Regional carbon stock assessment and the potential effects of land cover change

Abstract

Accurate assessment of carbon stocks remains a global challenge. High levels of uncertainty in Land Use, Land Use Change and Forestry reporting has hindered decision-makers and investors worldwide to support sustainable soil and vegetation management. Potential mitigation-driven activities and effects are likely to be locally/regionally unique. A spatially-targeted approach is thus required to optimise strategic carbon management. This study provides a new regional carbon assessment (tier 3) approach using biophysical-process modelling of high-resolution Land Cover (LC) data within a UK National Park (NFNP) to provide higher accuracy. Future Land Cover Change (LCC) scenarios were simulated. Vegetation-driven carbon dynamics were modelled by coupling two widely-used models, LPJ-GUESS and RothC-26.3. Transition and persistence analysis was conducted using Terrset's Land Change Modeller to predict likely future LCC for 2040 using Multi-Layer Perceptron Markov-Chain Analysis. Current total carbon in the NFNP is 7.32–8.73 Mt C, with current trajectories of LCC leading to minor losses of up to 0.39 Mt C. Alternative LCC scenarios indicated possible gains or losses of 1.27 Mt C, or 136.7 t C ha−1. The importance of vegetation-driven carbon storage was greater than the national average, with a VegC pool 12–14% of the soil organic C pool, placing greater significance on local/regional LC and management policy. The potential storage capacity of each LC class was ranked (highest to lowest): Coniferous > Broadleaved/Mixed > Coastal > Semi-natural Grassland > Heath > Improved Grassland > Arable (Cropland). Opportunities were prioritised to inform landscape-scale management to reduce future carbon losses and/or to enhance gains through LCC. Balancing the carbon budget relies upon maintaining existing LC. The more detailed LC classification facilitated accounting of management through stock change factors and disaggregation of classes, achieving greater detail and accuracy. Forthcoming policy decisions must optimise carbon storage at a local/regional landscape-scale.