Emissions of CH4and N2O over the United States and Canada based on a receptor-oriented modeling framework and COBRA-NA atmospheric observations

We report national scale estimates of CO2emissions from fossil-fuel combustion and cement production in the United States based directly on atmospheric observations, using a dual-tracer inverse modeling framework and CO2andΔ14CO2measurements obtained primarily from the North American portion of the National Oceanic and Atmospheric Administration’s Global Greenhouse Gas Reference Network. The derived US national total for 2010 is 1,653 ± 30 TgC yr−1with an uncertainty (1σ) that takes into account random errors associated with atmospheric transport, atmospheric measurements, and specified prior CO2and14C fluxes. The atmosphere-derived estimate is significantly larger (>3σ) than US national emissions for 2010 from three global inventories widely used for CO2accounting, even after adjustments for emissions that might be sensed by the atmospheric network, but which are not included in inventory totals. It is also larger (>2σ) than a similarly adjusted total from the US Environmental Protection Agency (EPA), but overlaps EPA’s reported upper 95% confidence limit. In contrast, the atmosphere-derived estimate is within1σof the adjusted 2010 annual total and nine of 12 adjusted monthly totals aggregated from the latest version of the high-resolution, US-specific “Vulcan” emission data product. Derived emissions appear to be robust to a range of assumed prior emissions and other parameters of the inversion framework. While we cannot rule out a possible bias from assumed prior Net Ecosystem Exchange over North America, we show that this can be overcome with additionalΔ14CO2measurements. These results indicate the strong potential for quantification of US emissions and their multiyear trends from atmospheric observations.

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Multiscale modeling of longleaf pine (Pinus palustris)

Canadian Journal of Forest Research ◽

10.1139/x07-070 ◽

2007 ◽

Vol 37 (11) ◽

pp. 2080-2089 ◽

Cited By ~ 3

Author(s):

E. Louise Loudermilk ◽

Wendell P. Cropper

Keyword(s):

United States ◽

Matrix Model ◽

Fire Regime ◽

Landscape Connectivity ◽

Longleaf Pine ◽

Pinus Palustris ◽

The United States ◽

Sensitivity Analyses ◽

Modeling Framework ◽

Landis Model

There are few remaining longleaf pine ( Pinus palustris Mill.) ecosystems left in the southeastern coastal plain of the United States. Restoration and maintenance of these remaining habitats requires an understanding of ecosystem processes at multiple scales. The focus of this study was to develop and evaluate a modeling framework for analyzing longleaf pine dynamics at the spatially explicit landscape scale and at the spatially implicit population scale. The landscape disturbance and succession (LANDIS) model was used to simulate landscape fire dynamics in a managed forest in north-central Florida. We constructed a density-dependent longleaf pine population matrix model using data from a variety of studies across the southeastern United States to extend an existing model. Sensitivity analyses showed that the most sensitive parameters were those from the original pine model, which was based on extensive observations of individual trees. A hybrid approach integrated the two models: the fire frequencies output from the LANDIS model were input to the matrix model for specific longleaf pine populations. These simulations indicated that small isolated longleaf pine populations are more vulnerable to fire suppression and that landscape connectivity is a critical concern. A frequent prescribed fire regime is nonetheless necessary to maintain even large longleaf pine sandhill communities that have better landscape connectivity.

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A VECTOR ANALYTICAL FRAMEWORK FOR POPULATION MODELING

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlvi-4-w2-2021-103-2021 ◽

2021 ◽

Vol XLVI-4/W2-2021 ◽

pp. 103-108

Author(s):

J. J. Moehl ◽

E. M. Weber ◽

J. J. McKee

Keyword(s):

United States ◽

Computational Efficiency ◽

Population Modeling ◽

Analytical Framework ◽

The United States ◽

Modeling Framework ◽

Output Data ◽

Implementation Methodology ◽

Computational Resources

Abstract. We propose a vector alternative to the typical raster based population modeling framework. When compared with rasters, vectors are more precise, have the ability to hold more information, and are more conducive to areal constructs such as building and parcel outlines. While rasters have traditionally provided computational efficiency, much of this efficiency is reduced at finer resolutions and computational resources are more plentiful today. Herein we describe the approach and implementation methodology. We also describe the output data stack for the United States and provide examples and applications.

