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Terrestrial ecosystems play a critical role in the global carbon cycle but have highly uncertain future dynamics. Ecosystem modelling that includes the scaling-up of underlying mechanistic ecological processes has the potential to improve the accuracy of future projections, while retaining key process-level detail. Over the past two decades, multiple modelling advances have been made to meet this challenge, including the Ecosystem Demography (ED) model and its derivatives including ED2 and FATES. Here, we present the global evaluation of the Ecosystem Demography model (ED v3.0), which likes its predecessors features the formal scaling of physiological processes of individual-based vegetation dynamics to ecosystem scales, together with integrated submodules of soil biogeochemistry and soil hydrology, while retaining explicit tracking of vegetation 3-D structure. This new version builds on previous versions and provides the first global calibration and evaluation, global tracking of the effects of climate and land-use change on vegetation 3-D structure, new spin-up process and input datasets, as well as numerous other advances. Model evaluation was performed with respect to a set of important benchmarking datasets, and model estimates were within observational constraints for multiple key variables including: (i) global patterns of dominant plant functional types (broadleaf vs evergreen); (ii) spatial distribution, seasonal cycle, and interannual trends of global Gross Primary Production (GPP); (iii) global interannual variability of Net Biome Production (NBP); and (iv) global patterns of vertical structure including leaf area and canopy height. With this global model version, it is now possible to simulate vegetation dynamics from local to global scales and from seconds to centuries, with a consistent mechanistic modelling framework amendable to data from multiple traditional and new remote sensing sources, including lidar.

Given the recent availability of high-resolution data on both current forest carbon stocks and restoration potential, the next geospatial and computational challenge is to utilize this data to iden...

Members of the U.S. Climate Alliance, a coalition of 24 states committed to achieving the emissions reductions outlined in the 2015 Paris Agreement, are considering policy options for inclusion of ...

Climate mitigation planning requires accurate information on forest carbon dynamics. Forest carbon monitoring and modeling systems need to step beyond the traditional Monitoring, Reporting, and Ver...

Fifth generation (5G) wireless networks promise to provide faster and more expansive data connectivity, exceeding thresholds from previous fourth generation (4G) technology. The deployment of 5G infrastructure requires allocating additional frequencies in radio bands at 24 gigahertz (GHz), potentially contaminating neighboring remote sensing bands used for weather forecasting and prediction. The current U.S. out-of-band emissions limit at 24 GHz of -20 dBW per 200 MHz is projected to degrade meteorological forecast accuracy by up to 30%, reducing the hurricane forecast lead time by 2 to 3 days, and endangering thousands of additional lives. Under the Weather Research and Forecasting Innovation Act of 2017 (Pub.L 115-25), the National Oceanic and Atmospheric Administration (NOAA) must develop more accurate and timely severe weather forecasts in order to protect life and property and reduce economic risk; however, the potential out-of-band interference from the roll out of 5G threatens this aim. Given U.S. economic reliance on accurate weather prediction (estimated to be in the trillions of dollars), we propose that Congress mandate stricter noise restrictions to adequately meet requirements of the Pub.L 115-25, while minimizing disruption to 5G deployment.

Aquatic environments are among Earth’s most species-diverse ecosystems, yet remain under-protected. Rivers face multiple threats, the most systemic being alteration of streamflow through human withdrawals or impediments by dams. Dam removal, once rare, is an increasingly used technique to restore stream flows. Marine populations are most threatened by overharvest, with more than 60% of commercial stocks classified as overfished or collapsed. Bycatch, the inadvertent capture of animals when fishing for something else, threatens non-target species, with losses to marine mammals alone between 300,000 and 600,000 individuals annually. Time and area closures, fishing effort reduction and gear modification can reduce bycatch mortality. Marine reserves produce increasing fish populations under “no-take” management as well as in commercial catches if harvest areas within the reserve are limited.

Habitats are sites having appropriate levels of biotic and abiotic features required for a species’ survival and reproduction, and so are vital elements of species conservation. Habitat loss, fragmentation, isolation (increasing distance between similar habitat types) and degradation are threats to habitat conservation. Contemporary tools for conserving habitat for threatened species include species-specific habitat suitability models (HSMs), “GAP” analysis (relating species’ distribution to distribution of protected areas), Light Detection and Ranging (LiDAR) technology employing laser scanning to measure habitat attributes, and dispersal models relating movement of individual organisms to relative “connectedness” of habitat patches. Human alteration of habitats cannot be eliminated, but can be mitigated through site-specific restrictions on human occupancy and disturbance, methods of resource extraction, and treatment of habitat following disturbance.

Global climate change poses a fundamentally different threat to biodiversity conservation than any other. Meta-analyses of species’ distribution studies demonstrate a “diagnostic fingerprint” of climate effects on species worldwide, characterized by advancement of spring events, range shifts, changes in local abundance, interactions across trophic levels, and climate-related extinctions. In some cases entire ecosystems, such as cloud forests and coral reefs, have been locally eliminated by warming temperatures. Conservation biologists are now using integrated vulnerability assessments (IVAs) assessing species exposure, sensitivity, and adaptive capacity to climate change to estimate their risk of extinction from it as well as models of species-specific “bioclimate envelopes” to determine climate vulnerability. Both science and policy-based strategies will be needed to reduce effects of climate change and prevent losses to global biodiversity.

