Details of Award
NERC Reference : NE/K00705X/1
Understanding the information content in diverse observations of forest carbon stocks and fluxes for data assimilation and modelling
Training Grant Award
- Lead Supervisor:
- Dr TL Quaife, University of Reading, Meteorology
- Grant held at:
- University of Reading, Meteorology
- Science Area:
- Atmospheric
- Terrestrial
- Overall Classification:
- Terrestrial
- ENRIs:
- Global Change
- Natural Resource Management
- Science Topics:
- None
- Abstract:
- This project will develop a mathematical framework and software tools to help better quantify the impact of diverse data sources on models of the carbon balance of forests. Such models are key to our understanding of how forests will respond to climate change and the extent to which they are able to sequester anthropogenic emissions of carbon dioxide. Large amounts of data relevant to the carbon balance of forests are now acquired on a routine basis, but for a given application it is not generally known what the optimal set of observations is (i.e. the actual variables measured and the frequency in space and time with which this should be done). Techniques for correctly evaluating the uncertainty in the data streams used to constrain models will lead to improved model predictions and, in turn, can be used to guide data acquisition by defining what the optimal data sets are for understanding a given problem. An important step in achieving this lies in the ability to define not just the error in a single observation but also how the errors in multiple observations co-vary with each other. Previous ecosystem data assimilation studies have typically employed the assumption that data uncertainties are independent from each each other (i.e. that they do not co-vary) but this has the potential to lead to bias and overconfidence in model predictions. The CASE partner is Forest Research, who are the research arm of the UK Forestry Commission and the project will be based at their field site at Alice Holt although in principle the techniques developed will be applicable to any forest. The aim of this project is to build a system to test the value of new observations to modelled estimates of the carbon balance of a forest ecosystem. The specific objectives are: 1) Construct modelling tools to determine the value of new observations for understanding the carbon balance of the forest at Alice Holt. 2) Develop a mathematically robust mechanism for specifying error covariances to allow for diverse data streams to be correctly incorporated into ecosystem carbon flux models. 3) Explore the role of new observations in estimating the carbon balance of the forest at Alice Holt and taking into account the covariance in data uncertainties. The first objective will allow the value of different observations to be assessed within the context of a modelling framework.The focus is on preparing the data and tools that will be required for the rest of the project. The second objective will examine previously developed techniques for quantifying the covariance of errors between observations and follows from work in the field of Numerical Weather Prediction undertaken by several of the academic supervisors and apply them to the existing data from a range of different instruments. The final objective, working closely with Forest Research, will be to determine the value of proposed observations and facilitate assessment of different field sampling strategies. Completion of this objective will involve the collection new data sets to inform specific model processes, the exact nature of which will be decided on the basis of the work in preceding objectives. The proposed application of this project is to enable better determination of the nature and frequency of data that are required to constrain estimates of the carbon balance of a forest. The project will provide a direct method to evaluate the efficacy of new sampling schemes and even hypothetical observations (such as those from yet to be launched satellite missions) on our understanding of how forests are changing with climate prior to investing any cost in making the observations themselves. This has the potential to be applied more broadly, for example looking at what variables are pertinent to the accurate prediction of standing forest biomass, or even to questions about entirely different ecosystems.
- NERC Reference:
- NE/K00705X/1
- Grant Stage:
- Completed
- Scheme:
- DTG - directed
- Grant Status:
- Closed
- Programme:
- Open CASE
This training grant award has a total value of £68,671
FDAB - Financial Details (Award breakdown by headings)
| Total - Fees | Total - Student Stipend | Total - RTSG |
|---|---|---|
| £13,978 | £49,194 | £5,499 |
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