Using Chesapeake Bay Models To Evaluate Dissolved Oxygen Sampling Strategies Aaron J. Bever, Marjorie A.M. Friedrichs, Carl T. Friedrichs Outline:  Models.

Slides:



Advertisements
Similar presentations
Assessing Sensitivity to Eutrophication of the Southern Puget Sound Basin Bos, J.K., Newton, J.A., Reynolds, R.A., Albertson, S.L. Washington State Dept.
Advertisements

Skill Assessment of Multiple Hypoxia Models in the Chesapeake Bay and Implications for Management Decisions Isaac (Ike) Irby - Virginia Institute of Marine.
OMSAP Public Meeting September 1999 The Utility of the Bays Eutrophication Model in the Harbor Outfall Monitoring Program James Fitzpatrick HydroQual,
TRMM Tropical Rainfall Measurement (Mission). Why TRMM? n Tropical Rainfall Measuring Mission (TRMM) is a joint US-Japan study initiated in 1997 to study.
Combining Observations & Numerical Model Results to Improve Estimates of Hypoxic Volume within the Chesapeake Bay Aaron Bever, Marjy Friedrichs, Carl Friedrichs,
CBEO Year 3 Planning Rebecca Murphy Dec. 9, 2008.
Evaluating Models for Chesapeake Bay Dissolved Oxygen: Helping Carl Friedrichs Virginia Institute of Marine Science Gloucester Point, Virginia, USA Presented.
Using process knowledge to identify uncontrolled variables and control variables as inputs for Process Improvement 1.
A Simple Model for Oxygen Dynamics in Chesapeake Bay Malcolm Scully 1)Background and Motivation 2)Simplified Modeling Approach 3)Importance of Physical.
National Environmental Research Institute, Aarhus University, Denmark Use of models for Maritime spatial planning Output from the data modelling workshop.
The Risk of Hypoxia in Narragansett Bay A Synthesis of Available Data.
Annapolis: July 18, 2006 Outline of talk: Objective: Improve BBL in 3D model. Estimates of shear stress. Evaluate bottom boundary layer.
OSMOSIS Primary Production from Seagliders April-September 2013 Victoria Hemsley, Stuart Painter, Adrian Martin, Tim Smyth, Eleanor Frajka-Williams.
US IOOS Modeling Testbed Leadership Teleconference May 3, 2011 Estuarine Hypoxia Team Carl Friedrichs, VIMS
Update on hydrodynamic model comparisons Marjy Friedrichs and Carl Friedrichs Aaron Bever (post-doc) Leslie Bland (summer undergraduate student)
The Physical Modulation of Seasonal Hypoxia in Chesapeake Bay Malcolm Scully Outline: 1)Background and Motivation 2)Role of Physical Forcing 3)Simplified.
In situ science in support of satellite ocean color objectives Jeremy Werdell NASA Goddard Space Flight Center Science Systems & Applications, Inc. 6 Jun.
Combining Observations & Numerical Model Results to Improve Estimates of Hypoxic Volume within the Chesapeake Bay JGR-Oceans, October 2013 issue Aaron.
Comparing observed and modeled estimates of hypoxic volume within the Chesapeake Bay, USA, to improve the observational sampling strategy Aaron J. Bever.
Interim Update: Preliminary Analyses of Excursions in the A.R.M. Loxahatchee National Wildlife Refuge August 18, 2009 Prepared by SFWMD and FDEP as part.
A T HREE- D IMENSIONAL W ATER Q UALITY M ODEL OF S OUTHERN P UGET S OUND Greg Pelletier, P.E., Mindy Roberts, P.E., Skip Albertson, P.E., and Jan Newton,
Bathymetry Controls on the Location of Hypoxia Facilitate Possible Real-time Hypoxic Volume Monitoring in the Chesapeake Bay Aaron J. Bever 1, Marjorie.
Isaac (Ike) Irby 1, Marjorie Friedrichs 1, Yang Feng 1, Raleigh Hood 2, Jeremy Testa 2, Carl Friedrichs 1 1 Virginia Institute of Marine Science, The College.
Neuse Estuary Eutrophication Model: Predictions of Water Quality Improvement By James D. Bowen UNC Charlotte.
Isaac (Ike) Irby 1, Marjorie Friedrichs 1, Yang Feng 1, Raleigh Hood 2, Jeremy Testa 2, Carl Friedrichs 1 1 Virginia Institute of Marine Science, The College.
The overarching goals of this workshop were to: 1)Review, summarize, and finalize the results from the U.S. IOOS Modeling Testbed model intercomparison.
Fig. 4. Target diagram showing how well the total 3D HV from each model is reproduced by different stations sets. Sets correspond to; min10: 10 stations.
Year 3 Research and Priorities Jeremy Testa Horn Point Laboratory December 9, 2008 Primary Scientific Question What C sources are missing from the Bay.
The Interconnectedness of the Chesapeake Bay Watershed Karen Sondak CREST Center for the Integrated Study of Coastal Ecosystem Processes and Dynamics University.
A Super-Regional Modeling Testbed for Improving Forecasts of Environmental Processes for the U.S. Atlantic and Gulf of Mexico Coasts Don Wright, SURA Principal.
Super-Regional Modeling Testbed to Improve Forecasts of Environmental Processes for the U.S. Atlantic and Gulf of Mexico Coasts Wright, L.D.; Signell,
