Transcript http://www.mwrp.net Muskegon Watershed Research Partnership The vision: Collaborative, Integrated, Relevant Science for a better future A Collaborative Approach to Understanding the Dynamics of the Muskegon Watershed: A Comprehensive Model,

http://www.mwrp.net
Muskegon Watershed
Research
Partnership
The vision:
Collaborative,
Integrated,
Relevant Science
for a better future
A Collaborative Approach to Understanding the Dynamics of the Muskegon Watershed:
A Comprehensive Model, Risk Assessment, and Tools for Use in Management
Principle Investigators
Mike Wiley, University of Michigan (Lead Institution)
Bryan C. Pijanowski, Purdue University
John Koches, Grand Valley State University
Paul Seelbach, Michigan Department of Natural Resources
Co-investigators:
Ed Rutherford (UM),
Paul Richards (UM),
David Jude (UM),
James Diana (UM)
Rich O'Neil (MDNR),
Doran Mason (NOAA)
Brian Eadie (NOAA)
R. Jan Stevenson (MSU),
David W. Hyndman (MSU),
Robert Walker (MSU),
Stuart Gage (MSU),
Rick Rediske (GVSU),
Paul Thorsnes (GVSU),
Gary Dawson (Consumers Energy),
Dale Black (Brooks TWP, Supervisor).
Affiliated MRP Stake-holder groups:
Muskegon Watershed Assembly;
MDNR,
MDEQ,
Consumers Energy Inc.,
Trout Unlimited,
Brooks Township,
Land Conservancy of West Michigan,
Timberland RC&D,
Lake Michigan Federation
Michigan Stream and Lake
Association
Integrated Modeling of the Muskegon River:
A New Approach to Ecological Risk Assessment for Great Lakes
Watersheds
Michael Wiley1, R. Jan Stevenson4, Bryan Pijanowski2, Paul Richards2, 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, Purdue University,
3 Institute for Fisheries Res., Michigan Department of Natural Resources
4 Departments of Zoology and Geology, Michigan State University,
5Annis Center, Water Research Institute, Grand Valley State University,
6 Dept. of Geology, Brocksport-SUNY
2 Department
Objective: Comprehensive forecasting tool for Ecosystem Management
in Great Lakes Tributaries
Watershed
Stakeholders’
Questions
Ecological
Inventory &
Assessment
Muskegon River Ecological Modeling System
MREMS
Integrated modeling
Management
scenario
evaluations
Talk Overview
Context: a work in progress
1. Time Line: where we are now…
2. Highlighted updates on the Mega-Modeling
3. Next steps
4. Impacts
5. Issues
Basin wide
Modeling
Framework
Fisheries
Model
Development
Model
integration and
risk assessment
Muskegon Watershed
Research Partnership
2001
2002
2003
2004
2005
2006
2007
MODELING
Project
ASSESSMENT
Project
Watershed
Estuary/ Lake
Michigan
Objective: Developing forecasting tools for Ecosystem Management
in Great Lakes Tributaries
Watershed
Stakeholders’
Questions
2007
Stakeholders’
Conference
2000,2002
Ecological
Inventory &
Assessment
2001-2003
Muskegon River Ecological Modeling System
MREMS
Integrated modeling
Management
scenario
evaluations
2006
2001-2005
Talk Overview
Context: a work in progress
1. Time Line: where we are now…
2. Highlighted updates on the MREMS-Modeling
3. Next steps
4. Impacts
5. Issues
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/Reg Tree
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
•More is better (some times)
Steelhead
Salmon
Walleye
Standing Stock Models
Sport fishes
Total fishes
Sensitive fishes
Total Algae
Filter-feeders
Grazing inverts
Example: comprehensive mechanistic modeling across scales
