Leveraging climate and remote sensing metrics for predicting forest carbon stock using Bayesian geostatistical modelling under a projected climate warming in Zimbabwe

Abstract

Climate change, driven by escalating carbon dioxide ($C{O}_2$) emissions, poses a significant threat to forest ecosystems and the livelihoods of communities reliant on them, especially for the global south countries and regions like the eastern highlands of Zimbabwe. The 2000 land redistribution programme reduced buffer zones between ecologically sensitive forests and land reform beneficiaries near major carbon reservoirs. In light of these challenges, this study aimed to assess the potential effects of climate change on a strategically important plantation forest ecosystem in Zimbabwe's eastern highlands. Using data from the Coupled Model Inter-comparison Project Phase 6 (CMIP6) of the Intergovernmental Panel on Climate Change (IPCC), we modelled and predicted changes in forest carbon (C) stock density under different climate scenarios: current (1970–2000), SSP5–4.5, and SSP5–8.5. Employing a hierarchical Bayesian geostatistical approach, we compared the baseline scenario (1970–2000) with projected scenarios (RCP4.5 and RCP8.5) for 2075 to estimate changes in forest carbon stock distribution. Our results indicated a decline in carbon stock concentration under future climate scenarios, reflecting the adverse impact of greenhouse gas emissions on forest growth. We found that the projected range of forest carbon stock under the RCP8.5 scenario for 2075 is notably lower ($2\le \mathrm{MgC}{\mathrm{ha}}^{-1}\le 44.9)$ than that of the baseline period (1970–2000) ($1\le \mathrm{MgC}{\mathrm{ha}}^{-1}\le 97)$, suggesting a substantial reduction in carbon storage. As the difference in posterior mean C stock (${\bar{\mu }}_1-{\bar{\mu }}_2)$, 52.1 MgCha^-1 is well above zero, we deduce that the posterior mean C stock distribution of the projected future RCP8.5 2075 climate projection is indeed credibly different from the current (1970–2000) climate scenario. Additionally, there is a high probability $(>90\%)$ that forest plantations will be adversely affected by the business-as-usual climate warming projection. Overall, our findings highlight the urgent need for climate change mitigation strategies, such as reforestation programs and careful selection of tree species for plantations, to safeguard forest ecosystems and the communities dependent on them. These insights are crucial for informing effective adaptation measures in the face of future climate uncertainties.