COMT
Chesapeake Bay Hypoxia Modeling
VIMS: Marjy Friedrichs (lead PI)
Carl Friedrichs (VIMS-PI)
Ike Irby (funded student)
Aaron Bever (consultant)
Jian Shen (collaborator)
Cathy Feng (collaborator)
WHOI: Malcolm Scully (WHOI-PI)
UMCES: Raleigh Hood (UMCES-PI)
Hao Wang (funded student)
Jeremy Testa (collaborator)
Wen Long (collaborator)
Meng Xia (collaborator)
NOAA-CSDL: Lyon Lanerolle (NOAA-PI)
Frank Aikman (collaborator)
EPA-CBP: Ping Wang, Lewis Linker, Carl Cerco (collaborators)
August 6-7, 2014
NOAA Center for Weather and Climate Prediction (NCWCP)
Chesapeake Hypoxia Objective
Assess the readiness/maturity of a suite of existing estuarine
community models for determining past, present and future hypoxia
events within the Chesapeake Bay, in order to accelerate the
transition of hypoxia model formulations and products from
“academic research” to “operational” centers
Chesapeake Hypoxia COMT “operational” centers include:
• Chesapeake Bay Ecological Prediction System (CBEPS)
- Quasi-operational short-term forecasts (R. Hood)
• NOAA/NOS/CO-OPS
- Truly-operational short-term forecasts (L. Lanerolle)
• EPA Chesapeake Bay Program (CBP)
- Regulatory scenario-based forecasts (M. Friedrichs, C. Friedrichs, I. Irby)
Outline
1. Short-term forecasts (days):
• CBOFS forecasting update
- M. Friedrichs and L. Lanerolle
• CBEPS forecasting update
- R. Hood
2. Seasonal forecasts (weeks/months):
• 2004 seasonal model comparisons
- M. Friedrichs and I. Irby
• 2014 seasonal forecasts by others
- C. Friedrichs
3. Interannual variability (~20-30 years):
• 1985-2005 model comparisons
- M. Friedrichs and A. Bever
• ChesROMS-1term interannual modeling
- M. Scully
4. Future Directions (M. Friedrichs)
Outline
1. Short-term forecasts (days):
• CBOFS forecasting update
- M. Friedrichs and L. Lanerolle
• CBEPS forecasting update
- R. Hood
2. Seasonal forecasts (weeks/months):
• 2004 seasonal model comparisons
- M. Friedrichs and I. Irby
• 2014 seasonal forecasts by others
- C. Friedrichs
3. Interannual variability (~20-30 years):
• 1985-2005 model comparisons
- M. Friedrichs and A. Bever
• ChesROMS-1term interannual modeling
- M. Scully
4. Future Directions (M. Friedrichs)
CBOFS
CBOFS Model Grid
NOAA’s Chesapeake Bay Operational
Forecast System (CBOFS)
Description:
•
•
•
•
•
•
Based on Regional Ocean Modeling System
(ROMS)
Bathymetry: cut-off at 2m depth (NOS)
Init Conds: NOAA T, S climatology for lower
Bay and CBP profiles for upper Bay
Rivers: discharge = USGS; T, S = CBP
Outer Bdy Conds: T, S = NOAA climatology
Outer Bdy Tides: tidal harmonic constituents
for WL and barotropic currents from ADCIRC
database
CBOFS Forecast Sites
Wind, water elevation, currents, T and S at many of the locations (not yet DO)
24 hour nowcast (with model-data comparison); 48 hour forecast
CBOFS model output
= observation
= nowcast
= forecast
Baltimore
Wind
Baltimore
Water Elevation
Baltimore Wind
Baltimore
Water Elevation
8/5
8/6
Time (EDT)
8/7
8/8
August 5, 2014
August 5, 2014
CBOFS model output
= observation
= nowcast
= forecast
Baltimore
Temperature
Baltimore
Salinity
Baltimore
Temperature
Baltimore
Salinity
8/5
8/6
Time (EDT)
8/7
8/8
August 5, 2014
August 5, 2014
CBOFS
CBOFS Model Grid
NOAA’s Chesapeake Bay Operational
Forecast System (CBOFS)
Proposed Improvements:
•
•
•
•
Add precipitation ✔
Newer version of ROMS ✔
Improved advection scheme for Bay ?
