Project description:Litter decomposition / accumulation are rate limiting steps in soil formation, carbon sequestration, nutrient cycling and fire risk in temperate forests, highlighting the importance of robust predictive models at all geographic scales. Using a data set for the Australian continent, we show that among a range of models, >60% of the variance in litter mass over a 40-year time span can be accounted for by a parsimonious model with elapsed time, and indices of aridity and litter quality, as independent drivers. Aridity is an important driver of variation across large geographic and climatic ranges while litter quality shows emergent properties of climate-dependence. Up to 90% of variance in litter mass for individual forest types can be explained using models of identical structure. Results provide guidance for future decomposition studies. Algorithms reported here can significantly improve accuracy and reliability of predictions of carbon and nutrient dynamics and fire risk.
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Project description:Forests are currently a substantial carbon sink globally. Many climate change mitigation strategies leverage forest preservation and expansion, but rely on forests storing carbon for decades to centuries. Yet climate-driven disturbances pose critical risks to the long-term stability of forest carbon. We quantify the climate drivers that influence wildfire and climate stress-driven tree mortality, including a separate insect-driven tree mortality, for the contiguous United States for current (1984-2018) and project these future disturbance risks over the 21st century. We find that current risks are widespread and projected to increase across different emissions scenarios by a factor of >4 for fire and >1.3 for climate-stress mortality. These forest disturbance risks highlight pervasive climate-sensitive disturbance impacts on US forests and raise questions about the risk management approach taken by forest carbon offset policies. Our results provide US-wide risk maps of key climate-sensitive disturbances for improving carbon cycle modeling, conservation and climate policy.
Project description:Unplanned fire is a major control on the nature of terrestrial ecosystems and causes substantial losses of life and property. Given the substantial influence of climatic conditions on fire incidence, climate change is expected to substantially change fire regimes in many parts of the world. We wished to determine whether it was possible to develop a deep neural network process for accurately estimating continental fire incidence from publicly available climate data. We show that deep recurrent Elman neural network was the best performed out of ten artificial neural networks (ANN) based cognitive imaging systems for determining the relationship between fire incidence and climate. In a decennium data experiment using this ANN we show that it is possible to develop highly accurate estimations of fire incidence from monthly climatic data surfaces. Our estimations for the continent of Australia had over 90% global accuracy and a very low level of false negatives. The technique is thus appropriate for use in estimating the spatial consequences of climate scenarios on the monthly incidence of wildfire at the landscape scale.