Combining Observational and Numerical Model Results to Improve Estimates of Hypoxic Volume in the Chesapeake Bay Aaron J. Bever 1,2, Marjorie A.M. Friedrichs.
Office of Coast Survey / Coast Survey Development Lab Transition, Progress, Challenges and Future Directions Richard Patchen NOAA’S National Ocean Service.
Is there any air down there? Using multiple 3D numerical models to investigate hypoxic volumes within the Chesapeake Bay, USA Aaron J. Bever 1, Marjorie.
Combining Observations & Numerical Model Results to Improve Estimates of Hypoxic Volume within the Chesapeake Bay Aaron Bever, Marjy Friedrichs, Carl Friedrichs,
Combining Observations & Models to Improve Estimates of Chesapeake Bay Hypoxic Volume* Aaron Bever, Marjorie Friedrichs, Carl Friedrichs, Malcolm Scully,
US IOOS Modeling Testbed Leadership Teleconference May 3, 2011 Estuarine Hypoxia Team Carl Friedrichs, VIMS
Evaluating Models of Chesapeake Bay Low Oxygen Dead Zones: Helping Federal Agencies Improve Water Quality Carl Friedrichs Virginia Institute of Marine.
CYCLE PO4 Wet Chemistry In-situ Nutrient analyzer.
Stratification and hypoxia on monthly to inter-annual timescales … plus Is hypoxic event timing related to spring-neap cycles? Codiga (GSO) Mar 21, 2013.
Introduction to Ecosystem Monitoring and Metabolism
Using ROMS Lagrangian-float simulations to estimate exchange rates between spatial compartments in a tidal estuary Mark Hadfield NIWA.
Status and Plans of the Global Precipitation Climatology Centre (GPCC) Bruno Rudolf, Tobias Fuchs and Udo Schneider (GPCC) Overview: Introduction to the.
Office of Coast Survey / CSDL Sensitivity Analysis of Temperature and Salinity from a Suite of Numerical Ocean Models for the Chesapeake Bay Lyon Lanerolle.
NOAA/NOS/OCS/Coast Survey Development Laboratory Lyon Lanerolle 1,2, Richard Patchen 1 and Frank Aikman III 1 1 National Oceanic and Atmospheric Administration.
By: Eliora Bujari 11/25/2008. Located in Southern Texas Bend Separated from the Gulf of Mexico by Mustang and North Padre Islands. Freshwater inflows.
Working With Simple Models to Predict Contaminant Migration Matt Small U.S. EPA, Region 9, Underground Storage Tanks Program Office.
This project is supported by the NASA Interdisciplinary Science Program The Estuarine Hypoxia Component of the Coastal Ocean Modeling Testbed: Providing.
Point Source Loads and Decision Criteria for Toxics Modeling Baltimore Harbor TMDL Stakeholder Advisory Group September 10, 2002.
Results of the US IOOS Testbed for Comparison of Hydrodynamic and Hypoxia Models of Chesapeake Bay Carl Friedrichs (VIMS) and the Estuarine Hypoxia Team.
Super-Regional Modeling Testbed Estuarine Hypoxia Team Carl Friedrichs (VIMS) – Team Leader Federal partners David Green (NOAA-NWS) – Transition to operations.
An Examination of Dissolved Oxygen Levels and the Effects of Macroalgae in Chincoteague Bay, Maryland Renee Harrington, Southampton College, Honors Thesis,
Controls of sub-surface dissolved oxygen in Massachusetts Bay, USA Amanda Hyde (Maine Maritime Academy), Doug Vandemark (University of New Hampshire),
Estuarine Hypoxia Component of Testbed 2 Marjorie Friedrichs, VIMS, lead Carl Friedrichs, VIMS, co-lead Wen Long and Raleigh Hood, UMCES Malcolm Scully,
1 of 31 The EPA 7-Step DQO Process Step 6 - Specify Error Tolerances 60 minutes (15 minute Morning Break) Presenter: Sebastian Tindall DQO Training Course.
U.S. IOOS Testbed Comparisons: Hydrodynamics and Hypoxia Marjy Friedrichs Virginia Institute of Marine Science Including contributions from the entire.
The Need for Sustainable, Integrative Long-Term Monitoring of the Gulf of Mexico Hypoxic Zone Summit on Long-Term Monitoring of the Gulf of Mexico Hypoxic.
Technical Support in Engineering Construction Phase of Craney Island Eastward Expansion Mac Sisson, Harry Wang, Jian Shen, Albert Kuo, and Wenping Gong.
Acknowledgments: The study is funded by the Deep-C consortium and a grant from BOEM. Model experiments were performed at the Navy DoD HPC, NRL SSC and.
The Properties of Mixtures: Solutions REVIEW. Solution – any substance that is evenly dispersed or distributed throughout another substance. A. homogeneous.
COMT Chesapeake Bay Hypoxia Modeling VIMS: Marjy Friedrichs (lead PI) Carl Friedrichs (VIMS-PI) Ike Irby (funded student) Aaron Bever (consultant) Jian.
Hypoxia Forecasts as a Tool for Chesapeake Bay Fisheries
Marjorie Friedrichs, Raleigh Hood and Aaron Bever
Lessons learned from Metro Vancouver
Physica Medica 32 (2016) 1570–1574 報告人:王俊淵
Estuarine Hypoxia Component of Testbed 2
Multi-year Trends and Event Response
US Environmental Protection Agency
Eutrophication indicators PSA & EUTRISK
Presentation transcript:

