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Ecological Observation Modeling Approach Dennis Ojima Natural Resource Ecology Laboratory MAY 2006 Tucson, Arizona

Ecological Observation Modeling Approach

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Ecological Observation Modeling Approach. Dennis Ojima Natural Resource Ecology Laboratory. MAY 2006 Tucson, Arizona. Collaborators David Schimel (NCAR), Steve Running (U of MT), Russ Monson (CU), Brit Stevens (NCAR), Jeff Hicke (CSU). Funding from NSF, NASA, NOAA. WHY NOW?. - PowerPoint PPT Presentation

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Page 1: Ecological Observation Modeling Approach

Ecological Observation ModelingApproach

Dennis OjimaNatural Resource Ecology Laboratory

MAY 2006Tucson, Arizona

Page 2: Ecological Observation Modeling Approach

CollaboratorsDavid Schimel (NCAR), Steve Running

(U of MT), Russ Monson (CU), Brit Stevens (NCAR), Jeff Hicke (CSU)

Funding from NSF, NASA, NOAA

Page 3: Ecological Observation Modeling Approach

WHY NOW?WHY NOW?• Grand Challenges facing Environmental

Sciences– Land Use; Climate change; Biodiversity;

Biogeochemical cycles; Infectious disease; Invasive species

• New Observations for Terrestrial Systems• New Cyber Infrastructure Developments• New Advancements in Quantitative Analysis• Development of Data-Model Fusion

Techniques

Page 4: Ecological Observation Modeling Approach

Land Use Change

More land was converted to cropland in the 30 years after 1950 than in the 150 years between 1700 & 1850

Cultivated Systems in 2000 cover 25% of Earth’s terrestrial surface

(Defined as areas where at least 30% of the landscape is in croplands, shifting cultivation, confined livestock production, or

freshwater aquaculture)Millennium Ecosystem Assessment 2005

Page 5: Ecological Observation Modeling Approach

Temperature Anomalies (2003)

Data source: (Jones and Moberg 2003). Processed by the U.S. NOAA NCDC Global Climate at the Glance Mapping System

Page 6: Ecological Observation Modeling Approach

MODIS Optical Density (Average 2001)

Page 7: Ecological Observation Modeling Approach

Global and Regional Telecommunications

Fire

Dust Storms

Nitrogen

BrownCloud

NitrogenNitrogen

Page 8: Ecological Observation Modeling Approach

CHALLENGES and NEEDSCHALLENGES and NEEDSOF OF

Terrestrial Environmental Terrestrial Environmental Observations and AnalysisObservations and Analysis

• Multiple Stresses• Interactive Sectors• Increasing Human Pressures

• Information Exchange to Multiple Publics– Science– Managers– Policy Makers– Public at Large

Page 9: Ecological Observation Modeling Approach

This is a Time of Great Opportunity

• Digital information explodes

• Bandwidth increases

• Wireless capabilities expand

• HPCC and IT technologies advance and pervade science and society

• Collaborative, multidisciplinary activities increase

• Integrative approaches demanded

Page 10: Ecological Observation Modeling Approach

FROM PETABYTA TO SOUNDBYTE

Page 11: Ecological Observation Modeling Approach

Multi-sensor/Multi-scale Modeling Framework

Nemani et al., 2003, EOM White & Nemani, 2004, CJRS

Page 12: Ecological Observation Modeling Approach

• Collect data from digital libraries, laboratories, and observation

• Analyze the data with models run on the grid

• Visualize and share data over the Web

• Publish results in a digital library

Changing How Science is Done

Page 13: Ecological Observation Modeling Approach

NEON: A continental research platform designed to provide the capacity to forecast future states of ecological systems for the advancement of science and the benefit of society

Nat

ion

al E

colo

gic

al O

bse

rvat

ory

Net

wo

rk (

NE

ON

)

Novel infrastructure that:

• allows scientists to observe the previously unobservable

• scale from m2 to continent

• evaluate fundamental theory at regional to continental scale

• enables a new forecasting and predictive capacity for ecology

• takes advantage of new and evolving in situ sensing technologies

• couples human and natural systems

Page 14: Ecological Observation Modeling Approach

From points to pixels

?

Create high res. productsby coupling high res. imagerywith field and tower data

Aggregate

Correlate

Some graphics courtesy of BigFoot project, layout courtesy of Shunlin LiangMultiple use of airborne or high res. satellite data for extrapolation of sites observations

Page 15: Ecological Observation Modeling Approach

• SCIENCE BASED: Developing and testing theory and models requires integration of complex in situ process data with large gridded data sets.

• MULTI-SCALED: Required data are multi-scale, many formats, originating in multiple disciplines.

