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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).
Automated calibration requires the use of an objective function to direct the optimisation process. The Source calibration tool implements single objective optimisation, which reduces the comparison between the observed and modelled data during the calibration period to a single number to be optimised (multiple objective optimisation is also available, see Multi-objective optimisation - Insight - SRG for information).
Source implements five different basic types of objective function:
- Nash Sutcliffe Coefficient of Efficiency (NSE)
- Flow duration (specifically, the NSE of the flow duration)
- Absolute bias
- Bias penaltyAbsolute bias
- 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:
- 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.
- 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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Overview information on the four optimisation techniques in Source is available in Vaze et al. (2011). Further information is in textbooks and papers, particularly for the genetic algorithm and uniform random sampling[DB1] . Publications on the shuffled complex evolution method include papers by Duan et al. (1992) and Sorooshian et al. (1993). Publications on the Rosenbrock method include the paper by Rosenbrock (1960).
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Availability
Provided with Source.
Some of these objective functions can be combined to create composite objective functions. For composite objective functions, the user is often able to enter a weight that determines the relative contribution of each objective function component to the
The following nine forms of objective function are available in Source:
- Match to Nash Sutcliffe Coefficient of Efficiency (NSE) of Daily Flows
- Minimise Absolute Bias between Observed and Modelled Flows (calculated using daily flows)
- Match to NSE of Daily Flows but Penalise Biased Solutions
- Match to NSE of Monthly Flows
- Match to NSE of Monthly Flows but Penalise Biased Solutions
- Combined Match to NSE and Match to Flow Duration Curve (Daily)
- Combined Match to NSE and Match to Logarithm of Flow Duration Curve (Daily)
- Combined Match to NSE of Logarithms of Daily Flows with Bias Penalty
- Combined Bias, Daily Flows and Daily Exceedance (Flow Duration) Curve (SDEB)
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Implementation
Background
The optimisation techniques and statistical measures of calibration performance used in Source are well established and are not described in detail here. 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
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.
The traditional formula for NSE is:
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where:
Qobsi is the observed flow on day i,
Qsimi is the modelled flow on day i,
N is the number of days
Alternatively,
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This formulation obviates the necessity to calculate the average of the observed flows before evaluating the denominator in the traditional version.
The choice of any particular objective function will depend on the intended application. Each of the pre-defined objective functions are formulated to put emphasis (reproduce as closely as possible) on different flow characteristics (Vaze et al, 2011).
- Match to Nash Sutcliffe Coefficient of Efficiency (NSE) of Daily Flows
Application of this objective function involves maximising the NSE (i.e. getting it as close to 1.0 as possible). The calculation of the NSE is in accordance with Nash and Sutcliffe (1970) and uses observed and modelled daily flow data for all days within the calibration period for which observed daily flow data, including zero flow values (i.e. cease to flow), is available.
The NSE tends to produce solutions that match high and moderate flows very well but often will produce poor fits to low flows. It will also tend to favour solutions that provide a good match to the timing and shape of runoff events (Vaze et al, 2011).
2. Minimise Absolute Bias between Observed and Modelled Flows
This objective function will produce a match on the overall volume of flow generated but often will produce a poor fit to the timing of flows (Vaze et al, 2011). It has the following form:
| Equation 3 |
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The evaluation of this objective function uses observed and modelled daily flow data for all days within the calibration period for which observed daily flow data, including zero flow values, is available.
3. Match to NSE of Daily Flows but Penalise Biased Solutions
This objective function is a weighted combination of the daily NSE and a logarithmic function of bias based on Viney et al (2009), and the aim is to find its maximum value.
| Equation 4 |
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where:
B is the bias; and
| Equation 5 |
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The evaluation of this objective function uses observed and modelled daily flow data for all days within the calibration period for which observed daily flow data, including zero flow values, is available.
This formulation makes sure that the models are calibrated predominantly to optimise NSE while ensuring a low bias in the total streamflow. It avoids solutions that produce biased estimates of overall runoff, which can produce marginal improvements in low flow performance over the NSE objective function. However, NSE-Bias will still be strongly influenced by moderate and high flows and by the timing of runoff events, which can still often result in poor fits to low flows (Vaze et al, 2011).
4. Match to NSE of Monthly Flows
This objective function works in the same way as for the case “Match to NSE of Daily Flows” except that monthly flows are used to evaluate the NSE instead of daily flows. The NSE calculation ignores observed and modelled data for all months where there are one or more days of missing data in the observed flow series.
