# Accounting for Climate Change in Extreme Sea Level Estimation

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Data

#### 2.2. Extreme Value Inference

#### 2.3. Existing Methodology

#### 2.4. Incorporating Interannual Variations to Skew Surge Distribution

#### 2.5. Spatial Pooling

#### 2.5.1. Improved Inference by Pooling

#### 2.5.2. Spatial Independence Diagnostics

`texmex`R package [38]. Specifically, we use skew surge daily maxima for each pairwise combination of the four study sites, using data on the same day and with lags of $\pm 1$ day to account for time lags between the peak of surge reaching each site, when events last multiple days. Here, we have lags $t=1$ and $t=-1$ so that site A is one day ahead or behind site B, respectively. Since the variables are not identically distributed, due to seasonality for example, this can affect the evaluation of $\chi $ and $\overline{\chi}$. We address this potential concern by also using the marginal distributional model of [15] ${F}_{Y}^{(d,j,k)}$, given by expression (7), to account for this through a transform the variables to identical uniform margins and then re-evaluate these measures. These results are discussed in Section 3.4.

## 3. Results

#### 3.1. Introduction

#### 3.2. Single-Site Analysis

#### 3.3. Return Level Estimation

#### 3.4. Spatial Pooling

## 4. Discussion

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Zsamboky, M.; Fernández-Bilbao, A.; Smith, D.; Knight, J.; Allan, J. Impacts of Climate Change on Disadvantaged UK Coastal Communities; Joseph Rowntree Foundation: York, UK, 2011; pp. 1–63. [Google Scholar]
- Seneviratne, S.; Nicholls, N.; Easterling, D.; Goodess, C.M.; Kanae, S.; Kossin, J.; Luo, Y.; Marengo, J.; McInnes, K.; Rahimi, M.; et al. Changes in climate extremes and their impacts on the natural physical environment. In Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation; A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change, (IPCC); Field, C.B., Barros, V., Stocker, T.F., Qin, D., Dokken, D.J., Ebi, K.L., Mastrandrea, M.D., Mach, K.J., Plattner, G.K., Allen, S.K., et al., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2012; Volume 3, pp. 109–230. [Google Scholar]
- Seneviratne, S.I.; Zhang, X.; Adnan, M.; Badi, W.; Dereczynski, C.; Di Luca, A.; Ghosh, S.; Iskandar, I.; Kossin, J.; Lewis, S.; et al. Weather and Climate Extreme Events in a Changing Climate. In Climate Change 2021: The Physical Science Basis; Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate, Change; Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S.L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M.I., et al., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2021; Volume 11, pp. 1513–1766. [Google Scholar] [CrossRef]
- Morice, C.P.; Kennedy, J.J.; Rayner, N.A.; Winn, J.; Hogan, E.; Killick, R.; Dunn, R.; Osborn, T.; Jones, P.; Simpson, I. An updated assessment of near-surface temperature change from 1850: The HadCRUT5 data set. J. Geophys. Res. Atmos.
**2021**, 126, e2019JD032361. [Google Scholar] [CrossRef] - Egbert, G.D.; Ray, R.D. Tidal prediction. J. Mar. Res.
**2017**, 75, 189–237. [Google Scholar] [CrossRef] - Pugh, D.; Woodworth, P. Sea-Level Science: Understanding Tides, Surges, Tsunamis and Mean Sea-Level Changes; Cambridge University Press: Cambridge, UK, 2014. [Google Scholar]
- Williams, J.; Horsburgh, K.J.; Williams, J.A.; Proctor, R.N. Tide and skew surge independence: New insights for flood risk. Geophys. Res. Lett.
**2016**, 43, 6410–6417. [Google Scholar] [CrossRef] - Howard, T.; Williams, S.D.P. Towards using state-of-the-art climate models to help constrain estimates of unprecedented UK storm surges. Nat. Hazards Earth Syst. Sci.
**2021**, 21, 3693–3712. [Google Scholar] [CrossRef] - Woodworth, P.L.; Player, R. The Permanent Service for Mean Sea Level: An Update to the 21st Century. J. Coast. Res.
**2003**, 19, 287–295. [Google Scholar] - Wahl, T.; Haigh, I.D.; Woodworth, P.L.; Albrecht, F.; Dillingh, D.; Jensen, J.; Nicholls, R.J.; Weisse, R.; Wöppelmann, G. Observed mean sea level changes around the North Sea coastline from 1800 to present. Earth-Sci. Rev.
**2013**, 124, 51–67. [Google Scholar] [CrossRef] - Calafat, F.M.; Wahl, T.; Tadesse, M.G.; Sparrow, S.N. Trends in Europe storm surge extremes match the rate of sea-level rise. Nature
**2022**, 603, 841–845. [Google Scholar] [CrossRef] - Weiss, J.; Bernardara, P. Comparison of local indices for regional frequency analysis with an application to extreme skew surges. Water Resour. Res.
