Linking models and data on vegetation structure

For more than a century, scientists have recognized the importance of vegetation structure in understanding forest dynamics. Now future satellite missions such as Deformation, Ecosystem Structure, and Dynamics of Ice (DESDynI) hold the potential to provide unprecedented global data on vegetation structure needed to reduce uncertainties in terrestrial carbon dynamics. Here, we briefly review the uses of data on vegetation structure in ecosystem models, develop and analyze theoretical models to quantify model-data requirements, and describe recent progress using a mechanistic modeling approach utilizing a formal scaling method and data on vegetation structure to improve model predictions. Generally, both limited sampling and coarse resolution averaging lead to model initialization error, which in turn is propagated in subsequent model prediction uncertainty and error. In cases with representative sampling, sufficient resolution, and linear dynamics, errors in initialization tend to compensate at larger spatial scales. However, with inadequate sampling, overly coarse resolution data or models, and nonlinear dynamics, errors in initialization lead to prediction error. A robust model-data framework will require both models and data on vegetation structure sufficient to resolve important environmental gradients and tree-level heterogeneity in forest structure globally.

Estimation of tropical forest height and biomass dynamics using lidar remote sensing at La Selva, Costa Rica

In this paper we present the results of an experiment to measure forest structure and biomass dynamics over the tropical forests of La Selva Biological Station in Costa Rica using a medium resolution lidar. Our main objective was to observe changes in forest canopy height, related height metrics, and biomass, and from these map sources and sinks of carbon across the landscape. The Laser Vegetation Imaging Sensor (LVIS) measured canopy structure over La Selva in 1998 and again in 2005. Changes in waveform metrics were related to field-derived changes in estimated aboveground biomass from a series of old growth and secondary forest plots. Pairwise comparisons of nearly coincident lidar footprints between years showed canopy top height changes that coincided with expected changes based on land cover types. Old growth forests had a net loss in height of -0.33 m, while secondary forests had net gain of 2.08 m. Multiple linear regressionwas used to relate lidar metrics with biomass changes for combined old growth and secondary forest plots, giving an r² of 0.65 and an RSE of 10.5 Mg/ha, but both parametric and bootstrapped confidence intervals were wide, suggesting weaker model performance. The plot level relationships were then used to map biomass changes across La Selva using LVIS at a 1 ha scale. The spatial patterns of biomass changes matched expected patterns given the distribution of land cover types at La Selva, with secondary forests showing a gain of 25 Mg/ha and old growth forests showing little change (2 Mg/ha). Prediction intervals were calculated to assess uncertainty for each 1 ha cell to ascertain whether the data and methods used could confidently estimate the sign (source or sink) of the biomass changes. The resulting map showed most of the old growth areas as neutral (no net biomass change), with widely scattered and isolated sources and sinks. Secondary forests in contrast were mostly sinks or neutral, but were never sources. By quantifying both the magnitude of biomass changes and the sensitivity of lidar to detect them, this work will help inform the formulation of future space missions focused on biomass dynamics, such as NASA’s Deformation Ecosystem Structure and Dynamics of Ice mission.

Regional forest growth rates measured by combining ICESat GLAS and Landsat data

Forest canopy heights derived from ICESat Geoscience Laser Altimeter System (GLAS) lidar data were combined with Landsat-based disturbance history maps to assess forest regeneration rates in three regions of the eastern United States (Maine, Virginia, and Mississippi). GLAS observations were screened for topographic relief and waveform quality, and canopy heights were obtained by visual inspection of each waveform. Regressing the GLAS heights against the age of last disturbance yielded vertical growth rates of 0.6 m/yr (Maine), 1.0 m/yr (Virginia), and 1.2 m/yr (Mississippi). Growth rates, when combined with height-biomass allometric relations, can be converted to estimates of aboveground wood productivity. The study demonstrates that large-footprint lidar data can be used to measure vertical growth rates when averaged spatially, thus providing unique information on forest regeneration for carbon cycle studies.

Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure

Abrupt forest disturbances generating gaps >0.001 km² impact roughly 0.4–0.7 million km² a⁻¹. Fire, windstorms, logging, and shifting cultivation are dominant disturbances; minor contributors are land conversion, flooding, landslides, and avalanches. All can have substantial impacts on canopy biomass and structure. Quantifying disturbance location, extent, severity, and the fate of disturbed biomass will improve carbon budget estimates and lead to better initialization, parameterization, and/or testing of forest carbon cycle models. Spaceborne remote sensing maps large-scale forest disturbance occurrence, location, and extent, particularly with moderate- and fine-scale resolution passive optical/near-infrared (NIR) instruments. High-resolution remote sensing (e.g., ∼1 m passive optical/NIR, or small footprint lidar) can map crown geometry and gaps, but has rarely been systematically applied to study small-scale disturbance and natural mortality gap dynamics over large regions. Reducing uncertainty in disturbance and recovery impacts on global forest carbon balance requires quantification of (1) predisturbance forest biomass; (2) disturbance impact on standing biomass and its fate; and (3) rate of biomass accumulation during recovery. Active remote sensing data (e.g., lidar, radar) are more directly indicative of canopy biomass and many structural properties than passive instrument data; a new generation of instruments designed to generate global coverage/sampling of canopy biomass and structure can improve our ability to quantify the carbon balance of Earth's forests. Generating a high-quality quantitative assessment of disturbance impacts on canopy biomass and structure with spaceborne remote sensing requires comprehensive, well designed, and well coordinated field programs collecting high-quality ground-based data and linkages to dynamical models that can use this information.

Impacts of tropical cyclones on U.S. forest tree mortality and carbon flux from 1851 to 2000

Tropical cyclones cause extensive tree mortality and damage to forested ecosystems. A number of patterns in tropical cyclone frequency and intensity have been identified. There exist, however, few studies on the dynamic impacts of historical tropical cyclones at a continental scale. Here, we synthesized field measurements, satellite image analyses, and empirical models to evaluate forest and carbon cycle impacts for historical tropical cyclones from 1851 to 2000 over the continental U.S. Results demonstrated an average of 97 million trees affected each year over the entire United States, with a 53-Tg annual biomass loss, and an average carbon release of 25 Tg y⁻¹. Over the period 1980–1990, released CO₂ potentially offset the carbon sink in forest trees by 9–18% over the entire United States. U.S. forests also experienced twice the impact before 1900 than after 1900 because of more active tropical cyclones and a larger extent of forested areas. Forest impacts were primarily located in Gulf Coast areas, particularly southern Texas and Louisiana and south Florida, while significant impacts also occurred in eastern North Carolina. Results serve as an important baseline for evaluating how potential future changes in hurricane frequency and intensity will impact forest tree mortality and carbon balance.

Carbon cycling under 300 years of land use change: Importance of the secondary vegetation sink

We have developed a dynamic land model (LM3V) able to simulate ecosystem dynamics and exchanges of water, energy, and CO₂ between land and atmosphere. LM3V is specifically designed to address the consequences of land use and land management changes including cropland and pasture dynamics, shifting cultivation, logging, fire, and resulting patterns of secondary regrowth. Here we analyze the behavior of LM3V, forced with the output from the Geophysical Fluid Dynamics Laboratory (GFDL) atmospheric model AM2, observed precipitation data, and four historic scenarios of land use change for 1700–2000. Our analysis suggests a net terrestrial carbon source due to land use activities from 1.1 to 1.3 GtC/a during the 1990s, where the range is due to the difference in the historic cropland distribution. This magnitude is substantially smaller than previous estimates from other models, largely due to our estimates of a secondary vegetation sink of 0.35 to 0.6 GtC/a in the 1990s and decelerating agricultural land clearing since the 1960s. For the 1990s, our estimates for the pastures' carbon flux vary from a source of 0.37 to a sink of 0.15 GtC/a, and for the croplands our model shows a carbon source of 0.6 to 0.9 GtC/a. Our process-based model suggests a smaller net deforestation source than earlier bookkeeping models because it accounts for decelerated net conversion of primary forest to agriculture and for stronger secondary vegetation regrowth in tropical regions. The overall uncertainty is likely to be higher than the range reported here because of uncertainty in the biomass recovery under changing ambient conditions, including atmospheric CO2 concentration, nutrients availability, and climate.

