Combining Observations & Numerical Model Results to Improve Estimates of Hypoxic Volume within the...

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

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)


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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)

,


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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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• 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

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)

Seven 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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Observed and Modeled Top-to-Bottom DS and Bottom DO in Central Chesapeake Bay

(by M. Scully)

Variability in DO is easier to model than salinity stratification. This is true for both the simple 1-term model (above) and the more complex EPA model.

plus 1-term DO model

ChesROMS model

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OUTLINE






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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


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


Hypoxic Volume Estimates

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• When observations and model are interpolated in same way, the match is reasonably good


• Much of this disparity could be due to temporal errors (red bars)

• When observations and model are interpolated in same way, the match is reasonably good


• 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






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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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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.

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


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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.

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


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Combining Observations & Numerical Model Results to Improve Estimates of Hypoxic Volume within the...

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