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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., Forest models can help understanding the processes that shape forest functioning, structure and diversity, since they can can simulate forest dynamics over spatio‐temporal scales unreachable by most empirical investigations. Here we describe the development of three widely applied but contrasting forest mo−delling approaches — species distribution models, individual‐based models and dynamic global vegetation models. We provide an overview of recent model applications and pinpoint ten key questions that could, and should, be tackled with forest models in the next decade.

This document is the first version of the Guidelines and protocols harmonizing activities across case studies of the H2020 BESTMAP project. It is intended to be updated in month 40 (D1.8).

Tree water access via roots is crucial for forest functioning and therefore forests have developed a vast variety of rooting strategies across the globe. However, Dynamic Global Vegetation Models (DGVMs), which are increasingly used to simulate forest functioning, often condense this variety of tree rooting strategies into biome-scale averages, potentially under- or overestimating forest response to intra- and inter-annual variability in precipitation. Here we present a new approach of implementing variable rooting strategies and dynamic root growth into the LPJmL4.0 DGVM and apply it to tropical and sub-tropical South-America under contemporary climate conditions. We show how competing rooting strategies which underlie the trade-off between above- and below-ground carbon investment lead to more realistic simulated intra-annual productivity and evapotranspiration, and consequently forest cover and spatial biomass distribution. We find that climate and soil depth determine a spatially heterogeneous pattern of mean rooting depth and belowground biomass across the study region.

ropical dry forests and savannas harbour unique biodiversity and provide critical ES, yet they are under severe pressure globally. We need to improve our understanding of how and when this pressure provokes tipping points in biodiversity and the associated social-ecological systems. We propose an approach to investigate how drivers leading to natural vegetation decline trigger biodiversity tipping and illustrate it using the example of the Dry Diagonal in South America, an understudied deforestation frontier. The Dry Diagonal represents the largest continuous area of dry forests and savannas in South America, extending over three million km² across Argentina, Bolivia, Brazil, and Paraguay. Natural vegetation in the Dry Diagonal has been undergoing large-scale transformations for the past 30 years due to massive agricultural expansion and intensification. Many signs indicate that natural vegetation decline has reached critical levels. Major research gaps prevail, however, in our understanding of how these transformations affect the unique and rich biodiversity of the Dry Diagonal, and how this affects the ecological integrity and the provisioning of ES that are critical both for local livelihoods and commercial agriculture. Fil: Thonicke, Kirsten. Institute for Climate Impact Research ; Alemania Fil: Langerwisch, Fanny. Institute for Climate Impact Research ; Alemania. Czech University of Life Sciences Prague; República Checa Fil: Baumann, Matthias. Humboldt Universität zu Berlin; Alemania. Technische Universitat Carolo Wilhelmina Zu Braunschweig.; Alemania Fil: Leitão, Pedro J.. Humboldt Universität zu Berlin; Alemania. Technische Universitat Carolo Wilhelmina Zu Braunschweig.; Alemania Fil: Václavík, Tomáš. Helmholtz Centre for Environmental Research; Alemania. Palacký University Olomouc; República Checa Fil: Alencar, Anne. Ministerio da Agricultura Pecuaria e Abastecimento de Brasil. Empresa Brasileira de Pesquisa Agropecuaria; Brasil Fil: Simões, Margareth. Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA); Brasil Fil: Scheiter, Simon. Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA); Brasil. Universidade Federal do Rio de Janeiro; Brasil Fil: Langan, Liam. Senckenberg Biodiversity and Climate Research Centre; Alemania Fil: Bustamante, Mercedes. Universidade do Brasília; Brasil Fil: Gasparri, Nestor Ignacio. Universidad Nacional de Tucumán. Facultad de Ciencias Naturales e Instituto Miguel Lillo. Instituto de Ecología Regional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina Fil: Hirota, Marina. Universidade Federal de Santa Catarina; Brasil. Universidade Estadual de Campinas; Brasil Fil: Börner, Jan. Universitat Bonn; Alemania Fil: Rajao, Raoni. Universidade Federal de Minas Gerais; Brasil Fil: Soares Filho, Britaldo. Universidade Federal de Minas Gerais; Brasil Fil: Yanosky, Alberto. Consejo Nacional de Ciencia y Tecnología; Paraguay Fil: Ochoa Quinteiro, José Manuel. Instituto de Investigación de Recursos Biológicos Alexander von Humboldt; Colombia Fil: Seghezzo, Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Investigaciones en Energía no Convencional. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Física. Instituto de Investigaciones en Energía no Convencional; Argentina Fil: Conti, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; Argentina Fil: de la Vega Leiner, Anne Cristina. Universität Greifswald; Alemania

