PDF version - Chesapeake Community Modeling Program

Transcription

Models of the Chesapeake Basin
Kevin Sellner
Executive Director
Chesapeake Research Consortium
www.chesapeake.org
G. Brush Class, JHU 10/22/09
Outline
•
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General
Air
Watersheds
Bay Physics
Biogeochemistry
Food web
Nowcasts/Forecasts
Climate Change?
Hydrologic Budget
3
Need data to describe hydrologic processes in watershed
B. Benham, VA Tech; brbenham@exchange.vt.edu
Watershed Characterization
Hydrography Soils Land Use Topography Pollutants
Management
Practices
Elevation
Watershed
Boundaries
Canopy Cover
Surface Roughness
Ponds
Percent Impervious
Erodibility
Streams
Cross-Sections
Slope
Rainfall
487 daily stations
192 hourly stations
Monthly Regression of
Latitude
Longitude
Altitude
Daily Intercept
1984-2005
5
Lauren Hay – USGS NRP
CMAQ Model
The Community Multiscale Air
Quality Model (CMAQ) has a domain
that covers the North American
continent at a 36 km x 36 km grid
scale and is nested at a finer 12 km x
12 km grid scale over the Chesapeake
watershed and Bay.
6
Atmospheric Deposition Estimates
Combining
a regression
model of
wetfall
deposition...
Jim Lynch, Jeff Grimm
Penn State
…with
CMAQ
estimates
of dry
deposition
for the
base…
Robin Dennis, EPA/
NOAA
…and using the
power of the
CMAQ model for
7
scenarios.
8
Important Considerations: Modeling Accuracy
9
Model Application Considerations
SPARROW (SPAtially Referenced
Regression on Watershed Attributes)
Smith et al., Water Resour. Res., 1997
Monitoring Data
Aquatic Landscape
Terrestrial Landscape
Predicted Flux,
Concentration and
Yield: Origin and Fate
Spatial arrangement of land and water
properties matters!
Steve Preston [spreston@usgs.gov]
Incremental Reach Watershed
Use reach network to relate watershed data
to monitored loads
Load leaving
the reach
=
Load generated within
upstream reaches and
transported to the reach via the
stream network
Load originating within the
reach’s incremental watershed
+ and delivered to the reach
segment
SPARROW - Example Applications
Identification of Spatial Distribution of Source Areas for
Understanding Impacts on Receiving Water Bodies
Chesapeake Bay SPARROW Model
HOT SPOT ID!
•uses a nonlinear regression model
approach
•annual output
•source variables include point
sources, urban area, fertilizer
application, manure generation, and
atmospheric deposition
• other variables include soil
permeability and instream-loss rates
for four stream-reach classes
HSPF (Hydrologic Simulation Program Fortran)
Watershed Model
Snapshot:
Hourly output is summed over
10 years of hydrology to
compare against other
management scenarios
Hourly Values:
Rainfall
Snowfall
Temperature
Evapotranspiration
Wind
Solar Radiation
Dewpoint
Cloud Cover
HSPF
Land Use Acreage
BMPs
Fertilizer
Manure
Atmospheric Deposition
Point Sources
Septic Loads
1991-2000
“Average Annual
Flow-Adjusted Loads”
13
G. Shenk, gshenk@chesapeakebay.net
Each segment consists of
separately-modeled land uses
•
•
•
•
•
•
•
•
High Density Pervious Urban
High Density Impervious Urban
Low Density Pervious Urban
Low Density Impervious Urban
Construction
Extractive
Wooded
Disturbed Forest
Plus Point Source
and Septic
• Corn/Soy/Wheat rotation (high
till)
• Corn/Soy/Wheat rotation (low
till)
• Other Crops
• Alfalfa
• Nursery
• Pasture
• Degraded Riparian Pasture
• Animal Feeding Operations
• Fertilized Hay
• Unfertilized Hay
– Nutrient management versions
of the above
14
A software solution was devised that
directs the appropriate water, nutrients,
and sediment from each land use type
within each land segment to each river
segment
External
Transfer
Module
N, P, Sediment
load
Each land use type simulation is completely independent.
