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Sensors, Cyberinfrastructure, and Water Quality in the Little Bear River: Adventures in Continuous Monitoring

Sensors, Cyberinfrastructure, and Water Quality in the Little Bear River: Adventures in Continuous Monitoring. Jeffery S. Horsburgh Amber Spackman Jones, David K. Stevens David G. Tarboton, Nancy O. Mesner. Three Breakout Topics. Designing continuous monitoring networks

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Sensors, Cyberinfrastructure, and Water Quality in the Little Bear River: Adventures in Continuous Monitoring

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  1. Sensors, Cyberinfrastructure, and Water Quality in the Little Bear River: Adventures in Continuous Monitoring Jeffery S. Horsburgh Amber Spackman Jones, David K. Stevens David G. Tarboton, Nancy O. Mesner

  2. Three Breakout Topics • Designing continuous monitoring networks • Sensor network telemetry and communication • Integrating optical measurements with other water quality data to improve predictions

  3. Observing Infrastructure Horsburgh, J. S., A. Spackman Jones, D. G. Tarboton, D. K. Stevens, and N. O. Mesner (2010), A sensor network for high frequency estimation of water quality constituent fluxes using surrogates, Environmental Modelling & Software, 25, 1031-1044, doi:10.1016/j.envsoft.2009.10.012.

  4. Designing Continuous Monitoring Networks

  5. “The Space Challenge” • How do water quality conditions vary throughout a watershed? • As a result of hydrologic features? • As a result of different land use? • As a result of management practices? • What processes (human and natural) drive the variability? • Sources - What are the sources of pollution and how much is coming from each source? • Transport pathways - How do pollutants reach the water bodies in the watershed? • Fate - what happens to the pollutants once they get into a water body?

  6. “The Time Challenge” • How and why does WQ change over time(minutes - years) • In response to natural events (seasons, storms, snowmelt, etc.) • In response to human events (reservoir management, diversions, return flows, etc.) • Are WQ conditions getting better or worse? • What might happen in the future? • Climate change? • Land use change?

  7. Little Bear River Sensor Network • 7 water quality and streamflow monitoring sites • Temperature • Dissolved Oxygen • pH • Specific Conductance • Turbidity • Water level/discharge • 4 weather stations • Air Temperature • Relative Humidity • Solar radiation • Precipitation • Barometric Pressure • Wind speed and direction • Soil moisture and temperature at 5 depths • Spread spectrum radio telemetry network

  8. Water Quality Issues • Nutrients (Primarily P) • Sediment

  9. Pollution Sources Urban Stormwater Runoff Wastewater Treatment Agriculture

  10. Objectives • Use high frequency measurements of discharge and turbidity to better quantify suspended sediment and total phosphorus fluxes • Design the observing infrastructure required to enable high frequency estimates of constituent fluxes using surrogates • Study how high-frequency sensor data collected at multiple sites improve our understanding of hydrology and water quality

  11. Sensor Deployment • How do we deploy the sensors so they are: • Representative • Secure • Lots of great guidance out there • Every site is different! • Can constrain site selection and network design

  12. Have you seen my turbidity sensor? It used to be right here!

  13. Location, Location, Location • Access? • Can you get permission from the landowner? • Can you get there all year long? • Does it freeze? • Cross section? • What sort of telemetry options will work? • Power?

  14. The Human Element • Huh… Why does the river all of the sudden get deeper during the middle of the summer?

  15. Site selection in network design • Your research questions matter – the space and time challenges • Sometimes the “right” site for the science isn’t accessible • Detailed scoping is required, and every site is different

  16. Sensor Network Telemetry and Communication

  17. Why Telemetry? • The remote technician – I don’t have to go to the field to check the status of my sensors! • Adaptive sampling – its raining at my weather station and the stage has increased in the stream, do I change the frequency of my observations? • What can we do with data in real time that we can’t do with offline data?

  18. Telemetry Network Design • Which technologies to choose? • Satellite • UHF/VHF/spread spectrum radios • Ethernet • Land line telephone • Cellular telephone • Mixed networks

  19. Considerations • Equipment cost • Regular service cost • Service availability • Terrain • Vegetation • Distance • Required bandwidth • Availability • Reliability • Power • Interference • Required expertise

  20. Viewshed Analysis • Radio telemetry network setup • Optimal placement of radio repeaters given monitoring site locations

  21. 5.2 Mountain Crest High School Remote Base Station Paradise Repeater UWRL Base Station Computer 1.3 2.9 1.9 Paradise Site East Fork Weather Site C S Confluence Site S 0.6 Lower East Fork Site S Key Lower South Fork Site 2.9 Internet Link S Radio Link 0.8 Stream Monitoring Site S Climate Monitoring Site C S Upper South Fork Site Telemetry

  22. Viewsheds and radios have nothing to do with hydrology and water quality …but, if you want to network sensors or have real time access to data you have to get this expertise…

  23. Data Integration

  24. Observing Infrastructure Horsburgh, J. S., A. Spackman Jones, D. G. Tarboton, D. K. Stevens, and N. O. Mesner (2010), A sensor network for high frequency estimation of water quality constituent fluxes using surrogates, Environmental Modelling & Software, 25, 1031-1044, doi:10.1016/j.envsoft.2009.10.012.

