the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 29 Nov 2024
Identification of hydro-meteorological drivers for low greenness events in Europe
Abstract. Extreme hydro-meteorological events can have a substantial impact on vegetation and ecosystems. In particular, with heatwaves and droughts projected to become more frequent due to climate change, understanding their effects on forests is crucial. The goal of our study is to find the most relevant predictors for forest damage in Europe at monthly to annual timescales. Using a Random Forest approach, we pinpoint hydro-meteorological conditions associated with low normalized difference vegetation index (NDVI) events. We train the model using the NDVI from the Advanced Very High Resolution Radiometers (AVHRR) as the predictand, and a range of variables from the ERA5 and ERA5-Land reanalysis as hydro-meteorological predictors. These predictors include maximum temperature at 2 meters and dewpoint temperature, precipitation, surface latent heat flux, and soil moisture up to 18 months before the observed damage. The random forest model exhibits a high prediction skill over most gridpoints in Europe, with a critical success index greater than 0.75 for 67 % of gridpoints. Notably, warm and dry conditions in spring and early summer emerge as essential predictors. Additionally, we emphasize a longer-term relationship between hydro-meteorological conditions and forest damage. For instance, low dewpoint temperatures one year before the studied summer impact broad-leaved forests, while soil moisture during the preceding autumn influences low greenness events in coniferous forests, albeit with location-specific variations.
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RC1: 'Comment on egusphere-2024-3482', Anonymous Referee #1, 02 Jan 2025
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This study analyzes the response of periods with relatively low NDVI to various climate variables in Europe, with a particular focus on forested areas. The authors use high spatial resolution data with long temporal coverage, complemented by ERA5-land data as climate inputs. Although the data used in this study has potential for this type of analysis, it appears that the research contributes minimally to the current understanding of climate impacts on forest health. Furthermore, the study is affected by significant uncertainties due to data aggregation and the statistical methods applied. Overall, the results are not informative, and the discussion section is lacking. I would not recommend accepting this article in its current format. To be considered for publication, it would need substantial revisions. Below, I provide some of the reasons for my decision.
Line 16: Forests consume large amounts of water, so I would not classify water storage and regulation as a service typically associated with forested lands. See, for instance, the review by Filoso et al. (2017).
Line 17: Additionally, biodiversity in homogeneous forest landscapes is generally much lower than in heterogeneous, fragmented landscapes.
Line 19: While an increase in forest activity is observed over large regions, how can this trend be explained? I think this assessment should be more balanced.
Lines 44-45: A critical issue to address is the skill of the forecasting model. While the theoretical framework might be compelling, if the model does not demonstrate operationally reasonable accuracy, the study's value is limited.
Lines 59-61: Recent studies suggest that using vegetation indices may not be the best approach for assessing tree health and climate impacts compared to studying secondary forest growth (Gazol et al., 2018; Hoek van Dijke et al., 2023). This should be considered if the primary aim is to evaluate the effects of climate variability on forest conditions. Moreover, it is widely known that NDVI tends to saturate at high Leaf Area Index (LAI) values (Carlson & Ripley, 1997). Why not use kNDVI, which addresses many of these issues (Camps-Valls et al., 2021)?
Lines 69-79: It is unclear whether the authors developed their own NDVI data using the original AVHRR raw images. If so, several issues must be considered, such as geometric correction, atmospheric correction, topographic correction, and data gap filling. These are not explained in the study, yet they are critical for evaluating the quality of the dataset. Developing a high-resolution LAC dataset for Europe since 1981 is a substantial task, and summarizing it in two brief paragraphs is insufficient.
Line 92: The significance of the 80% value is unclear. Does this refer to 80% of European forests, individual forest types, or forest patches?
Lines 103-105: How are land cover changes accounted for? In some regions, deforestation (e.g., due to forest fires) or reforestation (due to land abandonment) has been particularly intense. This should be considered since NDVI anomalies could be linked to land cover changes.
Line 113: This appears to be an error. AVHRR NDVI data typically has a spatial resolution of 1.1 km at the nadir.
Line 127: Have the normality assumptions for these variables been checked? I suspect that at least precipitation does not follow a normal distribution, and alternative probability distributions should be used to normalize z-scores.