Each river simulation is dependent on the local land use
type simulations and the upstream river simulations.
15
• Goal
Chesapeake Bay Watershed
Modeling at ESSIC/UMCP
Model water quality and quantity of the
Chesapeake Bay Watershed (CBW)
Provide river discharge, sediment, and
nutrient forecast for the prominent
tributaries in CBW
• Approach
Adopt a hydrologic and water quantity
model – Soil and Water Assessment
Tool (SWAT) for modeling CBW
An independent SWAT model for each
major river basin and some secondary
river basins on the MD shores
Pilot river basin: Rappahannock River
basin
Huan.Meng@noaa.gov
• Rappahannock SWAT
Model initial set-up requires information on topography, land
use, soil, point and non-point nutrient sources, etc.
Model calibration and validation using USGS observations of
flow, sediment, and nutrient
Model driving force: weather (solar radiation, temperature,
precipitation, wind speed, relative humidity)
Rappahannock SWAT has been successfully calibrated and validated
for river discharge, sediment load, and nutrient loads.
• Application
– A component of the Chesapeake Bay
Forecast System (CBFS) project at ESSIC/
UMCP - CBFS is an integrated atmosphere/
land/ocean (Bay) earth system
– Provides 14-day forecast of river flow,
sediment load , and nutrient loads for
Rappahannock River on a daily basis.
– The model output is used in the CBFS ocean
model as input from the Rappahannock
River basin.
Huan.Meng@noaa.gov
Penn State Integrated Hydrologic Model: PIHM
Qu and Duffy 2007
•
Modeling water movement
•
Soil saturation
•
Surface & Groundwater flows
•
Evapotranspiration
•
Output
• Water Budget
•Nutrient Loads
•Nutrient Concentrations
C. Duffy, cxd11@psu.edu
Irregular Mesh &
Stream Network
Elevations
Soil Classes
Land Cover
The Hydrochemical Model GWLF
• Simple approach to landscape modeling
– Lumped parameter model, monthly time step
– Visual basic (fast, efficient on a PC)
– Sufficiently detailed to mimic important processes
• Publications
– Lee et al. 2000. Biogeochem. 49: 143-173
– Lee et al. 2001. Biogeochem. 56: 311-348
– Fisher et al. 2006. Limnol. Oceanogr. 51: 435-447
– Used by others as well (Penn State, Howarth, etc.)
• Capable of predicting annual export
T. Fisher, UMCES-HPL; fisher@umces.edu
GWLF validation
%$
%&,"',
%$,"(,
(%,"'),
!&$$$
Historical Export from Tilghman Island
GWLF Output
" &!*
# %!!*
" #!!$!!*
# &!!'!!*
$ #!!!*
T. Fisher, UMCES-HPL
AnnAGNPS:
Uses NRCS Standards
Annualized Agricultural Non-Point Source Pollution computer model.
Processes
Databases
• Weather Generation - GEM
• Soils - NASIS
• Runoff – SCS Curve Number
• Crops and Operations –
Set by NRCS State
Agronomists
• Peak Runoff – TR-55
• Erosion - RUSLE
• Sediment Delivery - HUSLE
• HUWQ Databases –
Fertilizer, Pesticides,
Animal Wastes, etc.
AnnAGNPS: the pollutant loading model
Loadings by source of pollutant:
• Cells (land areas)water, sediment, & chemicals.
• Feedlotssoluble nutrients.
• Gulliessediment and chemicals.
• Point Sourceswater and chemicals.
• Reachessediment yield & chemical transport.
• Impoundmentssediment deposition.
