Articles | Volume 19, issue 15
https://doi.org/10.5194/bg-19-3575-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-19-3575-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Hydrodynamic and biochemical impacts on the development of hypoxia in the Louisiana–Texas shelf – Part 2: statistical modeling and hypoxia prediction
Yanda Ou
Department of Oceanography & Coastal Sciences, Louisiana State
University, Baton Rouge, LA 70803, USA
Bin Li
Department of Experimental Statistics, Louisiana State University,
Baton Rouge, LA 70803, USA
Department of Oceanography & Coastal Sciences, Louisiana State
University, Baton Rouge, LA 70803, USA
Center for Computation & Technology, Louisiana State University,
Baton Rouge, LA 70803, USA
Coastal Studies Institute, Louisiana State University, Baton Rouge,
LA 70803, USA
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Cited articles
Battaglin, W. A., Aulenbach, B. T., Vecchia, A., and Buxton, H. T.: Changes
in streamflow and the flux of nutrients in the Mississippi-Atchafalaya River
Basin, USA, 1980–2007, Scientific Investigations Report, Reston, VA,
U.S. Geological Survey,
https://doi.org/10.3133/sir20095164, 2010.
Bianchi, T. S., DiMarco, S. F., Cowan, J. H., Hetland, R. D., Chapman, P.,
Day, J. W., and Allison, M. A.: The science of hypoxia in the northern Gulf
of Mexico: A review, Sci. Total Environ., 408, 1471–1484,
https://doi.org/10.1016/j.scitotenv.2009.11.047, 2010.
Bleck, R.: An oceanic general circulation model framed in hybrid
isopycnic-Cartesian coordinates, Ocean Model., 4, 55–88,
https://doi.org/10.1016/S1463-5003(01)00012-9, 2002.
Bleck, R. and Boudra, D. B.: Initial testing of a numerical ocean
circulation model using a hybrid (quasi-isopycnic) vertical coordinate, J.
Phys. Oceanogr., 11, 755–770,
https://doi.org/10.1175/1520-0485(1981)011<0755:ITOANO>2.0.CO;2, 1981.
Chesney, E. J. and Baltz, D. M.: The effects of hypoxia on the northern Gulf
of Mexico Coastal Ecosystem: A fisheries perspective, in: Coastal Hypoxia:
Consequences for Living Resources and Ecosystems, Am. Geophys.
Union,
58, 321–354, https://doi.org/10.1029/CE058p0321, 2001.
Conley, D. J., Paerl, H. W., Howarth, R. W., Boesch, D. F., Seitzinger, S.
P., Havens, K. E., Lancelot, C., and Likens, G. E.: Controlling
Eutrophication: Nitrogen and Phosphorus, Science, 323, 1014–1015,
https://doi.org/10.1126/science.1167755, 2009.
Craig, J. K.: Aggregation on the edge: Effects of hypoxia avoidance on the
spatial distribution of brown shrimp and demersal fishes in the Northern
Gulf of Mexico, Mar. Ecol. Prog. Ser., 445, 75–95,
https://doi.org/10.3354/meps09437, 2012.
Craig, J. K. and Bosman, S. H.: Small Spatial Scale Variation in Fish
Assemblage Structure in the Vicinity of the Northwestern Gulf of Mexico
Hypoxic Zone, Estuar. Coast., 36, 268–285, https://doi.org/10.1007/s12237-012-9577-9, 2013.
Craig, J. K. and Crowder, L. B.: Hypoxia-induced habitat shifts and
energetic consequences in Atlantic croaker and brown shrimp on the Gulf of
Mexico shelf, Mar. Ecol. Prog. Ser., 294, 79–94,
https://doi.org/10.3354/meps294079, 2005.
Cummings, J. A.: Operational multivariate ocean data assimilation, Q. J. R.
Meteorol. Soc., 131, 3583–3604, https://doi.org/10.1256/qj.05.105, 2005.
Cummings, J. A. and Smedstad, O. M.: Variational Data Assimilation for the
Global Ocean, in: Data Assimilation for Atmospheric, Oceanic and Hydrologic
Applications, Vol. 2, edited by: Park, S. K. and Xu, L., Springer Berlin
Heidelberg, 303–343,
https://doi.org/10.1007/978-3-642-35088-7_13, 2013.
de Mutsert, K., Steenbeek, J., Lewis, K., Buszowski, J., Cowan, J. H., and
Christensen, V.: Exploring effects of hypoxia on fish and fisheries in the
northern Gulf of Mexico using a dynamic spatially explicit ecosystem model,
Ecol. Modell., 331, 142–150,
https://doi.org/10.1016/j.ecolmodel.2015.10.013, 2016.
