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Extreme Sea Levels Philip L. Woodworth Permanent Service for Mean Sea Level with thanks to David Pugh, David Blackman and Roger Flather

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Contents Introduction Introduction Annual Maxima method Annual Maxima method Joint Probability method Joint Probability method Complementary value of tide gauge data and numerical modelling Complementary value of tide gauge data and numerical modelling Changes in extremes with climate change Changes in extremes with climate change

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INTRODUCTION Coastal planners need to know the risk of flooding to structures such as houses, factories and power stations at the coast so that decisions can made on where to site them and protect them. High water extreme events typically result from a high water on a spring tide and a storm surge.

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Let Q(z) be the Probability of a level z being exceeded in any 1 year. (Dont worry for now about how to calculate Q(z)). Then the RETURN PERIOD T(z) = 1/Q(z) is the average time between which levels higher than z occur. The DESIGN RISK is the Probability that a given z will be exceeded during the design life (D years) of the structure.

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If Q(z) is the Probability of exceeding z in 1 year then (1 – Q(z)) is the Probability of NOT exceeding z in 1 year (1 – Q(z)) 2 is the Probability of NOT exceeding z in 2 years (1 – Q(z)) D is the Probability of NOT exceeding z in D years Then DESIGN RISK = 1 - (1 – Q(z)) D e.g. from next figure we see that if engineers build a structure to a DESIGN RETURN PERIOD T(z) of 100 years, then if the structure is required to exist for D = 100 years, there will be a 63.5 % chance of the level z being exceeded at some occasion in that time.

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Relationship between the risk of encountering an extreme sea level with a Return Period of 100 years in the lifetime of the structure, as a function of the intended period of operation of the structure

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Note that houses, power stations etc. at the coast all have D = 100 years or thereabouts. To get a small DESIGN RISK of being flooded in that time, we have to make the design return period T(z) as large as possible. For nuclear power stations, the design T(z) may be 100,000 or 1,000,000 years. In the Netherlands, houses are constructed with T(z) of 10,000 years. In the UK, T(z) is often as low as 1,000 years. e.g. if D = 100 years, and design risk is required to be just 0.1 (10% chance of flooding at some time during the 100 years) then a design return period T(z) of 950 years is needed. (Use the equations on previous pages to check this.)

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To calculate Q(z) for a range of values of z, we can use: Tide gauge data plus statistical models Numerical modelling information (plus statistical models) Tide gauge data and numerical modelling in combination In the following examples we shall use tide gauge data from Newlyn.

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Example data used here are taken from Newlyn (Mean Tidal Range 3 m)

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ANNUAL MAXIMA METHOD We have 84 complete years of Newlyn data (within ), so we have 84 ANNUAL MAXIMUM water levels. These are histogrammed on next figure. Note that Highest Astronomical Tide (HAT) (which is at z=3.0 m) was exceeded only 28 times, because in most years the astronomical tide did not approach HAT and the surges at high water were not big enough to take the combined level over HAT. Curve shows Q(z) = Probability of z being exceeded in any 1 year e.g. Q(z=HAT) = 0.33 = 28/84

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The Q(z) can be plotted against z for values of z for which we have data. This is called an Extreme Level Distribution. Alternatively, and more normal, is to plot the distribution in another way: z versus log(T(z)). The use of log(T) is such that it makes the resulting curve approximately linear – see next figure. This curve can be parameterised easily for interpolation, but that does not help if we need to extrapolate it in order to estimate the z values corresponding to higher T(z) values (or, if you prefer, very low probabilities Q(z)). To perform the extrapolation we need to assume one of the Generalised Extreme Level (GEV) family of curves.

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The GEV family of curves is derived from the shapes of the extremes of Gaussian- (or Normal-) type distributions and have the form: z = b + a (1 – e –kX ) where z is the level of interest and X = log(T(z)) In the previous example, k > 0.0. The special case k=0 is called a Gumbel Distribution and sometimes the GD is preferred as a simpler choice of curve to fit than the GEV curve which has the extra parameter (k). Once the GEV (or GD) curves have been fitted by least-squares (or, more usually these days, maximum likelihood) to the available data, then the curves can be extrapolated to larger T(z) values.

