Dataset:
State and Parameter Uncertainty Estimation (SPUE)
State and Parameter Uncertainty Estimation (SPUE)
| dc.date.accessioned | 2021-11-26T09:53:13Z | |
| dc.date.available | 2021-11-26T09:53:13Z | |
| dc.date.issued | 2020 | en_US |
| dc.description.abstract | This dataset includes streamflow and climate data as well as calibration scripts for performing SPUE. | en_US |
| dc.identifier.uri | http://hdl.handle.net/1959.4/resource/collection/resdatac_1047/1 | |
| dc.language | English | |
| dc.language.iso | EN | en_US |
| dc.rights | CC-BY | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | 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.accessRights.uri | https://purl.org/coar/access_right/c_abf2 | |
| 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 | https://doi.org/10.26190/5f28d447e95f9 | 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 |
| unsw.relation.school | School of Civil and Environmental Engineering | |
| unsw.relation.school | School of Civil and Environmental Engineering | |
| unsw.relation.school | School of Civil and Environmental Engineering | |
| unsw.subject.fieldofresearchcode | 040608 Surfacewater Hydrology | en_US |
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