MacBean, N.L.; (2011) Using earth observation and ground-based data to improve modelled CO2 and CH4 fluxes from peatlands. Doctoral thesis , UCL (University College London).

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Abstract

Peatlands are an important store of carbon; they contain ~15-30% of the world’s soil C while covering only ~3% of the land area. They are also the largest natural source of methane (CH4), but the global estimate is highly uncertain. Here, this issue is addressed by developing a combined data - modelling framework to provide optimal estimates of CO2 and CH4 fluxes from an upland UK peat site. An Observing Systems Simulation Experiment (OSSE), using a Bayesian inversion method, is implemented to investigate the CH4, NEP and soil moisture observation temporal frequency and uncertainty required to accurately constrain model parameters and to estimate model predictive uncertainty. The OSSE is used to examine the impact of parameter correlations, bias in low-resolution observations, and unknown model error. The highly model-sensitive parameters are constrained by almost all observations, with a corresponding improvement in model predictive uncertainty. However there is high degree of model equifinality. Biased observations and unaccounted-for model error can result in false confidence in inaccurate model predictions. The OSSE results demonstrate the importance of performing a synthetic experiment prior to using actual data. Finally, real data are used to calibrate the model, which is then used to determine the net CO2 and CH4 flux for the site. The results highlight a possible source of error in the model. It is suggested this is because of an inaccurate representation of the coupling between CO2 and CH4, due to an unaccounted for lag in the methanotrophic activity. This has significant implications for CH4 flux modelling, as many models use a similar formulation for CH4 dynamics. Results are compared with satellite and ground-based measurement characteristics and recommendations are made for the observation and modelling of ecosystems at small spatial scales. This information is useful for modellers, space agencies and field biologists.

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