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In practice, one can extrapolate out to values of T(z) which are approximately several times the record length (i.e. several times the 84 years in this example for Newlyn). Software now exists which can perform such calculations easily and produce formal errors on estimates of z corresponding to extrapolated T(z) values. It is very important to know such errors.

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Joint Probability Method The Annual Maxima method is wasteful in that it uses tide gauge information from only the highest high waters each year. This ignores all the other data from the rest of the year, which is a bit crazy! The JPM uses the fact that the statistics of the tide and of surges are largely independent (not completely true) and compiles separate tables of the distributions of both quantities. So, we can learn about the statistics of large positive surges even if they occur at low water, for example; in the Annual Maxima method such surges would not have contributed to the analysis. An advantage of the JPM is that it allows to estimate much smaller probabilities from the data alone, without need for the gross extrapolations of the Annual Maxima method. Also much shorter data sets can be used than in the AM method e.g. even 4 years might be useful compared to the 84 from before.

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The first step is to perform a tidal analysis (e.g. from the TASK-2000 package) such that the time series of (usually hourly) sea level values for the year is divided into tide and surge time series. The tidal series has a height frequency distribution as shown on the next page. The surge time series will have a distribution which is approximately Gaussian.

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The first step is to perform a tidal analysis such that the time series of (usually hourly) sea level values for the year is divided into tide and surge. The tidal series has a height frequency distribution as shown on the next page. The surge time series has a distribution which is approximately Gaussian. Then, inside a computer of course, we can make a 2-dimensional table which is like a 2-D version of the histogram used above for the Annual Maxima method. The following page shows a highly schematic example of the table, in practice many more rows and columns would be used. But mostly we need only consider the higher tide rows which have a chance of contributing to an overall high water extreme.

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Normalised frequency distributions for tide (vertical axis) and surge (horizontal axis). Surge = 0.1 for example means surge between 0.05 and 0.15 m A total level of 3.4 m (i.e. between 3.35 and 3.45) would be obtained 11% of the time (of the high tidal levels represented in the table) from tide+surge (0.04), (0.04) and (0.03)

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The statistics included in this table can be converted into Q(z) and T(z) form similar as for the Annual Maxima method enabling similar Extreme Level Distribution plots to be produced. For more details, see Pugh (1987) book

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WHAT CAN YOU DO IF YOU HAVE NO TIDE GAUGE DATA FROM A LOCATION WHERE YOU WANT TO HAVE EXTREME LEVEL INFORMATION? Simple regional approach methods Sophisticated spatial approach modelling of Coles and Tawn Use numerical tide+surge models

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Example of simple methods, where you have data, define: α 100 = 100-year return water level (HAT year return surge level) If the large surges always occurred at high astronomical tide, then this quantity would be 1.0. In practice of course, they do not always, so it is often much less than 1. Around the UK it is typically 0.8, falling to 0.7 in the southern North Sea where tide-surge interaction luckily causes surges to avoid high water. Once values of α 100 have been acquired for an area, then it may be possible to use the same value at sites where there are no good surge data (but some basic knowledge of the tide is still needed).

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WHAT CAN YOU DO IF YOU HAVE NO TIDE GAUGE DATA FROM A LOCATION WHERE YOU WANT TO HAVE EXTREME LEVEL INFORMATION? Simple regional approach methods Sophisticated spatial approach modelling of Coles and Tawn Use numerical tide+surge models

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POL NISE model grid (~12km) - nested in NEAC

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Storm surge extremes – numerical model approach Model runs forced by long met data sets produce realistic surge climatology - which can then be analysed like tide gauge observations. Two model runs are usually carried out for: 1. tide + met (air pressure and wind) forcing 2. tide only model fields stored hourly then 1 – 2 gives the storm surge component. Data can then be used for Annual Maxima or JPM as for tide gauge data, or be used with the gauge data as an interpolation tool.

