the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Submitted as: development and technical paper 27 Jan 2021
Submitted as: development and technical paper | 27 Jan 2021
Accounting for forest management in the estimation of forest carbon balance using the dynamic vegetation model LPJ-GUESS (v4.0, r9333): Implementation and evaluation of simulations for Europe
- 1Department of Physical Geography and Ecosystem Science, Lund University, Sweden
- 2Hawkesbury Institute for the Environment, Western Sydney University, Penrith NSW, Australia
- 3TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
- 1Department of Physical Geography and Ecosystem Science, Lund University, Sweden
- 2Hawkesbury Institute for the Environment, Western Sydney University, Penrith NSW, Australia
- 3TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
Abstract. Global forests are the main component of the land carbon sink, which acts as a partial buffer to CO2 emissions into the atmosphere. Dynamic vegetation models offer an approach to making projections of the development of forest carbon sink capacity in a future climate. Forest management capabilities in dynamic vegetation models are important to include the effects of age and species structure and wood harvest on carbon stocks and carbon storage potential. This article describes the introduction of a forest management module in the dynamic vegetation model LPJ-GUESS. Different age- and species-structure setup strategies and harvest alternatives are introduced. The model is used to represent current European forests and an automated harvest strategy is applied. Modelled carbon stocks and fluxes are evaluated against observed data at the continent and country levels. Including wood harvest in simulations increases the total European carbon sink by 32 % in 1991–2015 and improves the fit to the reported European carbon sink, growing stock and net annual increment (NAI). Growing stock (156 m3 ha−1) and NAI (5.4 m3 ha−1 y−1) densities in 2010 are close to reported values, while the carbon sink density in 2000–2007 (0.085 kgC m−2 y−1) is 63 % of reported values. The fit of modelled values and observations for individual European countries vary, but NAI is generally closer to observations when including wood harvest in simulations.
Mats Lindeskog et al.
Status: final response (author comments only)
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RC1: 'Comment on gmd-2020-440', Anonymous Referee #1, 22 Feb 2021
General comments
As I think it is urgently necessary to consider more realistic stand conditions as well as management impacts into estimates of carbon and wood storages, I welcome the incorporation of such features into LPG-GUESS. I think that the results obtained for managed forest area are indeed advancing modelling science. Despite being not the first modelling approach addressing biomass development in dependence on forest management, I guess it is worthwhile publishing the respective components specifically for LPJ, in particular since the now available options are quite comprehensive and the question addressed is still not well investigated. In my opinion, the presentation is well structured and the language is fluent (some spelling errors may occur that are not specifically pinpointed but this can easily be corrected). Some specific recommendations related to references, figures and issues that might be better addressed are given below.
Two main concerns remain that may require some more elaboration. Firstly, I am a bit irritated about the presentation of large-scale simulations that use the new features at a large scale without presenting any stand-level evaluation of managed sites in the paper. As it is, it is implicitly assumed that if the model represents PFTs under natural conditions correctly, it is also appropriately simulating the PFTs under management. Since it is known that growth depends on structural features (e.g. Bohn et al.) and this is what is changed here, this assumption might not hold, and thus should be demonstrated, at least in case of some examples. In fact, I think that lots of specific cases are available that could be used for such an exercise (e.g. sites in the Profound database). Please apologize in case such evaluations have been presented in other publications and I have overlooked the references.
With this respect, I am particular puzzled about the example simulation, where beech seems to perform the superior growth relative to spruce in the PNV (for the first 100 years) as well as (and particularly in) all managed forest options. This seems to be counter-intuitive to what I would expect for a site in southern Sweden (see e.g. Bolte et al. 2010), although I am aware of only one extensive biomass study which gives almost the same biomass/production values for both species in this area (Nihlgard 1972). However, although the particular conditions seem to be more representative to Poland than to Sweden (see e.g. Jagodizinki et al. 2020), I might misjudge the growth and will happily accept if I am proven wrong. In any case, I think that particularly (the) illustrating example(s) need some data or references that support their realism.
Second, it is unclear why old forests (one third of the area) are considered as PNV despite it is acknowledged by the authors that this leads to a simulated species composition widely divergent from the actual forests. It also leads to total forest estimates of carbon in the different compartments that are heavily influenced from this (unrealistic?) assumption. I guess that considering even a species composition from coarse data as is available from literature would largely decrease uncertainties related to the inventory as it is now.
Specific comments
L17/18: How is the underestimation of carbon sink explained if stock and net growth is fairly well met?
L29: Morin et al. 2017 not in ref. Do you mean Morin et al. 2018?
