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References & Bibliography

This section contains the complete bibliography and references for the Canopy-App model, including the scientific basis for the parameterizations and algorithms used.

Core Model References

Wind and Turbulence

Massman, W. J., Forthofer, J. M., & Finney, M. A. (2017). An improved canopy wind model for predicting wind adjustment factors and wildland fire behavior. Canadian Journal of Forest Research, 47(5), 594-603. https://doi.org/10.1139/cjfr-2016-0354

Katul, G. G., Mahrt, L., Poggi, D., & Sanz, C. (2004). One- and two-equation models for canopy turbulence. Boundary-Layer Meteorology, 113, 81-109. https://doi.org/10.1023/B:BOUN.0000037333.48760.e5

Makar, P., Staebler, R., Akingunola, A., et al. (2017). The effects of forest canopy shading and turbulence on boundary layer ozone. Nature Communications, 8, 15243. https://doi.org/10.1038/ncomms15243

Roughness Sublayer and Unified Parameterizations

Bonan, G. B., Patton, E. G., Harman, I. N., Oleson, K. W., Finnigan, J. J., Lu, Y., & Burakowski, E. A. (2018). Modeling canopy-induced turbulence in the Earth system: A unified parameterization of turbulent exchange within plant canopies and the roughness sublayer (CLM-ml v0). Geoscientific Model Development, 11, 1467-1496. https://doi.org/10.5194/gmd-11-1467-2018

Abolafia-Rosenzweig, R., He, C., Burns, S. P., & Chen, F. (2021). Implementation and evaluation of a unified turbulence parameterization throughout the canopy and roughness sublayer in Noah-MP snow simulations. Journal of Advances in Modeling Earth Systems, 13, e2021MS002665. https://doi.org/10.1029/2021MS002665

Biogenic Emissions

MEGAN Model Development

Guenther, A. B., Jiang, X., Heald, C. L., Sakulyanontvittaya, T., Duhl, T., Emmons, L. K., & Wang, X. (2012). The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions. Geoscientific Model Development, 5, 1471-1492. https://doi.org/10.5194/gmd-5-1471-2012

Guenther, A., Hewitt, C. N., Erickson, D., Fall, R., Geron, C., Graedel, T., Harley, P., Klinger, L., Lerdau, M., McKay, W. A., Pierce, T., Scholes, B., Steinbrecher, R., Tallamraju, R., Taylor, J., & Zimmerman, P. (1995). A global model of natural volatile organic compound emissions. Journal of Geophysical Research, 100(D5), 8873-8892. https://doi.org/10.1029/94JD02950

Guenther, A., Baugh, B., Brasseur, G., Greenberg, J., Harley, P., Klinger, L., Serça, D., & Vierling, L. (1999). Isoprene emission estimates and uncertainties for the Central African EXPRESSO study domain. Journal of Geophysical Research, 104(D23), 30625-30639. https://doi.org/10.1029/1999JD900391

Advanced MEGAN Parameterizations

Silva, S. J., Heald, C. L., & Guenther, A. B. (2020). Development of a reduced-complexity plant canopy physics surrogate model for use in chemical transport models: a case study with GEOS-Chem v12.3.0. Geoscientific Model Development, 13, 2569-2585. https://doi.org/10.5194/gmd-13-2569-2020

Clifton, O. E., Patton, E. G., Wang, S., Barth, M., Orlando, J., & Schwantes, R. H. (2022). Large eddy simulation for investigating coupled forest canopy and turbulence influences on atmospheric chemistry. Journal of Advances in Modeling Earth Systems, 14, e2022MS003078. https://doi.org/10.1029/2022MS003078

CO₂ Inhibition Studies

Possell, M., & Hewitt, C. N. (2011). Isoprene emissions from plants are mediated by atmospheric CO₂ concentrations. Global Change Biology, 17, 1595-1610. https://doi.org/10.1111/j.1365-2486.2010.02306.x

