Transitioning a Chesapeake Bay
Ecological Prediction System to
Operations
D. Green1, C. Brown1, F. Aikman1, A. Siebers1, H. Tolman1, M.
Ji1, D. Levin2, C. Friedrichs3, M. Friedrichs3, and R. Hood4
1
NOAA, 2 Washington College, 3 VIMS College of William & Mary,
4 HPL/UMCES University of Maryland
January 24, 2012
Outline
• Ecological Forecasting
• Chesapeake Pathfinder Project
• Next steps
2
Ecological Forecasting for a
Weather Ready Nation
Predict impacts
• Biological, chemical,
physical, and humaninduced changes on
ecosystems ecosystem
components, and people.
Address “what if”
questions
• Resource management
Transition science
• Leverage infrastructure
3
Seamless Suite of Services
• Local, short term
nowcasting and
forecasting beach water
quality, living resource
distribution (oysters, sea
nettles), development of
harmful algal blooms,
pathogens,…
• Long term scenarios
and seasonal outlooks,
estimating sea grass
restoration, disease
outbreaks, eutrophication
and hypoxia reduction,
recruitment of fisheries
species…
4
Integrated Science
• Physical
Temperature
Salinity
Current velocity
Sea Surface Height
• Biogeochemical
Nutrients
Phytoplankton, Zooplankton
Dissolved oxygen
• Organismal
Sea Nettles
Water-borne pathogens
Harmful algal blooms
Enable Informed Decisions
Observations
Environmental
Modeling
Ecological
Forecasting
&
Decision
Support
Tools
Local Regional
Products
&
Services
for
Stakeholders
Partners &
Users
Research
System development & partnerships
Linking needed components
Scaling to local decision making
6
System use and sustainability
Pathfinder: Sea Nettle Forecasting
1. Forecast surface
salinity and
temperature fields
SST
Likelihood of Chrysaora
2. Apply habitat model
3. Generate image
illustrating the
likelihood of
encountering sea
nettles
Habitat Model
Salinity
4. Disseminate daily
and 3-day forecast to
users
7
Migration to the NOAA Chesapeake ROMS
 UMD/NOAA migrated ecological forecasting models to CBOFS2
 Higher resolution allows better bathymetric representation
 Improves simulation of physical processes (particularly salinity)
 Provides more accurate forcing for our empirical and mechanistic models
8
Transition to Operations
• Research and monitoring to provide data for
developing and validating forecast models (statistical
and process models to overlay on environmental
variable forecast
– Builds on NESDIS/NOS/NMFS/NWS/UMD research, data and
observations
• Operational backbone modeling suite to create
forecasts of environmental variables
– Leverages NOS-supplied Chesapeake Bay Operational Forecast
System (CBOFS2) model and is enabled by NCEP infrastructure
– Modeling testbed and proving ground
• Forecast office that works with regional management
agencies and structure (e.g., Chesapeake Bay
Program) to ensure utility of and support for forecast
– Dissemination of products through NWS and NOS/NMFS offices
and information tools
9
Planned Sea Nettle Forecast
Concept of Operations
NOAA Chesapeake Bay
Office
Weather Forecast Offices
Address biological questions
Include Nettle Forecasts in text alerts
Link to Nettle
Web Page
National Oceanographic
Data Center
AWIPS
Nettle Web Site
Archive ρnettles
and CBOFS
output
FTP
Nettle model guidance,
and CBOFS SST & SSS
Calculate ρnettles using
CBOFS SST & SSS
Run CBOFS
NCEP Central Operations
Center for Sat. Applications &
Research
Habitat Model, JellyCams, Satellite Data
Oceans
Center for Operational
Oceanographic Products
& Services
Stage CBOFS output
Ocean Prediction Center
Satellites
Coast Survey Development
Laboratory
Develop and update CBOFS
Weather
Fisheries
10
Next Steps
Testbed and Proving Ground
• Regional Earth System
Model-based Operations
• Fully integrates
ecosystem model suite
for the Chesapeake Bay
and its watershed
• Assimilates in-situ and
satellite-derived data by
adapting and coupling
existing models
• Uses coupled air, land,
and coastal ocean
models in products and
services
11
Conclusion: Its Just the Beginning…
• Regional prediction
system can be easily
extended to other
forecasts:
o Harmful algal blooms
o Water-borne
pathogens
o Dissolved oxygen
(hypoxia)
concentrations
o …
• Prediction system and
approach transportable
to other regions
Likelihood of Vibrio
vulnificus on 20 April
2011.
