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

Slides:



Advertisements
Similar presentations
Bayes rule, priors and maximum a posteriori
Advertisements

NORMAL OR GAUSSIAN DISTRIBUTION Chapter 5. General Normal Distribution Two parameter distribution with a pdf given by:
Decomposition Method.
A COMPARISON OF HOMOGENEOUS AND MULTI-LAYERED BERM BREAKWATERS WITH RESPECT TO OVERTOPPING AND STABILITY Lykke Andersen, Skals & Burcharth ICCE2008, Hamburg,
Cloud Control with Distributed Rate Limiting Raghaven et all Presented by: Brian Card CS Fall Kinicki 1.
Sensitivity Analysis In deterministic analysis, single fixed values (typically, mean values) of representative samples or strength parameters or slope.
Reading LibQUAL+ Results The University of Chicago Library LibQUAL+™ Survey Supervisors’ Meeting June 16, 2004.
OCEN 201 Introduction to Ocean & Coastal Engineering Coastal Processes & Structures Jun Zhang
Lecture 12 Lecture 1L PAVEMENT CONDITION INDICES.
Coastal Vulnerability to Climate Change by David A.Y. Smith Smith Warner International.
Workshop September 2004 Sandilands, United Kingdom COMRISK Subproject 8 “Risk Assessment Lincolnshire, Pilot Studies” (SP8)
Integration of sensory modalities
Time-series modelling of aggregate wind power output Alexander Sturt, Goran Strbac 17 March 2011.
DEVELOPMENT PLANNING FOR COASTAL HAZARDS JUNE 30, 2006 BY ENGINEERING SECTION COASTAL ZONE MANAGEMENT UNIT COASTAL ENGINEERING FOR NATURAL HAZARDS.
The Statistics of Fingerprints A Matching Algorithm to be used in an Investigation into the Reliability of the Use of Fingerprints for Identification Bob.
Prague, March 18, 2005Antonio Emolo1 Seismic Hazard Assessment for a Characteristic Earthquake Scenario: Integrating Probabilistic and Deterministic Approaches.
Harris County (Fig. 3) was chosen as the area of study for this project. The three phases described in the methodology above was used for this area. Six.
By Rob Day, David Bardos, Fabrice Vinatier and Julien Sagiotto
CHAPTER 6 Statistical Analysis of Experimental Data
Chapter 5. Operations on Multiple R. V.'s 1 Chapter 5. Operations on Multiple Random Variables 0. Introduction 1. Expected Value of a Function of Random.
Scalable Text Mining with Sparse Generative Models
Variance Fall 2003, Math 115B. Basic Idea Tables of values and graphs of the p.m.f.’s of the finite random variables, X and Y, are given in the sheet.
Probability and Statistics in Engineering Philip Bedient, Ph.D.
Juan Carlos Ortiz Royero Ph.D.
HAZUS ®MH Coastal Flood Hazard Analysis FATIH C. DOGAN ABS CONSULTING.
Lecture II-2: Probability Review
Elec471 Embedded Computer Systems Chapter 4, Probability and Statistics By Prof. Tim Johnson, PE Wentworth Institute of Technology Boston, MA Theory and.
1 Assessment of Imprecise Reliability Using Efficient Probabilistic Reanalysis Farizal Efstratios Nikolaidis SAE 2007 World Congress.
Masoud Asadzadeh 1, Masoud Asadzadeh 1, Saman Razavi 1, Bryan Tolson 1 David Fay 2, William Werick 3, Yin Fan 2 2- Great Lakes - St. Lawrence Regulation.
Private & Confidential MS Frontier Re Modeling Research Pte. Ltd. Catastrophic Risk – A Flood Perspective Kunal Jadhav 12 April 2012.
Particle Filtering in Network Tomography
Background:Project Background * Work Statement * Relevance Study Area Methodology:Past Studies Data Preparation *? Actual Data Adjustments * Modeling Procedure.
Chanyoung Park Raphael T. Haftka Paper Helicopter Project.
A Statistical Analysis of Seedlings Planted in the Encampment Forest Association By: Tony Nixon.
Stormy Weather.
Particle Filters for Shape Correspondence Presenter: Jingting Zeng.
© 2008 Morningstar, Inc. All rights reserved. 3/1/2008 LCN Portfolio Performance.
CA-RTO: A Contention- Adaptive Retransmission Timeout I. Psaras, V. Tsaoussidis, L. Mamatas Demokritos University of Thrace, Xanthi, Greece This study.
Mixture Models, Monte Carlo, Bayesian Updating and Dynamic Models Mike West Computing Science and Statistics, Vol. 24, pp , 1993.