Addition of dissolved oxygen (Year 2)
Outline
1. Short-term forecasts (days):
• CBOFS forecasting update
- M. Friedrichs and L. Lanerolle
• CBEPS forecasting update
- R. Hood
2. Seasonal forecasts (weeks/months):
• 2004 seasonal model comparisons
- M. Friedrichs and I. Irby
• 2014 seasonal forecasts by others
- C. Friedrichs
3. Interannual variability (~20-30 years):
• 1985-2005 model comparisons
- M. Friedrichs and A. Bever
• ChesROMS-1term interannual modeling
- M. Scully
4. Future Directions (M. Friedrichs)
CBEPS
Chesapeake Bay Ecological Prediction System (CBEPS)
CBEPS is a parallel research operational model to CBOFS running at
UMCES.
Provides “operational” nowcasts and short-term (3-day) forecasts of Sea
Nettle, HAB, pathogen and also physical and biogeochemical properties for
research, management and public uses in Chesapeake Bay.
Sea Nettle forecasts have been transitioned to 24/7 operational mode in
NOAA using CBOFS.
CBEPS provides a testbed for other prototype/research operational
models.
We are currently focusing on mechanistic oxygen model tuning and skill
assessment.
CBEPS
ChesROMS Implementation and Availability
ChesROMS is the hydrodynamic engine that drives
CBEPS.
It is a Chesapeake Bay implementation of the
Regional Ocean Modelling System (ROMS version
3.0).
 Curvilinear horizontal grid (100 * 150).
 Sigma-coordinate vertical grid (20 levels).
 Includes all major tributaries.
 Both hindcast and operational implementations at
UMCES.
 Open Source (SourceForge).
CBEPS
Four Empirical Habitat Models for Ecological Forecasts
• Sea Nettles (Decker et al, 2007)
logistic regression model, based on T and S
• Karlodinium veneficum (Brown et al. 2013)
Neural Network based on T and S, and time of year
•
Vibrio cholera (Constantin de Magny et al., 2010)
logistic regression model, based on T and S
•
Vibrio vulnificus (Jacobs et al., 2010; 2014)
logistic regression model, based on T and S
CBEPS Ecological Forecasts (Sea Nettles and V. vulnificus)
 Sea Nettles (Chrysaora quinquecirrha) can become very abundant in Chesapeake Bay during
summer and they sting people.
 Vibrio vulnificus also becomes abundant during summer and infection is a potential human
health threat.
 T and S strongly constrain sea nettle and V. vulnificus distributions.
 Estimate (nowcast and forecast) T and S using ChesROMS.
 Provides input to empirical logistic regression models that predicts probability of sea nettle
and V. vulnificus occurrence.
CBEPS
Nowcasts/forecasts generated daily and posted on website
Surface Temperature
20 Jan 2014
Surface Salinity
Sea Nettles
CBEPS Operational Biogeochemical Model
 Based on Fennel et al. core
model bundled with ROMS
 NPZD type model with
parameterized/fixed sediment
denitrification
 Full oxygen model with air-sea
exchange
 Added DON
 ISS loading and sediment transport
 Atmospheric N deposition
 Diffuse N sources
 Anoxic/variable benthic
denitrification
 Water column denitrification
Brown et al. (2013) and manuscript in preparation, Wiggert et al. (2014)
CBEPS
Chesapeake Bay Ecological Prediction System (CBEPS)
Proposed Improvements/tasks:
•
•
•
•
•
Migrate CBEPS/ChesROMS to ROMS version 3.6
Fully tune and assess skill of our mechanistic biogeochemical oxygen model using a
25-year hindcast.
Implement a new web interface for CBEPS at UMCES.
Compare skill of multiple models from simple to complex using 25-year hindcasts
Transition these models to CBEPS to provide an ensemble of Chesapeake Bay
oxygen nowcasts and 3-day forecasts for operational intercomparison.
