Document

January 13, 2018 | Author: Anonymous | Category: Science, Biology, Ecology
Share Embed Donate


Short Description

Download Document...

Description

AmeriFlux, Yesterday, Today and Tomorrow

Dennis Baldocchi, UC Berkeley Margaret Torn and Deb Agarwal, Lawrence Berkeley National Lab Bev Law, Oregon State University Tom Boden, Oak Ridge National Laboratory

AmeriFlux, circa 2012

Growth in the Network

Data from Bai Yang and Tom Boden

Age of Flux Sites, and the Length of their Data Archive

Pros and Cons of a Sparse Flux Network • Pros – Covers Most Climate and Ecological Spaces – Long-Term Operation Experiences Extreme Events, Gradual Climate Change, and Disturbance – Gradients of Sites across Landscapes and Regions Span Range of Environmental and Ecological Forcing Variables – Clusters of Sites examine effects of Land Use Change, Management, and Disturbance (fire, drought, insects, logging, thinning, fertilizer, flooding, woody encroachment) – Robust Statistics due to Over-Sampling • Cons – Can’t Cover All Physical and Ecological Spaces or Complex Terrain – Current Record is too Short to Detect Climate or CO2-Induced Trends – Flux Depends on Vegetation in the Footprint – Bias Errors at Night, Under Low Winds

The Type of Network Affects the Type of Science

• Sparse Network of Intense Super-Sites and Clusters of Sites, Producing Mechanistic Information can Test, Validate and Parameterize Process and Mechanistic Models • Denser and More Extensive Network of LessExpensive Sites can Assist in Statistical and Spatial Up-Scaling of Fluxes with Remote Sensing

Climate Space of AmeriFlux Sites

Yang et al 2008, JGR Biogeosciences

AmeriFlux Sites, Circa 2003, and Ecosystem/Climate Representativeness

Hargrove, Hoffman and Law, 2003 Eos

Representativeness of AmeriFlux, Circa 2008 (blue is good!)

Yang et al. 2008 JGR Biogeosciences

Basis of a Successful Flux Network

It Takes People (Scientists, Postdocs, Students and Technicians)

Social Network that Facilitates Meetings, Workshops, Shared Leadership and a Shared/Central Data Base This Fosters Getting to Know Each Other, Collaboration, Communication, Common Vision, Shared Goals, And Joint Authorship of Synthesis Papers

Past and Current Leadership

Dave Hollinger, Chair 1997-2001

Bev Law, Chair 2001-2011

Margaret Torn Tom Boden AmeriFlux PI, 2012- AmeriFlux Data Archive

Published Use of AmeriFlux Data 184 Papers linked to key word ‘AmeriFlux’

246 Papers linked to key word ‘Fluxnet’

These Papers have been cited over 7000 Times

Issues of standardization, or not?

‘Know Thy Site’

Ray Leuning

Most Flux Instruments are Very Good; Pick the Instrument System that is Most Appropriate to Your Weather and Climate

Open-Path CO2 Fluxes were 1.7% Higher than Closed Path Fluxes

Schmidt et al. 2012, JGR Biogeosciences

Site Calibration with Roving Standard

Schmidt et al 2012 JGR Biogeosciences

Extrinsic Contributions • Data Contribute to Producing Better Models via Validation, Parameterization, Data-Assimilation & Defining Functional Responses – Land-Vegetation-Atmosphere-Climate • Energy Partitioning, Albedo, Energy Forcing, Land Use

– Remote Sensing, Light Use Efficiency Models • Regional and Global GPP models

– Ecosystem and Biogeochemical Cycling • Carbon Cycle, Disturbance, Phenology, Environmental Change, Plant Functional Types

– Hydrology • Evaporation , Soil Moisture, Ground-Water, Drought

Lessons Learned

What’s in the Data? • Magnitudes and Trends in Annual C and H2O Fluxes, by Plant Functional Type and Climate Space • Light-Use, Temperature, Rain Response Functions • Emergent-Scale Properties – Diffuse Light – Rain Pulses – Drought and Ground Water Access

• Disturbance – Insect Defoliation – Fire, Logging and Thinning – Drought and Mortality

• BioPhysical Forcings – Albedo and Temperature – Energy Partitioning with Land Use

C Fluxes are a Function of Time Since Disturbances, as well as Weather, Structure and Function GPP NEE Reco

Harvard Forest 1800

1400

-2

Carbon Flux Density, gC m y

-1

1600

1200 1000 800 0 -200 -400 -600 1990

1992

1994

1996

1998

2000

2002

2004

2006

Year

Urbanski et al. 2007 JGR Biogeosciences

2008

Light Response Curves of CO2 Flux are Quasi-Linear, Deviating from Monteith’s Classic Paper and Impacting the Interpretation of C Flux with Remote Sensing

Gilmanov et al 2010 Range Ecology & Management

Light Use Efficiency INCREASES with the Fraction of Diffuse Light

Niyogi et al 2004 GRL

Response Functions from Elevation/Climate Gradients

Anderson-Teixeira et al. 2010 GCB

Respiration is a function of Temperature, Soil Moisture, Growth, Rain Pulses And Temperature Acclimation