**2013**, 49, 2940–2951. [Google Scholar] [CrossRef] - Wong, T.E.; Sheets, H.; Torline, T.; Zhang, M. Evidence for Increasing Frequency of Extreme Coastal Sea Levels. Front. Clim.
**2022**, 4. [Google Scholar] [CrossRef] - Woodworth, P.L.; Menéndez, M.; Roland Gehrels, W. Evidence for century-timescale acceleration in mean sea levels and for recent changes in extreme sea levels. Surv. Geophys.
**2011**, 32, 603–618. [Google Scholar] [CrossRef] - D’Arcy, E.; Tawn, J.A.; Joly, A.; Sifnioti, D.E. Accounting for Seasonality in Extreme Sea Level Estimation. arXiv
**2022**, arXiv:2207.09870. [Google Scholar] [CrossRef] - Coles, S.G. An Introduction to Statistical Modeling of Extreme Values; Springer: London, UK, 2001. [Google Scholar]
- Environment Agency. Coastal Flood Boundary Conditions for the UK: Update 2018. Technical Summary Report. 2018. Available online: https://environment.data.gov.uk/dataset/6e856bda-0ca9-404f-93d7-566a2378a7a8 (accessed on 1 October 2021).
- Leadbetter, M.; Lindgren, G.; Rootzén, H. Extremes and Related Properties of Random Sequences and Processes; Springer: New York, NY, USA, 1983. [Google Scholar]
- Ferro, C.A.T.; Segers, J. Inference for clusters of extreme values. J. R. Stat. Soc. Ser. B
**2003**, 65, 545–556. [Google Scholar] [CrossRef] - Smith, R.L.; Weissman, I. Estimating the extremal index. J. R. Stat. Soc. Ser. B
**1994**, 56, 515–528. [Google Scholar] [CrossRef] - Ledford, A.W.; Tawn, J.A. Diagnostics for dependence within time series extremes. J. R. Stat. Soc. Ser. B
**2003**, 65, 521–543. [Google Scholar] [CrossRef] - Smith, R.L.; Tawn, J.A.; Coles, S.G. Markov chain models for threshold exceedances. Biometrika
**1997**, 84, 249–268. [Google Scholar] [CrossRef] - Fawcett, L.; Walshaw, D. Improved estimation for temporally clustered extremes. Environmetrics
**2007**, 18, 173–188. [Google Scholar] [CrossRef] - Graff, J. Concerning the recurrence of abnormal sea levels. Coast. Eng.
**1978**, 2, 177–187. [Google Scholar] [CrossRef] - Coles, S.G.; Heffernan, J.; Tawn, J.A. Dependence Measures for Extreme Value Analyses. Extremes
**1999**, 2, 339–365. [Google Scholar] [CrossRef] - Tawn, J.A. An extreme-value theory model for dependent observations. J. Hydrol.
**1988**, 101, 227–250. [Google Scholar] [CrossRef] - Dixon, M.J.; Tawn, J.A. The effect of non-stationarity on extreme sea-level estimation. J. R. Stat. Soc. Ser. C
**1999**, 48, 135–151. [Google Scholar] [CrossRef] - Pugh, D.; Vassie, J. Extreme sea levels from tide and surge probability. Coast. Eng.
**1978**, 16, 911–930. [Google Scholar] - Tawn, J.A. Estimating probabilities of extreme sea-levels. J. R. Stat. Soc. Ser. C
**1992**, 41, 77–93. [Google Scholar] [CrossRef] - Batstone, C.; Lawless, M.; Tawn, J.A.; Horsburgh, K.; Blackman, D.; McMillan, A.; Worth, D.; Laeger, S.; Hunt, T. A UK best-practice approach for extreme sea-level analysis along complex topographic coastlines. Ocean. Eng.
**2013**, 71, 28–39. [Google Scholar] [CrossRef] - Baranes, H.; Woodruff, J.; Talke, S.; Kopp, R.; Ray, R.; DeConto, R. Tidally driven interannual variation in extreme sea level frequencies in the Gulf of Maine. J. Geophys. Res. Ocean.
**2020**, 125, e2020JC016291. [Google Scholar] [CrossRef] - Eastoe, E.F.; Tawn, J.A. Modelling non-stationary extremes with application to surface level ozone. J. R. Stat. Soc. Ser. C
**2009**, 58, 25–45. [Google Scholar] [CrossRef] - Northrop, P.J.; Attalides, N.; Jonathan, P. Cross-validatory extreme value threshold selection and uncertainty with application to ocean storm severity. J. R. Stat. Soc. Ser. C
**2017**, 66, 93–120. [Google Scholar] [CrossRef] - Wadsworth, J.L. Exploiting Structure of Maximum Likelihood Estimators for Extreme Value Threshold Selection. Technometrics
**2016**, 58, 116–126. [Google Scholar] [CrossRef] - Davison, A.C.; Padoan, S.A.; Ribatet, M. Statistical modeling of spatial extremes. Stat. Sci.
**2012**, 27, 161–186. [Google Scholar] [CrossRef] - Dixon, M.J.; Tawn, J.A.; Vassie, J.M. Spatial modelling of extreme sea-levels. Environmetrics
**1998**, 9, 283–301. [Google Scholar] [CrossRef] - Huser, R.; Genton, M.G. Non-stationary dependence structures for spatial extremes. J. Agric. Biol. Environ. Stat.
**2016**, 21, 470–491. [Google Scholar] [CrossRef] - Southworth, H.; Heffernan, J.E.; Metcalfe, P.D. Texmex: Statistical Modelling of Extreme Values, R Package Version 2.4.8. 2020. Available online: https://cran.r-project.org/web/packages/texmex (accessed on 1 July 2022).
- Araújo, I.B.; Pugh, D.T. Sea levels at Newlyn 1915–2005: Analysis of trends for future flooding risks. J. Coast. Res.
**2008**, 24, 203–212. [Google Scholar] [CrossRef]

