Maumee River Sedimentation Project

USACE Great Lakes Sediment Workshop
Ann Arbor, MI (May 30-31, 2012)
Development of Integrated Tools
for Assessing
g Current and Future
Sedimentation in Great Lakes
Rivermouth Systems: Application
to Maumee System
Joseph DePinto, Todd Redder, Ric McCulloch,
Greg Peterson – LimnoTech
Brent LaSpada – USACE – Buffalo District
Funded by USACE-Buffalo District
through sub-contract to Ecology &
Environment, Buffalo, NY
Presentation Outline
 Overview of sedimentation issues
 GLRI sedimentation
di
t ti metric
ti
 Pilot Project for Toledo Harbor Navigation
Channel




Summary of key datasets
Challenges
g & data limitations
Available modeling tools
Integrated modeling approach
 Project
P j t Plan
Pl and
dS
Schedule
h d l
 Future Steps
Overview of Sedimentation
Issues in Great Lakes
 Maintenance dredging required for many Great
Lakes harbors

Confined disposal (high cost)
 ‘Open lake’ disposal (potential water quality impacts)
 Annual dredging cost: ~$20
$20 million

Toledo Harbor: $5M
 Green Bay Harbor: $2-3M
 Saginaw Harbor: $1M
 Duluth-Superior Harbor: $1M
 Resources aimed at addressing sedimentation
problem:
bl


Water Resources Development Act – Section 516(e)
Great Lakes Restoration Initiative (GLRI) focus area
Great Lakes Restoration Initiative
Sedimentation Metrics
Metrics for “Nearshore Health and Non-point Source Pollution Focus Area”
Toledo Harbor – Pilot Case for
g GLRI Metrics
Assessing
 Represents highest dredging maintenance cost of any Great
Lakes tributary:
Annual average dredge volume >640
>640,000
000 yd3
$5M per year (~25% of total maintenance dredging cost)
 ‘Confined’ (30%) & ‘open lake’ (70%) disposal
 “Critical” dredged material management status


 Sediment sources to Federal navigation channel:
 Maumee River is dominant loading source (primarily cohesive
sediments)
 Wind-wave
Wind wave resuspension focuses Maumee
Maumee-delivered
delivered
sediments and other sediments into navigation channel
Western Lake Erie Basin: 2005
Sediment Load Distribution
Other Detroit
6% 16%
Maumee
78%
Datasets to Support Metric
Assessment in Toledo Harbor
 USACE bathymetry surveys
 “Project
Project conditions”
conditions - annual survey
 “Before dredge” surveys
 “After dredge” surveys
 Maumee River data @ Waterville, OH:
 Mean daily flow (USGS)
 Daily total suspended solids since mid-70s
mid 70s
(Heidelberg U.)
 Suspended solids monitoring in Lake
Erie:



U. of Toledo long-term program (Tom
Bridgeman)
Multiple locations in Maumee Bay
Monthly spring/summer sampling (2002-2011)
Toledo Harbor:
Bathymetry Survey Data
 Provides estimates of deposition
for:
Dredging

Specific sub-reaches of navigation
channel for a given year
 Limited time periods (e.g., between
dredging events)
 Limitations:

Represents sediment deposited from:
Primary deposition via Maumee River
– Secondary deposition facilitated by
wind-wave resuspension
–

Surveys conducted at varying space
and time scales
–
Relative magnitude and timing of flow
and wind events must be considered
Maumee River:
Total Suspended Solids Loading
 Heidelberg University
monitoring @ Waterville, O
OH:

Total suspended solids (TSS) –
daily measurements
 Co-located with USGS flow
gaging station
 Represents
p
> 96% of total
Maumee watershed area
 Key data uses:

Quantify relative importance of
high flow events to overall load
 Support estimation of multi-year
trends in TSS load reductions
 Daily data are ideal for
specifying loading in a model
2011: 60% of sediment load
delivered via 3 spring events
Summary of Challenges & Data
Limitations for Metric Assessment
 Maumee River high flow events are:


Mostt significant
M
i ifi
t driver
di
off navigation
i ti channel
h
ld
deposition
iti
Highly variable – both seasonally and year-to-year
 Wind-wave resuspension

Contributes to total deposition in navigation channel
each year
 Needs to be separated 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 assessment
Modeling to Support
Toledo Harbor Assessment
 “Lower Maumee River – Maumee Bay” (LMR-MB)
Model

