the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 02 Aug 2024
Understanding and simulating cropland and non-cropland burning in Europe using the BASE (Burnt Area Simulator for Europe) model
Abstract. Fire interacts with many parts of the Earth system. However, its drivers are myriad and complex, interacting differently in different regions depending on prevailing climate regimes, vegetation types, socioeconomic development, and land use and management. Europe is facing strong increases in projected meteorological fire danger as a consequence of climate change, and has experienced extreme fire seasons and events in recent years. Here, we focus on understanding and simulating burnt area across a European study domain using remote sensing data and Generalised Linear Models (GLMs). We first examined fire occurrence across land cover types and found that all non-cropland vegetation types (NCV, comprising 26 % of burnt area) burned with similar spatial and temporal patterns, which were very distinct from those in croplands (74 % of burned area). We then used GLMs to predict cropland and NCV burnt area at ~9x9 km and monthly spatial and temporal resolution, respectively, which together we termed BASE (Burnt Area Simulator for Europe). Compared to satellite burned area products, BASE effectively captured the general spatial and temporal patterns of burning, explaining 32 % (NCV) and 36 % (cropland) of the deviance, and gave similar performance of state-of-the-art global fire models. The most important drivers were fire weather and monthly indices derived from gross primary productivity, followed by coarse socioeconomic indicators and vegetation properties. Crucially, we found that the drivers of cropland and NCV burning were very different, highlighting the importance of simulating burning in different land cover types separately. Through the choice of predictor variables, BASE was designed for coupling with dynamic vegetation and Earth System models, and thus enabling future projections. In particular, the strong model skill of BASE when reproducing seasonal and interannual dynamics of NCV burning (i.e. temporally evolving wildfire risk), and the novel inclusion of cropland burning, recommend it for this purpose. In addition to this, the BASE framework may serve as a basis for further studies using additional predictors to further elucidate drivers of fire in Europe. Through these applications, we suggest BASE may be a useful tool for understanding, and therefore adapting to, the increasing fire risk in Europe.
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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.
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Preprint
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Journal article(s) based on this preprint
Interactive discussion
Status: closed
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CC1: 'Comment on egusphere-2024-1973', Taimur Khan, 06 Aug 2024
To provide a comprehensive and constructive peer review, I'll address key aspects of the paper titled "Understanding and simulating cropland and non-cropland burning in Europe using the BASE (Burnt Area Simulator for Europe) model" by Matthew Forrest et al. The review will consider the paper's objectives, methodology, results, discussion, and overall contribution to the field.
Title and Abstract
The title is clear and concise, effectively summarising the paper's focus. The abstract provides a good overview of the study, highlighting the importance of fire modelling in Europe and the novel aspects of the BASE model. However, the abstract could be improved by including specific findings or key statistics to give readers a better sense of the study’s outcomes.Introduction
The introduction is thorough, providing a strong rationale for the study by situating it within the context of increasing wildfire risks in Europe due to climate change. The authors effectively summarise the complexities of fire dynamics and the limitations of existing models. The justification for focusing on Europe and the use of a new modelling approach (BASE) is well-articulated.Methodology
The methodology section is detailed and provides a clear explanation of the datasets and modelling techniques used. The use of Generalised Linear Models (GLMs) is appropriate given the study's aims, and the authors provide a sound justification for their use. However, the methodology could be improved by:- Clarifying Assumptions: The authors should more explicitly state the assumptions underlying their modelling approach, particularly in relation to the spatial and temporal resolution of the data.
- Model Validation: While the authors mention comparing BASE to satellite products, the process of model validation could be more robust. Including a section on the limitations and potential biases of the validation process would strengthen the credibility of the results.
Results
The results are presented clearly, with appropriate use of figures and tables to illustrate the findings. The distinction between cropland and non-cropland burning is a novel contribution, and the results support the authors' hypothesis that these two types of burning have different drivers. However, the results section could be enhanced by:- Providing More Context: Some of the results are presented without sufficient context or interpretation. For example, the significance of the differences in deviance explained by the model for cropland versus non-cropland could be discussed in more detail.
- Addressing Uncertainty: The authors should more explicitly address the uncertainty in their results, especially given the complexity of fire dynamics and the number of variables involved. Sections 3.3 & 3.4 address model performance to a certain degree, but it remained difficult to follow uncertainty of the model while reading through the results.
