Discussion
Vegetation change and climate variability were considered as main
factors affecting annual streamflow in this study, and the evaluation of
such impacts were implemented at mean annual scale. At seasonal or
monthly time scale, the climate variability impact on streamflow may be
different since the streamflow would be impacted by the seasonality of
rainfall which adds an additional layer of complexity. Another time
scale issue which should be focused on is the period selected for
bushfire impact assessment. This study mainly focuses on an immediate
post bushfire period. When the bushfire period considered is extended
the increased impact can be subdued due to regrowth and runoff will
decrease when there is more vegetation.
In this study, the estimation of streamflow change due to climate
variability was calculated by a sensitivity-based method based on the
Budyko framework (Budyko, 1958). This study not only tested the method
of Zhang et al., (2001), but also other Budyko curves such as Fu curves
(Fu, 1981) and both methods yield similar results. These tests further
indicate that Budyko curves are robust for estimating mean annual
streamflow change induced by climate variability. Generally speaking,
the approach can yield more reliable results if multiple linear
regression considered PET as well as P as descriptors to predict the
mean annual streamflow during the testing period. For this reason, the
multilinear regression considering P and PET was tested as well.
Overall, the fitting coefficient of determination
(R2 ) of single linear regression (0.74) and
multiple linear regression (0.76) are very similar. In addition, the
proportion of bushfire and climate variability impact on streamflow
didn’t show a dramatic change when the single regression was replaced by
multiple regression. As a result, we conclude that the linear regression
relationships between annual rainfall and annual streamflow are
satisfactory for estimating vegetation change induced impacts on
streamflow.
As shown in Figure 5, the relationship can be described by a linear
regression model. The fitting coefficient of determination
(R2 ) is 0.69 which is satisfactory. it is
possible to further improve quantitation of the relationship between
burnt percentage and percentage of mean annual streamflow increase due
to bushfire, which is constructed to specify how bushfire extent
influences the bushfire impact on streamflow.
This study uses two independent methods to estimate the effects of
vegetation change caused by bushfire and climate variability on annual
streamflow. The independent evaluation of the two impacts show that the
time-trend analysis and sensitivity-based methods are accurate for our
research purpose. Further, to meet the assumption that climate
variability and bushfire are the main dominant factors to affect the
streamflow, efforts has been made when choosing these target catchments
(i.e. adequate data record and impacted by bushfire to certain extent).
Our study clearly indicates the burnt percentage shows a good positive
relationship with percentage of mean annual streamflow change due to
bushfire, which suggests higher and more serious burnt percentage
contributes a greater increase in streamflow.