Wilkinson, M. J., Monson, R. K., Trahan, N., Lee, S., Brown, E., Jackson, R. B., Polley, H. W., Fay, P. A., & Fall, R. (2009). Leaf isoprene emission rate as a function of atmospheric CO₂ concentration. Global Change Biology, 15, 1189-1200. https://doi.org/10.1111/j.1365-2486.2008.01803.x

Leaf Age and Environmental Stress

Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P. I., & Geron, C. (2006). Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature). Atmospheric Chemistry and Physics, 6, 3181-3210. https://doi.org/10.5194/acp-6-3181-2006

Fang, H., Wei, S., & Liang, S. (2019). Validation of MODIS and CYCLOPES LAI products using global field measurement data. Remote Sensing of Environment, 119, 43-54.

Zou, J., Rogers, W. E., & Siemann, E. (2009). Plasticity of Sapium sebiferum seedling growth to light and water resources: Inter- and intraspecific comparisons. Basic and Applied Ecology, 10, 79-88.

Dry Deposition

Gas Dry Deposition Parameterizations

Zhang, L., Brook, J. R., & Vet, R. (2003). A revised parameterization for gaseous dry deposition in air-quality models. Atmospheric Chemistry and Physics, 3, 2067-2082. https://doi.org/10.5194/acp-3-2067-2003

Saylor, R. D. (2013). The Atmospheric Chemistry and Canopy Exchange Simulation System (ACCESS): model description and application to a temperate deciduous forest canopy. Atmospheric Chemistry and Physics, 13, 693-715. https://doi.org/10.5194/acp-13-693-2013

Urban Dry Deposition

Gao, J., & Shen, H. (2018). A review of dry deposition of air pollutants on building surfaces and its influence on indoor air quality. Building and Environment, 132, 205-217. https://doi.org/10.1016/j.buildenv.2018.02.046

Chemical Mechanisms

Goliff, W. S., Stockwell, W. R., & Lawson, C. V. (2013). The regional atmospheric chemistry mechanism, version 2. Atmospheric Environment, 68, 174-185. https://doi.org/10.1016/j.atmosenv.2012.11.038

Soil and Root Distribution

Zeng, X. (2001). Global vegetation root distribution for land modeling. Journal of Hydrometeorology, 2, 525-530. https://doi.org/10.1175/1525-7541(2001)002<0525:GVRDFL>2.0.CO;2

Fire and Flame Height

Alexander, M. E., & Cruz, M. G. (2012). Interdependencies between flame length and fireline intensity in predicting crown fire initiation and crown scorch height. International Journal of Wildland Fire, 21, 95-113. https://doi.org/10.1071/WF11001

Data Sources and Satellite Products

Canopy Structure Data

Lang, N., Jetz, W., Schindler, K., & Wegner, J. D. (2023). A high-resolution canopy height model of the Earth. Nature Ecology & Evolution, 7, 1778-1789. https://doi.org/10.1038/s41559-023-02206-6

Wei, S., Fang, H., Schaaf, C. B., He, L., & Chen, J. M. (2019). Global 500 m clumping index product derived from MODIS BRDF data (2001-2017). Remote Sensing of Environment, 232, 111296. https://doi.org/10.1016/j.rse.2019.111296

VIIRS Products

Myneni, R. (2023). VIIRS/NPP Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V002. NASA EOSDIS Land Processes Distributed Active Archive Center. https://doi.org/10.5067/VIIRS/VNP15A2H.002

Myneni, R. (2023). VIIRS/NOAA20 Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V002. NASA EOSDIS Land Processes Distributed Active Archive Center. https://doi.org/10.5067/VIIRS/VJ115A2H.002

GEDI Products

GEDI Product from North Arizona University. https://goetzlab.rc.nau.edu/index.php/gedi/. Plant area volume density data (2019-2022) with 14 vertical layers from surface to 70m above ground level.

