Applications of MIDAS regression in analysing trends in water quality

- We analyse the impact of rainfall and flow on trends in water quality.
- We use a flexible mixed data sampling approach to deal with the mixed data frequency.
- We propose a flexible parsimonious model of the impact of rainfall and flow on trend.
- Testing on a water quality data set illustrates the benefits of using the method.

SummaryWe discuss novel statistical methods in analysing trends in water quality. Such analysis uses complex data sets of different classes of variables, including water quality, hydrological and meteorological. We analyse the effect of rainfall and flow on trends in water quality utilising a flexible model called Mixed Data Sampling (MIDAS). This model arises because of the mixed frequency in the data collection. Typically, water quality variables are sampled fortnightly, whereas the rain data is sampled daily. The advantage of using MIDAS regression is in the flexible and parsimonious modelling of the influence of the rain and flow on trends in water quality variables. We discuss the model and its implementation on a data set from the Shoalhaven Supply System and Catchments in the state of New South Wales, Australia. Information criteria indicate that MIDAS modelling improves upon simplistic approaches that do not utilise the mixed data sampling nature of the data.