hours ~x00 km2
Dt = 1 day
decades ~ x00 km2
Dt = 0=fixed per run
Climate
Landscape
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
Individual Fish growth & mortality
Historical
Daily/Hourly 1985-2005
LTM2 Neural Net
Hec_HMS
Coupled to MODFLOW
Hec_RAS
Steelhead IBM
Tyler and Rutherford 2002
UPDATE Highlights
• Now using improved climate data (NEXRAD & Leaf area index
modeling for ET)
• Improved LTM2 for future and backcasts
• Multiple Versions of coupled hydro models
•
•
•
•
running (including hi-res Cedar,Brooks,Bigelow)
Hi-Res Hec-RAS Channel Hydraulics Model: Croton to below Newago
Lower River Fish and Productivity studies wrapping up
Dynamic Fish habitat models for L River
(includes new Temperature and Prey Models)
Hi-RES Steelhead IBM
NEXRAD for Expanded Muskegon
• NEXRAD data
•
•
•
•
becomes available
in 1996
4 km grid cells
Available for liquid
precipitation only
June-September
2003
Significant variation
even over very
short distances
Mukegon Expanded watershed boundary with NEXRAD gridcells used for
extracting spatially variable precipitation overlaied
Expanded Muskegon watershed boundary
•Standard Climate Run
• synthetic record
•1985- 2005
Grayling
Traverse City
Glennie Alcona Dam
Houghton Lake
Wellston Tippy
Cedar Creek Watershed
Gladwin
Baldwin
Hesperia
Stanton
Muskegon
Kent City
Grand Haven
Thiessen polygons with NCDC weather station names used for determining precipitation
across the expanded Muskegon model area and the Cedar Creek watershed
Dynamic Seasonal Vegetation Density based
on MODIS imagery for Expanded Muskegon
Leaf Area Index (LAI)
Expanded Muskegon watershed
<1
1-2
2-3
3-4
4-5
5-6
6-7
Cedar Creek watershed
1km resolution MODIS LAI grids showing vegetation density over
the expanded Muskegon and Cedar Creek watersheds
Future (Past) 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!)
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
•Present {1998}
1978
•1998
Urban to Ag
Forest to Ag
1977
Urban to Ag
Forest to Ag
1976
Urban to Ag
Forest to Ag
1975
Urban to Ag
Forest to Ag
1974
Urban to Ag
Forest to Ag
1973
Urban to Ag
Forest to Ag
1972
Urban to Ag
Forest to Ag
1971
Urban to Ag
Forest to Ag
1970
Urban to Ag
Forest to Ag
1969
Urban to Ag
Forest to Ag
1968
Urban to Ag
Forest to Ag
1967
Urban to Ag
Forest to Ag
1966
Urban to Ag
Forest to Ag
1965
Urban to Ag
Forest to Ag
1964
Urban to Ag
Forest to Ag
1963
Urban to Ag
Forest to Ag
1962
Urban to Ag
Forest to Ag
1961
Urban to Ag
Forest to Ag
1960
Urban to Ag
Forest to Ag
1959
Urban to Ag
Forest to Ag
1958
Urban to Ag
Forest to Ag
1957
Urban to Ag
Forest to Ag
1956
Urban to Ag
Forest to Ag
1955
Urban to Ag
Forest to Ag
1954
Urban to Ag
Forest to Ag
1953
Urban to Ag
Forest to Ag
1952
Urban to Ag
Forest to Ag
1951
Urban to Ag
Forest to Ag
1950
Urban to Ag
Forest to Ag
1949
Urban to Ag
Forest to Ag
1948
Urban to Ag
Forest to Ag
1947
Urban to Ag
Forest to Ag
1946
Urban to Ag
Forest to Ag
1945
Urban to Ag
Forest to Ag
1944
Urban to Ag
Forest to Ag
1943
Urban to Ag
Forest to Ag
1942
Urban to Ag
Forest to Ag
1941
Urban to Ag
Forest to Ag
1940
Urban to Ag
Forest to Ag
1939
Urban to Ag
Forest to Ag
1938
Urban to Ag
Forest to Ag
1937
Urban to Ag
Forest to Ag
1936
Urban to Ag
Forest to Ag
1935
Urban to Ag
Forest to Ag
1934
Urban to Ag
Forest to Ag
1933
Urban to Ag
Forest to Ag
