Transcript Integrated Modeling of the Muskegon River Ecosystem: A New Approach to Integrated Risk Assessment for Great Lakes Watersheds Michael Wiley1, Bryan Pijanowski2, Paul.

Integrated Modeling of the Muskegon River Ecosystem:
A New Approach to Integrated Risk Assessment for Great Lakes
Watersheds
Michael Wiley1, Bryan Pijanowski2, Paul Richards1, Catherine Riseng1,
David Hynman4, Ed Rutherford3, and, John Koches5
Funded by the Great Lakes Fisheries Trust
A product of the Muskegon Watershed Research Partnership
1School
of Natural Resources and Environment, University of Michigan
of Forestry and Natural Resources, Pudue University,
3 Institute for Fisheries Res., Michigan Department of Natural Resources
4 Department of Geology, Michigan State University, 5Annis Center Grand Valley State University
2 Department
http://www.mwrp.net
Muskegon Watershed
Research
Partnership
The vision:
~4% Lake Michigan’s ‘shed (2870 sq miles )
~5% Lake Michigan’s Q (2404 cfs )
Collaborative,
Integrated,
Relevant Science
for a better future
Objective: Developing forecasting tools for Ecosystem Management
in Great Lakes Tributaries
Watershed
Stakeholders’
Questions
2007
2000,2002
Ecological
Inventory &
Assessment
2001-2003
Muskegon River Ecological Modeling System
MREMS
Integrated modeling
Management
scenario
evaluations
2006
2001-2005
Design features:
Start and End with Stakeholders Questions
2D spatial org by river channel unit: VSEC or [NHD arcs]
Time represented by “frames” in Landscape trajectory
Collection (Integration) of many relevant Models
Variable time scales are OK
MREMS Components
Model
Predicts
Type
LTM 2
Land Use change
Neural net
MODFLOW
Groundwater flow
Simulation
MRI_DARCY
Groundwater upwelling
GIS
HEC-HMS
Surface water flows
Simulation
MRI_FDUR
Surface water flow frequencies
Regression
System
HEC-RAS
Surface water hydraulics
Simulation
GWLF
Surface dissolved loads
Simulation
MRI_LOADS
Surface dissolved loads
Regression
Fish/insect diversity
Fish/insect diversity
EPT taxa/ Sensitive fish
Algal Index
Regression
Regression
Regression
Regression
Growth rate
and
survivorship
Simulation
Simulation
Simulation
Kg/hec total mass
Kg/hec total mass
Kg/hec total mass
g/m2
g/m2
g/m2
Regression
SEM1
SEM1
SEM1
SEM1
SEM1
Regional Assessment
Models
All taxa
Sensitive taxa
EPT Index
Algal Index
Bioenergetic IB Models
Steelhead
Salmon
Walleye
Standing Stock Models
Sport fishes
Total fishes
Sensitive fishes
Total Algae
Filter-feeders
Grazing inverts
MREMS is really a data sharing protocol and directory structure
for a collection of interacting models
All participating models store spatially referenced output into specific year
and landscape scenario directories.
MREMS Directory Structure
MRI-DARCY
MODFLOW
surface loading
recharge
The use of redundant models in MREMS allows us to
cross validate results and use weight of evidence
arguments in resource risk assessment.
In this figure, output from 2 very different groundwater models, MRI-DARCY and MODFLOW,
are compared. Note correspondence between predicted loading to surface systems (light blue
areas on the right with redder areas on the left).