Using lidar data and a height-structured ecosystem model to estimate forest carbon stocks and fluxes over mountainous terrain

Accurately predicting forest dynamics and associated carbon fluxes requires both knowledge of the current state of the ecosystem and an understanding of the underlying processes and environmental conditions that influence the ecosystem processes. Here, we apply a combination of light detection and ranging (lidar) remote sensing (LVIS), an individual-based height-structured ecosystem model (ED), and detailed topographic and climate data to address these requirements to predict carbon dynamics at the Hubbard Brook Experimental Forest (HBEF) in the White Mountains of New Hampshire. Lidar data provided substantial constraints on model estimates of carbon stocks and annual net ecosystem production (ANEP). Lidar-initialized model estimates of carbon stocks (10.77 kg C m⁻²) were within 5% of the field estimates over the domain and accounted for the 44% decrease in carbon stocks observed between minimum and maximum elevation at HBEF. Lidar-initialized model estimates of ANEP (0.023 kg C m⁻² year⁻¹) also compared favorably with recent field estimates. Model estimates of ANEP strongly depended on fine-scale (1 ha) lidar data on vegetation structure, environmental gradients, and the dynamics of disturbance events. Substituting fine-scale (1 ha) data on vegetation structure and climate with domain-wide inputs increased model estimates of ANEP by 84%. Substituting fine-scale climate data with domain-wide inputs but initializing with fine-scale data on vegetation structure increased estimates of ANEP by 40%. Model simulations initialized with spatial heterogeneity in environmental conditions but that lacked corresponding spatial heterogeneity in vegetation structure were the most problematic because this configuration had serious inconsistencies in areas where the domain mean canopy height exceeded the local potential for vegetation (e.g., at high elevations). Lastly, failing to account for increased natural disturbance rates with elevation increased model estimates of ANEP by 43%. This research demonstrates that the combination of lidar data and a height-structured ecosystem model can be a powerful tool for estimating forest carbon stocks and fluxes, even in complex mountainous environments. To be most useful for constraining model predictions, lidar data need to be at the scale of the underlying environmental heterogeneity that determines plant vital rates.

Clustered disturbances lead to bias in large-scale estimates based on forest sample plots

Assessments from field plots steer much of our current understanding of global change impacts on forest ecosystem structure and function. Recent widespread observations of net carbon accumulation in field plots have suggested that terrestrial ecosystems may be a carbon sink possibly resulting from climate change and/or CO₂ fertilization. We hypothesize that field plots may inadequately sample inherently rare mortality events, leading to bias when plot level measurements are scaled up to larger domains. In this study, we constructed a simple computer simulation model of forest dynamics to investigate the effects of disturbance patterns on landscape-scale carbon balance estimates. The model was constructed to be a balanced biosphere at the landscape-scale with a uniform spatial pattern of forest growth rates. Disturbance gap-size distributions across the landscape were modelled with a power-law distribution. Small and frequent disturbances result in a well-mixed heterogeneous forest where even small sample plots represented domain-wide behaviour. However, with disturbances dominated by large and rare events, sample plots as large as 50 ha displayed significant bias towards growth. We suggest that the accuracy of domain level estimates of carbon balance from sample plots are highly sensitive to the distribution of disturbance events across the landscape, and to the number, size and distribution of field plots that comprise the estimate. Assumptions that small clusters of field plots may be representative of domain-wide conditions should only be made very cautiously, and warrant further investigation for verification