Discussion
The discussion effectively ties the results back to the broader context of fire modelling and management in Europe. The authors highlight the importance of their findings for future modelling efforts and for understanding the drivers of fire in different land cover types. However, the discussion could benefit from:- Critiquing the Model's Performance: While the authors emphasise the model’s strengths, they should also critically evaluate its weaknesses. For instance, the relatively low percentage of deviance explained (32-36%) suggests there may be important factors not captured by the model.
- Implications for Policy: The discussion touches on the potential policy implications of the findings but could go further in outlining specific recommendations for fire management or policy changes in Europe.
Conclusion
The conclusion is concise and summarises the main contributions of the study. However, it could be strengthened by including a brief discussion on the future direction of research in this area, particularly how the BASE model could be refined or extended to improve its predictive power.References
The references are appropriate and up-to-date, demonstrating the authors' thorough engagement with the relevant literature. However, the inclusion of more recent studies on fire modelling in Europe could further bolster the literature review.Overall Evaluation
This paper makes a valuable contribution to the field of fire modelling by developing and applying the BASE model to simulate cropland and non-cropland burning in Europe. The study is well-conceived and methodologically sound, but there are areas where the analysis could be deepened, particularly in terms of addressing uncertainties and the implications of the findings. With minor revisions, particularly in the discussion and methodology sections, this paper could be a significant addition to the literature on fire dynamics and climate change in Europe.Recommendation: Minor revision with specific attention to strengthening the discussion and clarifying methodological assumptions.
Citation: https://doi.org/10.5194/egusphere-2024-1973-CC1 - AC1: 'Reply on CC1', Matthew Forrest, 04 Oct 2024
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RC1: 'Comment on egusphere-2024-1973', Anonymous Referee #1, 21 Aug 2024
The authors of “Understanding and simulating cropland and non-cropland burning in Europe using the Base model” use generalized linear models to develop a fire model capable of predicting cropland and non-cropland burned area in Europe. This model is likely suitable for use in land surface and climate models. To my knowledge, few land surface models include the ability to model cropland fire. This work is timely, technically rigorous, and falls within the scope of bio-geosciences. I have several comments which are listed below.
- Intro: The lack of land surface models capable of representing cropland fire is mentioned in the discussion. I suggest discussing it in the intro as well as the motivation for this work.
- Table 1: Adding the data source and citations could help better inform the reader
- L270: How were the data points sampled? Was anything done to uniformly distribute the sampling across space, or account for spatial autocorrelation?
- L460: Some text comparing and contrasting these models with mechanistic models could be interesting. For example in mechanistic models, wildland fire is influenced by wind and terrain which impacts spread, whereas cropland fire appears to be a more complex phenomenon perhaps better suited to description using a statistical model.
- L575: The analysis of the role GDP and HDI play in the model is interesting. Can the authors provide insight into whether this is corelative or causative? Do these relationships apply in time (i.e. moving into the future)? What if there were abrupt changes in these metrics due to for example a short-term financial crisis?
- L655: Did the authors consider other remote-sensed burned products that might include small fires like GFED4s?
- L690: Finally, can the authors address if this model is specific to this region or could be transferred to other regions of the world? How involved do they believe the process of doing this would be?
Minor comments:
- L26 here and elsewhere rephrase meteorological fire danger for clarity
- L34 remove “of” just before “state of the art”
- L39-40 suggest rephrasing these sentences
- L49-52 split and shorten this sentence
- L58 rephrase “coherent political level” for clarity
- L210 revise “artefacts”
- Figure 1: Here and through the figures would be clearer if the acronyms were defined in the axis labels and figure captions
- Figure 5: Here and elsewhere the single shared legend and brief caption could be clearer if they provided information about the mean lines, uncertainty regions, etc.
Citation: https://doi.org/10.5194/egusphere-2024-1973-RC1 - AC2: 'Reply on RC1', Matthew Forrest, 04 Oct 2024
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RC2: 'Comment on egusphere-2024-1973', Anonymous Referee #2, 23 Aug 2024
The authors present an important contribution to a pertinent issue in wildfire modelling, adding an important perspective to the hot topic of the differences between natural vegetation versus cropland wildfire. The model is well-described and rigorous, finding concrete differences between the drivers of burning in cropland and non-cropland vegetation. Performance statistics are largely better than fireMIP models, but similar-to-worse than the other GLM based studies cited in the introduction. However, as this is the only study focussed on Europe and is at a finer resolution than most of the other studies, the only conclusion that can be reached through this comparison is that BASE’s performance meets an acceptable threshold for publication.
