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Federal Agencies Predict and Reduce Chesapeake Bay Dead Zones. Evaluating Models for Chesapeake Bay Dissolved Oxygen: Helping. Carl Friedrichs Virginia Institute of Marine Science Gloucester Point, Virginia, USA Presented to DPB Visitors, 12 July 2011 .

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Evaluating models for chesapeake bay dissolved oxygen helping

Federal Agencies Predict and Reduce Chesapeake Bay Dead Zones

Evaluating Models for Chesapeake Bay Dissolved Oxygen: Helping

Carl Friedrichs

Virginia Institute of Marine Science

Gloucester Point, Virginia, USA

Presented to DPB Visitors, 12 July 2011


Evaluating models for chesapeake bay dissolved oxygen helping1

Federal Agencies Predict and Reduce Chesapeake Bay Dead Zones

Evaluating Models for Chesapeake Bay Dissolved Oxygen: Helping

Outline

1) Introduction: Chesapeake Bay Dead Zone Effects and Causes

2) SURA Estuarine Model Testbed: Funding, Participants, Methods

3) Results of Oxygen Dead Zone Model Comparisons


Evaluating models for chesapeake bay dissolved oxygen helping

(UMCES, Coastal Trends)


Evaluating models for chesapeake bay dissolved oxygen helping

“HYPOXIA”

Oxygen ≤ ~ 2 mg/L

(UMCES, Coastal Trends)


Evaluating models for chesapeake bay dissolved oxygen helping

Goal 2: to enable long-term (≥ years) dead zone forecasts to aid in restoration (via EPA-CBP)

Goal 1: to enable short-term (≤ weeks) dead zone forecasts for hazard mitigation (via NOAA-NCEP)

Dead zone volume (km3)

(VIMS, ScienceDaily)

(UMCES, Coastal Trends)


Classically two primary factors nutrient input and stratification

Classic Factors Thought to Affect Dead Zones in Chesapeake Bay

Classically two primary factors: nutrient input and stratification

(www.vims.edu)


Evaluating models for chesapeake bay dissolved oxygen helping2

Federal Agencies Predict and Reduce Chesapeake Bay Dead Zones

Evaluating Models for Chesapeake Bay Dissolved Oxygen: Helping

Outline

1) Introduction: Chesapeake Bay Dead Zone Effects and Causes

2) SURA Estuarine Model Testbed: Funding, Participants, Methods

3) Results of Oxygen Dead Zone Model Comparisons


Evaluating models for chesapeake bay dissolved oxygen helping

NOAA/SURA Estuarine Hypoxia Dead Zone Modeling Testbed

  • Funded by NOAA through SURA (Southeastern Universities Research Association). Initially two years of funding to VIMS (~$1M) which started June 2010.

  • Part of a larger NOAA/SURA larger (~$5M) “Super-Regional Testbed to Improve Models of Environmental Processes on the U.S. Atlantic and Gulf of Mexico Coasts”.

  • Pilot projects in the larger “Super-Regional Testbed” are addressing three chronic issues of high relevance within the U.S. Gulf of Mexico-U.S. Atlantic Coast region:

  • Coastal Storm Surge Flooding

  • Estuarine Hypoxia Dead Zones

  • Shelf Hypoxia Dead Zones


Evaluating models for chesapeake bay dissolved oxygen helping

NOAA/SURA Estuarine Hypoxia Dead Zone Modeling Testbed

  • Carl Friedrichs (VIMS) – Team Leader

  • Federal partners

  • David Green (NOAA-NWS) – Transition to operations at NWS

  • Lyon Lanerole (NOAA-CSDL) – Transition to operations at CSDL; CBOFS2

  • Lewis Linker (EPA), Carl Cerco (USACE) – Transition to operations at EPA; CH3D, CE-ICM

  • Doug Wilson (NOAA-NCBO) – Integration w/observing systems at NCBO/IOOS

  • Non-federal partners

  • Marjorie Friedrichs, Aaron Bever (VIMS) – Metric development and model skill assessment

  • Yun Li, Ming Li (UMCES) – ROMS hydrodynamics in CB

  • Wen Long, Raleigh Hood (UMCES) – ChesROMS with NPZD water quality model

  • Scott Peckham, JisammaKallumadikal (CSDMS) – Multiple ROMS grids, forcings, O2 codes

  • Malcolm Scully (ODU) – ChesROMS with 1 term oxygen respiration model

  • Kevin Sellner (CRC) – Academic-agency liason; facilitator for model comparison

  • JianShen, Bo Hong (VIMS) – SELFE, FVCOM, EFDC models in CB

  • John Wilkin, Julia Levin (Rutgers) – ROMS-Espresso + 7 other MAB hydrodynamic models


