This study will explore an underlying pattern of behaviour in water quality time series by assessing N (TN, DON, NH4, NO3) and P (TP, PO4), SS (TSS, turbidity), pH, EC, and Chlorophyll-a. To make and justify statements about tendencies (pattern of behaviour) in these data,
trend estimation can be applied. In the case data can be assumed to be linear, trend estimation can be done by linear regression. If data have a monotonic upward or downward trend (other shapes than linear), trend analysis can be undertaken within a non-linear (Mann–Kendall Test),
and additive models (for data change more slowly). In this study, trends will be assessed using the Mann–Kendall trend test, as recommended by the World Meteorological Organization (WMO) as the most effective tool for hydro-climate variables analysis and trend analysis for water quality data.
The data will be checked for adequate representation across flow conditions to see if it is biased towards a particular flow category (e.g. low flow-biased sampling).