Seminar: Siyuan Wang

Different disturbance events lead to a varied response of terrestrial biomass, which regulates the terrestrial ecosystems' short- and long-term carbon cycle dynamics. Quantifying the disturbance regimes is essential to understanding and reducing the uncertainty of vegetation mortality and its effects on biomass. Based on the synthetic exercise, we revealed a strong link between three disturbance regime parameters, \mu (probability scale), \alpha (clustering degree), \beta (intensity slope), and the spatial pattern of emergent biomass. Relying on this connection, we applied the real-world observation from a high-resolution biomass dataset, the GlobBiomass with a spatial resolution of 25m, to infer the regional disturbance regime.

We first conducted a series of comparison exercises to test whether the current framework is robust for realistic regime retrieving, then the difference between the statistics of synthetic biomass results and GlobBiomass observations was examined. The performances of the selected metrics in capturing the spatial variability patterns of real-world biomass data were evaluated. We intend to integrate the parameter inversion model for the disturbance regime with the new remote sensing observations to produce the spatially continuous distribution of three disturbance regime parameters.
Given the novelty of assessing disturbance regimes with high-resolution biomass data, our study provides opportunities to evaluate and improve the representation of disturbance dynamics in dynamic vegetation and Earth System models.)