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
Mar 29, 2015
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
• Sensor network telemetry and communication
• Integrating optical measurements with other water quality data to improve predictions
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.
Designing Continuous Monitoring Networks
“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?
“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?
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
Water Quality Issues• Nutrients (Primarily P)• Sediment
Urban StormwaterRunoffAgriculture
Wastewater Treatment
Pollution Sources
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
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
Have you seen my turbidity sensor?
It used to be right here!
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?
The Human Element
• Huh… Why does the river all of the sudden get deeper during the middle of the summer?
• 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
Sensor Network Telemetry and Communication
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?
Telemetry Network Design
• Which technologies to choose?– Satellite– UHF/VHF/spread
spectrum radios– Ethernet– Land line telephone– Cellular telephone– Mixed networks
Considerations
• Equipment cost• Regular service
cost• Service availability• Terrain• Vegetation• Distance
• Required bandwidth
• Availability• Reliability• Power• Interference• Required expertise
• Radio telemetry network setup
• Optimal placement of radio repeaters given monitoring site locations
Viewshed Analysis
ParadiseRepeater
Mountain Crest High SchoolRemote Base Station
Upper SouthFork Site
Lower SouthFork Site
Lower EastFork Site
East ForkWeather Site
ConfluenceSite
UWRL BaseStation Computer
S
S
S
S
C
S
C
Key
Internet LinkRadio Link
Stream Monitoring Site
Climate Monitoring Site
SParadise
Site
0.8
2.9
0.6
2.91.3
1.9
5.2
Telemetry
• 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…
Data Integration
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.
Hydrologic Information Science
Hydrologic conditions(Fluxes, flows, concentrations)
Hydrologic environment(Dynamic earth)
Physical laws and principles(Mass, momentum, energy, chemistry)
It is as important to represent hydrologic environments precisely withdata as it is to represent hydrologic processes with equations
Hydrologic Information Science(Observations, data models, visualization
Hydrologic Process Science(Equations, simulation models, prediction)
Slide from David Maidment
The Data Deluge
One day = 48 observationsOne week = 336 observationsOne month = 1440 observationsOne year = 17,520 observationsTwo years = 35,040 observationsThree + 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.
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)
• Built on established software– MS SQL Server, ArcGIS server
• Open Source Community Code Repository– Sustainability
Ongoing Data Collection
Data presentation, visualization, and analysis through Internet
enabled applications
Internet ApplicationsPoint Observations Data
Historical Data Files
GIS Data
HydroServer
ODM Database
GetSitesGetSiteInfoGetVariableInfoGetValues
WaterOneFlowWeb Service
WaterML
Observations Data Model (ODM)
Soil moisture
data
Streamflow
Flux tower data
Groundwaterlevels
Water Quality
Precipitation& Climate
• 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.
Data Values – indexed by “What-where-when”
Space, S
Time, T
Variables, V
s
t
Vi
vi (s,t)
“Where”
“What”
“When”A data value
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)
Loading data into ODM
• 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
ODM Data Loader
SDL
SSIS
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.
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.
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?
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?
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.
Questions?
Support:EAR 0622374CBET 0610075