The regional model calibration results (Table 5 and Figure 11) suggest that the AWRA-L model performs satisfactorily in estimating most in the and its surrounding area when it is calibrated against in situ high streamflow and low streamflow, respectively.
It is noted that when the regional model is calibrated against observations from eight streamflow gauges it does not generate a uniform model performance. Though the AWRA-L model performs well overall, it performs poorly in some catchments and does not estimate the suite of hydrological response variables equally effectively. For instance, the high-streamflow model calibration under-predicts streamflow at catchment 003205 and the low-streamflow model calibration over-predicts at catchment 002105 (Table 5). For other gauges in the Galilee subregion, the model performs well in terms of model efficiency and bias.
A key characteristic of a regional calibration approach is that, unlike with local calibration, there is little degradation in prediction performance between model calibration and model prediction (Viney et al., 2014; ). This means that prediction performance in calibration provides a good guide to the expected performance in ungauged parts of the modelling domain. In other words, it is reasonable to expect that at all the Ed(1.0) values will be of the order of 0.3 to 0.4 and the biases will be of the order of –0.3 to +0.3. This, therefore, provides confidence in the prediction quality of the AWRA-L model outputs in each model node where there are no streamflow observations.
Although not directly calibrated towards any of the hydrological response variables, an assessment of how well the two calibration sets predict the hydrological response variables serves as a further assessment of model performance. In general, one or other of the two calibration sets provides predictions of the hydrological response variables with little bias. An exception is for the variable describing the number of , which both parameter sets severely under-predict. This suggests that less confidence may be ascribed to the prediction of this variable in Section 18.104.22.168 than to the prediction of the other variables.
Product Finalisation date
- 22.214.171.124 Methods
- 126.96.36.199 Review of existing models
- 188.8.131.52 Model development
- 184.108.40.206 Calibration
- 220.127.116.11 Uncertainty
- 18.104.22.168 Prediction
- Currency of scientific results
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