Search

Earth system models (ESMs) typically simplify the representation of land surface spectral albedo to two values, which correspond to the photosynthetically active radiation (PAR, 400–700 nm) and the near infrared (NIR, 700–2,500 nm) spectral bands. However, the availability of hyperspectral observations now allows for a more direct retrieval of ecological parameters and reduction of uncertainty in surface reflectance. To investigate sensitivity and quantify biases of incorporating hyperspectral albedo information into ESMs, we examine how shortwave soil albedo affects surface radiative forcing and simulations of the carbon and water cycles. Results reveal that the use of two broadband values to represent soil albedo can introduce systematic radiative-forcing differences compared to a hyperspectral representation. Specifically, we estimate soil albedo biases of ±0.2 over desert areas, which can result in spectrally integrated radiative forcing divergences of up to 30 W m−2, primarily due to discrepancies in the blue (404–504 nm) and far-red (702–747 nm) regions. Furthermore, coupled land-atmosphere simulations indicate a significant difference in net solar flux at the top of the atmosphere (>3.3 W m−2), which can impact global energy fluxes, rainfall, temperature, and photosynthesis. Finally, simulations show that considering the hyperspectrally resolved soil reflectance leads to increased maximum daily temperatures under current and future CO2 concentrations.

Recent progress in satellite observations has provided unprecedented opportunities to monitor vegetation activity at global scale. However, a major challenge in fully utilizing remotely sensed data to constrain land surface models (LSMs) lies in inconsistencies between simulated and observed quantities. For example, gross primary productivity (GPP) and transpiration (T) that traditional LSMs simulate are not directly measurable from space, although they can be inferred from spaceborne observations using assumptions that are inconsistent with those LSMs. In comparison, canopy reflectance and fluorescence spectra that satellites can detect are not modeled by traditional LSMs. To bridge these quantities, we presented an overview of the next generation land model developed within the Climate Modeling Alliance (CliMA), and simulated global GPP, T, and hyperspectral canopy radiative transfer (RT; 400–2,500 nm for reflectance, 640–850 nm for fluorescence) at hourly time step and 1° spatial resolution using CliMA Land. CliMA Land predicts vegetation indices and outgoing radiances, including solar-induced chlorophyll fluorescence (SIF), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near infrared reflectance of vegetation (NIRv) for any given sun-sensor geometry. The spatial patterns of modeled GPP, T, SIF, NDVI, EVI, and NIRv correlate significantly with existing data-driven products (mean R2 = 0.777 for 9 products). CliMA Land would be also useful in high temporal resolution simulations, for example, providing insights into when GPP, SIF, and NIRv diverge.

Humid tropical forests play a dominant role in the functioning of Earth but are under increasing threat from changes in land use and climate. How forest vulnerability varies across space and time and what level of stress forests can tolerate before facing a tipping point are poorly understood. Here, we develop a tropical forest vulnerability index (TFVI) to detect and evaluate the vulnerability of global tropical forests to threats across space and time. We show that climate change together with land-use change have slowed the recovery rate of forest carbon cycling. Temporal autocorrelation, as an indicator of this slow recovery, increases substantially for above-ground biomass, gross primary production, and evapotranspiration when climate stress reaches a critical level. Forests in the Americas exhibit extensive vulnerability to these stressors, while in Africa, forests show relative resilience to climate, and in Asia reveal more vulnerability to land use and fragmentation. TFVI can systematically track the response of tropical forests to multiple stressors and provide early-warning signals for regions undergoing critical transitions.

The purpose of this presentation is to make a contribution to the study of contemporary processes of grassroots organizations and project management by networks of community organizations in the Great Buenos Aires (Argentina). This is the result of a case study about the articulation process of a network of grassroots organizations placed in one of the poorest areas of the metropolitan area during the peak of the recent socio-economic crisis of Argentinean history. This network called “Organized Community” began a process of self-organization with the purpose of developing basic urban infrastructure in five neighborhoods. There are three main hypotheses. First, toward the failure of both the market and the state, population of these neighborhoods propels processes of local organization that are autonomous of political and governmental existing structures. Second, the articulation of a network of grassroots organizations allows them to consolidate, face bigger and more ambitious projects and to establish relationships and alliances with strategic actors (other networks, professional NGOs, international agencies, etc.). Finally, because of the way of functioning of the network almost as a collective organization (horizontal and open in an assembly manner) reaching the entire community in a novel way for Argentinean society, it generates tensions and competition, challenging well established political actors and machineries characterized by clientelistic practices and verticality.

