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- ItemA generic pixel-to-point comparison for simulated large-scale ecosystem properties and ground-based observations: An example from the Amazon region(Katlenburg-Lindau : Copernicus, 2018) Rammig, Anja; Heinke, Jens; Hofhansl, Florian; Verbeeck, Hans; Baker, Timothy R.; Christoffersen, Bradley; Ciais, Philippe; De Deurwaerder, Hannes; Fleischer, Katrin; Galbraith, David; Guimberteau, Matthieu; Huth, Andreas; Johnson, Michelle; Krujit, Bart; Langerwisch, Fanny; Meir, Patrick; Papastefanou, Phillip; Sampaio, Gilvan; Thonicke, Kirsten; von Randow, Celso; Zang, Christian; Rödig, EdnaComparing model output and observed data is an important step for assessing model performance and quality of simulation results. However, such comparisons are often hampered by differences in spatial scales between local point observations and large-scale simulations of grid cells or pixels. In this study, we propose a generic approach for a pixel-to-point comparison and provide statistical measures accounting for the uncertainty resulting from landscape variability and measurement errors in ecosystem variables. The basic concept of our approach is to determine the statistical properties of small-scale (within-pixel) variability and observational errors, and to use this information to correct for their effect when large-scale area averages (pixel) are compared to small-scale point estimates. We demonstrate our approach by comparing simulated values of aboveground biomass, woody productivity (woody net primary productivity, NPP) and residence time of woody biomass from four dynamic global vegetation models (DGVMs) with measured inventory data from permanent plots in the Amazon rainforest, a region with the typical problem of low data availability, potential scale mismatch and thus high model uncertainty. We find that the DGVMs under- and overestimate aboveground biomass by 25% and up to 60%, respectively. Our comparison metrics provide a quantitative measure for model-data agreement and show moderate to good agreement with the region-wide spatial biomass pattern detected by plot observations. However, all four DGVMs overestimate woody productivity and underestimate residence time of woody biomass even when accounting for the large uncertainty range of the observational data. This is because DGVMs do not represent the relation between productivity and residence time of woody biomass correctly. Thus, the DGVMs may simulate the correct large-scale patterns of biomass but for the wrong reasons. We conclude that more information about the underlying processes driving biomass distribution are necessary to improve DGVMs. Our approach provides robust statistical measures for any pixel-to-point comparison, which is applicable for evaluation of models and remote-sensing products.
- ItemThe importance of forest structure for carbon fluxes of the Amazon rainforest(Bristol : IOP Publishing, 2018) Rödig, Edna; Cuntz, Matthias; Rammig, Anja; Fischer, Rico; Taubert, Franziska; Huth, AndreasPrecise descriptions of forest productivity, biomass, and structure are essential for understanding ecosystem responses to climatic and anthropogenic changes. However, relations between these components are complex, in particular for tropical forests. We developed an approach to simulate carbon dynamics in the Amazon rainforest including around 410 billion individual trees within 7.8 million km2. We integrated canopy height observations from space-borne LIDAR in order to quantify spatial variations in forest state and structure reflecting small-scale to large-scale natural and anthropogenic disturbances. Under current conditions, we identified the Amazon rainforest as a carbon sink, gaining 0.56 GtC per year. This carbon sink is driven by an estimated mean gross primary productivity (GPP) of 25.1 tC ha−1 a−1, and a mean woody aboveground net primary productivity (wANPP) of 4.2 tC ha−1 a−1. We found that successional states play an important role for the relations between productivity and biomass. Forests in early to intermediate successional states are the most productive, and woody above-ground carbon use efficiencies are non-linear. Simulated values can be compared to observed carbon fluxes at various spatial resolutions (>40 m). Notably, we found that our GPP corresponds to the values derived from MODIS. For NPP, spatial differences can be observed due to the consideration of forest successional states in our approach. We conclude that forest structure has a substantial impact on productivity and biomass. It is an essential factor that should be taken into account when estimating current carbon budgets or analyzing climate change scenarios for the Amazon rainforest.