AnnAGNPS:
source accounting
10% of outlet
sediment from
gully
gully
cell A
cell B
80% of outlet
pest X from
cell C
cell C
feedlot
25% of outlet
nitrogen from
feedlot
cell D
cell F
cell E
watershed
outlet
SERC Empirical Models
Regressions of Land Use & Nutrients
"
!
!
!
!!
T. Jordan & D. Weller, SERC; jordant@si.edu, wellerd@si.edu
Maximum average restoration benefit
Piedmont
29% reduction
0.98 mg/l
Coastal Plain
54% reduction
0.56 mg/l
Current
buffer
Appalachian
Mountains
15% reduction
0.29 mg/l
Complete
buffer
Chesapeake Bay Program
Decision Support System
Land Use
Change Model
Management
Actions
Watershed
Model
Bay
Model
Criteria
Assessment
Procedures
Scenario
Builder
Airshed
Model
Sparrow
Effects
Allocations
29
G. Shenk, gshenk@chesapeakebay.net
Meta-Modeling with WOOOMM
WOOOMM is an
Online
Web-based, multi-user environment
Object-Oriented
Robert.Burgholzer@deq.virginia.gov
Model components are linked in
object-oriented fashion, similar to
Stella, PowerSim, or Simulink
Meta-Model
Has typical primitives: equations,
graphs, and tables; and has special
components for coupling environmental
models
Watershed
Model
Habitat
Model
Q, N
Water
Supply
Q-W
Stream
Model Q,
V, DO
WOOOMM Details
WOOOMM is Open Source
Detailed Components
And also Made of 100% Open Source
Real-time data from USGS and NOAA
Loose and Tight Coupling with HSPF
Summarize data and Geo-process using
PostgreSQL/PostGIS
Statistical Analysis with R
Currently Used For:
Water Supply planning model for
Commonwealth of Virginia
Links CBP-HSPF model, USGS
Gages, Habitat Suitability models
from IFIM studies
Multi-user capability allows
planners, modelers and permit
writers to develop and share model
scenarios
GW: ParFlow
Conceptual
Model
Atmospheric Forcing
Land Surface
Flow Divide
LSM LSM
LSM
LSM
LSM
Air
Root Zone
LSM
Vadose Zone
Water Table
Vegetation
LSM
LSM
Rout
ed
Wate
r
LSM
LSM
Flow
s
Line
•Water table location is
an output, driven by
topography
• Streams form where
land is saturated
Groundwater
C. Welty, UMBC; welty@umbc.edu
Source: R M Maxwell
Inputs and Outputs
• Inputs
– Climate data
– Soil and rock permeability, porosity
– Land surface elevations
• Outputs
– Pressure head and degree of saturation
(surface water, vadose zone, groundwater)
– Derived outputs
• Volumetric flow rate in streams
• Water table delineation
• Subsurface flow paths (using particle tracking code)
Goal is not calibration but rather process
understanding
Kollet and Maxwell 2008
Watershed Model
Input Data
Chesapeake Bay Land Change Model
(CBLCM)
County
Population
Projections
Growth
Allocation
Model
Future
Urban Area
Sewer Service
Areas
Sewer
Model
Slope,
Protected
lands,
Zoning,
Priority Funding
Areas
Land Use/
Land Cover
(NLCD)
Calibration
Metrics
Cellular
Automata
Model
1990 and 2000
Impervious
Surface
P. Claggett, USGS; PClagget@chesapeakebay.net
Sewer
outflows
Septic
loads
Proportions of
Urban infill,
New urban growth
Forest loss,
Farmland loss,
Growth on septic,
Growth on sewer
Determining Proportions of Farmland and Forest Loss:
Using SLEUTH
Results:
Salisbury, MD
2030Protected
1990
2000
Urban
Land
Impervious
growth
areas
Cover
Forecasted Urban Growth (2000 to 2030)
Farmland and Forest Land Loss
(2000 to 2030)
Forecasted Population Growth:
Sewer vs. Septic (2000 to 2030)
Bay Physics
Coupled hydrodynamic and water quality models
J. Fitzpatrick, HydroQual; jfitzpatrick@hydroqual.com
Bathymetry, Ocean Boundary, Tides, Winds, Ri
ver Discharge all Critical
H. Wang, VIMS; wang@vims.edu
CH3D Circulation Model
freshet
dry
Tracers are released from
Susquehanna River
1 month
!
