- Title
- Prediction of ungauged basins - uncertain criteria conditioning, regionalization and multimodel methods
- Creator
- Wyatt, Adam
- Relation
- University of Newcastle Research Higher Degree Thesis
- Resource Type
- thesis
- Date
- 2009
- Description
- Research Doctorate - Doctor of Philosophy (PhD)
- Description
- The purpose of rainfall-runoff modelling, like all environmental modelling is to generate simulations that accurately mimic those encountered in the system being modelled. Once this is achieved, the model may then be used to study the catchment response under conditions that have not previously been observed, such as the determination of extreme flood levels. The complex behaviour of the processes involved in the generation of streamflow mean that to achieve a usable model, simplifications must be made. This inevitably leads to the introduction of model error into the simulations, as these simplifications cannot reproduce the level of response variation encountered in a natural system. As a consequence, a model that performs well at some times may be inappropriate at other times. The MultiModel approach is an alternative method of rainfall-runoff modelling that uses numerous alternative process descriptions to generate a suite of unique rainfall runoff models. These models are calibrated and applied to allow for simulation responses that incorporate not only parameter variability but model structure variability. It is shown that the application of the MultiModel method to four test catchments produced simulated confidence limits that are much more likely to contain flood peaks that are beyond the range encountered during the calibration process than using a single model. This is due to the wider confidence limits generated as a result of the greater structure variability available to the MultiModel. The wider confidence limits are therefore a better reflection of our true understanding of the system being modelled. The prediction of ungauged basins presents an additional challenge to rainfallrunoff modelling. Most methods involve some form of regionalization of model parameters. These approaches are very limited in that they are restricted by model selection and application range. Two unique methods for the prediction of ungauged basins are presented that overcome these restrictions. The first attempts to condition a rainfall-runoff model using uncertain criteria, normally used as a supplement to more common calibration procedures. These criteria include estimates of flood peaks, baseflow, recession and saturated area. It is shown that combinations of these criteria provide a powerful means of constraining the parameter space and reducing the simulation uncertainty. The second approach to model conditioning for ungauged basins uses an alternative method of regionalization that focuses on the estimation of flow characteristics rather than model parameter values. Strong relationships between flow characteristics (such as runoff coefficients, flow duration curves and coefficient of variation) and catchment conditions (such as area, mean annual rainfall and evaporation) are identified for catchments across Australia. Using the estimated ranges of these flow characteristics as assessment criteria, a rainfall-runoff model is successfully conditioned to adequately reproduce the streamflow response of the four test catchments. In particular it is shown that the use of numerous characteristics in tandem further improves the conditioning for the test catchments.
- Subject
- model hypothesis uncertainty; prediction of ungauged basins; regionalisation
- Identifier
- http://hdl.handle.net/1959.13/41180
- Identifier
- uon:4684
- Rights
- Copyright 2009 Adam Wyatt
- Language
- eng
- Full Text
- Hits: 800
- Visitors: 1521
- Downloads: 468
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | ATTACHMENT01 | Abstract | 201 KB | Adobe Acrobat PDF | View Details Download | ||
| View Details Download | ATTACHMENT02 | Thesis | 7 MB | Adobe Acrobat PDF | View Details Download |