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Societal exposure to large fires has been increasing in recent years. Our fire forecasting system reveal an untapped and useful burned area predictive ability using seasonal climate models, which can play a crucial role in developing fire management strategies minimising the impact of adverse climate conditions.
Developed by Univ. of Barcelona; Univ. of Murcia; Barcelona Supercomputing Center
FIRECAST is a novel simulation tool for forecasting burned area anomalies through linking seasonal climate predictions with parsimonious empirical climate fire models.
See more information about this level and the TRL and SRL levels.
Our strategy for seasonally forecasting burned area anomalies consists in linking seasonal climate predictions with parsimonious empirical climate–fire models using the standardized precipitation index as the climate predictor for burned area.
Limitations/conditions under which this innovation does not work or is less effective
This innovation may be less effective over data-poor regions such as Africa and South America owing to the uncertainties of the observed near-real-time data.
Added value
The key contribution of this innovation is to provide skillful BA predictions using multi-model seasonal climate predictions at a global scale and for each season separately. The system reveals substantial BA predictability based on antecedent and forecasted climate conditions that can be exploited for fire risk management months ahead.
Societal exposure to large fires has been increasing in recent years. Our fire forecasting system reveal an untapped and useful burned area predictive ability using seasonal climate models, which can play a crucial role in developing fire management strategies minimising the impact of adverse climate conditions.
Developed by Univ. of Barcelona; Univ. of Murcia; Barcelona Supercomputing Center
FIRECAST is a novel simulation tool for forecasting burned area anomalies through linking seasonal climate predictions with parsimonious empirical climate fire models.
The main components of the system have been tested separately, and an initial integration exercise has been conducted.
Our strategy for seasonally forecasting burned area anomalies consists in linking seasonal climate predictions with parsimonious empirical climate–fire models using the standardized precipitation index as the climate predictor for burned area.
Limitations/conditions under which this innovation does not work or is less effective
This innovation may be less effective over data-poor regions such as Africa and South America owing to the uncertainties of the observed near-real-time data.
Added value
The key contribution of this innovation is to provide skillful BA predictions using multi-model seasonal climate predictions at a global scale and for each season separately. The system reveals substantial BA predictability based on antecedent and forecasted climate conditions that can be exploited for fire risk management months ahead.
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