Outline
1. Short-term forecasts (days):
• CBOFS forecasting update
- M. Friedrichs and L. Lanerolle
• CBEPS forecasting update
- R. Hood
2. Seasonal forecasts (weeks/months):
• 2004 seasonal model comparisons
- M. Friedrichs and I. Irby
• Success of 2014 forecasts by others
- C. Friedrichs
3. Interannual variability (~20-30 years):
• 1985-2005 model comparisons
- M. Friedrichs and A. Bever
• ChesROMS-1term interannual modeling
- M. Scully
4. Future Directions (M. Friedrichs)
Chesapeake Seasonal Comparisons
To assess the relative skill of our suite of Chesapeake
Bay hypoxia models on seasonal time scales, we are:
• Statistically comparing output from six Chesapeake Bay
models for 2004 (and 2005):
– Five ROMS models with varying biological complexity:
ChesROMS-ECB, ChesROMS-BGC, ROMS-RCA
ChesROMS-1term, CBOFS-1term (constant biology)
– EPA regulatory/operational biologically sophisticated model:
CH3D-ICM
– FVCOM unstructured grid (hydrodynamics only, coming soon…)
• Examining how well they reproduce the mean and
spatial/seasonal variability of:
– temperature, salinity, stratification, dissolved oxygen (DO),
chlorophyll-a, and nitrate
Chesapeake Hypoxia Model Comparisons
Compare
simulations to
observations at 10
main stem stations
for ~16 cruises in
2004 (and 2005)
Model Skill Assessment via Target Diagrams
2004 Model Comparison
normalized
bias
normalized
unbiased
RMSD
CH3D – ICM (EPA)
ChesROMS – ECB
ChesROMS – BGC
ROMS – RCA
ChesROMS – 1term
CBOFS
Bottom Temp
Bottom Salinity
Overall skill of all models (temporal + spatial variability):
• High in terms of bottom T and S
2004 Model Comparison
normalized
bias
normalized
unbiased
RMSD
CH3D – ICM (EPA)
ChesROMS – ECB
ChesROMS – BGC
ROMS – RCA
ChesROMS – 1term
CBOFS
Bottom Temp
Bottom Salinity
Stratification (max dS/dz)
Overall skill of all models (temporal + spatial variability):
• High in terms of bottom T and S
• Lower in terms of stratification
2004 Model Comparison
normalized
bias
normalized
unbiased
RMSD
CH3D – ICM (EPA)
ChesROMS – ECB
ChesROMS – BGC
ROMS – RCA
ChesROMS – 1term
CBOFS
Bottom Temp
Bottom Salinity
Stratification (max dS/dz)
Surface chlorophyll
Bottom nitrate
Overall skill of all models (temporal + spatial variability):
• High in terms of bottom T and S
• Lower in terms of stratification AND chlorophyll, nitrate
2004 Model Comparison
normalized
bias
normalized
unbiased
RMSD
CH3D – ICM (EPA)
ChesROMS – ECB
ChesROMS – BGC
ROMS – RCA
ChesROMS – 1term
CBOFS
Bottom Temp
Bottom Salinity
Stratification (max dS/dz)
Surface chlorophyll
Bottom nitrate
Bottom DO
Overall skill of all models (temporal + spatial variability):
• High in terms of bottom T and S
• Lower in terms of stratification AND chlorophyll, nitrate
• Models can reproduce seasonal DO without correct dS/dz & bio
2004 Model Comparison
normalized
bias
normalized
unbiased
RMSD
CH3D – ICM (EPA)
ChesROMS – ECB
ChesROMS – BGC
ROMS – RCA
ChesROMS – 1term
CBOFS
Bottom Temp
Bottom Salinity
Stratification (max dS/dz)
Surface chlorophyll
Bottom nitrate
Bottom DO
control run
Overall skill of all models (temporal + spatial variability):
• Low mixing experiment
2004 Model Comparison
normalized
bias
normalized
unbiased
RMSD
low
mixing
CH3D – ICM (EPA)
ChesROMS – ECB
ChesROMS – BGC
ROMS – RCA
ChesROMS – 1term
CBOFS
Bottom Temp
Bottom Salinity
Stratification (max dS/dz)
Surface chlorophyll
Bottom nitrate
Bottom DO
control run
Overall skill of all models (temporal + spatial variability):
• Low mixing experiment : stratification, bottom S and T
• Stratification skill
• Bottom T, S and DO skill
Temporal skill at individual stations
2004 Stratification (max dS/dz)
°North
ROMS-RCA
CBOFS
Latitude
CH3D-ICM
ChesROMS-ECB
ChesROMS-BGC
ChesROMS-1term
°South
All models consistently underestimate both the mean and seasonal
variability of stratification, particularly at the northern stations
Temporal skill at individual stations
2004 Chlorophyll
°North
ROMS-RCA
CBOFS
Latitude
CH3D-ICM
ChesROMS-ECB
ChesROMS-BGC
ChesROMS-1term
°South
Models have varying degrees of low skill for chlorophyll
Temporal skill at individual stations
2004 Bottom DO
°North
Latitude
CH3D-ICM
ChesROMS-ECB
ROMS-RCA
ChesROMS-BGC
CBOFS
ChesROMS-1term
°South
Despite underestimation of stratification & varying performance between models for