Using Chesapeake Bay Models To Evaluate Dissolved Oxygen Sampling Strategies Aaron J. Bever, Marjorie A.M. Friedrichs, Carl T. Friedrichs Outline:  Models and data used.  Methods of calculating hypoxic volume from 3-dimensional model results  How does the number of stations included in the interpolations influence hypoxic volume?  Is improved temporal resolution more important than adding stations?  The models show temporally variable (daily-weekly) DO concentrations and hypoxic volumes. Where do the models recommend the high- frequency data be collected, and how can this high-frequency data help the models? 22, February 2011: Chesapeake Bay Program office, Annapolis MD.

Models used: Two 3-D hydrodynamic models with dissolved oxygen  CH3D-ICM: Complex, multi-component ecosystem model presently used by the CBP. Extensively calibrate to CBP data. Model results provided by Ping Wang.  Regional Ocean Modeling System (ROMS) with a one-equation oxygen model: Constant respiration (no nutrients, primary production, etc.), oxygen saturation at the surface, diffusion of DO, advection of DO with water masses. No calibration to data. Model results provided by Malcolm Scully (ODU). Data used:  Chesapeake Bay Program vertical profiles of DO  Collected bay-wide monthly or bi-monthly  Takes about 7-14 days to sample all stations. Time-frame of investigation:  Calendar year 2004.

