Prospects for improving savanna biophysical models by using multiple-constraints model-data assimilation methods

Barrett, Damian J., Hill, Michael J., Hutley, Lindsay B., Beringer, Jason, Xu, Johnny H., Cook, Garry D., Carter, John O. and Williams, Richard J. (2005) Prospects for improving savanna biophysical models by using multiple-constraints model-data assimilation methods. Australian Journal of Botany, 53 7: 689-714. doi:10.1071/BT04139


Author Barrett, Damian J.
Hill, Michael J.
Hutley, Lindsay B.
Beringer, Jason
Xu, Johnny H.
Cook, Garry D.
Carter, John O.
Williams, Richard J.
Title Prospects for improving savanna biophysical models by using multiple-constraints model-data assimilation methods
Journal name Australian Journal of Botany   Check publisher's open access policy
ISSN 0067-1924
1444-9862
Publication date 2005
Sub-type Article (original research)
DOI 10.1071/BT04139
Volume 53
Issue 7
Start page 689
End page 714
Total pages 26
Place of publication Melbourne
Publisher CSIRO Publishing
Language eng
Subject 05 Environmental Sciences
Abstract A 'multiple-constraints' model-data assimilation scheme using a diverse range of data types offers the prospect of improved predictions of carbon and water budgets at regional scales. Global savannas, occupying more than 12% of total land area, are an economically and ecologically important biome but are relatively poorly covered by observations. In Australia, savannas are particularly poorly sampled across their extent, despite their amenity to ground-based measurement ( largely intact vegetation, low relief and accessible canopies). In this paper, we describe the theoretical and practical requirements of integrating three types of data ( ground-based observations, measurements of CO2/H2O fluxes and remote-sensing data) into a terrestrial carbon, water and energy budget model by using simulated observations for a hypothetical site of given climatic and vegetation conditions. The simulated data mimic specific errors, biases and uncertainties inherent in real data. Retrieval of model parameters and initial conditions by the assimilation scheme, using only one data type, led to poor representation of modelled plant-canopy production and ecosystem respiration fluxes because of errors and bias inherent in the underlying data. By combining two or more types of data, parameter retrieval was improved; however, the full compliment of data types was necessary before all measurement errors and biases in data were minimised. This demonstration illustrates the potential of these techniques to improve the performance of ecosystem biophysical models by examining consistency among datasets and thereby reducing uncertainty in model parameters and predictions. Furthermore, by using existing available data, it is possible to design field campaigns with a specified network design for sampling to maximise uncertainty reduction, given available funding. Application of these techniques will not only help fill knowledge gaps in the carbon and water dynamics of savannas but will result in better information for decision support systems to solve natural-resource management problems in this biome worldwide.
Keyword CHAIN MONTE-CARLO
SENSING DATA ASSIMILATION
NORTHERN AUSTRALIA
TROPICAL SAVANNA
TERRESTRIAL BIOSPHERE
PARAMETER-ESTIMATION
SEASONAL PATTERNS
CARBON-DIOXIDE
SURFACE-TEMPERATURE
VEGETATION COVER
Q-Index Code C1

Document type: Journal Article
Sub-type: Article (original research)
Collections: Excellence in Research Australia (ERA) - Collection
Sustainable Minerals Institute Publications
 
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Created: Wed, 11 Nov 2009, 12:45:09 EST