State and Parameter Uncertainty Estimation (SPUE) 2021-11-26T09:53:13Z 2021-11-26T09:53:13Z 2020 en_US
dc.description.abstract This dataset includes streamflow and climate data as well as calibration scripts for performing SPUE. en_US
dc.language English
dc.language.iso EN en_US
dc.rights CC-BY
dc.rights.uri en_US
dc.subject.other calibration en_US
dc.subject.other SPUE en_US
dc.subject.other hydrology en_US
dc.subject.other rainfall-runoff en_US
dc.subject.other uncertainty en_US
dc.subject.other states en_US
dc.subject.other soil moisture en_US
dc.title State and Parameter Uncertainty Estimation (SPUE) en_US
dc.type Dataset en_US
dcterms.accessRights open access
dcterms.rightsHolder Copyright 2020, University of New South Wales en_US
dspace.entity.type Dataset en_US
unsw.contributor.leadChiefInvestigator Kim, Shaun en_US
unsw.contributor.researchDataCreator Marshall, Lucy en_US
unsw.contributor.researchDataCreator Hughes, Justin en_US
unsw.contributor.researchDataCreator Sharma, Ashish en_US
unsw.contributor.researchDataCreator Vaze, Jai en_US
unsw.identifier.doi en_US
unsw.relation.OriginalPublicationAffiliation Kim, Shaun, Civil & Environ. Eng (Sum), Engineering, en_US
unsw.relation.OriginalPublicationAffiliation Marshall, Lucy, Civil & Environ. Eng (Sum), Engineering, en_US
unsw.relation.OriginalPublicationAffiliation Hughes, Justin, , CSIRO, en_US
unsw.relation.OriginalPublicationAffiliation Sharma, Ashish, Civil & Environ. Eng (Sum), Engineering, en_US
unsw.relation.OriginalPublicationAffiliation Vaze, Jai, , CSIRO, en_US
unsw.relation.faculty Engineering
unsw.relation.projectDesc Proposal abstract: For comprehensive uncertainty analyses in environmental modelling, the different sources of uncertainty need to be appropriately characterised. Otherwise, subsequent predictions are likely to be specious. Model structure uncertainty is a particularly difficult error source to quantify due to its epistemic nature, i.e. it stems from a lack of system knowledge. System mis-representation not only causes some immediate error in the simulated response but, due to the inaccurate estimation of the model states, causes further deterioration in following time steps, and hence, autocorrelated residuals. Data assimilation, which is extensively used in forecasting, corrects the internal model states during simulation. This is to compensate for any system perturbations that aren’t explicitly represented. However, appropriate prior characterisation of state uncertainty is required for successful assimilation. Observational data uncertainty is more easily estimated than structural uncertainty if knowledge is given about the measurement techniques and data processing. To date, structural uncertainty characterisations that assume known observational data uncertainty have not directly involved assessing state errors. Attempts at characterising state uncertainty have generally been in conjunction with data assimilation schemes, however, data assimilation is not the ideal framework for error characterisation. The aim of the proposed research is to develop, implement and evaluate various methods in characterising hydrological model structure uncertainty as error in the model states. The methods will be tested in synthetic and real case studies, and efficacy compared to that of existing structural error characterisation approaches. The developed methods will be organised into a framework that may be used for a vast range of modelling studies and will be demonstrated in real applications. This research has wide appeal since it will provide generic strategies in addressing structural uncertainty in modelling for environmental planning, management and forecasting. en_US
unsw.relation.projectTitle Theoretically robust methods for quantifying model structure errors in hydrological models en_US School of Civil and Environmental Engineering School of Civil and Environmental Engineering School of Civil and Environmental Engineering
unsw.subject.fieldofresearchcode 040608 Surfacewater Hydrology en_US
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