")(
!"'*%!" & '
&% 4 month
Typical events: 3 month longer for
dissolved substances coming from SR
enter BH during freshet event than
during dry event
Tracers are released from
mid-bay
B. Hong, VIMS; wang@vims.edu
Tide Comparison (ELCIRC)
Tidal elevation (m)
red: NOAA tide
Tolchester Beach, MD
blue: model
Lewisetta, VA
CBBT, VA
Eulerian-Lagrangian CIRCulation model
J. Cho, VIMS; wang@vims.edu
Semi-implicit Eulerian-Lagrangian Finite-Element model
Salinity - Floyd (SELFE)
M5
M3
Courtesy of Valle-Levinson
J. Cho, VIMS; wang@vims.edu
ANIMATION
QUODDY Velocity Field Animation
Velocity fields are used to
visualize transport of
materials, track & spread
contaminants and aid
navigation.
Exchange of estuarine
waters with the ocean is
through the current
patterns at the mouth of
the bay. The flushing rate
of the Bay determines its
ability to discharge or
retain pollutants.
T. Gross, UNESCO; t.gross@unesco.org
QUODDY Flapping Bay
W. Long, UMCES; wenlong@umces.edu
Temperature and salinity (2003)
• CB5.3
Mid Bay
Temperature
simulation
excellent
Salinity
simulation
not as good
R. Hood, UMCES; rhood@umces.edu
NOAA CSDL CBOFS2 Set-up and Validation
• Motivation : to upgrade present 2D barotropic (pressure alone
determines gradients) NOS CBOFS implementation and produce water
levels and fully 3D current, T, S fields for the public including
ecologists
• Validation sequence :
(i) Constant density 3-D baroclinic (pressure, T, and sal determine
gradient) simulation to validate tides - water levels and currents against
predictions,
(ii)Synoptic hindcast simulation (June 2003- September 2005) with full
suite of forcings to validate water levels, currents, T and S against
observed data; initialization and boundary T, S fields from
climatology, river forcing from USGS and Chesapeake Bay Program,
non-tidal open boundary water level forcing from NOS/CO-OPS
observations and meteorological forcing from NARR blended with
NOAA/NDBC buoy data for winds, air T, air P and DP/RH.
(iii) real-time simulations to monitor model performance and validate
against observations (in preparation for being fully operational);
currently running x 4 daily and outputs being monitored and will
become fully operational later in FY09
L. Lanerolle, NOA/NOS/CSDL; lyon.lanerolle@noaa.gov
Chesapeake POM
Guo & Valle-Levinson 2008
Princeton Ocean Model: International ocean modeling
software, available through the web.
Used throughout the open ocean community
http://www.aos.princeton.edu/WWWPUBLIC/htdocs.pom/
ADCIRC
• Vertically-integrated
continuity equation for
water surface elevation
• Models tides and wind
driven circulation, analysis
of hurricane storm surge
and flooding, dredging
feasibility and material
disposal studies, larval
transport studies, & near
shore marine operations.
• Used for FEMA CB flood
mapping
NOAA CSDL uses ADCIRC for
inundation/storm surge, the VDATUM
project (tidal datums), and for generating
tidal databases containing harmonic
constituents for water levels and currents.
R. Luettich, UNC; rick_luettich@unc.edu
CHIMP: ROMS-based Circulation Webpage
http://www.d.umn.edu/~jaustin/CHIMP.html
Fig. 3. Plan views. (a) Water height levels displayed via color mapping. (b) Velocity direction vectors with color mapped magnitudes
(Crouch et al. 2008).
