Land use and land cover changes have altered terrestrial ecosystem carbon storage, but their impacts on ecosystem sensitivity to drought and temperature fluctuations have not been evaluated spatially over the globe. We estimate drought and temperature sensitivities of ecosystems using vegetation greenness from satellite observations and vegetation biomass from dynamic global vegetation model (DGVM) simulations. Using a space-for-time substitution with satellite data, we first illustrate the effects of vegetation cover changes on drought and temperature sensitivity and compare them with the effects estimated from DGVMs. We also compare simulations forced by scenarios with and without land cover changes to estimate the historical land cover change effects. Satellite data and vegetation models both show that converting forests to grasslands results in a more negative or decreased positive sensitivity of vegetation greenness or biomass to drought. Significant variability exists among models for other types of land cover transitions. We identify substantial effects of historical land cover changes on drought sensitivity from model simulations with a generally positive direction globally. Deforestation can lead to either an increased negative sensitivity, as drought-tolerant forests are replaced by grasslands or croplands, or a decreased negative sensitivity since forests under current land cover are predicted to exhibit greater drought resistance compared to those under pre-industrial land cover. Overall, our findings emphasize the critical role of forests in maintaining ecosystem stability and resistance to drought and temperature fluctuations, thereby implying their importance in stabilizing the carbon stock under increasingly extreme climate conditions.

Bushfire fuel hazard is determined by fuel hazard that represents the type, amount, density, and three-dimensional distribution of plant biomass and litter. The fuel hazard represents a biological control on fire danger and may change in future with plant growth patterns. Rising atmospheric CO2 concentration (Ca) tends to increase plant productivity (‘fertilisation effect’) but also alters climate, leading to a ‘climatic effect’. Both effects will impact on future vegetation and thus fuel hazard. Quantifying these effects is an important component of predicting future fire regimes and evaluating fire management options. Here, by combining a machine learning algorithm that incorporates the power of large fine-resolution datasets with a novel optimality model that accounts for the climatic and fertilisation effects on vegetation cover, we developed a random forest model to predict fuel hazard at fine spatial resolution across the state of Victoria in Australia. We fitted and evaluated model performance with long-term (i.e., 20 years), ground-based fuel observations. The model achieved strong agreement with observations across the fuel hazard range (accuracy >65%). We found fuel hazard increased more in dry environments to future climate and Ca. The contribution of the ‘fertilisation effect’ to future fuel hazard varied spatially by up to 12%. The predictions of future fuel hazard are directly useful to inform fire mitigation policies and as a reference for climate model projections to account for fire impacts.