Modeling is essential to characterize and explore complex societal and environmental issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. Depending on the modeling purpose, many SES modelers also realize that involvement of stakeholders and experts is fundamental to support social learning and decision-making processes for achieving improved environmental and social outcomes. The contribution of this paper lies in identifying and formulating grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling. Eight challenges are delineated: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. These challenges limit our ability to effectively use SES modeling to provide the knowledge and information essential for supporting decision making. Whereas some of these challenges are not unique to SES modeling and may be pervasive in other scientific fields, they still act as barriers as well as research opportunities for the SES modeling community. For each challenge, we outline basic steps that can be taken to surmount the underpinning barriers. Thus, the paper identifies priority research areas in SES modeling, chiefly related to progressing modeling products, processes and practices.

In archaeology, we are accustomed to investing great effort into collecting data from fieldwork, museum collections, and other sources, followed by detailed description, rigorous analysis, and in many cases ending with publication of our findings in short, highly concentrated reports or journal articles. Very often, these publications are all that is visible of this lengthy process, and even then, most of our journal articles are only accessible to scholars at institutions paying subscription fees to the journal publishers. While this traditional model of the archaeological research process has long been effective at generating new knowledge about our past, it is increasingly at odds with current norms of practice in other sciences. Often described as ‘open science’, these new norms include data stewardship instead of data ownership, transparency in the analysis process instead of secrecy, and public involvement instead of exclusion. While the concept of open science is not new in archaeology (e.g., see Lake 2012 and other papers in that volume), a less transparent model often prevails, unfortunately. We believe that there is much to be gained, both for individual researchers and for the discipline, from broader application of open science practices. In this article, we very briefly describe these practices and their benefits to researchers. We introduce the Society of American Archaeology’s Open Science Interest Group (OSIG) as a community to help archaeologists engage in and benefit from open science practices, and describe how it will facilitate the adoption of open science in archaeology.

Archaeology has an opportunity to offer major contributions to our understanding of the long-term interactions of humans and the environment. To do so, we must elucidate dynamic socioecological processes that generally operate at regional scales. However, the archaeological record is sparse, discontinuous, and static. Recent advances in computational modeling provide the potential for creating experimental laboratories where dynamic processes can be simulated and their results compared against the archaeological record. Coupling computational modeling with the empirical record in this way can increase the rigor of our explanations while making more transparent the concepts on which they are based. We offer an example of such an experimental laboratory to study the long-term effects of varying landuse practices by subsistence farmers on landscapes, and compare the results with the Levantine Neolithic archaeological record. Different combinations of intensive and shifting cultivation, ovicaprid grazing, and settlement size are modeled for the Wadi Ziqlab drainage of northern Jordan. The results offer insight into conditions under which previously successful (and sustainable) landuse practices can pass an imperceptible threshold and lead to undesirable landscape consequences. This may also help explain long-term social, economic, and settlement changes in the Neolithic of Southwest Asia.

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.

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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

The evolution of Mediterranean landscapes during the Holocene has been increasingly governed by the complex interactions of water and human land use. Different land-use practices change the amount of water flowing across the surface and infiltrating the soil, and change water's ability to move surface sediments. Conversely, water amplifies the impacts of human land use and extends the ecological footprint of human activities far beyond the borders of towns and fields. Advances in computational modelling offer new tools to study the complex feedbacks between land use, land cover, topography and surface water. The Mediterranean Landscape Dynamics project (MedLand) is building a modelling laboratory where experiments can be carried out on the long-term impacts of agropastoral land use, and whose results can be tested against the archaeological record. These computational experiments are providing new insights into the socio-ecological consequences of human decisions at varying temporal and spatial scales.