M. Dinniman, ODU-CCPO; msd@ccpo.odu.edu
Tidal Biogeochemistry
Plankton & Impact of Hurricane Isabel
Unusual fall bloom observed after Isabel
The model can be used to
understand how hurricanes
affect plankton production and
hypoxia in Chesapeake Bay.
M. Li, UMCES; mingli@umces.edu
ChesROMS Biogeochemical Model - Work in Progress
(chief architect Dr. Jerry Wiggert)
Based on Fennel et al. core
model bundled with ROMS
NPZD type model with oxic
sediment denitrification
Adding DON
ISS loading
Atmospheric N deposition
Anoxic benthic denitrification
Water column denitrification
Longitudinal Section of Patuxent Showing Region
Boundaries and Fluxes Between Regions (Boxes)
Upper
Estuary
Middle
Estuary
Lower
Estuary
(Hagy et al. 2000)
Use flow balance and salt balance equations for each box to compute unknown
values for water flows (Q’s) and mixing rates (E’s), given salinities and FW inputs;
also used for main Bay (Hagy et al. 2000).
J. Testa, UMCES; jtesta@umces.edu
Box Model computations of
net production of oxygen,
inorganic nitrogen, and
dissolved silicate in both the
surface and bottom layer of
the Patuxent estuary
“Sweet spot Model”
Apply classical engineering river
model (Streeter-Phelps DO-sag)
Scavia et al. pers. comm.; scavia@umich.edu
Application to ChesBay
Susquehanna
Load ~N
Pycnocline
Diffusion
v
v
B
D
Advection
Organic matter
decay
Model Calibration
R2=0.73
Model
Data
Although some think of SOD
and nutrient fluxes as internal
sources in a estuary or lake
mass balance, they originate
from external sources.
The sediment flux model (SFM)
considers the deposition of
organic matter (phytoplankton
& detritus), its subsequent
decomposition (diagenesis),
burial of the more refractory
components to the deep
sediment bed, and the flux of
resulting end-products back to
the overlying water column.
Importance of overlying water column dissolved oxygen on sediment phosphate
release … under conditions of low dissolved oxygen PO4 fluxes increase.
1985-1988 SONE data (Boynton, Kemp, et.al. (circles) vs. model computations
Tidal Food Web
Coupled hydrodynamic and water quality models
Coupling of nutrients (N, P, Si),
primary production, and
dissolved oxygen in the water
column and the sediment bed
Chesapeake Bay Regional Estuarine
Ecology Model (CBREEM)
•
•
•
Purpose
– Generate historical patterns in
primary productivity for EwE
Introduction & Methods
– Two layer, simple hydrographic
model (monthly time steps for 50+
years)
– Use wind, rainfall, gage inflow,
and relative loading as inputs
– Solve for equilibrium velocity fields
on Richardson grids and make
chemical mass-balanced
calculations (Wright et al. 1986,
Hunter and Hearn 1991)
Results
– Chla (used as nutrient loading
forcing function for EwE)
NOAA Chesapeake Bay Office
Inorganic
Carbon
Three
Algal
Groups
Respiration
Respiration
Microzooplankton
Respiration
Dissolved
Organic
Carbon
External
Loads
Labile
Particulate
Organic
Carbon
In CBP
WQ
Model:
Carbon
Cycle
Refractory
Particulate
Organic
Carbon
C. Cerco, ACE; Carl.F.Cerco@usace.army.mil
Mesozooplankton
Sediments
Larval Pools
Oyster Larvae
Pelagic Prey Fish
Bay Anchovy
Anchovy Larvae
Ctenophore Larvae
Atlantic
Menhaden
Gelatinous Zooplankton
Zooplankton
Sea Nettles
Ctenophores
Acartia tonsa
Microzoopnktn
Phytoplankton
< 2 microns
Pelagic Bacteria
2-4 microns
HNAN
4-10 microns
Non reef fish
Benthic
Reef-assoc.
