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A Hierarchy of Physical Models for Ecological Applications. Lyon Lanerolle 1,2 , Richard Patchen 1 , Richard Stumpf 3 , Frank Aikman III 1 , Timothy Wynne 3 , Michelle Tomlinson 3 and Jiangtao Xu 1,4 - PowerPoint PPT Presentation
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O ffi c e o f C o a s t S u r v e y / C S D L
A Hierarchy of Physical Models for Ecological Applications
Lyon Lanerolle1,2, Richard Patchen1, Richard Stumpf3, Frank Aikman III1, Timothy Wynne3, Michelle Tomlinson3 and Jiangtao Xu1,4
1NOAA/NOS/OCS/Coast Survey Development Laboratory,1315 East-West Highway, Silver Spring, MD 20910; 2Earth Resources Technology (ERT) Inc.,10810 Guilford Road, Suite 105, Annapolis Junction, MD 20701; 3NOAA/NOS/NCCOS/Coastal and Oceanographic Assessment Status & Trends Branch,
1305 East-West Highway, Silver Spring, MD 20910; 4University Corporation for Atmospheric Research/Visiting Scientist Program, P. O. Box 3000, Boulder, CO 80307.
O ffi c e o f C o a s t S u r v e y / C S D L
Introduction and Motivation• Origin of modeling efforts due to research collaborations attempting to
improve HAB and Hypoxia predictions: 1D, 2D HAB set-ups due to NCCOS-CSDL collaboration3D HAB set-up due to NOAA/IOOS partnership projectHypoxia set-up due to SURA Testbed (Estuarine Hypoxia) project
• Hierarchy of modeling approaches due to :Different levels of complexity of processes (e.g. 1D, 2D, 3D, etc.)Nature of dominant physical processes (e.g. vertical mixing, upwelling, etc.)
• All applications are physically forced usually with no behavior; all based on Rutgers University’s ROMS model• Modeling set-ups have dual purposes : (a) research tool and (b) real-time, operational forecast system (if needed)
O ffi c e o f C o a s t S u r v e y / C S D L
1D Vertical Mixing ModelMotivation and Model Set-up
• Motivation : to study and predict cyanobacterial bloom/scum formation in water bodies
• Model Design Specifications : (i) highly portable (speedily applied to any water body) and (ii) minimum number of model inputs – a simple grid, representative bathymetric value, a Coriolis parameter, a T and S profile for initialization and a fixed-point time-series of met. variables
• Model set-up : ROMS with Bulk Fluxes, GLS k-ω closure, quadratic bottom drag (Cd=0.003), wall BCs, ∆t=300s (baroclinic)
• Calibration : using idealized fields (thereafter applied to Western Lake Erie)
• Computational Efficiency : 30-day simulation/per minute on LINUX box in serial mode (highly efficient)
Acknowledgment : This work was funded by National Center for Environmental Health at the Centers for Disease Control and Prevention (CDC)
O ffi c e o f C o a s t S u r v e y / C S D L
1D Vertical Mixing ModelWestern Lake Erie Application and Results
1. Periods of high/low viscosity due to met. conditions2. Simulated tracer and particles respond in the expected way
• 9 x 9 horizontal grid• 20 vertical σ-levels• 7.7m flat bathymetry (from Obs.)• IC from linear interp. of a surface and bottom T, S value• Met. forcing from NOAA/NDBC Marblehead, OH station
Marblehead, OH station
O ffi c e o f C o a s t S u r v e y / C S D L
2D Upwelling Transect ModelMotivation and Model Set-up
• Motivation : Try to enhance WFS HAB event predictions of NOS/CO-OPS Bulletins by accounting for upwelling
• Hypothesis : Upwelling contributes to HAB events on WFS
• Model Design Specifications : Set-up model to study upwelling driven flow with cross-shore transport component
• Model Set-up (ROMS): Grid : transect with 400 x 9 points, 80
vertical σ-levels Bathymetry : NOS soundings ICs : MODAS/Basin model (NGOM) for T
and S and geostrophic velocities
BCs : periodic and far-field radiationForcings : met. forcing only (VENF1-
CMAN and NAM)
Vertical Mixing : GLS k-ω model
Time Step : 150s (baroclinic)
O ffi c e o f C o a s t S u r v e y / C S D L
2D Upwelling Transect ModelModel Output and Results