Climate change and land-use change are two major drivers of biome shifts causing habitat and biodiversity loss. What is missing is a continental-scale future projection of the estimated relative impacts of both drivers on biome shifts over the course of this century. Here, we provide such a projection for the biodiverse region of Latin America under four socio-economic development scenarios. We find that across all scenarios 5-6% of the total area will undergo biome shifts that can be attributed to climate change until 2099. The relative impact of climate change on biome shifts may overtake land-use change even under an optimistic climate scenario, if land-use expansion is halted by the mid-century. We suggest that constraining land-use change and preserving the remaining natural vegetation early during this century creates opportunities to mitigate climate-change impacts during the second half of this century. Our results may guide the evaluation of socio-economic scenarios in terms of their potential for biome conservation under global change.

Climate change and land-use change are two major drivers of vegetation change causing habitat and biodiversity loss and posing a threat to the sustained provisioning of ecosystem goods and services. Following-up on the Millennium Ecosystem Assessment, the Sustainable Development Goals have been a fresh stimulus to the current interest in ecosystem services. Dynamic Global Vegetation Models (DGVMs) offer the possibility of integrating large amounts of geospatial data to quantify and project a large range of ecological variables important for ecosystem service provisioning under future scenarios. We outline how such model output could be used for projecting ecosystem service provisioning.

LEGATO stands for “Land-use intensity and Ecological EnGineering—Assessment Tools for risks and Opportunities in irrigated rice based production systems.”

International audience; Comparing 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 Published by Copernicus Publications on behalf of the European Geosciences Union. 5204 A. Rammig et al.: Pixel-to-point comparison for simulated large-scale ecosystem properties 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.

This paper synthesizes what is known about the physical and biophysical impacts of climate change and their consequences for societies and development under different levels of global warming in Latin America and the Caribbean (LAC). Projections show increasing mean temperatures by up to 4.5 °C compared to pre-industrial by the end of this century across LAC. Associated physical impacts include altered precipitation regimes, a strong increase in heat extremes, higher risks of droughts and increasing aridity. Moreover, the mean intensity of tropical cyclones, as well as the frequency of the most intense storms, is projected to increase while sea levels are expected to rise by ~0.2–1.1 mm depending on warming level and region. Tropical glacier volume is found to decrease substantially, with almost complete deglaciation under high warming levels. The much larger glaciers in the southern Andes are less sensitive to warming and shrink on slower timescales. Runoff is projected to be reduced in Central America, the southern Amazon basin and southernmost South America, while river discharge may increase in the western Amazon basin and in the Andes in the wet season. However, in many regions, there is uncertainty in the direction of these changes as a result of uncertain precipitation projections and differences in hydrological models. Climate change will also reduce agricultural yields, livestock and fisheries, although there may be opportunities such as increasing rice yield in several LAC countries or higher fish catch potential in the southernmost South American waters. Species range shifts threaten terrestrial biodiversity, and there is a substantial risk of Amazon rainforest degradation with continuing warming. Coral reefs are at increasing risk of annual bleaching events from 2040 to 2050 onwards irrespective of the climate scenario. These physical and biophysical climate change impacts challenge human livelihoods through, e.g., decreasing income from fisheries, agriculture or tourism. Furthermore, there is evidence that human health, coastal infrastructures and energy systems are also negatively affected. This paper concludes that LAC will be severely affected by climate change, even under lower levels of warming, due to the potential for impacts to occur simultaneously and compound one another., This research has been funded through the World Bank Project “Turn Down the Heat: Confronting the New Climate Normal,” and we are grateful to everybody involved in this activity for making it a success.

