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Many studies bias correct daily precipitation from climate models to match the observed precipitation statistics, and the bias corrected data are then used for various modelling applications. This paper presents a review of recent methods used to bias correct precipitation from regional climate models (RCMs). The paper then assesses four bias correction methods applied to the weather research and forecasting (WRF) model simulated precipitation, and the follow-on impact on modelled runoff for eight catchments in southeast Australia. Overall, the best results are produced by either quantile mapping or a newly proposed two-state gamma distribution mapping method. However, the differences between the methods are small in the modelling experiments here (and as reported in the literature), mainly due to the substantial corrections required and inconsistent errors over time (non-stationarity). The errors in bias corrected precipitation are typically amplified in modelled runoff. The tested methods cannot overcome limitations of the RCM in simulating precipitation sequence, which affects runoff generation. Results further show that whereas bias correction does not seem to alter change signals in precipitation means, it can introduce additional uncertainty to change signals in high precipitation amounts and, consequently, in runoff. Future climate change impact studies need to take this into account when deciding whether to use raw or bias corrected RCM results. Nevertheless, RCMs will continue to improve and will become increasingly useful for hydrological applications as the bias in RCM simulations reduces. [ABSTRACT FROM AUTHOR]

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]

The recent drought in the southern Murray-Darling Basin has seen a larger reduction in annual runoff in many places compared to historical droughts with similar annual rainfall reductions. Several reasons have been suggested for this, including proportionally less autumn and winter rainfall, fewer high rainfall years, and increased temperatures. Using the SIMHYD daily rainfall-runoff model and scenarios of rainfall and potential evapotranspiration, we investigate the causes of the observed runoff reduction over 1997-2008 in the Campaspe river basin, an area representative of the southern Murray-Darling Basin. This method accounts for 83% of the runoff reduction, with the reduction in annual rainfall accounting for 52%. The remainder is not explained by any single hydroclimatic feature but is mostly accounted for by the combination of changes in rainfall variability outside monthly and annual time scales (15%), changed seasonality of rainfall (11%), and increased potential evapotranspiration (5%). [ABSTRACT FROM AUTHOR]

Different methods have been used to obtain the daily rainfall time series required to drive conceptual rainfall-runoff models, depending on data availability, time constraints, and modeling objectives. This paper investigates the implications of different rainfall inputs on the calibration and simulation of 4 rainfall-runoff models using data from 240 catchments across southeast Australia. The first modeling experiment compares results from using a single lumped daily rainfall series for each catchment obtained from three methods: single rainfall station, Thiessen average, and average of interpolated rainfall surface. The results indicate considerable improvements in the modeled daily runoff and mean annual runoff in the model calibration and model simulation over an independent test period with better spatial representation of rainfall. The second experiment compares modeling using a single lumped daily rainfall series and modeling in all grid cells within a catchment using different rainfall inputs for each grid cell. The results show only marginal improvement in the 'distributed' application compared to the single rainfall series, and only in two of the four models for the larger catchments. Where a single lumped catchment-average daily rainfall series is used, care should be taken to obtain a rainfall series that best represents the spatial rainfall distribution across the catchment. However, there is little advantage in driving a conceptual rainfall-runoff model with different rainfall inputs from different parts of the catchment compared to using a single lumped rainfall series, where only estimates of runoff at the catchment outlet is required. [ABSTRACT FROM AUTHOR]

This study describes a daily rainfall, potential evaporation and streamflow data set compiled for the important water resources region of southeast Australia, and the application of six commonly used lumped conceptual rainfall-runoff models to estimate daily runoff across the region. The daily climate data set and the daily modelled runoff are available from 1895 to 2008 at 0.05° grid resolution across the region. The modelling exercise indicates that the rainfall-runoff models can generally be calibrated to reproduce the daily observed streamflow (for 232 catchments in the high runoff generation areas), and the regionalisation results indicate that the use of optimised parameter values from a gauged catchment nearby can model runoff reasonably well in the ungauged areas. There are differences between the six models, but they are relatively small when used to describe aggregated results across large regions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper shows the feasibility of using steady state water balances of gauged catchments to calibrate a spatially explicit evaporation model and then applying this to estimate mean annual runoff for 120 gauged catchments in the Murray-Darling Basin (MDB) of Australia from 2001 to 2005. We used remotely sensed leaf area indices from the Moderate Resolution Imaging Spectrometer (MODIS) mounted on the polar-orbiting Terra satellite with the Penman-Monteith equation, gridded meteorology, and a two-parameter biophysical model for surface conductance ( Gs) to estimate 8-day average evaporation at 1-km resolution. Parameters for the G s model were optimized using steady state water balance estimates (precipitation minus runoff) in the gauged catchments in three precipitation zones of the MDB, and the calibrated evaporation model was then used to estimate evaporation ( ERS) and runoff from gauged and ungauged catchments in the MDB. Mean annual calibrated estimates of ERS compared well with water balance estimates, indicated by a root-mean-square error (RMSE) of 78.6 mm/a and the Nash-Sutcliffe coefficient of efficiency ( CE) of 0.68. Reasonable agreement was obtained between the estimated mean annual runoff ( RRS) (rainfall minus ERS), and the measured runoff (RMSE = 71.0 mm/a and CE = 0.75). Cross validation showed that estimated ERS and RRS were almost as good as the calibrated ones. Furthermore, RRS has an accuracy similar to that of a seven-parameter conceptual rainfall-runoff model in the gauged catchments. The results show that the evaporation model can be easily applied to estimate steady state evaporation and runoff and that ERS can be used with rainfall-runoff models to improve accuracy of estimated runoff in ungauged catchments. [ABSTRACT FROM AUTHOR]