L41: Are the Pan et al. data really providing below- and aboveground carbon stocks for Europe? The numbers given here don’t seem to match the same values indicated for the same source in Table 2. Estimates from other sources seem to be also far different as e.g. app. 5/5 (Liski et al.), 9/14 (Goodale et al.), and 9/8 PgC (Pilli et al.). Please check. Consider to introduce the given references into Table 2-4 if appropriate.
L69ff: It is not clear, what are newly developed features that justify this publication and what are features that are only described in order to understand the scenarios modelled. Please rephrase and better indicate the expected benefit of the model improvement. (In other words: clarify the objective.)
L76ff: What is (are) the timestep(s) of the model?
L132ff: For the within-patch mixture of Fig.4a, the 60/40 ratio refers to while for the among-stand-types mixture of Fig. 4b the 60/40 ratio refers to groundcover area. I think that this should be indicated when presenting the different options.
L163: define LUH2
L192/Tab. 1: consider changing ‘N fertilization’ to ‘N fertilization/deposition’ since the continuous kind of application looks more similar to a deposition regime (and is more likely to happen anyway).
L205/Fig. 5: This is a nice presentation indicating the potential options that can be selected. However, I wonder what happens, if ‘old’ would be selected together with ‘small’ or vice versa in the thinning rule options? What would happen? Wouldn’t be a preference ‘from above’ or ‘from below’ be more suitable? What effect does this have on the simulation if biomass is taken e.g. preferentially from larger trees? I guess that the effect would depend on underlying assumptions about the biomass in different size groups – what are these assumptions?
L215ff: I am a bit unhappy with the unit ‘densmax’ being trees per ha since it implies that the stand is harvested after a certain number of trees has reached. However, the number is actually not defined and can be very different dependent on the diameter distribution. Densmax thus is an arbitrary density value in 1/m2 or similar unit. Similarly, I guess that ‘dens’ is also not indicating real tree numbers.
L232/233: As indicated in Tab. 1, irrigation seems to simply bypass drought stress. Here it is said that you actually calculate the (minimum) amount of water that is necessary to do this. Is this true? Check and homogenize.
L308: harvested volume was calculated from ‘killed vegetation carbon’? consider rewording.
L311: What does ‘reductions in wood products and residuals’ mean? Do you mean ‘due to’ instead of ‘in’? Consider that the argument is repeated a couple of lines later.
L330-332: irritating punctuation (needs “:” after forest, and “,” after pubescens)
L486: The simulations are initialized and driven by the given information but not ‘constrained’, correct? The word would be correct, if repeated inventory data would be used to parameterize or adjust the simulations which I think has not been done.
L494: As I take it, there are no observations but only estimates of ‘mean growing stock’ that are based on different information, sometimes on rather inhomogeneous sources, correct?
L526: You mean that the NPP decline that ORCHIDEE simulates is not simulated by LPJ, which might have various reasons that are caused by the model structure and processes. You don’t mean that the decline is a function as such that is ‘included’ or not ‘included’, correct? Could such a decline be related to nutrient depletion? How is the effect of nutrient export – that is a main concern of a sustainable management – considered anyway? I think it should at least be part of the discussion (see e.g. Parolari and Porporato; Sverdrup et al.)
L645: I don’t see the usefulness of this plot. The development of stand density in dependence on automated or self-thinning seems to be the same for all countries as is not surprising since it is treated with the same functions. So what would you like to show here?
Mentioned references
Bohn, F.J., May, F. and Huth, A. 2018 Species composition and forest structure explain the temperature sensitivity patterns of productivity in temperate forests. Biogeosciences, 15 (6), 1795-1813.
Bolte, A., Hilbrig, L., Grundmann, B., Kampf, F., Brunet, J. and Roloff, A. 2010 Climate change impacts on stand structure and competitive interactions in a southern Swedish spruce-beech forest. Eur. J. Forest Res., 129 (3), 261-276.
Ciais, P., Schelhaas, M.J., Zaehle, S., Piao, S.L., Cescatti, A., Liski, J. et al. 2008 Carbon accumulation in European forests. Nature Geosci., 1 (7), 425-429.
Goodale, C.L., Apps, M.J., Birdsey, R.A., Field, C.B., Heath, L.S., Houghton, R.A. et al. 2002 Forest Carbon Sinks in the Northern Hemisphere. Ecological Applications, 12 (3), 891-899.
JagodziÅski, A.M., Dyderski, M.K. and Horodecki, P. 2020 Differences in biomass production and carbon sequestration between highland and lowland stands of Picea abies (L.) H. Karst. and Fagus sylvatica L. Forest Ecol. Manage., 474, 118329.
