Aeolian science is faced with significant challenges that impact its ability to benefit from recent advances in information technology. The discipline deals with high-end systems in the form of ground and satellite based sensors, computer modeling and simulation, and wind tunnel experiments. Aeolian scientists also collect field data manually with observational methods that may differ significantly between studies with little agreement on even basic morphometric parameters and terminology. Data produced from these studies, while forming the core of research papers and reports, is rarely available to the community at large. Recent advances are also superimposed on an underlying semantic structure that dates to the 1800's or earlier that is confusing, with ambiguously defined, and at times even contradictory, meanings.
The aeolian “world-view” does not always fit within neat increments nor is defined by crisp objects. Instead change is continuous and features are fuzzy. Development of an ontological framework to guide spatiotemporal research is the fundamental starting point for organizing data in aeolian science. This requires a “rethinking” of how we define, collect, process, store and share data along with the development of a community-wide collaborative approach designed to bring the discipline into a data rich future. There is also a pressing need to develop efficient methods to integrate, analyze and manage spatial and temporal data and to promote data produced by aeolian scientists so it is available for preparing diagnostic studies, as input into a range of environmental models, and for advising national and international bodies that drive research agendas. This requires the establishment of working groups within the discipline to deal with content, format, processing pipelines, knowledge discovery tools and database access issues unique to aeolian science.
Achieving this goal requires the development of comprehensive and highly-organized databases, tools that allow aeolian scientists as well as those in related disciplines to access and analyze the wealth of data available, and a supporting infrastructure and community-wide effort that allows aeolian scientists to communicate their results in replicable ways to scientists and decision and policy makers. Fortunately, much of the groundwork required to move aeolian science into a data rich future has been developed in other data rich physical science fields, and within the computer science and information technology disciplines.
We thank the Bibliography of Aeolian Research authors J.E. Stout, A. Warren, T.E. Gill for access to BAR database and providing the Namibia portion of the database for semantic analysis. To Jeff Lee for encouraging this work and to Joan Drake, Larry Rodarte and two anonymous reviewers for constructive comments. We also thank the University of New Mexico’s Center for Rapid Environmental Assessment and Terrain Evaluation (CREATE) for use of their database processing system to develop and test many of the spatial and spatio-temporal approaches discussed in this paper. Xiaoping Yang thanks the National Natural Science Foundation of China (grant number 41430532) for supporting his desert research over the years.