muskegon watershed research partnership the vision: collaborative,integrated, relevant science for a...

Muskegon Watershed Muskegon Watershed ResearchResearch

PartnershipPartnership

The vision:The vision:

Collaborative,Collaborative,Integrated,Integrated,

Relevant Science Relevant Science for a better futurefor a better future

http://www.mwrp.net

A Collaborative Approach to Understanding the Dynamics of the Muskegon Watershed: A Comprehensive Model, Risk Assessment, and Tools for Use in Management

Principle InvestigatorsMike Wiley, University of Michigan (Lead Institution)

Bryan C. Pijanowski, Purdue UniversityJohn Koches, Grand Valley State University

Paul Seelbach, Michigan Department of Natural ResourcesCo-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 FederationMichigan Stream and Lake Association

Integrated Modeling of the Muskegon River: A New Approach to Ecological Risk Assessment for Great Lakes

WatershedsMichael Wiley1, R. Jan Stevenson4, Bryan Pijanowski2, Paul Richards2, Catherine Riseng1,

David Hynman4, Ed Rutherford3, and, John Koches5

Funded by the Great Lakes Fisheries TrustA product of the Muskegon Watershed Research Partnership

1School of Natural Resources and Environment, University of Michigan2 Department of Forestry and Natural Resources, Purdue University,

3 Institute for Fisheries Res., Michigan Department of Natural Resources4 Departments of Zoology and Geology, Michigan State University,

5Annis Center, Water Research Institute, Grand Valley State University, 6 Dept. of Geology, Brocksport-SUNY

Talk Overview

Context: a work in progress1. Time Line: where we are now…2. Highlighted updates on the Mega-Modeling3. Next steps4. Impacts5. Issues

Objective: Comprehensive forecasting tool for Ecosystem Management in Great Lakes Tributaries

Watershed Stakeholders’

Questions

Managementscenario

evaluations

EcologicalInventory &Assessment

MREMSIntegrated modeling

Muskegon River Ecological Modeling System

2001

2002

2003

2004

2005

2006

2007

MODELINGProject

ASSESSMENTProject

Basin wide Modeling Framework

FisheriesModel

Development

Model integration and risk assessment

Watershed Estuary/ Lake Michigan

Muskegon WatershedResearch Partnership

Talk Overview

Context: a work in progress1. Time Line: where we are now…2. Highlighted updates on the MREMS-Modeling3. Next steps4. Impacts5. Issues

Objective: Developing forecasting tools for Ecosystem Management in Great Lakes Tributaries


Questions

Managementscenario

evaluations




2000,2002

2001-2003

2006

2007Stakeholders’Conference

2001-2005

Model Predicts TypeLTM 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 RegressionSystem

HEC-RAS Surface water hydraulics Simulation

GWLF Surface dissolved loads Simulation

MRI_LOADS Surface dissolved loads Regression

Regional Assessment Models

All taxa Sensitive taxa EPT Index Algal Index

Fish/insect diversityFish/insect diversityEPT taxa/ Sensitive fishAlgal Index

Regression/Reg TreeRegressionRegressionRegression

Bioenergetic IB Models Steelhead Salmon Walleye

Growth rateandsurvivorship

Simulation Simulation Simulation

Standing Stock ModelsSport fishesTotal fishesSensitive fishesTotal AlgaeFilter-feedersGrazing inverts

Kg/hec total massKg/hec total massKg/hec total massg/m2

g/m2

g/m2

RegressionSEM1

SEM1

SEM1

SEM1

SEM1

MREMS Components

•More is better (some times)

Climate

Reach Hydrology

Reach Hydraulics

Local hydraulics and substratum

Individual Fish growth & mortality

Hec_HMSCoupled to MODFLOW

Hec_RAS

Steelhead IBMTyler and Rutherford 2002

hours ~x00 km2

decades ~ x00 km2

weeks ~x000 km2

days ~x km2

days ~x m2

days x cm2

Landscape

HistoricalDaily/Hourly 1985-2005

LTM2 Neural Net

Example: comprehensive mechanistic modeling across scales

t = 1 day

t = 0=fixed per run

Surfacet = 1 hr GW t = 1 day

t = .1 day

t = 1 day

t = 1 day

UPDATE Highlights UPDATE Highlights

• Now using improved climate data (Now using improved climate data (NEXRAD & Leaf area NEXRAD & Leaf area

index modeling for ETindex modeling for ET))• Improved LTM2 for future Improved LTM2 for future and backcastsand backcasts• Multiple Versions of coupled hydro modelsMultiple Versions of coupled hydro models

running running (including hi-res Cedar,Brooks,Bigelow)(including hi-res Cedar,Brooks,Bigelow)

• Hi-Res Hec-RAS Channel Hydraulics Model: Hi-Res Hec-RAS Channel Hydraulics Model: Croton to Croton to below Newagobelow Newago

