iEMSs

This year Tony Jakeman and Val Snow presented the iEMSs awards. They both noted that this round of nominations presented the strongest field to date! Thank you to the entire Awards Committee for their hard work.

This Joint Special Issue (JSI) follows on from our previous one on good modelling practice wherein 60+ papers have been published across the three participating journals including Ecological Modelling (ECOMOD), Environmental Modelling and Software (ENSO), and Socio-Environmental Systems Modelling (SESMO). Uncertainty is drawn out specifically for this JSI, also across the three journals, as it pervades so many aspects for modelling Socio-Ecological/Environmental Systems (SES). We now invite research that aims to improve the treatment of uncertainty throughout parts of the entire modelling cycle.

A major underlying theme in the JSI is that uncertainty is diverse in nature and pervasive, entering all stages of the modelling process. It also cannot be eliminated, so that an aim is to assess what gives confidence, not only in the results which should include multiple/pluralistic methods of model evaluation, but also in the process, including documentation of assumptions and methods.
Note that interpretation of what is an SES is considered widely. Systems for example may include hydrological, ecological, energy, policy, health and social sectors, often a combination of these or others. An aim is to share experiences of uncertainty management across sectors and disciplines.

Relevant topics may include but are not limited to those in the list below:
* Philosophical and pragmatic approaches to describing and treating uncertainty, its type and sources
* Methods for identifying and prioritizing treatment of sources, both quantitative and qualitative
* Data acquisition planning for reducing critical uncertainties
* Going beyond (but not excluding) common metrics and Bayesian methods in assessing model performance and realism, including use of multiple and/or qualitative methods, scenario cash testing of model assumptions, explicit assessment of model limitations
* Protocols for documenting uncertainty management, assumptions and related decisions undertaken in the modelling process (such as particularization of TRACE and note books)
* Integration of AI, such as LLMs and surrogate modelling, into the uncertainty assessment process to increase its effectiveness and efficiency
Communicating uncertainty results to end users, including use of narratives about uncertainty and graphical methods
* Software for any of the above
* Case studies that demonstrate holistic aspects of uncertainty treatment

For any queries in submitting your manuscript please contact:

Please ensure you read the Guide for Authors before writing your manuscript: SESMO submission guidelines