fish
Oysters
10-50 mic
On-reef inverts
50-100 mic
Off-reef inverts
> 100 microns
Benthic
Bacteria
DOC
N
P
POC
Si
Detritus Pools
Fulford et al. Ecological Applications in press
Algal Speciation Model of the Potomac Portion of the
Chesapeake Bay WQSTM
• Study Objectives
– Refine and improve representation
of dominant algal groups in the
Potomac
– Calibrate the revised Potomac
portion of the WQSTM
• Study Tasks
– Literature review
– Data analysis
– Development of new algal
speciation sub-model
– Calibrate Potomac portion of
WQSTM
Amanda Flynn, LimnoTech; aflynn@limno.com
Algal Speciation Model (continued)
• Revised model contains five (5) algal
groups
• Calibration runs were used to:
– understand the physical, chemical, and
biological dynamics represented in the
model
– determine model sensitivity to changes in
key input parameters
– improve representation of algal groups
• Model Output
– algal biomass, primary production
– water quality (N, P, DO, Light)
• Revised Potomac model can/will be used to
address local WQ issues and serve as a
research testbed for the WQSTM
SAV Model Interactions
CH3D
Hydrodynamic
Model
WindGenerated
Wave Model
Bottom Shear
Stress,
Currents
SAV Model
Optical Model
ROMS Bed
Model
Resuspension
ICM
Eutrophication
Model
SAV Unit Model
•Computes SAV density (mass/unit area)
as a function of irradiance and nutrients.
•Irradiance and epiphytes calculated
separately.
•Interacts with water column and bed
sediments.
SAV Sub-Grid
•SAV is computed on a sub-grid independent
of the hydrodynamic grid.
•Sub-grid areas are based on observed SAV
beds rather than arbitrary computational
elements.
•Sub-grid elements permit refined depth
increments for computation of available light.
Oysters
excretion
Filter
Feeders
feeding
Particulate
Organic
Matter
Deposit
Feeders
diagenesis
excretion
diagenesis
Dissolved
Oxygen
sediment-oxygen
demand
biodeposits
Sediments
Dissolved
Nutrients
sediment-water
exchange
Water
Column
respiration
filtration
settling
Particulate
Organic
Matter
Dissolved
Nutrients
Oxygen
Demand
respiration
Diagenesis Model
with Benthos
System-Wide Summary
Larval Pools
Oyster Larvae
Pelagic Prey Fish
Bay Anchovy
Anchovy Larvae
Ctenophore Larvae
Atlantic
Menhaden
Zooplankton
Sea Nettles
Ctenophores
Acartia tonsa
Microzoopnktn
Phytoplankton
< 2 microns
Pelagic Bacteria
2-4 microns
HNAN
4-10 microns
Non reef fish
Benthic
Reef-assoc.
fish
Oysters
10-50 mic
On-reef inverts
50-100 mic
Off-reef inverts
> 100 microns
Benthic
Bacteria
DOC
N
P
POC
Si
Detritus Pools
R. Fulford, USM; Richard.Fulford@usm.edu
Menhaden: Prey, Predation Story
Menhaden
Menhaden Controls
X. Zhang, NOAA Oxford Lab; xinsheng.zhang@noaa.gov
Menhaden
There do seem to be positive
relationships between menhaden
recruitment and bottom-up (i.e.
prey quality & quantity) variables.
Chlorophyll seems be better than
annually integrated primary
production in most of fits to date.
Data from Houde, Harding, and Annis
2006. Progress Report to NOAA
Chesapeake Bay Office
Menhaden Modeling
in Chesapeake Bay
• Research Question: Are Atlantic menhaden
(a planktivorous fish) impacting water quality?
