Sensors, Cyberinfrastructure, and Water Quality in the Little Bear

River: Adventures in Continuous Monitoring

Jeffery S. HorsburghAmber 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

KeyInternet 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