Impacts of Climate Change on Coastal (Nuclear) Energy Infrastructure - - PowerPoint PPT Presentation

Impacts of Climate Change on Coastal (Nuclear) Energy Infrastructure

Supporting Decision Making

ARCoES case study IAA case study ERIIP case study

Projections of potential future flooding to 2500 AD. Originally intended to focus on scenarios of sea level rise due to climate change to the 2020s, 2050s & 2080s and our best understanding of long-term change to 2100, 2200 & 2500 AD Online map viewer for exploring potential future flooding developed

• n NW region and selected nuclear

case studies. * Sizewell (mixed beach) * Hinkley Point (rocky shore and cliff) * Bradwell (estuary) * Sellafield (sand beach/dune)

Projections of long-term coastal change providing the landscape for coastal energy infrastructure: e.g. NW Region

1545-2017

Flood Modelling Approach

SWAB/XBeach-G LISFLOOD-FP

2 m LiDAR data Filtered to 10 m Sea defences reincorporated Surface roughness assigned

Model setup, Fleetwood example

Extreme water level hazard

Source: EA, Flood boundary conditions for UK mainland and islands Project: SC060064/TR2: Design sea level (Macmillan et al., 2011)

Sea level rise: plausible high-end projections to 2500 AD (Jevrejeva et al., 2012) RCP3PD RCP4.5 RCP6 RCP8.5

upper 95% and low 5% confidence level 5.49 m 1.95 m

Wave overtopping hazard

➢ Storm tide: 1 in 250 yr R.P. ➢ Waves: 1 in 100 yr R.P. ➢ SLR: 0.65 m

https://arcoes-dst.liverpool.ac.uk/

Flood depth

Hazard ratings

Hazard threshold (H) Degree Description < 0.75 Low Caution: Flood zone with shallow water or deep standing water 0.75 - 1.25 Moderate Dangerous for some (i.e., children): Flood zone with deep or faster flowing water 1.25 - 2.0 Significant Dangerous for most people: Flood zone with deep or fast flowing water > 2.0 Extreme Dangerous for all: Flood zone with deep or fast flowing water

Depth ×(Velocity + 0.5) + Debris Factor

[land use: pastoral/arable, woodland, urban] (DEFRA: Surendran et al., 2008)

£0 £5,000 £10,000 £15,000 £20,000 £25,000 £30,000

2 4

Cost per grid cell (25m2) Depth of Flood Water (m)

Cost of flooding

Depth damage curves Land use Housing - short duration saltwater events

Salt Water Depth Damage data for Housing, Road and Industrial flood inundation cells Inundation cost data for arable flood inundation cells

Costs for each land use and the total area inundated

Present-day management information

Acknowledgement of operation and pre-operational safety cases

• Fig. 1. Past and future sea-

level rise. For the past, proxy data are shown in light purple and tide gauge data in blue. For the future, the IPCC projections for very high emissions (red, RCP8.5 scenario) and very low emissions (blue, RCP2.6 scenario) are shown. Source: IPCC AR5 Fig. 13.27.

Providing context: Sea Level Rise Scenarios

Regional sea level rise projections for cities Figure S4: Projected regional sea level rise over the 21st century and uncertainty distributions for cities in Northern Europe under RCP8.5. Darker shading indicates the 17-83% range when using AR5 for all. Grinsted et al. (2015) Climate Research, Vol. 64: 15–23, doi: 10.3354/cr01309

Providing context: Sea Level Rise Scenarios

Mitrovica et al., 2001. Nature 409, 1026-1029.

Normalized global sea-level variations computed for the case of present-day ice mass variations in a, Antarctica b, Greenland c, melting of the mountain glaciers and ice sheets.

