Resolving scale-variance in the carbon dynamics of fragmented, mixed-use landscapes estimated using Model-Data Fusion

Abstract. Many terrestrial landscapes are heterogeneous. Mixed land cover and land-use generate a complex mosaic of fragmented ecosystems at fine spatial resolutions with contrasting ecosystem stocks, traits and processes, each differently sensitive to environmental and human factors. Representing spatial complexity within terrestrial ecosystem models is a key challenge for understanding regional carbon dynamics, their sensitivity to environmental gradients, and their resilience in the face of climate change. Heterogeneity underpins this challenge due to the trade-off between the fidelity of ecosystem representation within modelling frameworks and the computational capacity required for fine-scale model calibration and simulation. We directly address this challenge by quantifying the sensitivity of simulated carbon fluxes in a mixed-use landscape in the UK to the spatial resolution of the model analysis. We test two different approaches for combining EO data into the CARDAMOM Model-Data Fusion (MDF) framework, assimilating time series of satellite-based Earth Observation (EO) derived estimates of ecosystem leaf area and biomass stocks to constrain estimates of model parameters and their uncertainty for an intermediate complexity model of the terrestrial C cycle. In the first approach, ecosystems are calibrated and simulated at pixel-level, representing a "community average" of the encompassed land cover and management. This represents our baseline approach. In the second, we stratify each pixel based on land-cover (e.g. coniferous forest, arable/pasture etc.), and calibrate the model independently using EO data specific to each stratum. We test the scale-dependence of these approaches for grid resolutions spanning 1° to 0.05° over a mixed land-use region of the UK. Our analyses indicate that spatial resolution matters for MDF. Under the "community-average" baseline approach biological C fluxes (GPP, R_eco) simulated by CARDAMOM are insensitive to resolution. However, disturbance fluxes exhibit scale-variance that increases with greater landscape fragmentation, and for coarser model domains. In contrast, stratification of assimilated data based on fine-resolution land-use distributions resolved the resolution dependence, leading to disturbance fluxes that were approximately double the baseline experiments. The differences in simulated disturbance fluxes were sufficient to drive alternative interpretations of the terrestrial C balance: in the baseline experiment the live C pools suggest a strong C sink, whereas in the stratified experiment, the live C pools were approximately in steady-state as the C gains from NPP were balanced by losses due to the higher simulated harvest fluxes focused in conifer woodlands. We also find that stratifying the model domain based on land-use leads to differences in the retrieved parameters that reflect variations in ecosystem function between neighbouring areas of contrasting land-use. The emergent differences in model parameters between land-use strata give rise to divergent responses to future climate change. Accounting for fine-scale structure in heterogeneous landscapes (e.g. stratification) is therefore vital for ensuring the ecological fidelity of large-scale MDF frameworks. The need for stratification arises because land-use places strong controls on the spatial distribution of carbon stocks and plant functional traits, and on the ecological processes controlling the fluxes of C through landscapes, particularly those related to management and disturbance. Given the importance of disturbance to global terrestrial C fluxes, together with the widespread increase in fragmentation of forest landscapes, these results carry broader significance for the application of MDF frameworks to constrain the terrestrial C-balance at regional and national scales.