flow threshold for flood identification and temperature threshold for drought analysis.
Answers to the exercises are available here. If you obtained a different (correct) answer than those listed on the solutions page, please feel free to post your answer as a comment on that page.
Please install and load the lubridate package before starting this exercise.
Exercise 1
Load the data set. Name it hirnant and tidy it up, including column re-name, dismiss NA value, delete unnecessary rows and columns, and change flow and rain value to numeric.
Exercise 2
Convert time into POSIXct class using the strptime function with time zone consideration (Europe/London.)
Exercise 3
Create a plot that shows half-hourly and daily means of flow in one graphic:
A. Calculate the data frame of daily statistic value, including mean, min, max and standard deviation using the tapply function. Name it as dailyMean, dailyMin, dailyMax, dailySD.
B. Create a list of days (in a data frame) in which observations occurred using the unique function; use floor_date to round down the dates. Name it wholeDays.
C. Create new and tidy data frames consisting of 2a and 2b; name it dailyFlow.
D. Create a simple plot of the dailyFlow and set an axis to display weekly tick labels and date stamps every two weeks.
E. Plot the daily means on top using a double-thickness line.
Exercise 4
Create a ribbon plot showing the mean flow and its standard deviation.
A. Calculate upper and lower standard deviation bounds; name it upperSD and lowerSD.
B. Create an empty plot “Flow vs. Time” of the hirnant data frame.
C. Set the axis to display weekly tick labels and date stamps every two weeks.
D. Draw a standard deviation ribbon using the polygon function.
E. Plot the daily means on top using a double-thickness line.
Threshold Analysis and Visualization
Exercise 5
Data preparation
A. Use the aggregate function to get an hourly mean flow.
B. Create a new hourly time vector and use it for creating a new tidy data frame.
C. Add a new column to hourlyFlow named thresholdTemp.
D. Replace all observations lower than 23 with zero.
E. Sum up the number of hours on which 23 was exceeded using the tapply function. Add it as a new column to dailyFlow named flowhours23.
Plotting
A. Create an empty plot, setting up the x-axis ticks to appear weekly with date stamps every two weeks.
B. Re-sample the dates and flow hour variables at a 1 minute resolution using the approx function.
C. Use the polygon function to draw the red-shaded area.
D. Plot the daily flow hours > 23 m3/s using a double thickness line.
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