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Project Update: Upper Great Lakes Study Shore Protection Teleconference 29 March 2011 Mike Davies, Ph.D., P.Eng. Coldwater Consulting Ltd.

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Presentation on theme: "Project Update: Upper Great Lakes Study Shore Protection Teleconference 29 March 2011 Mike Davies, Ph.D., P.Eng. Coldwater Consulting Ltd."— Presentation transcript:

1 Project Update: Upper Great Lakes Study Shore Protection Teleconference 29 March 2011 Mike Davies, Ph.D., P.Eng. Coldwater Consulting Ltd.

2 Outline Shore Protection Performance Indicators - Review and discussion of model operation and results by Coldwater Consulting Ltd. (conference call) - Application in the Shared Vision Model and interpretation of metrics for o Regulation plan evaluation o Water level “restoration” o Multi-lake regulation and AM - Performance indicator fact sheet

3 Draft report - update Version 0.11 transmitted last week. Subsequent changes: We have moved Sections 5.6 and 5.7 to Chapter 7 (“Interpretation”). Chapter 6 has become a part of Chapter 5. Working on data gaps / future needs and Conclusions.

4 Model operation (function) Using Available: Wave, Surge, Bathymetric and Profile data Developed Wave transformation model (shoaling and refraction to pre-process WIS to 10m contour then linear theory (shoaling with breaking) to toe of structure Wave runup and overtopping model (probability-based using Eurotop) Downcutting model (Parametric toe scour – PTS, based on CPE simulations including reflection effects) Combined these ‘process’ models to simulate time evolution of damage “Life-Cycle simulations” One month time-step

5 Model operation (mechanics) UGLSP – Stand-alone model for prediction of life-cycle performance and cost of ownership of coastal structures SAT -.dll version of UGLSP suitable for operation from within Excel (integrated into SVM).

6 Methodology 107 yr Simulations (month-by-month) Erodibility Index Structure geometry Offshore waves, Surge Stochastic Structures 1,000 statistically-derived structures Summary statistics Plan 77b Plan 1887 Plan MH Plan S4S... Water level scenarios 25 sites

7 The ‘Stochastic Structure’ Probability-based representation of coastal structures Uses the observed statistical distribution of structure characteristics Extended throughout Upper Great Lakes domain using design water level scaling A 1,000 structure sample is generated that matches the target statistical distribution Split between Class 1 and Class 2 structures is 65/35% Crest elevations are defined relative to the 100-yr design water level Toe elevations are defined relative to chart datum TypeClass 1Class 2 Revetment82%29%71% Wall18%90%10%

8 Structure data Structure geometries and characteristics come from three datasets Lake and Cook Counties, IL Racine County, WI Collingwood-Wasaga, ON

9 Structure data Crest and Toe Distribution Crest elevations from the three datasets collected in Lakes Michigan and Huron (CD = m) were combined to produce a single dataset. Only structures broadly classified as revetment and seawalls were included. Crest elevation data from various Lake Michigan locations and fitted normal (Gaussian) distribution

10 Stochastic Structures Michigan-Huron Structure Type Mean (m, CD) Standard deviation (m) Notes Crest Revetment Toe0.00.5Cannot be higher than 1 m below crest Seawall Toe1.0Cannot be higher than 1 m below crest Berm--Must be 1 m below crest and 1 m above toe, or taken as toe elevation (i.e., no berm) Superior Structure Type Mean (m, CD) Standard deviation (m) Notes Crest Revetment Toe0.00.5Cannot be higher than 1 m below crest Seawall Toe1.0Cannot be higher than 1 m below crest Berm--Must be 1 m below crest and 1 m above toe, or taken as toe elevation (i.e., no berm)

11 Probabilistic Simulations Loop through all study sites (25) Loop through all months (12x107) Loop through all structures (1,000) Loop through all regulation plans (p77, 1887, S4H, MH, etc.) Downcutting – transform H eq from 10m contour to structure D/C uses a randomly generated wave of H eq from µ, σ(H eq ) of month Downcutting (parametric toe scour) Runup wave transformation is similar but with H max (the expected max H s that month) and associated monthly surge (random # based on µ, σ(Surge) of month) Wave runup computed using Eurotop (2007) Overtopping uses cdf of H s for that month Wave overtopping - Eurotop(2007), adapted for low-crested structures and to ensure smooth transitions between various algorithms  P(f) OT Structure maintenance costs Rebuild cost Overtopping cost = P(f) OT x rebuild cost

12 Structure costs Costs are based on the monthly cost of ownership. Overtopping cost = P(f) OT x rebuild cost Rebuild cost is computed each month based on structure type & height. Degradation cost = linear depreciation (50yrs for Class 1, 25 yrs for Class 2 - ) Cost for month = max(Overtopping, Degradation) Overtopping failure occurs when P(f) OT >0.5; Flag to output, triggers re-build Structure is rebuilt with crest 25% higher; structure has 12 month rebuild window. During rebuild window, structure cannot fail a second time. Downcutting cost increases cost of ownership by virtue of increased depth, taller structure being required. Downcutting allows large waves to reach the structure; increasing likelihood of failure due to overtopping. Growth algorithm: If downcutting deepens the toe, the crest height grows at a rate of 0.2 (Class 1) or 0.3 (Class 2) x the downcutting. This is based on Eurotop algorithms to maintain constant OT performance.

13 25 Modelling zones Zones are spatially distributed throughout Superior and Huron-Michigan Summary ‘forcing’ statistics are shown below.

14 25 Modelling zones Shore classification database used to identify substrates susceptible to downcutting Erodibility index was developed to guide calculation of downcutting – a major factor for shore protection in areas of erodible beds.

15 25 Modelling zones Extent of shore protection varies widely from 0 in NE Superior to 62% near Chicago

16 Surge Statistical analysis of 2yr return period surge elevations based on measured data (green diamonds)

17 Waves Waves are based on available hindcast datasets

18 Results

19 Total Costs

20 Example results: Plan 130

21 Interpretation Total Costs relative to 77A The numbered plans (Plan 122 through to Plan 130) all produce fairly similar results. For this reason, only results for Plan 55M49, Plan 126 and Plan BAL1 are discussed further

22 Spatial pattern of cost difference 55M49

23 Spatial pattern of cost difference BAL1

24 Cost and downcutting impact of 126 vs 77A

25 Overtopping 126 vs 77A

26 Dry times 1930s

27 Wet times 1960s

28 End


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