Lines 130-131: Cumulative conditions typically have negative consequences for vegetation (Bachmair et al., 2018), so the compartmentalization of the climate data could be problematic.
Line 189: The spatial resolution of the data seems too coarse. This contradicts the 0.1º spatial resolution indicated earlier. Additionally, several forested areas are missing from the analysis, such as large parts of the Mediterranean Iberian Peninsula, Norway, and the Irish and British Isles. This discrepancy contrasts with the maps in Figure B1, which record forest coverage over larger regions, but also show differences in coverage across years that are not explained. It is unclear why forest areas at 0.1º resolution in Figure B1 are not analyzed at this resolution but instead aggregated at 0.5º, which introduces uncertainties by mixing different land cover types. If climate and NDVI data are available at the same spatial resolution, there seems to be no reason for such aggregation, which reduces the coverage and introduces errors.
Line 215: Regarding the analysis, I wonder if overfitting might be an issue with the model outputs. For example, in the Jura forest in France, the explanatory climate variables are highly correlated—soil moisture is linked to precipitation, the primary infilling factor, but also to temperature, which affects atmospheric evaporative demand and land evapotranspiration. How can it be explained that soil moisture from the previous summer is the most important factor? It would seem that current soil moisture is more relevant than that from one year ago, which may indicate a statistical artifact from the analysis method used.
Section 4.2.2: This section is overly cryptic and excessively summarized. For instance, it is impossible to spatially assess the role of the different variables on NDVI. The summaries provided in Figure 3 are not informative. It is unclear which regions are more or less affected by the variables, nor is the overall efficiency of the predicted model clear. Similarly, Figure 4 is not informative, and it is difficult to extract any meaningful patterns from it.
Finally, the discussion section is very weak and lacks depth. Any scientific study must include a discussion that compares its findings with previous research to highlight contributions, gaps, and limitations. This is notably absent from the current manuscript.
References: Bachmair, S., Tanguy, M., Hannaford, J., & Stahl, K. (2018). How well do meteorological indicators represent agricultural and forest drought across Europe? Environmental Research Letters, 13(3). https://doi.org/10.1088/1748-9326/aaafda
Camps-Valls, G., Campos-Taberner, M., Moreno-Martínez, Á., Walther, S., Duveiller, G., Cescatti, A., Mahecha, M. D., Muñoz-Marí, J., García-Haro, F. J., Guanter, L., Jung, M., Gamon, J. A., Reichstein, M., & Running, S. W. (2021). A unified vegetation index for quantifying the terrestrial biosphere. Science Advances, 7(9). https://doi.org/10.1126/sciadv.abc7447
Carlson, T. N., & Ripley, D. A. (1997). On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment, 62(3), 241–252. https://doi.org/10.1016/S0034-4257(97)00104-1
Filoso, S., Bezerra, M. O., Weiss, K. C. B., & Palmer, M. A. (2017). Impacts of forest restoration on water yield: A systematic review. PLoS ONE, 12, e0183210.
Gazol, A., Camarero, J. J., Vicente-Serrano, S. M., Sánchez-Salguero, R., Gutiérrez, E., de Luis, M., Sangüesa-Barreda, G., Novak, K., Rozas, V., Tíscar, P. A., Linares, J. C., Martín-Hernández, N., Martínez del Castillo, E., Ribas, M., García-González, I., Silla, F., Camisón, A., Génova, M., Olano, J. M., … Galván, J. D. (2018). Forest resilience to drought varies across biomes. Global Change Biology, 24(5), 2143–2158. https://doi.org/10.1111/gcb.14082
Hoek van Dijke, A. J., Orth, R., Teuling, A. J., Herold, M., Schlerf, M., Migliavacca, M., Machwitz, M., van Hateren, T. C., Yu, X., & Mallick, K. (2023). Comparing forest and grassland drought responses inferred from eddy covariance and Earth observation. Agricultural and Forest Meteorology, 341. https://doi.org/10.1016/j.agrformet.2023.109635
Citation: https://doi.org/10.5194/egusphere-2024-3482-RC1
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