Liski, J., Perruchoud, D. and Karjalainen, T. 2002 Increasing carbon stocks in the forest soils of western Europe. Forest Ecol. Manage., 169 (1-2), 159-175.
Morin, X., Fahse, L., Jactel, H., Scherer-Lorenzen, M., García-Valdés, R. and Bugmann, H. 2018 Long-term response of forest productivity to climate change is mostly driven by change in tree species composition. Sci. Rep., 8 (1), 5627.
Nihlgård, B. 1972 Plant Biomass, Primary Production and Distribution of Chemical Elements in a Beech and a Planted Spruce Forest in South Sweden. Oikos, 23 (1), 69-81.
Parolari, A.J. and Porporato, A. 2016 Forest soil carbon and nitrogen cycles under biomass harvest: Stability, transient response, and feedback. Ecol. Modelling, 329, 64-76.
Pilli, R., Grassi, G., Kurz, W.A., Moris, J.V. and Viñas, R.A. 2016 Modelling forest carbon stock changes as affected by harvest and natural disturbances. II. EU-level analysis. Carbon Balance Manag., 11, 20.
Sverdrup, H., Thelin, G., Robles, M., Stjernquist, I. and Sörensen, J. 2006 Assesing nutrient sustainability of forest production for different tree species considering Ca, Mg, K, N and P at Björnstorp Estate, Sweden. Biogeochemistry, 81 (2), 219-238.
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RC2: 'Comment on gmd-2020-440', Anonymous Referee #2, 02 Apr 2021
Including a more realistic forest management in DGVMs is important, and this manuscript clearly addresses and demonstrates this. Although everything is presented and explained, it was difficult to follow all approaches and concepts in the first read, perhaps because I am not familiar with LPJ-GUESS. Including (many) different options to achieve the same result, and extensively listing alternative options or exceptions are confusing and distract from the main message. An example of the first case is the presentation of 3 alternative ways of creating an (initial) age class distribution (Section 2.2.2., Figure 2), which requires already quite a good understanding of the concepts on patch/stand/management type in LPJ-GUESS. An example of the second case is section 2.3.2.1 (Species selection). It is full of words like “may”, “can”, “or”, “optionally” and “possibly”.
I think it would be worthwhile to introduce early on in the paper predefined categories, and explain how these are implemented in LPJ-GUESS. For Europe, I would say three types of management are important to consider: age-class (even-aged) based forestry, uneven-aged forestry/continuous cover forestry, and pristine forests. Pristine forests would be implemented as Natural (PNV) in Figure 1, while even-aged and uneven-aged management would have a separate stand type/management type. Even-aged forestry would logically be represented by having stands of different age, with patches of the same age. Uneven-aged forestry would then be represented by one stand, containing patches of different ages.
Similarly, in Section 2.3 (Forest management routines), it would be very helpful to announce which systems will be covered in this section (simplified forestry, detailed forestry and continuous cutting), and connect this to the management types defined earlier. Also, I think it makes sense to present Table 1 already here in the introduction to this section. Table 1 could be extended with columns that indicate for the different systems (simplified, detailed, continuous) the options implemented. Please see the example attached.
The authors use the term “old-growth” forests for forests that have a certain age. However, in the literature old-growth forests is commonly used as a synonym for pristine forests, and the authors should avoid this confusion by using another term. The assumption that forests older than 140 years of age are not managed is not totally realistic, and the assumption that these have a species distribution equal to PNV is not realistic at all. I see no problem in applying assigning species to this class in the same way as for the younger age classes. Rather than assuming no management, these forests could be managed with low intensity using the continuous cover forests management type.
Line 107 mentions that 30% of leaf biomass is removed from the site in case of harvesting. This is way too high. Delimbing of felled trees is mostly done in the forest and in that case all branches and leaves remain in the forest. In case of harvest residue extraction, branches are left on piles for a while to dry out and drop the foliage. These piles may be at the roadside, not sure that counts as being in the forest or not. Foresters like to keep the foliage in the forest for nutrient recycling, and biomass plants prefer having as little as possible foliage. Harvest residue extraction is only applied at large scale in part of Europe’s forests (mainly Nordic countries). I would estimate this value at 5-10%. A sensitivity analysis on this number would be very valuable.
Line 200: the minimum diameter for harvesting can be lowered if no trees are available. I wonder if this is realistic. Harvesting small trees is costly and the revenue is small. In such cases I would think harvesting is simply not done at all.
Mats Lindeskog et al.
Mats Lindeskog et al.
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