Developed by LimnoTech in 2010 (funded by USACE
Buffalo District)
 Represents hydrodynamics, wind-wave dynamics,
sediment transport
 Provides a simulation tool for assessing sediment
management alternatives in the Bay
 Key inputs:



Maumee flow, TSS loading @ Waterville
Lake Erie boundary condition, other tributary inflows
Wind forcings
 Current calibration based on 2004-05 data:

Bathymetry
y
y change
g analysis
y
 Maumee Bay suspended solids data (U. Toledo)
Lower Maumee River – Maumee Bay
Model Framework
EFDC Model
“Simulating Waves
Nearshore” (SWAN)
Hydrodynamics
y
y
•Water level
•Current velocity
Hydrodynamic
Sub-Model
Wind-Wave
Sub-Model
•Current
velocity
Wind-Waves
•Significant height
•Direction
•Frequency
Shear
Stress
Sediment Transport
Sub-Model
Suspended Solids Animation
(beginning 5/12/2004)
Maumee Flow: 28,200 cfs
Data provided by:
Pete Richards and Dave Baker, Heidelberg University
Tom Bridgeman, University of Toledo
Integrated Modeling Approach
for Toledo Harbor
1. Conduct ‘bathymetry change’ analysis for 2004-
2009 period
2. Further corroborate (evaluate & refine as
necessary) LMR-MB model calibration based on
‘bathymetry change’ analysis and bay TSS data
3. Develop regressions to estimate % sediment
loading reductions for GLRI period of interest
(2009-2011) relative to 2005 – 2008
4. Model Application (2008-2011):

Develop baseline model simulations based on actual
observed sediment loads
 Conduct simulations with “scaled up” load, based on
estimated
i
d % reductions
d i
 Compute reductions in navigation channel deposition for
2008-2011 period (“scaled up” minus “baseline”)
Project Plan and Schedule
 Task 1 – Develop Quality Control Plan

June, 2012 (LimnoTech)
 Task 2 – Analyze USACE
S C bathymetry data


2004 – 2009
September, 2012 (Ecology and Environment)
 Task
T k 3 – Compile
C
il and
d analyze
l
TSS lloading
di
data at Waterville


1975 – 2011
September 2012 (LimnoTech)
September,
 Task 4 – Corroborate LMR-MB model


2004 – 2011 use bathymetry and Western Basin
TSS data
November, 2012 (LimnoTech)
 Task 5/6 – Apply model and report results


February,
y, 2013 – p
presentation to GLNPO
May, 2013 – final report and recommendations
Apply Approach to Other Priority Harbors
Source: USACE Website (http://www.lre.usace.army.mil/_kd/Items/actions.cfm?action=Show&item_id=8270&destination=ShowItem)
Questions?
 Acknowledgements:
 Funding: USACE Buffalo District
 Partners: Ecology & Environment
 Data Sources:
–
–
–
–
USACE Buffalo District
Heidelberg University
University of Toledo (T. Bridgeman)
GeoSea
 Contact Information:
Joseph V. DePinto
LimnoTech
Ann Arbor, MI
jdepinto@limno.com
EXTRA SLIDES
Summary
 GLRI sedimentation metrics require quantification
of navigation channel deposition in Great Lakes
harbors
 Piloting of integrated modeling approach
underway for Toledo Harbor:


Driven by daily sediment loading data,
data wind data
data, etc
etc.
Model constrained by 1) bathymetry change analysis,
and 2) Maumee Bay TSS data
 Loading
g reductions based on statistical analysis
y
of
Waterville TSS dataset
 Similar approach can eventually be applied to
other priority harbors to assess progress in
reducing sedimentation:



Saginaw Harbor
Green Bay Harbor
Duluth-Superior Harbor
Detroit
Huron
Stony
Grid Characteristics:
Grid
Characteristics:
• Curvilinear Grid
• 4,613 Horizontal Cells
• 26,387 Total Cells (3D)
Raisin
Ottawa
Maumee
Cedar
Portage
Navigation Channel
Navigation Channel
Ottawa
Maumee
Example of Wind
Wind--driven Resuspension
Event ((3/22/2005))
Maumee Flow: ~3,000
3,000 cfs
Maumee River Flow &
Total Suspended Solids (2011)
2011: 60% of load
delivered via 3 spring
p g events
Example of ModelModel-Calculated
Deposition Patterns (2004(2004-05)