GriddingMachine

Wang, Y., Konings, A. G., Le Vine, N., Prigent, C., Piles, M., Fluet-Chouinard, E., Gascoin, S., Mialon, A., Wigneron, J. P., Fan, L., Kerr, Y. H., & Fernandez-Moran, R. (2022). A new global dataset of microwave vegetation optical depth. Scientific Data, 9, 342. https://doi.org/10.1038/s41597-022-01346-x

Model Coupling and Applications

Large Eddy Simulation Studies

Clifton, O. E., Patton, E. G., Wang, S., Barth, M., Orlando, J., & Schwantes, R. H. (2022). Large eddy simulation for investigating coupled forest canopy and turbulence influences on atmospheric chemistry. Journal of Advances in Modeling Earth Systems, 14, e2022MS003078. https://doi.org/10.1029/2022MS003078

Monin-Obukhov Length Calculations

Essa, K. S. M. (1999). Estimation of Monin-Obukhov length using Richardson number and surface layer wind speed. International Atomic Energy Agency Technical Document, IAEA-TECDOC-1106, 215-224.

Software and Tools

Python Solar Calculations

Pysolar Documentation. Solar position calculations used for cosine solar zenith angle. https://pysolar.readthedocs.io/en/latest/

Fortran Namelist Processing

f90nml Documentation. Python library for reading and writing Fortran namelists. https://f90nml.readthedocs.io/en/latest/

Standards and Conventions

NetCDF Climate and Forecast Conventions

CF Conventions. Climate and Forecast metadata conventions for NetCDF files. http://cfconventions.org/

EPA Air Quality Standards

U.S. Environmental Protection Agency. W126 ozone exposure index calculation methodology. https://www.epa.gov/sites/default/files/2015-09/documents/w126_steps_to_calculate_revised_feb19.pdf

Data Repositories and Archives

NOAA Data Sources

NOAA/NESDIS GBBEPx. Global Biomass Burning Emissions Product. https://www.ospo.noaa.gov/Products/land/gbbepx/

NOAA VIIRS Green Vegetation Fraction. https://www.star.nesdis.noaa.gov/jpss/gvf.php

NOAA CLASS Archive. Comprehensive Large Array-data Stewardship System. https://www.aev.class.noaa.gov/

AWS Open Data

NOAA OAR ARL AWS Archive. https://registry.opendata.aws/noaa-oar-arl-nacc-pds/ - Global GFS meteorological files: https://noaa-oar-arl-nacc-pds.s3.amazonaws.com/inputs/ - Global 13-km canopy files: https://noaa-oar-arl-nacc-pds.s3.amazonaws.com/inputs/geo-files/

NCEI Archive

NOAA National Centers for Environmental Information. Global 1-km canopy data representative of 2020. https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.nodc:0295750

How to Cite

Primary Citation

When using Canopy-App in research, please cite:

Campbell, P. C., Moon, Z., Hung, W.-T., Marvin, M., Rasool, Q., et al. (2024). Canopy-App: A repository for low-level, stand-alone/column canopy parameterizations for atmospheric composition and air quality models. Zenodo. https://doi.org/10.5281/zenodo.8403649

Component-Specific Citations

When using specific components, please also cite the relevant scientific papers listed above:

  • Wind calculations: Massman et al. (2017)
  • Biogenic emissions: Guenther et al. (2012), Silva et al. (2020)
  • Dry deposition: Zhang et al. (2003), Saylor (2013)
  • Turbulence: Makar et al. (2017), Katul et al. (2004)

Data Sources

Please also acknowledge the data sources used in your application: - GFS meteorological data: NOAA/NCEP - VIIRS products: NASA/NOAA - GEDI canopy height: NASA/North Arizona University - Satellite-derived parameters: As listed in the data sources section above

This bibliography is maintained as part of the Canopy-App documentation. For the most current references and additional citations, please refer to the source code comments and the main README.md file.