The proposed grant would design SKOPE (Synthesized Knowledge of Past Environments) a cybertool that, given a location and a temporal interval, will integrate contemporary, historical, and paleoenvironmental data and return a synthesis of key environmental parameters relevant to humans. The proffered environmental knowledge will be documented with record of its provenance and, to the extent possible, assessments of its accuracy and spatial and temporal resolution. A stakeholder needs assessment will guide the software architecture design. The project will identify key sources of environmental and paleoenvironmental data and identify and generalize the core analytical, modeling, and data integration procedures that will be required. The first stage implementation, funded by a subsequent grant, will cover key environmental parameters for the Southwest US over the last 13,000 years, with a focus on the last 2000. Subsequent stages will extend the tool to additional areas and back in time, will incorporate additional classes of environmental data, and expand the analytical and inferential operations employed. The cybertool to be designed would be freely accessible on the Internet. Use of the DataONE (an NSF DataNet) cyberinfrastructure will facilitate discovery and extraction of modern, modeled or ancient environmental data from multiple relevant repositories. The DataONE Toolkit (ITK) will be used to prototype new analytical and visualization capabilities using workflow environments, R scripts, and other tools, and to in some cases capture computational provenance. Its use will speed development of a readily-extensible software design and will permit the SKOPE design effort to focus squarely on the environmental reconstruction task Intellectual Merit. Understanding coupled human and natural systems is a major research focus for the social and natural sciences. Implicit in the concept of a coupled natural and human system is the mutual dependency of human societies and their natural environments. Scholars examining anything other than short intervals in recent decades can assume neither a stable environment nor that todays environment was replicated in the past. They need environmental knowledge specific to their spatialtemporal problem contexts. However, in accounting for environmental change they are likely to find that state-of-the-art data on past environments are difficult to discover and even more difficult to integrate, process, and interpret. The project would provide such studies with synthesized, state-of-the-art knowledge of environments in the recent or remote past. The tool would have direct applicability in such fields as geography, sustainability, archaeology, sociology, political science, economics, anthropology, and ecology. For example, it would directly benefit geographers seeking to understand changing population distributions across the landscape through time, archaeologists examining the social consequences of abrupt or long-term climate changes, or ecologists investigating long-term changes in biodiversity or ancient species distributions. Broader Impacts. Understanding societal responses to long-term climate change or the socioecological interactions through which humans have transformed landscapes has obvious public policy implications. Social scientific insights on those issues depend heavily on having accurate environmental data of the sort that would be provided by the proposed cybertool. Planners could use its long-term environmental reconstructions to investigate vulnerabilities in existing infrastructure that are not revealed by experience in the recent historic record. The tool would eliminate the need or greatly reduce the costs for countless heritage management projects to do their own reconstructions. Students would have free access to high-quality environmental scenarios in which to situate their own studies. Similarly members of the general public might wish to know how ancient environments differed from contemporary ones where they live or where they choose to visit.
|Effective start/end date||9/1/14 → 2/28/17|
- National Science Foundation (NSF): $157,128.00