the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 23 Mar 2026
Global vegetation responses to wet and dry soil moisture extremes
Abstract. Hydrological extremes are continuing to intensify under climate change. However, the responses of vegetation to dry and wet soil moisture extremes, and the dominant drivers of these responses, have not yet been analyzed consistently. In this study, we utilize long-term observations of Normalized Difference Vegetation Index (NDVI) as a proxy of vegetation responses to soil moisture extremes. We then analyze related drivers with a machine-learning attribution approach to assess the role of pre-extreme vegetation conditions, characteristics of extremes, and of the environmental background. Vegetation generally loses greenness during dry extremes, indicated by widespread and consistent negative NDVI anomalies. This is mainly modulated by pre-extreme vegetation conditions and the characteristics of the extreme (especially seasonal timing) which reflect varying vegetation vulnerability. In contrast, wet extremes lead to more heterogeneous responses, including both positive and negative NDVI anomalies. This is modulated by multiple aspects including pre-extreme vegetation conditions, the characteristics of the extreme (especially seasonal timing) as well as environmental background variables such as climate (e.g., long-term mean air temperature, aridity) and topographic variability. This illustrates that vegetation response to wet extremes is complex and potentially influenced by different processes. Further, regions with negative NDVI anomalies during extremes that are strongly modulated by environmental background indicate localized vulnerability arising from adverse climatic, soil or topographic conditions, such that vegetation stress can occur even under extremes with less severity. These results highlight the roles of seasonal timing and of environmental background conditions for impacts of soil moisture extremes on vegetation. This clarifies the predictability of ecosystem responses to hydrological extremes, and serves as a basis for related management planning.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-1199', Anonymous Referee #1, 07 Apr 2026
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RC2: 'Comment on egusphere-2026-1199', Anonymous Referee #2, 18 May 2026
This study uses NDVI anomalies as a proxy for vegetation response to wet and dry soil moisture extremes and uses random forest feature importance to relate anomalies to various drivers. I agree with the authors that comparing the response of vegetation to wet and dry extremes is necessary and important. The results reiterate previous studies which show that wet extremes generate a more heterogeneous response than do dry extremes (and often increase, rather than decrease, NDVI). The results also highlight the complex factors that influence NDVI response to both wet and dry extremes. My detailed comments are below:
Methods:
Line 71: Is 0-100cm of soil thickness the best choice for both dry and wet extremes? I could imagine that the surface soil moisture might be more relevant for wet extremes in particular.
Line 84: Did you disregard events shorter than 5 days because of the 16-day NDVI resolution? If so, this reasoning (and its implications) is worth stating explicitly, since ideally shorter events could also be examined (particularly wet extremes). However, this 5 day threshold may still be too short given that you use 16-day NDVI and interpolate by repeating the same value for each day (Line 90). I would recommend further discussion of the implications and limitations of this resolution choice.
The RF description (Section 2.4) would benefit from several clarifications:
- Describe/define concurvity and its relevance (first mentioned in Line 142)
- Write ‘out of bag’ score the first time the acronym OOB is used
- How did you treat the wet vs dry extremes? Did you repeat the entire training/prediction process twice, once for dry extremes and once for wet extremes? (Line 152)
- You say you only considered negative NDVI anomalies? (Line 155) Doesn’t this eliminate the possibility of detecting wet or dry extremes that cause an increase in NDVI relative to expectation? (Such as what you say in Line 181 about greening during wet extremes?)
Results:
Figure 1. Would the number of events be proportional to the timeseries length, if events are chosen based on percentile thresholds? Does this mean that the different number of events per IPCC area is due to different numbers of pixels in each region or due to the number of events that could not be detected due to poor quality NDVI data?
- Additionally, you mention in Line 173 that “the most common reason for excluding an event was insufficient NDVI data availability.” Did you see that wet extremes were associated with reduced NDVI data quality to a greater degree than dry extremes?
- You show the data availability in Figure S7 and it appears to be the majority of the land surface area, so discussing these issues is important for contextualizing the frequency of extreme events you report.
You provide the OOB score and concurvity information in Figure S5, but the RF model performance is very important for interpreting your results. Could you report on the performance more explicitly in the main text? I would like to know how the RF performance varies across IPCC regions and across the wet vs dry extreme models and how this impacts your results.
Paragraph starting at Line 195: It would be helpful to clarify here whether the results refer to wet or dry extremes.
Figure 2. The map is great, but because you are showing the average NDVI anomalies, the information in Figure S6 (with the distribution of the anomalies) is possibly more relevant for the main text, especially because so many of the IPCC regions have both negative and positive anomalies.
Line 208-210: You describe the importance of antecedent conditions for wet extremes very definitively here, but in the abstract the takeaway seems to be that all of the factors you looked at are important. It would be helpful to clarify the main paper takeaways (for example if there are consistently important drivers or differences in drivers between the wet vs dry extremes) and make this message consistent between the text and the abstract. In general, it is a little bit hard to follow in the text which factors you describe as important for controlling the negative vs positive anomaly attribution for the wet vs dry extremes.
Figure 4: It would be helpful to see how far ahead the ‘leading variable’ is.
Section 3.4 (Line 246ish): When you look at the two regions with the most negative NDVI anomalies during wet and dry extremes, is it fair to look at just the negative NDVI response for wet extremes, when you found such a heterogeneous response to wet extremes, even for the sign of the NDVI anomaly? It would be worth mentioning this.
Small comments:
Line 54: You use several different phrases to refer to antecedent conditions. Consider picking one term.
Line 61: I wouldn’t necessarily describe this RF approach as “new,” although it does seem effective.
Line 117: Which version of GLEAM did you use?
Grammar/punctuation errors:
- Line 35, “…stomatal closure, decrease…”
- Line 40, “oxygen deficiency add nutrient uptake”
Citation: https://doi.org/10.5194/egusphere-2026-1199-RC2
Data sets
Data associated with Global vegetation responses to wet and dry soil moisture extremes Xueyan Cheng et al. https://zenodo.org/records/18234862
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This study presents a global-scale analysis of vegetation greenness responses to both dry and wet soil moisture extremes using long-term NDVI data and a machine-learning-based attribution framework. The manuscript is well structured, and the separation of drivers into pre-extreme conditions, extreme characteristics, and environmental background is conceptually clear and useful. The results confirming consistent vegetation browning during dry extremes and more heterogeneous responses during wet extremes are interesting and pretty important. I include my comments below, which I hope help the authors to strengthen the paper.