Discussion
Summarising, this study used multiple approaches to confirm that in
general streamflow in south-eastern Australia is declining based on the
41 years of data, and that this decline tends to be greater than the
associated decline in rainfall (Tables 4 and 6). In other words, there
is significant “amplification” of the observed rainfall declines.
However, the overall declines, for small forested catchments and
considering 41 years of data, is (still) very small in percentage and mm
year-1 terms, once variability of rainfall and
temperature is accounted for. In addition, using the LTPMK approach,
which accounts for the autocorrelation in the data and test for
significance of the Hurst coefficient, most of the approaches indicated
non-significant trends. This indicates that while there is a clear local
trend in the 41 years of some of the data, we cannot (yet) say with
confidence whether this represents a long-term declining trend in
streamflow.
Comparison to other
studies
In comparison, an analysis of Australian streamflow records for the
latter half of the 20th Century using a Mann-Kendall
test by Chiew and McMahon (1993) found significant declines at 7 of the
30 studied stations. That study points out that due to the high
inter-annual variability of Australian streamflow, a greater change
might be required to identify a statistically significant trend (Chiew
& McMahon, 1993). A similar observation can be made in this study
considering the significance under the LTPMK test. Significant larger
declines in streamflow have been observed by Petrone et al. (2010), for
catchments in southwest Western Australia between 1950-2008. A more
recent paper identified significant trends in 20 of 199 catchments,
concentrated in the South-East of Australia (Ajami et al., 2017). An
analysis of global data (Milly, Dunne, & Vecchia, 2005) similarly
indicated decreases in streamflow in Australia, but the database from
that study overlaps with the other studies.
In general, decreasing trends in rainfall in the south east of Australia
and increasing trends in temperature have been widely identified (Cai &
Cowan, 2012; Chowdhury, Beecham, Boland, & Piantadosi, 2015; Murphy &
Timbal, 2008). In contrast, positive trends in rainfall were found in
the 20th Century in earlier studies (Collins &
Della-Marta, 1999; Smith, 2004), although the data period was suggested
to be an issue (Smith, 2004). Some other studies have found much larger
decreases in annual rainfall (Gallant, Hennesy, & Risbey, 2007; Murphy
& Timbal, 2008) in the order of 1 mm per year and greater decreases in
streamflow (Petrone et al., 2010). The differences are most likely
related to the difference in aggregation time scale (annual anomalies
versus weekly data). Another complicating factor could have been that
many of these earlier studies had timeseries to 2008, which was at the
end of the millennium drought (van Dijk et al., 2013), while our study
included the drought breaking year 2010. This “local trend” phenomena
can also explain why many of our LTPMK tests showed no significant
trends. This highlights once again the need for careful consideration of
the trend tests applied to the data.
The low rainfall elasticities calculated from the rainfall runoff
simulations (Figure 4) differ from earlier research (Chiew, 2006), which
suggested much higher amplifications from their SimHyd modelling. There
are however differences with the original work (Chiew, 2006), which
could explain the outcomes. Our study simulates the streamflow on a
daily scale and scales this to annual data, while the original work
calibrated on monthly data and scaled to annual. As larger time scales
are smoother and show more significant trends than weekly and daily data
(see the monthly and annual Mann Kendall results in the supplementary
documents, document 3) this could have also affected the amplification
calculations.
There could be differences in the magnitude of the analysed trends in
this study compared to other studies, due to the differences in time
scale. A reduction in variance occurs when the data is summarised to
annual and monthly data (Cai & Cowan, 2008; Petrone et al., 2010). In
addition, there could be differences due to the translation of remotely
sensed (Ukkola et al., 2015), filtered (Trancoso et al., 2017) or
modelled data (Chiew et al., 2009) to streamflow declines. Several other
studies investigated mainly the difference between decades or specific
periods (Chiew et al., 2014; Potter et al., 2010; Saft et al., 2015).
Furthermore, our work concentrates on 13 single land cover (forested)
small catchments, while several older studies did not control for land
cover, but mainly selected “unimpaired” catchments (meaning no water
resource development). The data based analyses (triangles in Figure 4),
and the GAMM modelling supports the magnitude of the amplification
(Chiew, 2006) for the few catchments that showed significant trends.
However, the non-parametric rainfall elasticities (grey triangles in
Figure 4) could be biased (Fu et al., 2007) as this does not take into
account the full interaction between rainfall and temperature.