• Model formulation: Monte Carlo simulation of
seasonal migration in and out of bay
• Consumption (algae, zooplankton, detritus) based on
food availability (in water quality), fish output based
on bioenergetics formulation
• Conserve mass (fish outputs,
mass from mortality returns to
water quality model)
S. Daylander, ACE; Patricia.A.Dalyander@usace.army.mil
Menhaden Modeling in Chesapeake Bay
Effects of
Fish
Population
• Algae, fish both seasonally variant
• More fish, more nutrient recycling (like fertilizer), faster algal growth, BUT
at some point they eat so much algae can’t “keep up”
• Also has annual menhaden school movements animated as well as annual
changes in menhaden-induced longitudinal chlorophyll in the bay
Multi-Layered Bioenergetics Modeling
Fully Coupled NPZ-Menhaden-Piscivore Model
M.J. Brush, VIMS; brush@vims.edu
Time to filter the …
Entire bay
Source: M.J. Brush
Surface mixed layer
Effect of Juvenile + Adult Consumption
with Juvenile + Adult N excretion
Source: M.J. Brush
Fig. 7. As for Figs. 4-6, but effect of adding juvenile and adult menhaden
consumption and both juvenile and adult N excretion to the NPZ model.
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Source: M.J. Brush
Next steps: Climate Influence
Modeling Striped Bass Habitat Suitability
Habitat Requirements
Habitat Suitability Index
Response Scale Functions
Combine into habitat suitability index
Decision rules
Interpolated Data
Response functions based on Bain & Bain , 1982
High temperature-low oxygen habitat squeeze during July
1985
1997
1986
1998
1987
1999
1988
2000
1989
2001
1990
2002
1991
2003
1992
2004
1993
2005
1994
2006
1995
1996
Responses to high summer surface temperature &
low bottom DO vary among striped bass & menhaden
{
{
Habitat
Good
July 1996
Greater Habitat Overlap
{
July 1999
Smaller Habitat Overlap
Bad
{
S. Brandt, OSU; Stephen.Brandt@oregonstate.edu
SB Larvae Habitat Suitability at Peak Spawning Season
May 1996
May 1999
Larval Pools
Oyster Larvae
Pelagic Prey Fish
Bay Anchovy
Anchovy Larvae
Ctenophore Larvae
Atlantic
Menhaden
Zooplankton
Sea Nettles
Ctenophores
Acartia tonsa
Microzoopnktn
Phytoplankton
< 2 microns
Pelagic Bacteria
2-4 microns
HNAN
4-10 microns
Non reef fish
Benthic
Reef-assoc.
fish
Oysters
10-50 mic
On-reef inverts
50-100 mic
Off-reef inverts
> 100 microns
Benthic
Bacteria
DOC
N
P
POC
Si
Detritus Pools
R. Fulford, USM; Richard.Fulford@usm.edu
Oyster Impact on Summer Primary
Production
Oyster Impact on Summer
Secondary Production
CB Fisheries Ecosystem Model
• Developed in cooperation between NOAA
CBO, CRC, UBC with support from many
bay researchers using Ecopath with Ecosim
software (code base)
• A companion to the CB Fisheries
Ecosystem Plan
• Technical report (230 p) completed/in press
• Chesapeake Bay tidal waters
• 45 functional groups
• Replicates ecosystem history 1950 – present
H. Townsend, NCBO; howard.townsend@noaa.gov
NOAA Chesapeake Bay Office
Bacteria/Pathogen Modeling: Models from Other Regions
Background
• Meteorological,
physical, and
environmental data
• Useful for predictive
model development
H. Kelsey, UMCES; Heath.Kelsey@noaa.gov
Nowcasts/Forecasts
ChesROMS Operational Modeling
NCBO stable prediction of Sea nettles, K.
veneficum, & V. cholerae since January 2008
http://155.206.18.162/cbay_hab/; http://155.206.18.162/vvul/; W. Long, UMCES; wenlong@umces.edu
Ecological Forecasts (Sea Nettles):
T and S strongly constrain sea nettle distributions
Estimate T and S using ChesROMS
Provides input to an empirical logit model that predicts
probability of sea nettle occurrence
A fuzzy logic approach to predicting Prorocentrum minimum blooms
•
•
Fuzzy logic: Fuzzy logic addresses the uncertainty of descriptive categories (e.g.