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Prototyping a Methodology for Long-Term (1680–2100) Historical-to-Future Landscape Modeling for the Conterminous United States

Land ◽

10.3390/land10050536 ◽

2021 ◽

Vol 10 (5) ◽

pp. 536

Author(s):

Jordan Dornbierer ◽

Steve Wika ◽

Charles Robison ◽

Gregory Rouze ◽

Terry Sohl

Keyword(s):

United States ◽

Land Cover ◽

River Basin ◽

Landscape Change ◽

Spatial Scales ◽

The United States ◽

Modeling Framework ◽

Delaware River ◽

Future Landscape

Land system change has been identified as one of four major Earth system processes where change has passed a destabilizing threshold. A historical record of landscape change is required to understand the impacts change has had on human and natural systems, while scenarios of future landscape change are required to facilitate planning and mitigation efforts. A methodology for modeling long-term historical and future landscape change was applied in the Delaware River Basin of the United States. A parcel-based modeling framework was used to reconstruct historical landscapes back to 1680, parameterized with a variety of spatial and nonspatial historical datasets. Similarly, scenarios of future landscape change were modeled for multiple scenarios out to 2100. Results demonstrate the ability to represent historical land cover proportions and general patterns at broad spatial scales and model multiple potential future landscape trajectories. The resulting land cover collection provides consistent data from 1680 through 2100, at a 30-m spatial resolution, 10-year intervals, and high thematic resolution. The data are consistent with the spatial and thematic characteristics of widely used national-scale land cover datasets, facilitating use within existing land management and research workflows. The methodology demonstrated in the Delaware River Basin is extensible and scalable, with potential applications at national scales for the United States.

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Continued emissions of carbon tetrachloride from the United States nearly two decades after its phaseout for dispersive uses

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1522284113 ◽

2016 ◽

Vol 113 (11) ◽

pp. 2880-2885 ◽

Cited By ~ 17

Author(s):

Lei Hu ◽

Stephen A. Montzka ◽

Ben R. Miller ◽

Arlyn E. Andrews ◽

John B. Miller ◽

...

Keyword(s):

United States ◽

Carbon Tetrachloride ◽

San Francisco ◽

Environmental Protection Agency ◽

Gulf Coast ◽

The United States ◽

Bay Area ◽

The Us ◽

Atmospheric Observations ◽

Surrounding Areas

National-scale emissions of carbon tetrachloride (CCl4) are derived based on inverse modeling of atmospheric observations at multiple sites across the United States from the National Oceanic and Atmospheric Administration’s flask air sampling network. We estimate an annual average US emission of 4.0 (2.0–6.5) Gg CCl4 y−1 during 2008–2012, which is almost two orders of magnitude larger than reported to the US Environmental Protection Agency (EPA) Toxics Release Inventory (TRI) (mean of 0.06 Gg y−1) but only 8% (3–22%) of global CCl4 emissions during these years. Emissive regions identified by the observations and consistently shown in all inversion results include the Gulf Coast states, the San Francisco Bay Area in California, and the Denver area in Colorado. Both the observation-derived emissions and the US EPA TRI identified Texas and Louisiana as the largest contributors, accounting for one- to two-thirds of the US national total CCl4 emission during 2008–2012. These results are qualitatively consistent with multiple aircraft and ship surveys conducted in earlier years, which suggested significant enhancements in atmospheric mole fractions measured near Houston and surrounding areas. Furthermore, the emission distribution derived for CCl4 throughout the United States is more consistent with the distribution of industrial activities included in the TRI than with the distribution of other potential CCl4 sources such as uncapped landfills or activities related to population density (e.g., use of chlorine-containing bleach).

