In the Central Himalayas, where environmental conditions vary greatly, understanding the biophysical limitations on forest carbon is crucial for accurately determining the region’s forest carbon stocks. This study investigates the role of climate and disturbance on the spatial variation of two key forest carbon pools: aboveground carbon (AGC) and soil organic carbon (SOC). Using field-observed plot-level carbon pool estimates from Nepal’s national forest inventory and structural equation modeling, we explore the relationship between forest carbon stocks and proxies of environmental constraints. The forest AGC and SOC models explained 25 % and 59 % of the observed spatial variation in forest AGC and SOC, respectively. The climatic availability of water and energy in broad-scale gradients combined with the fine-scale gradients of terrain and disturbance intensity were found to influence forest carbon stocks, but the sign and strength of the statistical relationships differ for forest AGC and SOC. While AGC showed a negative relationship to disturbance, SOC was impacted by the availability of climatic energy. Disturbances such as selective logging and firewood collection result in immediate forest carbon loss, while soil carbon changes take longer to respond. The lower decomposition rates in the high-elevation region, due to lower temperatures, preserve organic matter and contribute to the high SOC stocks observed there. These results have important implications for forest carbon management and conservation in the Central Himalayas.

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.

Grassland responses to drought are strongly mediated by leaf phenology, with greening and browning being highly sensitive to soil moisture. However, this process is represented overly simplistically in most vegetation models, limiting their capacity to predict grassland responses to global change factors. We derive functions representing grassland phenological responses to soil water content (SWC), by fitting an empirical model to greenness data. Data were obtained from fixed cameras (phenocams) monitoring phenology at several grassland experiments in Sydney, Australia. The data-model synthesis showed that the sensitivity of growth to SWC exhibited a concave-down response in most species. For senescence, we found a strong nonlinear increase in senescence rate with declining SWC. Both findings contradict common assumptions in vegetation models. Incorporating nonlinear responses in the empirical model reduced the error in cover predictions by 12%. Model evaluation against data from drought treatments indicated that differential sensitivity of phenology to SWC helps explain differences among species’ responses to variable rainfall. Our work provides a new methodology, and new evidence, to support the development of improved representations of grassland phenology for vegetation models.

The mechanisms by which woody plants recover xylem hydraulic capacity after drought stress are not well understood, particularly with regard to the role of embolism refilling. We evaluated the recovery of xylem hydraulic capacity in young Eucalyptus saligna plants exposed to cycles of drought stress and rewatering. Plants were exposed to moderate and severe drought stress treatments, with recovery monitored at time intervals from 24 hrs to 6 months after rewatering. The percentage loss of xylem vessels due to embolism (PLV) was quantified at each time point using micro-computed tomography with stem water potential (Ψx) and whole plant transpiration (Eplant) measured prior to scans. Plants exposed to severe drought stress suffered high levels of embolism (47.38 ± 10.97 % PLV) and almost complete canopy loss. No evidence of embolism refilling was observed at 24 hrs, one week, or three weeks after rewatering despite rapid recovery in Ψx. Recovery of hydraulic capacity was achieved over a 6-month period by growth of new xylem tissue, with canopy leaf area and Eplant recovering over the same period. These findings indicate that E. saligna recovers slowly from severe drought stress, with potential for embolism to persist in the xylem for many months after rainfall.