Observed data are essential to understand, quantify and predict the risk posed by extreme climatic events. In the case of floods or heavy precipitation, such data take the form of long series measured at stream gauging or weather stations.
In a previous post, we illustrated a widely used method called principal component analysis (PCA). This method can be used as an exploratory tool to summarise a dataset made of hundreds or thousands of time series into just a few ‘summary’ time series called principal components.
In hydrology, it is frequent to analyse long time series coming from many sites. The figure below shows monthly streamflows at 207 sites in France for the period 1969-2014.
Investigating the hydrologic regime of 195 rivers in Australia revealed three types of flow seasonality. At some stations, the wettest period occurs during one particular season (winter or summer), whereas at others, similar flows are observed all year round.
Plotting hydrologic regimes Most hydrologic studies start by determining the average quantity of water in rivers and how it is distributed throughout the year.