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
Pollution Sources
UrbanTreatment
Stormwater
Wastewater
Runoff
Agriculture
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
Viewshed
Analysis
• Radio telemetry
network setup
• Optimal
placement of
radio repeaters
given monitoring
site locations
5.2
Mountain Crest High School
Remote Base Station
Paradise
Repeater
1.3
1.9
UWRL Base
Station Computer
2.9
Paradise
Site
East Fork
Weather Site
Confluence
Site
Key
Internet Link
Radio Link
Stream Monitoring Site
Climate Monitoring Site
0.6
Lower South
Fork Site
2.9
Lower East
Fork Site
0.8
Upper South
Fork Site
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
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
The Data Deluge
One years
Two
Three
day
week
month
year
+ years
===48
17,520
=35,040
336
observations
1440
= 50,000
observations
observations
observations
observations
+ 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
Point Observations Data
Internet Applications
Ongoing Data Collection
Historical Data Files
ODM Database
GIS Data
GetSites
GetSiteInfo
GetVariableInfo
GetValues
WaterML
WaterOneFlow
Web Service
HydroServer
Data presentation, visualization,
and analysis through Internet
enabled applications
Observations Data Model (ODM)
Streamflow
Precipitation
& Climate
Water Quality
Groundwater
levels
Soil
moisture
data
Flux tower data
• 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”
Time, T
“When”
t
A data value
vi (s,t)
s
“What”
Vi
Variables, V
“Where”
Space, S
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
ODM Data Loader
• Interactive ODM Data Loader
– Loads data from spreadsheets and
comma separated tables in simple
format
• Streaming Data Loader (SDL)
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
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 Human
Activities 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.
Support:
EAR 0622374
CBET 0610075
Questions?