Remaining trends
What the remaining trends in the streamflow (after removing rainfall and
temperature effects) exactly represent is still difficult to say.
Amplification has been mainly explained as an additional drying effect
(Chiew, 2006), mostly driven by the increases in temperature. Given that
in most catchments the remaining trend (Table 6) represents up to 80%
of the original trend in the streamflow (Table 4), this would correspond
to amplifications around 4 (the data suggests 0.98 - 4.5).
There is no indication that streamflow in these forested catchments has
decreased by a large amount (24 - 28%) due to increases in
evapotranspiration as a result of CO2 fertilisation
(Ukkola et al., 2015; van Dijk et al., 2013). Because we selected
homogeneous land cover (forestry), the larger declines found in earlier
studies (Ukkola et al., 2015; van Dijk et al., 2013) might be due to
trends in land management and land cover, such as changes in cropping
patterns, introduction of conservation tillage, or terracing and crop
variety effects. Furthermore, changes in the vegetation water use
efficiencies and changes in the tree physiology (Franks et al., 2013;
Ukkola et al., 2015) can also explain part of the remaining trends.
Conceptually, if trees become more water use efficient and produce more
leaf mass, then this could affect evapotranspiration and streamflow. In
addition, larger leaf area could also result in increased interception,
specifically for smaller rainfall events (Stoof et al., 2012). This
clearly highlights that more work in the area of vegetation effects on
streamflow would be useful, given the dominance of transpiration in the
global water balance (Jasechko et al., 2013), to support investigations
into climate change effects.
Distribution changes
Changes in the timing and distribution of rainfall could impact the
amplification of the rainfall signal in the streamflow. Shifts in the
main rainfall season from winter towards summer, or changes in the
average storm depth and timing between rainfall events can all affect
the runoff process (Ishak, Rahman, Westra, Sharma, & Kuczera, 2013;
Westra et al., 2012) and shift the balance between surface runoff and
groundwater flow. For example, Trancoso et al. (2017) recently found
declines of about 1 mm/year focussing on baseflow. We compared
streamflow and rainfall distribution curves across the four decades in
the data (supplementary data, document 4) but found no obvious or clear
patterns. But this analysis was essentially qualitative and a more
detailed analysis of changes in the distribution of flows and rainfall
is warranted.
Limitations
However, there are also some limitations to the present study. Small
catchments might have smaller temperature effects on streamflow (as
there is less time for evaporation to work on the catchment storage).
However, the advantages of controlling for routing and land cover in
smaller catchments were important for this study.
Some of the reasons for the low performance of the GAMM models could be
that the weekly data is too coarse to pick up some of the non-linear
rainfall responses, or that the AR1 model is not complex enough to model
the remaining storage delays in the weekly data despite the small
catchments. However, computational difficulties and missing data make
working with daily data in GAMM challenging.
The low impact of rainfall and ET on explaining variation in streamflow
contrasts the earlier work in this area using mainly linear regression
and with annual and monthly data (Cai & Cowan, 2008; Potter & Chiew,
2011). We believe that this difference has to do with the differences in
aggregation time scale and the non-linearity of the processes.
The value of using observed data to analyse trends is that the model
bias (Figure 5) is not affecting the results. However, finding the right
method of analysis is tricky (Fu et al., 2007). We chose to use
statistical modelling to remove the need to make the assumptions related
to mechanistic rainfall-runoff models. This also removes the need for
water balance closure, which can affect the parameter calibration
(Vervoort, Miechels, van Ogtrop, & Guillaume, 2014).
This study has concentrated on a relatively small group of Australian
catchments as an example, because we controlled for catchment size and
land cover. We identified about 5 more catchments that could be
included, but these were either very small or would skew the spatial
distribution of the catchments. However, future research could be to
take advantage of other (global) long term databases of unimpaired and
forested catchments, such as the Critical Zone Observatories in the US
(Troch et al., 2015) or the well-publicized French catchment database
(Oudin, Andréassian, Perrin, Michel, & Le Moine, 2008) and other
European catchments (Euser et al., 2013; Fenicia et al., 2014; Kirchner,
2009). This would identify whether the trends in streamflow are unique
to Australia, or link in with global changes in the hydrological cycle
(Huntington, 2006). Another avenue of future research would be to run
the same analysis on a comparison catchment dataset which is strictly
agricultural. Based on this research, we would hypothesise that these
catchments would demonstrate greater changes in streamflow and larger
elasticities.