Feng, Y., Fennel, K., Jackson, G. A., DiMarco, S. F., and Hetland, R. D.: A
model study of the response of hypoxia to upwelling-favorable wind on the
northern Gulf of Mexico shelf, J. Mar. Syst., 131, 63–73,
https://doi.org/10.1016/j.jmarsys.2013.11.009, 2014.
Fennel, K., Hetland, R., Feng, Y., and DiMarco, S.: A coupled physical-biological model of the Northern Gulf of Mexico shelf: model description, validation and analysis of phytoplankton variability, Biogeosciences, 8, 1881–1899, https://doi.org/10.5194/bg-8-1881-2011, 2011.
Fennel, K., Hu, J., Laurent, A., Marta-Almeida, M., and Hetland, R.:
Sensitivity of hypoxia predictions for the northern Gulf of Mexico to
sediment oxygen consumption and model nesting, J. Geophys. Res.-Ocean., 118,
990–1002, 2013.
Fennel, K., Laurent, A., Hetland, R., Justic, D., Ko, D. S., Lehrter, J.,
Murrell, M., Wang, L., Yu, L., and Zhang, W.: Effects of model physics on
hypoxia simulations for the northern Gulf of Mexico Mean for 2015: A model
intercomparison, J. Geophys. Res.-Ocean., 121, 5731–5750,
https://doi.org/10.1002/2015JC011516, 2016.
Forrest, D. R., Hetland, R. D., and DiMarco, S. F.: Multivariable
statistical regression models of the areal extent of hypoxia over the
Texas-Louisiana continental shelf, Environ. Res. Lett., 6, 045002,
https://doi.org/10.1088/1748-9326/6/4/045002, 2011.
Del Giudice, D., Matli, V. R. R., and Obenour, D. R.: Bayesian mechanistic
modeling characterizes Gulf of Mexico hypoxia: 1968–2016 and future
scenarios, Ecol. Appl., 30, 1–14, https://doi.org/10.1002/eap.2032, 2020.
Hazen, E. L., Craig, J. K., Good, C. P., and Crowder, L. B.: Vertical
distribution of fish biomass in hypoxic waters on the gulf of Mexico shelf,
Mar. Ecol. Prog. Ser., 375, 195–207, https://doi.org/10.3354/meps07791,
2009.
Hetland, R. D. and DiMarco, S. F.: How does the character of oxygen demand
control the structure of hypoxia on the Texas-Louisiana continental shelf?,
J. Mar. Syst., 70, 49–62, https://doi.org/10.1016/j.jmarsys.2007.03.002,
2008.
Jackman, S.: pscl: Classes and Methods for R Developed in the Political
Science Computational Laboratory, https://github.com/atahk/pscl/ (last access: 7 September 2021), 2020.
Justić, D. and Wang, L.: Assessing temporal and spatial variability of
hypoxia over the inner Louisiana-upper Texas shelf: Application of an
unstructured-grid three-dimensional coupled hydrodynamic-water quality
model, Cont. Shelf Res., 72, 163–179,
https://doi.org/10.1016/j.csr.2013.08.006, 2014.
Katin, A., Del Giudice, D., and Obenour, D. R.: Temporally resolved coastal
hypoxia forecasting and uncertainty assessment via Bayesian mechanistic
modeling, Hydrol. Earth Syst. Sci., 26, 1131–1143,
https://doi.org/10.5194/hess-26-1131-2022, 2022.
LaBone, E. D., Rose, K. A., Justic, D., Huang, H., and Wang, L.: Effects of spatial variability on the exposure of fish to hypoxia: a modeling analysis for the Gulf of Mexico, Biogeosciences, 18, 487–507, https://doi.org/10.5194/bg-18-487-2021, 2021.
Lambert, D.: Zero-inflated poisson regression, with an application to
defects in manufacturing, Technometrics, 34, 1–14, 1992.
Laurent, A. and Fennel, K.: Time-Evolving, Spatially Explicit Forecasts of
the Northern Gulf of Mexico Hypoxic Zone, Environ. Sci. Technol., 53,
14449–14458, https://doi.org/10.1021/acs.est.9b05790, 2019.