  25. Hydrologic Information Science It is as important to represent hydrologic environments precisely with data as it is to represent hydrologic processes with equations Physical laws and principles (Mass, momentum, energy, chemistry) Hydrologic Process Science (Equations, simulation models, prediction) Hydrologic conditions (Fluxes, flows, concentrations) Hydrologic Information Science (Observations, data models, visualization Hydrologic environment (Dynamic earth) Slide from David Maidment

  26. The Data Deluge One day = 48 observations One week = 336 observations One month = 1440 observations One year = 17,520 observations Two years = 35,040 observations Three + years = 50,000 + observations Times 7 Sites = 350,000 observations Times 10 + Variables per site = 3,500,000 observations Plus different versions of the data (raw versus checked) = 7,000,000 observations Plus 4 weather stations with 10 + variables = almost 12,000,000 observations You need some infrastructure to manage and share the data.

  27. http://hydroserver.codeplex.com • A platform for publishing space-time hydrologic datasets that is: • Autonomous with local control of data • Part of a distributed system that makes data universally available • Basis for Experimental Watershed or Observatory data management and publication system • Standards based approach to data publication • Accepted and emerging standards for data storage and transfer (OGC, WaterML) • Builton established software • MS SQL Server, ArcGIS server • Open Source Community Code Repository • Sustainability

  28. Point Observations Data Internet Applications Ongoing Data Collection Historical Data Files ODM Database GetSites GetSiteInfo GetVariableInfo GetValues GIS Data WaterML WaterOneFlow Web Service Data presentation, visualization, and analysis through Internet enabled applications HydroServer

  29. Observations Data Model (ODM) Streamflow Groundwater levels Precipitation & Climate Soil moisture data Flux tower data Water Quality • A relational database at the single observation level • Metadata for unambiguous interpretation • Traceable heritage from raw measurements to usable information • Promote syntactic and semantic consistency • Cross dimension retrieval and analysis Horsburgh, J. S., D. G. Tarboton, D. R. Maidment, and I. Zaslavsky (2008), A relational model for environmental and water resources data, Water Resources Research, 44, W05406, doi:10.1029/2007WR006392.

  30. Data Values – indexed by “What-where-when” Time, T t “When” A data value vi (s,t) “Where” s Space, S Vi “What” Variables, V

  31. ODM • Supports: • different types of data and different needs • a number of different queries – you can slice and dice the data however you want • Many analysis packages (MATLAB and R) can connect directly to a database to get data • Supports data publication using the CUAHSI Hydrologic Information System (HIS)

  32. Loading data into ODM ODM Data Loader • Interactive ODM Data Loader • Loads data from spreadsheets and comma separated tables in simple format • Streaming Data Loader (SDL) • Loads data from datalogger files on a prescribed schedule • Interactive configuration • SQL Server Integration Services (SSIS) • Microsoft application accompanying SQL Server useful for programming complex loading or data management functions SDL SSIS

  33. Managing Data Within ODM - ODM Tools • Query and export – export data series and metadata • Visualize – plot and summarize data series • Edit – delete, modify, adjust, interpolate, average, etc.

  34. Data Management and Publication Cyberinfrastructure Horsburgh, J. S., and D. G. Tarboton (2010), Components of an integrated environmental observatory information system, Computers & Geosciences, doi:10.1016/j.cageo.2010.07.003. Horsburgh, J. S., D. G. Tarboton, M. Piasecki, D. R. Maidment, I. Zaslavsky, D. Valentine, and T. Whitenack (2008), An integrated system for publishing environmental observations data, Environmental Modelling & Software, 24, 879-888, doi:10.1016/j.envsoft.2009.01.002.

  35. Wait a second – I’m not a computer scientist! Yes…but… • We are collecting more data – higher spatial and temporal resolutions • The way we store and manage data can either enhance or inhibit our analyses • Visualization and analysis of large datasets can be difficult and require specialized software • You will need to share data • Are we training our students to work in a data intensive environment?

  36. Data Management Requirements • What are the 20 queries that you want to do? • e.g., “Give me simultaneous observations of turbidity and TSS collected during the spring snowmelt period so I can develop a regression in R.” • How will you organize and manage your data to satisfy those queries? • What are the standards we will use as a community to share data and metadata?

  37. How do Natural Features and HumanActivities Affect WQ Conditions? Spatial distribution of total suspended solids fluxes in the Little Bear River for 2008. The areas of the node markers are proportional to the total suspended solids fluxes, which are expressed in metric tons.

  38. Support: EAR 0622374 CBET 0610075 Questions?

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