Modelling guidelinesterminology and guiding principles

By Jens Christian Refsgaard, Hans Jørgen Henriksen. 2004

447 citations recorded by the source corpus.

Subfields: Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/bd8ddbff819b2bc897ddc1ed6be75ab6bce893e5

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In Information Systems and Management

Citations in the corpus

Characterising performance of environmental models (2013, background): “Finally, a philosophical debate has aimed to differentiate verification from validation (Jakeman et al., 2006; Oreskes et al., 1994; Refsgaard and Henriksen, 2004).”
Linking science with environmental decision making: Experiences from an integrated modeling approach to supporting sustainable water resources management (2008, background): “…SAHRA IM effort on decision support (as discussed above in Section 4) and other environmental modeling frameworks discussed in the literature (e.g., Refsgaard and Henriksen, 2004; Jakeman et al., 2006; Refsgaard et al., 2007), we propose a generic framework for performing integrated modeling…”
Exploring, exploiting and evolving diversity of aquatic ecosystem models: a community perspective (2015, methodology): “(| | Refsgaard and Henriksen 2004).”
Verification and validation benchmarks (2008, background): “” Refsgaard and Henriksen [60] have recommended terminology and fundamental procedures for V&V that are applicable to a much wider range of simulations than just hydrological modeling.”
HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts (2007, background): “Clarke, 1973) and present (e.g. Jakeman et al., 2006; Refsgaard and Henriksen, 2004; Refsgaard et al., 2005) concerns about the need to develop consistent hydrological modelling protocols and terminologies, it is important to be clear about what does or does not constitute a time series forecasting ”
Uncertainty in the environmental modelling process - A framework and guidance (2007, methodology): “Uncertainty assessment of model simulations is therefore important, when models are used to support water management decisions (Beven and Binley, 1992; Beven, 2002; Pahl-Wostl, 2002; Jakeman and Letcher, 2003; Refsgaard and Henriksen, 2004; Pahl-Wostl, 2007; Vandenberghe et al., 2007).”
The role of uncertainty in climate change adaptation strategies—A Danish water management example (2013, background): “…and system data; model uncertainty, originating from the model structure including process equations, the model software and the model parameters (Refsgaard and Henriksen 2004); context uncertainty, such as the boundaries of the systems to be modelled, future climate and regulatory conditions…”
Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling (2014, methodology): “…development process employed in a paper, which is not only a requirement of quality assurance for model development (Scholten and Udink ten Cate, 1999; Scholten et al., 2001), but also needed to assist the interaction between manager, code developer and modellers (Refsgaard and Henriksen, 2004).”
HESS Opinions : The complementary merits of competing modelling philosophies in hydrology (2017, methodology): “…2012; Andréassian et al., 2009), models then often experience substantial performance decreases when used to predict the hydrological response for time periods they were not calibrated for (e.g. Seibert, 2003; Refsgaard and Henriksen, 2004; Kirchner, 2006; Coron et al., 2012; Gharari et al., 2013).”
A framework for dealing with uncertainty due to model structure error (2004, methodology): “In our definition [39] the term validation is not used in a universal sense, but is always restricted to clearly defined domains of applicability and performance accuracy (‘numerical universal’ in Popperian sense).”