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LC-08 Abstract

Modeling the Biogeochemical System of the Terrestrial Amazon: Issues for Sustainability

Berrien Moore — University of New Hampshire (US-PI)
Carlos Afonso Nobre — INPE - Instituto Nacional de Pesquisas Espaciais (SA-PI)


The objective of this research is to understand the interactive effects of changes in

land-use and climate on 1) carbon storage and nutrient dynamics, including trace gas

fluxes, in terrestrial ecosystems and 2) the prospect for sustainable land-use in


The specific product of the research will be a set of coupled, hierarchically

structured models accessible through a common model framework. This framework will provide

the means for investigating our principal objectives. We shall consider the LBA region

within the context of two broad environmental conditions: 1) natural ecosystems where

perturbations in biogeochemical states are driven primarily by natural variability of

climate and fire, and 2) disturbance gradients that are induced by human land-use

activities and/or human-induced climate change.

We will use our models of terrestrial biogeochemical cycles, vegetation dynamics,

hydrology and land-use change. We will concentrate on model improvements to ensure

applicability of all models to the LBA study region. The ecosystem and hydrology models

will be driven by the physical climate; whereas, the land-use model will be driven by

biophysical, ecological, and economic constraints. The linked models will be incorporated

into a Geographic Information systems (GIS) context, accessing numerous data sets from LBA

or data layers housed at our institutions. We will evaluate model performance by

comparison and field measurements from LBA as well from published data sources. We will

use satellite remote sensing analysis as a means to evaluate the spatial and temporal

patterns of model performance at the regional scale. Finally, we will apply formal

statistical methods to characterize model uncertainty, as we apply this work to the

question of the human impacts on the Amazonian landscape.

Change from both natural and anthropogenic sources must be appropriately understood.

Therefore, we will focus on three objectives:

  • The natural pattern of variability in net primary production, respiration, nutrient

    availability, and the flux of trace gases between terrestrial ecosystems and the


  • Human-altered land-cover and ecosystem distribution and condition

  • The associated changes in the pattern of net primary production, respiration, nutrient

    availability, and the flux of trace gases between terrestrial ecosystems and the


The Science Objectives

Our research effort links three Objectives. The First Objective focuses on the

biogeochemistry of terrestrial systems under the forcing of natural climate variability.

Among the topics of interest are the natural pattern of variability in net primary

production, respiration, nutrient availability, and the flux of trace gases between

terrestrial (including seasonally flooded) ecosystems and the atmosphere. Soil moisture

and inundation are also important components of this research because of their controls on

trace gas dynamics. In Objective Two, we address the problem of modeling the transient

dynamics of human-altered terrestrial ecosystems, including agricultural systems, stages

of succession, and the combined forcing of various land-use and climate change patterns.

The Third Objective both supports and synthesizes the preceding Objectives. It provides a

Geographical Information System (GIS) framework for model development, evaluation, and

application. Within the model evaluation theme we will exploit remote sensing, using AVHRR

and when available, MODIS and MISR data from EOS AM-1, as well as formal error analysis

based upon spatial-temporal statistical techniques. We will use the linked model to

explore impacts of land-use and climate change scenarios on the biogeochemistry of the


Key Tools

Amazonia contains some of the most productive ecosystems in the world, experiences

notable patterns of climate variability on seasonal to inter-annual time-scales, and is

undergoing significant changes in land-use. It is thus a region with important fluxes and

changes in fluxes of carbon, nutrients, and water. Regional-scale ecosystem modeling,

database development and remote sensing analysis will be important tools for evaluating

potential consequences of land-use change on the biogeochemistry in Amazonia. Key themes

and tools for our work will include modeling of natural and human-managed ecosystems,

remote sensing, GIS analysis, and large database assembly and dissemination.


Analysis of Natural Ecological Systems

We have used the Terrestrial Ecosystem Model/Water Balance Model (TEM/WBM) to explore

seasonal and climatic variations in net primary production (NPP), heterotrophic

respiration (Rh), and net ecosystem production (NEP) for South America, North America, and

the globe. TEM is a process-based ecosystem simulation model that uses spatially

referenced information on climate, elevation, soils, and vegetation to make monthly

estimates of important carbon and nitrogen fluxes and pool sizes. A key feature of TEM is

that the carbon, water, and nitrogen cycles are closely coupled.

In order to address the effect of climate variability and climate change, we are

developing a transient version of TEM and have used a preliminary version to explore the

response of terrestrial ecosystems to historical changes in atmospheric CO2

concentration and climate in Amazonia, the conterminous United States, high latitude

ecosystems, and the globe (see also DNDC discussion in the next subsection (i.e., III.

2)). Inter-annual variations of modeled NPP in Amazonia were primarily associated with

inter-annual variations of precipitation. Inter-annual variations in modeled Rh were

smaller and connected mainly with inter-annual variations of temperature. The different

transient responses of these two carbon fluxes indicate that even under natural

conditions, the region sometimes acts as a sink of atmospheric CO2, and at

other times as a source. Within Amazonia, the spatial distribution of carbon sources and

sinks also apparently change from year to year. This behavior appears to be linked to the

physical climate through ENSO events.

Our earlier work in Amazonia showed the dependence of NPP on water availability. The

capability to model and understand changes in the Amazon regional water system is

important to studies of terrestrial productivity, and also the significant effects of

floodplain inundation on trace gas emissions.