The NSE of monthly flows and NSE-Bias of monthly flows (below) can be useful for initial calibration because they tend to find solutions that will match the overall movement of water through the conceptual stores in the rainfall-runoff model, without being influenced by the timing of individual runoff events (Vaze et al, 2011).
5. Match to NSE of Monthly Flows but Penalise Biased Solutions
This objective function is the weighted combination of the monthly NSE and a logarithmic function of bias (Viney et al, 2009), and the aim is to find its maximum value. The equation used is the same as for the case “Match to NSE of Daily Flows but Penalise Biased Solutions” above. The NSE and Bias calculations ignore observed and modelled data for all months where there are one or more days of missing data in the observed flow series.
6. Combined Match to NSE and Match to Flow Duration Curve (Daily)
For this case the aim is to maximise the objective function, where:
| Equation 6 | Objective function = A * NSE daily daily flows + (1 - A) * NSE daily FDC |
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where:
A is a weighting factor whose value can be set by the modeller (0 ≤ A ≤ 1); and
NSE daily FDC is calculated using ranked value pairs of Qobsi and Qsimi.
This objective function and the following objective function are hybrids that compromise between the fit to the timing of high and moderate flows from the NSE component and the fit to the shape of the whole flow duration curve (FDC). The NSE-logFDC (below) will produce the closer fit to low flows (Vaze et al, 2011).
7. Combined Match to NSE and Match to Logarithm of Flow Duration Curve (Daily)
For this case the aim is to maximise the objective function, where:
| Equation 7 | Objective function = A * NSE daily flows + (1 - A) * NSE log10(daily FDC) |
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where:
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NSE log10(daily FDC) is calculated using ranked value pairs of log10(Qobsi+c) and log10(Qsimi+c).
c is the maximum of 1 ML and the 10th percentile of the observed flows. The use of this constant is intended to de-emphasise very small flows, which tend to be unreliable, and overcome the problem of trying to take logarithms of zero flows.
8. NSE Log Daily & Bias Penalty Objective Function
This objective function is given by:
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NSE(logarithms of daily flows) is calculated using value pairs of ln(Qobsi+c) and ln(Qsimi+c), where B and v are defined in the same way as above. The Bias Penalty is based on Viney et al (2009) and is:
| Equation 9 |
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This objective function captures the model’s ability to fit the shape of the observed daily flow hydrograph, with an emphasis on mid-range to low flows (in contrast to the arithmetic form of the NSE which tends to put an emphasis on medium to high flows), while ensuring a low bias in the total streamflow.
9. Combined Bias, Daily Flows and Daily Exceedance (Flow Duration) Curve (SDEB)
This objective function is based on the function introduced by Coron et al (2012) and has been successfully applied in a number of projects (e.g. Lerat et al, 2013). It has the following equation:
| Equation 10 |
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where:
α is a weighting factor whose value can be set by the modeller (0 ≤ α ≤ 1).
RQobsk is the k’th ranked observed flow of a total of N ranked flows,
RQsimk is the k’th ranked modelled flow of a total of N ranked flows, and
Other terms are as defined previously.
As explained by Lerat et al (2013), this function combines three terms: (i) the sum of squared errors on power transform of flow, (ii) the same sum on sorted flow values and (iii) the relative simulation bias.
The coefficient α and the power transform are used to balance the three terms within the objective function. The weighting factor α is used to reduce the impact of the timing errors on the objective function. This type of error can have a significant effect on the first term in the equation, where a slight misalignment of observed and simulated peak flow timing can result in large amplitude errors. Conversely, the second term is based on sorted flow values, which remain unaffected by timing errors. By way of example, Lerat et al (2013) in their study of the Flinders and Gilbert Rivers in Northern Australia used values of α of 0.1 for the Flinders calibration and 1.0 for the Gilbert calibration.
Using values of power transform of less than 1 has the effect of reducing the weight of the errors in high flows, where the flow data are known to be less accurate. Lerat et al (2013) found that a power transform of ½ led to the best compromise between high and low flow performance in their project. This value has been adopted in SourceThe choice of an appropriate objective function for calibration depends on the intended application of the model. Different objective functions are designed with the intention of emphasizing the fit of modelled flow to different aspects of the observed hydrograph (Vaze et al., 2011). The objective functions available in the Source Calibration Wizard are listed in Table 1, including useful references for further information.
