Spatial patterns in second-order impacts of human activity on climate, land and water

Zoller, Hannah; Lade, Steven J.; Gotangco Gonzales, C. Kendra; Fetzer, Ingo; Chaudhary, Nitin; Rocha, Juan C.

doi:10.5194/egusphere-2026-3452

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https://doi.org/10.5194/egusphere-2026-3452

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06 Jul 2026

| 06 Jul 2026

Spatial patterns in second-order impacts of human activity on climate, land and water

Hannah Zoller, Steven J. Lade, C. Kendra Gotangco Gonzales, Ingo Fetzer, Nitin Chaudhary, and Juan C. Rocha

Abstract. In order to assess the full systemic impact of anthropogenic pressures on climate and land, it is crucial to account for second-order impacts mediated by bio-geophysical processes, which typically display a high spatial heterogeneity. In this study, we systematically compare different clustering approaches to capture uni- and multivariate spatial patterns in second-order impacts of human activity on climate, land, and water. In a first step, we estimate effect sizes based on simulations from a spatial global vegetation model. In a second step, we approach the question of suitable spatial clustering. Following a top-down approach first, we map the global pattern of second-order impacts on common natural partitions of the Earth, like climate- or vegetation-zones. Cluster validity indices reveal a close alignment between the second-order impacts of land use change on climate and a biogeographic classification. In contrast, the second-order impacts of climate- and land use change on surface water runoff are best captured by the Köppen-Geiger climate zones. Following a bottom-up approach, we employ multivariate spatially constrained clustering to derive an integrative global partition. Several patches of tropical rainforest on the Indomalayan islands as well as large areas of warm grasslands in Australia are identified as high-impact clusters. The results of this study should be considered illustrative as they are based on only one dynamical vegetation model. Nevertheless, they emphasize the local nature of second-order impacts and elucidate both the potential and risks of spatial aggregation.

Received: 12 Jun 2026 – Discussion started: 06 Jul 2026

Competing interests: At least one of the (co-)authors serves as editor for the special issue to which this paper belongs.

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Hannah Zoller, Steven J. Lade, C. Kendra Gotangco Gonzales, Ingo Fetzer, Nitin Chaudhary, and Juan C. Rocha

Status: final response (author comments only)

RC1:
'Comment on egusphere-2026-3452', Anonymous Referee #1, 30 Jul 2026
Zoller et al. investigate an important and timely question: how strong and spatially heterogeneous the indirect impacts (so-called second-order impact) of human activities on climate, land, and water are, and how these impacts can be effectively classified into spatial clusters. The authors first quantify these impacts using the LPJmL land surface model and then compare two clustering approaches: a conventional top-down approach based on predefined geographical or ecological units (e.g., continents and plant functional types) and a bottom-up approach based on spatially constrained clustering. Through multiple evaluation metrics, the authors conclude that the bottom-up clustering generally outperforms top-down approach and identify several global hotspots of high-impact clusters.
This is an ambitious and comprehensive study that addresses a challenging problem. However, the manuscript is also technically complex, and after carefully evaluating the methodology and interpretation, I have several fundamental concerns regarding the reliability of the conclusions. I hope the following comments will help improve the manuscript.
Major Comments
The study does not explicitly represent land-atmosphere interactions. My primary concern is that the manuscript repeatedly attributes its findings to the interplay of land-atmosphere interactions (e.g., Lines 20, 24, 161, 228, and 278), whereas the underlying simulations are performed using the offline land surface model LPJmL. An offline land surface model can only respond to prescribed atmospheric forcing and cannot feed land-surface changes back to the atmosphere. Therefore, the two-way land-atmosphere interactions implied throughout the manuscript are not explicitly represented. Consequently, attributing the simulated patterns to the land-atmosphere interactions is misleading. More importantly, this limitation is not merely a matter of wording, but may affect the validity of the manuscript’s central conclusions. For example, land-cover change modifies surface albedo, evapotranspiration, and surface energy fluxes, which in turn alter atmospheric circulation, precipitation, vegetation growth, runoff, and carbon storage through atmospheric teleconnections. Previous studies have even shown that these atmospheric feedbacks can generate remote impacts comparable to, or larger than, the local responses in some regions. Such processes are entirely absent from the current framework. Therefore, I do not believe that this issue can be adequately addressed by the brief discussion of study limitation in Lines 359-360. Instead, the authors should explicitly acknowledge that their analysis only quantifies land-surface responses under prescribed atmospheric conditions and discuss how the omission of land-atmosphere feedbacks may influence their conclusions.

