Fire weather and likelihood: characterizing climate space for fire occurrence and extent in Puerto Rico

Beusekom, Ashley E. Van; Gould, William A.; Monmany, A. Carolina; Khalyani, Azad Henareh; Quiñones, Maya; Fain, Stephen J.; Andrade‐Núñez, María José; González, Grizelle

doi:10.1007/s10584-017-2045-6

Cited by 23 publications

(16 citation statements)

References 42 publications

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“…relative humidity or both take place [48,49]. This pattern is apparently independent of daily temperature thus we suggest to consider local conditions of wind and humidity in predicting models for Puerto Rico.…”

Section: Forest Ecology and Conservationmentioning

confidence: 70%

“…Instead, mean daily minimum temperature and daily thermal amplitude represented by the interaction between temperature maximum and minimum were more determinant for fire occurrence and of special consideration in predicting models. In another study using random forests and aggregating data into different day intervals we found that precipitation in fact explained fire occurrence better than temperature variables suggesting that precipitation variability rather than mean precipitation is a better predictor of fire occurrence in Puerto Rico [49]. In both cases, mean daily minimum temperature was more important than maximum temperature to explain fires.…”

Section: Forest Ecology and Conservationmentioning

confidence: 78%

“…The municipalities that registered more fire episodes between 2003 and 2011 were located mainly south of the island while municipalities with less number of fires reported were located north. The number of fires per 1000 persons was higher (40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50)(51)(52)(53)(54)(55)(56) in seven municipalities located south and west of the island (Figure 1a). The same spatial pattern was found in relation to municipality area, the highest numbers of fires per km 2 (10-17) were found in six municipalities located mainly in the south and located in the north west area of the island (Figure 1b).…”

Section: Resultsmentioning

confidence: 99%

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Characterizing Predictability of Fire Occurrence in Tropical Forests and Grasslands: The Case of Puerto Rico

Monmany¹,

Gould²,

Núñez³

et al. 2017

Forest Ecology and Conservation

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Global estimates of fire frequency indicate that over 70% of active fires occur in the tropics, and the size and frequency of fires are increasing every year. The majority of fires in the tropics are an unintended consequence of current land-use practices that promotes the establishment of grass and shrubland communities, which are more flammable and more adapted to fire than forests. In the Caribbean, wildland fires occur mainly in dry forests and in grasslands and crop lands. Climate change projections for the Caribbean indicate increasing area of drylands and subsequent increasing potential for wildland fire. We assessed the last decade of fire occurrence records for Puerto Rico to quantify the relative importance of time, climate, land cover, and population to inform predictive models of fire occurrence for projecting future scenarios of fire risk. Kruskal-Wallis, generalized linear models, robust regression, simple and multiple regressions, and tree models were used. We found that hour of the day (time), mean minimum temperature (climate), and percent forest cover (land cover) significantly influenced fire occurrence, while population showed a weak effect. Many variable interactions showed to be important. These significant variables and interactions should be considered in fire-predicting models for the island.

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Section: Forest Ecology and Conservationmentioning

confidence: 70%

Section: Forest Ecology and Conservationmentioning

confidence: 78%

Section: Resultsmentioning

confidence: 99%

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Characterizing Predictability of Fire Occurrence in Tropical Forests and Grasslands: The Case of Puerto Rico

Monmany¹,

Gould²,

Núñez³

et al. 2017

Forest Ecology and Conservation

Self Cite

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“…The high level of precision of RF model is likely due to the composition of a many individually trained decision trees in the model, and each of these trees creates a classification decision where the class with the maximum number of votes is determined to be the final classification rules for the input data. Thus, this method is recommended for future implementation of fire management decision systems, as also outlined by [47,48]. This study compared only three ML algorithms, which are implementable in GEE, but it would be interesting to incorporate other models, such as neural networks, where a model can be more complex and likely provide more accurate predictions.…”