The effects of deforestation on the hydrological cycle in Amazonia: a review on scale and resolution

This paper reviews the effects of deforestation on the hydrological cycle in Amazonia according to recent modeling and observational studies performed within different spatial scales and resolutions. The predictions that follow from future scenarios of a complete deforestation in the region point to a restrained water cycle, while the simulated effects of small, disturbed areas show a contrasting tendency. Differences between coarsely spatially averaged observations and finely sampled data sets have also been encountered. These contrasts are only partially explained by the different spatial resolutions among models and observations, since they seem to be further associated with the weakening of precipitation recycling under scenarios of extensive deforestation and with the potential intensification of convection over areas of land-surface heterogeneity. Therefore, intrinsic and interrelated scale and heterogeneity dependencies on the impact of deforestation in Amazonia on the hydrological cycle are revealed and the acknowledgement of the relevance of these dependencies sets a few challenges for the future.

Hurricane Katrina's Carbon Footprint on U.S. Gulf Coast Forests

Hurricane Katrina's impact on U.S. Gulf Coast forests was quantified by linking ecological field studies, Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) image analyses, and empirically based models. Within areas affected by relatively constant wind speed, tree mortality and damage exhibited strong species-controlled gradients. Spatially explicit forest disturbance maps coupled with extrapolation models predicted mortality and severe structural damage to ~320 million large trees totaling 105 teragrams of carbon, representing 50 to 140% of the net annual U.S. forest tree carbon sink. Changes in disturbance regimes from increased storm activity expected under a warming climate will reduce forest biomass stocks, increase ecosystem respiration, and may represent an important positive feedback mechanism to elevated atmospheric carbon dioxide.

The Tension between Fire Risk and Carbon Storage: Evaluating U.S. Carbon and Fire Management Strategies through Ecosystem Models

Fire risk and carbon storage are related environmental issues because fire reduction results in carbon storage through the buildup of woody vegetation, and stored carbon is a fuel for fires. The sustainability of the U.S. carbon sink and the extent of fire activity in the next 100 yr depend in part on the type and effectiveness of fire reduction employed. Previous studies have bracketed the range of dynamics from continued fire reduction to the complete failure of fire reduction activities. To improve these estimates, it is necessary to explicitly account for fire reduction in terrestrial models. A new fire reduction submodel that estimates the spatiotemporal pattern of reduction across the United States was developed using gridded data on biomass, climate, land-use, population, and economic factors. To the authors’ knowledge, it is the first large-scale, gridded fire model that explicitly accounts for fire reduction. The model was calibrated to 1° × 1° burned area statistics [Global Burnt Area 2000 Project (GBA-2000)] and compared favorably to three important diagnostics. The model was then implemented in a spatially explicit ecosystem model and used to analyze 1620 scenarios of future fire risk and fire reduction strategies. Under scenarios of climate change and urbanization, burned area and carbon emissions both increased in scenarios where fire reduction efforts were not adjusted to match new patterns of fire risk. Fuel reducing management strategies reduced burned area and fire risk, but also limited carbon storage. These results suggest that to promote carbon storage and minimize fire risk in the future, fire reduction efforts will need to be increased and spatially adjusted and will need to employ a mixture of fuel-reducing and non-fuel-reducing strategies.

The contributions of land-use change, CO₂ fertilization, and climate variability to the Eastern US carbon sink