The definition and scope of “second-order impacts” require clarification. The terminology “second-order impact" is introduced very early in the manuscript (Line 20), yet its definition remains ambiguous throughout the paper. First, I am not convinced that this relatively uncommon terminology is preferable to the more widely used term indirect impacts, unless the authors intend to distinguish a more specific concept. Second, the manuscript never clearly defines what processes are included within second-order impact and, equally importantly, which processes are intentionally excluded. For example, based on the modeling framework, atmospheric feedbacks discussed in Comment 1 are clearly beyond the scope of the study. Such boundaries should be explicitly stated when the term is first introduced to avoid confusion.

Justification for the selected indicators. The authors use carbon storage density, surface runoff, and natural vegetation cover as representative indicators of climate, water, and land impacts, respectively. However, the rationale for selecting these three variables is not sufficiently explained. For example, from a land-surface perspective, variables such as evapotranspiration or soil moisture may more directly characterize hydrological impacts than runoff, while GPP could arguably be a more representative measure of ecosystem functioning than vegetation cover. The manuscript would benefit from explaining the criteria used to select these indicators and discussing whether the major conclusions remain robust when alternative variables are considered.

The Title and Abstract do not adequately reflect the main findings. The main contribution of this study is not only the investigation of the spatial patterns in second-order impacts, but also the comparison between top-down and bottom-up clustering approaches, with the latter potentially demonstrating superior performance. However, the Abstract devotes relatively little attention to these central findings and instead remains rather general. A similar issue exists in the title, which does not reflect the methodological comparison that forms the core contribution of the study. As a result, even after reading the title and Abstract multiple times, it remains difficult to immediately grasp the primary objectives and conclusions of the manuscript. I recommend revising both sections to better emphasize the novelty and principal findings.

Consistency among different impact estimation methods. The manuscript adopts different methodologies to estimate the strength of second-order impacts for different components. For example, the effects on natural vegetation cover are quantified using one methodology, whereas climate-related impacts are estimated using another. The authors should clarify whether these different methods produce quantitatively comparable estimates. If the results are intended to be directly compared, the rationale for using different estimation methods should be justified. Alternatively, the authors should discuss the potential biases introduced by methodological inconsistency.

Minor Comments
Line 52: I do not consider a spatial resolution of 0.5° to constitute “high spatial resolution”. This description should be moderated.

Line 71: Please explain why only the last 30 years were selected for the analysis.

Both the Results and Discussion are considerably longer than necessary. I recommend reducing repetitive descriptions, removing some regional examples, and emphasizing the overarching conclusions. In addition, the Results should explain why the observed patterns emerge rather than only describing them. For example, in Lines 195-203, why does natural vegetation exert a negative influence on runoff primarily in Asian tropical rainforests? Does this relationship not exist in tropical rainforests elsewhere?

Since the bottom-up clustering consistently outperforms the top-down approach in most evaluation metrics, whether the extensive discussion of the top-down results is necessary. The manuscript would benefit from placing greater emphasis on the more successful clustering framework.

Under the bottom-up approach, different impact pathways (e.g., climate -> vegetation versus vegetation -> water) would likely produce substantially different spatial clusters. If so, how comparable are the resulting cluster systems? This issue deserves further discussion.

