Complexity in Catchment Modelling Systems and its Impact on Predictive Reliability

Ball, JE; Choi, K

Complexity in Catchment Modelling Systems and its Impact on Predictive Reliability

Ball, JE

Choi, K

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Publisher:: Conference Design Pty Ltd
Publication Type:: Conference Proceeding
Citation:: 30th Hydrology and Water Resources Symposium, 2006, pp. 247 - 252
Issue Date:: 2006-01

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Field	Value	Language
dc.contributor.author	Ball, JE https://orcid.org/0000-0001-8342-1916	en_US
dc.contributor.author	Choi, K	en_US
dc.contributor.editor	Brayford, G	en_US
dc.contributor.editor	Ratcliffe, S	en_US
dc.date	2006-12-04	en_US
dc.date.issued	2006-01	en_US
dc.identifier.citation	30th Hydrology and Water Resources Symposium, 2006, pp. 247 - 252	en_US
dc.identifier.isbn	0-8582579-0-4	en_US
dc.identifier.uri	http://hdl.handle.net/10453/14146
dc.description.abstract	In general, the calibration process involves minimisation of the deviation between recorded information and the simulation predictions through repeated adjustment of control parameters. Implementation of this process requires temporal and spatial information at an adequate resolution to achieve robust predictions from a catchment modelling system. Unfortunately, the available information usually is not adequate for this purpose and it becomes necessary to either use catchment average values or to use other techniques to infer the necessary information. Developments in information technology and the availability of digital information have facilitated the later approach (see, for example, Choi and Ball, 1999). Using the Centennial Park Catchment in Sydney, Australia as a test catchment, inference models for estimation of the control parameters necessary to implement the Storm water Management Model were developed. A number of alternative inference models were developed to assess the influence of inference model complexity and structure on the calibration of the catchment modelling system. These inference models varied from the assumption of a spatially invariant value (catchment average) to spatially variable with each sub-catchment having its own unique values. Furthermore, the influence of different measures of deviation between the monitored information and simulation predictions were considered. Presented herein will be the results of these investigations into the complexity and structure of models used in the calibration process.	en_US
dc.publisher	Conference Design Pty Ltd	en_US
dc.relation.ispartof	30th Hydrology and Water Resources Symposium	en_US
dc.relation.ispartof	Hydrology and Water Resources Symposium	en_US
dc.title	Complexity in Catchment Modelling Systems and its Impact on Predictive Reliability	en_US
dc.type	Conference Proceeding
utslib.location	Sandy Bay, Australia	en_US
utslib.location.activity	Launceston, Tasmania, Australia	en_US
utslib.for	0905 Civil Engineering	en_US
utslib.for	0907 Environmental Engineering	en_US
dc.location.activity	Launceston, Tasmania, Australia	en_US
dc.location.activity	Honk Kong, China
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CTWW - Centre for Technology in Water and Wastewater Treatment
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.place-of-publication	Sandy Bay, Australia	en_US
pubs.start-date	2006-12-04	en_US

Abstract:

In general, the calibration process involves minimisation of the deviation between recorded information and the simulation predictions through repeated adjustment of control parameters. Implementation of this process requires temporal and spatial information at an adequate resolution to achieve robust predictions from a catchment modelling system. Unfortunately, the available information usually is not adequate for this purpose and it becomes necessary to either use catchment average values or to use other techniques to infer the necessary information. Developments in information technology and the availability of digital information have facilitated the later approach (see, for example, Choi and Ball, 1999). Using the Centennial Park Catchment in Sydney, Australia as a test catchment, inference models for estimation of the control parameters necessary to implement the Storm water Management Model were developed. A number of alternative inference models were developed to assess the influence of inference model complexity and structure on the calibration of the catchment modelling system. These inference models varied from the assumption of a spatially invariant value (catchment average) to spatially variable with each sub-catchment having its own unique values. Furthermore, the influence of different measures of deviation between the monitored information and simulation predictions were considered. Presented herein will be the results of these investigations into the complexity and structure of models used in the calibration process.

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http://hdl.handle.net/10453/14146