Introduction
The effects of climate change will influence water resource availability
in the future (Huntington, 2006; Trenberth et al., 2014). This is
particularly of importance in Australia where major droughts have
drastically increased public concerns about water availability (Leblanc,
Tweed, Van Dijk, & Timbal, 2012). As a result, there have been several
studies focussing on climate change impacts on water resources (Betts et
al., 2007; Chien, Yeh, & Knouft, 2013; Chiew & McMahon, 2002; Chiew et
al., 2009).
Most of the earlier studies on future water availability have used
rainfall-runoff models with climate data from downscaled global
circulation models to highlight future scenarios (e.g. Chien et al.,
2013; Chiew et al., 2009; Leblanc et al., 2012; Vaze, Davidson, Teng, &
Podger, 2011). For Australia, most of these studies indicate a more
variable future and a decreased water availability (van Dijk et al.,
2013). The main explanation is decreased rainfall and increased
evaporation and therefore decreased streamflow. A drawback of such
studies is that they introduce at least two types of errors: the
calibration uncertainty of the rainfall-runoff model and the associated
uncertainty in the prediction; and uncertainty in future climate data,
either from downscaling global models (Leblanc et al., 2012), or from
scaling of historical data (Chiew, 2006; Fu, Charles, & Chiew, 2007).
Reductions in streamflow in Australia have also been attributed to
increased CO2 fertilisation of the vegetation due to
changes in the atmospheric CO2 concentrations (Trancoso,
Larsen, McVicar, Phinn, & McAlpine, 2017; Ukkola et al., 2015).
However, as Ajami et al. (2017) highlight, this effect varies along
aridity gradients and depends on other factors such as nutrient
availability. Biological work (Franks et al., 2013) suggests that tree
stomata adapt to changes in climate, i.e. there is a certain level of
bio-plasticity to reduce how water is transpired as a function of
temperature. This highlights further other, possibly interacting and
counteracting, factors that could influence future changes in
streamflow, well beyond the traditional forcing variables.
Another issue with the earlier work, including some of the latest work
on the changes in catchment responses with drought (Ajami et al., 2017;
Booij, Schipper, & Marhaento, 2019; Saft, Western, Zhang, Peel, &
Potter, 2015), is that very few of the studies have controlled for land
cover and therefore might have stationarity issues (Montanari et al.,
2013; Navas, Alonso, Gorgoglione, & Vervoort, 2019). Ajami et al.
(2017) analysed vegetation cover and found that runoff sensitivity to
precipitation varied with fractional vegetation cover. Moreover, they
confirmed that increased vegetation cover decreases runoff sensitivity,
confirming earlier work (Chiew, 2006). However, Ajami et al. (2017) also
point out that fractional vegetation cover covaries with precipitation,
therefore making the comparison difficult.
An alternative approach to biophysical modelling with future climate
scenarios to understand changes in future water yield, would be to
analyse historical data sets, as several older studies in Australia have
done (Booij et al., 2019; Cai & Cowan, 2008; Chiew, Whetton, McMahon,
& Pittock, 1995; Petrone, Hughes, Van Niel, & Silberstein, 2010;
Potter, Petheram, & Zhang, 2011; Trancoso et al., 2017). However, there
are several reasons why the issue of trends in streamflow might need
revisiting. Many of the older studies were based on annual data, a
limited geographical area (Petrone et al., 2010) and varied in terms
data periods and data aggregation in space and time. Some studies
focused on a few rivers (Petrone et al., 2010; Potter & Chiew, 2011),
some focussing on the whole Murray Darling water balance (Cai & Cowan,
2008) and some focussing on a large data base of catchments (Booij et
al., 2019; Chiew, 2006; Trancoso et al., 2017).
Finally, the models and types of analyses varied across the studies,
including simple linear regression on un-transformed and transformed
data (Cai & Cowan, 2008; Chiew, 2006; Chiew et al., 2014; Potter et
al., 2011), non-parametric Mann-Kendall and step change methods (Petrone
et al., 2010; Potter & Chiew, 2011), statistical analyses and using
rainfall runoff models (Chiew, 2006; Potter, Chiew, & Frost, 2010) and
applying an energy framework (Booij et al., 2019) and focussing on
baseflow (Trancoso et al., 2017). As a result, despite a consistent view
of a sharply declining streamflow in response to changing rainfall, it
is difficult to compare across studies.
Rather than using conceptual or deterministic modelling approaches, data
based statistical models allow quick identification of the main
variables explaining variations in streamflow. If the variables in the
statistical model represent real physical processes (Fu et al., 2007),
the model removes all variance based on the physical processes and the
remaining unexplained trend in the residuals can be analysed. Modern
regression approaches also allow non-linearity and the identification of
uncertainties (Wood, 2006), rather than being limited to linear
relationships. The disadvantage of statistical models is that they are
essentially a “black box” as they do not explicitly include any
physical processes. This means that, while the model can show
correlation, it does not necessarily prove causation, and a lack of
understanding of processes within a system may lead to an incorrect
interpretation of statistical relationships.
The above review highlights that it is therefore important to have
consistent methodology that includes several different statistical
analyses to cross check the trend results, as each analysis included
methodological and data uncertainty.
Given the range of approaches, the aim of this paper is 1) to offer a
combination of statistical approaches to analyse climate change trends
in streamflow data in forested catchments; 2) to use the increased
observed data length to revisit the work by Chiew (2006) and other past
research to investigate climate change trends on a subset of the
Australian hydrological reference stations across the continent; and 3)
to highlight the spatial and temporal variation in these rainfall and
temperature effects on streamflow across the Australian continent.
While the data in this paper focuses on Australia, the outcomes and
methodology are relevant for other regions that are experiencing changes
in rainfall and temperature and have predicted changes in streamflow.
In light of the complexity of the topic, and the number of existing
papers with somewhat conflicting results, all the data, explanations and
code for the analyses used in this paper have been stored as
supplementary data in an on-line repository, which can be accessed via:
https://doi.org/10.5281/zenodo.3757041