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Transcript of Ecological Forecasting for the Great Lakes Regional Data Exchange Workshop University at Buffalo May...
Ecological Forecasting for the Great Lakes
Regional Data Exchange WorkshopUniversity at Buffalo
May 15, 2008
Joseph AtkinsonGreat Lakes ProgramUniversity at Buffalo
Use of models in management/decision makingIn
crea
sing
util
ity
Data
Information
Knowledge and understanding
Decision making
Increasing resource and knowledge requirements
Modeling
Analysis and visualization
Synthesis and forecasting
Adaptive management
Management/modeling Issues• Water quantity and flows (hydrologic model)
– Hydropower, shipping, recreational boating – Controls at Lake Superior and Lake Ontario– Diversions – Changes in habitat (wetlands), fisheries
• Pollution, eutrophication (hydrodynamic, nutrients)– Algal blooms and HABs
• Invasive species (ecological model)• Persistent toxic chemicals (water quality model)
– Organics, metals, etc.; bioaccumulation– Contaminated sediments (IJC areas of concern)
(sediment transport model)• Climate change (multiple concerns, models)
Focus on integrated modeling approaches
Other issues/features• International waters• Closed basin circulation• Coastal flows, upwelling and downwelling• River/lake interactions• Vertical suspended solids structure (benthic
nepheloid layer)– Cycling of organics
• Vertical and horizontal (thermal bar) stratification
• Water/sediment interactions• Atmospheric deposition and exchange• Point and non-point source loads
What is a model?• Idealized representation of the real system
– Conceptual– Simple analytical– Physical
– Mathematical (numerical)
– Expressed in terms of “governing equations”• Differential equations describing conservation statements
(mass, momentum, energy, etc.)– Constitutive relations (equation of state, coefficients)– Incorporate approximations --- “all models are wrong”
• Scale and resolution (time and space)• Processes to be considered• Numerical approximations (computer solutions)
Light
Nutrients
Temperature
Grazing, mortality
Algal biomass kCtd
Cd
t
C
What are models used for?
• Integrate and synthesize data– ex: water level regulation in Lake Ontario
• Simulate the “real world”– Demonstrate understanding of system
• Allow experimentation, evaluation of “what if” scenarios
• Convey results – Graphics, tables, etc.– Management support, options, risk
Model application
Problem statement
CalibrationConfirmation
(system understanding)
Conceptual frameworkManagement, scientific questions
Processes to consider,Resolution
Model formulation
Scenarios(test management
options)
Solution method
Risk and uncertainty
iteration
Data
Examples
• Algal bloom monitoring and modeling (MERHAB)
• Source locations and resource sheds
• Integrated coastal ecosystem model
• New York Ocean and Great Lakes Ecosystem Conservation
• Sediment transport
Hydrodynamic and particle tracking tools
• Three-dimensional hydrodynamic model (Princeton Ocean Model, POM)
• Uses actual or historic meteorological data– Forecasting based on actual, current conditions
• Current applications using surface velocity field– Any level can be used
• POM produces velocity and diffusion fields
Hydrodynamic and particle tracking tools (con’d)
• Lagrangian (particle tracking) approach – Random walk algorithm– Conservative, passively transported particles
(like a water molecule)
• Gridless model, but interpolates from POM grid values
Random walk algorithm
Deterministic component = real velocity + pseudo velocity;Stochastic component = random walk based on diffusivity
tDzt
x
Duxx x
xnn
21In x direction,
(similar for y direction)
Iterative approach used to account for changes in velocity and diffusivity values at initial and final location
Particle movement = deterministic component + stochastic component
deterministic stochastic
Application to Lake Erie
• Forward and backward tracking
• August and May conditions– General circulation– Source areas
• One-day, one-week and one-month resource shed simulations
• Connection with watershed model
“Particles” move with predicted water flow
General circulation Point release (bloom tracking)
Forward tracking
Source regions - Western Basin Lake Erie
“Long-term” vision - MERHAB-LGL project
(Monitoring and Event Response for Harmful Algal Blooms)
• Provide predictions of algal bloom growth and movement, with certainty estimates, to predict potential impacts in Great Lakes basin
– “Early warning system”/management tool– Focus on Lakes Erie and Ontario
Approach
• Run hydrodynamic model (POM) continuously– Maintain initial conditions for forecast runs
• Click on map of lake, or enter location (web based application)
• Run hydrodynamic model for desired forecast period (several days to several weeks)
– Historical or forecast meteorological data – Produce velocity and diffusivity fields
• Run particle tracking/population model– Different modes possible:
o Multiple “particles”o Backtracking
User interface
Database
(MySQL)
Input module
Hydrodynamic model
(POM)
Data sources
(NOAA/NWS)
Run/Forecast module
Execution module
Output module
Particle tracking model
(PTM)
Basic system arrangement (web-based modeling interface):
Resource sheds - overview
• Resource sheds in coastal waters (Great Lakes)– Motivation– What are they?