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OTHER EXTREME LEVEL TECHNIQUES r largest method rather than the 1 largest method of Annual Maxima Revised JP Method Peaks over threshold Percentiles

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Some warnings about all methods: The methods are designed for mid-latitude climates where extremes come from winter storms. The methods are designed for mid-latitude climates where extremes come from winter storms. Experience is needed in dealing with data sets which have large outlier extremes. It is important to decide if they are representative or not, as they affect analysis results considerably. Experience is needed in dealing with data sets which have large outlier extremes. It is important to decide if they are representative or not, as they affect analysis results considerably. None of the methods work well for really extreme events e.g. tsunami None of the methods work well for really extreme events e.g. tsunami We have discussed extreme still water levels (tides + surges) only. Extreme waves, and tide-surge-wave interactions, also have to be considered. And extreme waves + currents for off-shore industry. We have discussed extreme still water levels (tides + surges) only. Extreme waves, and tide-surge-wave interactions, also have to be considered. And extreme waves + currents for off-shore industry. Refs. Pugh Tides, surges and mean sea-level, 1987, chapter 8; UK MAFF reports obtainable from Refs. Pugh Tides, surges and mean sea-level, 1987, chapter 8; UK MAFF reports obtainable from

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Tsunami Scenario: Cumbre Vieja volcano, La Palma, Canary Islands slides into the sea Tsunami waves O(5-10m) hit NW European Shelf. (Picture from Benfield Greig Hazard Research Centre, UCL)

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Some warnings about all methods: The methods are designed for mid-latitude climates where extremes come from winter storms. The methods are designed for mid-latitude climates where extremes come from winter storms. Experience is needed in dealing with data sets which have large outlier extremes. It is important to decide if they are representative or not, as they affect analysis results considerably. Experience is needed in dealing with data sets which have large outlier extremes. It is important to decide if they are representative or not, as they affect analysis results considerably. None of the methods work well for really extreme events e.g. tsunami None of the methods work well for really extreme events e.g. tsunami We have discussed extreme still water levels (tides + surges) only. Extreme waves, and tide-surge-wave interactions, also have to be considered. And extreme waves + currents for off-shore industry. We have discussed extreme still water levels (tides + surges) only. Extreme waves, and tide-surge-wave interactions, also have to be considered. And extreme waves + currents for off-shore industry. Refs. Pugh Tides, surges and mean sea-level, 1987, chapter 8; UK MAFF reports obtainable from Refs. Pugh Tides, surges and mean sea-level, 1987, chapter 8; UK MAFF reports obtainable from

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Changes of Extremes and Risk with Climate Change Simple approach which considers just a MSL change and resulting changes in z vs. T(z) Simple approach which considers just a MSL change and resulting changes in z vs. T(z) an order of magnitude increase in risk at Newlyn an order of magnitude increase in risk at Newlyn Complex approach which models changes of MSL, tides, surges etc. in a future climate Complex approach which models changes of MSL, tides, surges etc. in a future climate conclusions are very dependent on confidence in global climate models conclusions are very dependent on confidence in global climate models

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Changes of Extremes and Risk with Climate Change: Simple approach which considers just a MSL change and resulting changes in z vs. T(z) Simple approach which considers just a MSL change and resulting changes in z vs. T(z) an order of magnitude increase in risk at Newlyn an order of magnitude increase in risk at Newlyn Complex approach which models changes of MSL, tides, surges etc. in a future climate Complex approach which models changes of MSL, tides, surges etc. in a future climate conclusions are very dependent on confidence in global climate models conclusions are very dependent on confidence in global climate models

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Integrated effects of climate change on UK coastal extreme sea levels (As an example of such a complex approach and with a suspicion that things are getting worse)