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Validation of a Hydrodynamics and Sediment Transport Modeling Framework for the Evaluation of Offshore Wind Farms

Marine Technology Society Journal ◽

10.4031/mtsj.54.6.6 ◽

2020 ◽

Vol 54 (6) ◽

pp. 62-76

Author(s):

Craig Jones ◽

Grace Chang ◽

Jason Magalen ◽

Jesse Roberts

Keyword(s):

United States ◽

Alternative Energy ◽

Model Development ◽

Wind Farms ◽

Private Investment ◽

Ocean Waves ◽

The United States ◽

Offshore Wind ◽

Modeling Framework ◽

Offshore Wind Farms

AbstractDevelopment of alternative energy production in the United States continues at a rapid pace, with significant public and private investment in recent years. Offshore wind energy (herein referred to as offshore wind) has become a significant contributor to the global energy market, and the number of projects in the United States is rapidly increasing. As the technology continues to improve, the ability to deploy offshore wind turbines in deeper waters becomes increasingly feasible; however, differences in deployment environments bring unique challenges. To continue developing offshore wind as a viable renewable energy source, the United States must overcome three critical hurdles: (1) reduce the cost of generating offshore wind electricity, (2) accelerate the deployment and permitting process, and (3) integrate the new electrical source with the national grid. This work aims to help reduce time and costs associated with planning, development, and permitting by accurately predicting environmental responses to the presence of offshore wind arrays. We demonstrate that interactions between offshore wind infrastructure and the environment can be accurately assessed through a focused model development and validation process that considers the interrelationships between ocean waves, circulation, and seabed dynamics. Best practices are recommended to help guide future model development.

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Extracting Agronomic Information from SMOS Vegetation Optical Depth in the US Corn Belt Using a Nonlinear Hierarchical Model

Remote Sensing ◽

10.3390/rs12050827 ◽

2020 ◽

Vol 12 (5) ◽

pp. 827

Author(s):

Colin Lewis-Beck ◽

Victoria A. Walker ◽

Jarad Niemi ◽

Petruţa Caragea ◽

Brian K. Hornbuckle

Keyword(s):

United States ◽

Optical Depth ◽

The United States ◽

Development Stage ◽

District Level ◽

Corn Belt ◽

Model Parameters ◽

Growth Stages ◽

Modeling Framework ◽

Crop Development

Remote sensing observations that vary in response to plant growth and senescence can be used to monitor crop development within and across growing seasons. Identifying when crops reach specific growth stages can improve harvest yield prediction and quantify climate change. Using the Level 2 vegetation optical depth (VOD) product from the European Space Agency’s Soil Moisture and Ocean Salinity (SMOS) satellite, we retrospectively estimate the timing of a key crop development stage in the United States Corn Belt. We employ nonlinear curves nested within a hierarchical modeling framework to extract the timing of the third reproductive development stage of corn (R3) as well as other new agronomic signals from SMOS VOD. We compare our estimates of the timing of R3 to United States Department of Agriculture (USDA) survey data for the years 2011, 2012, and 2013. We find that 87%, 70%, and 37%, respectively, of our model estimates of R3 timing agree with USDA district-level observations. We postulate that since the satellite estimates can be directly linked to a physiological state (the maximum amount of plant water, or water contained within plant tissue per ground area) it is more accurate than the USDA data which is based upon visual observations from roadways. Consequently, SMOS VOD could be used to replace, at a finer resolution than the district-level USDA reports, the R3 data that has not been reported by the USDA since 2013. We hypothesize the other model parameters contain new information about soil and crop management and crop productivity that are not routinely collected by any federal or state agency in the Corn Belt.

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Linking agricultural crop management and air quality models for regional to national-scale nitrogen assessments

Biogeosciences ◽

10.5194/bg-9-4023-2012 ◽

2012 ◽

Vol 9 (10) ◽

pp. 4023-4035 ◽

Cited By ~ 56

Author(s):

E. J. Cooter ◽

J. O. Bash ◽

V. Benson ◽

L. Ran

Keyword(s):

