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![Changing levels of dissolved oxygen concentrations were measured at a U.S. Environmental Protection Agency station near Kent Point in the upper Chesapeake Bay. Red dots represent 34 observations made during 2004-2005. Gray curves represent the predictions of several individual models. The dark blue curve represents the model mean, and the turquoise curves give the 95% confidence interval. The model mean does better in matching the observations than any individual model. Source: Irby et al. [2016], CC BY 3.0 (https://creativecommons.org/licenses/by/3.0/legalcode)](profile/C-Nichols-2/publication/318986947/figure/fig1/AS:614096625991702@1523423453827/Changing-levels-of-dissolved-oxygen-concentrations-were-measured-at-a-US-Environmental.png)
Changing levels of dissolved oxygen concentrations were measured at a U.S. Environmental Protection Agency station near Kent Point in the upper Chesapeake Bay. Red dots represent 34 observations made during 2004-2005. Gray curves represent the predictions of several individual models. The dark blue curve represents the model mean, and the turquoise curves give the 95% confidence interval. The model mean does better in matching the observations than any individual model. Source: Irby et al. [2016], CC BY 3.0 (https://creativecommons.org/licenses/by/3.0/legalcode)
Source publication
An ocean modeling program is improving our ability to predict circulation along the U.S. West Coast, dead zones and other coastal ecosystem responses, and storm surges in island environments.
Context in source publication
Context 1
... project team compared the regulatory model used by Chesapeake Bay program managers with research models currently being used by the scientific community and found that the regulatory model performed as well as many of the scientific models. This result gives program managers and academic scientists more confidence in the regulatory model. The team also found that an ensemble mean of multiple models is better at predicting hypoxia than any individual model (Figure 1), illustrating the potential value of multimodel ensembles for decision-making concerning the Chesapeake ...
Citations
... Intercomparison is also well established for regional models, for example within the Integrated Ocean Observing System's Coastal Ocean Modeling Testbed (IOOS COMT) 178 . As part of the COMT, different physical models for simulation and prediction of coastal hypoxia in the Mississippi River outflow region were compared using the same simple oxygen model 179 . ...
Ocean biogeochemical models describe the ocean’s circulation, physical properties, biogeochemical properties and their transformations using coupled differential equations. Numerically approximating these equations enables simulation of the dynamic evolution of the ocean state in realistic global or regional spatial domains, across time spans from years to centuries. This Primer explains the process of model construction and the main characteristics, advantages and drawbacks of different model types, from the simplest nutrient–phytoplankton–zooplankton–detritus model to the complex biogeochemical models used in Earth system modelling and climate prediction. Commonly used metrics for model-data comparison are described, alongside a discussion of how models can be informed by observations via parameter optimization or state estimation, the two main methods of data assimilation. Examples illustrate how these models are used for various practical applications, ranging from carbon accounting, ocean acidification, ocean deoxygenation and fisheries to observing system design. Access points are provided, enabling readers to engage in biogeochemical modelling through practical code examples and a comprehensive list of publicly available models and observational data sets. Recommendations are given for best practices in model archiving. Lastly, current limitations and anticipated future developments and challenges of the models are discussed. Ocean biogeochemical models use coupled differential equations to describe the transformation of the ocean’s circulation, physical and biogeochemical properties under different conditions. This Primer introduces the process of model construction and explains the characteristics of various model types, from simple to complex, alongside their advantages and disadvantages.
... Users need to sift through data from local, national, and globally available datasets that can help address environmental issues, ranging from recurrent flooding to sealevel rise. Local university researchers are already applying new technologies such as unmanned vehicles to fill data gaps that may mask important processes, providing algorithms as evidenced by the COMT, and are defining levels of uncertainty in the data that are available for analysis (Luettich et al. 2017). Private sector companies are also applying big data for targeted solutions and predictive power such as apps that provide weather data to commercial and recreational fishermen. ...
