Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology

By Keith Beven, Jim Freer. 2001

2,210 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/db3c69373dfebcd348787a127bbdeae7a1e325b4

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Citations in the corpus

Model Performance Sensitivity to Objective Function during Automated Calibrations (2012, background): “This conclusion is con sistent with the equifinality concept of Beven and Freer (2001), which argues that multiple sets of parameters can simulate water shed characteristics reasonably well.”
Estimating Saturated Hydraulic Conductivity Using Genetic Programming (2007, background): “These aspects of uncertainty have led to the concept of equifi nality ( Beven and Freer, 2001 ), which argues that there are many different model structures and many different parameter sets within a chosen model structure that may be behavioral or acceptable in reproducing the observed behavior of ”
Spatial proximity, physical similarity, regression and ungaged catchments: A comparison of regionalization approaches based on 913 French catchments (2008, methodology): “[2005], who also included prior likelihood based on the GLUE framework [Beven and Freer, 2001].”
Consideration of measurement uncertainty in the evaluation of goodness-of-fit in hydrologic and water quality modeling (2007, methodology): “The root mean square method is widely accepted and has been used for uncertainty estimates related to discharge measurements (Sauer and Meyer, 1992) and water quality constituents (Cuadros-Rodriquez et al., 2002).”
Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling (2009, result): “The decomposition presented here highlights the fact that identical values of the NSE criterion are not necessarily indistinguishable – as is commonly (and erroneously) assumed in the literature in arguments relating to equifinality (Beven and Binley, 1992; Beven and Freer, 2001) – since the criteri”
Large-sample hydrology: a need to balance depth with breadth (2013, background): “, 2012), and on quantifying predictive uncertainty (Beven and Freer, 2001; Doherty and Johnston, 2003; Pokhrel et al., 2008).”
Ensemble predictions of runoff in ungauged catchments (2005, methodology): “The problem is compounded by model overparameterization, so that a multitude of significantly different MP sets lead to near-equivalent results (the equifinality problem [Beven and Freer, 2001]).”
Beyond microbes: Are fauna the next frontier in soil biogeochemical models? (2016, background): “…operating at large spatial scales in order to avoid problemswith computational constraints and the potential of developing amodel that can replicate existing data but inaccurately represents the real world and thus future scenarios (i.e. ‘equifinality’; Beven and Freer, 2001; Luo et al., 2009).”
Assessing uncertainty propagation through physically based models of soil water flow and solute transport (2006, background): “To date, uncertainty analyses of environmental models have typically focused on model inputs and parameter values, as evidenced by the range of schemes available for input uncertainty propagation and for evaluating plausible parameter values (e.g. Janssen et al., 1994; Bennett et al., 1998; Clausnit”
Using expert knowledge to increase realism in environmental system models can dramatically reduce the need for calibration (2013, background): “In contrast, due to their reduced storage capacity, wetlands are likely to generate fast flows at lower moisture levels, thus dominating event response during dry periods (cf. Beven and Freer, 2001b; Seibert, 2003; Molénat et al., 2005; Anderson et al., 2010; Birkel et al., 2010).”
A formal framework for scenario development in support of environmental decision-making (2009, background): “There exists an extensive literature on understanding and estimating uncertainties in environmental studies (e.g. Morgan and Henrion, 1990; Beven and Freer, 2001; Wagener and Gupta, 2005).”
Appraisal of the generalized likelihood uncertainty estimation (GLUE) method (2008, methodology): “Beven and Binley [1992] acknowledge that the choice of likelihood measure will greatly influence the resulting uncertainty intervals and so argue that this choice must be made explicit so they can be the ‘‘subject of discussion and justification’’ [ Beven and Freer, 2001, p. 18 ].”