Summary Anthropogenic global change compromises forest resilience, with profound impacts to ecosystem functions and services. This synthesis paper reflects on the current understanding of forest resilience and potential tipping points under environmental change and explores challenges to assessing responses using experiments, observations and models. Forests are changing over a wide range of spatio-temporal scales, but it is often unclear whether these changes reduce resilience or represent a tipping point. Tipping points may arise from interactions across scales, as processes such as climate change, land-use change, invasive species or deforestation gradually erode resilience and increase vulnerability to extreme events. Studies covering interactions across different spatio-temporal scales are needed to further our understanding. Combinations of experiments, observations and process-based models could improve our ability to project forest resilience and tipping points under global change. We discuss uncertainties in changing CO2 concentration and quantifying tree mortality as examples. Synthesis. As forests change at various scales, it is increasingly important to understand whether and how such changes lead to reduced resilience and potential tipping points. Understanding the mechanisms underlying forest resilience and tipping points would help in assessing risks to ecosystems and presents opportunities for ecosystem restoration and sustainable forest management.

This paper provides a comprehensive description of the newest version of the Dynamic Global Vegetation Model with managed Land, LPJmL4. This model simulates – internally consistently – the growth and productivity of both natural and agricultural vegetation in direct coupling with water and carbon fluxes. These features render LPJmL4 suitable for assessing a broad range of feedbacks within, and impacts upon, the terrestrial biosphere as increasingly shaped by human activities such as climate change and land-use change. Here we describe the core model structure including recently developed modules now unified in LPJmL4. Thereby we also summarize LPJmL model developments and evaluations (based on 34 earlier publications focused e.g. on improved representations of crop types, human and ecological water demand, and permafrost) and model applications (82 papers, e.g. on historical and future climate change impacts) since its first description in 2007. To demonstrate the main features of the LPJmL4 model, we display reference simulation results for key processes such as the current global distribution of natural and managed ecosystems, their productivities, and associated water fluxes. A thorough evaluation of the model is provided in a companion paper. By making the model source code freely available at a Gitlab server, we hope to stimulate the application and further development of LPJmL4 across scientific communities, not least in support of major activities such as the IPCC and SDG process.

Global change includes multiple stressors to natural ecosystems ranging from direct climate and land-use impacts to indirect degradation processes resulting from fire. Humid tropical forests are vulnerable to projected climate change and possible synergistic interactions with deforestation and fire, which may initiate a positive feedback to rising atmospheric CO 2 . Here, we present results from a multifactorial impact analysis that combined an ensemble of climate change models with feedbacks from deforestation and accidental fires to quantify changes in Amazon Basin carbon cycling. Using the LPJmL Dynamic Global Vegetation Model, we modelled spatio-temporal changes in net biome production (NBP); the difference between carbon fluxes from fire, deforestation, soil respiration and net primary production. By 2050, deforestation and fire (with no CO 2 increase or climate change) resulted in carbon losses of 7.4-20.3 Pg C with the range of uncertainty depending on socio-economic storyline. During the same time period, interactions between climate and land use either compensated for carbon losses due to wetter climate and CO 2 fertilization or exacerbated carbon losses from drought-induced forest mortality (―20.1 to + 4.3 Pg C). By the end of the 21st century, depending on climate projection and the rate of deforestation (including its interaction with fire), carbon stocks either increased (+ 12.6 Pg C) or decreased (―40.6 Pg C). The synergistic effect of deforestation and fire with climate change contributed up to 26-36 Pg C of the overall decrease in carbon stocks. Agreement between climate projections (n = 9), not accounting for deforestation and fire, in 2050 and 2098 was relatively low for the directional change in basin-wide NBP (19―37%) and aboveground live biomass (13―24%). The largest uncertainty resulted from climate projections, followed by implementation of ecosystem dynamics and deforestation. Our analysis partitions the drivers of tropical ecosystem change and is relevant for guiding mitigation and adaptation policy related to global change.

et al. 2006).A substantial, globally significant carbon flow exists be-tween the atmosphere, land ecosystems, and aquatic ecosys-tems in the Amazon basin, probably leading to a major, yet poorly constrained carbon export to the Tropical Atlantic. One estimate of the annual export of carbon to the ocean is 40 Tg C/yr (M