Atmospheric measurements and land-based inventories imply that terrestrial ecosystems in the northern hemisphere are taking up significant amounts of anthropogenic carbon dioxide (CO₂) emissions; however, there is considerable disagreement about the causes of this uptake, and its expected future trajectory. In this paper, we use the ecosystem demography (ED) model to quantify the contributions of disturbance history, CO₂ fertilization and climate variability to the past, current, and future terrestrial carbon fluxes in the Eastern United States. The simulations indicate that forest regrowth following agricultural abandonment accounts for uptake of 0.11 Pg C yr^-1 in the 1980s and 0.15 Pg C yr^-1 in the 1990s, and regrowth following forest harvesting accounts for an additional 0.1 Pg C yr^-1 of uptake during both these decades. The addition of CO₂ fertilization into the model simulations increases carbon uptake rates to 0.38 Pg C yr^-1 in the 1980s and 0.47 Pg C yr^-1 in the 1990s. Comparisons of predicted aboveground carbon uptake to regional-scale forest inventory measurements indicate that the model’s predictions in the absence of CO₂ fertilization are 14% lower than observed, while in the presence of CO₂ fertilization, predicted uptake rates are 28% larger than observed. Comparable results are obtained from comparisons of predicted total Net Ecosystem Productivity to the carbon fluxes observed at the Harvard Forest flux tower site and in model simulations free-air CO₂ enrichment (FACE) experiments. These results imply that disturbance history is the principal mechanism responsible for current carbon uptake in the Eastern United States, and that conventional biogeochemical formulations of plant growth overestimate the response of plants to rising CO₂ levels. Model projections out to 2100 imply that the carbon uptake arising from forest regrowth will increasingly be dominated by forest regrowth following harvesting. Consequently, actual carbon storage declines to near zero by the end of the 21st century as the forest regrowth that has occurred since agricultural abandonment comes into equilibrium with the landscape’s new disturbance regime. Incorporating interannual climate variability into the model simulations gives rise to large interannual variation in regional carbon fluxes, indicating that long-term measurements are necessary to detect the signature of processes that give rise to long-term uptake and storage.

Broadening students horizons: the development, delivery, and assessment of a new course in Earth System Science.

Earth System Science is an exceptionally interdisciplinary field requiring knowledge and skills from multiple scientific disciplines. Many important questions lie at the intersection of traditional disciplines and require a systems level approach. The emerging educational challenge is to train the next generation of scientists to address these topics. Here, we describe the development, delivery, and assessment of a new course in Earth System Science designed for advanced undergraduates and beginning graduate students. The course was designed to meet specific learning objectives, delivered in an inquiry-based learning environment, and assessed to determine the extent to which the learning objectives had been attained. The course consisted of readings from both texts and primary literature, lectures by UNH professors and NASA scientists, computer modeling labs, and interdisciplinary student-team research projects. Results emphasize the importance of pre-planning and resources, establishing clear and concise student learning objectives, creating of an inquiry-based learning centered environment, role-modeling how Earth System Science research is done, and meeting student demand and institutional challenges. This class can serve as a model course for upper level undergraduates and beginning graduate students to expand their disciplinary scope, skills, and readiness to address Earth System Science questions.

A water balance model to study the hydrological response to different scenarios of deforestation in Amazonia

Amazonia encloses some of the largest watersheds in the world, experiencing substantial amounts of rainfall annually and producing more runoff to the ocean than any other region. Amazonia experiences one of the highest rates of deforestation in the world and the hydrological effects of such a disturbance have already been investigated by several studies. Contrasting results exist, especially when different scales and degrees of heterogeneity are considered. This paper assesses the dependency of the hydrological impact of deforestation on these factors through application of a gridded water balance model. The model simulates different scenarios of deforestation based on straightforward water balance calculations. In all experiments performed, the scenarios conform to observations of decreased evapotranspiration within disturbed sites. Initially, by implying an uncoupling between small deforested áreas and circulation, the model suggests an increase in runoff locally. However, when the land-atmosphere coupling caused by intermediate levels of deforestation is reproduced through deviations on circulation, the model confirms that the water cycle may or may not become regionally accelerated, depending on the degree of heterogeneity associated. Finally, by simulating a scenario of complete deforestation, the model confirms expectations of a less intense water cycle in Amazonia. Due to the broad range of numerical models and observation networks currently available, the importance of the proper representation of both scale and heterogeneity of deforestation to the correct assessment of its hydrological effects is emphasized.

Despite our model results, there is need for more mechanistic studies on coupled land-surface and atmosphere interactions under varying conditions.

