Abstract:
Freshwater systems in southern Africa are under threat of climate change, not only from altered flow regimes
as rainfall patterns change, but also from biologically significant increases in water temperature. Statistical
models can predict water temperatures from air temperatures, and air temperatures may rise by up to 7 °C by
2100. Statistical water temperature models require less data input than physical models, which is particularly
useful in data deficient regions. We validated a statistical water temperature model in the lower Olifants River,
South Africa, and verified its spatial applicability in the upper Klaserie River. Monthly and daily temporal scale
calibrations and validations were conducted. The results show that simulated water temperatures in all cases
closely mimicked those of the observed data for both temporal resolutions and across sites (NSE>0.75 for
the Olifants River and NSE>0.8 for the Klaserie). Overall, the model performed better at a monthly than a
daily scale, while generally underestimating from the observed (indicated by negative percentage bias values).
The statistical models can be used to predict water temperature variance using air temperature and this use can
have implications for future climate projections and the effects climate change will have on aquatic species.
SIGNIFICANCE : • Statistical modelling can be used to simulate water temperature variance from observed air temperature,
which has implications for future projections and climate change scenarios.
• While there are many other factors affecting water temperature, air temperature accounts for up to 95%
of water temperature variance.
• The model used can successfully simulate water temperature variance for different rivers.
