To meet the modern standards of fire management agencies, weather data must be accurate, timely, representative of actual field conditions, and durable enough to ensure a continuous historical weather record. These can be achieved by working towards a dense network of Remote Automated Weather Stations (RAWS) that are correctly located, finely calibrated, and robust.
Station location is critical
A weather station’s location needs to reflect the conditions it’s trying to assess. A meteorological station at an airport, for example, will accurately gauge flying conditions in the valley bottom and the approach of broad weather fronts. A fire weather station, on the other hand, needs to gauge what weather conditions are like in the places where wildfires start—within the forest cover, close to the ground, on a level or sunny slope — and embedded in the local terrain. Wind speeds in a forest opening are typically 40% less than those at an airport station . Terrain and elevation can substantially alter temperature, relative humidity, and wind speed and direction.
Existing meteorological networks rarely cover the areas where fire danger information is required, particularly in jurisdictions with large wilderness areas. In British Columbia, for instance, the meteorological network has approximately 90 stations in the province, mostly located in urban areas, while another 260 RAWS are used to collect fire weather data throughout the province, particularly in fire-prone ecosystems.
More density equals more accuracy
The data accuracy of a fire weather network increases with its density. One modelling study set in Spain found that switching from a grid spacing of 100 km to 50 km in just the most critical fifth of an area would reduce annual burned area by 20% . The initial RAWS network in the US had a spacing of 120 km, but an 80 km distance is now considered the minimum necessary for a meaningful network. This reflects a shift in use from only coarse fire danger ratings to support of decisions that affect firefighter safety in specific locations . A study of the US RAWS network found that, in spite of a median distance of only 29 km between stations, only 5% of stations were redundant, indicating that such a dense network is useful for representative fire weather data.
The more varied the terrain is, the more stations are needed for accuracy. Complex terrain also requires greater judgement and analysis to determine station placement. Portable RAWS stations can be particularly useful in confirming or challenging the accuracy of the nearest weather station during a major fire event or during a prescribed burn.
Automation increases reliability and efficiency
According to the World Meteorological Organization, “the benefits of Automated Weather Stations include their cost effectiveness, high frequency data, better ability to detect extremes, deployment in hostile locations, faster access to data, consistency and objectiveness in measurement, and ability to perform automatic quality monitoring.”
Timeliness is essential for fire weather, and as discussed in the previous section, rudimentary weather stations don’t always report at the right time of day for calculating fire danger indices. Fire suppression operations particularly value having access to hourly data, because it could save lives.
Automated fire weather stations are purpose-built to transmit data without any intervention. Manual weather stations are difficult and expensive to staff in remote areas where few people live. They also require staff time on the receiving end to receive and process the information. Automated fire weather stations reduce human error by cutting out these processes, providing fire managers with peace of mind that observations are correct. Automation also frees up staff time to focus on other tasks, reducing budget expenditures.
Low volume vs. high volume precipitation
Fire weather operators have unique needs. Where standard meteorological stations are more focused on accurately capturing high-volume rainfall events, a fire weather station needs to be finely-tuned to gauge minute quantities of precipitation. The faint drizzle in the morning or the overnight dewfall—these small quantities of moisture can have a significant impact on the day’s fire behaviour. They are also easy to miss because of their rapid evaporation from a typical rain gauge. The best fire weather stations are designed to prevent this from happening.
Tailored to fire weather standards
To maintain data accuracy, stations should be built and maintained to rigorous fire weather network standards. The US fire weather network standards specify everything from sensor accuracy and station placement to calibration and maintenance schedules . These requirements are tailored to the unique demands of fire weather applications while upholding standards set by the World Meteorological Organization.
Maintenance simplicity reduces costs
Purpose-built remote fire weather stations are designed to make scheduled maintenance simple enough for a non-technical staff person to do. This reduces costs and makes it more affordable to keep up a regular maintenance schedule.
Durable stations prevent interruptions
A remote automated fire weather station must be virtually indestructible in order to maintain the continuous weather records so necessary for fire danger applications. Lightning strikes, fire, humidity, insects, and vandalism are just some of the harsh conditions they must withstand.
The value of a dedicated fire weather network
A dedicated fire weather network will result in more accurate, timely, and complete fire weather data. Better data means better decision-making, which in turn leads to lower expenditure on fighting fires and less damage to property, lives, and ecosystems. The cost of a dedicated fire weather network is very small in comparison with the cost of errors in fire suppression. Using very conservative estimates, an agency would need to reduce the area burned by only 2.5% in order to justify the cost (see box). If reductions are 20% as in the study previously mentioned , then the costs will be vastly outweighed by the benefits.
A dedicated fire weather network helps fire management agencies improve their ability to predict, prevent and fight wildfires. This article has shown how costly and dangerous errors can be made when fire weather data is incorrect or missing. A dedicated network of remote automated fire weather stations ensures fire weather data is timely, accurate, and representative of actual field conditions. This investment will be paid back many-fold in the form of more effective and efficient firefighting, reduced damages to property and ecosystems, and saved lives.
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A Cost-Benefit Analysis
Based on data collected between 1985 and 2012, the United States sees an average of 76,669 fires per year, impacting 4.96 million acres (20,075 km2). This is roughly 22% of the total land area of the United States.
Using the US average as a reference (which is lower than the global average) an area the size of Malaysia (330,000 km2) could expect to have fires impacting 726 km2 annually. Assuming* a cost of $100/hectare for fire suppression and $100/hectare for damages, this would result in approximately $14.5 million dollars in suppression and damage costs annually. The approximate cost of a fire weather network for an area this size, spaced at 80-km apart and amortized over ten years and including maintenance costs, would be covered by a 2.5% reduction in annual fire costs.
*Estimates of cost per hectare vary enormously. In the US, the 20-yr average cost of suppression alone is $600/haxxxi. Case studies in the US have shown suppression costs ranging from 53% to more typically only 5% of total costs incurred by fires. In 1997, fires in Malaysia, Indonesia, and Khazakstan incurred damage costs of $609,000/ha, $2,418/ha, and $717/ha respectively. In the Malaysian case, fire suppression only accounted for three percent of the total costs—the bulk were incurred from lost productivity due to evacuations, and lost tourism revenue. Other examples include Sri Lanka, where average damages from 1990-2000 were $60/ha, and Mongolia, where average damages from 1996-1997 were $13,000/ha.