Relative
abundance of
Karlodinium
veneficum on 20
April 2005. Low: 010, med: 11-2000
cells/ml, high: >
2000 cells/ml.
12
Background Material
13
Expanding Regional Capabilities
Beach/Water Quality – Case Study
• Issue: Water quality risk due to microbial and chemical
contamination threatens human/ecosystem health and
economics
• Solution: Water (beach) quality guidance
• Operational Concept: Routinely generate forecasts and warnings
daily, weekly, seasonal (including lead times) using hydrologic,
waves, precipitation, circulation, transport turbidity, nutrients,
waste, watershed and land computational models
• Collaborators: Include state and local managers, scientists,
health workers, fishers and regulators
• Output Product: Near-real time maps and decision support tools
showing water quality index and long-term scenarios, bacterial
content, water temperature, turbidity, beach closures, habitat
suitability, stock assessments, categorical risk assessment
• Dissemination: Online, Factsheets, and Media
• Outcome: Actions taken to improve Bay and public health, clean
water, promote restoration, land and resource management,
adaptation, and research
Indicators and Indices
14
http://www.eco-check.org/reportcard/chesapeake/2007/
Harmful Algal Bloom (Chlorophyll)
Monitoring & Forecast System
• Issue: HABs threaten human health and natural
resources
• Solution: Predict nature, extent, development and
movement of HAB species in Bay and its tidal
tributaries.
• Operational Concept: Routinely generate forecasts
using data from hydrodynamic computer models
and NOAA satellites.
• Collaborators: Include state natural resource
partners
• Output Product: Near-real time maps showing
when and where to expect initiation and landfall
• Dissemination: Online and Media
• Outcome: Actions taken to monitor and mitigate
HAB effects.
Nowcast of K.veneficum abundance
(Experimental product)
http://155.206.18.162/cbay_hab/
15
Dissolved Oxygen [DO]
Monitoring & Forecast System
•
•
•
•
•
•
•
Issue: Some areas of the Bay have low oxygen levels
threatening survival of species.
Solution: Predictions and forecasts of hypoxia, including
uncertainty related to nutrient loading and river flow
Operational Concept: Routinely generate predictions and
forecasts on synoptic to seasonal scales using data from
hydrodynamic, circulation, watershed, atmospheric and water
quality models
Collaborators: Include state managers, scientists and fishers
Output Product: Maps and decision support tools showing
concentration and dead zones, habitat suitability, and marine
assessments
Dissemination: Online and Media
Outcome: Regional actions taken to promote restoration and
recovery
http://www.ecocheck.org/forecast/chesapea
ke/overview/
16
Living Resource Distribution/Oyster
Monitoring & Forecast System
•
•
•
•
•
•
•
Issue: Oyster populations are at low levels and productivity
varies depending on salinity, water quality, habitat conditions,
and disease.
Solution: Annual forecast of oyster biomass including
harvests and other related mortality/disease information
Operational Concept: Routinely generate forecasts and
outlooks using data from hydrodynamic, circulation,
watershed, water quality, atmospheric and ecosystem models
Collaborators: Include state managers, scientists and fishers
Output Product: Maps and decision support tools showing
habitat suitability, stock assessments, management and
larvae tracking
Dissemination: Online and Media
Outcome: Actions taken to promote oyster restoration and
disease research
Chesapeake Bay Oyster
Larvae Tracker (CBOLT)
http://csc.noaa.gov/cbolt/
17
Disease Pathogen Progression
&– Forecast System
• Issue: Monitoring
Bacterial and viral pathogens
microorganisms capable of causing disease threaten shellfish, fish species and human
health
• Solution: Predict nature, extent, and spatially
dependence of pathogens, including virulence
probabilities in Bay and tidal tributaries
• Operational Concept: Routinely generate
short- and long-term predictions using data
from hydrodynamic and climate models,
temperature and salinity, vibrio and multiple
species, pathogen models and remote
sensing data.
• Collaborators: Include water quality and
resource mangers, environmental, health and
safety planners, and health officials
• Output Product: Near-real time predictions
and maps showing when and where to expect
outbreaks or likelihood of occurrence, and
long-term scenarios
• Dissemination: Online, Factsheets and Media
Near-real-time maps of V. cholerae
likelihood Experimental product
http://155.206.18.162/pathogens/
18