Quantifying i the GLRI Metric for Annual Sediment Deposition in Great Lakes Harbors:

Transcription

1 USACE 516(e) Annual Meeting Ann Arbor, MI (May 15, 2013) Quantifying i the GLRI Metric for Annual Sediment Deposition in Great Lakes Harbors: A Pilot Evaluation for Toledo Harbor Todd Redder Joe DePinto LimnoTech Ann Arbor, MI Funded by GLRI via USACE-Buffalo District

2 Presentation Outline Overview of GLRI sediment deposition metric Pilot evaluation: Tld Toledo Harbor Key datasets & challenges Model ldevelopment t& calibration Model application for GLRI metric Summary & application to other Great Lakes Summary & application to other Great Lakes harbors

3 Great Lakes Restoration Initiative (GLRI) Sediment Deposition Metric Metrics for Nearshore Health and Non point Source Pollution Focus Area

4 Toledo Harbor: A Pilot Case for Assessing GLRI Metrics Highest dredging maintenance cost of any Great Lakes tributary: ~25% of total GL maintenance dredging cost Annual average dredge volume > 640,000 yd 3 Critical dredged material management status Sediment sources to Federal navigation channel: Maumee River is dominant loading source Wind wave resuspension focuses Maumeedelivered and other sediments into NC Key datasets: Daily flow, suspended solids (@ Waterville) Bathymetry surveys (PC, before/after dredge) Bay/WLEB suspended solids (U. of Toledo) Western Lake Erie Basin: Sediment Load Distribution

5 Summary of Challenges & Data Limitations for Metric Assessment Maumee River high flow events are: Most significant driver of nav channel deposition Highly variable both seasonally and year to year Wind wave resuspension Contributes to total deposition in nav channel each year Needs to be distinguished from direct Maumee deposition Bathymetry data provide a limited assessment of deposition patterns/trends: Not all channel areas are surveyed each year GLRI targeted deposition changes (< 3%) are too small to be detected in bathymetry change analysis A well constrained simulation model can fill in data gaps and support GLRI metric ti assessment

6 Assessment Approach for Assessing GLRI Sedimentation Reduction Metric 1. Data acquisition & analysis: Establish daily sediment loading for Maumee River Waterville) Develop targets for change in sediment bed elevation (E & E) 2. Develop & calibratesediment transport model ( ) 09) Integrate all relevant datasets (bathymetry, TSS in river/bay/wleb) Account for direct and indirect (resuspension mediated) deposition 3. Quantify change in Maumee River loading post 2008: Actual : actual loading for period Adjusted : : loads adjusted to reflect sediment delivery 4. Apply model to quantify effective % reduction in sedimentation for timeframe ( actual vs. adjusted loading)

7 Lower Maumee River Maumee Bay Model Simulating Waves Nearshore (SWAN) Wind-Wave Sub-Model Framework Hydrodynamics y Water level Current velocity Wind-Waves Significant height Direction Frequency EFDC Model Hydrodynamic Sub-Model Shear Stress Current velocity 4 Sediment Transport Sub-Model (SNL-EFDC)

8 Navigation Channel Navigation Channel Open Lake Disposal Ottawa Western Lake Erie Basin Maumee CDF Maumee R. mouth

9 Maumee River Annual Flow & Sediment Loading ( ) Calibration ( ) Application ( )

10 Bed Elevation Change Data Spatial & Temporal Variability 2006 Head of Nav Channel (Station 0+00) Mouth Station CDF Station Western Lake Erie Basin Station Lake Erie

11 Comparison of Simulated to Observed Deposition in Nav Channel (s/f 2007 s/f 2008) May 08 Jul 08 Apr 08 May 08

12 Model Data Suspended Solids Comparison (June 18, 2004)

13 Comparison of Sediment Plume to MODIS Satellite Image (4/18/2006)

14 Model Application Approach 1. Regression of event mean concentration (EMC) vs. event peak flow rate for (R 2 = 0.54) 2. Regression used to specify adjusted EMCs and sediment loads to apply for Adjusted case represents 19% load increase relative to actual case 95% CI: 8 29% load increase 3. Simulations run for based on: Actual case: actual TSS loading Adjusted case: TSS loading based on regression 4. Evaluate reduction in deposition Overall % reduction Relative contributions of direct vs. wind wave mediated deposition Relative contributions of Maumee River vs. other sources

15 Maumee River Sediment Loadings ( ) Error bars indicate range for regression upper/lower 95% C.I.

16 GLRI Metric Evaluation Actual vs. Adjusted Loading Cases ( ) GLRI Target = 2.5%

17 Summary & Next Steps GLRI deposition metric requires quantification of navigation channel deposition in Great Lakes harbors Piloting of integrated t modeling dli approach successful lfor Tld Toledo Harbor: Maumee River sediment loading reduction: 19% (range: 8 29%) Overall reduction in nav channel deposition: 10% (range: 4 16%) Annual reductions to be reported for (TBD for ) Similarapproach approach could be applied to other priority harbors to assess progress in reducing sedimentation: Saginaw Harbor, Green Bay Harbor, Duluth Superior Harbor, etc. Constrain model with available bathymetry survey data Evaluate/address adequacy of sediment loading data Models can be used to estimate load reduction required to achieve 2.5% q deposition reduction

18 Questions?