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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]

Global warming can potentially lead to changes in future rainfall and runoff and can significantly impact the regional hydrology and future availability of water resources. All the large-scale climate impact studies use the future climate projections from global climate models (GCMs) to estimate the impact on future water availability. This paper presents results from a detailed assessment to investigate the capability of 15 GCMs to reproduce the observed historical annual and seasonal mean rainfalls, the observed annual rainfall series and the observed daily rainfall distribution across south-east Australia. The assessment shows that the GCMs can generally reproduce the spatial patterns of mean seasonal and annual rainfalls. However, there can be considerable differences between the mean rainfalls simulated by the GCMs and the observed rainfall. The results clearly show that none of the GCMs can simulate the actual annual rainfall time series or the trend in the annual rainfall. The GCMs can also generally reproduce the observed daily (ranked) rainfall distribution at the GCM scale. The GCMs are ranked against their abilities to reproduce the observed historical mean annual rainfall and daily rainfall distribution, and, based on the combined score, the better GCMs include MPI-ECHAM5, MIUB, CCCMA_T47, INMCM, CSIRO-MK3·0, CNRM, CCCMA_T63 and GFDL 2·0 and those with poorer performances are MRI, IPSL, GISS-AOM, MIROC-M, NCAR-PCM1, IAP and NCAR-CCSM. However, the reduction in the combined score as we move from the best- to the worst-performing GCMs is gradual, and there is no evident cut-off point or threshold to remove GCMs from climate impact studies. There is some agreement between the results here and many similar studies comparing the performance of GCMs in Australia, but the results are not always consistent and do significantly disagree with several of the studies. Copyright © 2010 John Wiley & Sons, Ltd. [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]