Forest regrowth in the Democratic Republic of the Congo: A field data/vegetation model comparison

Tropical forests of Central Africa play a fundamental role in the global carbon cycle, the regional moisture recycling, and also act as biodiversity hotspots. Yet, afrotropical forests experience important anthropogenic pressure, including slash-and-burn agriculture that is particularly widespread in the region. Despite their significance, the post-disturbance recovery dynamics of these forests remain poorly understood, particularly compared to the other tropical regions. With increasing anthropogenic pressures and projected shifts in rainfall regimes in the future, an improved understanding of forest regrowth processes is critical to anticipate the future of the Congo Basin carbon sink and simulate the land carbon sink of secondary forests/

This study presents a comprehensive model intercomparison of forest regrowth trajectories in the Democratic Republic of the Congo, combining ground inventory data with outputs from multiple Dynamic Global Vegetation Models (DGVMs). We compiled a harmonized dataset of field sites, representing dozens of site/age combination, along wide climatic gradients in the country. Multiple DGVMs were benchmarked against empirical regrowth curves derived from plot networks, with additional models currently under evaluation to extend the model ensemble. Each model was forced by consistent climate and land-use datasets but exhibited heterogeneous process representations and carbon allocation schemes.

Results reveal a systematic overestimation of above-ground biomass accumulation across models, particularly during the first decades of succession. Modelled forests typically regained 80–100% of their pre-disturbance biomass within 50 years, whereas inventory data indicate substantially slower recovery rates, often below 60%. Sensitivity analyses showed that the divergence between simulated and observed regrowth trajectories could be linked to differences in parameterization of turnover rates and demography. Furthermore, the influence of climatic drivers varied markedly across models: while some exhibited strong sensitivity to precipitation seasonality, others were dominated by temperature and radiation effects. Such discrepancies highlight structural uncertainties in how models capture key processes controlling regrowth, including recruitment limitation, and resource constraints.

Our findings underscore the need for process-based improvements based on existing field data. By confronting models with empirical data from the Congo Basin, this intercomparison provides an essential step toward reducing uncertainties in projections of African forest resilience under climate and land-use change.