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

WatershedsMichael Wiley1, Bryan Pijanowski2, Paul Richards1, 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, Pudue University,

3 Institute for Fisheries Res., Michigan Department of Natural Resources4 Department of Geology, Michigan State University, 5Annis Center Grand Valley State University

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

~4% Lake Michigan’s ‘shed (2870 sq miles )~5% Lake Michigan’s Q (2404 cfs )

Design features:

Start and End with Stakeholders Questions2D spatial org by river channel unit: VSEC or [NHD arcs]Time represented by “frames” in Landscape trajectoryCollection (Integration) of many relevant Models Variable time scales are OK

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

Watershed Stakeholders’

Questions

Managementscenario

evaluations

EcologicalInventory &Assessment

MREMSIntegrated modeling

Muskegon River Ecological Modeling System

2000,2002

2001-2003

2006

2007

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

RegressionRegressionRegressionRegression

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

MREMS Directory Structure

MREMS is really a data sharing protocol and directory structurefor a collection of interacting models

All participating models store spatially referenced output into specific year and landscape scenario directories.

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).

MRI-DARCY MODFLOWsurface loadingrecharge

surface loadingrecharge

Climate

Reach Hydrology

Reach Hydraulics

Local hydraulics and substratum

Fish growth & mortality

Hec_HMSSMA/ MODFLOW

Hec_RAS

Steelhead IBM

hours ~x00 km2

decades ~ x00 km2

weeks ~x000 km2

days ~x km2

days ~x m2

days x cm2

Landscape

HistoricalDaily 1980-2000

LTM2 Neural Net

River Segment ID @ Time Frame yr=2020

t = 1 day

t = 0=fixed per run

Surfacet = 1 hr GW t = 1 day

t = .1 day

t = 1 day

t = 1 day

Climate

Reach Hydrology

Reach Hydraulics

Local hydraulics and substratum

Fish growth & mortality

Landscape

Modeling to forecast Modeling to understand

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

Historicalreconstruction

Air Photointerpretation

1830 1978 2020 2040

Neural Netprojection

Neural Netprojection

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

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 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.

grams SRP /day @ Q10

.005 – 6.5 6.5 – 13 13 – 19 19 – 26 26 – 32 32 – 39 39 - 45

HydrologicModel

LoadingModel

1830

1978

2040

VSECFramework

Landcover maps (year)Data Source

Survey notes

Aerial photos

LTM prediction

VSECFramework

1830

1978

2040

SRP Load maps

1830 1978 2040

Database queries Output mapping

Figure 2. An illustration, from the current Muskegon River study, of our method for linking valley scale ecologicalclassification (VSEC) units to landscape models. A: Sample sites are used to represent the entire VSEC unit theyoccur in, based on the mapping objective of ecological homogeneity B: VSEC unit ID # is used to geo-reference andquery the associated catchment, buffers, site databases etc. C: Query results are used as inputs for regional models ofrelevant processes as illustrated here for soluble phosphate load. All segments are processed simultaneously in a matrixmodeling environment. Once modeling is completed predicted results are mapped back into the GIS using the VSECspatial framework. Coupled to changing input data sources on landcover distributions, this process can generate bothforecasts and hindcasts of ecological status.

A. B.

C.

Sample site

Unit catchment

VSEC unitUnit channel buffer

Together the landscape trajectoryand VSEC unitstructure provides MREMSan explicit time x spaceFramework for linking diverse MREMS componentmodels

MWRP Stakeholders’ WorkshopExamples 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?

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

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 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)

Mega-Model 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.

What will we do with the Model?

•August 24 2002 @ Annis Center•Representatives from 13 stakeholder organizations and 9 of the project PIs met •To develop scenarios to be evaluated with theMuskegon Watershed Mega 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)

Muskegon Watershed Research PartnershipModeling Endpoints Workshop

What’s next on the MREMS What’s next on the MREMS agenda?agenda?

• Lower river hydrology and fisheries models Lower river hydrology and fisheries models completed by end of 2005completed by end of 2005

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

• Final report out to Stakeholders winter Final report out to Stakeholders winter 20072007

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