Corruption of accuracy and efficiency of Markov chain Monte Carlo simulation by inaccurate numerical implementation of conceptual hydrologic models

By G. Schoups, Jasper A. Vrugt, Fabrizio Fenicia, N. V. D. Giesen. 2010

83 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/05299000205b29dc30ab39f277411ee2034ec869

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In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

Ancient numerical daemons of conceptual hydrological modeling: 2. Impact of time stepping schemes on model analysis and prediction (2010, background): “Markov chain sampling could be particularly vulnerable to getting trapped on macroscale optima [e.g., Vrugt et al., 2009; Schoups et al., 2010], but importance samplers will also converge slower for highly irregular probability distributions, and importantly, these irregularities would considerably…”
A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non‐Gaussian errors (2010, methodology): “Daily records of precipitation and potential evaporation are input into a lumped conceptual rainfall‐runoff model [Schoups et al., 2010] based on the FLEX modeling system [Fenicia et al., 2007] to simulate daily streamflow.”
Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation (2016, methodology): “Case Study III: Dynamic simulation model The third case study considers HYDRUS-1D, a variably saturated porous flow model written in Fortran by Šimůnek et al. (1998). This case study is taken from Scharnagl et al.”
Bayesian calibration of a large‐scale geothermal reservoir model by a new adaptive delayed acceptance Metropolis Hastings algorithm (2011, background): “…data [Oliver et al., 1997], calibrating conceptual rainfallrunoff models [Kuczera and Parent, 1998; Bates and Campbell, 2001; Renard et al., 2010; Schoups et al., 2010], estimating the permeability distribution for a two phase flow model [Efendiev et al., 2005], and assessing parameter…”
Towards a comprehensive assessment of model structural adequacy (2012, methodology): “…solutions, as opposed to incorrect simulations of reality), and to result in highfrequency oscillations of the Likelihood function, causing severe problems during model identification [Kavetski et al., 2003, 2006a, 2006b; Kavetski and Clark, 2010; Kavetski and Kuczera, 2007; Schoups et al., 2010].”