Section: Discussionmentioning

confidence: 99%

Exploratory Analysis of Driving Force of Wildfires in Australia: An Application of Machine Learning within Google Earth Engine

Sulova

Arsanjani

2020

Remote Sensing

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Recent studies have suggested that due to climate change, the number of wildfires across the globe have been increasing and continue to grow even more. The recent massive wildfires, which hit Australia during the 2019–2020 summer season, raised questions to what extent the risk of wildfires can be linked to various climate, environmental, topographical, and social factors and how to predict fire occurrences to take preventive measures. Hence, the main objective of this study was to develop an automatized and cloud-based workflow for generating a training dataset of fire events at a continental level using freely available remote sensing data with a reasonable computational expense for injecting into machine learning models. As a result, a data-driven model was set up in Google Earth Engine platform, which is publicly accessible and open for further adjustments. The training dataset was applied to different machine learning algorithms, i.e., Random Forest, Naïve Bayes, and Classification and Regression Tree. The findings show that Random Forest outperformed other algorithms and hence it was used further to explore the driving factors using variable importance analysis. The study indicates the probability of fire occurrences across Australia as well as identifies the potential driving factors of Australian wildfires for the 2019–2020 summer season. The methodical approach and achieved results and drawn conclusions can be of great importance to policymakers, environmentalists, and climate change researchers, among others.

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“…Tropical dry forests in Puerto Rico are home to 24 species of cactus, of which five are native columnar, semi-epiphytic, or globular cactus inhabiting the main island (Liogier 1994;Carrera-Martínez et al 2018). These dry forests are more likely to ignite under normal climatic conditions than tropical wet or rain forests (Monmany et al 2017) and, at least in Puerto Rico, fire frequency in these forests is expected to increase (Van Beusekom et al 2018). Cactus species are historically considered fire intolerant (Benson and Walkington 1965), with average mortality considerably higher than other succulent species (Thomas 1991).…”

Section: Introductionmentioning

confidence: 99%

Effects of fire on native columnar and globular cacti of Puerto Rico: a case study of El Faro, Cabo Rojo

et al. 2020

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Background: Many Puerto Rican ecosystems evolved without a regular fire regime. As such, many native plants lack adaptations necessary to survive even low-intensity fires. Human-caused fires are increasing in frequency, in part promoted by the presence of invasive grasses. During the afternoon of 19 February 2014, a wildfire burned a large portion of the dry forest in El Faro Natural Reserve, Cabo Rojo, Puerto Rico. Using a previously monitored population, we documented the mortality and morphological damage on three columnar and semi-epiphytic cactus species (Pilosocereus royenii [L.] Byles & Rowley, Harrisia portoricensis Britton, and Leptocereus quadricostatus Britton) and one globular species (Melocactus intortus [Mill.] Urb.) in relation to canopy cover and cactus growth strategy. Results: Our results suggest that M. intortus and H. portoricensis were more susceptible to fire damage. Cacti growing in areas dominated by grasses and shrubs were significantly more likely to burn than those growing under a closed tree canopy. Apart from L. quadricostatus, cacti that survived at least one year after the fire had lost spines, developed scarring tissue in some or all ribs, and few were able to regrow. Conclusions: Because extensive tissue damage and environmental alterations were observed during the postfire survey, which occurred between 14 to 19 months after the fire, we argue there will be significant delayed mortality and that the mortality estimates presented here are conservative.

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Fire weather and likelihood: characterizing climate space for fire occurrence and extent in Puerto Rico

Cited by 23 publications

References 42 publications

Characterizing Predictability of Fire Occurrence in Tropical Forests and Grasslands: The Case of Puerto Rico

Characterizing Predictability of Fire Occurrence in Tropical Forests and Grasslands: The Case of Puerto Rico

Exploratory Analysis of Driving Force of Wildfires in Australia: An Application of Machine Learning within Google Earth Engine

Effects of fire on native columnar and globular cacti of Puerto Rico: a case study of El Faro, Cabo Rojo

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