“hot”, “cold”, “warm”, or “low”, “medium”, “high”) by allowing a parameter’s
value to exist in multiple categories simultaneously, and to varying degrees.
Objectives:
– Examine the success of a hybrid fuzzy logic and decision tree model for
predicting harmful algal blooms
– Provide better predictive success than traditional statistics
– Provide a reliable, robust forecasting tool for managers
J. Anderson, MSU; jon.anderson@morgan.edu
Fuzzy Decision Tree for Station CB3.3C
Environmental conditions are 1.5 months
prior to prediction time-point
J. Goodwin, UMCES; jgoodwin@hpl.umces.edu
Oyster Habitat
Model Input/Output
• Monthly spatial maps of modeled oyster growth (from 1985 – current) based on salinity, dissolved
oxygen and temperature (see http://chesapeakebay.noaa.gov/IEA/Oyster.htm) for individual plots)
• Trends over time in changes of modeled oyster growth (Figure A)
Potential
• Compare growth model trends with other factors important to oyster survival and growth (e.g.,
disease, substrate) using a GIS platform (e.g. Historical Yates Bars/substrate w/ model output on
Figure B)
LTRANS (Lagrangian Transport Model)
Iteratively (repeatedly) solve for
the new position at each time step
to develop trajectories of particle
motion
t=2
t=1
t=3
t=4
t=5
E. North, UMCES; enorth@umces.edu
Percent settled
particles
0 – 10%
10 – 20
20 – 30
30 – 40
40 – 50
50 – 60
60 – 70
70 – 80
80 – 90
90 - 100
There are clear
spatial patterns
in transport success
When combined
with demographics,
they could be used
to guide restoration
and management
• To design, develop, test, and implement an agent-based simulation model
in a multiplayer “game” format.
– The research objective was to model and simulate a complex
environmental system composed of many interrelated subsystems, with
no central coordinating authority, with many independent decisionmaking entities (agents), and where the behavior of the complex system
displays unpredictable, emergent outcomes
– The agent-based simulation had to instantiate a large number software
agents and a smaller number of “live” agents – game players – in
various decision-making roles
– http://www.virginia.edu/vpr/baygame.html
© Copyright 2009 The Rector and Visitors of the University of Virginia
G. Learmonth, jl5c@virginia.edu
105
Game
Model
106
Crab fishery
107
April Test Result
• The test of the Bay Game presented a
realistic scenario for a 20-year planning
horizon
• The Bay Health Index responded correctly
to the decisions made by the “live” agent/
players and the software agents – they
“saved the Bay”
• Players were not constrained to behave in
their own best interests, e.g., some went
bankrupt!
108
Chesapeake Inundation Prediction System
(CIPS) – Part of the Chesapeake Bay
Observing System
• High resolution
visualization of storm
surge and rive flooding
of specific areas
• Much finer resolution
than current NOAA
operational model
SLOSH
• Goal: Another
flooding tool for
NOAA’s storm ‘tool
box’
B. Stamey, Noblis, Inc.; barry.stamey@noblis.org
The intersection of Union and King
Streets in Old Town Alexandria, VA
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Hurricane Isabel SLR=0.0m
Flood Depth vs Sea Level Rise
National Airport
2
200cm
150cm
1.8
100cm
50cm
1.6
0
1.4
Meters
1.2
1
0.8
0.6
0.4
0.2
0
0
20
40
60
Hours
80
100
120
Thank you!
Kevin Sellner
Chesapeake Research Consortium
www.chesapeake.org
sellnerk@si.edu

PDF version - Chesapeake Community Modeling Program

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