Laurent, A., Fennel, K., Ko, D. S., and Lehrter, J.: Climate change
projected to exacerbate impacts of coastal Eutrophication in the Northern
Gulf of Mexico, J. Geophys. Res.-Ocean., 123, 3408–3426,
https://doi.org/10.1002/2017JC013583, 2018.
Matli, V. R. R., Fang, S., Guinness, J., Rabalais, N. N., Craig, J. K., and
Obenour, D. R.: Space-Time Geostatistical Assessment of Hypoxia in the
Northern Gulf of Mexico, Environ. Sci. Technol., 52, 12484–12493,
https://doi.org/10.1021/acs.est.8b03474, 2018.
Mattern, J. P., Fennel, K., and Dowd, M.: Sensitivity and uncertainty
analysis of model hypoxia estimates for the Texas-Louisiana shelf, J.
Geophys. Res.-Ocean., 118, 1316–1332, https://doi.org/10.1002/jgrc.20130,
2013.
McCarthy, M. J., Carini, S. A., Liu, Z., Ostrom, N. E., and Gardner, W. S.:
Oxygen consumption in the water column and sediments of the northern Gulf of
Mexico hypoxic zone, Estuar. Coast. Shelf Sci., 123, 46–53,
https://doi.org/10.1016/j.ecss.2013.02.019, 2013.
Mississippi River/Gulf of Mexico Watershed Nutrient Task Force: Action Plan
for Reducing, Mitigating, and Controlling Hypoxia in the Northern Gulf of
Mexico, Washington, DC, US Environmental Protection Agency, https://www.epa.gov/sites/default/files/2015-03/documents/2001_04_04_msbasin_actionplan2001.pdf (last access: 29 July 2022), 2001.
Mississippi River/Gulf of Mexico Watershed Nutrient Task Force: Gulf Hypoxia
Action Plan 2008 for Reducing, Mitigating, and Controlling Hypoxia in the
Northern Gulf of Mexico and Improving Water Quality in the Mississippi River
Basin, Washington, DC, US Environmental Protection Agency, https://www.epa.gov/sites/default/files/2015-03/documents/2008_8_28_msbasin_ghap2008_update082608.pdf (last access: 29 July 2022), 2008.
Monteith, J. and Unsworth, M.: Principles of environmental physics: plants,
animals, and the atmosphere, 4th Edn., Academic Press,
https://doi.org/10.1016/C2010-0-66393-0, 2014.
Murray, F. W.: On the Computation of Saturation Vapor Pressure, J. Appl.
Meteorol. Climatol., 6, 203–204,
https://doi.org/10.1175/1520-0450(1967)006<0203:OTCOSV>2.0.CO;2, 1967.
Murrell, M. C. and Lehrter, J. C.: Sediment and Lower Water Column Oxygen
Consumption in the Seasonally Hypoxic Region of the Louisiana Continental
Shelf, Estuar. Coast., 34, 912–924, https://doi.org/10.1007/s12237-010-9351-9, 2011.
Obenour, D. R., Michalak, A. M., and Scavia, D.: Assessing biophysical
controls on Gulf of Mexico hypoxia through probabilistic modeling, Ecol.
Appl., 25, 492–505, https://doi.org/10.1890/13-2257.1, 2015.
Ou, Y.: Data for Hydrodynamic and biochemical impacts on the development of hypoxia in the Louisiana–Texas shelf – Part 2: statistical modeling and hypoxia prediction, LSU [data set], https://lsu.app.box.com/folder/168361434653?s=8qhpz2glpxlbsu9z9m6g4cudcegfseh4, last access: 26 July 2022.
Picard, A., Davis, R. S., Gläser, M., and Fujii, K.: Revised formula for
the density of moist air (CIPM-2007), Metrologia, 45, 149–155,
https://doi.org/10.1088/0026-1394/45/2/004, 2008.
Purcell, K. M., Craig, J. K., Nance, J. M., Smith, M. D., and Bennear, L.
S.: Fleet behavior is responsive to a large-scale environmental disturbance:
Hypoxia effects on the spatial dynamics of the northern Gulf of Mexico
shrimp fishery, PLoS One, 12, e0183032, https://doi.org/10.1371/journal.pone.0183032, 2017.
Rabalais, N. N. and Baustian, M. M.: Historical Shifts in Benthic Infaunal
Diversity in the Northern Gulf of Mexico since the Appearance of Seasonally
Severe Hypoxia, Diversity, 12, 49, https://doi.org/10.3390/d12020049, 2020.
