Document Type

Article

Publication Date

1-2018

Publication Title

Earth System Dynamics

ISSN

2190-4987

Volume

9

Issue

2

DOI

http://dx.doi.org/10.5194/esd-9-895-2018

Abstract

The unprecedented use of Earth's resources by humans, in combination with increasing natural variability in natural processes over the past century, is affecting the evolution of the Earth system. To better understand natural processes and their potential future trajectories requires improved integration with and quantification of human processes. Similarly, to mitigate risk and facilitate socio-economic development requires a better understanding of how the natural system (e.g. climate variability and change, extreme weather events, and processes affecting soil fertility) affects human processes. Our understanding of these interactions and feedback between human and natural systems has been formalized through a variety of modelling approaches. However, a common conceptual framework or set of guidelines to model human–natural-system feedbacks is lacking. The presented research lays out a conceptual framework that includes representing model coupling configuration in combination with the frequency of interaction and coordination of communication between coupled models. Four different approaches used to couple representations of the human and natural system are presented in relation to this framework, which vary in the processes represented and in the scale of their application. From the development and experience associated with the four models of coupled human–natural systems, the following eight lessons were identified that if taken into account by future coupled human–natural-systems model developments may increase their success: (1) leverage the power of sensitivity analysis with models, (2) remember modelling is an iterative process, (3) create a common language, (4) make code open-access, (5) ensure consistency, (6) reconcile spatio-temporal mismatch, (7) construct homogeneous units, and (8) incorporating feedback increases non-linearity and variability. Following a discussion of feedbacks, a way forward to expedite model coupling and increase the longevity and interoperability of models is given, which suggests the use of a wrapper container software, a standardized applications programming interface (API), the incorporation of standard names, the mitigation of sunk costs by creating interfaces to multiple coupling frameworks, and the adoption of reproducible workflow environments to wire the pieces together.

Comments

Derek T. Robinson1, Alan Di Vittorio2, Peter Alexander3,4, Almut Arneth5, C. Michael Barton6, Daniel G. Brown7, Albert Kettner8, Carsten Lemmen9, Brian C. O'Neill10, Marco Janssen11, Thomas A. M. Pugh12,13, Sam S. Rabin5, Mark Rounsevell3,5, James P. Syvitski14, Isaac Ullah15, and Peter H. Verburg16

1Department of Geography and Environmental Management, University of Waterloo, Waterloo, Ontario, Canada
2Climate and Environmental Sciences Department, Lawrence Berkley National Laboratory, Berkeley, California, USA
3School of Geosciences, University of Edinburgh, Drummond Street, Edinburgh, EH8 9XP, UK
4Land Economy and Environment Research, SRUC, West Mains Road, Edinburgh, EH9 3JG, UK
5Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research – Atmospheric Environmental Research (IMK-IFU), Garmisch-Partenkirchen, Germany
6School of Human Evolution & Social Change and Center for Social Dynamics and Complexity, Arizona State University, Tempe, Arizona, USA
7School of Environmental and Forest Sciences, University of Washington, Seattle, Washington, USA
8Dartmouth Flood Observatory, Community Surface Dynamics Modeling System, Institute of Arctic and Alpine Research, University of Colorado, Boulder, Colorado, USA
9Institute of Coastal Research, Helmholtz-Zentrum Geesthacht, Geesthacht, Germany
10National Center for Atmospheric Research, Boulder, Colorado, USA
11School of Sustainability, Arizona State University, Tempe, Arizona, USA
12School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
13Birmingham Institute of Forest Research, University of Birmingham, Birmingham, UK
14Community Surface Dynamics Modeling System, University of Colorado, Boulder, Colorado, USA
15Department of Anthropology, San Diego State University, San Diego, California, USA
16Environmental Geography Group, Institute for Environmental Studies, VU University Amsterdam, Amsterdam, the Netherlands


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Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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