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A prolonged drought across southern Australia has led to renewed interest in water resource development of northern Australia (NA), with an increase in demand for runoff predictions from ungauged catchments in Australia’s tropics. However, wet-dry tropical environments have an extended dry season with periods of low or no flow, and from an ecological perspective, dry-season flows are of vital importance (e.g. Erskine et al., 2003). The dry-season is also the period when potential consumptive water use is likely to be high. Thus it is important to ensure dry-season flows are well predicted. One of the key challenges in undertaking robust water resource assessments across NA is the relatively low density of hydrological data. Using data from 105 catchments in tropical Australia, five daily rainfall-runoff models (RRM) and three methods of regionalising model parameters were compared for the simulation of dry-season flows. To ensure low-flows were well modelled the approach of Petheram et al. (2012) was adopted, where the simulated and observed terms in the objective function were raised to the power of λ. Here six calibration runs were undertaken for each model, using values of 1.25, 1, 0.75, 0.5, 0.25 and 0.05 for λ. To select the ‘best all-round’ parameter set we used the method of Petheram et al. (2012), where for each model and for each catchment the ‘best all-round’ calibrated parameter set was selected based on the weighted combination of different Nash-Sutcliffe Efficiency metrics. We then compared the best performing RRM and best method of regionalisation against regression-based predictive methods for a wetdry tropical environment. We found that the adoption of multiple criteria to select an optimal parameter set resulted in an improved ability to simulate low flows with no loss in predictive capacity for high flows. An educated transposition of parameter sets from gauged to ungauged catchments was found to be better than random assignment of model parameters, whilst assigning model parameters on the basis of spatial proximity outperformed physical similarity methods. For simulating the lower-half of the flow duration curve no clear method was best for regionalising model parameters and no method was better than randomly assigning intact model parameter sets. The best performing multi-model ensemble (Sacramento and IhacresClassic) and the best method of parameter regionalisation (spatial proximity) performed similarly to statistical regression approaches in predicting mean annual flows. However, the regression approaches demonstrated more skill predicting low-flow metrics. There may be opportunities to improve low-flow NSE metrics under calibration mode through ‘smarter’ calibration procedures. However, it is thought unlikely that this will result in improved performance under prediction mode. Erskine, W.E., G.W. Begg, P. Jolly, A. Georges A., A. O'Grady, D. Emaus, N. Rea, P. Dostine, S. Townsend, A. and Padovan (2003). Recommended environmental water requirements for the Daly River, Northern Territory, based on ecological, hydrological and biological principles., Darwin, NT. Petheram, C., P. Rustomji, F.H.S. Chiew, and J. Vleehsouwer (2012). Rainfall-runoff modelling in northern Australia: a guide to modelling strategies in the tropics. Submitted to special issue on Tropical Hydrology, Journal of Hydrology.

Regionalising low-flow responses in large tropical catchments: a comparison of rainfall-runoff modelling and regression approaches