the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Contrasting drought legacy effects on gross primary productivity in a mixed versus pure beech forest
- 1Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, D-07745, Germany
- 2Department of Ecology, University of Innsbruck, Innsbruck, A-6020, Austria
- 3Bioclimatology, University of Göttingen, Göttingen, D-37077, Germany
- 4Joint Research Centre, European commission, Ispra (VA), 21027, Italy
- 5Forestry Research and Competence Centre Gotha, Gotha, D-99867, Germany
- 6Department of Environmental Systems Science, ETH, Zürich, 8092, Switzerland
- 7Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, 8903, Switzerland
- 1Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, D-07745, Germany
- 2Department of Ecology, University of Innsbruck, Innsbruck, A-6020, Austria
- 3Bioclimatology, University of Göttingen, Göttingen, D-37077, Germany
- 4Joint Research Centre, European commission, Ispra (VA), 21027, Italy
- 5Forestry Research and Competence Centre Gotha, Gotha, D-99867, Germany
- 6Department of Environmental Systems Science, ETH, Zürich, 8092, Switzerland
- 7Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, 8903, Switzerland
Abstract. Droughts affect terrestrial ecosystems directly and concurrently, and can additionally induce lagged effects in subsequent seasons and years. Such legacy effects of drought on vegetation growth and state have been widely studied in tree-ring records and satellite-based vegetation greenness, while legacies on ecosystem carbon fluxes are still poorly quantified and understood. Here, we focus on two ecosystem monitoring sites in central Germany with similar climate but characterized by different species and age structures. Using eddy-covariance measurements, we detect legacies on gross primary productivity (GPP) by calculating the difference between random-forest model estimates of potential GPP and observed GPP. Our results showed that at both sites, droughts caused significant legacy effects on GPP at seasonal and annual time scales which were partly explained by reduced leaf development. The GPP reduction due to drought legacy effects is of comparable magnitude to the concurrent drought effects, but differed between two neighbouring forests with divergent species and age structures. The methodology proposed here allows quantifying the temporal dynamics of legacy effects at the sub-seasonal scale and separating legacy effects from model uncertainties. Application of the methodology at a larger range of sites will help quantify whether the identified lag effects are general and on which factors they may depend.
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Xin Yu et al.
Status: open (until 13 Jun 2022)
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RC1: 'Comment on bg-2022-99', Anonymous Referee #1, 09 May 2022
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In this manuscript, Yu et al. investigate drought legacy effects in GPP at two contrasting forest types in Germany. This manuscript represents several notable advances, including: 1) direct observation of GPP legacy effects, 2) a method to quantify sub-annual legacies, 3) incorporating uncertainty in legacy effect calculations, and 4) a neat idea to get at the mechanism behind GPP legacies. In addition, the manuscript is written very clearly and is quite compelling to read. This is a great contribution to the literature and only have a few suggestions.
Major comments:
1) The approach to calculate a “tree ring width” based on dendrometer bands is interesting. However, due to bark shrinkage and expansion, these processes aren’t exactly analogous. I think there needs to be an acknowledgement of this and a discussion of how these biases might play out.
2) How well does the RF model predict GPP during drought years, if trained on non-drought data? Or, just trained on a subset of droughts and used to predict other droughts? The answer to this question has implications for the interpretation of the legacy effect calculation.
3) Along those lines, there also needs to be some information regarding model fit, predictive ability, variable importance, etc. in the methods or results. Does model fit differ across sites, years, etc? It seems like a model with a lot of uncertainty at one site or one year may drastically alter the legacy effect calculation. What variables are most important for predicting fluxes at these sites?
4) It doesn’t seem like there is any mention in the methods regarding how the length and size of legacies were calculated. It is implied that GPP recovers when it hits the uncertainty boundary, but not explicitly stated.
Minor comments:
L118: What constitutes “good” gapfilling?
L133: I might be missing something, but this doesn’t seem to mention how WAI is calculated. WAIt depends on the calculation of WAIt-1, which is undefined. So, the definition seems circular.
L197-199: Detrended how?
L198: Is this annual, or a mean from the growing season? The latter would probably be more relevant.
L443: Some of your citations do exactly this, so perhaps cut this sentence.
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RC2: 'Comment on bg-2022-99', Anonymous Referee #2, 19 May 2022
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The authors present a sophisticated collaborative work and the development of a new method to separate GPP legacy effects. The analysis clearly demonstrates the value and importance of long term flux measurements with eddy covariance in combination with biometric data for the evaluation of concurrent and legacy effects of ecosystem GPP on different temporal scales. The potential applicability to other ecosystems is attractive as well. Only very few remarks needed for clarification.
Regarding the importance of changes in the energy balance caused by drought and legacy effects, a bit more evaluation of evapotranspiration would improve the paper even more. Even though transpiration seems not to be influenced by drought / drought legacy here, it is unclear whether the term ‘transpiration’ in the manuscript is standing for ‘evapotranspiration’ from eddy covariance data.
Mortality of trees is mentioned to be already caused by droughts. Can the effect of mortality / less trees over time be separated already? Have these trees been in the flux footprint? It should also be mentioned whether the biomass data from dendrometers and the litter harvest were from within the footprint.
Specific remarks:
L 212ff: could you clarify a bit more the description of the model setting with EVI anomalies? It seems not to be totally clear how structural effects are removed
L 223: “…other factors in addition to…”
L 331/332: “…using eddy-covariance data at two forests in central Germany in the same climate but with different management and species composition.“ I suggest to repeat here briefly what these forests have in common and where they differ.
L 338: “…if they appear only in critical periods of the growing season,…” –check formulation
L 349: “Finally, our approach allows determining the uncertainties in estimated legacy effects…” replace one ‘estimate’
L 365: “…negative legacies on GPP (reduced uptake) in the…” -just for the reader’s convenience
L 399: “…of stand age the heat and drought impact on carbon….”
L 431 + 432: this should probably be evaporation instead of evapotranspiration
Fig. 2:
As a) represents DE-Hai for 2003 and following years and b) represents DE-Hai for 2018, I suggest to write such:
Figure 2: “Daily GPP in the selected drought and legacy years at a), DE-Hai 2003, b) DE-Hai 2018 and c) DE-Lnf showing the 2003 droughts and following legacy years, respectively."
Similar for Fig. S2
Fig 5: seasonal GPP anomalies: lines ResEVI (structural effect) in figures hard to distinguish from Res. Could you e.g. zoom in to the periods discussed?
Xin Yu et al.
Xin Yu et al.
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