Forward-Scan Sonar Tomographic Reconstruction PHD Filter Multiple Target Tracking Bayesian Multiple Target Tracking in Forward Scan Sonar.
PCB 3043L - General Ecology Data Analysis. OUTLINE Organizing an ecological study Basic sampling terminology Statistical analysis of data –Why use statistics?
A Process Control Screen for Multiple Stream Processes An Operator Friendly Approach Richard E. Clark Process & Product Analysis.
1 Snow depth distribution Neumann et al. (2006). 2.
Chapter 7 Sampling Distributions Statistics for Business (Env) 1.
Literature review IBC 464
COMPARISON OF ANALYTICAL AND NUMERICAL APPROACHES FOR LONG WAVE RUNUP by ERTAN DEMİRBAŞ MAY, 2002.
Chapter 23 Process Capability. Objectives Define, select, and calculate process capability. Define, select, and calculate process performance.
Nearshore Waves and Erosion Model Quantifying the Coastal Protection Benefits Provided by Natural Habitats.
WORKSHOP ON LONG-WAVE RUNUP MODELS Khairil Irfan Sitanggang and Patrick Lynett Dept of Civil & Ocean Engineering, Texas A&M University.
Los Angeles District Los Angeles District 86 th CERB 3-4 June 2009 Los Angeles District Activities and Data Utilization Arthur T. Shak, SPL Navigation.
Introduction to the TOPMODEL
PCB 3043L - General Ecology Data Analysis.
Protein Family Classification using Sparse Markov Transducers Proceedings of Eighth International Conference on Intelligent Systems for Molecular Biology.
Database Management Systems, R. Ramakrishnan 1 Algorithms for clustering large datasets in arbitrary metric spaces.
Custom Reports: SCGs and VCGs. Standard Comparison Group (SCG)
Target Releas e Component 1 Component 3 Baseline Flow Component 2 Design a regulation plan for Lake Superior that:  is easily interpretable (piecewise.
Enhancements to IIIG LTMS By: Todd Dvorak
1 Chapter 8: Model Inference and Averaging Presented by Hui Fang.
VIEWING SURVEY DATA IN CONTEXT: CRITICAL, MAINTENANCE AND DESIGN TRIGGER LEVELS Jonathan Clarke Canterbury City Council.
Performance Comparison of Ad Hoc Network Routing Protocols Presented by Venkata Suresh Tamminiedi Computer Science Department Georgia State University.
OVERVIEW OF CLARA MODEL IMPROVEMENT TESTING Kenneth Kuhn – RAND Corporation Jordan Fischbach – RAND Corporation David Johnson – Purdue University.
UGLSP – Supplemental Slides Development of an Assessment Tool to Evaluate Impacts to Shore Protection Infrastructure due to Fluctuating Water Levels (RFP.
PCB 3043L - General Ecology Data Analysis.
Anguilla & Vicinity Anguilla Probable Storm Effects
Update on Great Lakes Coastal Methodology
Update on “Channel Models for 60 GHz WLAN Systems” Document
Mathematical Foundations of BME Reza Shadmehr
The Normal Distribution
MULTIFAN-CL implementation of deterministic and stochastic projections
Improving estimates of confidence intervals around smoking quit rates
Presentation transcript:

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

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

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.

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

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).

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

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%

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

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

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)

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

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.

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

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.

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

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

Waves Waves are based on available hindcast datasets

Results

Total Costs

Example results: Plan 130

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

Spatial pattern of cost difference 55M49

Spatial pattern of cost difference BAL1

Cost and downcutting impact of 126 vs 77A

Overtopping 126 vs 77A

Dry times 1930s

Wet times 1960s

End