Modelling feedbacks between human and natural processes in the land system

Abstract

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 Published by Copernicus Publications on behalf of the European Geosciences Union. D. T. Robinson et al.:Modelling feedbacks between human and natural processes in the land system 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.

Societal breakdown as an emergent property of large-scale behavioural models of land use change

“…An, 2012;Brown et al, 2017;Robinson et al, 2018). However, their focus on underlying processes makes them suitable for scaling out and scaling up across entire coherent land systems (Rounsevell et al, 2012a). Recent conceptual and technical developments make this scaling feasible (Arneth et al, 2014;, and associated studies suggest that microscale behavioural processes can have significant macroscale effects (Bai et al, 2016;Blanco et al, 2017a;Brown et al, 2018b;Calvin and Bond-Lamberty, 2018).…”

Modelling how people and nature are intertwined

“…5) Include feedbacks. Feedback representation is a growing research direction towards a next generation of socialecological models (Robinson et al 2018). The work of Synes and colleagues reminds us that we can only adequately find out how to best manage our environment if we acknowledge how strongly we are intertwined with nature.…”

Explaining and Measuring Social-Ecological Pathways: The Case of Global Changes and Water Security

“…Consequently, very few studies manage change over time. The first attempt to model feedback between water security, risk and economic growth is very recent [4] and researchers started to draw and share lessons from their experience in modeling of coupled human-nature systems [12]. ("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" ( [12], p.…”

“…Current literature offers mainly in-depth investigations highlighting mechanisms and triggers of changes. However, there is a need to scale-up the knowledge toward more generalizable results [11,12]. Relying on SES and resilience thinking, we propose an institutional and cognitive model of change for SES where institutions and natural resource systems co-evolve.…”