Urban infrastructures are mostly interdependent in various ways. A variety of qualitative explanations is presented in the literature to analyze and address resiliency and vulnerability. Unfortunately, most of the explanations do not provide an objective resilience index computation. This chapter attempts to develop resilience indices and computational methods for urban infrastructures in order to lower disasters risk subjected to urban infrastructures.
... The most recent COMT projects began in 2013 and included participants from academia, the private sector, and government agencies (see Table 3). Phase 2 COMT projects further advanced the operational use of models for the prediction of extreme events and chronic conditions [39]. Of particular importance was modeling of low or depleted oxygen (hypoxia) and the storm surge Redrawn based on [34] with permission from Journal of Geophysical Research, 2020. ...
... The most recent COMT projects began in 2013 and included participants from academia, the private sector, and government agencies (see Table 3). Phase 2 COMT projects further advanced the operational use of models for the prediction of extreme events and chronic conditions [39]. Of particular importance was modeling of low or depleted oxygen (hypoxia) and the storm surge inundation of coastal areas adjacent to steep sloped bathymetry. ...
... The model viewer provides interactive access to archived COMT model data utilizing Sci-WMS, a COMT-supported, Python-based web mapping service for visualizing geospatial data. An animated example of storm wave visualization for Puerto Rico during Hurricane Georges in 1998 is offered in [39] and can be accessed at https://eos.org/science-updates/a-test-bed-for-coastal-and-ocean-modeling. While individual researchers have their own visualization and analysis tools for use with their particular numerical models, the model viewer facilitates collaboration by allowing simultaneous visualization of results from different models through inter-model comparisons. ...
Beginning in 2003, the Southeastern Universities Research Association (SURA) enabled an open-access network of distributed sensors and linked computer models through the SURA Coastal Ocean Observing and Predicting (SCOOP) program. The goal was to support collaborations among universities, government, and industry to advance integrated observation and modeling systems. SCOOP improved the path to operational real-time data-guided predictions and forecasts of coastal ocean processes. This was critical to the maritime infrastructure of the U.S. and to the well-being of coastal communities. SCOOP integrated and expanded observations from the Gulf of Mexico, the South Atlantic Bight, the Middle Atlantic Bight, and the Chesapeake Bay. From these successes, a Coastal and Ocean Modeling Testbed (COMT) evolved with National Oceanic and Atmospheric Administration (NOAA) funding via the Integrated Ocean Observing System (IOOS) to facilitate the transition of key models from research to operations. Since 2010, COMT has been a conduit between the research community and the federal government for sharing and improving models and software tools. SCOOP and COMT have been based on strong partnerships among universities and U.S. agencies that have missions in ocean and coastal environmental prediction. During SURA's COMT project, which ended September 2018, significant progress was made in evaluating the performance of models that are progressively becoming operational. COMT successes are ongoing.
... Testing model reliability involves comparing "hindcasted" model output for prior recorded events with data from ocean observing systems such as the Gulf of Mexico Coastal Ocean Observing System. In the initiative we propose here, we do not intend to develop new models or to engage in rigorous model inter-comparisons as was done in SURA's earlier model testbed programs 7,26 . Rather, we propose to assist local and regional officials and planners in selecting the most appropriate existing models for their specific needs and local conditions and providing the academic expertise required to run those models for anticipated future scenarios. ...
... Abandonment of the system, however, would negatively impact other agencies such as the NOAA U.S. IOOS Program, U.S. Coast Guard (USCG), and the National Park Service (NPS) as well as organizations such as MARACOOS and the CBF that use CBIBS directly. NOAA funded research programs, such as the Coastal and Ocean Modeling Testbed for example, have also relied on CBIBS data (in this case, to assess an estuarine hypoxia model)(Luettich et al., 2017). ...