1932
Urban to Ag
Forest to Ag
1931
Urban to Ag
Forest to Ag
1930
Urban to Ag
Forest to Ag
1929
Urban to Ag
Forest to Ag
1928
Urban to Ag
Forest to Ag
1927
Urban to Ag
Forest to Ag
1926
Urban to Ag
Forest to Ag
1925
Urban to Ag
Forest to Ag
1924
Urban to Ag
Forest to Ag
1923
Urban to Ag
Forest to Ag
1922
Urban to Ag
Forest to Ag
1921
Urban to Ag
Forest to Ag
1920
Urban to Ag
Forest to Ag
1919
Urban to Ag
Forest to Ag
1918
Urban to Ag
Forest to Ag
1917
Urban to Ag
Forest to Ag
1916
Urban to Ag
Forest to Ag
1915
Urban to Ag
Forest to Ag
1914
Urban to Ag
Forest to Ag
1913
Urban to Ag
Forest to Ag
1912
Urban to Ag
Forest to Ag
1911
Urban to Ag
Forest to Ag
1910
Urban to Ag
Forest to Ag
1909
Urban to Ag
Forest to Ag
1908
Urban to Ag
Forest to Ag
1907
Urban to Ag
Forest to Ag
1906
Urban to Ag
Forest to Ag
1905
Urban to Ag
Forest to Ag
1904
Urban to Ag
Forest to Ag
1903
Urban to Ag
Forest to Ag
1902
Urban to Ag
Forest to Ag
1901
Urban to Ag
Forest to Ag
1900
•Circa 1900
Urban to Ag
Forest to Ag
SCHEMATIC MODEL OF MREMS-HEC
COUPLED WITH MODFLOW
INTERNALLY-DRAINED AREAS
Houses
PRECIP
TOPOGRAPHICALY
CONNECTED
Road
EVAP
PRECIP
EVAP
SNOWPACK
Custom SMA
Module 1,2
IMP RUNOFF
RECHARGE
RECHARGE
MODFLOW2
RUNOFF
STREAM
HEC-HMS
1. P. Richards, SUNY:Brockport
based on GWLF hydrology code
2. Custom implementation by D. Hyndman and A. Kendall, Michigan State University
routing
MODFLOW takes the recharge data and iterates
a steady state solution to Darcy’s law for each Day
Of the simulation period.
MREMS can be used to evaluate effects of alternate land use patterns
Historical climate > Obs and forcast Land cover >HEC_HMS*
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.
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)
MREMS scenario 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.
At the end of each time step………
B = A* K/T * (head – stream elev*)
B = baseflow average for month
A = Cell area
K = conductivity
T=1
MRW Outline
Modeled Depth to Water
in meters
High : 177.8
Low : 0
GW flux (cfs/km)
Coupled Modflow MREMS model
Groundwater flux per river km
40
Evart
30
20
10
0
0
50
100
150
200
River length (km)
250
300
350
GIS used to assemble reach-scale channel models from multiple data sources
Air Photos (1998)
Field survey reconnaissance
Acoustic Doppler profiling
Cross-section profiles were then extracted (using GeoRAS) for HEC_RAS
LM
Valley segment Hydraulic model 12.91 km for 18.2
167 transects
20-50 cells per transect (Q dependent)
10335.49
9722.78*10434.*
Typically ~4000-5000 cells
9645.68* 10532.5*
9122.06*
Depth, Velocity, Substrate
10700.9*
9568.594
8451.590
8886.17*
10917.5*
Prey density inferred from
7952.06*
8623.7738811.511
11070.65
7723.46*
11208.1*
cell substrate
7420.68*
R
11344.8*
7266.48*
7188.16*
7109.840
Area1
Area0
Area2
11812.1*
11970.77
182
12034.6*
12098.49
12160.0*
12221.54
5730.18*
5641.44*
5552.707
5410.12*
3518.43*
3618.385 3789.26*
11656.3*
7022.23*
6934.620
6550.720
6475.64*
6400.55*
6325.478
6028.507
5888.78*
1943.01*
2663.146
1328.43*
2818.500
1186.350
183.230*
2910.08*
1117.94*
451.471
3001.66*
1049.53*
3093.249
535.085*
981.133
3255.86*
795.253*
3418.475
11496.3*
4202.10*
5253.43*
5168.03*
5082.642
4541.97* 4839.145
HEC_RAS Simulations run for 1 year
12979.97
12844.51
12726.27
12655.3*
12584.38
musk_18_2
Plan: Plan 02
2/10/2005
