United Kingdom UK: Urban Land Area Where Elevation is Below 5 Meters: % of Total Land Area data was reported at 1.195 % in 2010. This stayed constant from the previous number of 1.195 % for 2000. United Kingdom UK: Urban Land Area Where Elevation is Below 5 Meters: % of Total Land Area data is updated yearly, averaging 1.195 % from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 1.195 % in 2010 and a record low of 1.195 % in 2010. United Kingdom UK: Urban Land Area Where Elevation is Below 5 Meters: % of Total Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s UK – Table UK.World Bank: Land Use, Protected Areas and National Wealth. Urban land area below 5m is the percentage of total land where the urban land elevation is 5 meters or less.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Weighted Average;

The data are simulated instantaneous sea surface elevations above time-mean sea level due to tides alone (tideAnom) and due to tide and meteorological surge (tideSurgeAnom). The data were produced by the Met Office Hadley Centre, using data made available by the Swedish Meteorological and Hydrological Institute (SMHI) and the Climate Model Intercomparison Project, phase 5 (CMIP5). The data were produced to investigate the impact of simulated atmospheric storminess change on extreme sea levels. To produce the data, atmospheric winds and pressure from the SMHI Regional Atmospheric Model RCA4 was used to drive the CS3 continental shelf model. The data are the resulting simulated sea surface elevations. Five CMIP5 RCP8.5 simulations were downscaled in this way: EC-EARTH, HadGEM2-ES, MPI-ESM-LR, IPSL-CM5A-MR, CNRM-CM5. The data covers the period 2007 to 2099, and applies to the UK coast.

Monthly and annual values of sea level are sent to the Permanent Service for Mean Sea Level (PSMSL) by national authorities, with details of gauge location, missing days, and definition of the datum to which measurements are referred. Received data are checked for consistency. If possible, values are reduced to a Revised Local Reference (RLR). This involves the identification of a stable, permanent benchmark which ensures continuity with subsequent data. Geodetic information on benchmarks from new geodetic techniques (SLR + VLBI/GPS) related to the IERS global geodetic network will also be databanked by PSMSL.

 There are more than 46000 stations with approx. 1800 tide gauge
 stations with 20 years+ data, and approx. 120 stations with data prior
 to 1900 (earliest is 1806). Data are normally distributed on magnetic
 tape in the IOC recommended GF3 format. Small quantities can be sent over
 electronic mail or on floppy disk. Data can also be accessed via
 the INTERNET: "http://www.pol.ac.uk/psmsl/"

 Also a Global Sea Level Observing System (GLOSS) Station Handbook,
 describing in detail the 287 sea level stations in the GLOSS network,
 is available. GLOSS measures the Global Level of the Sea Surface, a
 smooth sea surface layer after averaging out waves, tides and short
 period meteorological events. A full description of each gauge in the
 GLOSS network is provided including tide gauge details, benchmark
 information, data delivery systems and the GLOSS national contact
 point. Details of data availability in national or project data banks
 is included, together with details of the monthly and annual means
 held by the PSMSL. The handbook is provided on CDROM.

'''DEFINITION'''

The OMI_EXTREME_SL_NORTHWESTSHELF_slev_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea level measured by tide gauges along the coast. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset omi_extreme_sl_northwestshelf_slev_mean_and_anomaly_obs, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (Pérez Gómez et al 2018).

'''CONTEXT'''

Sea level (SLEV) is one of the Essential Ocean Variables most affected by climate change. Global mean sea level rise has accelerated since the 1990’s (Abram et al., 2019, Legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (WCRP, 2018). Basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one metre by the end of the century (Vousdoukas et al., 2020, Tebaldi et al., 2021). This will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves (Boumis et al., 2023). The increase in extreme sea levels over recent decades is, therefore, primarily due to the rise in mean sea level. Note, however, that the methodology used to compute this OMI removes the annual 50th percentile, thereby discarding the mean sea level trend to isolate changes in storminess. The North West Shelf area presents positive sea level trends with higher trend estimates in the German Bight and around Denmark, and lower trends around the southern part of Great Britain (Dettmering et al., 2021).

