River and stream water quality and ecology - Greater Wellington ...

4.2 Temporal trends
River and stream water quality and ecology in the Wellington region: State and trends
4.2.1 Approach to analysis
A selection of 14 water quality variables were analysed for temporal trends,
utilising RSoE monitoring data collected monthly over the period July 2006 to
June 2011. An initial assessment examined trends between September 2003
and June 2010 (see Ballantine & Booker 2011); the September 2003 start date
represented a logical follow-on from the 1997 to 2003 trend period used by
Milne and Perrie (2005) in the last river and stream SoE technical report.
However, a change in Greater Wellington’s analytical laboratory (including
detection limits for some variables) in July 2006 was deemed to have produced
‘step changes’ in some water quality variables (Ballantine & Booker 2011) 17 ,
confounding trend assessments. As a result, the data record for the five years
ending June 2011 was used for temporal trend analysis in this report.
Following methodology outlined in Ballantine and Booker (2011), temporal
trends were analysed using the Seasonal Kendall trend test in NIWA’s Time
Trends software (Version 3.20). Trend analysis was undertaken on both raw
data and – for the 44 sites where flow values or estimates were available (see
Appendix 4) – flow-adjusted data. Seasonal Kendall trend tests were
undertaken using 12 ‘seasons’ (ie, reflecting monthly water quality sampling)
and flow-adjustment was performed in the Time Trends software using
LOWESS (Locally Weighted Scatterplot Smoothing) with a 30% span.
Prior to analysis, the nutrient data sets, which often contain a high proportion
of censored values (ie, concentrations reported as below the analytical
detection limit), were processed as follows:
If a data set contained less than 10% censored data, the censored values
were halved;
For data sets containing between 10% and 80% censored data, censored
values were estimated using Regression on Order Statistics (ROS); and
For data sets with greater than 80% censored data, trend analysis was not
undertaken due to the high level of uncertainty present in the dataset.
For the remaining variables (eg, TOC and E. coli) censored values typically
comprised well below 10% of the values in the data set for each site (in all
cases they were below 17%) and the censored data were simply halved before
undertaking trend analysis.
While estimation of censored data using ROS is considered the best approach
for dealing with censored data when undertaking water quality trend analysis
(Ballantine & Booker 2011), it is important to note that as the proportion of
censored data increases so too does any associated error. Ballantine and Booker
(2011) advise that professional judgement is exercised when interpreting trend
results that contain higher proportions of censored values. For this reason, only
results for data sets that contained no more than 30% censored values are
17 This is despite analytical methods remaining constant for many variables.
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