EFAS hydrological modelling performance is shown as one query-able and one non query-able layers under the 'Performance' menu. For EFAS-4, the layer has been revamped, described here. Details on the method used to derive the information is provided in EFAS hydrological model performance.

Performance metric information

The modified KGE (Kling-Gupta Efficiency) represents a good synthesis of the river discharge simulation quality. Its components can be singularly used to assess hydrological dynamics: temporal errors (correlation), bias errors (bias ratio), and variability errors (variability ratio).

• The KGE ranges from -Inf to 1, with a perfect value of 1.
• The correlation ranges from -1 to 1, with a perfect value of 1.
• The bias and variability ratios range from -Inf to +Inf, with a perfect value of 1.

Performance intepretation

• If KGE > 0.6: The quality of the simulation is generally considered good.
• If KGE < 0.6: The KGE components should be analysed to identify the component with the lowest score.
  • If correlation > 0.6, the simulation should be considered with caution if bias or spread ratios < 0.9 or > 1.1.
  • If correlation < 0.4, the simulation should be considered with caution.

Model performance layers

The hydrological model performance is shown for each river gauge where observational data allows us to conduct a hydrological model performance.

Layers in the EFAS-IS mapviewer

Two layers are provided, showing the summary performance metrics as coloured coded symbols, either as dots located at the catchment outlet (layer: 'Model performance - Points') or as polygons representing the catchment footprint (layer: 'Model performance - Catchments').

Pop-up windows information

Hydrological model performance metric

The evaluation metric KGE' and its components are represented in speedometer-like figures.

Monthly discharge climatology

Climatological discharge main statistics (median, interquartile range and outliers) are calculated for each month (displayed over a 14-month period starting in September) for both observed and simulated discharges. The superposition of both shows the (dis)agreement between the two time series, giving a visual confirmation of the KGE and its components.

While the correlation is high if simulation and observation co-vary, the correspondence between the medians and the outliers reflect the bias and variability ratio respectively. The following figure helps to determine systematic errors for specific months or seasons and helps to identify the cause of a potentially low KGE score.

Simulated and modeled hydrographs

Full discharge time series at the model time-step are shown as hydrographs, with observed values highlighted as polygons. The time series plots help to identify particular periods of low model performance, and to understand where a low KGE value might come from. For example, if correlation is fairly good, but the model fails to capture low flows or peak amplitudes correctly, the discharge hydrograph helps to visually identify the quality of the gauged station's observations.