Studies of Managed and Disturbed Ecosystems

We are currently developing a model for land-cover and land-use change. The model consists

of: 1) a land evaluation module that assesses the suitability and availability of land for

crops and pasture based on biophysical constraints such as climate, soil and topography,

and 2) a land-use module, GEOMOD, for simulating spatial patterns of land-use/land-cover

as well as the rates of change from landscape to regional scales based upon biophysical

factors and socio- economic factors (e.g., population density, land tenure system, timber

price). Algorithms within GEOMOD represent the principles of adjacency, dispersion,

regional heterogeneity, relative growth, energy efficiency, and resource quality.

Conversion of land to pasture and cropland has had a significant impact on the

biogeochemistry of many areas of Amazonia, and these ecosystems must be included in any

regional analysis. The DNDC model can be used to simulate carbon and nutrient

biogeochemistry in agro-ecosystems. DNDC has simulated 30 years of pasture biogeochemistry

following conversion of forest to pasture. Estimated SOM and N2O fluxes are in general

accord with a chronosequence study in Costa Rica and simulation of NO flux from fertilized

maize grown on recently cleared land is also in agreement with measurement. DNDC has

successfully simulated N2O fluxes and soil organic matter (SOM) dynamics in temperate and

subtropical regions. DNDC has been used to simulate N2O flux from agricultural

lands in the U.S., and is currently being used to evaluate N2O emissions from

agricultural lands in China, including wet subtropical areas. Tropical agriculture and

pasture simulations with DNDC have been focused on Costa Rica, and will be adapted to the

LBA region.

Management of Data and GIS Capabilities

Our NASA EOS-IDS and Hughes Applied Information Systems are collaborative partners in a

prototyping project that will provide full GIS capability over the internet and is fully

interoperable with NASA's EOS Data and Information System Core System (ECS). One aspect of

this effort to provide a means for LBA participants to conduct geo-spatial data searches

and queries via the internet. We currently have a very rough prototype

( using a Java client and server, a Spatial Data Engine

(SDE/ESRI) client and server, and an Oracle database (which provides access to several

data layers including information from the Humid Tropical Forest Inventory Project (HTFIP)

which is part of NASA's Landsat Pathfinder Project); additional data layers are available

from our EOS-IDS research effort including Hydro-Climatology layers provided by the UNH

Global Hydrology Research Group). Recently approved funding through the NASA CAN

addressing Federation of the EOSDIS will allow a significant expansion of this capability.

Remote Sensing

Issues related to validating spatial patterns of model predictions have been explored

using remote sensing. We examined model results of the Vegetation/Ecosystem Modeling and

Analysis Project of net primary productivity (NPP), estimated in the conterminous USA at a

spatial resolution of 0.5 by 0.5 degree grids. One goal was to check the realism of the

spatial variability of model estimates using long-term monthly mean NDVI for each 0.5x0.5

grid cell, using the fact that NPP and NDVI are linearly related. Correlation for the

entire domain were relatively high (R2=0.6-0.7). However, comparison of the mean deviates

of both NDVI and simulated NPP (each grid cell value subtracted from the mean of all grid

cell values in an ecosystem type) were uncorrelated within biomes. Thus the models

appeared to be representing across-biome patterns of NPP, but no conclusion could be made

about within-biome variability. This type of analysis is potentially very powerful for

evaluating model-predicted NPP.

A remotely sensed vegetation index can also yield insight into patterns of response of

ecosystems to climate, providing a means to evaluate model response and model-based

hypotheses. We used global AVHRR data and gridded air temperature from the Microwave

Sounding Unit to estimate the magnitude of immediate and lagged response to temperature.

Patterns of zero-, one-, and two-year lagged responses of NDVI to temperature variability

were ecosystem dependent and consistent with the hypothesis that biogeochemical mechanisms

play an important role in mediating global relationships between CO2 and


We have successfully retrieved canopy biophysical variables, including the fraction of

PAR absorbed by the canopy (fAPAR) and albedo, using a radiative transfer model and AVHRR

data for a transect in the Central African Republic. Though observations of a single pixel

have single sun-sensor geometry, we gathered neighboring pixels in temporally composited

scenes, having similar functional ecosystem type, in order to simulate a multiple sampling

of geometry within approximately 0.5x0.5 "cells". With the advent of MISR data

from EOS AM-1, we expect both improved accuracy and the ability to perform inversions

using much smaller spatial windows.

Research Team Responsibilities

  • Berrien Moore III: modeling

  • Jerry Melillo: Biogeochemistry and terrestrial ecology

  • Steve Pacala: Modeling and terrestrial ecology

  • Charles Vorosmarty: hydrology and biogeochemical modeling

  • Bruce Peterson: hydrology and biogeochemical modeling

  • Changsheng Li: transient ecosystems and biogeochemical modeling

  • Steve Frokling: trace gases and biogeochemical modeling

  • Bobby Braswell: remote sensing and biogeochemical modeling

  • Ernest Linder: spatial statistics

  • Xiangming Xiao: remote sensing and terrestrial ecosystem modeling

We recognize that this research proposal is ambitious. The effort will draw significant

support from our EOS IDS grant and other currently funded activities. Our effort will be

supported intellectually primarily by LBA investigators and by other institutions,

including the MIT Joint Program on Policy and Global Change, the Oak Ridge National

Laboratory DAAC, and the University of Texas (Dr. J. Famiglietti).

Last Updated: October 1998

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