Besides Figure 8, I strongly recommend adding a summary figure or table explaining the dominant mechanisms responsible for the identified high positive and high negative impact clusters. This would substantially improve readability and reduce the need for lengthy textual descriptions.

Line 393: Please explain why a decrease in natural vegetation cover leads to increased evapotranspiration in these regions. This result appears counterintuitive and requires further explanation.
Citation: https://doi.org/10.5194/egusphere-2026-3452-RC1
RC2: 'Comment on egusphere-2026-3452', Fabian Stenzel, 05 Aug 2026

Dear authors,
I appreciate the chance to read your present study ("Spatial patterns in second-order impacts of human activity on climate, land and water"), in which you derive 4 grid-cell based interaction strengths between climate and land-use change as drivers, and affecting runoff, carbon storage, and vegetation structure, based on existing simulations from the DGVM LPJmL. To cluster the gridded results for larger units you then apply two classes of clustering approaches. The first (“top-down”) is based on predefined spatial units such as continents or biomes, and the second (“bottom-up”) is based on analyzing the interaction strength maps with a clustering algorithm. You find that the “bottom-up” approach generally gives much better results and then use it, finding 19 clusters with strong effects in at least 2 of the 4 interaction dimensions, and 3 “high impact clusters” where even 3 out of 4 dimensions show strong effects.
The paper is situated in the analysis of regionally resolved interactions between different Earth system processes, and thus especially relevant for the planetary boundaries and tipping points frameworks, as well as more broadly studying Earth system interactions.
The paper generally reads well, and I support the publication in ESD after reviewing some aspects of it. My major comments refer to the details of the selection and description of the LPJmL simulations and their discussion, as well as the balance between different parts of the manuscript, that partially have been also raised by an anonymous reviewer (R1) before me.
1. Please give more details for the LPJmL simulations you used and discuss their limitations:
- Which specific LPJmL version did you use?
- Which land-use input did you use?
- Please mention, how you calculate the carbon storage density. I suppose it should be the sum of vegetation, soil, and litter carbon.
- Please note that using monthly climate inputs (that internally have to be distributed over the days in a month) leads to ambiguity, that can be prevented by using (more up-to-date) daily data e.g. from ISIMIP (GSWP3-W5E5) or TRENDY (CRU-JRA). This is especially relevant, since you compare the differences between runs. I am sure you have checked, that the seed with which monthly values are distributed to days is the same between runs, otherwise you have a climate effect, where you don’t expect one.
- LPJmL4.X.Y as described by Schaphoff et al. 2018 has 11 PFTs. Your text in (p.6 line 99) could be mistaken so that LPJmL4 would have 12. I believe you use barren land as land cover type 12, but strictly speaking this is not a PFT. Please also add how you calculate it, because you need separate grid cell fractions for CFTs for this (barren = 1 – sum(PFT fpc) – sum(CFT fpc).
- With LPJmLv4 instead of v5, you are missing N limitation, thus overestimating the CO2 fertilization effect.
- The sensitivity analysis using 6 more (GCM based) climate input datasets could be integrated better. Currently in the Methods (p. 5 lines 91-92) it is not clear that this is only for sensitivity tests and that the results won’t go into the main analysis.

2. Details of the clustering approaches.
What exactly are the inputs to the “bottom-up” approach? As far as I understood it, the final effect maps are also going in, so naturally much better clusters are derived than for “top-down”. Is that a fair comparison between the two cluster algorithms?
Like this it appears that the result is clear before hand, and only serves as an argument to focus on the bottom-up clustering results. This is not a problem per se, but could be stated more clearly. I would additionally suggest to shorten the analysis of the “top-down” part, if it is then discarded anyways.
At the same time, the policy implications of using dynamic and thus hard to name and locate clusters could be discussed in more detail.

