Decision-making in model construction: Unveiling habits

Babel, Lucie; Vinck, Dominique; Karssenberg, Derek

doi:10.1016/j.envsoft.2019.07.015

Cited by 23 publications

(20 citation statements)

References 57 publications

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“…The use of multiple hydrological models aims at accounting for the uncertainty associated with the models' structure, their numerical implementation, and their processes representation. The idea is to consider a diversity of models that represent the uncertainty at play (Babel et al, 2019). Such multi-model approaches are in particular extremely relevant for probabilistic flood forecasting (Kauffeldt et al, 2016) or for long term climate change impact predictions (Andrianaki et al, 2019;Kobierska et al, 2011Kobierska et al, , 2013, where different model formulations can lead to different model sensitivities with respect to the climate forcing and thus to different results.…”

Section: On Model Structural Uncertainty and Multi-model Approachesmentioning

confidence: 99%

“…This can include, on the one hand, the experience available in the modeling group and, on the other hand, code and data availability. Some of these practical aspects are not reflected in the literature: Peer‐reviewed articles involve a reshaping of the narration (Babel et al, 2019; Pontille, 2007; Rinck, 2010), that is, the chronology of decisions is often modified to fit a standard paper structure and the drivers behind the decisions are adapted retrospectively. The practical aspects that drove model selection are thus difficult to assess objectively without additional information.…”

Section: The Motivations Behind the Model Choicementioning

confidence: 99%

“…While education seems to play only a marginal role, sociological aspects, such as recommendations from colleagues or peers, have a nonnegligible impact (38.8%), mainly among researchers not involved in model development. Babel et al (2019) showed, based on interviews with model developers, that the choices made during model conception and development can be indirectly influenced by various actors. Some transferred knowledge becomes self‐evident for the developer, who embraces external influences.…”

Section: The Motivations Behind the Model Choicementioning

confidence: 99%

“…The strong link between the institutes and some models (see also Figure 5) brings the advantage that the model at hand is thus well known, that the code is available, and that specialists for potential modifications are present. All of these factors are a positive aspect, as in-house experience (or habits) can contribute to efficiency and expertise (Babel et al, 2019). Care has to be taken that habits do not come at the expense of adequacy.…”

Section: The Motivations Behind the Model Choicementioning

confidence: 99%

See 3 more Smart Citations

Why do we have so many different hydrological models? A review based on the case of Switzerland

2021

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Hydrology plays a central role in applied and fundamental environmental sciences, but it is well known to suffer from an overwhelming diversity of models, particularly to simulate streamflow. We discuss here in detail how such diversity did arise based on the example of Switzerland. The case study's relevance stems from the fact that Switzerland, despite being a small country, shows a variety of hydro‐climatological regimes, of water resources management challenges, and of hydrological research institutes that led to a model diversification that stands exemplary for the diversification that arose also at larger scales. Our analysis, based on literature review, personal inquiry, and an author survey, summarizes the main driving forces behind model diversification. We anticipate that this review not only helps researchers from other fields but in particular also the international hydrology community to understand why we have so many different streamflow models. This article is categorized under: Science of Water > Science of Water Science of Water > Hydrological Processes Science of Water > Methods

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Section: On Model Structural Uncertainty and Multi-model Approachesmentioning

confidence: 99%

Section: The Motivations Behind the Model Choicementioning

confidence: 99%

Section: The Motivations Behind the Model Choicementioning

confidence: 99%

Section: The Motivations Behind the Model Choicementioning

confidence: 99%

See 2 more Smart Citations

Why do we have so many different hydrological models? A review based on the case of Switzerland

2021

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“…Babel et al. (2019) revealed, based on interviews with modelers from several fields in the Earth sciences, that habits play a large role in modeling decisions. Mayer et al.…”

Section: Introductionmentioning

confidence: 99%

It Takes a Village to Run a Model—The Social Practices of Hydrological Modeling

Melsen

2022

Water Resources Research

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Hydrological models are frequently used tools for scientific research. In 2010, about 75% of the hydrological scientific publications related to runoff were based on a model study (Burt & McDonnell, 2015). The bibliometric analysis of Addor and Melsen (2019) demonstrates a steady increase in scientific publications based on hydrological models over time. Models are thus an accepted method in scientific hydrological research.Every hydrological model is prone to uncertainty (Oreskes et al., 1994), and in order to draw robust conclusions based on models, this uncertainty has to be made transparent (Renard et al., 2010). Transparency of the uncertainty is not only needed for scientific rigor (Gupta et al., 2012), but also to support practical applications based on the scientific insights (McMillan et al., 2017). The use of models thus comes with (societal) responsibility (Hamilton et al., 2019;Melsen, Vos, et al., 2018). Estimating and quantifying uncertainty in hydrological models is a well-established research field-although challenges remain (Liu & Gupta, 2007) and uncertainty can be framed in different ways (Guillaume et al., 2017).Often, this field takes a technical lens: the model is taken as a starting point, and from there the uncertainty is estimated, for example, in structure, parameters, and/or data (Wagener et al., 2004), for instance, through sampling. However, this notion of uncertainty only comprehends "technical" uncertainty, while models are also prone to methodological uncertainty (Funtowicz & Ravetz, 1993). Methodological uncertainty arises from differences in approaches and methods that are evaluated as appropriate for the research question. Different modelers evaluate

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