Time series is a sequence of values collected over time on a particular variable. A time series can consist of the values of a variable observed at discrete times, averaged over a given time interval, or recorded continuously with time (Machiwal & Jha 2012). It may consist of only deterministic events, only stochastic events, or a combination of deterministic and stochastic events (Machiwal & Jha 2012). Stochastic events refer to random events and deterministic means that no randomness is involved and, generally, a hydrologic time series is composed of a stochastic component superimposed on a deterministic component (Machiwal & Jha 2012).
Time series in hydrology can be analyzed to a) detect a trend due to another random hydrologic variable, b) develop and calibrate a model, c) predict future characteristics of a variable (Machiwal & Jha 2012). The application of time series analysis is diverse; for instance, it can be used to evaluate global trends of soil moisture (Dorigo et al. 2012), to analyze river discharges (Papa et al. 2012), to detect glacial lake outburst floods (Veh et al. 2018) or to detect rainfall patterns (Wang et al. 2016).
The visualization of the data variability over the time can be a useful tool to identify patterns or to compare the behavior of different samples. The use of software for Geographic Information Systems (GIS) allows to identify the location of the samples and to compile the information that the samples have. Open-source software like QGIS offers excellent tools to achieve this objective. This tutorial will explain how to use the Time Manager Plugin.
To begin with, go to Plugins/ Manage and Install Plugins :
In the Search bar, write TimeManager and click on Install plugin :
Make sure that you have a Date column in your attribute table and that it follows the format YYYY-MM-DD HH:MM:SS :
Change the symbology to Heatmap so we can have a better visualization of the data. Make sure to select Weight points by SELECT THE ATRIBUTE OF INTEREST, in this example, the attribute of interest is called NORMAL. If you want to have a basemap change the opacity of the layer so you can see through it:
For this instance, we will label the layer with the precipitation values, which are in the ‘NORMAL’ column. Open the Expression Dialog and write NORMAL, the string concatenation symbol || and the letters ‘ mm’:
We can add a Buffer to highlight the label:
Now, go to Time Manager and click on Settings:
Select the layer of interest, in this case it will be Climate1, the start time will be the column with the correct date format and the end time as ‘Same as start’:
According on your time frame, select the one that fits best with your investigation. For this instance, months is the best one, since we have monthly data:
You can change the speed of the frame using the animation options, the greater the number of milliseconds, the slower the video:
In summary, using QGIS to visualize time series is an excellent option. It works for different purposes, including hydrological studies.
You can download the input data for this tutorial here.
- Dorigo, W. et al., 2012. Evaluating global trends (1988-2010) in harmonized multi-satellite surface soil moisture. Geophysical Research Letters, 39(18).
Machiwal, D. & Jha, M.K., 2012. Hydrologic time series analysis: theory and practice, Springer Science \& Business Media.
- Papa, F. et al., 2012. Ganga-Brahmaputra river discharge from Jason-2 radar altimetry: an update to the long-term satellite-derived estimates of continental freshwater forcing flux into the Bay of Bengal. Journal of Geophysical Research: Oceans, 117(C11).
Veh, G. et al., 2018. Detecting Himalayan glacial lake outburst floods from Landsat time series. Remote Sensing of Environment, 207, pp.84–97.
- Wang, X. et al., 2016. An Adaptive Density-Based Time Series Clustering Algorithm: A Case Study on Rainfall Patterns. ISPRS International Journal of Geo-Information, 5(11), p.205.