Transcript ppt

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.
3.
•
Observed DO has coarse spatial resolution = spatial error
•
Observed DO is not a “snapshot” = temporal error
Use 3D models to improve EPA-CBP interpolations of hypoxic volume.
Corresponding paper: JGR-Oceans, October 2013 issue
p.1 of 22
Combining Observations & Numerical Model Results to Improve
Estimates of Hypoxic Volume within the Chesapeake Bay
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)
p.2 of 22
Combining Observations & Numerical Model Results to Improve
Estimates of Hypoxic Volume within the Chesapeake Bay
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)
p.3 of 22
,
Combining Observations & Numerical Model Results to Improve
Estimates of Hypoxic Volume within the Chesapeake Bay
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)
p.3 of 22
,
Combining Observations & Numerical Model Results to Improve
Estimates of Hypoxic Volume within the Chesapeake Bay
General COMT Estuarine Hypoxia modeling methods:
• 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)
Motivation for Better Hypoxia Modeling for 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
p.4 of 22
Five hydrodynamic
models configured for
Chesapeake Bay
EFDC
Shen
VIMS
p.5 of 22
DC
UMCES-ROMS
Li & Li
UMCES
CH3D
Cerco & Wang
USACE
Five hydrodynamic
models configured for
Chesapeake Bay
TODAY’S TALK
EFDC
Shen
VIMS
CH3D
Cerco & Wang
USACE
Dx ~ 0.5 km
Dz ~ 1.5 m
Dx ~ 1 km
Dz ~ 1-2 m
Dx ~ 2 km
Dz ~ 1-2 m
p.5 of 22
UMCES-ROMS
Li & Li
UMCES
Eight dissolved oxygen (DO) models configured for the Bay
o ICM: EPA-CBP model; 27-component ecosystem
model (multi P, multi Z, C/N/P/Si/DO, etc.)
o BGCs: 3 NPZD-type (~10 component) models
o 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)
p.6 of 22
Eight dissolved oxygen (DO) models configured for the Bay
o ICM: EPA-CBP model; 27-component ecosystem
model (multi P, multi Z, C/N/P/Si/DO, etc.)
o BGCs: 3 NPZD-type (~10-component) models
o 1eqn: Simple one equation respiration
TODAY’S TALK
(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)
p.6 of 22
Coupled hydrodynamic-DO models
Today’s talk = Four combinations:
o
o
o
o
CH3D
CBOFS
ChesROMS
ChesROMS
+
+
+
+
ICM  EPA model
1term
1term
1term+DD
-- 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
p.7 of 22
Model skill: Bottom DO
Total RMSD2 = Bias2 + unbiased RMSD2
-- 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.
p.8 of 22
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
1.
Relation to US-IOOS Modeling Testbed program and general methods.
2.
Use 3D models to examine uncertainties in interpolating hypoxic volume.
3.
•
Observed DO has coarse spatial resolution = spatial error
•
Observed DO is not a “snapshot” = temporal error
Use 3D models to improve EPA-CBP interpolations of hypoxic volume.
Corresponding paper: JGR-Oceans, October 2013 issue
p.9 of 22
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
p.10 of 22
(“CBP” = EPA Chesapeake Bay Program)
Hypoxic Volume Estimates
• When observations
and model are
interpolated in
same way, the
match is
reasonably good
20
CH3D-ICM
= Absolute Match
Hypoxic Volume, km
3
10
0
20
ChesROMS+1term
10
0
20
Observations-derived
10
0
p.11 of 22
05/01
06/01
07/01
08/01
09/01
Date in 2004, Month/Day
10/01
11/01
Integrated 3D HV
Absolute Match
• But interpolated
HV underestimates
actual HV for every
cruise
06/01
07/01
08/01
09/01
Date in 2004, Month/Day
10/01
11/01
Integrated 3D HV
Absolute Match
20 CH3D-ICM
CH3D-ICM
= Absolute Match
10
3
0
• When observations
05/01
and model are
interpolated in
same way, the
match is
reasonably good
Hypoxic Volume Estimates
Hypoxic Volume, km
10
Cruise Date Range
0
20
ChesROMS+1term
ChesROMS+1term
10
0
20
Observations-derived
Data-derived
10
0
p.11 of 22
05/01
06/01
09/01
08/01
07/01
Date in 2004, Month/Day
10/01
11/01
Integrated 3D HV
Absolute Match
06/01
• 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)
07/01
08/01
09/01
Date in 2004, Month/Day
20
10/01
11/01
Integrated 3D HV
Absolute Match
Spatial Match
Spatial Match Range
Cruise Date Range
CH3D-ICM
10
3
05/01
Hypoxic Volume Estimates
Hypoxic Volume, km
0
0
20
ChesROMS+1term
10
0
20
=
Observations-derived
10
0
p.12 of 22
05/01
06/01
07/01
08/01
09/01
Date in 2004, Month/Day
10/01
11/01
Integrated 3D HV
Absolute Match
Spatial Match
Spatial Match Range
Date in 2004, Month/Day
• 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
p.13 of 22
Integrated 3D HV
Absolute Match
Spatial Match
Spatial Match Range
Cruise Date Range
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
p.14 of 22
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
1.
Relation to US-IOOS Modeling Testbed program and general methods.
2.
Use 3D models to examine uncertainties in interpolating hypoxic volume.
3.
•
Observed DO has coarse spatial resolution = spatial error
•
Observed DO is not a “snapshot” = temporal error
Use 3D models to improve EPA-CBP interpolations of hypoxic volume.
Corresponding paper: JGR-Oceans, October 2013 issue
p.15 of 22
Improving observation-derived hypoxic volumes
Blue triangles = 13 selected CBP stations
 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
p.16 of 22
Improving observation-derived hypoxic volumes
 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.
Smoothed approximation
of CF vs. HV
Integrated 3D HV = (1 + CF) (Interpolated HV)
p.17 of 22
Improving observation-derived hypoxic volumes
 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
p.18 of 22
Before
Scaling
After
Scaling
Improving observation-derived hypoxic volumes
Blue triangles = 13 selected CBP stations
 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?
p.19 of 22
Improving observation-derived hypoxic volumes
Modeled
Integrated 3D
vs.
Spatial Match for
Different Station Sets
(a) 2004
p.20 of 22
(b) 2005
After being “scaling-corrected”, an
interpolation based on these 13 stations
did especially well in reproducing 3D HV.
Interannual (1984-2012) corrected (i.e., scaled) time
series of observed Hypoxic Volume
 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.
p.21 of 22
Combining Observations & Numerical Model Results to Improve
Estimates of Hypoxic Volume within the Chesapeake Bay
Summary/Conclusions
 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
p.22 of 22