developed by: host updated: 1.05.05 u3-m12-s1 principal investigators bruce munson – umd education...

Developed by: Host Updated: 1.05.05 U3-m12-s1

Principal Investigators

Bruce Munson – UMD Education

Rich Axler – NRRI- Limnology

George Host – NRRI – Data issues

Cindy Hagley – MN Sea Grant -Outreach

Carl Richards – MN Sea Grant

Glenn Merrick – Lake Superior College

Staff

Norm Will – NRRI - Programming

Elaine Ruzycki – NRRI- RUSS-QA/QC

Jerry Henneck - Hardware

Jane Reed – NRRI- Web design

Zandy Zwiebel – MN Sea Grant

www.waterontheweb.org


A Brief History

Remote Underwater Sampling Station technology developed at NRRI in mid-1990s

1996 – NSF Advanced Technology Education grant to create “Water on the Web” Online curriculum incorporating real-time data from RUSS units into

college-level lesson plans Focus on lakes in Minnesota

1999 – EPA EMPACT program funds Lake Access Deliver real-time data for Minneapolis lakes

2000 – NSF ATE grant Water Science Technician Training – 2 yr curriculum National scope Teacher training workshops in NY, WI, TX, NC, WA

2001 – EPA EMPACT funds DuluthStreams Real-time river/stream data added to WOW

San Marcos, TXSkagit VCC, WA


RUSS unit – Remote Underwater Sampling Station

Remote monitoring systems for environmental data

“RUSS”

Remote Underwater Sampling Station

Real-time water quality data acquisition

Data transmitted to WOW web site


Solar Panels

Triple Hull(with electronics

and power supply units)

RUSS Design: Platform & Power Supply


Solar Panels



Profiler(Variable-buoyancy

device)

Multiprobe(sensor package)

RUSS Design: Profiler


Profiler: Leveling device

Variable buoyancy Will achieve target depth

within 0.2 m Typically set to collect

data at 1 m intervals in water column


Profiler: Sensor package

Standard Hydrolab or YSI Sonde attached to Leveler

Measures Temperature pH Dissolved Oxygen Conductivity Turbidity Chl a


RUSS deployment Lake Independence, MN


RUSS Deployment - Ice Lake, MN


On-board Data Processing and Storage

Remote Programming, Data Acquisition and Retrieval (RePDAR) unit CPU Memory Telemetry

Data is stored in ring memory on RePDAR Profiles are stored in a data

buffer Can be downloaded on

demand


Solar Panels



Wireless Communications (cellular/RF/Satellite/900 MHz)

Profiler(Variable-buoyancy

device)

Multiprobe(sensor package)

RUSS Design: Communications


Data Flow: RUSS unit to the WOW web site

DVT

Archive

EXCEL

HTML

Data Importer


Lots of data for lakes and streams

5-6 variables per depth 10-20 depths per profile 4 profiles per day ~200 days per season

96,000 points per lake 4-6 lakes in WOW 2-6 years worth of data

2,400,000 data points in archives


Lots of data for lakes and streams

5-6 variables per depth 10-20 depths per profile 4 profiles per day ~200 days per season

96,000 points per lake 4-6 lakes in WOW 2-6 years worth of data And then there’s stream

data – 5 minute intervals

2,400,000 data points in archives


Online Data Visualization Tools

Profile plotter (parameters vs depth)

Color mapper (parameters vs depth)

DxT (depth vs time)


WOW Site tour…

Curriculum materials Downloadable annotated Powerpoint files Online water quality data Online resources (primers, supporting materials) IMS map server (deliver watershed, landuse etc)

Two types of curricula Basic Science – HS,CC, 4 yr college Water Science Technician Training –modular 2

semester course In depth – Lake Ecology module


Wrap-up

Successes Extensive and comprehensive site for water science

information 300+ pages Broad user base

~40 cooperating institutions (CC, HS, 4yr) 1300 person mailing list Steadily increasing site use (30K hits/m)

Active ‘comments’ section Scaleable – regional national

Challenges – Sustainability? Data feeds expensive – need agency partners Certification – no defined standards for water science Alternative funding models?


Thank you!


Adapted from Ratti and Garton (1994)

Precision, accuracy and bias

Precision: Tendency to have

values closely clustered around the mean

Accuracy: Tendency of an

estimator to predict the value it was intended to estimate

Bias: A systematic error in

prediction


Unbiased Biased

Not

Pre

cise

Pre

cise

The yellow curlingrocks representmeans from repeated samples

Green dots are the mean value

Spread is analogous to the standard error Accurate Not Accurate


Data Visualization Tools: Profile Plotter

Accesses and plots individual profiles by parameter

Can ‘step through’ or animate time steps


Data Visualization Tools: Profile Plotter


DVT:Color Mapper


2D visualizations: Temperature by Depth and TimeData Visualization Tools: DxT Profiler

developed by: host updated: 1.05.05 u3-m12-s1 principal investigators bruce munson – umd education...

Documents

realtime data

profiler slide

wow slide

data buffer

communications slide

wa slide

demand slide

russ units