Tropical forest degradation from logging, fire, and fragmentation not only alters carbon stocks and carbon fluxes, but also impacts physical land surface properties such as albedo and roughness length. Such impacts are poorly quantified to date due to difficulties in accessing and maintaining observational infrastructures, as well as the lack of proper modeling tools for capturing the interactions among biophysical properties, ecosystem demography, canopy structure, and biogeochemical cycling in tropical forests. As a first step to address these limitations, we implemented a selective logging module into the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) by mimicking the ecological, biophysical, and biogeochemical processes following a logging event. The model can specify the timing and aerial extent of logging events, splitting the logged forest patch into disturbed and intact patches; determine the survivorship of cohorts in the disturbed patch; and modifying the biomass and necromass (total mass of coarse woody debris and litter) pools following logging. We parameterized the logging module to reproduce a selective logging experiment at the Tapajós National Forest in Brazil and benchmarked model outputs against available field measurements. Our results suggest that the model permits the coexistence of early and late successional functional types and realistically characterizes the seasonality of water and carbon fluxes and stocks, the forest structure and composition, and the ecosystem succession following disturbance. However, the current version of FATES overestimates water stress in the dry season and therefore fails to capture seasonal variation in latent and sensible heat fluxes. Moreover, we observed a bias towards low stem density and leaf area when compared to observations, suggesting that improvements are needed in both carbon allocation and establishment of trees. The effects of logging were assessed by different logging scenarios to represent reduced impact and conventional logging practices, both with high and low logging intensities. The model simulations suggest that in comparison to old-growth forests the logged forests rapidly recover water and energy fluxes in 1 to 3 years. In contrast, the recovery times for carbon stocks, forest structure, and composition are more than 30 years depending on logging practices and intensity. This study lays the foundation to simulate land use change and forest degradation in FATES, which will be an effective tool to directly represent forest management practices and regeneration in the context of Earth system models.

There is large uncertainty whether Amazon forests will remain a carbon sink as atmospheric CO2 increases. Hence, we simulated an old-growth tropical forest using six versions of four terrestrial models differing in scale of vegetation structure and representation of biogeochemical (BGC) cycling, all driven with CO2 forcing from the preindustrial period to 2100. The models were benchmarked against tree inventory and eddy covariance data from a Brazilian site for present-day predictions. All models predicted positive vegetation growth that outpaced mortality, leading to continual increases in present-day biomass accumulation. Notably, the two vegetation demographic models (VDMs) (ED2 and ELM-FATES) always predicted positive stem diameter growth in all size classes. The field data, however, indicated that a quarter of canopy trees didn't grow over the 15-year period, and while high interannual variation existed, biomass change was near neutral. With a doubling of CO2, three of the four models predicted an appreciable biomass sink (0.77 to 1.24 Mg ha−1 year−1). ELMv1-ECA, the only model used here that includes phosphorus constraints, predicted the lowest biomass sink relative to initial biomass stocks (+21%), lower than the other BGC model, CLM5 (+48%). Models projections differed primarily through variations in nutrient constraints, then carbon allocation, initial biomass, and density-dependent mortality. The VDM's performance was similar or better than the BGC models run in carbon-only mode, suggesting that nutrient competition in VDMs will improve predictions. We demonstrate that VDMs are comparable to nondemographic (i.e., “big-leaf”) models but also include finer scale demography and competition that can be evaluated against field observations.

In the version of this Article originally published, the first sentence of the Acknowledgements “We would like to thank D. Papale, M. Zapparoli, L. Portoghesi, M. di Porcia e Brugnera, and H. Verbeeck for feedback that helped improve the manuscript.” should have been “We would like to thank: F. Berzaghi's PhD thesis committee: D. Papale, M. Zapparoli and L. Portoghesi; A. Swann and S. Wasser for their conception of this idea and their mentorship during the initial work on the project; and M. di Porcia e Brugnera and H. Verbeeck for feedback that helped improve the manuscript.” This has now been amended.

