Postgraduate Education, Training and Capacity Building in Water, Environment and Infrastructure

Adapting to Change

Nigel Wright

Professor of Hydraulic Engineering and River Basin Development

UNESCO-IHE Institute for Water Education

What will change?

• Climate change• Urbanisation• Globalisation vs. stakeholder participation• New technologies

Adaptability

Change is nothing new - we have always had to change.

However, this change comes with increasing uncertainty:

What is the size of the danger we are adapting to?

Will a new defence need to deal with 30cm or 2m of sea level rise?

Resilience

Gerard Doornbos, Hoogheemraadschap van Rijnland, September 2007:

“The Netherlands is a very safe place…

until there is a flood”.

Flooding in the UK July 2007

• 400,000 without water.• 15,000 without electricity.

Must include uncertainty in our designs

• We can establish the best design for 0.5m sea level rise.

• However, what if the prediction is 0.25-1.0m sea level rise?

• The solution must have future adaptability built in.

Hall & Hine, IAHR 2007

Hall & Hine, IAHR 2007

Public perception

We cannot deliver certainties as before.

Flooding will happen, particularly with localised urban flooding due to more extreme rainfall events.

A policy of keeping our feet dry at all costs is no longer feasible.

Must emphasise that whilst we can alleviate we cannot prevent.

Learn from developing countries

In the absence of money to invest in primary defences, developing countries have had to adapt.

• Cyclone Shelters.• Resilient construction.• Working at a local scale.

Modelling opportunity/challenges

• Remotely sensed data• Urban cases:

•Sub-surface/surface interaction;•Building representation.

• Multiple simulations for uncertainty and risk analysis.• Continuous simulations.

Multiple simulations

• There is no one model for all cases.

• To assess flood risk we must simulate different failure modes at multiple failure points in the system.

• This points two ways: simpler models and greater use of computer power.

Combining data sources

LiDAR/map-based data

Urban

Multiple simulations

• There is no one model for all cases.

• To assess flood risk we must simulate different failure modes at multiple failure points in the system.

• This points two ways: simpler models and greater use of computer power.

Simpler models

• GIS-based models.• Raster-based cell storage models.

Parallelisation

Massively-parallel systems based on PC architecture (1000+ processors).

or

Cheaper multi-core systems.

Carlisle 2005, Env. Agency:

Carlisle simulation

• LiDAR data at 1m.• Solution with 5 million cells.

• Originally on HPC, but now on multi-core PCs.

• Used to inform coarser resolution models.

115 Km reach,53.5Km upstream of junction, 62 Kms downstreamSurface of Km2

36 processorsdl = 50 m

Key figures• 400-700 m inmediate ice dam collapse

• 9 -18 M cumecs peak

• 1D wave heights of 250 m

• 1D wave speeds of 45 m/s

• 1D Froude number of 3.5

• 1D Xs every 300 m, 2D dl=50 m

Summary

• Adaptability to future uncertainty must be built in.• Risk management not defence.• Learn from others.• Innovate with technologies.

Thank You!

Questions and remarks