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Integrating Real-time Geosciences Data into the EarthCube Framework


Mike Daniels, lead and confirmed presenter

V. Chandrasekar, confirmed presenter

Manil Maskey, confirmed presenter

David Arctur, confirmed presenter

Ethan Davis, confirmed presenter

Jiri Kadlec, confirmed presenter


Projects, Committees and Groups

Cloud-Hosted Real-time Data for the Geosciences (CHORDS) Building Block

Building Block for Integrating Discrete and Continuous Data (DisConBB)

Science Committee

Technology and Architecture Committee

Engagement Team



The importance of real-time scientific data is increasing, particularly when informed decisions must be made rapidly. Such real-time data have the potential to drastically improve experiments by permitting optimal allocation of experimental resources, while simultaneously serving as a new tool for non-scientific decision makers during extreme events. The CHORDS team is addressing an urgent need for tools to bring real-time sensor web technology within reach of geoscientists lacking cyberinfrastructure skills and resources. A separate, also-important application focus taken up by the DisConBB team is high-resolution streamflow and flood forecasting at the national scale in near-real time. This research has only become practical in the last year, through the integration of advancements in models for weather forecasting, land-surface hydrology, and streamflow routing. EarthCube has enabled these two project teams to explore how their separate sciences and technologies could leverage and advance each other’s work.

Due to the advancement of sensor networks and real-time access to the resulting data, many new transient phenomena in space-time have the potential to be observed which may otherwise go unnoticed. While EarthCube proposes to provide an unprecedented framework for disseminating and analyzing historical data sources, the use of real-time data raises an additional set of complex challenges: 1) “off-the-shelf” infrastructure does not exist for distributing real-time measurements made by smaller research groups, 2) sensors may fail, 3) latency issues may arise due to hardware and network constraints, 4) data and information may not be presented in a fashion that is easily analyzed and interpreted by decision-makers during a hazardous or time-critical event, 5) unexpected or new phenomenon during a field experiment may trigger the need to rapidly shift modeling and sampling strategies and 6) standards and protocols for real-time data streams being fed to advanced visualization tools and model assimilation are limited in many cases.

The CHORDS and DisConBB teams are addressing these issues with easy-to-use tools that establish and run sensor webs for research with little to no technology guidance required, which then feed real-time data into downstream tools. For example, CHORDS real-time streams would feed into DisConBB mappings and tools for transforming WaterML into netCDF-CF encoded timeseries that are more easily aggregated and visualized. Applications of this capability could potentially provide important sources of observation data for comparing, validating and calibrating national streamflow and flood forecasting models that are being developed for DisConBB.


Initial Outline of Session Presentation/Demos

  • Why real-time data is important: Getting timely information and data to decision makers during hazards, field projects or other time-critical events (Chandrasekar)

  • Integration of atmospheric and hydrologic models for national-scale flood forecasting (Arctur)

  • Lowering the barrier to disseminating sensor observations in real-time (Daniels)

  • Metadata and web services for real-time data (Maskey)

  • Connecting real-time observation data into flood forecasting workflows (Arctur)

  • Visualizing snow and streamflow observations and forecasts in a web-based application (Kaldec)


Background and Experience of Audience

Some knowledge of real-time data from observing sensors would be helpful, as well as those who interface with data streams through standard Open Geospatial Consortium (OGC) services, WaterML, etc. Users of real-time data from the standpoints of emergency managers, field program PIs, modelers, geoscientists and small sensor teams would be particularly welcomed as this will provide the teams with valuable guidance, requirements and next steps.


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