The Ecosystem Demography model version 2.2 (ED-2.2) is a terrestrial biosphere model that simulates the biophysical, ecological, and biogeochemical dynamics of vertically and horizontally heterogeneous terrestrial ecosystems. In a companion paper (Longo et al., 2019a), we described how the model solves the energy, water, and carbon cycles, and verified the high degree of conservation of these properties in long-term simulations that include long-term (multi-decadal) vegetation dynamics. Here, we present a detailed assessment of the model's ability to represent multiple processes associated with the biophysical and biogeochemical cycles in Amazon forests. We use multiple measurements from eddy covariance towers, forest inventory plots, and regional remote-sensing products to assess the model's ability to represent biophysical, physiological, and ecological processes at multiple timescales, ranging from subdaily to century long. The ED-2.2 model accurately describes the vertical distribution of light, water fluxes, and the storage of water, energy, and carbon in the canopy air space, the regional distribution of biomass in tropical South America, and the variability of biomass as a function of environmental drivers. In addition, ED-2.2 qualitatively captures several emergent properties of the ecosystem found in observations, specifically observed relationships between aboveground biomass, mortality rates, and wood density; however, the slopes of these relationships were not accurately captured. We also identified several limitations, including the model's tendency to overestimate the magnitude and seasonality of heterotrophic respiration and to overestimate growth rates in a nutrient-poor tropical site. The evaluation presented here highlights the potential of incorporating structural and functional heterogeneity within biomes in Earth system models (ESMs) and to realistically represent their impacts on energy, water, and carbon cycles. We also identify several priorities for further model development.

Earth system models (ESMs) have been developed to represent the role of terrestrial ecosystems on the energy, water, and carbon cycles. However, many ESMs still lack representation of within-ecosystem heterogeneity and diversity. In this paper, we present the Ecosystem Demography model version 2.2 (ED-2.2). In ED-2.2, the biophysical and physiological processes account for the horizontal and vertical heterogeneity of the ecosystem: The energy, water, and carbon cycles are solved separately for a series of vegetation cohorts (groups of individual plants of similar size and plant functional type) distributed across a series of spatially implicit patches (representing collections of microenvironments that have a similar disturbance history).We define the equations that describe the energy, water, and carbon cycles in terms of total energy, water, and carbon, which simplifies the differential equations and guarantees excellent conservation of these quantities in long-term simulation (

Coarse dead wood is an important component of forest carbon stocks, but it is rarely measured in Amazon forests and is typically excluded from regional forest carbon budgets. Our study is based on line intercept sampling for fallen coarse dead wood conducted along 103 transects with a total length of 48 km matched with forest inventory plots where standing coarse dead wood was measured in the footprints of larger areas of airborne lidar acquisitions. We developed models to relate lidar metrics and Landsat time series variables to coarse dead wood stocks for intact, logged, burned, or logged and burned forests. Canopy characteristics such as gap area produced significant individual relations for logged forests. For total fallen plus standing coarse dead wood (hereafter defined as total coarse dead wood), the relative root mean square error for models with only lidar metrics ranged from 33% in logged forest to up to 36% in burned forests. The addition of historical information improved model performance slightly for intact forests (31% against 35% relative root mean square error), not justifying the use of a number of disturbance events from historical satellite images (Landsat) with airborne lidar data. Lidarderived estimates of total coarse dead wood compared favorably with independent ground-based sampling for areas up to several hundred hectares. The relations found between total coarse dead wood and variables quantifying forest structure derived from airborne lidar highlight the opportunity to quantify this important but rarely measured component of forest carbon over large areas in tropical forests.

