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ProbFire: a probabilistic fire early warning system for Indonesia
Recurrent extreme landscape fire episodes associated with drought events in Indonesia pose severe environmental, societal and economic threats. The ability to predict severe fire episodes months in advance would enable relevant agencies and communities to more effectively initiate fire-preventative...
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Published in: | Natural hazards and earth system sciences 2022-02, Vol.22 (2), p.303-322 |
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creator | Nikonovas, Tadas Spessa, Allan Doerr, Stefan H Clay, Gareth D Mezbahuddin, Symon |
description | Recurrent extreme landscape fire episodes associated with drought events in Indonesia pose severe environmental, societal and economic threats. The ability to predict severe fire episodes months in advance would enable relevant agencies and communities to more effectively initiate fire-preventative measures and mitigate fire impacts. While dynamic seasonal climate predictions are increasingly skilful at predicting fire-favourable conditions months in advance in Indonesia, there is little evidence that such information is widely used yet by decision makers. |
doi_str_mv | 10.5194/nhess-22-303-2022 |
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subjects | Analysis Climate Climate prediction Climatic changes Climatology Datasets Drought Droughts Early warning systems Economic benefits El Nino Fires Forecasting Forest fires Indonesia Lead time Mathematical models Multilayer perceptrons Peatlands Precipitation Prediction models Predictions Probability theory Regions Seasonal forecasting Statistical analysis Temperature Warning systems Weather forecasting |
title | ProbFire: a probabilistic fire early warning system for Indonesia |
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