**Figure 1.**Global mean temperature anomalies from the HadCRUT5 dataset in 1915–2020, relative to the period 1961–1990, with associated uncertainties in red [4].

**Figure 2.**Histograms of (

**a**) ${\Delta}_{\lambda}^{\left(\tilde{k}\right)}$ over 100 years and (

**b**) ${\Delta}_{\lambda}^{\left(m\right)}$ with a ${1}^{\circ}$C increase in GMT, as percentages, for all day d and peak tide x combinations at each site.

**Figure 3.**Confidence intervals for parameter estimates: (

**a**) ${\widehat{\delta}}_{\lambda ,s}^{\left(\tilde{k}\right)}$ and (

**b**) ${\widehat{\delta}}_{\lambda ,s}^{\left(m\right)}$ at Newlyn for $s=1,2,3,4$ denoting winter, spring, summer and autumn, respectively.

**Figure 4.**Confidence intervals for parameter estimates ${\widehat{\delta}}_{\lambda}^{\left(\tilde{k}\right)}$ (

**a**) and ${\widehat{\delta}}_{\sigma}^{\left(\tilde{k}\right)}$ (

**b**) for all sites.

**Table 1.**Location (latitude and longitude), observation period, percentage of missing data, highest astronomical tide (HAT) in metres and estimated mean sea level (MSL) trend in mm/yr for Heysham, Lowestoft, Newlyn and Sheerness.