• AGILE: Rapid prototyping and development cycle to maximize user control of information systems, implies incorporating existing state-of-the-art components rather than de novo development

• USER-DRIVEN: Data systems must allow user-driven, knowledge-based querying of multiple data types

Information Technology for Biogeosciences

Page 16: Ecological Observation Modeling Approach

ACME-CME: (Aircraft) C in the

Mountains Experiment

Sponsors: NSF-NASA Collaborating Inst: CU-NCAR-CSU-NOAA

Page 17: Ecological Observation Modeling Approach

ACME-CMEACME-CME• To understand carbon dynamics in

montane forest regions by developing new methods for estimating carbon exchange at local to regional scales

• “Bottom-Up” (plot and tower obs) and “Top-Down” (aircraft and satellite obs) constraints

• Evaluate factors affecting C-exchange in complex terrain as compared to flat landscapes to better understand the significance and contribution of mountain areas to the continental carbon budget.

Page 18: Ecological Observation Modeling Approach

IntegratingAcrossScalesThroughTop-down&Bottom-upApproaches

Page 19: Ecological Observation Modeling Approach

F

VOCCO

VOCCO

CO2

Biogenic sourceMissoula: Urban source

CH4

Carbon-containing pollutant transport: 10s-1000s of km

Wildfire source

Downslope flows and subsequent venting of CO2

Tower

Soil Chamber

WindWind

Upw

ind Profiles

Erosion and organic matter transport

Dow

nwind P

rofiles

NE

EFootprint

Courtesy of Steve Running, U Montana

Page 20: Ecological Observation Modeling Approach

Model-data fusion: processes at the scale of biosphere-atmosphere

exchange

PLANT WOOD CARBON

PLANT LEAF CARBON

Photosynthesis Autotrophic Respiration

Leaf Creation

VEGETATION

SOIL CARBON

Wood Litter Leaf Litter

Heterotrophic Respiration

Precipitation

Niwot Ridge, Colorado

SipNET

Page 21: Ecological Observation Modeling Approach
Page 22: Ecological Observation Modeling Approach
Page 23: Ecological Observation Modeling Approach

Airborne Carbon in the Mountains Experiment

Page 24: Ecological Observation Modeling Approach
Page 25: Ecological Observation Modeling Approach
Page 26: Ecological Observation Modeling Approach
Page 27: Ecological Observation Modeling Approach

SPACENET Model Structure

Plant Carbon

Soil CarbonSoil Moisture

Drainage

Precip. Transpiration

Photosynthesis (Phenology,Soil Moisture,

Tair, VPD, PAR)

Plant Respiration(Plant C, Tair)

Litterfall(Plant C, Phenology)

Soil Respiration(Soil C, Soil Moisture,

Tsoil)

Page 28: Ecological Observation Modeling Approach
Page 29: Ecological Observation Modeling Approach
Page 30: Ecological Observation Modeling Approach

NE

E (

g C

/m2 p

er h

alf-

dail

y ti

me

step

)

NE

E (

g C

/m2 p

er h

alf-

dail

y ti

me

step

)

Initial guess parameters Optimized parameters

Blue: ModelRed: Data

Dates: 11/1/98 – 10/31/02Each point represents one half-daily time step

Model vs. Data: Unoptimized & Optimized Parameters

Cum

ulat

ive

NE

E (

g C

/m2 )

Cum

ulat

ive

NE

E (

g C

/m2 )

Page 31: Ecological Observation Modeling Approach

DODS Aggregation

Server

GrADS-DODSServer

Regional carbon data assimilation system based on RAMS atmosphere model

4D VARAssimilation

System

Compare, minimize

Estimated Fluxes

ObservingSystem

CO2

Observations

http-BasedInterface

4D VAR Assimilation

System

RAMS

Optimizer

RAMS Adjoint

CO2,

And Meteorological Observations

1st Guess fluxesFrom

SPACENET

Airborne and surface CO2,

obs

Carbon Data-Model Assimilation (C-DAS)

http://dataportal.ucar.edu/CDAS/

Page 32: Ecological Observation Modeling Approach

Multiple Modeling Approach Multiple Modeling Approach for Terrestrial C Fluxesfor Terrestrial C Fluxes

1) Inverse modeling of atmospheric chemistry•Constrain C sinks; little info on why or exactly

where2) Biogeochemical models

•Test physiological changes; little land use or disturbance

3) Land-use bookkeeping models•Track land-use change, but no climate or

physiology4) Flux towers

• Integrated site-level measurements, but relatively few sites

5) Forest inventories

Page 33: Ecological Observation Modeling Approach
Page 34: Ecological Observation Modeling Approach

ACME/CDAS ApproachACME/CDAS Approach

• Bring together observationalists and modelers to form an integrated approach to improving our understanding of the global carbon cycle.

• Initial effort: Network design exercises based on a selected assimilation modeling strategy.

• Ongoing: Further development of the assimilation tool and support for testing and planning/educational use by the community.

Page 35: Ecological Observation Modeling Approach

• Developing and testing theory and models requires integration of complex in situ process data with large gridded data sets.

• Required data are multi-scale, many formats, originating in multiple disciplines.

• Rapid prototyping and development cycle to maximize user control of information systems, implies incorporating existing state-of-the-art components rather than de novo development

• Data systems must allow user-driven, knowledge-based querying of multiple data types

Information Technology for Global Environmental Change Sciences