the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 11 Jul 2025
LUCATOOv1 – A new land use change allocation tool and its application to the planetary boundary for land system change with the LPJmL model
Abstract. Anthropogenic alterations to terrestrial ecosystems resulting from land use transformation and agricultural intensification represent a significant driving force of global environmental change. The planetary boundary for land system change is one approach to determine an upper tolerable limit to such modifications, measured in terms of the remaining extent of major forest biomes in temperate, tropical, and boreal climatic zones on the different continents. Here, we introduce a land use change reallocation tool (LUCATOO) that can accurately represent the spatial distribution of agricultural land use for different statuses and transgression levels of the planetary boundary for land system change. By representing such configurations of global land cover and land use patterns, the tool facilitates a systematic assessment of the impacts of afforestation and deforestation scenarios on the status of this and other interconnected planetary boundaries. LUCATOO has been developed in the programming language R, is openly accessible, and can be readily adapted for land use change scenarios in applications beyond the planetary boundaries framework.
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RC1: 'Comment on egusphere-2025-2202', Anonymous Referee #1, 12 Aug 2025
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General comments
This study introduces a new land allocation tool to produce spatially explicit datasets consistent with different states of the LSC planetary boundary. The conceptual framework is enticingly simple and forms a novel contribution. Using the PB framework, the authors construct unique scenarios which provide a much-needed contribution to the currently limited diversity of LULCC scenarios in the literature. However, the simplicity of the approach is not supported with sufficient discussion about its potential limitations. In particular, the manuscript would benefit from further discussion about the land use allocation algorithm employed which omits key factors known to influence LULCC, including land productivity, land use and conversion costs, and socioeconomic factors. Including these factors could result in significantly different patterns of LULCC than those simulated here and add further layers of uncertainty not explored.
Specific comments
(L47) While forest loss is a prominent example, it might be worth highlighting that the impacts of LULCC are not just limited to forests but can affect a diverse range of ecosystems. For example: peatland drainage resulting in carbon emissions, soil carbon loss due to overgrazing of semi-natural grasslands. Additionally, LULCC impacts aren’t necessarily binary (e.g. forest vs. no forest) but can exist on a continuum based on different levels of land use intensity. While this study focuses on absolute deforestation and afforestation, it’s important to note the complexity of forest degradation more generally.
(L50-54) A little more background about how the 50% and 85% boundaries were chosen would be useful. It’s not clear how the third sentence of that paragraph leads on from the previous two. Where are we currently with respect to these boundaries? I think there needs to be more justification for setting a lower boundary for temperate forests or at least a discussion of the limitations of this assumption. The original source (Steffen et al. 2015) states “this is a provisional boundary only” – are there no recent updates? This lower boundary is justified in Steffen et al. by referencing Snyder et al 2004. However, I find this claim is not at all clear just from that paper - the climate impacts of removing temperate forests seems comparable to other biomes.
(L167-169) Why were continents chosen as the regional boundaries? That seems somewhat arbitrary. Wouldn’t ecologically relevant boundaries such as ecoregions be more appropriate? Or national boundaries given the importance of domestic policies.
(L197) Uniform intensification is rather unrealistic. LULCC is influenced by a range of factors including land productivity and costs, national and international demand for commodities, and proximity to existing managed lands. This leads to complex LULCC patterns which are rarely spatially uniform, particularly on continental scales. A discussion on the limitations of this assumption is needed.
(L215) Figure 5. The notation used in the figure could be improved. It’s not clear whether i, m, p etc. are parameters, variables or sets. For example, using the key in the top right corner, I would translate “i < LU_scn” as “subset of scenario dataset is less than scenario dataset” – while I can guess the intended meaning with the help of the caption, it’s perhaps a bit unconventional. Maybe it would be clearer with something like Si < SLU_scn where S is the LSC boundary variable. “m = i*fac.re[m]” and similar is particularly difficult to parse – is this representing the transformation of subset i into subset m? In which case, perhaps this could be written as “m = fre(i)” where fre() is the reduction function?
(L226-230) Some background information on previous assessments would fit well in the introduction (see comment for L50-54).
(L255) Figure 6. Higher resolution image needed. It’s interesting that each scenario shows either reforestation (planetary boundary) or deforestation (risk and strong transgression) in all biomes but not a mixture of both. Why is that? Given that different biomes in different continents are at or below the planetary boundaries (Table 1), shouldn’t result in a more heterogenous response? Also, the uniform application of the intensity factors within each biome is very apparent here. I think there needs to be discussion whether this is realistic, given that observed LULCC is spatially (and temporally) heterogeneous.
(L265-267) It’s not clear how this has been demonstrated. You have produced maps consistent with the PB-LSC boundary but there was no further analysis of how other PBs are affected under this scenario. Or is this referencing Richardson et al. 2023 (as it appears so further down)?
(L289) What did Drüke et al. 2024 find?
(L295-298 and L306) This is an important point of discussion that should be expanded on (also see previous comment)
(L310-321) – As previously commented, the reallocation of CFTs based purely on area is an important limitation here. A more detailed allocation tool would consider other factors such as potential yields, land suitability and production costs as well as trade-offs between agricultural expansion and intensification. Similarly, afforestation could be prioritised based on preservation of ecosystem services such as biodiversity and carbon storage. On a more fundamental level, it’s also not clear whether the scenarios presented here are internally consistent – for example, is the amount of deforestation in the strong transgression scenario even feasible given socioeconomic constraints? How much demand growth (food, timber etc.) would be required to cause this much deforestation?
(L329) “cannot be adjusted or modified to depict specific anthropogenic pressure levels” – to the contrary, many land system models work explicitly with “anthropogenic pressure levels”, although these can be expressed in different ways (e.g. demand for commodities, marginal utility of ecosystem services). Prominent examples include the major IAMs (IMAGE, REMIND-MAgPIE etc.) and other frameworks such as LandSyMM. While these haven’t extensively explored the PB framework, there’s no reason why PB-oriented scenarios couldn’t be constructed within these models.
Technical corrections
(L65) Replace “allocation models” with “land use models”
(L90) “The following _” section?
(L260-264) Too repetitive and non-specific, particularly “bridge the conceptual gap of an adjustable depiction”
Citation: https://doi.org/10.5194/egusphere-2025-2202-RC1
Model code and software
LUCATOO - A new land use change allocation tool and its application to the planetary boundary for land system change with the LPJmL5 model Arne Tobian et al. https://doi.org/10.5281/zenodo.14525230
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