Ensemble evaluation of hydrological model hypotheses

By T. Krueger, Jim Freer, John N. Quinton, Christopher J. A. Macleod, G. Bilotta, R. Brazier, P. Butler, P. Haygarth. 2010

106 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/6d54adf326597348a3537aba659969f56ae388c6

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In Civil and Structural Engineering

In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

Uncertainty in streamflow rating curves: methods, controls and consequences (2014, background): “Several recent papers have highlighted the need for, and critical role of, independent/ empirical data for hydrological model improvement (Silberstein, 2006; Fenicia et al., 2008; Krueger et al., 2010; Renard et al., 2010; Schertzer et al., 2010; McMillan et al., 2012).”
Time‐varying sensitivity analysis clarifies the effects of watershed model formulation on model behavior (2013, background): “…1991; Refsgaard and Knudsen, 1996; Gan et al., 1997; Wagener et al., 2001; Fenicia et al., 2006, 2007; Clark et al. 2008; deVos et al., 2010; Krueger et al., 2010; Staudinger et al., 2011], where optimal performance is used to choose between competing model structures on a per-watershed…”
The role of expert opinion in environmental modelling (2012, result): “In principle, this requirement is similar to that of other measured data (e.g. Krueger et al., 2009; Krueger et al., 2010), despite differences in the acquisition processes and associated sources of bias and imprecision between both types of data.”
a, b careful: The challenge of scale invariance for comparative analyses in power law models of the streamflow recession (2015, background): “Correlation between power law parameters has been observed in recession curves [Krueger et al., 2010; McMillan et al., 2014] but is not widely discussed in the recession literature.”
Pursuing the method of multiple working hypotheses for hydrological modeling (2011, methodology): “Though in most applications of GLUE the different models corresponded to different parameter sets within a single model structure (e.g., uniformly sampled from the feasible parameter space), multiple model structures could also be evaluated using essentially the same approach [Krueger et al., 2010].”
Rating curve estimation under epistemic uncertainty (2015, background): “Other authors modified the individual data point likelihoods by limiting the acceptable distance between the data point and the rating curve and/or exchanging the normal distribution for a uniform or pyramidal distribution, drawing on fuzzy set concepts (Pappenberger et al., 2006; Krueger et al., 20”