Table 1. List of Source calibration objective functions.Anchor Table 1 Table 1
| Objective Function Name | Description | Reference |
|---|---|---|
| NSE Daily | Maximise the NSE of daily flows | Vaze et al. (2011), Section 6 |
| NSE Monthly | Maximise the NSE of monthly flows | Vaze et al. (2011), Section 6 |
| NSE Log Daily | Maximise the NSE of the logarithm of daily flows | |
| Minimise Absolute Bias | Minimise the Absolute value of the relative bias | Vaze et al. (2011), Section 6 |
| NSE Daily & Bias Penalty | Maximise the NSE of daily flows and bias penalty | Vaze et al. (2011), Section 6 |
| NSE Log Daily & Bias Penalty | Maximise the NSE of the logarithm of daily flows and bias penalty | |
| NSE Monthly & Bias Penalty | Maximise the NSE of monthly flows and bias penalty | Vaze et al. (2011), Section 6 |
| NSE Daily & Flow Duration | Maximise the NSE of daily flows and the NSE of the flow duration | Vaze et al. (2011), Section 6 |
| NSE Daily & Log Flow Duration | Maximise the NSE of daily flows and the NSE of the flow duration of log flows | Vaze et al. (2011), Section 6 |
| Square-root Daily, Exceedance and Bias | Minimise a combination of the bias, daily Flows and daily exceedance (flow duration) curve | Lerat et al., 2013 |
Implementation Details
The Bivariate Statistics SRG User Guide entry provides general information on the objective function equations and their interpretation. Implementation details that are specific to the Source Calibration Wizard are described below.
Missing Data
It is common for observed time series of hydrological processes to contain missing values. Also, the observed and modelled time series may have different start and end dates. The Source calibration tool calculates the objective function values using only data from those time steps for which both observed and modelled data is available. In other words, the calibration objective function are calculated using observed and modelled data has been filtered to include only:
- data from within the calibration period, and
- data for time steps with complete data pairs.
Monthly Flows
The NSE Monthly objective function uses monthly streamflow values. These are calculated as follows:
- If the model is run on a daily time step, monthly flows are calculated by summing the observed and modelled daily flow values. The NSE calculation ignores observed and modelled data for all months where there are one or more days of missing data in the observed flow series.
- If the model is run on a monthly time step, then the monthly values are unchanged.
Data
Input data
Details on data to be input by the modeller are provided in the Source User Guide. Requirements Requirements for data series inputs to the various objective functions are included in the descriptions of each objective function, above.
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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), or not using manual optimisation. Modellers can also select which objective function they wish to use. The other parameters the modeller can input are described in the following table:in Table 2.
Table 2. Objective function parameters. Refer to the Bivariate Statistics SRG User Guide entry for further information on their implementation.Anchor Table 2 Table 2
Objective Function | Parameter | Parameter Description | Units | Default | Range |
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| NSE Daily & Flow Duration | a | Weight on NSE in the combined objective | Dimensionless | 0.5 | 0 ≤ |
| α ≤ 1 |
| NSE Daily & Log Flow Duration | a | Weight on NSE in the combined objective | Dimensionless | 0.5 | 0 ≤ α ≤ 1 |
Output data
Outputs include results of the evaluation of the selected objective function and other calibration performance statistics.
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Black, D.C., Wallbrink, P.J., Jordan, P.W., Waters, D., Carroll, C., and Blackmore, J.M. (2011). Guidelines for water management modelling: towards best practice model application. eWater Cooperative Research Centre, Canberra, Australia. September. ISBN: 978-1-921543-46-3. Available via: www.ewater.com.au.
Coron, L., Andrassian, V., Perrin, P., Lerat, J., Vaze, J., Bourqui, M., and Hendrickx, F. (2012) Crash testing hydrological models in contrasted climate conditions: an experiment on 216 Australian catchments. Water Resources Research, 48, W05552, doi:10.1029/ 2011WR011721.
Duan, Q., Sorooshian, S. and Gupta, V. (1992). Effective and Efficient global optimization for conceptual rainfall-runoff models. Water Resources Research, 28(4), 1015-1031.
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Vaze, J., Jordan, P., Beecham, R., Frost, A., Summerell, G. (2011). Guidelines for rainfall-runoff modelling: Towards best practice model application. eWater Cooperative Research Centre, Canberra, ACT. ISBN 978-1-921543-51-7. Available via www.ewater.com.au.Viney, N.R., Perraud, J-M., Vaze, J., Chiew F.H.S., Post, D.A. and Yang, A. (2009). The usefulness of bias constraints in model calibration for regionalisation to ungauged catchments. In: 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation, July 2009, Cairns: Modelling and Simulation Society of Australian and New Zealand and International Association for Mathematics and Computers in Simulation: 3421-3427.