Providing context: Sea Level Fall

Shennan et al. (2012) http://onlinelibrary.wile y.com/doi/10.1002/jqs. 1532/pdf

Example of 2D XBeach Output, Hurricane Sandy 2012. Credit: Kees Nederhoff https://www.youtube.com/watch?v=qw7kvt-aBdU

Working with ‘Natural’ Coastal Resilience using Models - WWNP

XBeach is a 1D/2D model for wave propagation, long waves and mean flow, sediment transport and morphological changes

• f the nearshore area,

beaches, dunes and back barrier during storms. It takes a time varying water level, wave spectra and a 1D profile

• r 2D elevation grid of

the coastline.

Saltmarshes and Wave Energy Dissipation

Saltmarsh and beach slope offshore. Eroded marsh states simulated by removing the relevant section of landward side of the saltmarsh. The missing section of the profile is replaced by assuming the slope of the beach would continue until it meets the new position of the marsh edge.

Change in Wave Run-up and Gravel Beach Profile: XBeach-G

Gravel Barrier-Beach Profile Run-up vs. Crest Height Run-up vs. Beach Width Run-up vs. Beach Slope

The 0.5% probability or 1 in 200 year flood event

Example Scenarios 3 = EWL: 2.20 m Hs: 5.61 m 30 = EWL: 5.28 m Hs: 0.92 m

Joint Probability Analysis

Scenarios Likely Range (m) Mean (m) RCP2.6 0.28-0.61 0.44 RCP4.5 0.36 – 0.71 0.53 RCP6.0 0.38 – 0.73 0.55 RCP8.5 0.52-0.98 0.74

‘Hold the Line’ Interventions

Managed Realignment: Learning from the Past

Tidal Lagoons – Multiple Benefits

8-10M m3 c.50M m3 c.75M m3 ‘Advance the Line’ Interventions: Mega-recharge/Sandscaping

Sandscaping: Coastal Evolution Modelling (NOC/BGS)

CEM model outputs of coastal erosion rates from small-scale sand and gravel recurve interventions.

UKCP09 High Emission Scenario Sea-Level Rise Projections for 2100

• UKCP09 provides SLR projections on an annual basis up to 2100, the projections

used in this project are the high emission scenario projections

• Normal distribution

showing the projected SLR distribution at annual intervals.

• Sampled randomly as part
• f a Monte Carlo analysis.
• The distribution of

maximum flood water depths (5th, 50th 95th %) can be derived for a defined point on the model domain that corresponds to the location of an energy infrastructure asset.

Economic Basis for Intervention: Percentage of Simulations for NW Substations where ‘Real Option’ is Exercised

Substati

• n Ref

No 2020 2030 2040 2050 2060 2070 2080 2090 2100 27 6.33 20.15 34.91 49.78 64.28 76.58 58 0.018 3.90 109 3.38 14.46 26.99 40.57 54.03 111 0.46 11.46 28.15 45.64 62.15 75.99 86.65 93.41 104 0.10 6.23 17.08 29.30 42.14 110 14.47 40.28 63.41 81.03 91.88 97.23 99.24 99.85 99.98

Spatial and temporal variability in the % probability that the real option to invest in defences around specific substations is exercised as a function of projected sea-level rise.

ARCoES Key Messages 1

Nuclear power stations are located on shorelines that are resilient to storm surges. Flooding due to storm surges, wave overtopping of sea defences and high river flows may combine to produce greater areas and depths of flooding. This risk is likely to increase in the future with sea-level rise. Flood events with the same joint probability may result from different combinations of wave height and extreme water level, causing different flood extents and depths. Incremental projections of future sea-level rise identify ‘tipping points’ at which the extent of flooding increases greatly, requiring a step change in management strategy and resourcing. These thresholds may be explored using an open-source decision-support tool.

ARCoES Key Messages 2

The Real Options methodology provides a probabilistic assessment of the

• ptimal time for investment in energy distribution assets to build resilience to

future sea-level rise. Projections of future flooding identify coastal locations that are less suited for the next generation of energy infrastructure. Implementation of tidal barrages/lagoons as a means for combining energy generation with enhanced sea defence capability has the potential to both decrease and increase future flooding in adjacent areas. Innovative sea defence by ‘Sandscaping’ works with natural processes to reduce coastal erosion and simultaneously create habitats and environments.