Any regular interaction of land and river during flooding affects carbon pools within the terrestrial system, riverine carbon and carbon exported from the system. In the Amazon basin carbon fluxes are considerably influenced by annual flooding, during which terrigenous organic material is imported to the river. The Amazon basin therefore represents an excellent example of a tightly coupled terrestrial–riverine system. The processes of generation, conversion and transport of organic carbon in such a coupled terrigenous–riverine system strongly interact and are climate-sensitive, yet their functioning is rarely considered in Earth system models and their response to climate change is still largely unknown. To quantify regional and global carbon budgets and climate change effects on carbon pools and carbon fluxes, it is important to account for the coupling between the land, the river, the ocean and the atmosphere. We developed the RIVerine Carbon Model (RivCM), which is directly coupled to the well-established dynamic vegetation and hydrology model LPJmL, in order to account for this large-scale coupling. We evaluate RivCM with observational data and show that some of the values are reproduced quite well by the model, while we see large deviations for other variables. This is mainly caused by some simplifications we assumed. Our evaluation shows that it is possible to reproduce large-scale carbon transport across a river system but that this involves large uncertainties. Acknowledging these uncertainties, we estimate the potential changes in riverine carbon by applying RivCM for climate forcing from five climate models and three CO2 emission scenarios (Special Report on Emissions Scenarios, SRES). We find that climate change causes a doubling of riverine organic carbon in the southern and western basin while reducing it by 20 % in the eastern and northern parts. In contrast, the amount of riverine inorganic carbon shows a 2- to 3-fold increase in the entire basin, independent of the SRES scenario. The export of carbon to the atmosphere increases as well, with an average of about 30 %. In contrast, changes in future export of organic carbon to the Atlantic Ocean depend on the SRES scenario and are projected to either decrease by about 8.9 % (SRES A1B) or increase by about 9.1 % (SRES A2). Such changes in the terrigenous–riverine system could have local and regional impacts on the carbon budget of the whole Amazon basin and parts of the Atlantic Ocean. Changes in riverine carbon could lead to a shift in the riverine nutrient supply and pH, while changes in the exported carbon to the ocean lead to changes in the supply of organic material that acts as a food source in the Atlantic. On larger scales the increased outgassing of CO2 could turn the Amazon basin from a sink of carbon to a considerable source. Therefore, we propose that the coupling of terrestrial and riverine carbon budgets should be included in subsequent analysis of the future regional carbon budget.

International audience; Deforestation in Amazon is expected to decrease evapotranspiration (ET) and to increase soil moisture and river discharge under prevailing energy-limited conditions. The magnitude and sign of the response of ET to deforestation depend both on the magnitude and regional patterns of land-cover change (LCC), as well as on climate change and CO 2 levels. On the one hand, elevated CO 2 decreases leaf-scale transpiration, but this effect could be offset by increased foliar area density. Using three regional LCC scenarios specifically established for the Brazilian and Boli-vian Amazon, we investigate the impacts of climate change and deforestation on the surface hydrology of the Amazon Basin for this century, taking 2009 as a reference. For each LCC scenario, three land surface models (LSMs), LPJmL-DGVM, INLAND-DGVM and ORCHIDEE, are forced by bias-corrected climate simulated by three general circulation models (GCMs) of the IPCC 4th Assessment Report (AR4). On average, over the Amazon Basin with no deforestation, the GCM results indicate a temperature increase of 3.3 • C by 2100 which drives up the evaporative demand, whereby Published by Copernicus Publications on behalf of the European Geosciences Union. 1456 M. Guimberteau et al.: Impacts of future deforestation and climate change on the Amazon hydrology precipitation increases by 8.5%, with a large uncertainty across GCMs. In the case of no deforestation, we found that ET and runoff increase by 5.0 and 14 %, respectively. However, in southeast Amazonia, precipitation decreases by 10 % at the end of the dry season and the three LSMs produce a 6 % decrease of ET, which is less than precipitation, so that runoff decreases by 22%. For instance, the minimum river discharge of the Rio Tapajós is reduced by 31 % in 2100. To study the additional effect of deforestation, we prescribed to the LSMs three contrasted LCC scenarios, with a forest decline going from 7 to 34 % over this century. All three scenarios partly offset the climate-induced increase of ET, and runoff increases over the entire Amazon. In the southeast , however, deforestation amplifies the decrease of ET at the end of dry season, leading to a large increase of runoff (up to +27 % in the extreme deforestation case), offsetting the negative effect of climate change, thus balancing the decrease of low flows in the Rio Tapajós. These projections are associated with large uncertainties, which we attribute separately to the differences in LSMs, GCMs and to the uncertain range of deforestation. At the subcatchment scale, the uncertainty range on ET changes is shown to first depend on GCMs, while the uncertainty of runoff projections is predominantly induced by LSM structural differences. By contrast , we found that the uncertainty in both ET and runoff changes attributable to uncertain future deforestation is low.

