Page Comparison

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Modellers have the option of selecting one optimisation technique , multiple optimisation techniques (in parallel), or combinations two optimisation techniques (in series).

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1. Nash Sutcliffe Coefficient of Efficiency (NSE) 
2. Flow duration (specifically, the NSE of the flow duration)
3. Absolute bias
4. Bias penalty
   
5. Absolute bias
6. Square-root daily, exceedance and bias
   

The NSE can be applied to daily or monthly data, and the NSE and flow duration objectives can be applied to data that has been transformed by taking the logarithm. Source also allows the user to create some composite objective functions, of which there are two types:

1. Combinations of the individual objective functions listed above. For example, the objective for calibrating streamflow at a gauging site could be a combination of the NSE and bias penalty.
2. Combinations of the objectives for different model outputs. For example, a model could be calibrated using a weighted combination of the objective functions for values at two or more different gauging sites.

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Further information on the first seven of these objective functions is available in Vaze et al. (2011), Section 6. Guidance on model calibration is available in many publications, including various eWater Best Modelling Practice Guidelines (Black et al., 2011; Vaze et al., 2011; Black and Podger, 2012; and Lerat, 2012).

Structure & processes

Background

As the optimisation techniques and statistical measures of calibration performance used in Source are well established, they are not re-described here. However, as the objective functions used in the optimisation techniques have been customised for Source, further information on these follows and as many of them rely on the Nash Sutcliffe Coefficient of Efficiency (NSE), its formulation is restated below.

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The flow duration objective function sorts the observed and modelled data values in increasing order and then calculates the NSE of the sorted data.

Absolute Bias

The equation for the absolute value of the relative bias is:

Bias Penalty

The bias penalty objective function is described in Viney et al. (2009). The equation is given by:

where B is the absolute value of the relative bias, as defined in equation (3).

Combined Objective Functions

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