• Lower River Fish and Productivity studies wrapping Lower River Fish and Productivity studies wrapping upup

• Dynamic Fish habitat models for L RiverDynamic Fish habitat models for L River(includes new Temperature and Prey Models)(includes new Temperature and Prey Models)

• Hi-RES Steelhead IBMHi-RES Steelhead IBM

NEXRAD for Expanded NEXRAD for Expanded MuskegonMuskegon

Mukegon Expanded watershed boundary with NEXRAD gridcells used for extracting spatially variable precipitation overlaied

• NEXRAD data NEXRAD data becomes available becomes available in 1996in 1996

• 4 km grid cells4 km grid cells• Available for liquid Available for liquid

precipitation onlyprecipitation only• June-September June-September

20032003• Significant Significant

variation even variation even over very short over very short distancesdistances

Baldwin

Stanton

Kent City

Houghton Lake

Gladwin

Grayling

Hesperia

Wellston Tippy

Muskegon

Traverse City

Grand Haven

Glennie Alcona Dam

Cedar Creek Watershed

Thiessen polygons with NCDC weather station names used for determining precipitation across the expanded Muskegon model area and the Cedar Creek watershed

Expanded Muskegon watershed boundary

•Standard Climate Run• synthetic record •1985- 2005

Dynamic Seasonal Vegetation Density Dynamic Seasonal Vegetation Density based on MODIS imagery for Expanded based on MODIS imagery for Expanded MuskegonMuskegon

1km resolution MODIS LAI grids showing vegetation density over the expanded Muskegon and Cedar Creek watersheds

Leaf Area Index (LAI)

<1

1-2

2-3

3-4

4-5

5-6

6-7

Cedar Creek watershed

Expanded Muskegon watershed

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!)

Future (Past) Landuse change in MREMSis 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.

Historicalreconstruction

Air Photointerpretation

1830 1978 2020 2040

Neural Netprojection

Neural Netprojection

Historical data sets augmented by neural net predictions provide a temporal framework

•Present {1998}

1978

Urban to AgForest to Ag

•1998

1977


1976


1975


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•Circa 1900

SCHEMATIC MODEL OF MREMS-HEC SCHEMATIC MODEL OF MREMS-HEC COUPLED WITH COUPLED WITH MODFLOWMODFLOW

strestreamam

INTERNALLY-DRAINEDINTERNALLY-DRAINED AREASAREAS TOPOGRAPHICALYTOPOGRAPHICALYCONNECTEDCONNECTED

RoadRoadHousesHouses

EVAPEVAP

RECHARGERECHARGE

EVAPEVAP

RUNOFFRUNOFF

IMP RUNOFFIMP RUNOFF

PRECIPPRECIP

STREAMSTREAM

RECHARGERECHARGE

PRECIPPRECIP

SNOWPACKSNOWPACK

MODFLOWMODFLOW22

HEC-HMSrouting

Custom SMAModule 1,2

1. P. Richards, SUNY:Brockport based on GWLF hydrology code2. Custom implementation by D. Hyndman and A. Kendall, Michigan State University

MODFLOWMODFLOW takes the recharge data and iterates takes the recharge data and iteratesa steady state solution to Darcy’s law for each Daya steady state solution to Darcy’s law for each DayOf the simulation period.Of the simulation period.

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.

MREMS can be used to evaluate effects of alternate land use patterns

1978

2040

1830

Cedar Creek

Historical climate > Obs and forcast Land cover >HEC_HMS*

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 hydro

% DD 1 Channel2

Response % SedLoad

3 %TDS4 Fish

spp. loss

Cedar Creek -13 % aggrade +26 % +32% 3-4 Brooks Creek -22 % aggrade +72 % +20% 1-2 Main River @ Evart 0 % No change +1% +20% 2-3 Main River @ Reedsburg 0 % No change +6 % +3% 0-1

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 ourCurrent conditions, identifying risks that lie ahead, and a testing ground for alternate Management Scenarios.

MRW Outline

Modeled Depth to Water

in meters

High : 177.8

Low : 0

At the end of each time step………At the end of each time step………

B = A* K/T * (head – stream elev*)B = A* K/T * (head – stream elev*)B = baseflow average for monthB = baseflow average for monthA = Cell areaA = Cell areaK = conductivityK = conductivityT = 1 T = 1

Coupled Modflow MREMS modelGroundwater flux per river km

0

10

20

30

40

0 50 100 150 200 250 300 350

River length (km)

GW

flux

(cfs

/km

)

Evart

/CROTON//FLOW/01JAN1999/1DAY/OBS/

1999 2000 2001 2002 2003 2004

1999 2000 2001 2002 2003 2004

0

50

100

150

200

250

GIS used to assemble reach-scale channel models from multiple data GIS used to assemble reach-scale channel models from multiple data sources sources

