Analysis of Trends in Low-Flow Time Series of Canadian Rivers - Hydrologic Time Series Analysis: Theory and Practice - page 200

and dry days). Scientific research on the identification of trends in time series
of hydrological variables is still continuing, however using improved
approaches and with an enhanced focus on the interpretation of trends. It is
important to note that the majority of the studies on trends over the last two
decades were driven mainly by concerns of climate change and less due to the
influence of other factors like agricultural and industrial developments that
could also influence time evolution of hydrological variables.
Many different trend analysis methods and their modifications particularly
to address the influence of serial correlations, when assessing local/site
significance of trends, and cross correlations, when assessing field/regional
significance of trends, have been proposed in the literature. These methods
are presented and discussed in Chapter 4 of this topic (Section 4.3). The trend
analysis methods are further addressed in Section 10.2 of this chapter under
four topics: (i) assumptions about the data distribution (parametric and
nonparametric), (ii) the type of trend model (linear and nonlinear), (iii)
assumptions about the serial structure of the hydrological time series (i.e.,
serial independence versus dependence), and (iv) assessment of field
significance. These items play a fundamental role for a sound and
comprehensive analysis of trends in a particular watershed or in a region. A
later section of this chapter contains a case study on trend analysis in time
series of annual and seasonal low flows observed at selected gauging stations
included in the Canadian Reference Hydrometric Basin Network (RHBN).
The river basins of RHBN are minimally affected by human activities and
therefore provide an excellent dataset for investigation of trends in time series
of hydrological variables. Concluding remarks are provided in the last section
of the chapter. It should be noted that most of the contents of this chapter are
based on the review of various trend analysis methods presented in Khaliq et
al. (2009a) and analyses of Monte Carlo simulated and observational data
reported in Khaliq et al. (2008, 2009a, b) and Khaliq and Gachon (2010) due
to focus of the case study on Canadian basins. Also, appropriate figures from
the published literature have been included and new insights about the analysis
of trends are presented and discussed.
10.2 Components of Trend Analysis Framework
In order to perform a meaningful trend analysis for a given problem, one has
to address a number of issues that affect the overall outcome of such an
analysis. Some of these topics are discussed below. Undoubtedly, good quality
and longer observational records are equally important topics that also deserve
adequate attention.
10.2.1 Assumptions about Data Distribution
In general, trend analyses are performed using parametric and nonparametric
approaches. An example of the parametric approach could be a non-stationary