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The term ‘hydrologic non-stationarity’ has been used to describe many things, ranging from different climate-runoff relationships evident in different periods within a long hydroclimate time series to changes in hydroclimate characteristics and dominant hydrological processes in an increasingly warmer and higher CO 2 world. This paper presents several examples of observed ‘hydrologic non-stationarity’ in far south-eastern Australia exposed by the prolonged 1997–2009 “Millennium” drought, focussing on the implications of this hydrologic non-stationarity on hydrological modelling and prediction. The runoff decline during the drought was unprecedented in the instrumental historical record. It was caused not only by the lower annual rainfall, but also by changes in other climate characteristics (lack of any high rainfall years, change in rainfall seasonality and higher temperatures) and dominant hydrological processes (reduced surface–groundwater connection and farm dams intercepting proportionally more water during dry periods). Hydrological models developed and calibrated against pre-1997 data cannot predict adequately the flow volumes and runoff characteristics during the drought. However, as the Millennium drought has exposed these extreme conditions, models can now be developed and calibrated to represent these, as well other conditions observed in the instrumental historical records (i.e., hydrologic non-stationarity that has already been observed). Such models should be able to satisfactorily predict the near-term runoff which will be influenced mainly by the rainfall inputs. However, further into the future, runoff will be increasingly influenced by higher temperatures and changed ecohydrological processes under higher CO 2. Reliably modelling these is difficult because of the complex interactions and feedbacks between many variables and processes in a future environment not seen in the past (i.e., hydrologic non-stationarity that has not been observed). [ABSTRACT FROM AUTHOR]

This paper presents the climate change impact on mean annual runoff across continental Australia estimated using the Budyko and Fu equations informed by projections from 15 global climate models and compares the estimates with those from extensive hydrological modeling. The results show runoff decline in southeast and far southwest Australia, but elsewhere across the continent there is no clear agreement between the global climate models in the direction of future precipitation and runoff change. Averaged across large regions, the estimates from the Budyko and Fu equations are reasonably similar to those from the hydrological models. The simplicity of the Budyko equation, the similarity in the results, and the large uncertainty in global climate model projections of future precipitation suggest that the Budyko equation is suitable for estimating climate change impact on mean annual runoff across large regions. The Budyko equation is particularly useful for data-limited regions, for studies where only estimates of climate change impact on long-term water availability are needed, and for investigative assessments prior to a detailed hydrological modeling study. The Budyko and Fu equations are, however, limited to estimating the change in mean annual runoff for a given change in mean annual precipitation and potential evaporation. The hydrological models, on the other hand, can also take into account potential changes in the subannual and other climate characteristics as well as provide a continuous simulation of daily and monthly runoff, which is important for many water availability studies. [ABSTRACT FROM AUTHOR]

This paper uses the Budyko and Fu models to estimate mean annual runoff in 0.05° grid cells across southeast Australia, and assesses the estimates against observations in 323 catchments. The results show that the Budyko model can provide reasonable estimates of runoff and is suitable for data limited regions. In regions where there are some streamflow data available, the calibration of a single parameter in the Fu model gives better runoff estimates than the Budyko model and similar to regionalised rainfall-runoff models. In drier regions, the consideration of different vegetation types in the Fu model can improve the modelling results. [ABSTRACT FROM AUTHOR]

The statistical behaviour and distribution of high-resolution (6 min) rainfall intensity within the wet part of rainy days (total rainfall depth >10mm) is investigated for 42 stations across Australia. This paper compares nine theoretical distribution functions (TDFs) in representing these data. Two goodness-of-fit statistics are reported: the Root Mean Square Error (RMSE) between the fitted and observed within-day distribution; and the coefficient of efficiency for the fit to the highest rainfall intensities (average intensity of the 5 highest intensity intervals) across all days at a site. The three-parameter Generalised Pareto distribution was clearly the best performer. Good results were also obtained from Exponential, Gamma, and two-parameter Generalized Pareto distributions, each of which are two parameter functions, which may be advantageous when predicting parameter values. Results of different fitting methods are compared for different estimation techniques. The behaviour of the statistical properties of the within-day intensity distributions was also investigated and trends with latitude, Köppen climate zone (strongly related to latitude) and daily rainfall amount were identified. The latitudinal trends are likely related to a changing mix of rainfall generation mechanisms across the Australian continent. [ABSTRACT FROM AUTHOR]

This paper presents a simple approach for estimating long-term runoff and diffuse pollution loads in urban catchments, and discusses conceptual modelling methods for simulating daily runoff and pollution loads. The modelling results for several catchments in Australian capital cities are presented. The study indicates that long-term and dailyrunoff can be estimated reasonably accurately using simple approaches. However, the water quality characteristic can vary considerably between catchments, and in the absence of data, the models can only provide a guide to the probable range of diffuse pollution load generated from a catchment. [ABSTRACT FROM AUTHOR]