Land conversion and changing climate are expected to significantly alter tropical forest hydrology. We used a land surface model integrated with a river routing scheme to analyze the hydrological alterations expected in the Tapajós River basin, a large portion of the Brazilian Amazon, caused by two environmental drivers: climate and land use. The model was forced with two future climate scenarios (years 2026–2045) from the Earth System Model HadGem2-ES with moderate (+4.5 W/m2 radiative forcing value in the year 2100 with respect to preindustrial levels) and severe (+8.5 W/m2) representative atmospheric carbon dioxide pathways (Representative Concentration Pathways). We tested the sensitivity of our results to the uncertainty in future climate projections by running simulations with IPSL-CM5 (wettest scenarios) and GISS-E2 (driest scenarios). Human land use effects on vegetation were evaluated using a limited and an extreme deforestation scenario. Our analysis indicates that climate change is predicted to reduce river flows across seasons (up to 20%) and bring a considerable shift in flow seasonality toward a later onset (nearly 1.5 months) and increase in interannual variability. While land use change partially counteracts the climate-driven diminishing trend in river flows, it is expected to contribute to a further increase in interannual and intraannual variability. From a water management perspective, the overall reduction of river flows and their increased variability, combined with the shift and the shortening of the wet season, could potentially affect the productivity of the large hydropower systems planned for the region and the growing demand for agricultural and transport expansion.

Forest degradation is common in tropical landscapes, but estimates of the extent and duration of degradation impacts are highly uncertain. In particular, selective logging is a form of forest degradation that alters canopy structure and function, with persistent ecological impacts following forest harvest. In this study, we employed airborne laser scanning in 2012 and 2014 to estimate three-dimensional changes in the forest canopy and understory structure and aboveground biomass following reduced-impact selective logging in a site in Eastern Amazon. Also, we developed a binary classification model to distinguish intact versus logged forests. We found that canopy gap frequency was significantly higher in logged versus intact forests even after 8 years (the time span of our study). In contrast, the understory of logged areas could not be distinguished from the understory of intact forests after 6–7 years of logging activities. Measuring new gap formation between LiDAR acquisitions in 2012 and 2014, we showed rates 2 to 7 times higher in logged areas compared to intact forests. New gaps were spatially clumped with 76 to 89% of new gaps within 5 m of prior logging damage. The biomass dynamics in areas logged between the two LiDAR acquisitions was clearly detected with an average estimated loss of −4.14 ± 0.76 MgC ha−1 y−1. In areas recovering from logging prior to the first acquisition, we estimated biomass gains close to zero. Together, our findings unravel the magnitude and duration of delayed impacts of selective logging in forest structural attributes, confirm the high potential of airborne LiDAR multitemporal data to characterize forest degradation in the tropics, and present a novel approach to forest classification using LiDAR data.

Late Quaternary crustal uplift is well recognized in northeast Sicily, southern Italy, a region also prone to damaging earthquakes such as the 1908 "Messina" earthquake (Mw 7.1), the deadliest seismic event reported within the Italian Earthquake Catalogue. Yet it is still understudied if, within the Milazzo Peninsula, crustal uplift rates are varying spatially and temporally and whether they may be either influenced by (i) local upper‐plate faulting activity or (ii) deep geodynamic processes. To investigate the long‐term crustal vertical movements in northeast Sicily, we have mapped a flight of Middle‐Late Pleistocene marine terraces within the Milazzo Peninsula and in its southern area and refined their chronology, using a synchronous correlation approach driven by published age controls. This has allowed a new calculation of the associated crustal uplift rates, along a north–south oriented coastal‐parallel transect within the investigated area. Our results show a decreasing uplift rate from south to north across the Milazzo Peninsula and beyond, and that the associated rates of uplift have been constant through the Late Quaternary. This spatially varying yet temporally constant vertical deformation helps to constrain the amount of uplift, allowing us to explore which is the driving mechanism(s), proposing a few related scenarios. We discuss our results in terms of tectonic implications and emphasize the importance of using appropriate approaches, as such applying a synchronous correlation method, to refine chronologies of undated palaeoshorelines when used for tectonic investigations. [ABSTRACT FROM AUTHOR]

