How to Make a Barranco: Modeling Erosion and Land-Use in Mediterranean Landscapes

Barton, C. Michael; Ullah, Isaac; Heimsath, Arjun M.

doi:10.3390/land4030578

Cited by 26 publications

(24 citation statements)

References 58 publications

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“…Some questions, though, are not easily extricated from their geographic setting. For example, models used to explore routes of hominin dispersals out of Africa (Mithen and Reed 2002) or land use in Neolithic Mediterranean contexts (Barton et al 2015) may be dependent on specific terrestrial and marine environments or spatially explicit biophysical processes. In such cases, it may be necessary to include placespecific geospatial information.…”

Section: Some Practical Considerationsmentioning

confidence: 99%

Combining Geographic Information Systems and Agent-Based Models in Archaeology: Part 2 of 3

Davies¹,

Romanowska²,

Harris³

et al. 2019

Preprint

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Archaeologists are using spatial data in increasingly sophisticated analyses and invoking more explicit considerations of space in their interpretations. Geographic information systems (GIS) have become standard technology for professional archaeologists in the collection and management of spatial data. Many calls have been made to develop and adapt digital geospatial technologies for interpretation and understanding past social dynamics, but this has been limited to some extent by the static nature of map-oriented GIS approaches. Here, we illustrate how coupling GIS with agent-based modeling (ABM) can assist with more dynamic explorations of past uses of space and geospatial phenomena.

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Section: Some Practical Considerationsmentioning

confidence: 99%

Combining Geographic Information Systems and Agent-Based Models in Archaeology: Part 2 of 3

Davies¹,

Romanowska²,

Harris³

et al. 2019

Preprint

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“…Naming conventions of data files indicate data type, temporality, and data permanence (intermediary data versus final data). Two versions of the MML have been developed: one where the coupler is an independent piece of wrapping software coded in Java (in MML v1; Barton et al, 2015a) and one where the coupler is integrated into the main model codebase (in Python) of a reduced version of MML (in MML-Lite; Barton et al, 2015b). Subcomponent models are also either independent software scripts coded in Python (Landscape components) or in Java (ABM) in the MML v1 or are coded in a monolithic Python codebase in the MML-Lite.…”

Section: Feedback Implementationmentioning

confidence: 99%

Modelling feedbacks between human and natural processes in the land system

et al. 2018

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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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“…Surface-process models (sometimes combined with agentbased models) have suggested that in semiarid areas with significant topographic relief, even limited settlement by Neolithic farmers or pastoralists can have outsized impacts on local and downstream dynamics of intermittent watercourses, initiating arroyo/barranco formation that reduced agricultural productivity by eroding fields in some areas and burying them in others (86). These processes can happen even without climate change, although they can be exacerbated by climate change.…”

Section: Climate and The Spread Of Farming In Asiamentioning

confidence: 99%

Twenty-first century approaches to ancient problems: Climate and society

Guedes

Crabtree

Bocinsky

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

110

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By documenting how humans adapted to changes in their environment that are often much greater than those experienced in the instrumental record, archaeology provides our only deep-time laboratory for highlighting the circumstances under which humans managed or failed to find to adaptive solutions to changing climate, not just over a few generations but over the longue durée. Patterning between climatemediated environmental change and change in human societies has, however, been murky because of low spatial and temporal resolution in available datasets, and because of failure to model the effects of climate change on local resources important to human societies. In this paper we review recent advances in computational modeling that, in conjunction with improving data, address these limitations. These advances include network analysis, niche and species distribution modeling, and agent-based modeling. These studies demonstrate the utility of deep-time modeling for calibrating our understanding of how climate is influencing societies today and may in the future.climate change | archaeology | computational modeling | agent-based modeling

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How to Make a Barranco: Modeling Erosion and Land-Use in Mediterranean Landscapes

Cited by 26 publications

References 58 publications

Combining Geographic Information Systems and Agent-Based Models in Archaeology: Part 2 of 3

Combining Geographic Information Systems and Agent-Based Models in Archaeology: Part 2 of 3

Modelling feedbacks between human and natural processes in the land system

Twenty-first century approaches to ancient problems: Climate and society

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