Paul Hanson, Fang-Pang Lin, Miron Livny, Chin Wu, Chris Solomon,

Many colleagues of the GLEON

Paul Hanson, Fang-Pang Lin, Miron Livny, Chin Wu, Chris Solomon,

Many colleagues of the GLEON

Transforming ecological sensor networks from data collectors to

knowledge generators

Transforming ecological sensor networks from data collectors to

knowledge generators

Questions

1. What are the patterns and surprises in sensor data, and what do they tell us about how external drivers influence lake physical, chemical, and biological processes?

2. How do large gradients in geology, hydrology, and climate influence lake responses to external drivers?

3. What are the essential emergent characteristics from lakes that allow us to generalize processes from a few, highly instrumented lakes to regional and global scales?

Tim

e (m

inut

es)

GLEON Projected Growth

109

2008

107 108

20122010 2014

Year

Number of data

10-1

100

101

102

Sandbox threshold(Select, visualize data)

Level 1 model threshold(Transformations, simple QA/QC)

Level 2 model threshold(0,1-D models)

Level 3 model threshold(3-D models)

Query time with current system

Acceptable thresholds for different tasks.

Acceptable thresholds for different tasks.

Level 2 models

Level 1 models

Lake Observatory

+ I T Development

GLEONObservational Data

RepositoriesQuery and display observational data

dbBadgerSoftware suiteStream data

Web, e.g.,dbBadger

Mendota buoyLSPA

New to this proposal

Existing

XY

Z

10-4

10-3

10-2

10-1

100

100

105

1010

Frequency (Hz)

Power Spectrum

chlorophyll

phycocyanin

dissolved oxygen

hourdayweek

Pow

er

Spectral anal.

3D hydrodynamicswavelet

Multi-dimensional simulated

data repositorySurprise anal.

dcoc\simulations\CompareSimulationsMassesFluxes.m

Dep

th (

m)

Dep

th (

m)

Tem

pera

ture

(°C

)P

AR

log 10

(µm

ol m

-2 s

ec-1)

A) TB B) SP

C) TB D) SP

Tem

pera

ture

(°C

)P

AR

log 10

(µm

ol m

-2 s

ec-1)

Dep

th (

m)

Dep

th (

m)

Hanson, Hamilton, Stanley, Langman, Preston in prep.

Trout Bog Sparkling Lake

100 150 200 250 3007

8

9

10

100 150 200 250 3009

10

11

12

100 150 200 250 3000

2

4

6

100 150 200 250 3002

4

6

8

DIC

(m

g L

-1)

100 150 200 250 3000

20

40

100 150 200 250 3000

20

40

Chl

(µ

g L

-1)

100 150 200 250 3000

10

20

30

100 150 200 250 3000

10

20

30T (

°C)

Day of year

A) TB epi

C) TB hypo

E) TB epi

G) TB hypo

I) TB epi

K) TB hypo

dcoc\simulations\PlotSeriesWithConfidence.m

100 150 200 250 3000

10

20

30

100 150 200 250 3000

10

20

30

100 150 200 250 3000

10

20

30

100 150 200 250 3000

10

20

30

Day of year

B) SP epi

D) SP hypo

F) SP epi

H) SP hypo

J) SP epi

L) SP hypoDetection bandPulse

100 150 200 250 30020

30

40

100 150 200 250 30020

30

40

DO

C (

mg

L-1

)

A) TB epi

C) TB hypo

100 150 200 250 3002.5

3

3.5

4

4.5

100 150 200 250 3002

3

4

B) SP epi

D) SP hypo

Day of year Day of year

How does a large spring pulse of DOC affect other variables?

How does a large spring pulse of DOC affect other variables?

Morphometry

Hydrology, loading

Hydrodynamics

Physical, chemical processes

Landscape setting

Microbial processes

Higher trophic level processes

Predictive uncertaintyhighlow

Meteorology

Strong couplingWeak coupling

phys

ical

chem

ical

biol

ogic

al

Sys

tem

leve

lEcosystem

Physical

GLEON sites

10-4

10-3

10-2

10-1

100

100

105

1010

Frequency (Hz)

Power Spectrum

chlorophyll

phycocyanin

dissolved oxygen

Lake Mendota 2008 July thru Sept

hourdayweek

Pow

er

16 16.1 16.2 16.3 16.4 16.5 16.6 16.7 16.8 16.9 17-1

-0.5

0

0.5

1

Days

Time scale: 10min, 0.0069444 days

16 16.1 16.2 16.3 16.4 16.5 16.6 16.7 16.8 16.9 17-1.5

-1

-0.5

0

0.5

1

1.5

Days

Time scale: 60min, 0.041667 days

10 min scale

60 min scale

PhycoChlDO

Technology will…

1. access large repositories of data, and move data seamlessly through a web of models and repositories;

2. accomplish a complex series of tasks in dependable ways; 3. support the interconnection of models, some of which are

extremely compute intensive, in flexible and fast ways; 4. provide on-demand access to GLEON scientists from

around the world.

This functionality extends existing GLEON technology by leveraging proven workflow and distributed technologies available through Condor and data access, visualization and transport technologies through NCHC.