• Algal cells simulated using Lagrangian particles (blue square – begin, black circle – end)• Model capable of running as Nowcast/Forecast system and generating above graphic• Computational Efficiency : 7-day simulation/hour or better on LINUX box in serial mode
Particles respond to wind-driven upwelling (~31 August)
O ffi c e o f C o a s t S u r v e y / C S D L
3D Nested/Coupled ModelMotivation and Modeling Strategy
• Motivation : Try to enhance predictions of NOS/CO-OPS Bulletin HAB patch extent and movement on WFS - which is inherently 3D in nature
• Model Design Specifications: Need a fully 3D model of WFS Need to include enough of shelf Need Tampa Bay and Charlotte Harbor Need to be computationally efficient• Modeling Strategy : Nest/Couple high-resolution ROMS
model to already available basin-scale, POM-based NOAA/NOS Gulf of Mexico Model (NGOM)
O ffi c e o f C o a s t S u r v e y / C S D L
3D Nested/Coupled ModelModel Coupling and Set-up
• Grid : Covers WFS and refined along coast, Tampa Bay and Charlotte Harbor; 298 x 254 horizontal points and 30 vertical σ-levels• Bathymetry : NOS soundings (need to match at lateral boundaries) • Initial Conditions : NGOM interpolated water levels, T and S (spin-up from rest)• Boundary Conditions : NGOM interpolated surface forcings (wind stress, air P, heat flux); SST correction; water levels, currents, T and S at lateral boundaries; tides from ADCIRC added; 19 additional rivers (Tampa Bay and Charlotte Harbor) • ROMS details : Coupling BCs, Quadratic bottom drag, GLS k-ω model, ∆t=90 s (baroclinic)• Computational Efficiency : 24-day simulation/hour [MPI, IBM Power 6 cluster , 96 proc.]
NGOM ROMS
O ffi c e o f C o a s t S u r v e y / C S D L
3D Nested/Coupled ModelModel Results
Tracer patch method : Passive/Inert tracer evolution within ROMSParticle tracking method : CSDL’s Chesapeake Bay Oyster Larvae Tracker (CBOLT)
Observed Initial Patch Digitized Initial Patch
Initialization HAB patch
7-day Hindcast
Need 3D velocities as 2D depth-averaged velocities miss near-
shore upwelling behavior
O ffi c e o f C o a s t S u r v e y / C S D L
Water Quality ModelMotivation and Model Set-up
• Motivation : to study & predict spatio-temporal evolution of hypoxia in Chesapeake Bay
• Strategy : begin with simplest WQ model and then build up to complex models
• Model : examine hypoxia via DO using a 1-equation model with constant respiration (Malcolm Scully/ODU)
• Model Set-up : embed DO model within NOAA/NOS Chesapeake Bay Operational Forecast System (CBOFS)
DO in ROMS is a passive/inert tracer• Simulation : synoptic hindcast from June 01,
2003 - August 31, 2005• ICs and BCs : DO saturation from T and S [Weiss
(1970)]; no river DO sources• Computational Efficiency : 6-day sim./hour
[MPI, IBM Power 6 cluster , 96 proc.]
Const. resp. rate of 0.55 gO2/m3/day
Fixed at saturation (surface also)
O ffi c e o f C o a s t S u r v e y / C S D L
Water Quality ModelModel Results (Preliminary)
• Total DO content (kg) diminishes during the summer months as expected• Hypoxic volumes show agreement with those derived from CBP
observations*• Hypoxic zones present in deep, narrow channels during summer months*Courtesy of Malcolm Scully/ODU and Rebecca Murphy/JHU
DO ≤ 2 mg/L at 1m above botm.
DO ≤ 2 mg/L
DO ≤ 1 mg/L
DO ≤ 0.2 mg/L
O ffi c e o f C o a s t S u r v e y / C S D L
Summary and Conclusions• Through collaborative efforts to study and predict (with improved
accuracy) HABs and Hypoxia a suite of physical models have been developed at NOAA/NOS/OCS/Coast Survey Development Lab.
• The models range in complexity depending on the nature of the dominant processes driving HABs and Hypoxia; they span multiple dimensions (1D - 3D)
• Predictive capabilities of the models have been demonstrated and they reveal insights in to the driving physical mechanisms
• These models, although developed as research tools, also have the potential to be cast in to Operational Forecast Systems (OFS) to routinely generate HAB and Hypoxia forecasts :
http://tidesandcurrents.noaa.gov/hab/
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