The use of nonparametric tests for monotonic trend has flourished in recent years to support routine water quality data analyses. The validity of an assumption of independent, identically distributed error terms is an important concern in selecting the appropriate nonparametric test, as is the presence of missing values. Decision rules are needed for choosing between alternative tests and for deciding whether and how to pre-process data before trend testing. Several data pre-processing procedures in conjunction with the Mann-Kendall tau and the Seasonal Kendall test (with and without serial correlation correction) are evaluated using synthetic time series with generated serial correlation and missing data. A composite test (pre-testing for serial correlation followed by one of two trend tests) is evaluated and was found to perform satisfactorily.(
In Chesapeake Bay
in the United States, decades of management efforts
have resulted in modest reductions of nutrient loads from the watershed,
but the corresponding improvements in estuarine water quality have
not consistently followed. Generalized additive models were used to
directly link river flows and nutrient loads from the watershed to
nutrient trends in the estuary on a station-by-station basis, which
allowed for identification of exactly when and where responses are
happening. Results show that Chesapeake Bay’s total nitrogen
and total phosphorus conditions are mostly improving after accounting
for variation in freshwater flow. Almost all of these improving nutrient
concentrations in the estuary can be explained by reductions in watershed
loads entering through 16 rivers and 145 nearby point sources, with
the nearby point source reductions being slightly more effective at
explaining estuarine nutrient trends. Overall, these two major types
of loads from multiple locations across the watershed are together
necessary and responsible for the improving estuarine nutrient conditions,
a finding that is highly relevant to managing valuable estuarine resources
worldwide.
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