Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.662298
Title: Estimation of landscape carbon budgets : combining geostatistical and data assimilation approaches
Author: Spadavecchia, Luke
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2008
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Abstract:
Quantification of carbon (C) budgets at the landscape or catchment scale is generally achieved using process-based models as scaling tools. Such models require some metric of the exchange surface capability (e.g. Leaf Area Index, LAI) and a set of rate parameters for C processing. The net C exchange is then determined by driving the model with meteorological observations. Regional fields of parameters and drivers may be derived by upscaling site level measurements, constrained using Earth Observation data such as vegetation indices and digital elevation models (DEMs). I explore issues of error and uncertainty when upscaling C model parameters and drivers, and the effect of these uncertainties on the final analysis of the carbon budget. Two study areas focus the research: a region of tundra in Arctic Sweden and a ponderosa pine stand in Oregon. I use geostatistical techniques to develop fields of LAI and meteorology, complete with error statistics, whilst the distributions of rate parameters for a C model are derived via the Ensemble Kalman filter (EnKF). I report that the use of DEM data can provide LAI fields with an r2 ~50% greater than those derived from EO data alone. In particular I find strong relationships between LAI, elevation and topographic exposure. I explore the use of spatio-temporal geostatistics to improve meteorological fields, but report a better interpolation skill when temporal autocorrelations are ignored. Variation in parameters has a much larger effect on the uncertainty of the carbon budge (~50%) than driver uncertainty (~10%). The combined uncertainty in parameterisation and meteorology may result in a 53% uncertainty in total C uptake.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.662298  DOI: Not available
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