# Presentation: 888593

## Perspective
A New Modeling Framework
Flux Towers and Models

- A New Modeling Framework
- Flux Towers and Models

## Ikonos (1m, pan)

## Ikonos (1m, pan)

## Landsat

## Fire

## Logging

## Succession

- (Data from: Uhl, *J. Ecol*., 1987 and Saldariaga *et al*., *J. Ecol.* 1988 for San Carlos, Venezuela)

## There is a need and challenge for data and models at multiple scales, and for both that integrate across scales.

## The Ecosystem Demography Model (ED)

- Princeton University	University of New Hampshire

## ED

- 	Need: The need to track fine-scale heterogeneity in ecosystem structure and fluxes

**Notes:**
So to connect to detail in fine scale data, and capture important structure and fluxes at small scales.

## Sources of heterogeneity	

- Weather/Climate
- Soils
- Topography
- Disturbance
- Land use
- ...

## ED

- Resulting form: A system of partial differential equations for each grid cell

## ED

- Leaf Physiology

- Growth and
- Reproduction

- Mortality

- Natural Disturbance

- Decomposition,
- Nutrients and
- Hydrology

- Biodiversity

- Patch

- Region

- Hours

- Centuries

- Land Use

- Spatial Scales:

- Time Scales:

## ED

## Double demography

- Small-scale disturbances within stands (e.g. treefalls)
- Stand-scale disturbances (e.g. fires, clear cuts, hurricanes...)

## ED “Mean Field”

## ED “Age-Structured”

## ED Connections to Data

- Time (yrs)

- Time (yrs)

- Stand Data (San Carlos)

- Flux-Tower Data (Manaus)

- Regional and Continental Data

## ED US  Land Use History 1700-1990

- (Based on Houghton, Hackler, and Lawrence, *Science* 1999)

## ED Land Use History 1700-1990

- Primary

- Crop

- Pasture

- Secondary

- 1850

- 1700

- 1920

- 1990

## ED US Total Carbon 1700-1990 (kg/m2)

- 1700

- 1850

- 1920

- 1990

## ED US Net Carbon Flux

- To Atmosphere

- To Land

## Data Needs

- Initial conditions
- 	(soil type, soil depth, land use, soil C, soil nutrients, above ground biomass, vegetation composition, ...)
- Meteorological drivers
- 	(temp, humidity, precipitation, solar radiation)
- Natural and anthropogenic disturbances (blowdowns, fire, land use, deforestation, selective harvesting,...)
- Data for testing
- 	(Ground based, remote sensing, atmospheric measurements, and experiments)

## Remote Sensing

**IKONOS**

**Lidar**

**GOES Fire**

**EOS...**

## Location of IKONOS Scenes, Eddy-flux towers, and key field sites in LBA

- http://www.esip.unh.edu

## Ground Based Data

- Inventories
- Censuses
- Process studies
- Manipulative experiments

## Atmospheric Data

- Flask Network
- Aircraft Profiles
- Isotopes
- CO2 satellite

## Flux Towers??

- Features:
- 	*small spatial scale (km)
- 	*fast time scale (min)
- 	*networked

- Many Important Benefits:
- 	*ecophysiology
- 	*variability in fluxes
- 	*detection system
- 	*...

## It is very important that these benefits be translated into testing and improving our understanding and our models.

## Are towers by themselves well suited for 

- Are towers by themselves well suited for
- large-scale or long-term net carbon fluxes?

- Unsampled heterogeneity?

- Small biases?

- Unsampled losses?

## Final Thoughts

**Need to simultaneously understand data from **
**“****all” time and space scales **

- Need to use multiple methods working together

- Syntheses from flux-tower studies likely to
- be a very powerful contribution

- No one method can do the job