Whilst the paper’s conclusion emphasises predictive applicability to the projection of future burned area, the overall performance is not greater than existing global and regional modelling methods. The crucial contribution of this paper is the use of a twin-approach to cropland and “natural” vegetation wildfires. The authors demonstrate that this can substantially reduce confounding effects in specific drivers (e.g. population density or fire weather) that differ between land-cover types, and that trends in NCV/cropland fires can be disaggregated. The paper thus reflects an important contribution to the next generation of fire models, towards improving overall model performance and also to understanding the complex dual effects of climate warming and land-use change over time.
Specific Comments:
- L95: provide evidence/citation that existing global fire models do in fact perform worse in other biomes due to this training bias. Is this the case for all widely-adopted fire-enable DGVMs?
- L180/L190: explain why MEPI and PHI are defined as relative to GPP maximum month of prior year; as maxima are less stable against interannual variability than, for example, the study-period mean, what was the advantage to this formulation?
- L200/Table 1: In table 1 it is stated that FAPAR12 models fine fuel build up over twelve months. At lower values of fAPAR there is a roughly linear relationship with LAI (LAI ~ - 0.5 ln(1 - fAPAR)). This is only one component of leaf litter accumulation. But this neglects leaf mass per area and leaf lifespan, which can vary substantially between needleleaf/broadleaf/deciduous/evergreen. So what does fAPAR12 actually mean, and why is it selected over the more physical GPP? Could it be that the modelled GPP product does not give as spatially reliable a map as the remotely-sensed fAPAR? This could explain the decreased spatial (but improved interannual) performance when GPP replaces fAPAR (table 2). This issue can either be addressed with a good explanation of FAPAR12’s physical effect vs GPP12 in the method section, or by discussing the implications further in section 4.1.3.
- L214/223: Two sources of GDP data are cited, which was used in this study?
- L478: Could it be that these regions are too wet to seasonally burn?
- L568: Spain does differ, but there also appears to be more simulated cropland fire in France, Poland and the Baltics. It would be good to either acknowledge this difference, or to justify that this visual difference in the maps is not as significant as the Spanish case (e.g. due to GLM ‘smearing’ or colormap choice).
- L687: Consider changing “so is suitable for projecting changes in fire hazard over annual-to-decadal time scales” to “so is suitable for projecting differing changes in fire hazard between cropland and non-cropland vegetation over annual-to-decadal time scales”. Not to say it cannot be used in general projections, but that this is the unique value of the model compared to similarly performing fire models.
Technical Corrections:
- L245-250: give the list of actual land cover classes in the supplementary for better readability.
- L393: add full-stop.
- L490/491: correct “1)” and “2)” to (1) and (2) in sentence.
Citation: https://doi.org/10.5194/egusphere-2024-1973-RC2 - AC3: 'Reply on RC2', Matthew Forrest, 04 Oct 2024
Interactive discussion
Status: closed
-
CC1: 'Comment on egusphere-2024-1973', Taimur Khan, 06 Aug 2024
To provide a comprehensive and constructive peer review, I'll address key aspects of the paper titled "Understanding and simulating cropland and non-cropland burning in Europe using the BASE (Burnt Area Simulator for Europe) model" by Matthew Forrest et al. The review will consider the paper's objectives, methodology, results, discussion, and overall contribution to the field.
Title and Abstract
The title is clear and concise, effectively summarising the paper's focus. The abstract provides a good overview of the study, highlighting the importance of fire modelling in Europe and the novel aspects of the BASE model. However, the abstract could be improved by including specific findings or key statistics to give readers a better sense of the study’s outcomes.Introduction
The introduction is thorough, providing a strong rationale for the study by situating it within the context of increasing wildfire risks in Europe due to climate change. The authors effectively summarise the complexities of fire dynamics and the limitations of existing models. The justification for focusing on Europe and the use of a new modelling approach (BASE) is well-articulated.Methodology
The methodology section is detailed and provides a clear explanation of the datasets and modelling techniques used. The use of Generalised Linear Models (GLMs) is appropriate given the study's aims, and the authors provide a sound justification for their use. However, the methodology could be improved by:- Clarifying Assumptions: The authors should more explicitly state the assumptions underlying their modelling approach, particularly in relation to the spatial and temporal resolution of the data.