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Floods in the IoM 2002 Douglas Ramsey "the worst in living memory" £4m damage in 3 hours Pictures from

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Aim To derive insight into on changes and trends in extreme sea levels from existing information To derive insight into on changes and trends in extreme sea levels from existing information Changes in extreme SL at the coast result from: Changes in extreme SL at the coast result from: a) global MSL change + regional variations b) regional land movements c) tidal changes due to increased SL d) changes in storm surges due to changes in "storminess"

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a) MSL change UK mean sea level (MSL) is rising UK mean sea level (MSL) is rising Plot shows MSL "relative" (to the land) as measured by tide gauges Plot shows MSL "relative" (to the land) as measured by tide gauges Corrected for local land movements, the "absolute" MSL trend is about +1mm/y = 10cm/century Corrected for local land movements, the "absolute" MSL trend is about +1mm/y = 10cm/century IPCC predicts +47cm by 2100 IPCC predicts +47cm by 2100

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b) Land movements Land subsidence or uplift can result from: Land subsidence or uplift can result from: –post-glacial rebound –water extraction –sediment compaction etc. Estimates (mm/y) based on geological data (Shennan, 1989) are shown here Estimates (mm/y) based on geological data (Shennan, 1989) are shown here Recent results (Shennan, in press) are not included Recent results (Shennan, in press) are not included

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c) Tidal changes Tides are modified by SL rise Tides are modified by SL rise Increased depth longer wavelength Increased depth longer wavelength Figure shows the change in MHW due to an assumed 50cm rise in MSL Figure shows the change in MHW due to an assumed 50cm rise in MSL Changes at the coast are cm Changes at the coast are cm

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d) Extreme storm surge Computed change in 50-year surge elevation Computed change in 50-year surge elevation "2 CO 2 "-"control" Produced from 30-y runs of surge models forced by met data from ECHAM4 T106 time- slice expts. Produced from 30-y runs of surge models forced by met data from ECHAM4 T106 time- slice expts. Caution! Similar studies with other climate GCMs, different sampling and extreme value analysis give different results. Caution! Similar studies with other climate GCMs, different sampling and extreme value analysis give different results. (from STOWASUS-2100 EU ENV4-CT )

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Change in relative extreme SL Taking the sum of changes in MSL + MHW + S50 + land movement (Scottish uplift will decrease the change) Taking the sum of changes in MSL + MHW + S50 + land movement (Scottish uplift will decrease the change) we obtain change in extreme sea level (cm) relative to the land for 2075 shown in the plot we obtain change in extreme sea level (cm) relative to the land for 2075 shown in the plot Caution! - uncertainty in each component Caution! - uncertainty in each component

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Rate of change of relative SL Assuming the changes in relative extreme SL occur between 1990 and 2075 Assuming the changes in relative extreme SL occur between 1990 and 2075 Mean rates are shown … c.f. official UK advice (boxed numbers) Mean rates are shown … c.f. official UK advice (boxed numbers)

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Coastal areas at risk Areas below year return period level Areas below year return period level By 2100: By 2100:the 1 in 1000-y level may become a 1 in 100-y level

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Conclusions Some of the methods used to compute extreme levels have been described but see refs. for more details. Some of the methods used to compute extreme levels have been described but see refs. for more details. Also a case study of possible changes in extremes around the UK has been described we suggest that other countries conduct similar studies. Also a case study of possible changes in extremes around the UK has been described we suggest that other countries conduct similar studies. Note that the IPCC Third Assessment Report discussed extensively changes in MSL, but pointed out that it is primarily the extreme events which do damage, and that far more study is required than has been made so far on extremes and on their possible changes in future. Note that the IPCC Third Assessment Report discussed extensively changes in MSL, but pointed out that it is primarily the extreme events which do damage, and that far more study is required than has been made so far on extremes and on their possible changes in future. So the GLOSS community must include this topic in its programme of work. So the GLOSS community must include this topic in its programme of work.

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