Rabalais, N. N. and Turner, R. E.: Gulf of Mexico Hypoxia: Past, Present,
and Future, Limnol. Oceanogr. Bull., 28, 117–124,
https://doi.org/10.1002/lob.10351, 2019.
Rabalais, N. N., Turner, R. E., and Wiseman, W. J.: Gulf of Mexico hypoxia,
a.k.a. “The dead zone,” Annu. Rev. Ecol. Syst., 33, 235–263,
https://doi.org/10.1146/annurev.ecolsys.33.010802.150513, 2002.
Rabalais, N. N., Turner, R. E., Sen Gupta, B. K., Boesch, D. F., Chapman,
P., and Murrell, M. C.: Hypoxia in the northern Gulf of Mexico: Does the
science support the plan to reduce, mitigate, and control hypoxia?, Estuar. Coast., 30,
753–772, https://doi.org/10.1007/BF02841332, 2007a.
Rabalais, N. N., Turner, R. E., Gupta, B. K. S., Platon, E., and Parsons, M.
L.: Sediments tell the history of eutrophication and hypoxia in the northern
Gulf of Mexico, Ecol. Appl., 17, 129–143,
https://doi.org/10.1890/06-0644.1, 2007b.
Rabotyagov, S. S., Kling, C. L., Gassman, P. W., Rabalais, N. N., and
Turner, R. E.: The economics of dead zones: Causes, impacts, policy
challenges, and a model of the gulf of Mexico Hypoxic Zone, Rev. Environ.
Econ. Policy, 8, 58–79, https://doi.org/10.1093/reep/ret024, 2014.
Reyes, B. A., Pendergast, J. S., and Yamazaki, S.: Mammalian peripheral
circadian oscillators are temperature compensated, J. Biol. Rhythms, 23,
95–98, https://doi.org/10.1177/0748730407311855, 2008.
Saha, S., Moorthi, S., Pan, H.-L., Wu, X., Wang, J., Nadiga, S., Tripp, P., Kistler, R., Woollen, J., Behringer, D., Liu, H., Stokes, D., Grumbine, R., Gayno, G., Wang, J., Hou, Y.-T., Chuang, H. Y., Juang, H.-M. H., Sela, J., Iredell, M., Treadon, R., Kleist, D., Van Delst, P., Keyser, D., Derber, J., Ek, M., Meng, J., Wei, H., Yang, R., Lord, S., van den Dool, H., Kumar, A., Wang, W., Long, C., Chelliah, M., Xue, Y., Huang, B., Schemm, J.-K., Ebisuzaki, W., Lin, R., Xie, P., Chen, M., Zhou, S., Higgins, W., Zou, C.-Z., Liu, Q., Chen, Y., Han, Y., Cucurull, L., Reynolds, R. W., Rutledge, G., and Goldberg, M.: NCEP Climate Forecast System Reanalysis (CFSR) 6-hourly Products, January 1979 to December 2010, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/D69K487J, 2010.
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Hou, Y.-T., Chuang, H., Iredell, M., Ek, M., Meng, J., Yang, R., Mendez, M. P., van den Dool, H., Zhang, Q., Wang, W., Chen, M., and Becker, E.: NCEP Climate Forecast System Version 2 (CFSv2) 6-hourly Products, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/D61C1TXF, 2011.
Scavia, D., Evans, M. A., and Obenour, D. R.: A scenario and forecast model
for gulf of mexico hypoxic area and volume, Environ. Sci. Technol., 47,
10423–10428, https://doi.org/10.1021/es4025035, 2013.
Scavia, D., Bertani, I., Obenour, D. R., Turner, R. E., Forrest, D. R., and
Katin, A.: Ensemble modeling informs hypoxia management in the northern Gulf
of Mexico, P. Natl. Acad. Sci. USA, 114, 8823–8828,
https://doi.org/10.1073/pnas.1705293114, 2017.
Siegel, A. F. and Wagner, M. R.: Chapter 12 – Multiple Regression:
Predicting One Variable From Several Others, in: Practical Business
Statistics, edited by: Siegel, A. F. and Wagner, M. R., Academic Press, London, 371–431,
https://doi.org/10.1016/B978-0-12-820025-4.00012-9, 2022.
Simpson, J. H.: The shelf-sea fronts: implications of their existence and
behaviour, Philos. T. R. Soc. Lond. Ser. A, 302,
531–546, https://doi.org/10.1098/rsta.1981.0181, 1981.
