Modelling feedbacks between human and natural processes in the land system

Abstract

The unprecedented use of Earth's resources by humans, in combination with the increasing natural variability in natural processes over the past century, is affecting evolution of the Earth system. To better understand natural processes and their potential future trajectories requires improved integration with 5 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. To capture and formalize our understanding of the interactions and feedback between human and natural systems a variety of modelling approaches are used. While integrated assessment models are widely 10 recognized as supporting this goal and integrating representations of the human and natural system for global applications, an increasing diversity of models and corresponding research have focused on coupling models specializing in specific human (e.g., decision-making) or natural (e.g., erosion) processes at multiple scales. Domain experts develop these specialized models with a greater degree of detail, accuracy, and transparency, with many adopting open-science norms that use new technology for 15 model sharing, coupling, and high performance computing. We highlight examples of four different approaches used to couple representations of the human and natural system, which vary in the processes represented and in the scale of their application. The examples illustrate how groups of researchers have attempted to overcome the lack of suitable frameworks for coupling human and natural systems to answer questions specific to feedbacks between human and natural systems. We draw from these 20 examples broader lessons about system and model coupling and discuss the challenges associated with maintaining consistency across models and representing feedback between human and natural systems in coupled models.Earth Syst. Dynam. Discuss., https://doi

Eight grand challenges in socio-environmental systems modeling

“…household and firms, to community, province/state and nation) ( Figure 2). Modeling of such complex, multi-scale systems requires clarity on the representation of scales in each type of subsystem, and matching a conceptual representation of variables and processes with their data (Scholes et al, 2013;Robinson et al, 2018). Furthermore, since spatial and temporal domains between social and environmental systems tend not to overlap, they need to be coherently matched to allow for coupled modeling (Gibson et al, 2000).…”

“…In addition to the need for conceptual consistency, empirical models of coupled SES require downscaling and upscaling of social and environmental processes to match other subsystems within the model (Figure 2). In the literature, there are examples that demonstrate approaches to resolving these scale differences while maintaining ontological and process consistency between coupled models that representing SES (Robinson et al 2018). While up/down-scaling approaches are actively used in environmental system analysis (Fowler et al, 2007;van Ittersum et al, 2013;Vereecken et al, 2007;Winsemius et al, 2013), they are in their infancy for social systems.…”

Modelling how people and nature are intertwined

“…Generic models can represent a wide range of species, systems and environments with only re-calibration or minor amendments (Grimm and Berger 2016). Feedback representation is a growing research direction towards a next generation of socialecological models (Robinson et al 2018). 4) Use ontologies: commonly agreed upon definitions of concepts and the relations among them.…”

Initial Land Use/Cover Distribution Substantially Affects Global Carbon and Local Temperature Projections in the Integrated Earth System Model

“…ESM ensembles commonly comprise different initial atmospheric conditions to estimate a range of internal model climate variability due to stochastic effects, but only rarely use different initial or bounding LULCC states, even though these effects may be larger because they are associated with input error and are not simply stochastic (Meiyappan & Jain, 2012). Considerable effort goes into improving fine‐scale biogeochemical processes in models (e.g., Tang & Riley, 2017; Zaehle et al, 2015), while less effort has been directed toward improving and standardizing LULCC data and implementation (e.g., Di Vittorio et al, 2018; Robinson et al, 2018). This means that relatively accurate models of particular vegetation types may be applied to the wrong places, with the wrong extents, at the wrong times, thus generating error in both initial conditions and during future projection.…”