A manifesto for the equifinality thesis

By Keith Beven. 2006

2,448 citations recorded by the source corpus.

Subfields: Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/d882f7133e542ddc398a8b4e71428fc63423d1e1

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In Environmental Engineering

In Water Science and Technology

Citations in the corpus

Multi-model ensemble hydrologic prediction using Bayesian model averaging (2007, background): “This has motivated a number of researchers to advocate multi-model methods for hydrologic predictions [33,17,1,3].”
Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities (2012, background): “Moreover, the inverse problem is often poorly constrained, which can result in parameters in one part of the model assigned unrealistic values to compensate for unrealistic parameters in another part of the model (Beven, 2006).”
Bayesian calibration of building energy models with large datasets (2017, positive): “Consequently, calibrating these models with limited data can often lead to overparameterization and equifinality (i.e., the model parameters are not uniquely identifiable) (Beven, 2006).”
Searching for the Holy Grail of scientific hydrology: Q t =( S, R, Δt ) A as closure (2006, background): “This di ﬀ erence is one explanation for the results of tracer experiments that suggest that in many small catchments, the hydrograph is dominated by the displacement of pre-event water (see discussion of the history of these ideas in Beven, 2006b”
Assessing uncertainty and complexity in regional-scale crop model simulations (2017, methodology): “Parameter uncertainty, similar to other types of environmental modelling approaches (Beven, 2006; Stainforth et al., 2005), was found to be an important aspect of groundnut growth prediction.”
Calibration and uncertainty analysis of the SWAT model using Genetic Algorithms and Bayesian Model Averaging (2009, methodology): “This is because when the calibrated hydrological models are applied to another set of data independent of the calibration data, the hydrologic conditions may change and therefore impact the predictive interval estimation (Beven, 2006; Liu and Gupta, 2007; Zhang et al., 2009a).”
Relationship Between Remotely-sensed Vegetation Indices, Canopy Attributes and Plant Physiological Processes: What Vegetation Indices Can and Cannot Tell Us About the Landscape (2008, result): “Second is the problem of equifinality, the tendency of models with different levels of complexity and different starting assumptions to produce similar results [82].”
Influence of uncertain boundary conditions and model structure on flood inundation predictions. (2006, methodology): “Models that result in predictions outside that range are rejected [ 66 ].”
Parameter uncertainty analysis for an operational hydrological model using residual-based and limits of acceptability approaches (2018, positive): “The extended concept of behavioral models in the GLUE limits of acceptability approach (GLUE LOA) (Beven, 2006) attempts to overcome this drawback through use of error bounds of the observational dataset.”
A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists (2017, background): “Equifinality (competingmodels and parameter sets that can generate similar outcomes; Beven, 2006) is another long-standing issue.”
GLUE: 20 years on (2014, methodology): “This may be because of the limitations of the available measurement techniques or number of measurements in providing what the model needs or an evaluation variable that is commensurable with a model predicted variable (Beven, 2006, 2009a).”
Efficient prediction uncertainty approximation in the calibration of environmental simulation models (2008, methodology): “Instead, we present substantially more efficient alternatives to the standard GLUE analysis that serve to address a future research question recently noted by Beven [2006a] regarding how to efficiently search for behavioral parameter sets.”
Bridging the gap between GLUE and formal statistical approaches: approximate Bayesian computation (2013, methodology): “This is perhaps more obvious if we use the following notation where I (A) is a simple indicator function that is “1” if A is true and “0” otherwise, and δ t ; t = { 1 ,... ,n } constitutes the effective observation error that takes into account multiple sources of error (Beven, 2006).”
The influence of parametric uncertainty on the relationships between HBV model parameters and climatic characteristics (2015, background): “The selected version of the HBV model includes 14 parameters and could suffer from equifinality problems (Beven 2006).”