Here we keep an overview of publications from our team members that relate to the overarching theme of the LEGIT project
Remmers J.O.E., Ter Horst R., Nabavi E., Proske U., Teuling A.J., Vos J., Melsen L.A. (2025), HESS Opinions: Reflecting and acting on the social aspects of modeling, Hydrology and Earth System Sciences, 29 (20), pp. 5371 – 5382, doi: 10.5194/hess-29-5371-2025
Hydrological models are generally acknowledged as subjective and uncertain, yet they are often still perceived as neutral, meaning they are seen as not taking sides. This notion of neutrality has several, potentially harmful, consequences. One is the marginalization of certain stakeholders: failing to acknowledge or incorporate alternative perspectives on the issue, which might have warranted a different (modeling) approach. In the critical social sciences, the non-neutrality in methods and research results is an established topic of debate. We propose that in order to deal with it in hydrological modeling, the hydrological modeling network (from commissioner to modeler to end-user) can learn from, and with, critical social sciences. This is a call for responsible modeling – modeling that is accountable, transparent, power-sensitive, situated and reproducible and this responsibility is carried by all actors related to the modeling study. To support our proposition, we structure our argument around four key pillars: (1) the social dimensions of and within hydrological modeling, (2) insights from the critical social sciences, (3) building bridges between disciplines, and (4) reflecting on what the hydrological modeling network can learn. The main take-away, from our perspective, is that responsible modeling is a collective responsibility, shared by all actors in the modeling network. We provide several actionable recommendations for individual actors to increase their share in facilitating responsible modeling.
Proske U., Melsen L.A. (2025), How Climate Model Developers Deal With Bugs, Earth’s Future, 13 (8), e2025EF006318, doi: 10.1029/2025EF006318.
Selected for EOS highlight:
https://eos.org/research-spotlights/when-is-a-climate-model-good-enough
General circulation models (GCMs) are not only powerful tools to understand Earth’s climate system and to forecast the weather. They are also large software programs written by humans. As such, they contain coding mistakes, so-called bugs. Researchers communicate results generated with GCMs and document new model versions, but seldom explicitly communicate the bugs they find in their models, let alone the practices surrounding them. This study portrays practices around bugs that were found during recent ICON development, and the workflow from getting a suspicion to fixing and communicating the bug. Eleven qualitative in-depth interviews were conducted with domain scientists and scientific programmers involved in ICON development. The interviews detail the workflow for dealing with bugs, highlighting that it is only partly standardized. For example, scientific testing is complicated by the fact that there is no absolute truth in terms of results that the model could be tested against. Thus testing resists standardization, so that dealing with bugs remains a laborious process. Being confronted and dealing with bugs, modelers aim for a model that is “good enough” rather than perfect. This stance is pragmatic and relaxes exuberant expectations for GCMs, especially considering their bugs. However, the goal of “good enough” is troubling with regard to GCMs’ use as universal tools, with high societal stakes. Who decides that the model is “good enough,” and what for?