Model Uncertainty in Water Science: Conceptualization, Assessment and Communication
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".
Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 31696
Special Issue Editors
Interests: uncertainty quantification; multi-model techniques; Bayesian model averaging; socio-hydrology; climate change impacts on hydrological cycle
Interests: model error and uncertainty; model selection and combination; stochastic hydro(geo)logical modelling; risk assessment
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Special Issue Information
Dear Colleagues,
Substantial research has been devoted to uncertainty quantification in water sciences in the last few decades. More recently, an actively-investigated aspect is the uncertainty arising from the definition of alternative model conceptualizations describing complex hydro(geo)logical systems. Such systems are, more often than not, conceptualized on the basis of limited, or even biased, data and knowledge, therefore accepting multiple interpretations. This ‘conceptualization problem’ will have substantial impacts on uncertainty quantification and ultimately on risk assessment and water management. This line of research has opened multiple questions from ways to properly define alternative conceptualizations, the value of data/information/knowledge to reduce model uncertainty, model structure diagnostics, frameworks to efficiently combine (or select from) multiple conceptualizations/working hypotheses, to the link with robust risk assessment frameworks. This Special Issue aims to collate contributions addressing the conceptualization, assessment and communication of model uncertainty in water sciences and how these aspects interlink with other domains such as information theory, risk assessment, safety research, water management and decision making. We welcome a broad spectrum of contributions in the form of state-of-the-art reviews, case studies and methodological papers on topics ranging from (but not limited to) frequentist and Bayesian approaches, multi-model techniques, value of data to reduce uncertainties, to applied aspects covering water management, decision making and risk management under model uncertainty.
Dr. Rodrigo Rojas
Dr. Anneli Guthke
Guest Editors
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Keywords
- conceptual model uncertainty
- model structural adequacy
- predictive uncertainty
- perceptual model development
- model structure diagnostics
- multi-model approaches
- data-worth analysis
- decision making under model uncertainty
- risk assessment under model uncertainty
- communication of model uncertainty
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