United States ◽

Air Quality ◽

Ambient Air ◽

The United States ◽

Fertilizer Application ◽

Nitrogen Fertilizers ◽

System Level ◽

Agricultural Crop ◽

Modeling Framework ◽

Continental Scale

Abstract. While nitrogen (N) is an essential element for life, human population growth and demands for energy, transportation and food can lead to excess nitrogen in the environment. A modeling framework is described and implemented to promote a more integrated, process-based and system-level approach to the estimation of ammonia (NH3) emissions which result from the application of inorganic nitrogen fertilizers to agricultural soils in the United States. The United States Department of Agriculture (USDA) Environmental Policy Integrated Climate (EPIC) model is used to simulate plant demand-driven fertilizer applications to commercial cropland throughout the continental US. This information is coupled with a process-based air quality model to produce continental-scale NH3 emission estimates. Regional cropland NH3 emissions are driven by the timing and amount of inorganic NH3 fertilizer applied, soil processes, local meteorology, and ambient air concentrations. Initial fertilizer application often occurs when crops are planted. A state-level evaluation of EPIC-simulated, cumulative planted area compares well with similar USDA reported estimates. EPIC-annual, inorganic fertilizer application amounts also agree well with reported spatial patterns produced by others, but domain-wide the EPIC values are biased about 6% low. Preliminary application of the integrated fertilizer application and air quality modeling system produces a modified geospatial pattern of seasonal NH3 emissions that improves current simulations of observed atmospheric particle nitrate concentrations. This modeling framework provides a more dynamic, flexible, and spatially and temporally resolved estimate of NH3 emissions than previous factor-based NH3 inventories, and will facilitate evaluation of alternative nitrogen and air quality policy and adaptation strategies associated with future climate and land use changes.

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Precipitation and temperature drive continental-scale patterns in stream invertebrate production

Science Advances ◽

10.1126/sciadv.aav2348 ◽

2019 ◽

Vol 5 (4) ◽

pp. eaav2348 ◽

Cited By ~ 9

Author(s):

C. J. Patrick ◽

D. J. McGarvey ◽

J. H. Larson ◽

W. F. Cross ◽

D. C. Allen ◽

...

Keyword(s):

United States ◽

Secondary Production ◽

Structural Equation ◽

Terrestrial Ecosystems ◽

The United States ◽

Equation Modeling ◽

Modeling Framework ◽

Continental Scale ◽

Temperature And Precipitation ◽

Water Ecosystems

Secondary production, the growth of new heterotrophic biomass, is a key process in aquatic and terrestrial ecosystems that has been carefully measured in many flowing water ecosystems. We combine structural equation modeling with the first worldwide dataset on annual secondary production of stream invertebrate communities to reveal core pathways linking air temperature and precipitation to secondary production. In the United States, where the most extensive set of secondary production estimates and covariate data were available, we show that precipitation-mediated, low–stream flow events have a strong negative effect on secondary production. At larger scales (United States, Europe, Central America, and Pacific), we demonstrate the significance of a positive two-step pathway from air to water temperature to increasing secondary production. Our results provide insights into the potential effects of climate change on secondary production and demonstrate a modeling framework that can be applied across ecosystems.

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ESP v1.0: methodology for exploring emission impacts of future scenarios in the United States

Geoscientific Model Development ◽

10.5194/gmd-4-287-2011 ◽

2011 ◽

Vol 4 (2) ◽

pp. 287-297 ◽

Cited By ~ 38

Author(s):

D. H. Loughlin ◽

W. G. Benjey ◽

C. G. Nolte

Keyword(s):

United States ◽

Growth Factors ◽

Air Quality ◽

Air Pollutants ◽

Energy System ◽

The United States ◽

Air Pollutant ◽

Population Projections ◽

Pollutant Emissions ◽

Modeling Framework

Abstract. This article presents a methodology for creating anthropogenic emission inventories that can be used to simulate future regional air quality. The Emission Scenario Projection (ESP) methodology focuses on energy production and use, the principal sources of many air pollutants. Emission growth factors for energy system categories are calculated using the MARKAL energy system model. Growth factors for non-energy sectors are based on economic and population projections. These factors are used to grow a 2005 emissions inventory through 2050. The approach is demonstrated for two emission scenarios for the United States. Scenario 1 extends current air regulations through 2050, while Scenario 2 adds a hypothetical CO2 mitigation policy. Although both scenarios show significant reductions in air pollutant emissions through time, these reductions are more pronounced in Scenario 2, where the CO2 policy results in the adoption of technologies with lower emissions of both CO2 and traditional air pollutants. The methodology is expected to play an important role within an integrated modeling framework that supports the US EPA's investigations of linkages among emission drivers, climate and air quality.

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