- Model Validation: While the authors mention comparing BASE to satellite products, the process of model validation could be more robust. Including a section on the limitations and potential biases of the validation process would strengthen the credibility of the results.
Results
The results are presented clearly, with appropriate use of figures and tables to illustrate the findings. The distinction between cropland and non-cropland burning is a novel contribution, and the results support the authors' hypothesis that these two types of burning have different drivers. However, the results section could be enhanced by:- Providing More Context: Some of the results are presented without sufficient context or interpretation. For example, the significance of the differences in deviance explained by the model for cropland versus non-cropland could be discussed in more detail.
- Addressing Uncertainty: The authors should more explicitly address the uncertainty in their results, especially given the complexity of fire dynamics and the number of variables involved. Sections 3.3 & 3.4 address model performance to a certain degree, but it remained difficult to follow uncertainty of the model while reading through the results.
Discussion
The discussion effectively ties the results back to the broader context of fire modelling and management in Europe. The authors highlight the importance of their findings for future modelling efforts and for understanding the drivers of fire in different land cover types. However, the discussion could benefit from:- Critiquing the Model's Performance: While the authors emphasise the model’s strengths, they should also critically evaluate its weaknesses. For instance, the relatively low percentage of deviance explained (32-36%) suggests there may be important factors not captured by the model.
- Implications for Policy: The discussion touches on the potential policy implications of the findings but could go further in outlining specific recommendations for fire management or policy changes in Europe.
Conclusion
The conclusion is concise and summarises the main contributions of the study. However, it could be strengthened by including a brief discussion on the future direction of research in this area, particularly how the BASE model could be refined or extended to improve its predictive power.References
The references are appropriate and up-to-date, demonstrating the authors' thorough engagement with the relevant literature. However, the inclusion of more recent studies on fire modelling in Europe could further bolster the literature review.Overall Evaluation
This paper makes a valuable contribution to the field of fire modelling by developing and applying the BASE model to simulate cropland and non-cropland burning in Europe. The study is well-conceived and methodologically sound, but there are areas where the analysis could be deepened, particularly in terms of addressing uncertainties and the implications of the findings. With minor revisions, particularly in the discussion and methodology sections, this paper could be a significant addition to the literature on fire dynamics and climate change in Europe.Recommendation: Minor revision with specific attention to strengthening the discussion and clarifying methodological assumptions.
Citation: https://doi.org/10.5194/egusphere-2024-1973-CC1 - AC1: 'Reply on CC1', Matthew Forrest, 04 Oct 2024
-
RC1: 'Comment on egusphere-2024-1973', Anonymous Referee #1, 21 Aug 2024
The authors of “Understanding and simulating cropland and non-cropland burning in Europe using the Base model” use generalized linear models to develop a fire model capable of predicting cropland and non-cropland burned area in Europe. This model is likely suitable for use in land surface and climate models. To my knowledge, few land surface models include the ability to model cropland fire. This work is timely, technically rigorous, and falls within the scope of bio-geosciences. I have several comments which are listed below.
- Intro: The lack of land surface models capable of representing cropland fire is mentioned in the discussion. I suggest discussing it in the intro as well as the motivation for this work.
- Table 1: Adding the data source and citations could help better inform the reader
- L270: How were the data points sampled? Was anything done to uniformly distribute the sampling across space, or account for spatial autocorrelation?
- L460: Some text comparing and contrasting these models with mechanistic models could be interesting. For example in mechanistic models, wildland fire is influenced by wind and terrain which impacts spread, whereas cropland fire appears to be a more complex phenomenon perhaps better suited to description using a statistical model.
- L575: The analysis of the role GDP and HDI play in the model is interesting. Can the authors provide insight into whether this is corelative or causative? Do these relationships apply in time (i.e. moving into the future)? What if there were abrupt changes in these metrics due to for example a short-term financial crisis?
- L655: Did the authors consider other remote-sensed burned products that might include small fires like GFED4s?
- L690: Finally, can the authors address if this model is specific to this region or could be transferred to other regions of the world? How involved do they believe the process of doing this would be?
Minor comments:
- L26 here and elsewhere rephrase meteorological fire danger for clarity
- L34 remove “of” just before “state of the art”
- L39-40 suggest rephrasing these sentences
- L49-52 split and shorten this sentence
- L58 rephrase “coherent political level” for clarity
- L210 revise “artefacts”
- Figure 1: Here and through the figures would be clearer if the acronyms were defined in the axis labels and figure captions
- Figure 5: Here and elsewhere the single shared legend and brief caption could be clearer if they provided information about the mean lines, uncertainty regions, etc.
