the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 20 Feb 2023
Fire risk: an integrated modelling approach
Abstract. Wildfires are key to landscape transformation and vegetation succession, but also to socio-ecological values loss. Fire risk mapping can help to manage the most vulnerable and relevant ecosystems impacted by fires. However, few studies provide accessible daily dynamic results at different spatio-temporal scales. We develop a fire risk model for Sicily (Italy), an iconic case of the Mediterranean basin, integrating a fire hazard model with an exposure and vulnerability analysis under present and future conditions. The integrated model is data-driven but can run dynamically at a daily time-step, providing spatially and temporally explicit fashion results through the k.LAB platform. K.LAB provides an environment for input data integration, employing modeling methods such as Geographic Information System, Remote Sensing and Bayesian Network algorithms. All data and models are semantically annotated, open and downloadable in agreement with the FAIR principles (Findable, Accessible, Interoperable and Reusable). The fire risk analysis reveals that 45 % of vulnerable areas of Sicily are at high probability of danger in 2050. The risk model outputs also include qualitative risk indexes, which can make the results more understandable for non-technical stakeholders. We argue that this approach is well suited to aid in landscape management and preventing wildfires due to climate change.
-
Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
-
Preprint
(2515 KB)
-
Supplement
(563 KB)
-
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(2515 KB) - Metadata XML
-
Supplement
(563 KB) - BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-138', Marj Tonini, 09 Mar 2023
In this paper, authors introduce the implementation an innovative software tool to ass fire risk using both the well-known static drivers and dynamic drivers, including weather conditions before the fire event. In addition, fire hazard is evaluated considering the vulnerability of exposed elements under present and future conditions. The model has ben developed under k.LAN platform using FAIR data and resources, which makes it open and freely accessible for other researchers and stakeholders. Authors use Sicily as case study to illustrate this software implementation. Results allows to: (i) assess the relative importance of the driving variables to fire hazard; and (ii) elaborate risk maps and exposure maps for two periods, 2020 and 2050, under climate change scenarios. Different indicators for model evaluation, based on confusion matrix, are provide in the paper.
This manuscript addresses relevant scientific and technical questions within the scope of NHESS and up to international standard. The accurate description of the data, methods, experiments and computations, as well as the results obtained, allow the reproducibility of the study. Data and code have been made available on an open repository, Zenodo. English language is good and the number and quality of the references appropriate.
This paper provide a good contribution to the research in fire risk. In my opinion it can be accepted after few revisions and technical corrections indicated in the supplement.
- AC1: 'Reply on RC1', Alba Marquez, 15 May 2023
-
RC2: 'Comment on egusphere-2023-138', Anonymous Referee #2, 27 Mar 2023
The authors develop a spatial prediction of fire probability for Sicily, created using artificial intelligence algorithm, specifically a Bayesian Network using discretized explanatory variables. The explanatory variables include meteorological data, land surface properties and human influence components. The fire probability model is built using data between 2007 to 2019. Fire probability is determined for current conditions, 2020, and future conditions, 2050, based on future driving variables from the CMIP5 RCP 8.5 (business as usual) scenario. The fire probability is separated into low, mid and high regimes and combined with ecosystem information to determine potential risk in different ecosystems and vulnerable areas.
Overall the work is a valuable contribution to the body of literature. The work to detemrine a predicted increase in future fires for the vulnerable ecosystems of wildland urban interface, wildland agriculture interface, and nationally protected areas is a particularly important result. This work has the potential to help minimize predicted damage due to increasing fires in the future, by highlighting areas to focus mitigation and conservation efforts. I have several comments to be addressed in the supplement file, roughly in order of importance.
- AC2: 'Reply on RC2', Alba Marquez, 15 May 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-138', Marj Tonini, 09 Mar 2023
In this paper, authors introduce the implementation an innovative software tool to ass fire risk using both the well-known static drivers and dynamic drivers, including weather conditions before the fire event. In addition, fire hazard is evaluated considering the vulnerability of exposed elements under present and future conditions. The model has ben developed under k.LAN platform using FAIR data and resources, which makes it open and freely accessible for other researchers and stakeholders. Authors use Sicily as case study to illustrate this software implementation. Results allows to: (i) assess the relative importance of the driving variables to fire hazard; and (ii) elaborate risk maps and exposure maps for two periods, 2020 and 2050, under climate change scenarios. Different indicators for model evaluation, based on confusion matrix, are provide in the paper.
This manuscript addresses relevant scientific and technical questions within the scope of NHESS and up to international standard. The accurate description of the data, methods, experiments and computations, as well as the results obtained, allow the reproducibility of the study. Data and code have been made available on an open repository, Zenodo. English language is good and the number and quality of the references appropriate.
This paper provide a good contribution to the research in fire risk. In my opinion it can be accepted after few revisions and technical corrections indicated in the supplement.
- AC1: 'Reply on RC1', Alba Marquez, 15 May 2023
-
RC2: 'Comment on egusphere-2023-138', Anonymous Referee #2, 27 Mar 2023
The authors develop a spatial prediction of fire probability for Sicily, created using artificial intelligence algorithm, specifically a Bayesian Network using discretized explanatory variables. The explanatory variables include meteorological data, land surface properties and human influence components. The fire probability model is built using data between 2007 to 2019. Fire probability is determined for current conditions, 2020, and future conditions, 2050, based on future driving variables from the CMIP5 RCP 8.5 (business as usual) scenario. The fire probability is separated into low, mid and high regimes and combined with ecosystem information to determine potential risk in different ecosystems and vulnerable areas.
Overall the work is a valuable contribution to the body of literature. The work to detemrine a predicted increase in future fires for the vulnerable ecosystems of wildland urban interface, wildland agriculture interface, and nationally protected areas is a particularly important result. This work has the potential to help minimize predicted damage due to increasing fires in the future, by highlighting areas to focus mitigation and conservation efforts. I have several comments to be addressed in the supplement file, roughly in order of importance.
- AC2: 'Reply on RC2', Alba Marquez, 15 May 2023
Peer review completion
Journal article(s) based on this preprint
Data sets
Fire Sicily Alba Marquez Torres https://doi.org/10.5281/zenodo.7616451
Model code and software
Fire Sicily Alba Marquez Torres https://doi.org/10.5281/zenodo.7616451
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 347 | 161 | 16 | 524 | 38 | 9 | 7 |
- HTML: 347
- PDF: 161
- XML: 16
- Total: 524
- Supplement: 38
- BibTeX: 9
- EndNote: 7
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
Alba Marquez Torres
Giovanni Signorello
Sudeshna Kumar
Greta Adamo
Ferdinando Villa
Stefano Balbi
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(2515 KB) - Metadata XML
-
Supplement
(563 KB) - BibTeX
- EndNote
- Final revised paper