Ancient numerical daemons of conceptual hydrological modeling: 2. Impact of time stepping schemes on model analysis and prediction

By Dmitri Kavetski, M. Clark. 2010

173 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/4e1e1eefb6c5773690078936206442ee30e9a03e

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Citations in the corpus

Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation (2016, background): “Such outlier chains are often the consequence of poor model numerics, for instance, explicit solvers with a too large integration time step are660 known to produce spurious local optima (Clark and Kavetski, 2010; Schoups et al., 2010b).”
Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications (2015, unknown): “, 2006; Zambrano-Bigiarini and Rojas, 2013); (c) determine the priorities for theoretical and site-specific model development (e.g., Hill and Tiedeman, 2007; Saltelli et al., 2008; Kavetski and Clark, 2010); and (d) identify the advantageous placement and timing of new measurements (e.”
Bridging the gap between GLUE and formal statistical approaches: approximate Bayesian computation (2013, background): “…solvers has demonstrated that explicit (Euler based) time-stepping schemes introduce considerable streamﬂow simulation errors and spurious local minima, “pits”, and irregularities in the objective function space (Kavetski et al., 2003, 2006c; Kavetski and Clark, 2010; Schoups et al., 2010b).”
Corruption of accuracy and efficiency of Markov chain Monte Carlo simulation by inaccurate numerical implementation of conceptual hydrologic models (2010, methodology): “Note that this does not necessarily mean that the joint posterior distribution became bimodal [see Kavetski and Clark, 2010, Figure 9].”
The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020, unknown): “In addition to improving properties of the original models (e.g., Kavetski and Clark 2010), more research is needed to equip SA algorithms to handle model failures, which is becoming a more pressing issue as the complexity of mathematical models grows.”
Comparison of joint versus postprocessor approaches for hydrological uncertainty estimation accounting for error autocorrelation and heteroscedasticity (2014, methodology): “…the groundwater flux in GR4J is implemented using a fixed-step explicit Euler time stepping scheme, which is susceptible to numerical artefacts [Kavetski and Clark, 2010]; and (iv) GR4J has a relatively low number of calibrated parameters, which may restrict its predictive ability once the…”
Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modeling heteroscedastic residual errors (2017, methodology): “A quasi-Newton optimization method with 100 multistarts is used [Kavetski and Clark, 2010].”
Pursuing the method of multiple working hypotheses for hydrological modeling (2011, unknown): “This can seriously, and unnecessarily, compromise model development, analysis, and application [Kavetski and Clark, 2010, 2011].”
A comprehensive evaluation of various sensitivity analysis methods: A case study with a hydrological model (2014, background): “Another potential cause for equifinality may be due to a phenomenon known as " numerical daemon " by Kavetski and Clark (2010).”