Highlights from ARR Book 7

Book 7 of Australian Rainfall and Runoff is titled Application of Catchment Modelling Systems.  It has been written by experienced people and there is some great information. A few, paraphrased, highlights follow.

  • Its often challenging to get good calibrations for all the available historical events and there may good reasons why.

Difficulties in calibrating a model to observed flood events of different magnitude should be taken as an indication of the changing role of processes.

In many cases a significant change occurs between floods that are mostly contained within the stream channel and floods in which floodplain storage plays an important role in the routing process.

If the model has only been calibrated to in-bank floods, confidence in its ability to represent larger floods will be lower.

  • Calibration needs to focus on what the model is to be used for, not just ensuring past events are well represented.

The focus of model calibration is not just to develop a model that is well calibrated to the available flood data.  Application of the model to the design requirements must be the primary focus.

It is often the case that calibration floods are relatively frequent while design applications require much rarer floods.  In this case, work in refining the model calibration to the frequent floods may not be justified.

Parameter values should account for the expected future design conditions, rather than an unrepresentative calibration event.

Calibration usually works with historic flood events while the design requirements are for probabilistic events.  The parameters calculated for the historic events may not be applicable to the design flood events.

  • On using all available data.

Even if the data is of poor quality or incomplete, it is important that the model calibration be at least consistent with the available information.

Even poor quality observations may be sufficient to apply a ‘common sense test’.

…at least ensure that model performance is consistent with minimal data [available]…

  • On inconsistent data

Effort should be concentrated on resolving the source of the inconsistency rather than pursing further calibration.

  • Dealing with poor calibration.

It is far more important to understand why a model may not be calibrating well at a particular location than to use unrealistic parameter values to ‘force’ the model to calibrate.

  • Don’t expect your model to provide a good fit to all data.

It is extremely unlikely that your simple model is perfectly representing the complex real world well, all your data has been collected without error, or is unaffected by local factors.

  • The appearance of great calibrations may mean:

The model has been overfitted to the data with unrealistic parameter values, or

Some of the data, that does not fit well, has been ignored or not presented.

  • Checking adopted parameters.

Calibration events should be re-run with adopted parameters and results should show at least reasonable performance for all of the calibration events.

  • Confirming model suitability for design events

Model performance, for design events, should be confirmed using Flood Frequency Analysis results, if available, or regional flood frequency information.

Book 7 also has worthwhile guidance on uncertainty analysis, model checking and reporting.

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