Location | Observation Period | % Missing | HAT (m) | MSL Trend (mm/yr) | |
---|---|---|---|---|---|

Heysham | $54.{03}^{\circ}$ N, $2.{92}^{\circ}$ W | 1964–2016 | 17 | 10.72 | 1.52 |

Lowestoft | $2.{47}^{\circ}$ N, $1.{75}^{\circ}$ E | 1964–2020 | 4 | 2.92 | 2.27 |

Newlyn | $50.{10}^{\circ}$ N, $5.{54}^{\circ}$ W | 1915–2016 | 17 | 6.10 | 1.73 |

Sheerness | $51.{45}^{\circ}$ N, $0.{74}^{\circ}$ E | 1980–2016 | 19 | 6.26 | 1.81 |

**Table 2.**Parameter estimates for the Models $R1-R4$ with AIC and BIC scores for each model fit at each site (including Model $R0$). The minimum AIC and BIC scores are highlighted in red and blue, respectively, for each site. The 95% confidence intervals are given in parentheses for parameter estimates.

Heysham | Lowestoft | Newlyn | Sheerness | |
---|---|---|---|---|

Model $R0$ | ||||

AIC | 12,234.21 | 15,312.08 | 24,498.77 | 9286.58 |

BIC | 12,275.89 | 15,354.88 | 24,543.93 | 9326.94 |

Model $R1$ | ||||

${\delta}_{\lambda}^{\left(\tilde{k}\right)}$ | 0.091 (−0.008, 0.191) | −0.061 (−0.150, 0.028) | 0.215 (0.154,0.276) | −0.114 (−0.219, −0.010) |

AIC | 12,232.89 | 15,312.26 | 24,453.12 | 9283.96 |

BIC | 12,282.91 | 15,363.61 | 24,507.31 | 9332.40 |

Model $R2$ | ||||

${\delta}_{\lambda ,1}^{\left(\tilde{k}\right)}$ | 0.161 (−0.033, 0.335) | 0.063 (−0.106, 0.232) | 0.114 (−0.011, 0.238) | −0.032 (−0.228, 0.164) |

${\delta}_{\lambda ,2}^{\left(\tilde{k}\right)}$ | 0.034 (−0.161, 0.230) | −0.141 (−0.322, 0.040) | 0.197 (0.077, 0.316) | −0.250 (−0.468, −0.032) |

${\delta}_{\lambda ,3}^{\left(\tilde{k}\right)}$ | 0.207 (0.013, 0.400) | −0.094 (−0.266, 0.078) | 0.209 (0.089, 0.328) | −0.189 (−0.405, 0.026) |

${\delta}_{\lambda ,4}^{\left(\tilde{k}\right)}$ | −0.047 (−0.261, 0.167) | −0.081 (−0.264, 0.102) | 0.338 (0.217, 0.460) | −0.021 (−0.221, 0.178) |

AIC | 12,235.30 | 15,315.29 | 24,452.52 | 9286.48 |

BIC | 12,310.32 | 15,392.33 | 24,533.80 | 9359.14 |

Model $R3$ | ||||

${\delta}_{\lambda}^{\left(m\right)}$ | 0.204 (0.074, 0.334) | −0.012 (−0.12, 0.427) | 0.336 (0.245, 0.427) | −0.164 (−0.304, −0.024) |

AIC | 12,227.14 | 15,314.04 | 24,451.34 | 9283.20 |

BIC | 12,277.16 | 15,365.39 | 24,505.53 | 9331.64 |

Model $R4$ | ||||

${\delta}_{\lambda ,1}^{\left(m\right)}$ | 0.256 (−0.002, 0.514) | 0.103 (−0.107, 0.312) | 0.135 (−0.058, 0.329) | −0.079 (−0.340, 0.181) |

${\delta}_{\lambda ,2}^{\left(m\right)}$ | 0.111 (−0.143, 0.365) | −0.067 (−0.282, 0.149) | 0.322 (0.144, 0.501) | −0.374 (−0.672, −0.076) |

${\delta}_{\lambda ,3}^{\left(m\right)}$ | 0.416 (0.167, 0.665) | −0.048 (−0.259, 0.163) | 0.393 (0.212, 0.574) | −0.273 (−0.568, 0.022) |

${\delta}_{\lambda ,4}^{\left(m\right)}$ | 0.010 (−0.274, 0.293) | −0.040 (−0.262, 0.182) | 0.478 (0.300, 0.655) | 0.008 (−0.253, 0.269) |

AIC | 12,227.92 | 15,318.49 | 24,450.27 | 9284.69 |

BIC | 12,302.94 | 15,395.53 | 24,531.56 | 9357.34 |

**Table 3.**Parameter estimates for the Models S1-4 with AIC and BIC scores for each model fit at each site (including Model $S0$). The minimum AIC and BIC scores are highlighted in red and blue, respectively, for each site. The 95% confidence intervals are given in parentheses for parameter estimates.

