Louisiana Universities Marine Consortium; R.E.Turner, Louisiana State University. Funded by: NOAA, Center for Sponsored
Coastal Ocean Research. http://www.gulfhypoxia.net/.
National Oceanographic and Atmospheric Administration
(NOAA), 1996. State of the Coastal Environment, Classified
Shellfish Growing Waters. Silver Spring: National Oceanic and
Atmospheric Association.
National Oceanographic and Atmospheric Administration
(NOAA), 2009. Ocean and coastal resource management. In
Depth: A Quick Look at the Sources and Impacts of Nonpoint
Source Pollution. http://coastalmanagement.noaa.gov/issues/
wq_quick.html,
http://www.seaweb.org/resources/briefings/
nonpointsource.php.
Nebel, B. J., and Wright, R. T., 1999. Environmental Science: The
Way the World Works, 7th ed. Prentice Hall.
Oberrecht, K., 2002. Nonpoint Source Pollution and Pacific Northwest Estuaries. South Slough National Estuarine Research
Reserve, Charleston. http://www.oregon.gov/dsl/ssnerr/docs/
emipubs/npspoll.pdf.
Schubel, J. R., 1977. Sediment and the quality of the estuarine
environment: some observations. In Suffet, I. H. (ed.), Fate
of Pollutants in the Air and Water Environments. Part 1.
Mechanism of Interaction between Environments and the
Mathematical Modeling and the Physical Fate of Pollutants.
New York: Wiley, pp. 399–424.
U.S. Census Bureau, 2011. Census 2010. http://factfinder2.census.
gov/faces/nav/jsf/pages/index.xhtml.
USEPA, 1993. Guidance specifying management measures for
sources of nonpoint source pollution in coastal waters. EPA
Report # EPA 840-B-92-002. http://water.epa.gov/polwaste/
nps/czara/index.cfm.
USEPA, 2013. Clean Water Act Section 319. http://water.epa.gov/
polwaste/nps/cwact.cfm.
Cross-references
Anoxia, Hypoxia, and Dead Zones
Clean Water Act
Coastal Erosion Control
Eutrophication
Nitrogen
Phosphorus
Water Quality
Watershed
NONSTATIONARY FORCING
Sebastián Solari
1
, Miguel A. Losada Rodríguez
2 and
Miguel Ortega-Sánchez
2
1
Instituto de Mecánica de los Fluidos e Ingeniería
Ambiental, Universidad de la República, Montevideo,
Uruguay
2
Andalusian Institute for Earth System Research,
University of Granada, Granada, Spain
Definition
The met-ocean forcing agents relevant for estuarine
dynamics (e.g., sea-level pressure, wind, rain, water
levels) have several temporal variability scales. The analysis of these variables is divided into short-term and
medium- and long-term analysis. Medium- and long-term
analysis is focused on understanding and modeling time
series of state variables (e.g., mean wind speed at 10 m
height, significant wave height) whose characteristic time
scale is O ($hours). On the other hand, short-term analysis
focuses on the instantaneous variables, whose characteristic time scale is O (minutes to seconds).
The processes whose characteristic time scale is
O (days to weeks), such as synoptic scale processes and
cycles of spring and neap tides, lead to autocorrelation in
the time series of the state variables, as well as to crosscorrelation among variables generated by the same physical process. An example is the passage of a storm: strong
winds are generated, leading to local waves and storm
surge. Then, the processes of time scale O (days to weeks)
are superimposed on the processes of scale O (months)
(e.g., seasons), which in turn are modulated by pluriannual
processes O (years) such as El Niño/Southern Oscillation,
as well as by long-term trends and processes of larger time
scales (e.g., climate change).
For analyzing and modeling, the met-ocean agents in
the medium- and long-term, multivariate nonstationary
stochastic models, with multiple scales of variation, are
required. Monbet et al. (2007) review several models
applied for modeling and simulation of wind and wave
time series. The methods are classified as parametric and
nonparametric. The Translated Gaussian Process (TGP)
method (Walton and Borgman, 1990) is the most widely
used nonparametric method, while the most frequently
used parametric methods are based on autoregressive
models (e.g., Guedes Soares et al., 1996; Stefanakos
et al., 2006). More recently, Solari and Losada (2011)
introduced a parametric methodology based on the use
of nonstationary mixture distribution functions and
copulas.
Bibliography
Guedes Soares, C., Ferreira, A. M., and Cunha, C., 1996.
Linear models of the time series of significant wave height on
the southwest coast of Portugal. Coastal Engineering, 29,
149–167.
Monbet, V., Ailliot, P., and Prevosto, M., 2007. Survey of stochastic
models for wind and sea state time series. Probabilistic Engineering Mechanics, 22, 113–126.
Solari, S., and Losada, M. A., 2011. Non-stationary wave height climate modeling and simulation. Journal of Geophysical
Research, 116(C9), 1–18.
Stefanakos, C., Athanassoulis, G., and Barstow, S. F., 2006. Time
series modeling of significant wave height in multiple scales,
combining various sources of data. Journal of Geophysical
Research, 111, C10001.
Walton, T. L., and Borgman, L. E., 1990. Simulation of
non-stationary, non-gaussian water levels in the Great Lakes.
Journal of Waterway Port Coastal Ocean Engineering, 116(6),
664–685.