'''COPERNICUS MARINE SERVICE KEY FINDINGS'''

The completeness index criteria is fulfilled by 33 stations in 2023, one less than in 2022 (32). The mean 99th percentiles present a large spatial variability related to the tidal pattern, with largest values found in East England and at the entrance of the English channel, and lowest values along the Danish and Swedish coasts, ranging from the 3.08 m above mean sea level in Immingan (East England) to 0.45 m above mean sea level in Tregde (Norway). The standard deviation of annual 99th percentiles ranges between 2-3 cm in the western part of the region (e.g.: 2 cm in Harwich, 3 cm in Dunkerke) and 7-8 cm in the eastern part and the Kattegat (e.g. 8 cm in Stenungsund, Sweden). The 99th percentile anomalies for 2023 show overall slightly negative values except in the Kattegat (Eastern part), with maximum significant values of +11 cm in Hornbaek (Denmark), and +10 cm in Ringhals (Sweden).

'''DOI (product):''' https://doi.org/10.48670/moi-00272

The dataset contains 39148 years of sea level data from 1355 station records, with some stations having alternative versions of the records provided from different sources. GESLA-2 data may be obtained from www.gesla.org. The site also contains the file format description and other information. The text files contain headers with lines of metadata followed by the data itself in a simple column format. All the tide gauge data in GESLA-2 have hourly or more frequent sampling. The basic data from the US National Atmospheric and Oceanic Administration (NOAA) are 6-minute values but for GESLA-2 purposes we instead settled on their readily-available 'verified hourly values'. Most UK records are also hourly values up to the 1990s, and 15-minute values thereafter. Records from some other sources may have different sampling, and records should be inspected individually if sampling considerations are considered critical to an analysis. The GESLA-2 dataset has global coverage and better geographical coverage that the GESLA-1 with stations in new regions (defined by stations in the new dataset located more than 50 km from any station in GESLA-1). For example, major improvements can be seen to have been made for the Mediterranean and Baltic Seas, Japan, New Zealand and the African coastline south of the Equator. The earliest measurements are from Brest, France (04/01/1846) and the latest from Cuxhaven, Germany and Esbjerg, Denmark (01/05/2015). There are 29 years in an average record, although the actual number of years varies from only 1 at short-lived sites, to 167 in the case of Brest, France. Most of the measurements in GESLA-2 were made during the second half of the twentieth century. The most globally-representative analyses of sea level variability with GESLA-2 will be those that focus on the period since about 1970. Historically, delayed-mode data comprised spot values of sea level every hour, obtained from inspection of the ink trace on a tide gauge chart. Nowadays tide gauge data loggers provide data electronically. Data can be either spot values, integrated (averaged) values over specified periods (e.g. 6 minutes), or integrated over a specified period within a longer sampling period (e.g. averaged over 3 minutes every 6 minutes). The construction of this dataset is fundamental to research in sea level variability and also to practical aspects of coastal engineering. One component is concerned with encouraging countries to install tide gauges at locations where none exist, to operate them to internationally agreed standards, and to make the data available to interested users. A second component is concerned with the collection of data from the global set of tide gauges, whether gauges have originated through the GLOSS programme or not, and to make the data available. The records in GESLA-2 will have had some form of quality control undertaken by the data providers. However, the extent to which that control will have been undertaken will inevitably vary between providers and with time. In most cases, no further quality control has been made beyond that already undertaken by the data providers. Although there are many individual contributions, over a quarter of the station-years are provided by the research quality dataset of UHSLC. Contributors include: British Oceanographic Data Centre; University of Hawaii Sea Level Center; Japan Meteorological Agency; US National Oceanic and Atmospheric Administration; Puertos del Estado, Spain; Marine Environmental Data Service, Canada; Instituto Espanol de Oceanografica, Spain; idromare, Italy; Swedish Meteorological and Hydrological Institute; Federal Maritime and Hydrographic Agency, Germany; Finnish Meteorological Institute; Service hydrographique et oceanographique de la Marine, France; Rijkswaterstaat, Netherlands; Danish Meteorological Institute; Norwegian Hydrographic Service; Icelandic Coastguard Service; Istituto Talassographico di Trieste; Venice Commune, Italy;