Heysham | Lowestoft | Newlyn | Sheerness | |
---|---|---|---|---|

Model $S0$ | ||||

AIC | −3091.53 | −3672.07 | −10,152.63 | −2974.317 |

BIC | −3064.77 | −3644.20 | −10,122.42 | −2948.854 |

Model $S1$ | ||||

${\delta}_{\sigma}^{\left(\tilde{k}\right)}$ | −0.009 (−0.032, 0.013) | −0.006 (−0.024, 0.011) | 0.001 (−0.003, 0.005) | 0.016 (−0.013, 0.044) |

AIC | −3088.558 | −3670.55 | −10,150.80 | −2973.05 |

BIC | −3056.448 | −3637.11 | −10,114.54 | −2942.50 |

Model $S2$ | ||||

${\delta}_{1}^{\left(\tilde{k}\right)}$ | 0.022 (−0.034, 0.078) | −0.041 (−0.090, 0.007) | 0.004 (−0.009, 0.016) | 0.023 (−0.032, 0.078) |

${\delta}_{2}^{\left(\tilde{k}\right)}$ | 0.022 (−0.014, 0.059) | −0.030 (−0.055, −0.006) | 0.006 (−0.002, 0.014) | −0.001 (−0.036, 0.035) |

${\delta}_{3}^{\left(\tilde{k}\right)}$ | −0.025 (−0.051, 0.001) | 0.012 (−0.010, 0.034) | −0.003 (−0.009, 0.003) | 0.023 (−0.010, 0.055) |

${\delta}_{4}^{\left(\tilde{k}\right)}$ | −0.035 (−0.079, 0.008) | −0.015 (−0.053, 0.023) | 0.001 (−0.008, 0.011) | 0.008 (−0.039, 0.054) |

AIC | −3095.28 | −3674.12 | −10,146.27 | −2971.19 |

BIC | −3047.12 | −3623.96 | −10,091.89 | −2925.36 |

Model $S3$ | ||||

${\delta}_{\sigma}^{\left(m\right)}$ | −0.011 (−0.033, 0.011) | −0.008 (−0.026, 0.009) | −0.001 (−0.008, 0.006) | 0.006 (−0.020, 0.032) |

AIC | −3088.42 | −3670.90 | −10,149.07 | −2972.43 |

BIC | −3056.32 | −3637.46 | −10,112.82 | −2941.87 |

Model $S4$ | ||||

${\delta}_{1}^{\left(m\right)}$ | 0.036 (−0.027, 0.099) | −0.050 (−0.105, 0.004) | −0.0003 (-0.021, 0.020) | 0.025 (-0.042, 0.091) |

${\delta}_{2}^{\left(m\right)}$ | 0.029 (−0.012, 0.070) | −0.037 (−0.061, −0.012) | 0.005 (−0.008, 0.018) | −0.015 (−0.054, 0.023) |

${\delta}_{3}^{\left(m\right)}$ | −0.027 (−0.054, −0.00009) | 0.017 (−0.006, 0.039) | −0.003 (−0.013, 0.006) | 0.013 (−0.018, 0.045) |

${\delta}_{4}^{\left(m\right)}$ | −0.030 (−0.081, 0.021) | −0.024 (−0.066, 0.017) | −0.005 (−0.021, 0.010) | −0.006 (−0.053, 0.040) |

AIC | −3093.73 | −3677.95 | −10145.39 | −2970.79 |

BIC | −3045.56 | −3627.79 | −10091.01 | −2924.96 |

**Table 4.**Estimates of the 1100 and 10,000 year sea level return levels (in metres), relative to the mean sea level in 2017, using Models $R4$ and $S0$ for the GPD rate and scale parameters, respectively, for skew surges with GMT as a fixed covariate equal to anomalies of −0.19 ${}^{\circ}$C (as in 1915), 0.92 ${}^{\circ}$C (as in 2020) and 1.92 ${}^{\circ}$C.

