A framework for dealing with uncertainty due to model structure error

By Jens Christian Refsgaard, J. P. van der Sluijs, James D. Brown, P. Keur. 2004

554 citations recorded by the source corpus.

Subfields: Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/419b7c11053c6a5e0dd4df537c3437cf02811912

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In Environmental Engineering

In Water Science and Technology

Citations in the corpus

Uncertainty in the environmental modelling process - A framework and guidance (2007, background): “Refsgaard et al. (2006) present a new framework for dealing with uncertainty due to model structure error, based on alternative conceptual models and assessment of their pedigree and adequacy.”
Selecting among five common modelling approaches for integrated environmental assessment and management (2013, background): “Such a task canbe complex for even relatively simple integrated models (see for example Norton and Andrews, 2006; Norton et al., 2006; Refsgaard et al., 2006).”
Exploring the quality of evidence for complex and contested policy decisions (2008, unknown): “For an assessment of the qualiﬁcation of the knowledge base for a particular model structure, for example, pedigree analysis (see box 1) (van der Sluijs 2005, Refsgaard et al 2006) or a model quality checklist (Risbey et al 2005) can be used.”
Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors (2010, methodology): “[5] Unlike data uncertainty, which can be estimated by analyzing sampling and measurement designs [Refsgaard et al., 2006], structural error is much harder to characterize.”
On model selection criteria in multimodel analysis (2007, background): “... recognition has led to a growing tendency among hydrologists to postulate several alternative hydrologic models for a site and use various criteria to (1) rank these models, (2) eliminate some of them, and/or (3) weigh and average predictions and statistics generated by multiple models (Neuman [”
HESS Opinions : The complementary merits of competing modelling philosophies in hydrology (2017, methodology): “…the model against observations as used for conceptual models, the use of regularization (e.g. Pokhrel et al., 2008; Samaniego et al., 2010), data assimilation (e.g. Shi et al., 2014) and similar techniques (e.g. Refsgaard et al., 2006) has proven helpful in identifying feasible model parameters.”