Jug Bay Abiotic Data Warehouse

Meteorological, hydrological, and astronomic data relevant to many research studies

This is just the beginning of information describing the data warehouse, results, and design decisions made during its development. Please check back for updates soon as the following outline is expanded and fleshed out.

Why was this developed?

Jug Bay Wetlands Sanctuary is a component in the Chesapeake Bay National Estuarine Research Reserve. Long-term research and monitoring includes:

  • Marbled salamander (Ambystoma opacum)migration
  • Vernal pool formation and conditions
  • Turtle radio telemetry tracking, box turtle nesting
  • bird activity and migration
  • Marsh vegetation surveys
  • Stream monitoring - fish, benthic macroinvertebrate populations

Much of the above research can benefit from an easy to use source of weather and river conditions since those are often uncontrolled variables potentially affecting field observations. Information about the phase of the moon, length of day, and related parameters are also potential explanatory factors.

How does it work?

Data include:

  • Weather data
    • National Weather Service data from the nearest station (several miles away)
    • On site NERRS weather station (since 2003) system wide common data via their Central Data Management Office (CDMO)
    • Daily readings from an on-site traditional weather station (19xx - 201x), replaced in 20xx with a Davis station recording data every xx minutes
    • Other weather data from the local area at times available from various Internet sources
  • River water
    • "Continuous monitoring" on-site of temperature, turbidity, salinity, chlorophyll a, dissolved oxygen and water depth collected by the Maryland Department of Natural Resources for NERRS. The data is available via DNR's Eyes on the Bay and the NERRS CDMO.
    • Periodic nutrient data from grab samples taken by Maryland DNR
    • US Geological Society (USGS) river flow and/or height from two stations upstream from Jug Bay
  • Astronomy
    • Sun/moon rise/set, twilight hours
    • Moon phase, lunar illumination
    • Predicted tides
    • Seasons

Fundamental design objectives

  • Friendly, easy to use user interface, especially for routine activities and analysis
  • Robust features for advanced analysis
  • Facilitate analysis such as degree-days, cumulative precipitation by water year, etc., that can be important for ecological research
  • Automate data download from Internet sources as much as possible

Initial results using the data warehouse

  • Outreach and education illustration of water quality impact
  • Investigation of river inundation of a beaver pond as a factor in the observed change in fish species in the pond
  • Data mining visualization for factors in turtle movement
  • Analysis of early growth conditions for wild rice (Zizania aquatica) as a hypothesis to explain inter-annual observations of different coverage extents

Lessons learned

  • Consistent handling of time - local time zone versus UTC (GMT) , standard versus daylight saving time
  • Data formats from some Internet sources change frequently and without notice while others are quite stable
  • Missing or inconsistent metadata - the information that should be provided to explain the actual data. For example, one source stated the time was EST or EDT as was in effect on that date, but the data values showed no skipped or duplicated value at the change between standard time and daylight saving time
  • Quality assurance and quality control information that was not clear. For example, one data source indicated that daily temperatures were compared to climatic norms without specifying the time frame for the norms. It became obvious that really hot days in summer and cold days in winter were both flagged as outside of x standard deviations of normal. This may have been true based on an annual average, but not likely to be true if the climatic norms used for comparison were months.

Technical details

  • Multiple, linked database files
  • Reusable data conversion functions and views
  • User-controllable data entry range checking and additional validation logic

See also...

This is just the beginning of information describing the data warehouse, results, and design decisions made during its development. Please check back for updates soon as the following outline is expanded and fleshed out.