Assessing Global Disturbance Regimes based on High-resolution biomass observations

Different disturbance events lead to varied response patterns in terrestrial biomass, while regulating the terrestrial ecosystems' short- and long-term carbon cycle dynamics. Quantifying the disturbance regimes is essential for understanding and reducing the uncertainty of climate factors affecting vegetation mortality and its responses on the carbon cycle. Based on model-based exercise, we built a machine learning model to predict three disturbance regime parameters, μ (probability scale), α (clustering degree), β (intensity slope) using the spatial pattern of emergent biomass. Here, relying on the model relationships, we utilize Earth observation data of high-resolution biomass, the GlobBiomass with a spatial resolution of 25m, to infer regional disturbance regime statistics.

We first conduct a series of comparison exercises to test whether the current framework is robust for retrieving realistic disturbance regimes, including varying factors controlling: (i) the impacts of disturbance shape setting; (ii) the recovery pattern after perturbation; and (iii) the downsampling process for the biomass simulation. It was found that different model settings mainly lead to the inconsistency of texture features, and the disturbance regime prediction accuracy was maintained with different shape settings,  or even higher after downsampling with a mean of 0.98 for Nash-Sutcliffe model efficiency coefficient (NSE).

Given the robustness in the framework for retrieving disturbance regimes statistics from modelled biomass results we contrasted these spatial patterns with local GlobBiomass patterns across the world. The comparison between model and observations show data aggregation needs that provide information on aspects of scale and spatial resolution required for simulations. Finally, we provide a sparse, but globally distributed, characterization of disturbance regimes based on remote sensing observations and discuss potential climate links, and mechanisms behind, the spatially continuous distribution of disturbance regime.

Given the novelty of the assessment of 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.