chl and nitrate, models still reproduce mean and seasonal variability of DO similarly well
2004 Comparison Implications
• Models reproduce bottom DO better than variables that are
primary influences on DO (stratification, chlorophyll, nitrate)
 Seasonal DO variability is sensitive to temperature (solubility effect),
and the models reproduce temperature very well
 DO may be very sensitive to future increases in Bay temperature
• Multiple models have similar skill as EPA model in terms of
seasonal variability along the main stem of Chesapeake for T, S,
stratification and DO
 More confidence in regulatory model results for DO
 Models do similarly well, regardless of complexity
 Hypoxia forecasting is possible with simple
biological formulations (for time scales < 1 year)
Outline
1. Short-term forecasts (days):
• CBOFS forecasting update
- M. Friedrichs and L. Lanerolle
• CBEPS forecasting update
- R. Hood
2. Seasonal forecasts (weeks/months):
• 2004 seasonal model comparisons
- M. Friedrichs and I. Irby
• 2014 seasonal forecasts by others
- C. Friedrichs
3. Interannual variability (~20-30 years):
• 1985-2005 model comparisons
- M. Friedrichs and A. Bever
• ChesROMS-1term interannual modeling
- M. Scully
4. Future Directions (M. Friedrichs)
Hypoxia Forecasts by Others for July 2014
http://ian.umces.edu/ecocheck/forecast/chesapeake-bay/2014/
(also announced via NOAA/USGS joint press release on 6/24/14)
(1)
(2)
Forecast (1) by Scavia & Evans, University of Michigan (NOAA/OAR/CPO/RISA/GLISA)
Forecast (2) by Testa & Murphy, UMCES/CBP (NOAA/NMFS/OHC/CBPO)
Statistical forecast approach
Forecast (1) by Scavia & Evans,
University of Michigan. Non-linear
function of Jan-May nitrogen input,
with coefficients calibrated by
previous three years of Jan-May
nitrogen input:
(red =
pre-1985)
Forecast (2) by Testa & Murphy, UMCES/Johns Hopkins. Linear regression based on 19852012, except for 1993 outlier:
34
Hypoxia Observation for early July 2014
Source: MD DNR
95% range
predicted by
Scavia &
Evans
Testa &
Murphy
(Scaled to
hypoxic
volume for
Maryland
main stem.)
Hypoxia Observation for early July 2014
LOWEST HYPOXIC VOL MEASURED IN 30 YEAR HISTORY OF CBP
“Likely the result of sustained winds of Arthur on July 4th”
-- Maryland DNR, 18 July 2014
Source: MD DNR
95% range
predicted by
Scavia &
Evans
Testa &
Murphy
OBSERV.
July 7-10
Hypoxia Observation for late July 2014
Source: MD DNR
95% range
predicted by
Scavia &
Evans
Testa &
Murphy
(Scaled to
hypoxic
volume for
Maryland
main stem.)
Hypoxia Observation for late July 2014
STILL LOWEST HYPOXIC VOL MEASURED IN 30 YEAR HISTORY
“Mild temperatures may have helped the destratification”
-- Maryland DNR, 29 July 2014
Source: MD DNR
95% range
predicted by
Scavia &
Evans
Testa &
Murphy
OBSERV.
July 21-24
Outline
1. Short-term forecasts (days):
• CBOFS forecasting update
- M. Friedrichs and L. Lanerolle
• CBEPS forecasting update
- R. Hood
2. Seasonal forecasts (weeks/months):
• 2004 seasonal model comparisons
- M. Friedrichs and I. Irby
• 2014 seasonal forecasts by others
- C. Friedrichs
3. Interannual variability (~20-30 years):
• 1985-2005 model comparisons
- M. Friedrichs and A. Bever
• ChesROMS-1term interannual modeling
- M. Scully
4. Future Directions (M. Friedrichs)
20-year Hypoxic Volume comparison
Interpolated:
observations
ChesROMS-1term
CH3D-ICM
based on 13
main stem stations
20-year Hypoxic Volume comparison
complex EPA model
slope = 0.52 ± 0.02
R2 = 0.57
constant biology model
slope = 1.03 ± 0.02
R2 = 0.84
Interpolated:
observations
ChesROMS-1term
CH3D-ICM
On interannual time scales, constant biology (1-term) model does
significantly better than the regulatory model in terms of reproducing our
best estimate of hypoxic volume
Outline
1. Short-term forecasts (days):
• CBOFS forecasting update
- M. Friedrichs and L. Lanerolle
• CBEPS forecasting update
- R. Hood
2. Seasonal forecasts (weeks/months):
• 2004 seasonal model comparisons
- M. Friedrichs and I. Irby
• 2014 seasonal forecasts by others
- C. Friedrichs
3. Interannual variability (~20-30 years):
• 1985-2005 model comparisons
- M. Friedrichs and A. Bever
• ChesROMS-1term interannual modeling
- M. Scully
4. Future Directions (M. Friedrichs)
A simple model with a 1-term dissolved oxygen formulation (no biological
variability) can capture the seasonal cycle of hypoxia in Chesapeake Bay.