Legend
WS PF 1
Ground
Bank Sta
Levee
Ground
8.36
7.78 8.53
7.4*
9.59*
8.94
9.47
8.83* 9.20
8.72
10.24
10.39*
10.54
7.135*
7.02
6.84
0.36
.485*
0.61
Elevation (m)
208
206
204
202
200
198
196
194
192
190
12.13
6.345*
6.23
5.93
5.73
5.595*
5.46
1.75
1.312.57
1.09 2.72
2.86*
.99*3.00
0.89
3.32
3.52
10.75
10.98
11.18
11.32
11.49
11.64
11.88
12.00
12.75
12.49
5.25
5.12*
4.99
3.65*
test
3.905* 4.27
4.54
4.75
Plan: Pl an 2001
5/11/2005
L
WS 31
0
2000
4000
6000
8000
Main Channel Distance (m)
10000
12000
14000
Example cross-section
unsteady (continuous) run for: VSEC unit 18.2: yr=2001
Cross section ID= 6742.67 (meters up from downstrean end of 18.2)
Flows can be driven by hydrographs
from gage records or MREMS hydrologic models
Dynamic Fish Habitat Modeling
•Adult Walleye habitat
•2001 by month
• VSEC 18.2
•HSI-based
Steelhead IBM operating in
Muskegon River VSEC 18.1-4
-2
YOY Density (number * m )
10
1
0.1
Data
2001
2003
0.01
100
150
200
250
Day of Year
300
350
Model development
•Ed Rutherford and Crew
Presence/Absence
Chinook location
present
Classification Tree
First split:
Groundwater vs. Runoff
Second split:
Drainage Area
Gradient
•Ed Rutherford and Crew
Results
Smolt per river system
350,000
300,000
200,000
150,000
100,000
50,000
B
at
te
et
Li Ma sie
ttl
e ni
Pe M ste
re an e
M ist
ar ee
q
Pe uet
n t te
w
at
er
W
M
us hite
ke
go
G n
ra
n
Pi d
ge
o
St n
.J
o
G e
al
ie
n
0
Pl
Smolts
250,000
River
Total Smolts: 1,025,902
Chinook smolt estimate
Lower River Productivity
GVSU,MSU,UM
Mosaic of aerial photos from the lower Muskegon River
watershed, showing habitat maps of a wetland area
(left) and stream segment (right), as well as preliminary
data collected on algal biomass in wetland, stream and
lake sites in spring 2004. Larger circles indicate more
productivity.
What’s next on the MREMS
agenda?
• Model integration completed June 2006
• Landscape and Hydrology Scenario runs
•
•
completed by Sept 2006
Stake-holder scenario modeling completed by
Dec 2006
Final report out to Stakeholders Summer 2007
GLFT-MWRP Technology Transfer and Diffusion
Spin-off or Linked /Leveraged Funded Research
•EPA-STAR ILWIMI Lake Michigan Assessment
•EPA-STAR Multi-stressor dynamics
•UM/NOAA Isotopic analysis of lower river foodwebs
•USGS Great Lakes Aquatic-GAP analysis
•GLFT/GLFC Big River Habitat Methodology
•GLFC Modeling Lamprey Habitats
•NOAA (pending) Integrated Assessment with MDNR
•GLFT (pending) Hires IBM modeling expansion
•13 papers published & In Press in peer-reviewed outlets
•15 more currently in review and submission
•>60 talks/posters at National/International Scientific Meetings
•Regional/ National/ International impacts
•Collaborative studies: Muskegon Watershed Assembly, MDNR, MDEQ
•Collaborative educational presentations: Michigan Lakes and Streams Assosciation
•MDEQ nutrient criteria legislation
•MDNR Ecoregional planning and groundwater protection programing
•National Nutrient Criteria Working Group
•Western States EMAP
•Poyang Lake Watershed Partnership (China)
•Ganges River Modeling and assessment (India)
•Landuse change/planning (E.Africa)
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
Issues:
Data & Topic Volumes! :Complexity
Coordination & Communication
2007 and Disposition
How might variations in hydrology affect
habitat and fish recruitment in the Lower
River?