- ItemTackling unresolved questions in forest ecology: The past and future role of simulation models([S.l.] : John Wiley & Sons, Inc., 2021) Maréchaux, Isabelle; Langerwisch, Fanny; Huth, Andreas; Bugmann, Harald; Morin, Xavier; Reyer, Christopher P.O.; Seidl, Rupert; Collalti, Alessio; Dantas de Paula, Mateus; Fischer, Rico; Gutsch, Martin; Lexer, Manfred J.; Lischke, Heike; Rammig, Anja; Rödig, Edna; Sakschewski, Boris; Taubert, Franziska; Thonicke, Kirsten; Vacchiano, Giorgio; Bohn, Friedrich J.Understanding the processes that shape forest functioning, structure, and diversity remains challenging, although data on forest systems are being collected at a rapid pace and across scales. Forest models have a long history in bridging data with ecological knowledge and can simulate forest dynamics over spatio-temporal scales unreachable by most empirical investigations.We describe the development that different forest modelling communities have followed to underpin the leverage that simulation models offer for advancing our understanding of forest ecosystems.Using three widely applied but contrasting approaches - species distribution models, individual-based forest models, and dynamic global vegetation models - as examples, we show how scientific and technical advances have led models to transgress their initial objectives and limitations. We provide an overview of recent model applications on current important ecological topics and pinpoint ten key questions that could, and should, be tackled with forest models in the next decade.Synthesis. This overview shows that forest models, due to their complementarity and mutual enrichment, represent an invaluable toolkit to address a wide range of fundamental and applied ecological questions, hence fostering a deeper understanding of forest dynamics in the context of global change.
- ItemTree mortality submodels drive simulated long-term forest dynamics: assessing 15 models from the stand to global scale(Ithaca, NY : ESA, 2019) Bugmann, Harald; Seidl, Rupert; Hartig, Florian; Bohn, Friedrich; Bruna, Josef; Cailleret, Maxime; Francois, Louis; Heinke, Jens; Henrot, Alexandra-Jane; Hickler, Thomas; Huelsmann, Lisa; Huth, Andreas; Jacquemin, Ingrid; Kollas, Chris; Lasch-Born, Petra; Lexer, Manfred J.; Merganic, Jan; Merganicova, Katarna; Mette, Tobias; Miranda, Brian R.; Nadal-Sala, Daniel; Rammer, Werner; Rammig, Anja; Reineking, Bjoern; Roedig, Edna; Sabate, Santi; Steinkamp, Jorg; Suckow, Felicitas; Vacchiano, Giorgio; Wild, Jan; Xu, Chonggang; Reyer, Christopher P.O.Models are pivotal for assessing future forest dynamics under the impacts of changing climate and management practices, incorporating representations of tree growth, mortality, and regeneration. Quantitative studies on the importance of mortality submodels are scarce. We evaluated 15 dynamic vegetation models (DVMs) regarding their sensitivity to different formulations of tree mortality under different degrees of climate change. The set of models comprised eight DVMs at the stand scale, three at the landscape scale, and four typically applied at the continental to global scale. Some incorporate empirically derived mortality models, and others are based on experimental data, whereas still others are based on theoretical reasoning. Each DVM was run with at least two alternative mortality submodels. Model behavior was evaluated against empirical time series data, and then, the models were subjected to different scenarios of climate change. Most DVMs matched empirical data quite well, irrespective of the mortality submodel that was used. However, mortality submodels that performed in a very similar manner against past data often led to sharply different trajectories of forest dynamics under future climate change. Most DVMs featured high sensitivity to the mortality submodel, with deviations of basal area and stem numbers on the order of 10–40% per century under current climate and 20–170% under climate change. The sensitivity of a given DVM to scenarios of climate change, however, was typically lower by a factor of two to three. We conclude that (1) mortality is one of the most uncertain processes when it comes to assessing forest response to climate change, and (2) more data and a better process understanding of tree mortality are needed to improve the robustness of simulated future forest dynamics. Our study highlights that comparing several alternative mortality formulations in DVMs provides valuable insights into the effects of process uncertainties on simulated future forest dynamics. © 2019 The Authors.