Economic impact of weather forecasts for forest fires

Sol, B.

doi:10.1002/met.5060010210

Cited by 3 publications

(1 citation statement)

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“…Several other fire danger rating systems are used throughout Europe. Sweden uses the Angstrom Index [36], Russia the Russian Ignition Index [37,38], France the fire danger rated using numerical risk [39,40], Portugal the Canadian Fire Weather Index for daily fire danger values on an approximately 1 km grid, Spain the Spanish Forest Fire Index (SFFI), which is an adaptation of the U.S NFDRS Ignition component. Finally, there were some efforts to introduce a wildfire danger rating system on a global scale.…”

Section: Introductionmentioning

confidence: 99%

Leveraging a Wildfire Risk Prediction Metric with Spatial Clustering

Aryal

2022

Fire

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Fire authorities have started widely using operational fire simulations for effective wildfire management. The aggregation of the simulation outputs on a massive scale creates an opportunity to apply the evolving data-driven approach to closely estimate wildfire risks even without running computationally expensive simulations. In one of our previous works, we validated the application with a probability-based risk metric that gives a series of probability values for a fire starting at a start location under a given weather condition. The probability values indicate how likely it is that a fire will fall into different risk categories. The metric considered each fire start location as a unique entity. Such a provision in the metric could expose the metric to scalability issues when the metric is used for a larger geographic area and consequently make the metric hugely intensive to compute. In this work, in an investigative effort, we investigate whether the spatial clustering of fire start locations based on historical fire areas can address the issue without significantly compromising the accuracy of the metric. Our results show that spatially clustering all fire start locations in Tasmania into three risk clusters could leverage the probability-based risk metric by reducing the computational requirements of the metric by a theoretical factor in thousands with a mere compromise of approximately 5% in accuracy for two risk categories of high and low, thereby validating the possibility of the leverage of the metric with spatial clustering.

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Section: Introductionmentioning

confidence: 99%

Leveraging a Wildfire Risk Prediction Metric with Spatial Clustering

Aryal

2022

Fire

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Using willingness‐to‐pay to assess the economic value of weather forecasts for multiple commercial sectors

Rollins

Shaykewich²

2003

Meteorological Applications

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Economic Valuation of a New Meteorological Information Service: Conjoint Analysis for a Pollen Forecast System

Lee

Jang²,

et al. 2014

Weather, Climate, and Society

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This study aims to investigate the public’s preferences for and quantitatively measure the economic value of a pollen forecast system, a new meteorological information service, in South Korea. To directly measure the economic value of the pollen forecast system and its attributes in terms of the public’s preferences, this study used conjoint analysis and a discrete choice model. In the conjoint survey, seven factors were considered as attributes of the pollen forecast system: the forecast area, forecast interval, information type, forecast period, delivery media, forecast accuracy, and price. Based on survey data from the six largest metropolitan areas in South Korea, the authors estimated people’s utility function by a rank-ordered logit model and calculated the relative importance (RI) and marginal willingness to pay (MWTP) for each attribute. People considered price, meaning the additional tax burden, to be the most important attribute, with a relative importance of 42.3%. In descending order of relative importance, price was followed by forecast accuracy, forecast interval, forecast area, forecast period, and information type; these attributes had MWTP values of 0.133, −0.017, 0.098, 0.059, and 0.011 USD month−1, respectively. The results of this study can serve as guidance of government investments in the pollen forecast system and provide an empirical basis for the application of conjoint analysis to economic valuation studies on a wider range of meteorological information services.

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Economic impact of weather forecasts for forest fires

Cited by 3 publications

References 0 publications

Leveraging a Wildfire Risk Prediction Metric with Spatial Clustering

Leveraging a Wildfire Risk Prediction Metric with Spatial Clustering

Using willingness‐to‐pay to assess the economic value of weather forecasts for multiple commercial sectors

Economic Valuation of a New Meteorological Information Service: Conjoint Analysis for a Pollen Forecast System

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