In Amazon forests, the relative contributions of climate, phenology, and disturbance to net ecosystem exchange of carbon (NEE) are not well understood. To partition influences across various timescales, we use a statistical model to represent eddy-covariance-derived NEE in an evergreen eastern Amazon forest as a constant response to changing meteorology and phenology throughout a decade. Our best fit model represented hourly NEE variations as changes due to sunlight, while seasonal variations arose from phenology influencing photosynthesis and from rainfall influencing ecosystem respiration, where phenology was asynchronous with dry-season onset. We compared annual model residuals with biometric forest surveys to estimate impacts of drought disturbance. We found that our simple model represented hourly and monthly variations in NEE well (R 2 Combining double low line 0.81 and 0.59, respectively). Modeled phenology explained 1 % of hourly and 26 % of monthly variations in observed NEE, whereas the remaining modeled variability was due to changes in meteorology. We did not find evidence to support the common assumption that the forest phenology was seasonally light- or water-triggered. Our model simulated annual NEE well, with the exception of 2002, the first year of our data record, which contained 1.2 MgC ha'1 of residual net emissions, because photosynthesis was anomalously low. Because a severe drought occurred in 1998, we hypothesized that this drought caused a persistent, multi-year depression of photosynthesis. Our results suggest drought can have lasting impacts on photosynthesis, possibly via partial damage to still-living trees.

Despite sustained declines in Amazon deforestation, forest degradation from logging and fire continues to threaten carbon stocks, habitat, and biodiversity in frontier forests along the Amazon arc of deforestation. Limited data on the magnitude of carbon losses and rates of carbon recovery following forest degradation have hindered carbon accounting efforts and contributed to incomplete national reporting to reduce emissions from deforestation and forest degradation (REDD+). We combined annual time series of Landsat imagery and high-density airborne lidar data to characterize the variability, magnitude, and persistence of Amazon forest degradation impacts on aboveground carbon density (ACD) and canopy structure. On average, degraded forests contained 45.1% of the carbon stocks in intact forests, and differences persisted even after 15 years of regrowth. In comparison to logging, understory fires resulted in the largest and longest-lasting differences in ACD. Heterogeneity in burned forest structure varied by fire severity and frequency. Forests with a history of one, two, and three or more fires retained only 54.4%, 25.2%, and 7.6% of intact ACD, respectively, when measured after a year of regrowth. Unlike the additive impact of successive fires, selective logging before burning did not explain additional variability in modeled ACD loss and recovery of burned forests. Airborne lidar also provides quantitative measures of habitat structure that can aid the estimation of co-benefits of avoided degradation. Notably, forest carbon stocks recovered faster than attributes of canopy structure that are critical for biodiversity in tropical forests, including the abundance of tall trees. We provide the first comprehensive look-up table of emissions factors for specific degradation pathways at standard reporting intervals in the Amazon. Estimated carbon loss and recovery trajectories provide an important foundation for assessing the long-term contributions from forest degradation to regional carbon cycling and advance our understanding of the current state of frontier forests.

Systematic biases in eddy covariance measurements of net ecosystem-atmosphere carbon dioxide exchange (NEE) are ubiquitous in forests when turbulence is low at night. We propose an alternative to the conventional bias correction, the friction velocity (u*) filter, by hypothesizing that these biases have two separate, concurrent causes: (1) a subcanopy CO2 storage pool that eludes typical storage measurements, creating a turbulence-dependent bias, and (2) advective divergence loss of CO2, creating a turbulence-independent bias. We correct for (1) using a simple parametric model of missing storage (MS). Prior experiments have inferred (2) directly from atmospheric measurements (DRAINO). For sites at which DRAINO experiments have not been performed or are infeasible, we estimate (2) empirically using a PAR-extrapolated advective respiration loss (PEARL) approach. We compare u* filter estimates of advection and NEE to MS-PEARL estimates at one temperate forest and two tropical forest sites. We find that for tropical forests, u* filters can produce a range of extreme NEE estimates, from long-term forest carbon emission to sequestration, that diverge from independent assessments and are not physically sustainable. Our MS model eliminates the dependence of nighttime NEE on u*, consistent with findings from DRAINO studies that nighttime advective losses of CO2 are often not dependent on the strength of turbulence. Our PEARL estimates of mean advective loss agree with available DRAINO measurements. The MS-PEARL correction to long-term NEE produces better agreement with forest inventories at all three sites. Moreover, the correction retains all nighttime eddy covariance data and is therefore more widely applicable than the u* filter approach, which rejects substantial nighttime data—up to 93% at one of the tropical sites. The full MS-PEARL NEE correction is therefore an equally defensible and more practical alternative to the u* filter, but leads to different conclusions about the resulting carbon balance. Our results therefore highlight the need to investigate which approach's underlying hypotheses are more physically realistic.

