Evaluating Models of Chesapeake Bay Low Oxygen Dead Zones: Helping Federal Agencies Improve Water Quality

Carl Friedrichs Virginia Institute of Marine Science

Gloucester Point, Virginia, USAPresented to VIMS Council, 28 January 2011

Evaluating Models of Chesapeake Bay Low Oxygen Dead Zones: Helping Federal Agencies Improve Water Quality

Outline

1) Introduction: Chesapeake Bay Dead Zone Effects and Causes

2) SURA Estuarine Hypoxia Testbed: Goals, Participants, Methods

3) Preliminary Results of Oxygen Model Comparison

(UMCES, Coastal Trends)

(UMCES, Coastal Trends)

HYPOXIADO ≤ ~ 2 mg/L

(VIMS, ScienceDaily)(UMCES, Coastal Trends)

Classically two primary factors: nutrient input and stratification

Classic Factors Thought to Affect Dead Zones in Chesapeake Bay

(www.vims.edu)

(Kemp et al. 2009)

Seemingly Contradictory Trends in Bay Oxygen and Related Factors:

Hypoxia continually increasing in Chesapeake Bay with a marked jump around 1985

(Kemp et al. 2009)

Seemingly Contradictory Trends in Bay Oxygen and Related Factors:

But nutrient loading has been flat to decreasing since 1970.

(Kemp et al. 2009)

Seemingly Contradictory Trends in Bay Oxygen and Related Factors:

River discharge isn’t changing much.

(Kemp et al. 2009)

Seemingly Contradictory Trends in Bay Oxygen and Related Factors:

Something that has drastically changed: The “North Atlantic Oscillation Index”

Since 1980, summer winds over the Chesapeake Bay have been more from the west, which reduces the amount of transport of high DO water from shoals into the deep channel during summer. (It has nothing to do with nutrients, fresh water input or absolute stratification in the deep channel.)

(Scully 2010)

NAO is a climatic shift that controls the direction of summer Bay winds:

(Scully 2010)

Accounting for Change in Wind Explains Dead Zone Growth:

Evaluating Models of Chesapeake Bay Low Oxygen Dead Zones: Helping Federal Agencies Improve Water Quality

Outline

1) Introduction: Chesapeake Bay Dead Zone Effects and Causes

2) SURA Estuarine Hypoxia Testbed: Goals, Participants, Methods

3) Preliminary Results of Oxygen Model Comparison

NOAA/SURA Estuarine Hydrodynamics and Hypoxia Modeling Testbed

Funded by NOAA through SURA (Southeastern Universities Research Association). Initially one year of funding to VIMS (~$800K) which started June 2010.

Part of a larger NOAA/SURA larger (~$4M) “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 Inundation• Estuarine Hypoxia• Shelf Hypoxia

• Carl Friedrichs (VIMS) – Team LeaderFederal 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/IOOSNon-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, Jisamma Kallumadikal (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• Jian Shen, Bo Hong (VIMS) – SELFE, FVCOM, EFDC models in CB• John Wilkin, Julia Levin (Rutgers) – ROMS-Espresso + 7 other MAB hydrodynamic models

NOAA/SURA Estuarine Hydrodynamics and Hypoxia Modeling Testbed

Methods: Multiple Hydrodynamic modelsCH3D

(EPA/USACE Chesapeake Bay Program)CBOFS2

(L. Lanerolle, NOAA-CSDL)

Methods: Dissolved Oxygen Models

Two dissolved DO models highlighted today:

(1) 1-term DO model in ROMS model: single respiration term (not dependent on nutrients; M. Scully/NOAA)

(2) CE-QUAL-ICM Multi-component model: includes wathershed, nutrients, algae, zooplankton, SAV, benthos, fish… (CBP/EPA/USACE)

• Final run = 1985 – 2005.• It is not practical to rerun the final 57K grid CH3D model

with alternate forcing.• EPA CBP plans next “release” of updated hydrodynamic

model by 2017.• Successor hydrodynamic model has not been chosen,

although USACE favors adding third dimension to an existing finite element inundation model.

• Timing is ideal for Estuarine Hypoxia Team to provide guidance to CBP concerning favorable attributes of CB

(Slide courtesy Rich Batiuk, EPA)

EPA/USACE CE-QUAL-ICM forced by CH3D and detailed watershed inputs

EPA/USACE CE-QUAL-ICM model (cont.)

(Slide courtesy C. Cerco, USACE)

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

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

Evaluating Models of Chesapeake Bay Low Oxygen Dead Zones: Helping Federal Agencies Improve Water Quality

Outline

1) Introduction: Chesapeake Bay Dead Zone Effects and Causes

2) SURA Estuarine Hypoxia Testbed: Goals, Participants, Methods

3) Preliminary Results of Oxygen Model Comparison

Observed and Modeled Top-to-Bottom DS and Bottom DO in Central Chesapeake Bay

(by M. Scully)

Variability in DO is easier to model than and unrelated to stratification. This is true for both the simple 1-term model (above) and the more complex EPA model.

plus 1-term DO model

NOAA/UMCES/ODU ROMS model

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

Date in 2004

Hyp

oxic

Vol

ume

in k

m3

10

5

0

1-term DO model

Multi-term DO model

EPA observations

One-term DO model does about as well as much more complex model.

Observed and Modeled Chesapeake Bay Hypoxic Volume for 2004

(by M. Scully, L. Lanerolle, A. Bever, M. Friedrichs)

One-term DO model does about as well as much more complex model.

(by M. Scully)

2004 Hypoxic Volume Sensitivity Tests (1-term model)

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

Date in 2004

Hyp

oxic

Vol

ume

in k

m3

20

10

0

Base Case

Seasonal changes in hypoxia are not a function of seasonal changes in freshwater.

2004 Hypoxic Volume Sensitivity Tests (1-term model)

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

Date in 2004

Hyp

oxic

Vol

ume

in k

m3

20

10

0

Base Case

Constant River discharge

(by M. Scully)

Seasonal changes in hypoxia are almost entirely due to seasonal changes in wind.

2004 Hypoxic Volume Sensitivity Tests (1-term model)

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

Date in 2004

Hyp

oxic

Vol

ume

in k

m3

20

10

0

Base CaseJuly wind year-round

(by M. Scully)

Seasonal changes in hypoxia are almost entirely due to seasonal changes in wind.

2004 Hypoxic Volume Sensitivity Tests (1-term model)

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

Date in 2004

Hyp

oxic

Vol

ume

in k

m3

20

10

0

Base Case

January wind year-round

(by M. Scully)

Evaluating Models of Chesapeake Bay Low Oxygen Dead Zones: Helping Federal Agencies Improve Water Quality

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.

-- Simple models that largely neglect biology appear to predict CB seasonal dead zone variability as well as much more complex ecosystem models.

-- 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., “ensemble models” similar to hurricane prediction).