Heysham | Lowestoft | Newlyn | Sheerness | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

1 | 100 | 10,000 | 1 | 100 | 10,000 | 1 | 100 | 10,000 | 1 | 100 | 10,000 | |

−0.19 ${}^{\circ}$C | 10.61 | 11.52 | 12.45 | 3.47 | 4.60 | 5.81 | 6.07 | 6.55 | 6.94 | 6.41 | 7.17 | 7.98 |

−0.92 ${}^{\circ}$C | 10.63 | 11.56 | 12.50 | 3.49 | 4.61 | 5.83 | 6.09 | 6.57 | 6.95 | 6.42 | 7.18 | 7.99 |

−1.92 ${}^{\circ}$C | 10.65 | 11.60 | 12.55 | 3.50 | 4.63 | 5.85 | 6.11 | 6.60 | 6.97 | 6.44 | 7.19 | 8.00 |

**Table 5.**Kendall’s $\tau $, $\chi $ and $\overline{\chi}$ measures of dependence for daily maximum skew surge observations at pairs of sites. We show the dependence over lags −1 (LHS site is 1 day behind RHS), 0 and 1 (LHS site is 1 day ahead of RHS); in bold we show the largest dependence over these lags. $\chi $ and $\overline{\chi}$ are measures of extremal dependence for exceedances of the 0.95 quantile.

Heysham–Lowestoft | Heysham–Newlyn | Heysham–Sheerness | Lowestoft–Newlyn | Lowestoft–Sheerness | Newlyn–Sheerness | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

lag | −1 | 0 | 1 | −1 | 0 | 1 | −1 | 0 | 1 | −1 | 0 | 1 | −1 | 0 | 1 | −1 | 0 | 1 |

Observations | ||||||||||||||||||

$\tau $ | 0.133 | 0.160 | 0.309 | 0.287 | 0.322 | 0.259 | 0.153 | 0.149 | 0.298 | 0.089 | 0.040 | 0.034 | 0.155 | 0.510 | 0.238 | 0.137 | 0.168 | 0.196 |

$\chi $ | 0.095 | 0.129 | 0.270 | 0.127 | 0.145 | 0.076 | 0.092 | 0.111 | 0.259 | 0.017 | 0 | 0 | 0.145 | 0.509 | 0.200 | 0.054 | 0.077 | 0.121 |

$\overline{\chi}$ | 0.200 | 0.251 | 0.424 | 0.249 | 0.276 | 0.160 | 0.195 | 0.224 | 0.412 | 0.040 | −0.018 | −0.055 | 0.274 | 0.645 | 0.344 | 0.120 | 0.158 | 0.237 |

Transform to Uniform (0,1) | ||||||||||||||||||

$\tau $ | 0.103 | 0.130 | 0.289 | 0.285 | 0.318 | 0.244 | 0.108 | 0.102 | 0.262 | 0.086 | 0.036 | 0.028 | 0.143 | 0.523 | 0.228 | 0.139 | 0.173 | 0.200 |

$\chi $ | 0.026 | 0.040 | 0.180 | 0.103 | 0.122 | 0.053 | 0.056 | 0.036 | 0.173 | 0 | 0 | 0 | 0.095 | 0.494 | 0.174 | 0.003 | 0.016 | 0.050 |

$\overline{\chi}$ | 0.069 | 0.100 | 0.321 | 0.215 | 0.236 | 0.114 | 0.123 | 0.084 | 0.313 | −0.012 | −0.107 | −0.134 | 0.198 | 0.634 | 0.316 | 0.003 | 0.035 | 0.114 |

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**MDPI and ACS Style**

D’Arcy, E.; Tawn, J.A.; Sifnioti, D.E. Accounting for Climate Change in Extreme Sea Level Estimation. *Water* **2022**, *14*, 2956.
https://doi.org/10.3390/w14192956

**AMA Style**

D’Arcy E, Tawn JA, Sifnioti DE. Accounting for Climate Change in Extreme Sea Level Estimation. *Water*. 2022; 14(19):2956.
https://doi.org/10.3390/w14192956

**Chicago/Turabian Style**

D’Arcy, Eleanor, Jonathan A. Tawn, and Dafni E. Sifnioti. 2022. "Accounting for Climate Change in Extreme Sea Level Estimation" *Water* 14, no. 19: 2956.
https://doi.org/10.3390/w14192956