surface loading
recharge
River Segment ID @ Time Frame yr=2020
hours ~x00 km2
Dt = 1 day
decades ~ x00 km2
Dt = 0=fixed per run
Climate
Historical
Daily 1980-2000
Landscape
LTM2 Neural Net
weeks ~x000 km2
Surface Dt = 1 hr
GW Dt = 1 day
Reach Hydrology
days ~x km2
Dt = .1 day
Reach Hydraulics
days ~x m2
Dt = 1 day
Local hydraulics and substratum
days x cm2
Dt = 1 day
Fish growth & mortality
Hec_HMS
SMA/ MODFLOW
Hec_RAS
Steelhead IBM
Modeling to forecast
Modeling to understand
Climate
Landscape
Reach Hydrology
Reach Hydraulics
Local hydraulics and substratum
Fish growth & mortality
All modeling output is organized spatially using MRI-VSEC valley segment Map
[Valley Segment Ecological Classification Units, Seelbach et al. 1997]
VSEC channel reach units constitute the 2D organization of the model “Ecosystem”
Individual Model output is always organized by VSEC unit
Historical data sets augmented by neural net predictions provide a temporal framework
1830
Historical
reconstruction
1978
Air Photo
interpretation
2020
Neural Net
projection
2040
Neural Net
projection
Future Landuse change in MREMS
is handled by an enhanced version (LTM2)
of Pijanowski et al.’s Land Transformation Model
Pijanowski, B.C., D. G. Brown, G. Manik and B. Shellito (2002a)
Using Artificial Neural Networks and GIS to Forecast Land Use Changes:
A Land Transformation Model. Computers, Environment and Urban Systems. 26, 6:553-575.
Increasing the hidden layers from 1 to 2 increased model performance significantly.
On average, one hidden layer correctly predicted around 50% of the cells to transition;
the best 2 hidden layer model predicted 79% correctly. (which reflects a 50% increase in model performance!)
Together the
landscape trajectory
and VSEC unit
structure provides
MREMS
an explicit
time x space
Framework for
linking diverse
MREMS component
models
MWRP Stakeholders’ Workshop
Examples of selected Modeling queries
What is the effect of different rates of urban development?
What is the effect of with differing lot size constraints?
Effects of Minimum Setbacks for new construction from surface water edge?
How and where is channel erosion being affected by development?
What is effect of Great Lake water level changes on channel erosion and deposition?
How do headwater and main stem dam operations affect ecological integrity?
How do wetland losses & urbanization affect river hydrology and fish?
MREMS can be used to evaluate effects of alternate land use patterns
1830
1978
2040
Cedar Creek
Figure 6 - Modeled hydrographs for Cedar Creek using observed 1998 and LTM
projected 2040 landcover scenarios. Precipitation and temperature patterns, and
all other variables held constant. Days are arbitrary simulation dates.
Table 2. Example of multiple ecological responses predicted by MREMS in
preliminary runs for a “Fast Growth” scenario. Change rates for a 1998 to 2040
time frame comparison.
Site
D hydro
% DD 1
Channel
Response
%D
3
SedLoad
%TDS
Cedar Creek
Brooks Creek
Main River @ Evart
Main River @ Reedsburg
-13 %
-22 %
0%
0%
aggrade
aggrade
No change
No change
+26 %
+72 %
+1%
+6 %
+32%
+20%
+20%
+3%
1
2
4
Fish
spp.
loss
3-4
1-2
2-3
0-1
%DD: Percent change in Dominant Discharge (determines the size of the equilibrium
channel); product of HEC_HMS run and empirical load model.
2
Channel response: expected response based on %DD
3
%SL: Percent increase in average daily sediment load [tonnes/day]
4
%TDS: Percent change in median Total Dissolved Solids concentration(ppm)
Mega-Model Runs target the entire watershed and provide a time-dependent context for understanding our
Current conditions, identifying risks that lie ahead, and a testing ground for alternate Management Scenarios.
Muskegon Watershed Research Partnership
Modeling Endpoints Workshop
•August 24 2002 @ Annis Center
•Representatives from 13 stakeholder organizations
and 9 of the project PIs met
•To develop scenarios to be evaluated with the
Muskegon Watershed Mega model
What will we do
with the Model?
•Goal was to develop 3-4 scenarios in
each of 3 areas (land use, hydrology, sedimentation/ersosion)
•Land use management scenarios (12)
•Hydrologic management scenarios (13)
•Sediment management scenarios (12)
What’s next on the MREMS
agenda?
• Lower river hydrology and fisheries models
•
•
completed by end of 2005
Stake-holder scenario modeling completed by
summer 2006
Final report out to Stakeholders winter 2007
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