Can this same model capture true inter-annual variability of hypoxic volume?
Simulation Period: 1984 – 2013 (30 years)
Forcing: 1) River Discharge from USGS gauging stations
2) Observed tides and sub-tidal elevation (Duck, NC and Lewes, DE)
3) Atmospheric surface fluxes from NCEP North American Regional
Reanalysis (NARR), including wind.
4) Oceanic temperature and salinity from World Ocean Atlas
Modeled Bottom DO
July
1989
July
1999
Comparison between Modeled and Observed Hypoxic
Volumes (CBP data)
Monthly Correlations (r) between Model and CBP data
r=0.82
May
June
July
Aug
Sept
< 2.0
mg/L
0.13
0.59
0.64
0.66
0.58
< 1.0
mg/L
0.21
0.49
0.61
0.67
0.60
< 0.2
mg/L
0.07
0.36
0.45
0.46
0.45
Comparison (r) between Observed Hypoxic Volumes and
Susquehanna Nitrogen Loading (Jan - May)
May
June
July
Aug
Sept
< 2.0 mg/L
0.32
0.41
0.58
0.39
0.15
< 1.0 mg/L
0.28
0.49
0.66
0.49
0.16
< 0.2 mg/L
0.31
0.62
0.76
0.39
0.09
Comparison (r) between Modeled and Observed Hypoxic Volumes (CBP data)
May
June
July
Aug
Sept
< 2.0 mg/L
0.13
0.59
0.64
0.66
0.58
< 1.0 mg/L
0.21
0.49
0.61
0.67
0.60
< 0.2 mg/L
0.07
0.36
0.45
0.46
0.45
What Variables are Most Important for Inter-Annual
Variability of Hypoxic Volume (June-Aug)?
OBSERVATIONS
Jan-May
River
Discharge
Jan-May
Nitrogen
Load
June-Aug
Wind
Speed
March-May
Stratification
< 2.0 mg/L
0.73
0.69
-0.44
0.70
< 1.0 mg/L
0.79
0.75
-0.41
0.76
< 0.2 mg/L
0.75
0.80
0.07
0.57
1-Term Model
Jan-May
River
Discharge
Jan-May
Nitrogen
Load
June-Aug
Wind
Speed
March-May
Stratification
< 2.0 mg/L
0.36
0.36
-0.85
0.46
< 1.0 mg/L
0.37
0.37
-0.84
0.47
< 0.2 mg/L
0.37
0.36
-0.83
0.47
Summary:
1) 1-term model is more correlated with observed hypoxic volumes
than observed anoxic volumes suggesting hypoxia is more
physically controlled and anoxia is more biogeochemically
controlled.
2) 1-term model is not very sensitive to changes in river discharge
suggesting that the significant relationship between observed
hypoxic volumes and discharge is largely a biological response to
nutrient delivery.
3) Inter-annual variability in the 1-term model is largely driven by
variations in summer wind speed. Observations show similar, but
weaker response.
4) Because of the strong relationship between river discharge, nitrogen
load and spring stratification, the strong correlation between spring
stratification and observed hypoxic volumes represents both a
physical and biological response.
5) Inter-annual variability in 1-term model is correlated (weakly) with
spring stratification and as result, exhibits variability that is consistent
with the biological variability seen in the observations (even though
the model does not account for biological processes!)
Future Work
1. Short-term forecasts (days):
•
•
CBOFS – new advection scheme and new 1term model
CBEPS – implement a new web interface; transition multiple
models to provide ensemble of CB DO forecasts
2. Seasonal forecasts (weeks/months):
•
•
2004-05 seasonal comparisons – incorporate FVCOM results;
complete manuscript
2014 seasonal forecasts – assess success of existing forecasts
using multiple models
3. Interannual variability (~20-30 years):
•
•
1985-2005 model comparisons – complete interannual runs for
additional models
ChesROMS-1term interannual modeling – complete manuscript
4. Scenario-based forecasts:
•
Redo EPA’s Chesapeake TMDL using alternate model
Extra Slides
Spatial variability of stratification for each month
CH3D
ChesROMS
EFDC
CBOFS
month
UMCES-ROMS