The underpinnings of land-use history: three centuries of global gridded land-use transitions, wood harvest activity, and resulting secondary lands.

To accurately assess the impacts of human land use on the Earth system, information is needed on the current and historical patterns of land-use activities. Previous global studies have focused on developing reconstructions of the spatial patterns of agriculture. Here, we provide the first global gridded estimates of the underlying land conversions (land-use transitions), wood harvesting, and resulting secondary lands annually, for the period 1700–2000. Using data-based historical cases, our results suggest that 42–68% of the land surface was impacted by land-use activities (crop, pasture, wood harvest) during this period, some multiple times. Secondary land area increased 10–44 × 106 km²; about half of this was forested. Wood harvest and shifting cultivation generated 70–90% of the secondary land by 2000; permanent abandonment and relocation of agricultural land accounted for the rest. This study provides important new estimates of globally gridded land-use activities for studies attempting to assess the consequences of anthropogenic changes to the Earth's surface over time.

Field work and statistical analyses for enhanced interpretation of satellite fire data

Because their broad spatial and temporal coverage, satellites provide the main source of fire data for Amazonia. A key to the application of these tools for environmental studies is the appropriate interpretation of the data they provide. To enhance the interpretation of satellite fire data for this region, we collected ground-based data on fires in 2001 and 2002 using a simple and passive method, and statistically related these data to corresponding estimates from AVHRR and MODIS fire products using error matrices. Multiple methods of analyses from simple to complex produced qualitatively similar results. Total accuracies for both fire products were very high (> 99%) and dominated by accurate (> 99%) non-fire detection. Kappa statistics and fire-detection accuracies were substantially lower, with omission errors higher than commission errors. Results calculated using several different sets of spatial-matching parameters of analysis showed that Kappa was 1–10.6% for AVHRR, and 0–1.4% for MODIS. User's accuracy for fires was 0–40% for AVHRR and 3–100% for MODIS. Producer's accuracy for fires was 0–8% for AVHRR and 0–1% for MODIS. Statistical evaluations of potential explanatory factors showed that fire size and sampling time were dominant factors for low accuracies. Results from this study indicate that current satellite fire products are providing a limited sample of the fire activity in the region, and that ground-based analyses can substantially contribute to the interpretation of these products.

Beyond Potential Vegetation: Combining LIDAR Data and a Height-Structured Model for Carbon Studies

Carbon estimates from terrestrial ecosystem models are limited by large uncertainties in the current state of the land surface. Natural and anthropogenic disturbances have important and lasting influences on ecosystem structure and fluxes that can be difficult to detect or assess with conventional methods. In this study, we combined two recent advances in remote sensing and ecosystem modeling to improve model carbon stock and flux estimates at a tropical forest study site at La Selva, Costa Rica (10°25′ N, 84°00′ W). Airborne lidar remote sensing was used to measure spatial heterogeneity in the vertical structure of vegetation. The ecosystem demography model (ED) was used to estimate the consequences of this heterogeneity for regional estimates of carbon stocks and fluxes. Lidar data provided substantial constraints on model estimates of both carbon stocks and net carbon fluxes. Lidar-initialized ED estimates of aboveground biomass were within 1.2% of regression-based approaches, and corresponding model estimates of net carbon fluxes differed substantially from bracketing alternatives. The results of this study provide a promising illustration of the power of combining lidar data on vegetation height with a height-structured ecosystem model. Extending these analyses to larger scales will require the development of regional and global lidar data sets, and the continued development and application of height structured ecosystem models.

