Natural and anthropogenic disturbances are important drivers of tree mortality, shaping the structure, composition, and biomass distribution of forest ecosystems. Differences in disturbance regimes, characterized by the frequency, extent, and intensity of disturbance events, result in structurally different landscapes. Characterizing different disturbance regimes through landscape-scale forest structure provides a unique perspective for diagnosing the impacts and potential carbon-climate feedbacks from terrestrial ecosystems. In this study, we design a model-based experiment to investigate the links between disturbance regimes and spatial biomass patterns. We generate over 850 thousand biomass patterns, from 2,142 combinations of μ, α, and β under different primary productivity and background mortality scenarios. We characterize the emergent biomass patterns via synthesis statistics, including central tendency statistics; different moments of the distribution; information-based and texture features. We further follow a multi-output regression approach that takes the biomass synthesis statistics and gross primary production (GPP) as independent variables to retrieve the three disturbance regimes parameters. Results show confident inversion of all three “true” disturbance parameters, with Nash-Sutcliffe efficiency of  94.8% for μ, 94.9% for α, and 97.1% for β. Overall, these results demonstrate the association between biomass patterns and disturbance statistics that emerge from different underlying disturbance regimes. By doing so, it overcomes the known issue of equifinality between mortality rates and total biomass. Given the increasing availability of Earth observation of biomass, our findings open a new avenue to better understand and parameterize disturbance regimes and their links with vegetation dynamics under climate change. Ultimately, at a large scale, this approach would improve our current understanding of controls and feedback at the biosphere-atmosphere interface in the current Earth system models.

In a model simulating dynamics of a system, parameters can represent system sensitivities and unresolved processes, therefore affecting model accuracy and uncertainty. Taking a light use efficiency (LUE) model as an example, which is a typical approach to estimate gross primary productivity (GPP), we propose a Simultaneous Parameter Inversion and Extrapolation approach (SPIE) to overcome issues stemming from plant-functional-type(PFT)-dependent parameterizations. SPIE refers to predicting model parameters using an artificial neural network based on collected variables, including PFT, climate types, bioclimatic variables, vegetation features, atmospheric nitrogen and phosphorus deposition and soil properties. The neural network was optimized to minimize GPP errors and constrain LUE model sensitivity functions. We compared SPIE with 11 typical parameter extrapolating methods, including PFT- and climate-specific parameterizations, global and PFT-based parameter optimization, site-similarity, and regression approaches. All methods were assessed using Nash-Sutcliffe model efficiency(NSE), determination coefficient and normalized root mean squared error, and contrasted with site-specific calibrations. Ten-fold cross-validated results showed that SPIE had the best performance across sites, various temporal scales and assessing metrics. None of the approaches performed similar to site-level calibrations(NSE=0.95), but SPIE was the only approach showing positive NSE(0.68). The Shapley value, layer-wise relevance and partial dependence showed that vegetation features, bioclimatic variables, soil properties and some PFTs are determining parameters. The proposed parameter extrapolation approach overcomes strong limitations observed in many standard parameterization methods. We argue that expanding SPIE to other models overcomes current limits and serves as an entry point to investigate the robustness and generalization of different models.