Xu et al. 2004 Global Biogeochemical Cycles

Rain-Induced Pulses in Respiration: Long –Term Studies Capture More Pulses, Better Statistics

Ma et al. 2012 AgForMet

Disturbance, Fire and Thinning

Dore et al. 2012 GCB

Insect Defoliation, 2007

Clark et al. 2010 GCB

Disturbance Dynamics C Flux = f(time since disturbance)

Amiro et al. 2010 JGR Biogeosci

Flux Phenology

Gonsamo et al 2012 JGR Biogeosci

Satellite vs Flux Phenology

Gonsamo et al 2012 JGR Biogeosci

It’s Not only CO2! Effects of Precipitation and Energy on Evaporation

MI Budyko

Williams et al. 2012 WRR

Long-Term Studies can Assess Links between Drought and Fluxes

Schwalm et al 2012 Nature Geoscience

Net Negative Effects on Carbon and Water Fluxes are Strong: What about 2012?

Schwalm et al 2012 Nature Geoscience

Land Use and Climate

Forests are warmer than nearby Grasslands

Lee et al Nature 2011

Light Use Efficiency Models: Upscale Fluxes from Towers to Regions GPP    fPAR( NVDVI )  PAR

Yuan et al. 2007, AgForMet

   0 f (Ts ) f ( )

Heinsch et al 2006 IEEE

   0 f (VPD) f (Tmin )

C and Water fluxes Derived from Satellite-Snap Shots Scale with Daily Integrated Fluxes from Eddy Covariance

Sims et al 2005 AgForMet

Ryu et al. 2011 AgForMet

Seasonal Maps of NEE, via Regression Tree Analysis, on AmeriFlux and Modis Data

Xiao et al. 2008 AgForMet

What is the Truth?; How Good is Good-Enough?

Chen et al 2011 Biogeosciences

Regional Estimates of Fire, Drought, Hurricanes on NEE

Xiao et al. 2011 AgForMet

Using Flux Data to Validate Dynamic Vegetation Models-ORCHIDEE

Krinner et al 2005 GBC

Data-Model Fusion/Assimilation

Sacks et al. 2006 GCB

Model Hierarchy Testing: How Much Detail is Needed?

Bonan et al 2012 JGR Biogeosci

Testing Phenology Predictions in Ecosystem-Dynamic Models

The total bias in modeled annual GEP was +35 ± 365 g C m-2 yr-1 for deciduous forests +70 ± 335 g C m-2 yr-1 for evergreen forests across all sites, models, and years;

Richardson et al, 2012 GCB

It‘s Not Just About CO2:

Significant change in albedo with 3 disturbance types Hurricane

Beetles

Fire

Albedo change produces radiative forcing of same magnitude as CO2 forcing in case studies of forest mortality from hurricane defoliation, pine beetles, and fire. Beetle effect occurs mostly after snags fall

O’Halloran et al 2012 GCB

Albedo Scales with Nitrogen We can Use Albedo to Parameterize N and Ps Capacity in Models!

Hollinger et al 2009 Global Change Biology

The Albedo-N Correlation may be Spurious Knyazikhin et al 2012 PNAS report that the previously reported correlation is an artifact—it is a consequence of variations in canopy structure, rather than of %N.

• an increase in the amount of absorbing foliar constituents enhances absorption and correspondingly decreases canopy reflectance • When the BRF data are corrected for canopy-structure effects, the residual reflectance • variations are negatively related to %N at all wavelengths in the interval 423–855 nm.

To infer leaf biochemical constituents, e.g., N content, from remotely sensed data, BRF spectra in the interval 710–790 nm provide critical information for correction of structural influences

Validating and Improving Climate Drivers, like Net Radiation Fields

Jin et al 2011 RSE

Radiation and Evaporation Maps

Jin et al 2011 RSE

Testing Ecohydrology Theories for Soil Moisture

Miller et al 2007 Adv Water Res

Current and Future Collaborations • COSMOS and Soil Moisture Fields • Validation of Satellite based estimates of CO2, LIDAR, Albedo, and Soil Moisture (SMOS, SMAP, AIRMOSS) • Priors for CO2-Satellite Inversions (GOSAT, OCO) • Data-Model Assimilation • Phenology and Pheno-Camera Networks • FLUXNET and NEON

Importance of Site Metadata, A Plea for more LIDAR data to Test New Satellite Products and Force 3D Ecosystem Dynamic Models

Simard et al 2011 JGR Biogeosciences

Medvigy et al 2009 JGR Biogeoscience

AmeriFlux Plans • DOE grant to LBL to Manage 10-12 Long Term Clusters of Flux Towers – Ensure Cohort of Long Term Sites Extend into the Future to Address Ecological and Climate Questions on their Native Time Scales

• Continue Operation of Roving ‘Calibration’ system to All AmeriFlux Sites • Central Data Archiving, Processing and Data Distribution – Open Access, Prompt Submission, Uniform Processing

• Spare Sensors for Emergencies

Registered AmeriFlux Sites

View more...

Comments

Copyright � 2017 NANOPDF Inc.
SUPPORT NANOPDF