Cross-references
Estuarine Circulation
Tidal and Nontidal Oscillations
Tidal Hydrodynamics
NONSTATIONARY FORCING
461
Coastal Ocean Research. http://www.gulfhypoxia.net/.
National Oceanographic and Atmospheric Administration
(NOAA), 1996. State of the Coastal Environment, Classified
Shellfish Growing Waters. Silver Spring: National Oceanic and
Atmospheric Association.
National Oceanographic and Atmospheric Administration
(NOAA), 2009. Ocean and coastal resource management. In
Depth: A Quick Look at the Sources and Impacts of Nonpoint
Source Pollution. http://coastalmanagement.noaa.gov/issues/
wq_quick.html,
http://www.seaweb.org/resources/briefings/
nonpointsource.php.
Nebel, B. J., and Wright, R. T., 1999. Environmental Science: The
Way the World Works, 7th ed. Prentice Hall.
Oberrecht, K., 2002. Nonpoint Source Pollution and Pacific Northwest Estuaries. South Slough National Estuarine Research
Reserve, Charleston. http://www.oregon.gov/dsl/ssnerr/docs/
emipubs/npspoll.pdf.
Schubel, J. R., 1977. Sediment and the quality of the estuarine
environment: some observations. In Suffet, I. H. (ed.), Fate
of Pollutants in the Air and Water Environments. Part 1.
Mechanism of Interaction between Environments and the
Mathematical Modeling and the Physical Fate of Pollutants.
New York: Wiley, pp. 399–424.
U.S. Census Bureau, 2011. Census 2010. http://factfinder2.census.
gov/faces/nav/jsf/pages/index.xhtml.
USEPA, 1993. Guidance specifying management measures for
sources of nonpoint source pollution in coastal waters. EPA
Report # EPA 840-B-92-002. http://water.epa.gov/polwaste/
nps/czara/index.cfm.
USEPA, 2013. Clean Water Act Section 319. http://water.epa.gov/
polwaste/nps/cwact.cfm.
Cross-references
Anoxia, Hypoxia, and Dead Zones
Clean Water Act
Coastal Erosion Control
Eutrophication
Nitrogen
Phosphorus
Water Quality
Watershed
NONSTATIONARY FORCING
Sebastián Solari
1
, Miguel A. Losada Rodríguez
2 and
Miguel Ortega-Sánchez
2
1
Instituto de Mecánica de los Fluidos e Ingeniería
Ambiental, Universidad de la República, Montevideo,
Uruguay
2
Andalusian Institute for Earth System Research,
University of Granada, Granada, Spain
Definition
The met-ocean forcing agents relevant for estuarine
dynamics (e.g., sea-level pressure, wind, rain, water
levels) have several temporal variability scales. The analysis of these variables is divided into short-term and
medium- and long-term analysis. Medium- and long-term
analysis is focused on understanding and modeling time
series of state variables (e.g., mean wind speed at 10 m
height, significant wave height) whose characteristic time
scale is O ($hours). On the other hand, short-term analysis
focuses on the instantaneous variables, whose characteristic time scale is O (minutes to seconds).
The processes whose characteristic time scale is
O (days to weeks), such as synoptic scale processes and
cycles of spring and neap tides, lead to autocorrelation in
the time series of the state variables, as well as to crosscorrelation among variables generated by the same physical process. An example is the passage of a storm: strong
winds are generated, leading to local waves and storm
surge. Then, the processes of time scale O (days to weeks)
are superimposed on the processes of scale O (months)
(e.g., seasons), which in turn are modulated by pluriannual
processes O (years) such as El Niño/Southern Oscillation,
as well as by long-term trends and processes of larger time
scales (e.g., climate change).
For analyzing and modeling, the met-ocean agents in
the medium- and long-term, multivariate nonstationary
stochastic models, with multiple scales of variation, are
required. Monbet et al. (2007) review several models
applied for modeling and simulation of wind and wave
time series. The methods are classified as parametric and
nonparametric. The Translated Gaussian Process (TGP)
method (Walton and Borgman, 1990) is the most widely
used nonparametric method, while the most frequently
used parametric methods are based on autoregressive
models (e.g., Guedes Soares et al., 1996; Stefanakos
et al., 2006). More recently, Solari and Losada (2011)
introduced a parametric methodology based on the use
of nonstationary mixture distribution functions and
copulas.
Bibliography
Guedes Soares, C., Ferreira, A. M., and Cunha, C., 1996.
Linear models of the time series of significant wave height on
the southwest coast of Portugal. Coastal Engineering, 29,
149–167.
Monbet, V., Ailliot, P., and Prevosto, M., 2007. Survey of stochastic
models for wind and sea state time series. Probabilistic Engineering Mechanics, 22, 113–126.
Solari, S., and Losada, M. A., 2011. Non-stationary wave height climate modeling and simulation. Journal of Geophysical
Research, 116(C9), 1–18.
Stefanakos, C., Athanassoulis, G., and Barstow, S. F., 2006. Time
series modeling of significant wave height in multiple scales,
combining various sources of data. Journal of Geophysical
Research, 111, C10001.
Walton, T. L., and Borgman, L. E., 1990. Simulation of
non-stationary, non-gaussian water levels in the Great Lakes.
Journal of Waterway Port Coastal Ocean Engineering, 116(6),
664–685.
Cross-references
Estuarine Circulation
Tidal and Nontidal Oscillations
Tidal Hydrodynamics
NONSTATIONARY FORCING
461