• Hydrodynamic and particle tracking tools
• Application to Lake Erie
• Integration with watershed model
Motivation
• Determine source of materials (resources) to a particular area– Zebra mussels– Algae blooms
• Understand physical “connectivity” among different areas of the lake
What are they?(how are they calculated?)
• Particle tracking, used in combination with hydrodynamic model, to illustrate circulation and flow patterns– backtracking
• “Single release” – all locations from which materials originate at a common time– One day, one week, one month, etc.
• Pathlines – full trajectories over time period of interest
• “Continuous release” - particle positions plotted for continuous release to “fill in” all locations that may be contributing to a location of interest during the chosen time period
One-day backtracks (August)
One-week
One-week (May)
One-month
Density plots
Example Resource Shed Distributions Defined with Particle BacktrackingExample Resource Shed Distributions Defined with Particle Backtracking(in Western & Central Lake Erie)(in Western & Central Lake Erie)
1 day1 day
1 week1 week
2 weeks2 weeks
3 weeks3 weeks
1 month1 month
Central Basin Site 311 August 31Central Basin Site 311 August 31
00
maxmax
Example Resource Shed Distributions Defined with Particle BacktrackingExample Resource Shed Distributions Defined with Particle Backtracking(in Western & Central Lake Erie)(in Western & Central Lake Erie)
1 day1 day
1 week1 week
2 weeks2 weeks
3 weeks3 weeks
1 month1 month
Western Basin Site 835 August 31Western Basin Site 835 August 31
00
maxmax
General components – coastal ecosystem model
(intensive monitoring study in Lake Ontario summer 2008)
• Want to test “biological filtering”, or “near-shore shunt” hypothesis
• Include interactions with shore and with open water
• Combined physical/chemical/biological structure• Synthesize data, evaluate system responses to
various stressors, provide predictive capabilities (hypothesis testing)
Considerations
• Define state variables
• Desired temporal and spatial resolution– Nested model?– Same resolution for all components?
• Data availability
• Match watershed model(s) with lake model
• Time period of simulation
Data needs
• Meteorological (wind speed and direction, air temp., dew point, etc.)
• Point, non-point sources– Flows, temperatures, concentrations, ….
• Benthic conditions– Sediment, algae, ….
• In-lake currents and temperatures, concentrations, ….
• Desired level of detail in time and space
Possible approaches (model team)
• Existing models:– POM (hydrodynamic)– Saginaw Bay model (food web interactions,
bioaccumulation)– Particle tracking– LOTOX (water quality)– Delft/Elcom (hydrodynamics, water quality)– Cladophora growth– Watershed (?) – SWAT, other– Others (?)
• Canada/US – 3 focus areas each (proposed)
Proposed model
Coastal zone ecosystem model
Watershed,hydrological
Hydrodynamics
Particle tracking
Sediment transport
Ecological (nutrients, lower food web)
Chemical fate and transport
Cladophora growth
Input data:Geometry, bathymetry, topographyLand use, soil typeInitial conditionsMeteorology
Output:Tributary flows, loadingsLake circulation, water temperature, bottom shearP concentrations, biomass
Simple 2 - box model
inflows outflows
transport
Near-shore region
Off-shore region
“Basic” model• Mass balance for near-shore (NS) region:
or
• Mass balance for off-shore (OS) region:
or
NSNSNSOSNSexNSinNSNS VCkCCQQCQC
dt
VCd
OSOSOSOSNSexOSOS VCkCCQ
dt
VCd
NS
OSexinNSNS
NS
exNS
V
CQQCCk
V
dt
dC
NSOS
exOSOS
OS
exOS CV
QCk
V
Q
dt
dC
Sample results
0.0000001
0.000001
0.00001
0.0001
0.001
0.01
0.1
1
0 10 20 30 40 50
Time (days)
C (
mg
/l)
C_NS C_OS
Conclusions
• We’re ready