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 Federal Agencies Predict and Reduce Chesapeake Bay Dead Zones

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 Federal Agencies Predict and Reduce Chesapeake Bay Dead Zones

(UMCES, Coastal Trends)

“HYPOXIA” Oxygen ≤ ~ 2 mg/L

(UMCES, Coastal Trends) Goal 2: to enable long-term (≥ years) dead zone forecasts to aid in restoration (via EPA-CBP) (VIMS, ScienceDaily) Goal 1: to enable short-term (≤ weeks) dead zone forecasts for hazard mitigation (via NOAA-NCEP) Dead zone volume (km 3 )

Classically two primary factors: nutrient input and stratification Classic Factors Thought to Affect Dead Zones in Chesapeake Bay (

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 Federal Agencies Predict and Reduce Chesapeake Bay Dead Zones

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 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

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, Jisamma Kallumadikal (CSDMS) – Multiple ROMS grids, forcings, O 2 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 Hypoxia Dead Zone Modeling Testbed

Methods -- 5 Hydrodynamic Models (so far)

o ICM: CBP model; complex biology (dozens of equations) o bgc: NPZD-type biogeochemical model (4 main equations) o 1eqn: Simple one equation respiration (1 equation) o 1term-DD: depth-dependent net respiration (1 parameter) (not a function of x, y, temperature, nutrients…) o 1term: Constant net respiration (1 constant parameter) Methods -- 5 Dissolved Oxygen Models (so far) o CH3D + ICM o EFDC + 1eqn, 1term o CBOFS2 + 1term, 1term+DD o ChesROMS + 1term, 1term+DD, bgc Methods -- 8 Multiple combinations (so far)

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

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 Federal Agencies Predict and Reduce Chesapeake Bay Dead Zones

Salinity Stratification and Bottom Oxygen in Central Chesapeake Bay (by M. Scully) 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. plus 1-term DO model ChesROMS model Salinity stratification Dissolved oxygen

(from A. Bever, M. Friedrichs) Multiple models reproduce hypoxic volume reasonably well and together provide a useful uncertainty estimate. Results: Dead Zone Volume Model Comparison Volume of low oxygen water (km 3 ) Level of model uncertainty Circles are observations

(by M. Scully) Dead Zone Volume Model Sensitivity Tests (ChesROMS + 1-term DO model) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Date in 2004 Dead Zone Volume in km Base Case What leads to the large increase in dead zone size in the summer?

Changes in dead zone size are not a function of seasonal changes in freshwater. Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Date in Base Case Constant River discharge (by M. Scully) Dead Zone Volume Model Sensitivity Tests (ChesROMS + 1-term DO model) Dead Zone Volume in km 3

Seasonal changes in dead zone size are almost entirely due to changes in wind. Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Date in Base Case July wind year-round (by M. Scully) Dead Zone Volume Model Sensitivity Tests (ChesROMS + 1-term DO model) Dead Zone Volume in km 3

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Date in Base Case January wind year-round (by M. Scully) Seasonal changes in dead zone size are almost entirely due to changes in wind. Dead Zone Volume Model Sensitivity Tests (ChesROMS + 1-term DO model) Dead Zone Volume in km 3

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). Evaluating Models for Chesapeake Bay Dissolved Oxygen: Helping Federal Agencies Predict and Reduce Chesapeake Bay Dead Zones