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Application of a Coupled Physical-Biogeochemical Model to Simulate and Forecast the Ecological Variability of Chesapeake Bay. Outline. ChesROMS Community Model Biogeochemical Model Implementation & Waypoints Assessment of Ecosystem Model Solutions Concluding Remarks.

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slide1
Application of a Coupled Physical-Biogeochemical Model to Simulate and Forecast the Ecological Variability of Chesapeake Bay
outline
Outline
  • ChesROMS Community Model
  • Biogeochemical Model Implementation & Waypoints
  • Assessment of Ecosystem Model Solutions
  • Concluding Remarks

MODIS Image from Kemp et al. 2005

chesroms community model
ChesROMS Community Model
  • ROMS 3.0
  • Curvilinear Horizontally
  • σ-coordinate Vertically
  • Includes major tributaries
  • Coarse mesh for model development (100*150*20)
  • Forcing: Tides, Winds, Heat Fluxes and Rivers
  • Validated Physical Model w/ 15-Year Hindcast (Xu et al., accepted)
  • Currently expanding the biogeochemical model
  • Goal: Improved Simulation of BGC processes & Water Quality Fields
  • Use Output to inform Ecological Models (HABs, pathogens, etc.)
  • Open Source Available at:

http://sourceforge.net/projects/chesroms/

ChesROMS Team:

Chris Brown, Tom Gross, Brooke Denton, Raleigh Hood, Mohan Karyampudi, Lyon Lanerolle, Wen Long, Raghu Murtugudde, Dave Potsiadlo, M. Bala Krishna Prasad, Jerry Wiggert, Jiangtao Xu

cbp sampling sites
CBP Sampling Sites
  • Chesapeake Bay Program
  • (http://chesapeakebay.net/)
  • Data Used For:
  • Initial Conditions
  • River Boundary Conditions
  • Solution Validation Sites (following Xu & Hood, 2006)
    • CB3.3C (Upper Bay)
    • CB5.3 (Mid-bay)
    • C6.3 (Lower Bay)
  • Chlorophyll
  • Dissolved Oxygen
  • DON, PON
  • Freshwater Flux
  • NO3/NO2/NH4
  • TSS

CB4.1C (upper bay)

CB5.3 (middle bay)

CB6.3 (lower bay)

Map Courtesy of Chesapeake Bay Program

chesapeake bay ecological prediction system cbeps
Chesapeake Bay Ecological Prediction System (CBEPS)
  • Ocean Quality Control System (OQCS)
    • Automatic retrieval of historical and real-time data for validation and model forcing
  • Ocean Hydrodynamic Modeling System (OHMS)
    • ChesROMS and Empirical Habitat Models
  • Ocean Model Assessment System (OMAS)
    • Skill assessment of model predictions against data acquired by OQCS
  • Ocean Model Dissemination System (OMDS)
    • Data archive and forecast dissemination
    • Utilizes data interoperability techniques to facilitate efficient provision of model results to end users

Brown, et al., J. Mar. Sys., 2013.

bgc modeling targets implementation goals
BGC Modeling Targets & Implementation Goals
  • Phytoplankton Bloom Dynamics
    • Capture Spatio-temporal Physical-Biogeochemical Interactions Associated with Estuarine Circulation
  • Particulate and Dissolved Constituents
    • N-cycling Linkage of Water Column & Benthos
  • Dissolved Oxygen Evolution
    • Denitrification Onset - Offers Insight into N Balances & Budget

Improved ChesROMS BGC Realism ->

More Robust Ecological Forecast System (CBEPS)

Hindcast Year Chosen for Model Implementation is 1999

(“Typical” Conditions; Model Physics Validated)

Xu, et al., Est. and Coasts., 2011.

slide7

ChesROMS Biogeochemical Flows

Aspects of Implementation

1) Benthic NH4 Efflux & NO3 Uptake ramp up as overlying DO decreases

2) Reduce POM sinking in bottom layer

i) Promote O2 Demand in Water Column

ii) Promote BGC link to Estuarine Circulation

Sensitivity Explorations

  • 1) Reduce DL Sinking Velocity
  • 2) Particle Aggregation (Stickiness)
  • i) Regulates Bloom Dynamics, POM Loads & Sinking/Export of Organic Matter
  • ii) Tends to Degrade O2 Evolution (WC DO Increases)

Overall Goals

Overcome “Tension” in BGC Mechanisms

Bloom Dynamics <-> Hypoxia Realism <-> DIN Concentrations <-> Bloom Dynamics

DO is Indicated by the Light Blue Background

summary of sensitivity studies

Nitrate

Index

Station

DO

Test 64 -> 85: ⬇ DL Sink Velocity (0.5 x); ⬆ Max Nitrification Rate (4x)

Test 64 -> 91: Constant Phytoplankton Growth Rate

Test 91 -> 96: ⬇ Non-Dim Zooplankton Growth Rate (0.8x)

Test 96 -> 100: ⬆ Coagulation Param (1.5x)

Test 91 -> 104: Zooplankton Grazing ≠ f(Temperature)

Test 100 -> 105: ⬇ DL Sink Velocity (0.5 x)

Chlorophyll

Ammonium

Summary of Sensitivity Studies
hypoxic volume km 3 comparisons
Extension of Hypoxic Volume Envelope for Model

Overall, the 4 mg/ml threshold is a closer fit to the CBP-based Hypoxic Volume

Seasonal variability consisting of onset and dissipation timing are reasonable

Initial Baseline Solution (Test 64)

Test 64 -> 85: ⬇ DL Sink Velocity (0.5 x); ⬆ Max Nitrification Rate (4x)

Test 91 -> 96: ⬇ Non-Dim Zooplankton Growth Rate (0.8x)

New Baseline Solution (Test 105)

Hypoxic Volume (km3) Comparisons
baseline solution

Dissolved Oxygen

Chlorophyll

Baseline Solution

Upper Bay (4.1C)

Mid-Bay (5.3)

Lower Bay (6.3)

Upper Bay

Mid-Bay

Lower Bay

extending the model
Extending the Model

Ideally, Phys-BGC Model Will Naturally Capture Interannual Variability

or

Model Provides Additional Insights into the Chesapeake System

summary
Retain Organic Matter in Water Column

Promotes Water Column Oxygen Demand (to a point)

BUT! Oxic N-cycling Promotes O2 Production

Model Suggests a “Pulsing” of low DO conditions in bottom waters through the summer

Linkage to variability in modeled phytoplankton biomass

Bottom Dissolved Oxygen

Summary
  • Refining Hypoxic Fidelity in the Model
  • How to Amplify Denitrification in the Water Column and Anoxia Establishment?
    • Adjust the Nitrification - Denitrification Transition

Chesapeake Bay System and Availability of CBP Data Provide an Ideal Proving Ground for Development of the Biogeochemical Module