Simpson, J. H. and Bowers, D.: Models of stratification and frontal movement
in shelf seas, Deep-Sea Res. Pt. A, 28, 727–738,
https://doi.org/10.1016/0198-0149(81)90132-1, 1981.
Simpson, J. H. and Hunter, J. R.: Fronts in the Irish Sea, Nature, 250,
404–406, https://doi.org/10.1038/250404a0, 1974.
Simpson, J. H., Allen, C. M., and Morris, N. C. G.: Fronts on the
Continental Shelf, J. Geophys. Res., 83, 4607–4614,
https://doi.org/10.1029/JC083iC09p04607, 1978.
Smith, M. D., Asche, F., Bennear, L. S., and Oglend, A.: Spatial-dynamics of
hypoxia and fisheries: The case of Gulf of Mexico brown shrimp, Mar. Resour.
Econ., 29, 111–131, https://doi.org/10.1086/676826, 2014.
Turner, R. E., Rabalais, N. N., and Justic, D.: Predicting summer hypoxia in
the northern Gulf of Mexico: Riverine N, P, and Si loading, Mar. Pollut.
Bull., 52, 139–148, https://doi.org/10.1016/j.marpolbul.2005.08.012, 2006.
Turner, R. E., Rabalais, N. N., and Justic, D.: Gulf of Mexico hypoxia:
Alternate states and a legacy, Environ. Sci. Technol., 42, 2323–2327,
https://doi.org/10.1021/es071617k, 2008.
Turner, R. E., Rabalais, N. N., and Justić, D.: Predicting summer
hypoxia in the northern Gulf of Mexico: Redux, Mar. Pollut. Bull., 64,
319–324, https://doi.org/10.1016/j.marpolbul.2011.11.008, 2012.
van 't Hoff, J. H. and Lehfeldt, R. A.: Lectures in theoretical and physical
chemistry: Part I: Chemical dynamics, London: Edward Arnold, London, Edward Arnold, London, OCLC number 220605730, 1899.
Venables, W. N. and Ripley, B. D.: Modern Applied Statistics with S,
Fourth, Springer, New York, https://doi.org/10.1007/978-0-387-21706-2,
2002.
Wang, L. and Justić, D.: A modeling study of the physical processes
affecting the development of seasonal hypoxia over the inner Louisiana-Texas
shelf: Circulation and stratification, Cont. Shelf Res., 29, 1464–1476,
https://doi.org/10.1016/j.csr.2009.03.014, 2009.
Warner, J. C., Armstrong, B., He, R., and Zambon, J. B.: Development of a
Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) Modeling System,
Ocean Model., 35, 230–244, https://doi.org/10.1016/j.ocemod.2010.07.010,
2010.
Wood, S. N.: Thin plate regression splines, J. R. Stat. Soc. Ser. B, 65, 95–114, https://doi.org/10.1111/1467-9868.00374, 2003.
Wood, S. N.: Fast stable restricted maximum likelihood and marginal
likelihood estimation of semiparametric generalized linear models, J. R.
Stat. Soc. Ser. B, 73, 3–36,
https://doi.org/10.1111/j.1467-9868.2010.00749.x, 2011.
Yu, L., Fennel, K., and Laurent, A.: A modeling study of physical controls
on hypoxia generation in the northern Gulf of Mexico, J. Geophys. Res.-Ocean., 120, 5019–5039, https://doi.org/10.1002/2014JC010634, 2015.
Zambresky, L.: A verification study of the global WAM model, December 1987–November 1988, ECMWF, Shinfield Park, Reading, https://www.ecmwf.int/node/13201 (last access: 29 July 2022), 1989.
Zeileis, A., Kleiber, C., and Jackman, S.: Regression Models for Count Data
in R, J. Stat. Softw., 27, 1–25, https://doi.org/10.18637/jss.v027.i08, 2008.
Short summary
Over the past decades, the Louisiana–Texas shelf has been suffering recurring hypoxia (dissolved oxygen < 2 mg L−1). We developed a novel prediction model using state-of-the-art statistical techniques based on physical and biogeochemical data provided by a numerical model. The model can capture both the magnitude and onset of the annual hypoxia events. This study also demonstrates that it is possible to use a global model forecast to predict regional ocean water quality.
Over the past decades, the Louisiana–Texas shelf has been suffering recurring hypoxia (dissolved...
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