... Abandonment of the system, however, would negatively impact other agencies such as the NOAA U.S. IOOS Program, U.S. Coast Guard (USCG), and the National Park Service (NPS) as well as organizations such as MARACOOS and the CBF that use CBIBS directly. NOAA funded research programs, such as the Coastal and Ocean Modeling Testbed for example, have also relied on CBIBS data (in this case, to assess an estuarine hypoxia model)(Luettich et al., 2017). ...
The National Oceanic and Atmospheric Administration (NOAA) collects ecosystem data to support coastal resource conservation and management activities by studying stressors that impact estuaries such as the Chesapeake Bay, which is the largest in the United States. This paper seeks to help NOAA justify its existence and its budget by utilizing Monte Carlo simulation as a financial modeling tool, with such simulations providing insights on how to allocate identified resources. The results of the study offer an innovative method for helping government managers decide how much money to spend, what to spend it on, and how to acquire resources for the Chesapeake Bay Interpretive Buoy System. Moreover, this paper also demonstrates how an experiential project in graduate business education can be used to support sustainability efforts by addressing community-focused issues while improving student connection between theory and application at the same time.
... Recently, the COMT program has been expanded to consider deep ocean islands and examine NOAA's existing operational models used to predict hazardous surge and wave conditions that occur there (Luettich et al., 2017;van der Westhuysen et al., 2015). Currently, there is a research and knowledge gap with respect to storm surge response for these islands. ...
As part of a U.S. Integrated Ocean Observing System (IOOS) funded Coastal and Ocean Modeling Testbed (COMT), hindcasts of waves and storm surge for 2017 Hurricanes Irma and Maria are examined and compared to wave and water level gauge data in the vicinity of Puerto Rico and the U.S. Virgin Islands. The region is characterized by adjacent deep ocean water, narrow shelves, and coral reef systems providing coastal protection. The storm physics are analyzed using an unstructured grid third‐generation wave circulation coupled modeling system (ADCIRC+SWAN) with respect to tides, winds, atmospheric pressure, waves, and wave radiation stress‐induced setup. The water level response is generally dominated by the pressure deficit of the hurricanes. Wind‐driven surge is important over the shallow shelf to the east of Puerto Rico and wave‐induced setup becomes significant at locations in close proximity to the coastline. Contrary to conditions along the Gulf of Mexico shelf, geostrophically induced setup is negligible. Characteristics from a range of meteorological forcing models are assessed, and the associated errors in the hydrodynamic response are quantified. A data‐assimilated tropical planetary boundary model leads to the smallest atmospheric pressure, water level and wave property errors across both storms. Through comparisons between ADCIRC+SWAN and SLOSH‐FW (a structured grid first‐generation wave circulation coupled model), it is shown that the response to atmospheric forcing is similar; however, nearshore wave setup is smaller in SLOSH‐FW due to its coarser resolution here. Further, in addition to erroneous wind‐driven surge through depth limiting over the open ocean, numerical oscillations in the water level time series develop in SLOSH‐FW likely due to its small domain size.
... In the light of these recent multibillion dollar damaging hurricane events, combined with the threat of future sea level rise, it is timely and relevant to thoroughly examine the state-of-the-art in storm tide modeling for PRVI and similar deep ocean islands as part of the U.S. IOOS funded Coastal and Ocean Modeling Testbed (Luettich et al., 2017). In particular, this study conducts a detailed analysis of a high-resolution depth-integrated coastal ocean modeling system in terms of its ability to capture the entire frequency spectrum of 6-min coastal sea level variations around PRVI compared to tide gauges observations during 2017. ...