Citation: https://doi.org/10.5194/egusphere-2024-1973-RC1 - AC2: 'Reply on RC1', Matthew Forrest, 04 Oct 2024
-
RC2: 'Comment on egusphere-2024-1973', Anonymous Referee #2, 23 Aug 2024
The authors present an important contribution to a pertinent issue in wildfire modelling, adding an important perspective to the hot topic of the differences between natural vegetation versus cropland wildfire. The model is well-described and rigorous, finding concrete differences between the drivers of burning in cropland and non-cropland vegetation. Performance statistics are largely better than fireMIP models, but similar-to-worse than the other GLM based studies cited in the introduction. However, as this is the only study focussed on Europe and is at a finer resolution than most of the other studies, the only conclusion that can be reached through this comparison is that BASE’s performance meets an acceptable threshold for publication.
Whilst the paper’s conclusion emphasises predictive applicability to the projection of future burned area, the overall performance is not greater than existing global and regional modelling methods. The crucial contribution of this paper is the use of a twin-approach to cropland and “natural” vegetation wildfires. The authors demonstrate that this can substantially reduce confounding effects in specific drivers (e.g. population density or fire weather) that differ between land-cover types, and that trends in NCV/cropland fires can be disaggregated. The paper thus reflects an important contribution to the next generation of fire models, towards improving overall model performance and also to understanding the complex dual effects of climate warming and land-use change over time.
Specific Comments:
- L95: provide evidence/citation that existing global fire models do in fact perform worse in other biomes due to this training bias. Is this the case for all widely-adopted fire-enable DGVMs?
- L180/L190: explain why MEPI and PHI are defined as relative to GPP maximum month of prior year; as maxima are less stable against interannual variability than, for example, the study-period mean, what was the advantage to this formulation?
- L200/Table 1: In table 1 it is stated that FAPAR12 models fine fuel build up over twelve months. At lower values of fAPAR there is a roughly linear relationship with LAI (LAI ~ - 0.5 ln(1 - fAPAR)). This is only one component of leaf litter accumulation. But this neglects leaf mass per area and leaf lifespan, which can vary substantially between needleleaf/broadleaf/deciduous/evergreen. So what does fAPAR12 actually mean, and why is it selected over the more physical GPP? Could it be that the modelled GPP product does not give as spatially reliable a map as the remotely-sensed fAPAR? This could explain the decreased spatial (but improved interannual) performance when GPP replaces fAPAR (table 2). This issue can either be addressed with a good explanation of FAPAR12’s physical effect vs GPP12 in the method section, or by discussing the implications further in section 4.1.3.
- L214/223: Two sources of GDP data are cited, which was used in this study?
- L478: Could it be that these regions are too wet to seasonally burn?
- L568: Spain does differ, but there also appears to be more simulated cropland fire in France, Poland and the Baltics. It would be good to either acknowledge this difference, or to justify that this visual difference in the maps is not as significant as the Spanish case (e.g. due to GLM ‘smearing’ or colormap choice).
- L687: Consider changing “so is suitable for projecting changes in fire hazard over annual-to-decadal time scales” to “so is suitable for projecting differing changes in fire hazard between cropland and non-cropland vegetation over annual-to-decadal time scales”. Not to say it cannot be used in general projections, but that this is the unique value of the model compared to similarly performing fire models.
Technical Corrections:
- L245-250: give the list of actual land cover classes in the supplementary for better readability.
- L393: add full-stop.
- L490/491: correct “1)” and “2)” to (1) and (2) in sentence.
Citation: https://doi.org/10.5194/egusphere-2024-1973-RC2 - AC3: 'Reply on RC2', Matthew Forrest, 04 Oct 2024
Peer review completion
Journal article(s) based on this preprint
Data sets
Data for fitting Burnt Area Simulator for Europe (BASE v1.0) Matthew Forrest https://zenodo.org/records/12580343
Model code and software
BASE-v1.0: Submission Release (v1.0) Matthew Forrest https://zenodo.org/records/12580481
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Matthew Forrest
Jessica Hetzer
Maik Billing
Simon P. K. Bowring
Eric Kosczor
Luke Oberhagemann
Oliver Perkins
Dan Warren
Fátima Arrogante-Funes
Kirsten Thonicke
Thomas Hickler
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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