Re-sampling Time Series Dataset in Hydrology: Exercises

Robust data is a key for robust result in hydrological analysis. Problems that often happen with time series data analysis are missing value and different time scale measurement. Those two problems can be solved by doing spatial/temporal interpolation and or/ spatial modelling.
In this exercise, we will discuss how to resample time series dataset with NA value and different time scale measurement. We will use two datasets with different time scale measurement. Flow observations were measured in 30 minutes interval while river temperature in 15 minutes interval.
The most common used method is cubic spline. A cubic spline essentially fits a curve through a set of data points, and then allows you to then specify the locations along the curve for which you would like to generate data.

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.
Dataset is generated from previous exercise.
flow
temp

Please install and load lubridate, imputeTS, hydroTSM, hydroGOF package before starting the exercise

Exercise 1
Load data and tidying it up including delete unnecessary column, converting time to POSIXct class

Exercise 2
create a spline function (essentially the equation for a curve plotted through the observed temperature)

Exercise 3
evaluate tempSpline at times where we have observed flow to give approximate temperature for each of these points

Exercise 4
creating a new 15-minute data frame containing both the temperature and flow

Exercise 5
plot temperature and set axis ticks to weekly intervals with date stamps every two weeks

Exercise 6
plot the discharge on a secondary axis. Here, we use the par command to tell R to use the same window for the new plot and draw legend

Learn more about time series and Forecasting in the online course Time Series Analysis and Forecasting in R. In this course you will learn how to:

• A complete introduction on Forecasting
• Work thru an exponentional smoothing instruction
• And much more

Exercise 7
Load new dataset for analysing missing value
flow

Exercise 8
Check NA value composition and fill the missing values using imputation

Exercise 9
using na.interpolation for imputation; use three method including interpolation,Kalman and mean method

Exercise 10
Join date and time, the original and prediction value into one data frame

Exercise 11
use ggof function to plot the original and prediction value

Related exercise sets:

Spatial Data Analysis: Introduction to Raster Processing (Part 1) Spatial Data Analysis: Introduction to Raster Processing: Part-3 Density-Based Clustering Exercises Data Manipulation with data.table (part -2) Big Data Manipulation in R Exercises