This study applies a baroclinic‐coupled depth‐integrated modeling system to the North Atlantic Ocean, where an unstructured mesh is used to focus resolution down to ∼30 m along the coasts of Puerto Rico and the U.S. Virgin Islands. Ocean baroclinicity is incorporated through one‐way coupling from operational data‐assimilated Global Ocean Forecasting System 3.1 temperature and salinity fields at just 12% additional computational time. The main objectives are to provide a comprehensive analysis of observed and modeled coastal sea levels (spanning from seasonal to supertidal variations) in Puerto Rico and the U.S. Virgin Islands during 2017 and to evaluate the associated model performance with and without baroclinic coupling at 14 National Oceanic and Atmospheric Administration/National Ocean Service tide gauges deployed in the region. It is found that baroclinic coupling increases modeled energy across the entire frequency spectrum, which is more commensurate with observations. In particular, density‐driven effects such as the seasonal cycle and sea level setdown due to trailing cold wakes from passing hurricanes are largely reproduced. Supertidal shelf‐resonant seiching at one to two cycles per hour is observed and modeled at a number of locations, where excitation of these modes is often promoted by the baroclinic coupling. Baroclinicity improves the yearlong model skill at every tide gauge, where the mean total skill is increased from 87% to 93% accuracy (54% to 85% for the nontidal residual). In September 2017 during Hurricanes Irma and Maria, baroclinicity increases model skill at 10 out of 14 tide gauges even when the barotropic mode is adjusted to have no mean offset from the observations.
... DO was also not allowed to go negative, effectively setting a zero respiration rate in locations with anoxic conditions. As part of a multiple model comparison supported by the NOAA-IOOS Coastal and Ocean Modeling Testbed (Luettich et al., 2013(Luettich et al., , 2017, SRM has been shown to generate oxygen concentrations with similar accuracy to those generated from mechanistic coupled hydrodynamic-biogeochemical models (Irby et al., 2016). The SRM model (Bever et al., 2013) also accurately captures the timing of onset and breakup of hypoxia within the temporal resolution of the current bimonthly regional monitoring. ...
Low levels of dissolved oxygen (DO) occur in many embayments throughout the world and have numerous detrimental effects on biota. Although measurement of in situ DO is straightforward with modern instrumentation, quantifying the volume of water in a given embayment that is hypoxic (hypoxic volume (HV)) is a more difficult task; however, this information is critical for determining whether management efforts to increase DO are having an overall impact. This paper uses output from a three-dimensional numerical model to demonstrate that HV in Chesapeake Bay can be estimated well with as few as two vertical profiles. In addition, the cumulative hypoxic volume (HVC; the total amount of hypoxia in a given year) can be calculated with relatively low uncertainty (<10%) if continuous DO data are available from two strategically positioned vertical profiles. This is because HV in the Chesapeake Bay is strongly constrained by the geometry of the embayment. A simple Geometric HV calculation method is presented and numerical model results are used to illustrate that for calculating HVC, the results using two daily-averaged profiles are typically more accurate than those of the standard method that interpolates bimonthly cruise data. Bimonthly data produce less accurate estimates of HVC because high-frequency changes in oxygen concentration, for example, due to regional-weather- or storm-induced changes in wind direction and magnitude, are not resolved. The advantages of supplementing cruise-based sampling with continuous vertical profiles to estimate HVC should be applicable to other systems where hypoxic water is constrained to a specific area by bathymetry.
The shape of the coast and the processes that mold it change together as a complex system. There is constant feedback among the multiple components of the system, and when climate changes, all facets of the system change. Abrupt shifts to different states can also take place when certain tipping points are crossed. The coupling of rapid warming in the Arctic with melting sea ice is one example of positive feedback. Climate changes, particularly rising sea temperatures, are causing an increasing frequency of tropical storms and “compound events” such as storm surges combined with torrential rains. These events are superimposed on progressive rises in relative sea level and are anticipated to push many coastal morphodynamic systems to tipping points beyond which return to preexisting conditions is unlikely. Complex systems modeling results and long-term sets of observations from diverse cases help to anticipate future coastal threats. Innovative engineering solutions are needed to adapt to changes in coastal landscapes and environmental risks. New understandings of cascading climate-change-related physical, ecological, socioeconomic effects, and multi-faceted morphodynamic systems are continually contributing to the imperative search for resilience. Recent contributions, summarized here, are based on theory, observations, numerically modeled results, regional case studies, and global projections.