Focusing on hypoxic volume and spatial model estimates of dissolved oxygen. Hypoxic Volume was calculated from the models in five ways.  Total hypoxic volume from 3D model results.  Hypoxic volume estimated by the model using the observed station locations (snapshot in time).  Hypoxic volume using observed station locations at the exact time each was observed. (directly comparable to the observed hypoxic volume).  Hypoxic volume estimated using all Chesapeake Bay Program stations.  Hypoxic volume estimated using a subset of Chesapeake Bay Program stations and targeted specific locations.  Hypoxic volume was calculated using the Chesapeake Bay Program Visual Basic volume calculator software.

Hypoxic volume calculated from station locations for three of the five methods and the observations.  Absolute Time Match (directly comparable to observations)  Time-snapshot  All Stations

 Adding more stations may not improve the estimates of hypoxic volume. Estimates using all CBP station locations is nearly identical to using the subset actually observed in  Better resolving the time-variation in low DO will help better estimate hypoxic volume, and better validate/calibrate the models. Models suggest estimates of hypoxic volume can be improved through better observing the time-variation in low DO, or DO at strategic locations.

Fraction of Time Hypoxia Occurred ICM Model 1-Eq. DO Model Chesapeake Bay Program station locations. Using the model estimates to investigate strategic locations where hypoxia occurs.

ICM Model 1-Eq. DO Model Using the model estimates to investigate strategic locations where the variability in DO is high. Standard Deviation of Bottom Oxygen Concentration Choose station locations based on the frequency of hypoxia, variability in DO concentration, and CBP station locations. Calculate the hypoxic volume based on station subsets to help determine how important different locations are.

Start with a minimum number of stations Selected Station Locations 1-Eq. DO Model

Selected Station Locations ICM Model Start with a minimum number of stations

Include all CBP stations Selected Station Locations 1-Eq. DO Model

Selected Station Locations ICM Model Include all CBP stations

By simply including more stations the hypoxic volume estimated from the stations may have improved, but not as well as hoped. Strategic placement based on model estimates should help:  Improve the data by using the models to target locations of moderate dissolved oxygen variability near the edge of where hypoxia occurs.  Allows for a more robust estimate of hypoxic volume through time. Will help examine how the “real” hypoxic volume changes over the 1-2 weeks CBP profiles are being collected.  Helps improve relationships between short-term (daily to weekly) DO forcing factors and the observed DO concentrations.  Improve the models by strategically adding high frequency data to locations where validation is needed.  Near the northern and southern hypoxia extent to determine spatial dimensions through time.  In a known hypoxic region to determine the vertical DO/hypoxia extent through time. Instead

Minimum number of stations: Plus one near the northern hypoxic extent ICM Model 1-Eq. DO Model

Minimum number of stations: Plus one near the south-eastern extent of hypoxia ICM Model 1-Eq. DO Model

Improvements in hypoxic volume estimates from adding a station.  Hypoxic volume estimates from the 1-Eq. DO model improved when a single strategic station was added.  Hypoxic Volume estimates from the ICM model were unchanged by adding either of these stations.  Stations on the outer range of hypoxia, and where the variability in DO is high, have the potential to improve estimates of hypoxic volume and significantly contribute to our understanding of the driving forces behind DO concentration within the Chesapeake Bay.

Potential Instrument Locations Based on Model Estimates 1-Eq. model shown; ICM model shows similar results. Northern Bay Middle Bay

Extra Slides

Hypoxic volume from the stations and the total estimated hypoxic volume from the 3D fields. 1-Eq. Model

Hypoxic volume from the stations and the total estimated hypoxic volume from the 3D fields. ICM

Potential Instrument Locations: ICM model

Spatial model estimated bottom DO concentration (mg/L). 1-Eq. Model 2 mg/L is contoured in black.

Spatial model estimated bottom DO concentration (mg/L). ICM model: Much more calibrated to observed profiles, much less variable than 1-Eq. 2 mg/L is contoured in black. X and Y axis are in UTM, per ICM output.