Deforestation rates have declined in the Brazilian Amazon since 2005, yet degradation from logging, fire, and fragmentation has continued in frontier forests. In this study we quantified the aboveground carbon density (ACD) in intact and degraded forests using the largest data set of integrated forest inventory plots (n = 359) and airborne lidar data (18,000 ha) assembled to date for the Brazilian Amazon. We developed statistical models relating inventory ACD estimates to lidar metrics that explained 70% of the variance across forest types. Airborne lidar-ACD estimates for intact forests ranged between 5.0 ± 2.5 and 31.9 ± 10.8 kg C m−2. Degradation carbon losses were large and persistent. Sites that burned multiple times within a decade lost up to 15.0 ± 0.7 kg C m−2 (94%) of ACD. Forests that burned nearly 15 years ago had between 4.1 ± 0.5 and 6.8 ± 0.3 kg C m−2 (22–40%) less ACD than intact forests. Even for low-impact logging disturbances, ACD was between 0.7 ± 0.3 and 4.4 ± 0.4 kg C m−2 (4–21%) lower than unlogged forests. Comparing biomass estimates from airborne lidar to existing biomass maps, we found that regional and pantropical products consistently overestimated ACD in degraded forests, underestimated ACD in intact forests, and showed little sensitivity to fires and logging. Fine-scale heterogeneity in ACD across intact and degraded forests highlights the benefits of airborne lidar for carbon mapping. Differences between airborne lidar and regional biomass maps underscore the need to improve and update biomass estimates for dynamic land use frontiers, to better characterize deforestation and degradation carbon emissions for regional carbon budgets and Reduce Emissions from Deforestation and forest Degradation (REDD+).

The complex three-dimensional (3-D) structure of tropical forests generates a diversity of light environments for canopy and understory trees. Understanding diurnal and seasonal changes in light availability is critical for interpreting measurements of net ecosystem exchange and improving ecosystem models. Here, we used the Discrete Anisotropic Radiative Transfer (DART) model to simulate leaf absorption of photosynthetically active radiation (IAPAR) for an Amazon forest. The 3-D model scene was developed from airborne lidar data, and local measurements of leaf reflectance, aerosols, and PAR were used to model lAPAR under direct and diffuse illumination conditions. Simulated lAPAR under clear-sky and cloudy conditions was corrected for light saturation effects to estimate light utilization, the fraction of lAPAR available for photosynthesis. Although the fraction of incoming PAR absorbed by leaves was consistent throughout the year (0.80-0.82), light utilization varied seasonally (0.67-0.74), with minimum values during the Amazon dry season. Shadowing and light saturation effects moderated potential gains in forest productivity from increasing PAR during dry-season months when the diffuse fraction from clouds and aerosols was low. Comparisons between DART and other models highlighted the role of 3-D forest structure to account for seasonal changes in light utilization. Our findings highlight how directional illumination and forest 3-D structure combine to influence diurnal and seasonal variability in light utilization, independent of further changes in leaf area, leaf age, or environmental controls on canopy photosynthesis. Changing illumination geometry constitutes an alternative biophysical explanation for observed seasonality in Amazon forest productivity without changes in canopy phenology.

A survey of the inner Galaxy region of Galactic longitude l ∈[+15,+50] and latitude b ∈ [-4, +4] is performed using one-third of the High Altitude Water Cherenkov Observatory, operated during ts construction phase. To address the ambiguities arising from unresolved sources in the data, we use a maximum likelihood technique to identify point source candidates. Ten sources and candidate sources are identified in this analysis. Eight of these are associated with known TeV sources but not all have ifferential fluxes that are compatible with previous measurements. Three sources are detected with significances >5 sigma; after accounting for statistical trials, and are associated with known TeV sources.