3. Definition of second-order impacts
Like reviewer R1, I also believe that your current definition of “second-order impacts” based on direct and indirect effects is not sufficiently clear. The example you give rather confuses me, as I would say deforestation IS the vegetation cover change, thus zero-order, and everything that follows next would be direct or first-order impact (like runoff/ET changes).
Maybe the notion of local vs. remote effects can help you to improve the definition? I admit that for remote effects besides river discharge, LPJmL offline is not the right tool.

4. Justification of using LPJmL offline
Unlike reviewer R1, I believe that it is still worthwhile doing the analysis with LPJmL. Since the authors are using historical land-use and weather patterns, even if not directly coupled, the climate inputs includes land-atmosphere interactions to the historical land-use changes.
This is however only true for the factual wLUC simulation, and not the counterfactual noLUC simulation that uses the same climate inputs. This limitation needs to be stated clearly.

5. Definition of impacts on climate change
You chose carbon storage density changes as a metric for climate change. Since grid-cells in LPJmL are not the same size, carbon storage density changes in gC/m2 does not impact atmospheric CO2 concentrations in the same way everywhere. Secondly, the translation from carbon storage decline to atmospheric CO2 increase is certainly not linear. These limitations needs to stated more clearly.

I hope my comments (please see some additional inline comments below) can help you to further improve your study.
Best regards,
Fabian Stenzel
Potential conflict of interest: I am affiliated with SRC as a guest researcher and work in a common project together with study co-author Ingo Fetzer.
-------------------------
Additional comments:
p.2 line 36: If you want to compare with different hierarchical clustering approach, aimed at reducing computational costs please see Ehret et al. 2020 (https://doi.org/10.5194/hess-24-4389-2020) or Priesner et al. 2026 (https://doi.org/10.1140/epjs/s11734-026-02526-1) who additionally compare different distance metrics.
p.4 lines 78-80: The way you define it, you are rather talking about small changes in the land use input, rather than “noise” (numerical non-deterministic behavior?) in the model.
p.8 lines 161-162: And certainly also the different methodology, by regressing values for each year instead of a 30 year average, right?
p.10 lines 191-192: Can you really draw this conclusion from the Figure (and what does “best explain” mean – “highest RS”, or per no. of clusters so “most top-left”)? It is hard to see (you also highlighted “non climate” partitions), maybe plot only 3 colors for different symbols, red for climate and blue for PFT, and orange for biome to confirm this.
p.13 lines 266-267: I don’t find this very surprising, as each PFTs parameters describe a direct relationship with climate, especially in the temperature limits and water/radiation requirements. It would be interesting to single out the climate from the CO2 effect, by increasing CO2 regularly but keeping the climate as 1901-1930 and vice versa.
Fig. 6: Some combinations are hard to distinguish, e.g. same symbol and similar color as in “climates P” and “continents biomes”. In panel B the names are offset from the boxes (which I believe) they belong to. There is no clear method for when to draw boxes, right? It appears a bit arbitrary. In general everything is very small, even when zooming in.
Fig. A8: Labels appear erroneous. “Boreal broadleaved evergreen” does not exist in LPJmL. “Temperate needleleaved evergreen” is missing.
Fig. D1A: The dashed boxes are off.

Citation: https://doi.org/10.5194/egusphere-2026-3452-RC2

Hannah Zoller, Steven J. Lade, C. Kendra Gotangco Gonzales, Ingo Fetzer, Nitin Chaudhary, and Juan C. Rocha

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https://doi.org/10.5194/egusphere-2026-3452-supplement

Hannah Zoller, Steven J. Lade, C. Kendra Gotangco Gonzales, Ingo Fetzer, Nitin Chaudhary, and Juan C. Rocha

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Short summary

Human pressures on one Earth system component typically entail second-order effects on other components. The strength of such effects can show a high spatial variation and corresponding studies often report them by climate- or vegetation zone. We systematically compare the ability of different Earth partitions to capture the spatial pattern of second-order effects. Our results provide a reference point and highlight the risk of information loss inherent in commonly used aggregation approaches.


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