Risk-Based Coastal Asset Management Adam Hosking Louise Trim Ben Hamer Richard Harpin Gulf Coast Hurricane Preparedness, Response, Recovery & Rebuilding Conference. Mobile, AL. November 11-14, 2008

Outline UK Strategic Risk Analysis Applications of approach Asset Management System Conclusions

Strategic Risk Analysis in the UK

Risk Management - Background In UK 5 million people in 2 million homes live in flood risk areas Over 120,000 residential and commercial properties at erosion risk Significant geographic variation Widespread flooding experienced in 1998 and 2000 “Lessons Learned” led to an enhanced emphasis on flood risk management UK Government Flood & Erosion Risk Management Objective: In order to ensure the best use of public money, target spending in those areas of greatest flood risk, whilst seeking to maximise the return on investment in terms of reduction of flood risk. Erosion figures shown are from Defra’s National Appraisal of Assets at Risk Study (2002) which suggested that £7.7b of capital assets were at risk from coastal erosion. That figure was low compared to flood, but nevertheless a substantial financial risk to our nation.

Risk Assessment for Strategic Planning (RASP) Structured approach to the analysis and management of the flooding system Focus on risk, explicitly recognising that defences and flood plains perform as ‘systems’ (HR Wallingford, 2001) www.rasp-project.net

Performance of flood risk structures represented as ‘fragility curves’ Analysis Philosophy Figure 1: Halcrow have been involved in two studies related to improving the prediction of the flood pathway within the risk based framework. The pathway is defined by fragility curves. A fragility curve is used to define the relationship in the framework between the ‘source’ or ‘loading’ and the probability of failure. Where probability of failure is related to the failure to perform a flood risk role ie., breach or overtopping. Fragility curves are developed for the five key condition grades ranging from very good to very poor Performance of flood risk structures represented as ‘fragility curves’ Relates loading on an asset to the conditional probability of failure of that asset given that loading.

Predicting to flooding pathway True Fragility Curve RASP Figure 2:Fragility curves illustrate the relationship between the loading on an asset and the conditional probability of failure of the asset given that loading. Based on: field inspections, empirical models, limit state equations, expert judgement Developed for approx. 60 generic asset types

Asset Condition Grades Fragility curves are developed for the five key condition grades ranging from very good to very poor Five condition grades Based on asset performance, not just traditional condition.

Receptor The Analytical Approach Pathway Source Pathway Load Probability Source Damage Receptor Depth Probability of failure Load Pathway www.rasp-project.net

Performance-based Asset Management System (PAMS) PAMS project: Developing performance-based approach to identifying and prioritizing management of flood defences Ongoing studies, including: Improved failure mode analysis to determine key performance features, and hence condition grade Local applications to determine site specific fragility curves Fragility curves are developed for the five key condition grades ranging from very good to very poor www.pams-project.com

Applications of approach

Tiered Approach (HR Wallingford, 2002)

Application of RASP Set-up within GIS National Assessments: 2002, 05, 06 & 08 2004 Foresight Future Flooding Used to inform policy and decision making Provide consistent national flood risk mapping (public access) Local scale assessments: Project development Assist in the planning maintenance and replacement of assets

Asset Management System

Asset Management System (AMS) Based on existing GIS data platform (SANDS) Import, visualize and manipulate asset data Basis for consistent data capture Record asset types and condition, hence fragility Identify asset systems Link assets to risk areas and receptors Use cost models to optimise investment strategies

Key Components of System Base mapping Asset inventory Relationship between loading and asset condition (fragility curve) Cost models for upgrading from different condition grades (hence improving performance) for a given asset type Flood extent and receptor data

System Components

System Components

System Components

System Components

System Components

System Components

Ongoing Development Use of optimization engine to automate decision-making Tool to provide user with top ten outcomes (against multi-criteria objectives), to enable considered selection of preferred option Possible development for use at higher, regional & national level to inform policy makers and budget setters

Conclusions Benefits of Risk-based Asset Management: provides for systematic and co-ordinated approach to asset management requires comprehensive asset information (facilitates collection) focus on asset function and reduction of risk avoid surprises (physical or financial) evidence-based decision-making AMS benefits: built on established database platform consistent data storage and analysis facilitates improved decision-making maximize risk management benefits from finite budgets

THANK YOU! Adam Hosking Tel: (813) 876 6800 Halcrow Inc, Tampa, FL Tel: (813) 876 6800 Email: hoskingas@halcrow.com