Aaron Bever , Marjy Friedrichs, Carl Friedrichs , Malcolm Scully, Lyon Lanerolle
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Transcript of Aaron Bever , Marjy Friedrichs, Carl Friedrichs , Malcolm Scully, Lyon Lanerolle
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Combining Observations & Numerical Model Results to Improve Estimates of Hypoxic Volume within the Chesapeake Bay
Aaron Bever, Marjy Friedrichs, Carl Friedrichs, Malcolm Scully, Lyon Lanerolle
OUTLINE / SUMMARY
1. Relation to US-IOOS Modeling Testbed program and general methods.
2. Use 3D models to examine uncertainties in interpolating hypoxic volume.
• Observed DO has coarse spatial resolution = spatial error
• Observed DO is not a “snapshot” = temporal error
3. Use 3D models to improve EPA-CBP interpolations of hypoxic volume.
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Corresponding paper: JGR-Oceans, October 2013 issue
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Relationship to US-IOOS Modeling Testbed:
Part of Coastal & Ocean Modeling Testbed (COMT) Project headed by Rick Luettich (UNC), funded by NOAA US-IOOS Office
COMT Mission: Accelerate the transition of scientific and technical advances from the modeling research community to improve federal
agencies’ operational ocean products and services
Phase I (2010-12): Estuarine Hypoxia, Shelf Hypoxia, and Coastal Inundation Modeling Testbeds; Cyber-infrastructure to advance
interoperability and archiving
Phase II (2013-2015): Added West Coast Model Integration. (See Poster Session for initial results of Estuarine Hypoxia Phase II)
Combining Observations & Numerical Model Results to Improve Estimates of Hypoxic Volume within the Chesapeake Bay
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Present Estuarine Hypoxia COMT Team:
Virginia Institute of Marine Science:Marjy Friedrichs (lead PI), Carl Friedrichs (co-PI), Ike Irby (PhD student),
Aaron Bever (past Post-Doc, now consultant), Jian Shen (collaborator), Cathy Feng (collaborator)
Woods Hole Oceanographic Institution:Malcolm Scully (co-PI)
Univ. Maryland Center for Environmental Studies:Raleigh Hood (co-PI), Hao Wang (PhD student),
Jeremy Testa (collaborator), Wen Long (collaborator)
NOAA Coastal Survey Development Lab:Lyon Lanerolle (co-PI), Frank Aikman (collaborator)
,
Combining Observations & Numerical Model Results to Improve Estimates of Hypoxic Volume within the Chesapeake Bay
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Present Estuarine Hypoxia COMT Team:
Virginia Institute of Marine Science:Marjy Friedrichs (lead PI), Carl Friedrichs (co-PI), Ike Irby (PhD student),
Aaron Bever (past Post-Doc, now consultant), Cathy Feng (collaborator), Jian Shen (collaborator)
Woods Hole Oceanographic Institution:
Malcolm Scully (co-PI)
Univ. Maryland Center for Environmental Studies:Raleigh Hood (co-PI), Hao Wang (PhD student),
Jeremy Testa (collaborator), Wen Long (collaborator)
NOAA Coastal Survey Development Lab:Lyon Lanerolle (co-PI), Frank Aikman (collaborator)
,
Combining Observations & Numerical Model Results to Improve Estimates of Hypoxic Volume within the Chesapeake Bay
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• Compare relative skill and strengths/weaknesses of various Chesapeake Bay models
• Assess how model differences affect water quality simulations
• Recommend improvements to agency operational products associated with managing hypoxia (DO < 2 mg/L)
General COMT Estuarine Hypoxia modeling methods:
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Combining Observations & Numerical Model Results to Improve Estimates of Hypoxic Volume within the Chesapeake Bay
• 16 million people and > $1 Trillion in industries in CB watershed
• EPA Clean Water Act and a recent Presidential Executive Order both require a reduction of hypoxia in Chesapeake Bay
• Reducing hypoxia in Chesapeake through required nutrient reductions over next 15 years is expected to cost > $20 Billion
Motivation for Better Hypoxia Modeling for Chesapeake Bay:
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EFDCShenVIMS
CH3DCerco & Wang
USACE
UMCES-ROMSLi & Li
UMCES
Five hydrodynamic models configured for
Chesapeake Bay
DC
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EFDCShenVIMS
CH3DCerco & Wang
USACE
UMCES-ROMSLi & Li
UMCES
Five hydrodynamic models configured for
Chesapeake BayTODAY’S TALK
Dx ~ 2 kmDz ~ 1-2 m
Dx ~ 1 kmDz ~ 1-2 m
Dx ~ 0.5 kmDz ~ 1.5 m
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o ICM: EPA-CBP model; 27-component ecosystemmodel (multi P, multi Z, C/N/P/Si/DO, etc.)
