Decision Support Modeling: Data Assimilation, Uncertainty Quantification, and Strategic Abstraction. Issue 3 (30th December 2019)
- Record Type:
- Journal Article
- Title:
- Decision Support Modeling: Data Assimilation, Uncertainty Quantification, and Strategic Abstraction. Issue 3 (30th December 2019)
- Main Title:
- Decision Support Modeling: Data Assimilation, Uncertainty Quantification, and Strategic Abstraction
- Authors:
- Doherty, John
Moore, Catherine - Abstract:
- Abstract: We present a framework for design and deployment of decision support modeling based on metrics which have their roots in the scientific method. Application of these metrics to decision support modeling requires recognition of the importance of data assimilation and predictive uncertainty quantification in this type of modeling. The difficulties of implementing these procedures depend on the relationship between data that is available for assimilation and the nature of the prediction(s) that a decision support model is required to make. Three different data/prediction contexts are identified. Unfortunately, groundwater modeling is generally aligned with the most difficult of these. It is suggested that these difficulties can generally be ameliorated through appropriate model design. This design requires strategic abstraction of parameters and processes in a way that is optimal for the making of one particular prediction but is not necessarily optimal for the making of another. It is further suggested that the focus of decision support modeling should be on the ability of a model to provide receptacles for decision‐pertinent information rather than on its purported ability to simulate environmental processes. While models are compromised in both of these roles, this view makes it clear that simulation should serve data assimilation and not the other way around. Data assimilation enables the uncertainties of decision‐critical model predictions to be quantified andAbstract: We present a framework for design and deployment of decision support modeling based on metrics which have their roots in the scientific method. Application of these metrics to decision support modeling requires recognition of the importance of data assimilation and predictive uncertainty quantification in this type of modeling. The difficulties of implementing these procedures depend on the relationship between data that is available for assimilation and the nature of the prediction(s) that a decision support model is required to make. Three different data/prediction contexts are identified. Unfortunately, groundwater modeling is generally aligned with the most difficult of these. It is suggested that these difficulties can generally be ameliorated through appropriate model design. This design requires strategic abstraction of parameters and processes in a way that is optimal for the making of one particular prediction but is not necessarily optimal for the making of another. It is further suggested that the focus of decision support modeling should be on the ability of a model to provide receptacles for decision‐pertinent information rather than on its purported ability to simulate environmental processes. While models are compromised in both of these roles, this view makes it clear that simulation should serve data assimilation and not the other way around. Data assimilation enables the uncertainties of decision‐critical model predictions to be quantified and maybe reduced. Decision support modeling requires this. Abstract : Article Impact Statement : A conceptual framework that assists modelers in selecting appropriate complexity for decision‐support modeling is presented. … (more)
- Is Part Of:
- Ground water. Volume 58:Issue 3(2020)
- Journal:
- Ground water
- Issue:
- Volume 58:Issue 3(2020)
- Issue Display:
- Volume 58, Issue 3 (2020)
- Year:
- 2020
- Volume:
- 58
- Issue:
- 3
- Issue Sort Value:
- 2020-0058-0003-0000
- Page Start:
- 327
- Page End:
- 337
- Publication Date:
- 2019-12-30
- Subjects:
- Groundwater -- Periodicals
Wells -- Periodicals
Eau souterraine -- Périodiques
Puits -- Périodiques
Grondwater
Eau souterraine
Puits
Electronic journals
Périodique électronique (Descripteur de forme)
Ressource Internet (Descripteur de forme)
551.49 - Journal URLs:
- http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1745-6584 ↗
http://onlinelibrary.wiley.com/ ↗
http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1745-6584 ↗
http://www.blackwell-synergy.com/loi/gwat ↗
http://www.umi.com/proquest ↗ - DOI:
- 10.1111/gwat.12969 ↗
- Languages:
- English
- ISSNs:
- 0017-467X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4219.450000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 13118.xml