Fluxes of organic and inorganic carbon within the Amazon basin are considerably controlled by annual flooding, which triggers the export of terrigenous organic material to the river and ultimately to the Atlantic Ocean. The amount of carbon imported to the river and the further conversion, transport and export of it depend on temperature, atmospheric CO2, terrestrial productivity and carbon storage, as well as discharge. Both terrestrial productivity and discharge are influenced by climate and land use change. The coupled LPJmL and RivCM model system (Langerwisch et al., 2016) has been applied to assess the combined impacts of climate and land use change on the Amazon riverine carbon dynamics. Vegetation dynamics (in LPJmL) as well as export and conversion of terrigenous carbon to and within the river (RivCM) are included. The model system has been applied for the years 1901 to 2099 under two deforestation scenarios and with climate forcing of three SRES emission scenarios, each for five climate models. We find that high deforestation (business-as-usual scenario) will strongly decrease (locally by up to 90 %) riverine particulate and dissolved organic carbon amount until the end of the current century. At the same time, increase in discharge leaves net carbon transport during the first decades of the century roughly unchanged only if a sufficient area is still forested. After 2050 the amount of transported carbon will decrease drastically. In contrast to that, increased temperature and atmospheric CO2 concentration determine the amount of riverine inorganic carbon stored in the Amazon basin. Higher atmospheric CO2 concentrations increase riverine inorganic carbon amount by up to 20 % (SRES A2). The changes in riverine carbon fluxes have direct effects on carbon export, either to the atmosphere via outgassing or to the Atlantic Ocean via discharge. The outgassed carbon will increase slightly in the Amazon basin, but can be regionally reduced by up to 60 % due to deforestation. The discharge of organic carbon to the ocean will be reduced by about 40 % under the most severe deforestation and climate change scenario. These changes would have local and regional consequences on the carbon balance and habitat characteristics in the Amazon basin itself as well as in the adjacent Atlantic Ocean.

1. Anthropogenic global change compromises forest resilience, with profound impacts to ecosystem functions and services. This synthesis paper reflects on the current understanding of forest resilience and potential tipping points under environmental change and explores challenges to assessing responses using experiments, observations and models. 2. Forests are changing over a wide range of spatio-temporal scales, but it is often unclear whether these changes reduce resilience or represent a tipping point. Tipping points may arise from interactions across scales, as processes such as climate change, land-use change, invasive species or deforestation gradually erode resilience and increase vulnerability to extreme events. Studies covering interactions across different spatio-temporal scales are needed to further our understanding. 3. Combinations of experiments, observations and process-based models could improve our ability to project forest resilience and tipping points under global change. We discuss uncertainties in changing CO2 concentration and quantifying tree mortality as examples. 4. Synthesis. As forests change at various scales, it is increasingly important to understand whether and how such changes lead to reduced resilience and potential tipping points. Understanding the mechanisms underlying forest resilience and tipping points would help in assessing risks to ecosystems and presents opportunities for ecosystem restoration and sustainable forest management.

Floodplain forests, namely the Várzea and Igapó, cover an area of more than 97 000 km2. A key factor for their function and diversity is annual flooding. Increasing air temperature and higher precipitation variability caused by climate change are expected to shift the flooding regime during this century, and thereby impact floodplain ecosystems, their biodiversity and riverine ecosystem services. To assess the effects of climate change on the flooding regime, we use the Dynamic Global Vegetation and Hydrology Model LPJmL, enhanced by a scheme that realistically simulates monthly flooded area. Simulation results of discharge and inundation under contemporary conditions compare well against site-level measurements and observations. The changes of calculated inundation duration and area under climate change projections from 24 IPCC AR4 climate models differ regionally towards the end of the 21st century. In all, 70% of the 24 climate projections agree on an increase of flooded area in about one third of the basin. Inundation duration increases dramatically by on average three months in western and around one month in eastern Amazonia. The time of high- and low-water peak shifts by up to three months. Additionally, we find a decrease in the number of extremely dry years and in the probability of the occurrence of three consecutive extremely dry years. The total number of extremely wet years does not change drastically but the probability of three consecutive extremely wet years decreases by up to 30% in the east and increases by up to 25% in the west. These changes implicate significant shifts in regional vegetation and climate, and will dramatically alter carbon and water cycles.