o BGCs: 3 NPZD-type (~10 component) modelso 1eqn: Simple one equation respiration
(includes SOD)o 1term-DD: depth-dependent respiration
(not a function of x, y, temperature, nutrients…), surface DO = saturation
o 1term: Same, but constant net respiration(constant with depth)
Eight dissolved oxygen (DO) models configured for the Bay
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o ICM: EPA-CBP model; 27-component ecosystemmodel (multi P, multi Z, C/N/P/Si/DO, etc.)
o BGCs: 3 NPZD-type (~10-component) modelso 1eqn: Simple one equation respiration
(includes SOD)o 1term-DD: depth-dependent respiration
(not a function of x, y, temperature, nutrients…), surface DO = saturation
o 1term: Same, but constant net respiration(constant with depth)
TODAY’S TALK
Eight dissolved oxygen (DO) models configured for the Bay
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Today’s talk = Four combinations:
o CH3D + ICM EPA modelo CBOFS + 1termo ChesROMS + 1termo ChesROMS + 1term+DD
Coupled hydrodynamic-DO models
-- Physical models are similar, but grid resolution differs-- Biological/DO models differ dramatically-- All models run for 2004 and 2005 and compared to EPA Chesapeake Bay Monitoring Program’s DO observations
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-- The models all have significant skill (normalized RSMD < 1) in reproducing observed bottom dissolved oxygen (DO).-- EPA regulations require hypoxic volume be interpolated from observations.-- Unlike observations, model output is continuous in space and time.-- So use the continuous model output to estimate uncertainties caused by CBP interpolations of discontinous observed data and improve interpolation.
Model skill: Bottom DOTotal RMSD2 = Bias2 + unbiased RMSD2
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OUTLINE
1. Relation to US-IOOS Modeling Testbed program and general methods.
2. Use 3D models to examine uncertainties in interpolating hypoxic volume.
• Observed DO has coarse spatial resolution = spatial error
• Observed DO is not a “snapshot” = temporal error
3. Use 3D models to improve EPA-CBP interpolations of hypoxic volume.
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Combining Observations & Numerical Model Results to Improve Estimates of Hypoxic Volume within the Chesapeake Bay
Aaron Bever, Marjy Friedrichs, Carl Friedrichs, Malcolm Scully, Lyon Lanerolle
Corresponding paper: JGR-Oceans, October 2013 issue
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Four Types of Hypoxic Volume Estimates
Interpolation Method used for #1 - #3: CBP Interpolator (inverse dist. weighting) Hypoxic Volume (HV) = DO < 2 mg/L
#1) Observations Of 99 CBP stations (red dots), 30-65
are sampled each “cruise”, each cruise takes 1 to 2 weeks
#2) Modeled Absolute Match: Same 30-65 stations are “sampled” at
same time/place as observations are available
#3) Modeled Spatial Match: Same stations are “sampled” in space,
but samples are taken synoptically (i.e., all at once in time)
#4) Integrated 3D Model: Hypoxic Volume is computed from
integrating over all model grid cells(“CBP” = EPA Chesapeake Bay Program)
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CH3D-ICM
ChesROMS+1term
Observations-derived
= Absolute Match
Hypoxic Volume Estimates• When observations
and model are interpolated in same way, the match is reasonably good
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CH3D-ICM
ChesROMS+1term
Data-derived
= Absolute MatchCH3D-ICM
ChesROMS+1term
Observations-derived
Hypoxic Volume Estimates• When observations
and model are interpolated in same way, the match is reasonably good
• But interpolated HV underestimates actual HV for every cruise
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CH3D-ICM
ChesROMS+1term
Observations-derived
Hypoxic Volume Estimates
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• When observations and model are interpolated in same way, the match is reasonably good
• But interpolated HV underestimates actual HV for every cruise
• Much of this disparity could be due to temporal errors (red bars)
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• When observations and model are interpolated in same way, the match is reasonably good
• But interpolated HV underestimates actual HV for every cruise
• Much of this disparity could be due to temporal errors (red bars)
• Same pattern across all 4 models for both 2004 & 2005
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Spatial errors show interpolated HV is almost always too low (up to 5 km3)
The temporal errors from non-synoptic sampling can be as large as spatial errors (~5 km3)
