The variety of missions and observational campaigns in Earth and Space Science has led to a vast number of files containing low-level datasets with native, incompatible arrays. While higher-level datasets re-interpret this low-level data onto common, comparable arrays, this standardization moves scientists farther away from the observations, constraining the analysis and science that can be performed. SpatioTemporal Adaptive Resolution Encoding (STARE) provides a parallelizable, scalable index for co-aligning data with different native spatiotemporal formats efficiently across distributed computing resources. STARE is particularly useful for opening low-level datasets to intercomparison and integrative analysis by providing “array” indexes that carry spatiotemporal semantics, unifying datasets with previously incomparable native array indexing. We are developing STARE as a software library with both C++ and Python APIs and are integrating STARE indexing with existing data transfer tools (OPeNDAP). By organizing data in a hierarchical format and taking advantage of the Hierarchical Data Format’s (HDF) virtualization features, STARE may provide an end user with familiar HDF usability with STARE-enhanced performance and data unification on the back end. Furthermore, STARE’s spatial encoding can be used to index and integrate datasets associated with other planetary bodies, bringing scalability and unification of diverse data for planetary and space science as well.

Earth system models synthesize the science of interactions among multiple biophysical and, increasingly, human processes across a wide range of scales. Ecohydrologic models are a subset of earth system models that focus particularly on the complex interactions between ecosystem processes and the storage and flux of water. Ecohydrologic models often focus at scales where direct observations occur: plots, hillslopes, streams, and watersheds, as well as where land and resource management decisions are implemented. These models complement field-based and data-driven science by combining theory and data to create virtual laboratories. Ecohydrologic models are tools that managers can use to ask “what if” questions and domain scientists can use to explore the implications of new theory or measurements. Recent decades have seen substantial advances in ecohydrologic models, building on both new domain science and advances in software engineering and data availability. The increasing sophistication of ecohydrologic models however, presents a barrier to their widespread use and credibility. Because they are “black boxes,” what the models actually do is rarely clear—even to those who design and use them—and this opacity leads to mistrust and complicates the interpretation of model results. For models to effectively advance our understanding of how plants and water interact, we must improve how we visualize not only model outputs, but also the underlying theories that are encoded within the models. In this paper, we outline a framework for increasing the usefulness of ecohydrologic models through better visualization. We outline four complementary approaches, ranging from simple best practices that leverage existing technologies, to ideas that would engage novel software engineering and cutting edge human-computer interface design. Our goal is to open the ecohydrologic model black box in ways that will engage multiple audiences, from novices to model developers, and support learning, new discovery, and environmental problem solving.