Abstract
With the rise of large-scale environmental models comes new challenges for how we best utilize this information in research, management and decision making. Interactive data visualizations can make large and complex datasets easier to access and explore, which can lead to knowledge discovery, hypothesis formation and improved understanding. Here, we present a web-based interactive data visualization framework, the Interactive Catchment Explorer (ICE), for exploring environmental datasets and model outputs. Using a client-based architecture, the ICE framework provides a highly interactive user experience for discovering spatial patterns, evaluating relationships between variables and identifying specific locations using multivariate criteria. Through a series of case studies, we demonstrate the application of the ICE framework to datasets and models associated with three separate research projects covering different regions in North America. From these case studies, we provide specific examples of the broader impacts that tools like these can have, including fostering discussion and collaboration among stakeholders and playing a central role in the iterative process of data collection, analysis and decision making. Overall, the ICE framework demonstrates the potential benefits and impacts of using web-based interactive data visualization tools to place environmental datasets and model outputs directly into the hands of stakeholders, managers, decision makers and other researchers.
Highlights
Large-domain modeling is an important advancement in the environmental sciences
We discuss some of the limitations of both the Interactive Catchment Explorer (ICE) framework itself as well as our understanding of how ICE applications affect user thinking and decision making, and we provide some suggestions for how these limitations could be addressed in future research
The ICE framework demonstrates a Web-based interactive data visualization approach to explore the spatial patterns in environmental datasets and model outputs
Summary
Models covering broad spatial areas are expected to improve our ability to study, monitor and manage natural resources at regional, continental and even global scales [1,2,3] Modeling at this scale is increasingly feasible thanks to the growing computational power of desktop and cloud computing platforms, as well as the availability of large-scale and spatially continuous meteorological and geospatial datasets [3]. Geospatial datasets and model outputs spanning large areas can contain a wealth of information Due to their sheer size and complexity, model datasets are often inaccessible to the vast majority of interested stakeholders, resource managers, policy makers and researchers. Other stakeholders and researchers, whose backgrounds, goals and interests likely differ from the original model developers, could benefit from using these datasets to form their own hypotheses, discover new patterns and develop a better understanding of the processes and systems in their own area of interest
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