Using the Statistics tab of the Results Manager, you can carry out several types of statistical analyses on scenario results. For simplicity, they can be categorised into two groups:
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If a single result is selected from the left-hand tree menu of the Results Manager window, the statistics tab will show univariate statistics (Figure #1). Univariate statistics provide information on a single variable result and are intended to summarise and reveal patterns in that variableresult. See Univariate statistics - SRG.
For multiple results in a custom chart, the statistics tab shows both univariate comparison and bivariate statistics for two of the results in the chart. Univariate comparison statistics are the same as univariate statistics for a single result, except that the
- Univariate comparison statistics are the differ from
- Bivariate statistics compare two variables for the purpose of determining empirical relationships between them. See Bivariate statistics - SRG.
For statistics for a single result, you can:
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statistics for a single result, you can:
- Choose the period type (Table 1) used to calculate the statistics from the drop-down menu;
- Change the Water Year Start from the default date (the default is set in Project Options);
- View the result used to calculate the statistics under Data;
- View the statistics themselves.
For multiple results in a custom chart, the statistics tab shows both univariate comparison (Figure 2) and bivariate statistics (Figure 3) for two of the results in the chart:
- Univariate comparison statistics are the same as univariate statistics for a single result, except that by default the statistics are only calculated from data that overlaps between the two results (see Table 2). To calculate univariate statistics for all data in each result, disable Calculate using overlapping data only.
- Bivariate statistics compare two results for the purpose of determining empirical relationships between them. See Bivariate statistics - SRG.
For statistics of multiple results, you can also:
- Toggle on or off two Statistics Options for how the two results are compared (Table 3);
- View and change the result used as the X Data (reference) and the Y Data for statistic calculations (in Figure #22, this is Downstream Flow Volume); and
- Select to view either Univariate Comparison or Bivariate statistics for the two results.
Table 1. Basic statistical analysis, period types
| Period type | Description |
|---|---|
| Total | Provides statistics for the entire run. This is the default period. |
| Yearly summary | Provides annual statistics, with one row for each year in the run. |
| Monthly summary | Provides monthly statistics by combining the data for each month, regardless of the year. There are 12 rows, one for each calendar month. For example, the January row displays statistics calculated from data for every January for all years in the run. |
| MonthxYear summary | Provides monthly statistics by month and year, with one row for each month-year pair in the run. For example, December 1999, January 2000, February 2000 ... etc. |
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- NSE (Nash-Sutcliffe Efficiency) measures the relative magnitude of the model error variance compared to the measured data variance. It can be applied at any time step size (eg. daily, monthly). See Bivariate Statistics SRG - Nash-Sutcliffe Efficiency
- The NSE of Flow Duration measures the relative magnitude of the model error variance compared to the measured data variance. It can be applied at any time step size (eg. daily, monthly). See Bivariate Statistics SRG - Flow Duration.
- Relative Bias measures the magnitude of the model errors relative to the magnitude of the observations. See Relative Bias.
- Bias Penalty is log transformation of the absolute value of the relative bias. In Source, the bias penalty is always used in combination with the NSE and is not available on its own. It is designed to be used in model calibration to penalise biased solutions. See Bivariate Statistics SRG - Bias Penalty.
For more detailed information on bivariate statistics, see Bivariate Statistics - SRG.
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Statistic | Definition | Range |
|---|---|---|
Values Used | The number of time steps for which there are complete data pairs ie. both the X data and Y data time series have values. These pairs are used to calculate the bivariate statistics. Time steps where either series has missing values are not used. | 0 to +∞ |
Values not used from X | The number of time steps in the X Data series that are not used in bivariate statistics calculations because either the X data or the Y data have missing values for those time steps. | 0 to +∞ |
Values not used for Y | The number of time steps in the Y Data series that are not used in bivariate statistics calculations because either the X data or the Y data have missing values for those time steps. | 0 to +∞ |
Pearson's Correlation (r) | Pearson's correlation coefficient measures the linear correlation between two variables. Pearson's correlation coefficient is symmetric, meaning that the value will be the same regardless of which time series is defined as X data (reference) and which as the Y data. See Pearson's Correlation Coefficient. | -1 to 1 |
Volume Bias (%) | Relative bias expressed as a percentage. See Bivariate Statistics SRG - Relative Bias. | 0 to 100 |
Minimise Absolute Bias | The absolute value of the relative bias. See Bivariate Statistics SRG - Relative Bias. | 0 to +∞ |
| NSE Daily | The NSE for using a daily time step. See Bivariate Statistics SRG - Nash-Sutcliffe Efficiency. | -∞ to 1 |
NSE Daily & Bias Penalty | The difference between NSE daily and the Bias Penalty. See Bivariate Statistics SRG - NSE Daily & Bias Penalty. | -∞ to 1 |
NSE Daily & Flow Duration | Combines the NSE Daily and Flow Duration using a user-defined weighting factor. See Bivariate Statistics SRG - NSE Daily & Flow Duration. | -∞ to 1 |
| NSE Daily & log Flow Duration | Combines the NSE Daily and log Flow Duration using a user-defined weighting factor. Log flow duration is the NSE of flow duration of the logarithm of data, calculated using a daily time step. See Bivariate Statistics SRG - NSE Daily & log Flow Duration. | -∞ to 1 |
| NSE Log Daily | The NSE of the logarithm of data, using a daily time step. See Bivariate Statistics SRG - NSE of Log Data. | -∞ to 1 |
| NSE Log Daily & Bias Penalty | The difference between NSE Log daily and the Bias Penalty. See Bivariate Statistics SRG - NSE Log Daily & Bias Penalty. | -∞ to 1 |
| NSE Monthly | The NSE using a monthly time step. See Bivariate Statistics SRG - Nash-Sutcliffe Efficiency. | -∞ to 1 |
| NSE Monthly & Bias Penalty | The difference between NSE monthly and the Bias Penalty. See Bivariate Statistics SRG - NSE Monthly & Bias Penalty. | -∞ to 1 |
| Square-root Daily, Exceedance and Bias | Combines three terms, the sum of errors on power transformed flow, the same sum on sorted flow values, and the relative simulation bias. See Bivariate Statistics SRG - Sum of Daily Flows, Daily Exceedance (Flow Duration) Curve and Bias. | 0 to +∞ |
Figure #4. Bivariate statistics, Total period
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where xi is the ith value of the flow series x, xt is the user-defined threshold value for x, yj is the jth value of the flow series y, and yt is the user-defined threshold for y. This statistic can be used to calculate several useful parameters such as Volumetric runoff coefficients and Stream Erosion Index(SEI). To calculate SEI, it is necessary to know the flow threshold (critical flow) below which no erosion is expected to occur within a waterway. This threshold can be represented (EarthTech, 2005) as a percentage of the pre‐development two-year ARI peak flow at the location in question. The percentage depends on the stream bed material and usually varies between 10 – 50%. The pre-development two-year ARI peak discharge can be estimated using flood frequency analysis or the rational method as described in Australian Rainfall and Runoff (Pilgrim, D.H., 2001). Flux Files containing the pre- and post-development outflows can be generated from MUSIC model run. SEI can be calculated by creating a custom chart in Results Manager where X data is the post-development outflow, Y data is the pre-development outflow, and both the X data and Y data threshold values are manually set to the value of critical flow using the appropriated fields next to the volumetric ratio. Although Results Manager can calculate a volumetric ratio for any two data series, currently this statistic supports data expressed as either volume (eg. ML) or rate (eg. m3/s). |
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