egusphere-2024-3595

General comments:

The authors developed the statistical simulation model for fire burnt area which was supposed to be incorporated into DGVM. The proposed GLMs as the functions of multiple environmental factors, would give the potential for a very light simulation tool on global scale wildfire.

However, one of my concerns is that the wildfire would be ignited considerably by lighting and expand their burning severity depending on the dry litter amount as fuel, both of which are not considered this time. The predictability for the long-term future is not secured as long as you do not use an accumulated ecosystem carbon stock, which DGVM has the advantage to simulate, but you use the GPP related index which is not necessarily related to fire severity. Statistical models cannot guarantee accuracy when a regime-shift for fire occurrence has happened mechanistically.

Considering the fewer predictors selected for the general representation of wildfire, the linear regression is shown not to be the best model for wildfire though still easy to use compared to machine learning or process-based models. Correlation coefficients for each term should be rearranged when this model was introduced to DGVM to use their simulated GPP and other ecosystem information which must be more or less different from MODIS or satellite products.

CEAM, BONA, MIDE, and TENA showed a 2-fold difference in interannual variations, which suggests that this model could be parameterized or separately made for vegetation types. A single statistical model cannot estimate the average BA for specific areas.

Minor Comments:

Page 3, Lines 90-91, you have to identify the typical model name that you criticize here. What you propose here is still a simple GLM based module, so I feel this sentence as contradiction

Page 3, Line 94-95, Use of remote sensing data will reduce the advantage of DGVM which enables the longterm vegetation shift simulation in future. also wanna now th direction from start to end

Page 17, Line 330 does this mean that HDI is going down in these years? you would better show the number in the decadal trend of averaged globally

Page 22, Lines 425-427: sounds repeated. you merge these two sentences into one.

Page 23, Lines 463: you also have to mention about the SPITFIRE-based model performance. GlobFIRM is an old version, and we know this is not accurate

Figure 1: you should add more explanation on the shape of frames, rectangles, rounds, diamonds. what is the data and what is the process

Table 2: explain the condition for color

Figure 4: Specify the years for the average

Figure 5: The y-axis should be in 10^6 to reduce the number of digits.

General comment

The manuscript entitled “A statistical global burned area model for seamless integration into Dynamic Global Vegetation Models” by Blessing Kavhu and colleagues develops a Generalized Linear Model (GLM) with 19 predictors. The authors designed and tested 26 models using burned area data from the Global Fire Emissions Database version 5 (GFED5) and combinations of the selected predictors. Model 25 was chosen as the best-performing model, with an explained deviance of 0.568 and a Normalized Mean Error (NME) of 0.718. The authors identified key predictors such as Fire Weather Index (FWI), and Percentage Non-Tree Cover (PNTC), which strongly influence fire occurrence, and Human Development Index (HDI), Gross Primary Productivity Index (GPPI), and Population Density (PPN), which are negatively associated with fire occurrence. While the model demonstrates limited accuracy in predicting global annual burned area variability (Figure 5), it performs well in capturing global seasonal variability (Figure 8). The authors also discussed the comparison between predicted and observed data in terms of spatio-temporal variability at the GFED regional level.

In general, I have concerns regarding the alignment of the manuscript’s title with its methods and objectives. The current title suggests that the authors developed statistical models (GLMs) seamlessly integrated into Dynamic Global Vegetation Models (DGVMs). However, upon reviewing the manuscript, it becomes clear that the GLM was built independently of any DGVM, and the integration is only theoretically explained. According to my understanding, true integration with DGVMs requires a lot and long modification processes, testing within specific DGVM frameworks, reparameterization, new input-output verification, module integration, and validation process. The integration process involves technical adjustments such as modifying input data formats, calibrating modules, and ensuring compatibility with existing model components (e.g., physical, physiological, vegetation, or disturbance, and biogeochemical modules). Without actual implementation and demonstrated results, the claim of "seamless integration" remains unsupported. I suggest revising the title to reflect the study's scope and contributions more accurately. For example, the title could emphasize the development of a GLM, its evaluation of wildfire drivers, and its ability to predict spatio-temporal variability in burned area data.

Additionally, the study workflow needs to be presented more systematically. I recommend referencing workflows from published manuscripts in this field and ensuring that critical methodological details, such as data sources, temporal coverage of input data, and prediction periods, are clearly outlined. The abstract section is also not structurally strong enough, it should be rearranged. A clear and detailed workflow will greatly aid readers in understanding the study's methodology. Furthermore, the term "prediction" should be adjusted to "historical prediction" to reflect the study’s temporal scope (2002–2018).

Overall, I recommend major revision before this manuscript can be considered for publication in Biogeosciences. Addressing the points mentioned above, along with detailed reviewer's comments, will significantly enhance the manuscript's clarity and alignment with its objectives. Please find detailed comments in the supplement.