[1] This paper describes the modeling of climate change impact on runoff across southeast Australia using a conceptual rainfall-runoff model SIMHYD and presents the results and assesses the robustness of the modeling approach. The future climate series is obtained by scaling the historical series, informed by 15 global climate models (GCMs), to reflect a 0.9°C increase in global average surface air temperature, using a daily scaling method that considers changes in the future mean seasonal rainfall and potential evapotranspiration as well as in the daily rainfall distribution. The majority of the modeling results indicate that there will be less runoff in southeast Australia in the future. However, there is considerable uncertainty, with the results ranging from a 17% decrease to a 7% increase in the mean annual runoff averaged across the study area for the 0.9°C global warming. The model assessments indicate that the modeling approach is generally robust and can be used to estimate the climate impact on runoff. The modeled mean annual runoff is generally within 10-20% of the observed runoff. The modeling results for an independent test period are only slightly poorer than the calibration period, indicating that a satisfactorily calibrated rainfall-runoff model can be used to estimate runoff for another climate period. The modeled impact on various runoff characteristics as estimated by two rainfall-runoff models investigated here differ by less than 10%, which is relatively small compared to the range of modeled runoff results using rainfall projections from different GCMs.

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Abstract:The first step towards developing a reliable seasonal runoff forecast is identifying the key predictors that drive rainfall and runoff. This paper investigates the lag relationships between rainfall across Australia and runoff across southeast Australia versus 12 atmospheric-oceanic predictors, and how the relationships change over time. The analysis of rainfall data indicates that the relationship is greatest in spring and summer in northeast Australia and in spring in southeast Australia. The best predictors for spring rainfall in eastern Australia are NINO4 [sea surface temperature (SST) in western Pacific] and thermocline (20°C isotherm of the Pacific) and those for summer rainfall in northeast Australia are NINO4 and Southern Oscillation Index (SOI) (pressure difference between Tahiti and Darwin). The relationship in northern Australia is greatest in spring and autumn with NINO4 being the best predictor. In western Australia, the relationship is significant in summer, where SST2 (SST over the Indian Ocean) and II (SST over the Indonesian region) is the best predictor in the southwest and northwest, respectively.The analysis of runoff across southeast Australia indicates that the runoff predictability in the southern parts is greatest in winter and spring, with antecedent runoff being the best predictor. The relationship between spring runoff and NINO4, thermocline and SOI is also relatively high and can be used together with antecedent runoff to forecast spring runoff. In the northern parts of southeast Australia, the atmospheric-oceanic variables are better predictors of runoff than antecedent runoff, and have significant correlation with winter, spring and summer runoff. For longer lead times, the runoff serial correlation is reduced, especially over the northern parts, and the atmospheric-oceanic variables are likely to be better predictors for forecasting runoff. The correlations between runoff versus the predictors vary with time, and this has implications for the development of forecast relationship that assumes stationarity in the historical data.

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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.