1. Integrated Modeling of the Muskegon River Ecosystem: A New Approach to Integrated Risk Assessment for Great Lakes Watersheds Michael Wiley 1, Bryan Pijanowski 2, Paul Richards 1, Catherine Riseng1, David Hynman 4, Ed Rutherford 3, and, John Koches 5 Funded by the Great Lakes Fisheries Trust A product of the Muskegon Watershed Research Partnership 1 School of Natural Resources and Environment, University of Michigan 2 Department of Forestry and Natural Resources, Pudue University, 3 Institute for Fisheries Res., Michigan Department of Natural Resources 4 Department of Geology, Michigan State University, 5 Annis Center Grand Valley State University

2. Muskegon Watershed Research Partnership The vision: Collaborative,Integrated, Relevant Science for a better future http://www.mwrp.net ~4% Lake Michigan’s ‘shed (2870 sq miles ) ~5% Lake Michigan’s Q (2404 cfs )

3. 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 Objective: Developing forecasting tools for Ecosystem Management in Great Lakes Tributaries Watershed Stakeholders’ Questions Management scenario evaluations Ecological Inventory & Assessment MREMS Integrated modeling Muskegon River Ecological Modeling System 2000,2002 2001-2003 2006 2007 2001-2005

4. ModelPredictsType LTM 2Land Use changeNeural net MODFLOWGroundwater flowSimulation MRI_DARCYGroundwater upwellingGIS HEC-HMSSurface water flowsSimulation MRI_FDURSurface water flow frequenciesRegression System HEC-RASSurface water hydraulicsSimulation GWLFSurface dissolved loadsSimulation MRI_LOADSSurface dissolved loadsRegression Regional Assessment Models All taxa Sensitive taxa EPT Index Algal Index Fish/insect diversity EPT taxa/ Sensitive fish Algal Index Regression Bioenergetic IB Models Steelhead Salmon Walleye Growth rate and survivorship Simulation Standing Stock Models Sport fishes Total fishes Sensitive fishes Total Algae Filter-feeders Grazing inverts Kg/hec total mass g/m 2 Regression SEM 1 MREMS Components

5. MREMS Directory Structure 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.

6. 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 MODFLOW surface loading recharge surface loading recharge

7. ClimateReach HydrologyReach HydraulicsLocal hydraulics and substratum Fish growth & mortality Hec_HMS SMA/ MODFLOW Hec_RAS Steelhead IBM hours ~x00 km 2 decades ~ x00 km 2 weeks ~x000 km 2 days ~x km 2 days ~x m 2 days x cm 2 Landscape Historical Daily 1980-2000 LTM2 Neural Net River Segment ID @ Time Frame yr=2020  t = 1 day  t = 0=fixed per run Surface  t = 1 hr GW  t = 1 day  t =.1 day  t = 1 day

8. ClimateReach HydrologyReach HydraulicsLocal hydraulics and substratum Fish growth & mortality Landscape Modeling to forecast Modeling to understand

9. 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

10. Historical reconstruction Air Photo interpretation 1830 1978 2020 2040 Neural Net projection Neural Net projection Historical data sets augmented by neural net predictions provide a temporal framework

11. 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 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.

12. Together the landscape trajectory and VSEC unit structure provides MREMS an explicit time x space Framework for linking diverse MREMS component models

13. 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?

14. 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

15. 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 Channel 2 Response %  SedLoad 3 %TDS 4 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 inDominant 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)

16. 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.

17. 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 the Muskegon 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 Partnership Modeling Endpoints Workshop

18. What’s next on the MREMS agenda? Lower river hydrology and fisheries models completed by end of 2005 Lower river hydrology and fisheries models completed by end of 2005 Stake-holder scenario modeling completed by summer 2006 Stake-holder scenario modeling completed by summer 2006 Final report out to Stakeholders winter 2007 Final report out to Stakeholders winter 2007

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