Afforestation and reforestation have the potential to provide effective climate mitigation through forest carbon sequestration. Strategic reforestation activities, which account for both carbon sequestration potential (CSP) and economic opportunity, can provide attractive options for policymakers who must manage competing social and environmental goals. In particular, forest carbon pricing can incentivize reforestation on private land, but this may require landholders to forego other profits. Here, we utilize an ambitious geospatial approach to quantify economic opportunities for reforestation in the state of Maryland (USA) based on high-resolution remoting sensing, ecosystem modeling, and economic analysis. Our results identify spatially-explicit areas of economic opportunity where the potential revenue from forest carbon outcompetes the expected profit of existing cropland at the hectare scale. Specifically, we find that under a baseline economic scenario of $20 per ton of carbon (5% rental rate) and decadal average crop profitability, a transition to forest on agricultural land would be more profitable than 23.2% of cropland in Maryland under a 20 year land-use commitment. Accounting for variations in carbon and crop pricing, 5.5%–55.4% of cropland would be immediately outcompeted by expected forest carbon revenue, with the potential for an additional 0.5%–10.6% of outcompeted cropland within 20 years. Under the baseline economic scenario, an annual allocation of $5.8 million towards a carbon rental program could protect 6.93 Tg C (3.4% of the state’s total remaining CSP) on reforested croplands. This moderate yearly cost is equal to 9.7% of Maryland’s average annual auction proceeds from participation in the Regional Greenhouse Gas Initiative (between 2014 and 2018), and 19.3% of the average annual subsidy payments for corn, soy, and wheat allocated over the same period. This methodological approach may be useful for state governments, not-for-profit organizations, or regional climate initiatives interested in identifying strategic areas for reforestation.

The inclusion of forest carbon in climate change mitigation planning requires the development of models able to project potential future carbon stocks—a step beyond traditional monitoring, reporting and verification frameworks. Here, we updated and expanded a high-resolution forest carbon modelling approach previously developed for the state of Maryland to 11 states in the Regional Greenhouse Gas Initiative (RGGI) domain, which includes Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont. In this study, we employ an updated version of the Ecosystem Demography (ED) model, an improved lidar initialization strategy, and an expanded calibration/validation approach. High resolution (90 m) wall-to-wall maps of present aboveground carbon, aboveground carbon sequestration potential, aboveground carbon sequestration potential gap (CSPG), and time to reach sequestration potential were produced over the RGGI domain where airborne lidar data were available, including 100% of eight states, 62% of Maine, 12% of New Jersey, and 0.65% of New York. For the eight states with complete data, an area of 228 552 km2, the contemporary forest aboveground carbon stock is estimated to be 1134 Tg C, and the forest aboveground CSPG is estimated to be larger at >1770 Tg C. Importantly, these estimates of the potential for added aboveground carbon sequestration in forests are spatially resolved, are further partitioned between continued growth of existing trees and new afforested/reforested areas, and include time estimates for realization. They are also assessed for sensitivity to potential changes in vegetation productivity and disturbance rate in response to climate change. The results from this study are intended as input into regional, state, and local planning efforts that consider future climate mitigation in forests along with other land-use considerations.

Large-scale airborne lidar data collections can be used to generate high-resolution forest aboveground biomass maps at the state level and beyond as demonstrated in early phases of NASA’s Carbon Monitoring System program. While products like aboveground biomass maps derived from these leaf-off lidar datasets each can meet state- or substate-level measurement requirements individually, combining them over multiple jurisdictions does not guarantee the consistency required in forest carbon planning, trading and reporting schemes. In this study, we refine a multi-state level forest carbon monitoring framework that addresses these spatial inconsistencies caused by variability in data quality and modeling techniques. This work is built upon our long term efforts to link airborne lidar, National Agricultural Imagery Program imagery and USDA Forest Service Forest Inventory and Analysis plot measurements for high-resolution forest aboveground biomass mapping. Compared with machine learning algorithms (r 2 = 0.38, bias = −2.3, RMSE = 45.2 Mg ha−1), the use of a linear model is not only able to maintain a good prediction accuracy of aboveground biomass density (r 2 = 0.32, bias = 4.0, RMSE = 49.4 Mg ha−1) but largely mitigates problems related to variability in data quality. Our latest effort has led to the generation of a consistent 30 m pixel forest aboveground carbon map covering 11 states in the Regional Greenhouse Gas Initiative region of the USA. Such an approach can directly contribute to the formation of a cohesive forest carbon accounting system at national and even international levels, especially via future integrations with NASA’s spaceborne lidar missions.

NASA Carbon Monitoring System projects NNX12AN07G, NNX14AP12G, and 80NSSC17K0710; National Science Foundation Grant No. DGE1322106