Ecological Forecasting for the Great Lakes Regional Data Exchange Workshop University at Buffalo May 15, 2008 Joseph Atkinson Great Lakes Program University at Buffalo

Use of models in management/decision making Increasing utility 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 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 Calibration Confirmation (system understanding) Conceptual framework Management, 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 In 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: oMultiple “particles” oBacktracking

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 Backtracking (in Western & Central Lake Erie) 1 day 1 week 2 weeks 3 weeks 1 month Central Basin Site 311 August 31 0 max

Example Resource Shed Distributions Defined with Particle Backtracking (in Western & Central Lake Erie) 1 day 1 week 2 weeks 3 weeks 1 month Western Basin Site 835 August 31 0 max

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, topography Land use, soil type Initial conditions Meteorology Output: Tributary flows, loadings Lake circulation, water temperature, bottom shear P 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

Sample results

Conclusions We’re ready