SENSORS, CYBERINFRASTRUCTURE, AND EXAMINATION OF HYDROLOGIC AND HYDROCHEMICAL RESPONSE IN

THE LITTLE BEAR RIVER OBSERVATORY TEST BED

Jeffery S. HorsburghDavid K. Stevens, David G. Tarboton,

Nancy O. Mesner, and Amber Spackman Jones

Support:CBET 0610075

2

• Sensors and sensor networks

• Cyberinfrastructure development

• Data publication• Demonstrating techniques

and technologies for design and implementation of large-scale environmental observatories

WATERS Network 11 Environmental Observatory Test Beds

National Hydrologic Information ServerSan Diego Supercomputer Center

Little Bear River Test Bed• How can high frequency measurements of turbidity help us

quantify suspended sediment and total phosphorus fluxes?– Special case of a general problem of using surrogates to quantify hard

to measure constituents– Important for managing water quality and for environmental

observatory planning• How can high-frequency sensor data collected at multiple

sites improve hydrologic and hydrochemical process understanding?

• Across Test Beds: How can cyberinfrastructure facilitate data management and community data sharing?

Little Bear River Sensor Network• 7 water quality and

streamflow monitoring sites– Temperature– Dissolved Oxygen– pH– Specific Conductance– Turbidity– Water level/discharge

• 2 weather stations– Temperature– Relative Humidity– Solar radiation– Precipitation– Barometric Pressure– Wind speed and direction

• Spread spectrum radio telemetry network

Estimates of TSS and TP from Turbidity

• Least squares regression for TSS

• Regression with maximum likelihood estimation for TP (censored data)

0200

400600

8001000

12001400

16001800

0

500

1000

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2500

f(x) = 1.30792914332919 x + 3.58013179379194R² = 0.910185772582305

Turbidity (NTU)

Tota

l Sus

pend

ed S

olid

s (m

g L-

1)0 250 500 750 1000

0

0.2

0.4

0.6

0.8

1

1.2

f(x) = 0.0008470968812 x + 0.0350416933036R² = 0.910132197672157

Turbidity (NTU)To

tal P

hosp

horu

s (m

g L-

1)

Little Bear River Near Paradise, UT

TP and TSS Loading 2006• TSS and TP from

turbidity using surrogate relationships

• ~50-60% of the annual load occurs during one month of the year

• Provides information about flow pathways Little Bear River Near Paradise, UT

Effects of Sampling Frequency

Spring 2006

Upper South Fork Little Bear (2007- 2008)Two Component separation based on conductance:

• 43 % Baseflow• 57 % Quickflow

Baseflow does not extend into the peaks of the snowmelt hydrograph

Estimates of Biological Parameters

SiteAverage DO Deficit

(mg L-1)ka

(day-1)R

(mg L-1 day-1)Pavg

(mg L-1 day-1)Mendon -1.62 2.1 6.2 4.1Wellsville -0.97 44.1 100.8 58.1Paradise 0.61 42.0 29.6 56.3Lower South Fork -0.06 12.3 4.7 6.2

ODMDatabase

WaterOneFlowWeb ServicesGetSitesGetSiteInfoGetVariableInfoGetValues

1. Data Collection•Stream guaging•Water quality monitoring•Groundwater level monitoring•Climate Monitoring

2. Data Files are Loaded into ODM Using Controlled Vocabularies

3. Implementation of WaterOneFlow Web Services and Registration with Central Registry

ODMDatabase

ExcelFiles

TextFiles

AccessFiles

BinaryFiles

Central Registry

WATERS Test Bed Data Publication Network

June 17, 2008Item Total Number

ODM Databases 31Data Sources 41Monitoring Sites 3,767Variables 202Measurement Methods 99Data Values 41,651,095

Conclusions• Early snowmelt generates the vast majority of the annual TSS and TP load via

surface pathways from snowmelt close to the streams that carry TP and TSS loads• Water quality constituent loads estimated using weekly or monthly data:

– Are not representative of the high variability in discharge and constituent concentrations – Tend to under predict the true loading

• Discharge from slow subsurface pathways (i.e., baseflow) is relatively constant throughout the year and does not extend to a great degree into the peaks of the spring snowmelt hydrograph

• More than half of the annual discharge is from fast pathways (i.e., quickflow) that dominate the spring snowmelt hydrograph and dilute the relatively constant baseflow

• Metrics based on high-frequency profiles of DO concentrations and saturation deficits, are useful indicators of instream biological activity and can easily be calculated from high-frequency data

Conclusions• Organization of data using the HIS enabled

data management, analysis, and publication• Cyberinfrastructure demonstrates how

common systems can support a larger community