What does interdisciplinarity look like in practice : Mapping interdisciplinarity and its limits in the environmental sciences

In this paper we take a close look at current interdisciplinary modeling practices in the environmental sci- ences, and suggest that closer attention needs to be paid to the nature of scientific practices when investigating and planning interdisciplinarity. While interdisciplinarity is often portrayed as a medium of novel and trans- formative methodological work, current modeling strategies in the environmental sciences are conservative, avoiding methodological conflict, while confining interdisciplinary interactions to a relatively small set of pre-existing modeling frameworks and strategies (a process we call crystallization). We argue that such prac- tices can be rationalized as responses in part to cognitive constraints which restrict interdisciplinary work. We identify four salient integrative modeling strategies in environmental sciences, and argue that this crystalliza- tion, while contradicting somewhat the novel goals many have for interdisciplinarity, makes sense when con- sidered in the light of common disciplinary practices and cognitive constraints. These results provide cause to rethink in more concrete methodological terms what interdisciplinarity amounts to, and what kinds of inter- disciplinarity are obtainable in the environmental sciences and elsewhere.Peer reviewe

Beyond land cover change: Towards a new generation of Land Use Models

Land use models play an important role in exploring future land change dynamics and are instrumental to support the integration of knowledge in land system science. However, only modest progress has been made in achieving these aims due to insufficient model evaluation and limited representation of the underlying socio-ecological processes. We discuss how land use models can better represent multi-scalar dynamics, human agency and demand-supply relations, and how we can achieve learning from model evaluation. By addressing these issues we outline pathways towards a new generation of land use models that allow not only the assessment of future land cover pattern changes, but also stimulate envisioning future land use by society to support debate on sustainability solutions and help design alternative solutions

Coupled land use and ecological models reveal emergence and feedbacks in socio-ecological systems

Acknowledgements: This work was supported by an EPSRC Doctoral Training Centre grant (EP/G03690X/1). Supplementary material (Appendix ECOG‐04039 at ). Appendix 1.Peer reviewedPublisher PD

Earth system economics: a biophysical approach to the human component of the Earth system

Unidad de excelencia María de Maeztu CEX2019-000940-MThe study of humans has largely been carried out in isolation from the study of the non-human Earth system. This isolation has encouraged the development of incompatible philosophical, aspirational, and methodological approaches that have proven very difficult to integrate with those used for the non-human remainder of the Earth system. Here, an approach is laid out for the scientific study of the global human system that is intended to facilitate seamless integration with non-human processes by striving for a consistent physical basis, for which the name Earth system economics is proposed. The approach is typified by a foundation on state variables, central among which is the allocation of time amongst activities by human populations, and an orientation towards considering human experience. A framework is elaborated which parses the Earth system into six classes of state variables, including a neural structure class that underpins many essential features of humanity. A working example of the framework is then illustrated with a simple numerical model, considering a global population that is engaged in one of two waking activities: provisioning food or doing something else. The two activities are differentiated by their motivational factors, outcomes on state variables, and associated subjective experience. While the illustrative model is a gross simplification of reality, the results suggest how neural characteristics and subjective experience can emerge from model dynamics. The approach is intended to provide a flexible and widely applicable strategy for understanding the human-Earth system, appropriate for physically based assessments of the past and present, as well as contributing to long-term model projections that are naturally oriented towards improving human well-being

Transition Pathways to Sustainable Agricultural Water Management: A Review of Integrated Modeling Approaches

Agricultural water management (AWM) is an interdisciplinary concern, cutting across traditional domains such as agronomy, climatology, geology, economics, and sociology. Each of these disciplines has developed numerous process-based and empirical models for AWM. However, models that simulate all major hydrologic, water quality, and crop growth processes in agricultural systems are still lacking. As computers become more powerful, more researchers are choosing to integrate existing models to account for these major processes rather than building new cross-disciplinary models. Model integration carries the hope that, as in a real system, the sum of the model will be greater than the parts. However, models based upon simplified and unrealistic assumptions of physical or empirical processes can generate misleading results which are not useful for informing policy. In this article, we use literature and case studies from the High Plains Aquifer and Southeastern United States regions to elucidate the challenges and opportunities associated with integrated modeling for AWM and recommend conditions in which to use integrated models. Additionally, we examine the potential contributions of integrated modeling to AWM — the actual practice of conserving water while maximizing productivity

Computational models that matter during a global pandemic outbreak: A call to action

The COVID-19 pandemic is causing a dramatic loss of lives worldwide, challenging the sustainability of our health care systems, threatening economic meltdown, and putting pressure on the mental health of individuals (due to social distancing and lock-down measures). The pandemic is also posing severe challenges to the scientific community, with scholars under pressure to respond to policymakers’ demands for advice despite the absence of adequate, trusted data. Understanding the pandemic requires fine-grained data representing specific local conditions and the social reactions of individuals. While experts have built simulation models to estimate disease trajectories that may be enough to guide decision-makers to formulate policy measures to limit the epidemic, they do not cover the full behavioural and social complexity of societies under pandemic crisis. Modelling that has such a large potential impact upon people’s lives is a great responsibility. This paper calls on the scientific community to improve the transparency, access, and rigour of their models. It also calls on stakeholders to improve the rapidity with which data from trusted sources are released to the community (in a fully responsible manner). Responding to the pandemic is a stress test of our collaborative capacity and the social/economic value of research

Exploring low-carbon futures: A web service approach to linking diverse climate-energy-economy models

© 2019 by the authors. The use of simulation models is essential when exploring transitions to low-carbon futures and climate change mitigation and adaptation policies. There are many models developed to understand socio-environmental processes and interactions, and analyze alternative scenarios, but hardly one single model can serve all the needs. There is much expectation in climate-energy research that constructing new purposeful models out of existing models used as building blocks can meet particular needs of research and policy analysis. Integration of existing models, however, implies sophisticated coordination of inputs and outputs across different scales, definitions, data and software. This paper presents an online integration platform which links various independent models to enhance their scope and functionality. We illustrate the functionality of this web platform using several simulation models developed as standalone tools for analyzing energy, climate and economy dynamics. The models differ in levels of complexity, assumptions, modeling paradigms and programming languages, and operate at different temporal and spatial scales, from individual to global. To illustrate the integration process and the internal details of our integration framework we link an Integrated Assessment Model (GCAM), a Computable General Equilibrium model (EXIOMOD), and an Agent Based Model (BENCH). This toolkit is generic for similar integrated modeling studies. It still requires extensive pre-integration assessment to identify the ‘appropriate’ models and links between them. After that, using the web service approach we can streamline module coupling, enabling interoperability between different systems and providing open access to information for a wider community of users