the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 02 Jan 2024
On the potential of using smartphone sensors for wildfire hazard estimation
Abstract. Weather conditions that can enhance wildfire potential are a problem faced by many countries around the world. Wildfires can have major economic impacts as well as prolonged effects on populations and ecosystems. Distributing information on fire hazards to the public and first responders in real-time is crucial for fire risk management and risk reduction. Although most fires today are caused by people, weather conditions determine if and how fast the fire spreads. In particular, research has shown that atmospheric vapor pressure deficit (VPD) is a key parameter predicting the dryness of vegetation and the available fuel for fires. VPD is determined from the environmental air temperature and relative humidity, both of which are readily obtained from smartphones carried by the public. In this study we use smartphone data from the OpenSignal company, collected during almost 4 years and from more than 40,000 users per day, to estimate VPD values. We have found that smartphone data can provide useful information about fire risk and danger. Here we present two case studies from wildfires in Israel and Portugal in which VPD is calculated using calibrated temperature and relative humidity measurements from smartphones. Given the exponential growth in the number of smartphones around the globe, we propose applying smartphone data for meteorological research and fire-weather applications.
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RC1: 'Review on nhess-2023-211', Anonymous Referee #1, 06 Jan 2024
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AC1: 'Reply on RC1', Colin Price, 15 Feb 2024
The comment was uploaded in the form of a supplement: https://nhess.copernicus.org/preprints/nhess-2023-211/nhess-2023-211-AC1-supplement.pdf
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AC1: 'Reply on RC1', Colin Price, 15 Feb 2024
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CC1: 'Comment on nhess-2023-211', carolina ojeda leal, 21 Feb 2024
In this article, the authors present a useful tool available to virtually anyone (Android cell phones) to address the challenge that poses wildfires with 2 study cases in Portugal and Israel. Overall, it is well written in academic English, easy to follow, provides a large range of well-done figures, and gives 2 interesting study cases with thorough research.
My comments to improve the paper are more focused on the main theme of the article because the technical issues were treated by reviewer 1 and resolved by the authors:
1. I think it could be necessary to work on the title, abstract, and discussion about what could be the most interesting part of the experiment which is the citizen science. It could bring novelty to the topic of wildfire hazard monitoring empowering citizens of all ages. A possible title could be: "Experiencing the Potential of Use of Android Smartphone Sensors in Wildfire Hazard Estimation through Citizen Science".
2. Also, in my opinion, it was not very clear in the introduction, methodology, and discussion which could be the main reader of this article. Paragraphs 355-370 enumerated some potential readers, but they are mentioned at the end of the article and did not permeate through the entire manuscript. Was it written for citizen science advocates? For wildfire researchers? For public policy specialists in wildfires, climate, and disasters? For engineers who work in big data? For governments in low-income countries who cannot afford an abundance of meteorological stations? This lack of definition debilitated an otherwise good article and must be clearly stated throughout the manuscript, please consider a major revision from peers in those abovementioned topics.
3. It will be very important to add in paragraph 145 that the WeatherSignal App does not operate in IOS (iPhone) and it is available only for Android smartphones. This should be repeated in the limitations of this study (paragraphs 365 and 370).
3. In aspects of formatting, please use the table model from the journal.
Citation: https://doi.org/10.5194/nhess-2023-211-CC1 -
AC2: 'Reply on CC1', Colin Price, 25 Feb 2024
The comment was uploaded in the form of a supplement: https://nhess.copernicus.org/preprints/nhess-2023-211/nhess-2023-211-AC2-supplement.pdf
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AC2: 'Reply on CC1', Colin Price, 25 Feb 2024
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RC2: 'Comment on nhess-2023-211', Anonymous Referee #2, 10 Mar 2024
REVIEW OF MANUSCRIPT NHESS-2023-211
“On the potential of using smartphone sensors for wildfire hazard estimation”
Summary and recommendation
The manuscript presents a study of using smartphone-based T/RH measurements to estimate VPD and subsequently assess wildfire hazard. The use of smartphone-based data is certainly a novel aspect of the work presented and something that the wildfire community could examine in the future for assessing wildfire hazard or/and fire danger. However, despite the arguments presented by the authors in the manuscript, I, unfortunately, remain skeptical towards the added value of the proposed approach. I recommend to reconsider the manuscript for publication subject to a major revision that, in my view, would allow for better highlighting any possible added value of the proposed use of smartphone-based data for assessing the conduciveness of weather to wildfires.
Major remarks
- In L41, the authors claim that smartphone microsensor data “ay provide additional and highly complimentary data”, as compared to traditional weather station data. Further, in L91-93, the authors state that “smartphone sensors have the potential for providing useful information about VPD at high spatial resolution and high temporal resolution even in remote areas with few official weather stations”.
I am very worried that the analysis conducted by the authors and the results presented do not support the above two arguments made to support the added value of using smartphone data.
First, the authors conduct their analysis on a 1 x 1 degree grid, on which they interpolate the smartphone-based VPD estimates. This spatial resolution is too coarse for any assessment of wildfire hazard/danger and hence, the presented results cannot be used for supporting the authors’ claim about smartphone microsensors providing information about VPD at high spatial resolution.
Second, the authors computed gridded VPD data by taking the daily mean values of T and RH. This is a rather crude approach that introduces significant implications. By considering the daily mean T/RH values, the authors put together data that may have been collected both outdoors (daytime) and indoors (nighttime). In addition, there have been recent studies highlighting the importance of nighttime VPD for the effectiveness of fire suppression operations. During nighttime, fuels may recover part of their moisture content; when VPD remains high at night, this process is hindered and fuels’ flammability remains high, thus supporting the rapid spread of fire. By taking a daily mean T/RH value to estimate a kind of daily mean VPD, this important information is neglected.
Third, smartphone data availability is directly related to the population density. Where more people gather, more data will be available. This typically includes populated areas and very less often remote areas. In addition, when the smartphone data are gathered from people located in an urban area, one should take into account that conditions may be significantly different from a remote, mountainous area. By using these data to assess conditions in another totally different environment (in terms of land cover, topography, etc.), one should be aware of the significant uncertainty introduced through the extrapolation of the data.
In summary, I highly encourage the authors to reconsider the entire structure of their work to better highlighted the possible added value of smartphone data for wildfire applications. Some suggestions may include:
- Decrease the spatial resolution of the grid on to which the smartphone data are interpolated, possibly to match the resolution of one of the publicly available reanalysis datasets (e.g., ERA5 at 0.25 x 0.25 degrees).
- Comparison of the smartphone-based VPD against ERA5-based (or any other data source) VPD and VPD obtained from interpolation from weather stations. This would allow evaluating whether smartphones can provide additional information or not.
- Refrain from averaging daily T/RH values and focus on examining nighttime/daytime data.
Citation: https://doi.org/10.5194/nhess-2023-211-RC2 -
AC3: 'Reply on RC2', Colin Price, 18 Mar 2024
The comment was uploaded in the form of a supplement: https://nhess.copernicus.org/preprints/nhess-2023-211/nhess-2023-211-AC3-supplement.pdf
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