Uncertainty in the environmental modelling process - A framework and guidance

By Jens Christian Refsgaard, J. P. van der Sluijs, Anker Lajer Højberg, Peter A. Vanrolleghem. Environmental Modelling & Software, 2007

1,219 citations recorded by the source corpus.

Subfields: Environmental Engineering, Ocean Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/b12896a0f679d32398532c2359aa4277cb5a9435

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Citations in the corpus

Global emission inventories for C4-C14 perfluoroalkyl carboxylic acid (PFCA) homologues from 1951 to 2030, Part I: production and emissions from quantifiable sources. (2014, background): “Instead, in those cases where the data were sufficient, parameters such as emission factors or levels of PFCA impurities in products were estimated in two scenarios (lower and higher) (Morgan, 2001; Refsgaard et al., 2007).”
An overview of methods to evaluate uncertainty of deterministic models in decision support (2015, neutral): “From the management point of view, uncertainty is, quite simply, the lack of exact knowledge, regardless of what is the cause of this deficiency (Refsgaard et al., 2007).”
Modelling with stakeholders (2010, background): “Refsgaard et al. (2007) stress the importance of model evaluation in the context of uncertainties during the process, not only at the end.”
Linking science with environmental decision making: Experiences from an integrated modeling approach to supporting sustainable water resources management (2008, background): “…in Section 4) and other environmental modeling frameworks discussed in the literature (e.g., Refsgaard and Henriksen, 2004; Jakeman et al., 2006; Refsgaard et al., 2007), we propose a generic framework for performing integrated modeling efforts in an effective and efficient manner to achieve…”
A critical review of integrated urban water modelling - Urban drainage and beyond (2014, methodology): “Refsgaard et al. (2007) list four key levels of uncertainty: (1) statistical (quantifiable), (2) scenario (explorable), (3) qualitative uncertainty (neither quantifiable nor explorable) and (4) recognised ignorance (Van der Keur et al., 2008).”
The role of expert opinion in environmental modelling (2012, methodology): “general (Refsgaard et al., 2007)”
Statistical inference for stochastic simulation models--theory and application. (2011, background): “A common example is the sum of the squared distances between Sobs and the mean of Ssim (Refsgaard et al. 2007; Winkler & Heinken 2007), but many other measures are possible (Smith et al. 2008; Zinck & Grimm 2008; Duboz et al. 2010, see also Schröder & Seppelt 2006, for objective functions used in…”
The role of uncertainty in climate change adaptation strategies—A Danish water management example (2013, methodology): “2.2 Typology Our typology, which is adapted from Walker et al. (2003), Refsgaard et al. (2007) and van der Keur et al. (2008), characterises all uncertainties according to three dimensions nature, level and source.”
Parameter uncertainty analysis for an operational hydrological model using residual-based and limits of acceptability approaches (2018, methodology): “An increased awareness of these modeling uncertainties and the need for quality control of such models requires the integration of uncertainty analysis into the modeling process from the very beginning (Beven, 1989; Saltelli et al., 2006; Refsgaard et al., 2007).”
GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran (2015, methodology): “Refsgaard et al. (2007) investigated 14 different methods commonly used in uncertainty assessment including sensitivity analysis, data uncertainty engine, error propagation equations, inverse modeling (predictive uncertainty), inverse modeling (parameter estimation), expert elicitation, Monte Carlo…”
Model-based policymaking or policy-based modelling? How energy models and energy policy interact (2021, methodology): “Each step holds different possibilities for policymakers to affect the modelling, with the largest effect possible in the initial steps, especially in problem definition and data/assumptions, as it can affect the technical modelling steps in between [37].”
Integrated environmental modeling: A vision and roadmap for the future (2013, background): “Further, Matott et al. (2009) Application of these ideas to more complicated IEMs is only beginning to emerge (Ascough et al., 2008; Bastin et al., in this issue; Beven, 2007; McIntosh et al., 2011; Refsgaard et al., 2007), and there is still much to learn in developing methods and case studies.”
Crop modelling for integrated assessment of risk to food production from climate change (2015, background): “…such as numerical uncertainty that can be statistically quantified (probability theory), scenario uncertainty, imperfect understanding of the issue (Refsgaard et al., 2007; Gabbert et al., 2010) with some potentially relevant unknowns frequently ignored (Knutti, 2010; R€otter et al., 2012a).”