Simulating Regional Vegetation-climate Dynamics for Middle America: Tropical Versus Temperate Applications

High economic costs of reduced carbon sinks and declining biome stability in Central American forests

Tropical forests represent important supporting pillars for society, supplying global ecosystem services (ES), e.g., as carbon sinks for climate regulation and as crucial habitats for unique biodiversity. However, climate change impacts including implications for the economic value of these services have been rarely explored before. Here, we derive monetary estimates for the effect of climate change on climate regulation and habitat services for the forests of Central America. Our results projected ES declines in 24-62% of the study region with associated economic costs of $51-314 billion/year until 2100. These declines particularly affected montane and dry forests and had strong economic implications for Central America's lower-middle income countries (losses of up to 335% gross domestic product). In addition, economic losses were mostly higher for habitat services than for climate regulation. This highlights the need to expand the focus from mere maximization of CO₂ sequestration and avoid false incentives from carbon markets.

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

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.

Potential land use adjustment for future climate change adaptation in revegetated regions

To adapt to future climate change, appropriate land use patterns are desired. Potential natural vegetation (PNV) emphasizing the dominant role of climate can provide a useful baseline to guide the potential land use adjustment. This work is particularly important for the revegetated regions with intensive human perturbation. However, it has received little attention. This study chose China's Loess Plateau, a typical revegetated region, as an example study area to generate the PNV patterns with high spatial resolution over 2071-2100 with a process-based dynamic vegetation model (LPJ-GUESS), and further investigated the potential land use adjustment through comparing the simulated and observed land use patterns. Compared with 1981-2010, the projected PNV over 2071-2100 would have less forest and more steppe because of drier climate. Subsequently, 25.3-55.0% of the observed forests and 79.3-91.9% of the observed grasslands in 2010 can be kept over 2071-2100, and the rest of the existing forested area and grassland were expected to be more suitable for steppes and forests, respectively. To meet the request of China's Grain for Green Project, 60.9-84.8% of the existing steep farmland could be converted to grassland and the other for forest. Our results highlight the importance in adjusting the existing vegetation pattern to adapt to climate change. The research approach is extendable and provides a framework to evaluate the sustainability of the existing land use pattern under future climate.