Evaluating models for chesapeake bay dissolved oxygen helping

Methods --5 Hydrodynamic Models (so far)


Evaluating models for chesapeake bay dissolved oxygen helping

  • Methods --5 Dissolved Oxygen Models (so far)

  • ICM: CBP model; complex biology (dozens of equations)

  • bgc: NPZD-type biogeochemical model (4 main equations)

  • 1eqn: Simple one equation respiration (1 equation)

  • 1term-DD: depth-dependent net respiration (1 parameter)

    • (not a function of x, y, temperature, nutrients…)

  • 1term: Constant net respiration (1 constant parameter)

    • CH3D + ICM

    • EFDC + 1eqn, 1term

    • CBOFS2 + 1term, 1term+DD

    • ChesROMS + 1term, 1term+DD, bgc

    Methods -- 8 Multiple combinations (so far)


    Methods dissolved oxygen from 50 cbp epa monitoring station locations

    Methods: Dissolved Oxygen from ~50 CBP/EPA Monitoring Station Locations

    http://www.eco-check.org/


    Evaluating models for chesapeake bay dissolved oxygen helping3

    Federal Agencies Predict and Reduce Chesapeake Bay Dead Zones

    Evaluating Models for Chesapeake Bay Dissolved Oxygen: Helping

    Outline

    1) Introduction: Chesapeake Bay Dead Zone Effects and Causes

    2) SURA Estuarine Model Testbed: Funding, Participants, Methods

    3) Results of Oxygen Dead Zone Model Comparisons


    Evaluating models for chesapeake bay dissolved oxygen helping

    Salinity Stratification and Bottom Oxygen in Central Chesapeake Bay

    ChesROMS model

    Salinity stratification

    plus 1-term DO model

    Dissolved oxygen

    Variability in dissolved oxygen in the central Bay is easier to model than and unrelated to salinity stratification. This is true for all of the models tested.

    (by M. Scully)


    Results dead zone volume model comparison

    Results: Dead Zone Volume Model Comparison

    Level of model uncertainty

    Volume of low oxygen water (km3)

    Circles are observations

    Multiple models reproduce hypoxic volume reasonably well and together provide a useful uncertainty estimate.

    (from A. Bever, M. Friedrichs)


    Dead zone volume model sensitivity tests chesroms 1 term do model

    Dead Zone Volume Model Sensitivity Tests (ChesROMS + 1-term DO model)

    20

    10

    0

    Base Case

    Dead Zone Volume in km3

    Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

    Date in 2004

    What leads to the large increase in dead zone size in the summer?

    (by M. Scully)


    Dead zone volume model sensitivity tests chesroms 1 term do model1

    Dead Zone Volume Model Sensitivity Tests (ChesROMS + 1-term DO model)

    20

    10

    0

    Base Case

    Dead Zone Volume in km3

    Constant River discharge

    Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

    Date in 2004

    Changes in dead zone size are not a function of seasonal changes in freshwater.

    (by M. Scully)


    Dead zone volume model sensitivity tests chesroms 1 term do model2

    Dead Zone Volume Model Sensitivity Tests (ChesROMS + 1-term DO model)

    20

    10

    0

    July wind year-round

    Base Case

    Dead Zone Volume in km3

    Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

    Date in 2004

    Seasonal changes in dead zone size are almost entirely due to changes in wind.

    (by M. Scully)


    Dead zone volume model sensitivity tests chesroms 1 term do model3

    Dead Zone Volume Model Sensitivity Tests (ChesROMS + 1-term DO model)

    20

    10

    0

    Base Case

    Dead Zone Volume in km3

    January wind year-round

    Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

    Date in 2004

    Seasonal changes in dead zone size are almost entirely due to changes in wind.

    (by M. Scully)


    Evaluating models for chesapeake bay dissolved oxygen helping4

    Federal Agencies Predict and Reduce Chesapeake Bay Dead Zones

    Evaluating Models for Chesapeake Bay Dissolved Oxygen: Helping

    Conclusions

    -- Dead zones are highly detrimental to Chesapeake Bay living resources.

    -- Seasonal and interannual variability in the Chesapeake Bay dead zone is controlled largely by variability in the wind.

    -- Improved forecasts of CB dead zone extent in response to land use and climate change would benefit from the use of better wind models and multiple ecosystem models (i.e., “ensembles of models” similar to hurricane prediction).


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