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A variety of modelling studies have suggested tree rooting depth as a key variable to explain evapotranspiration rates, productivity and the geographical distribution of evergreen forests in tropical South America. However, none of those studies have acknowledged resource investment, timing and physical constraints of tree rooting depth within a competitive environment, undermining the ecological realism of their results. Here, we present an approach of implementing variable rooting strategies and dynamic root growth into the LPJmL4.0 (Lund-Potsdam-Jena managed Land) dynamic global vegetation model (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 simulation of intra-annual productivity and evapotranspiration and consequently of forest cover and spatial biomass distribution. We find that climate and soil depth determine a spatially heterogeneous pattern of mean rooting depth and below-ground biomass across the study region. Our findings support the hypothesis that the ability of evergreen trees to adjust their rooting systems to seasonally dry climates is crucial to explaining the current dominance, productivity and evapotranspiration of evergreen forests in tropical South America. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Tropical 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. Inspired by social-ecological systems theory, our approach helps to explain: (i) how drivers of natural vegetation decline affect the functioning of ecosystems, and thus ecological integrity, (ii) under which conditions, where, and at which scales the loss of ecological integrity may lead to biodiversity tipping points, and (iii) how these biodiversity tipping points may impact human well-being. Implementing such an approach with the greater aim of furthering more sustainable land use in the Dry Diagonal requires a transdisciplinary collaborative network, which in a first step integrates extensive observational data from the field and remote sensing with advanced ecosystem and biodiversity models. Secondly, it integrates knowledge obtained from dialogue processes with local and regional actors as well as meta-models describing the actor network. The co-designed methodological framework can be applied not only to define, detect, and map biodiversity tipping points across spatial and temporal scales, but also to evaluate the effects of tipping points on ES and livelihoods. This framework could be used to inform policy making, enrich planning processes at various levels of governance, and potentially contribute to prevent biodiversity tipping points in the Dry Diagonal and beyond. [ABSTRACT FROM AUTHOR]

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 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. [ABSTRACT FROM AUTHOR]

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 as coherently linked through their water, carbon, and energy 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 review LPJmL model developments and evaluations in the field of permafrost, human and ecological water demand, and improved representation of crop types. We summarize and discuss LPJmL model applications dealing with the impacts of historical and future environmental change on the terrestrial biosphere at regional and global scale and provide a comprehensive overview of LPJmL publications since the first model 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 https://gitlab.pik-potsdam.de/lpjml/LPJmL, we hope to stimulate the application and further development of LPJmL4 across scientific communities in support of major activities such as the IPCC and SDG process. [ABSTRACT FROM AUTHOR]

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 eveloped 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. [ABSTRACT FROM AUTHOR]

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 CO2 levels. On the one hand, elevated CO2 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 Bolivian 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 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 south-east 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Neglecting any atmospheric feedback to precipitation, deforestation in Amazon, i.e., replacement of trees by shallow rooted short vegetation, is expected to decrease evapotranspiration (ET). Under energy-limited conditions, this process should lead to higher soil moisture and a consequent increase in river discharge. The magnitude and sign of the response of ET to deforestation depends both on land-cover change (LCC), and on climate and CO2 concentration changes in the future. Using three regional LCC scenarios recently established for the Brazilian and Bolivian Amazon, we investigate the combined impacts of deforestation and climate change on the surface hydrology of the Amazon basin for this century at sub-basin scale. 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)for different scenarios of the IPCC 4th Assessment Report (AR4). The GCM results indicate that by 2100, without deforestation, the temperature will have increased by a mean of 3.3 °C (a range of 1.7 to 4.5 °C) over the Amazon basin, intensifing the regional water cycle, whereby precipitation, ET and runoff increase by 8.5, 5.0 and 14%, respectively. However, under this same scenario in south-east Amazonia, precipitation decreases by 10% at the end of the dry season and the three LSMs estimate a 6% decrease of ET, which does not compensate for lower precipitation. Runoff in southeastern Amazonia decreases by 22%, reducing minimum river discharge from the Rio Tapajós catchment by 31% in 2100. The low LCC scenario projects a 7% decline in the area of Amazonian forest by 2100, as compared to 2009; for the high LCC scenario the projection is a 34% decline. In the extreme deforestation scenario, forest loss partly offsets (-2.5%) the positive effect of climate change on increasing ET and slightly amplifies (+3.0%) the increaseof runoff. Effects of deforestation are more pronounced in the southern part of the Amazon basin, in particular in the Rio Madeira catchment where up to 56% of the 2009 forest area is lost. The effect of deforestation on water budgets is more severe at the end of the dry season in the Tapajós and the Xingu catchments where the decrease of ET due to climate change is amplified by forest area loss. Deforestation enhances runoff during this period (+35%) offsetting the negative effect of climate change (-22%), and balances the decrease of low flows in the Rio Tapajós. [ABSTRACT FROM AUTHOR]

Understanding the effect of climate on the distribution and composition of vegetation is crucial to investigate its response to climate change. However, process-based vegetation models usually use comparably coarse-grain climatic data, which underestimate topographic effects on local climate.To evaluate potential effects of topoclimate on vegetation redistribution predicted by the process-based vegetation model LPJmL, we compared modelled vegetation based on different climate inputs, namely coarse-grained climatic data, data corrected for temperature lapse rate according to local elevation and data incorporating topographic effects responsible for cool-air pooling and anisotropic heat load in a hilly region in the Czech Republic.The results showed the pronounced effect of terrain topography as well the large discrepancy between coarse-scale climate and locally adapted climate. Our results thus give a hint on how sensitive modelled vegetation distribution is to climate input correction. We found that despite future climate change, including higher temperatures and less precipitation, the current vegetation might be able to remain in some local refuges, which are however not captured by coarse-scale climatic dataset. Therefore, the inclusion of topographic effects is crucial for realistic estimates of the vegetation redistribution in the response to climate change. [ABSTRACT FROM AUTHOR]