Human-induced changes in US biogenic volatile organic compound emissions: evidence from long-term forest inventory data

Volatile organic compounds (VOCs) emitted by woody vegetation influence global climate forcing and the formation of tropospheric ozone. We use data from over 250,000 re-surveyed forest plots in the eastern US to estimate emission rates for the two most important biogenic VOCs (isoprene and monoterpenes) in the 1980s and 1990s, and then compare these estimates to give a decadal change in emission rate. Over much of the region, particularly the southeast, we estimate that there were large changes in biogenic VOC emissions: half of the grid cells (1° × 1°) had decadal changes in emission rate outside the range -2.3% to +16.8% for isoprene, and outside the range 0.2–17.1% for monoterpenes. For an average grid cell the estimated decadal change in heatwave biogenic VOC emissions (usually an increase) was three times greater than the decadal change in heatwave anthropogenic VOC emissions (usually a decrease, caused by legislation). Leaf-area increases in forests, caused by anthropogenic disturbance, were the most important process increasing biogenic VOC emissions. However, in the southeast, which had the largest estimated changes, there were substantial effects of ecological succession (which decreased monoterpene emissions and had location-specific effects on isoprene emissions), harvesting (which decreased monoterpene emissions and increased isoprene emissions) and plantation management (which increased isoprene emissions, and decreased monoterpene emissions in some states but increased monoterpene emissions in others). In any given region, changes in a very few tree species caused most of the changes in emissions: the rapid changes in the southeast were caused almost entirely by increases in sweetgum (Liquidambar styraciflua) and a few pine species. Therefore, in these regions, a more detailed ecological understanding of just a few species could greatly improve our understanding of the relationship between natural ecological processes, forest management, and biogenic VOC emissions.

Projecting the future of the U.S. carbon sink

Atmospheric and ground-based methods agree on the presence of a carbon sink in the coterminous United States (the United States minus Alaska and Hawaii), and the primary causes for the sink recently have been identified. Projecting the future behavior of the sink is necessary for projecting future net emissions. Here we use two models, the Ecosystem Demography model and a second simpler empirically based model (Miami Land Use History), to estimate the spatio-temporal patterns of ecosystem carbon stocks and fluxes resulting from land-use changes and fire suppression from 1700 to 2100. Our results are compared with other historical reconstructions of ecosystem carbon fluxes and to a detailed carbon budget for the 1980s. Our projections indicate that the ecosystem recovery processes that are primarily responsible for the contemporary U.S. carbon sink will slow over the next century, resulting in a significant reduction of the sink. The projected rate of decrease depends strongly on scenarios of future land use and the long-term effectiveness of fire suppression.

Consistent Land- and Atmosphere-Based U.S. Carbon Sink Estimates

For the period 1980-89, we estimate a carbon sink in the coterminous United States between 0.30 and 0.58 petagrams of carbon per year (petagrams of carbon = 1015 grams of carbon). The net carbon flux from the atmosphere to the land was higher, 0.37 to 0.71 petagrams of carbon per year, because a net flux of 0.07 to 0.13 petagrams of carbon per year was exported by rivers and commerce and returned to the atmosphere elsewhere. These land-based estimates are larger than those from previous studies (0.08 to 0.35 petagrams of carbon per year) because of the inclusion of additional processes and revised estimates of some component fluxes. Although component estimates are uncertain, about one-half of the total is outside the forest sector. We also estimated the sink using atmospheric models and the atmospheric concentration of carbon dioxide (the tracer-transport inversion method). The range of results from the atmosphere-based inversions contains the land-based estimates. Atmosphere- and land-based estimates are thus consistent, within the large ranges of uncertainty for both methods. Atmosphere-based results for 1980-89 are similar to those for 1985-89 and 1990-94, indicating a relatively stable U.S. sink throughout the period.

Contributions of land-use history to carbon accumulation in US forests

Carbon accumulation in forests has been attributed to historical changes in land use and the enhancement of tree growth by CO₂ fertilization, N deposition, and climate change. The relative contribution of land use and growth enhancement is estimated by using inventory data from five states spanning a latitudinal gradient in the eastern United States. Land use is the dominant factor governing the rate of carbon accumulation in these states, with growth enhancement contributing far less than previously reported. The estimated fraction of aboveground net ecosystem production due to growth enhancement is 2.0 ± 4.4%, with the remainder due to land use.