Similar patterns across all 4 models for both 2004 & 2005
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OUTLINE
1. Relation to US-IOOS Modeling Testbed program and general methods.
2. Use 3D models to examine uncertainties in interpolating hypoxic volume.
• Observed DO has coarse spatial resolution = spatial error
• Observed DO is not a “snapshot” = temporal error
3. Use 3D models to improve EPA-CBP interpolations of hypoxic volume.
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Combining Observations & Numerical Model Results to Improve Estimates of Hypoxic Volume within the Chesapeake Bay
Aaron Bever, Marjy Friedrichs, Carl Friedrichs, Malcolm Scully, Lyon Lanerolle
Corresponding paper: JGR-Oceans, October 2013 issue
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Blue triangles = 13 selected CBP stations
Improving observation-derived hypoxic volumes
Reduce Temporal errors:
1. Choose subset of 13 CBP stations
2. Routinely sampled within 2.3 days of each other
3. Characterized by high DO variability
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Improving observation-derived hypoxic volumes
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Integrated 3D HV = (1 + CF) (Interpolated HV)
Smoothed approximation of CF vs. HV
Reduce Spatial errors:
1. For each model and each cruise, derive a Correction Factor (CF) as a function of interpolated HV that “corrects” this 13-station Spatial Match HV to equal the Integrated 3D HV.
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Reduce Spatial errors:
1. For each model and each cruise, derive a Correction Factor (CF) as a function of interpolated HV that “corrects” this 13-station Spatial Match HV to equal the Integrated 3D HV. 2. Apply smoothed CF (as a function of HV) to HV time-series
3. Scaling-corrected “interpolated” HV more accurately represents true HV
Before Scaling
AfterScaling
Improving observation-derived hypoxic volumes
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Blue triangles = 13 selected CBP stations
Improving observation-derived hypoxic volumes
Reduce Temporal errors:
1. Choose subset of 13 CBP stations
2. Routinely sampled within 2.3 days of each other
3. Characterized by high DO variability
But why 13 stations?
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Improving observation-derived hypoxic volumes
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Modeled Integrated 3D
vs.Spatial Match for
Different Station Sets
(a) 2004 (b) 2005
After being “scaling-corrected”, an interpolation based on these 13 stations did especially well in reproducing 3D HV.
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Interannual (1984-2012) corrected (i.e., scaled) time series of observed Hypoxic Volume
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Time-series of corrected hypoxic volume for 1984-2012 are provided within JGR article (annual maximum HV, annual duration of HV, annual cumulative HV), and corrected HV for every CBP cruise is provided in JGR electronic supplement.
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Information from multiple models (2004-2005) has been used to assess uncertainties in present CBP interpolated hypoxic volume estimates
• Temporal uncertainties: up to ~5 km3
• Spatial uncertainties: up to ~5 km3
These are significant, given maximum HV is ~10-15 km3
A method for correcting interpolated HV time series for temporal and spatial errors has been presented, based on the 3D structure of multiple model DO results
• 13 stations (sample in 2 days) do as well for HV as 40-60 or more• Corrected HV for 1984-2012 are downloadable from JGR website
Summary/Conclusions
Combining Observations & Numerical Model Results to Improve Estimates of Hypoxic Volume within the Chesapeake Bay
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