Air Photos (1998)Field survey reconnaissanceAcoustic Doppler profiling

Cross-section profiles were then extracted (using GeoRAS) for HEC_RAS

182 12979.97

12844.5112726.27

12655.3*12584.38

12221.5412160.0*

12098.49

12034.6*

11970.7711812.1*

11656.3*

11496.3*

11344.8*11208.1*11070.65

10917.5*10700.9*

10532.5*10434.*

10335.49

9722.78*9645.68*

9568.5949122.06*

8886.17*8811.5118623.773

8451.5907952.06*

7723.46*7420.68*

7266.48*7188.16*7109.840

7022.23*

6934.6206550.720

6475.64*6400.55*

6325.4786028.507

5888.78*

5730.18*5641.44*5552.707

5410.12*

5253.43*

5168.03*

5082.642

4839.1454541.97*4202.10*

3789.26*3618.385

3518.43*

3418.475

3255.86*

3093.2493001.66*2910.08*2818.500

2663.1461943.01*

1328.43*1186.3501117.94*1049.53*981.133

795.253*

535.085*

451.471183.230*

LMR

Area0Area1

Area2

HEC_RAS Simulations run for 1 year

167 transects20-50 cells per transect (Q dependent)Typically ~4000-5000 cellsDepth, Velocity, SubstratePrey density inferred from cell substrate

Valley segment Hydraulic model 12.91 km for 18.2

0 2000 4000 6000 8000 10000 12000 14000190192194196198200202204206208

test Plan: Plan 2001 5/11/2005

Main Channel Distance (m)

Ele

vatio

n (m

)

Legend

WS 31DEC2001 2400

Ground

12.75

12.49 12.13

12.00 11.88

11.64 11.49

11.32 11.18 10.98

10.75 10.54 10.39*

10.24 9.59*

9.47 9.20 8.94

8.83* 8.72 8.53

8.36 7.78

7.4*

7.135* 7.02

6.84

6.345* 6.23 5.93

5.73 5.595* 5.46

5.25 5.12*

4.99 4.75 4.54 4.27 3.905* 3.65* 3.52

3.32 3.00 2.86* 2.72

2.57 1.75

1.31 1.09 .99* 0.89

0.61 .485*

0.36

musk_18_2 Plan: Plan 02 2/10/2005 Legend

WS PF 1Ground

Bank Sta

Levee

Ground

Example cross-sectionunsteady (continuous) run for: VSEC unit 18.2: yr=2001Cross 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 Dynamic Fish Habitat ModelingModeling

•Adult Walleye habitat•2001 by month• VSEC 18.2•HSI-based

Steelhead IBM operating inSteelhead IBM operating inMuskegon River VSEC 18.1-4Muskegon River VSEC 18.1-4

Day of Year

100 150 200 250 300 350

YO

Y D

ensi

ty (

num

ber

* m

-2)

0.01

0.1

1

10

Data20012003

First split:

Groundwater vs. Runoff

GradientDrainage Area

Second split:

Classification Tree

Presence/Absence location

Model development

Chinook present

•Ed Rutherford and Crew

Results

Total Smolts: 1,025,902

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

River

Sm

olt

s

Chinook smolt estimateSmolt per river system

•Ed Rutherford and Crew

Lower River Productivity Lower River Productivity GVSU,MSU,UMGVSU,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 What’s next on the MREMS agenda?agenda?

• Model integration completed June 2006Model integration completed June 2006

• Landscape and Hydrology Scenario runs Landscape and Hydrology Scenario runs completed by Sept 2006completed by Sept 2006

• Stake-holder scenario modeling completed Stake-holder scenario modeling completed by Dec 2006by Dec 2006

• Final report out to Stakeholders Summer Final report out to Stakeholders Summer 20072007

GLFT-MWRP Technology Transfer and DiffusionSpin-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)

Issues:Data & Topic Volumes! :ComplexityCoordination & Communication2007 and Disposition

Objective: Developing forecasting tools for Ecosystem Management in Great Lakes Tributaries


Questions

Managementscenario

evaluations




2000,2002

2001-2003

2006

2007

2001-2005

How might variations in hydrology affect How might variations in hydrology affect habitat and fish recruitment in the Lower habitat and fish recruitment in the Lower River?River?

/CROTON//FLOW/01JAN1999/1DAY/OBS/

1999 2000 2001 2002 2003 2004

1999 2000 2001 2002 2003 2004

0

50

100

150

200

250

muskegon watershed research partnership the vision: collaborative,integrated, relevant science for a...

Documents

michigan state university

ecological risk assessment

great lakes fisheries

lake association slide

paul richards um

purdue university john

stevenson msu

school of natural resources