AbstractA continuous, smoothed contour dataset at 500 m intervals for all land south of 60°S, excluding the Balleny Islands. The vertical datum of the contours is EGM2008. Contours are extracted primarily from the PGC Reference Elevation Model of Antarctica (REMA) v1.1 with certain islands filled from Copernicus WorldDEM. Peter I Øy contours are from the Norwegian Polar Institute. Sources of individual line segments are contained in the attribute table and full compilation information is given in the lineage statement.Data compiled, managed and distributed by the Mapping and Geographic Information Centre and the UK Polar Data Centre, British Antarctic Survey on behalf of the Scientific Committee on Antarctic Research.Further information and useful linksMap projection: WGS84 Antarctic Polar Stereographic, EPSG 3031. Note: by default, opening this layer in the Map Viewer will display the data in Web Mercator. To display this layer in its native projection use an Antarctic basemap.The currency of this dataset is November 2022 and will be reviewed every 6 months. This feature layer will always reflect the most recent version.For more information on, and access to other Antarctic Digital Database (ADD) datasets, refer to the SCAR ADD data catalogue.A related high resolution dataset at 100 m intervals is also published via Living Atlas.For background information on the ADD project, please see the British Antarctic Survey ADD project page.LineageAll processing described here was performed in ArcGIS Pro version 2.6.A composite Digital Elevation Model (DEM) was created comprising of three datasets from the Reference Elevation Model of Antarctica v1.1: ‘REMA_100m_peninsula_dem_filled’, ‘REMA_100m_dem’ and ‘REMA_200m_dem_filled’. These DEMs were first converted from ellipsoidal height to height above EGM2008 geoid and then mosaicked together in respective order at 100 m spatial resolution. This 100 m DEM was smoothed by performing ‘Focal Statistics’ using a 40x40 cell size.500 m contours were extracted and all contours with a height <1m were deleted, as well as erroneous offshore contours. In certain locations, primarily some islands on the Antarctic Peninsula, REMA data was insufficient to produce contours. In these places, contours were produced from the ‘Copernicus WorldDEM 90m’ DEM and smoothed by 4 km using a PAEK smoothing algorithm. Contours for Peter I Øy were incorporated from the Norwegian Polar Institute Data at 100 m intervals: 500 m intervals were extracted and smoothed by 800 m, to match the appropriate resolution of the main contours.All contours were merged together and lines <5 km in length were deleted. Further lines <20 km were deleted in ‘non-mountainous’ regions, so as to avoid deleting small mountain peak contours but to still simplify the main dataset. These regions were interpreted manually using the hillshade of the DEM used to produce the contours.Original DEM sources and citations:REMA: Howat, I. M., Porter, C., Smith, B. E., Noh, M.-J., and Morin, P.: The Reference Elevation Model of Antarctica, The Cryosphere, 13, 665-674, https://doi.org/10.5194/tc-13-665-2019 , 2019Copernicus WorldDEM: produced using Copernicus WorldDEM™-90 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved.Norwegian Polar Institute (2014). Map data / kartdata Peter I Øy 1:50 000 (P50 Kartdata). Norwegian Polar Institute. https://doi.org/10.21334/npolar.2014.29105abcCitationPlease cite this item as: 'Gerrish, L., Fretwell, P., & Cooper, P. (2020). Medium resolution vector contours for Antarctica (7.3) [Data set]. UK Polar Data Centre, Natural Environment Research Council, UK Research & Innovation. https://doi.org/10.5285/0779002b-b95d-432f-b035-b952c36aa5c9'. If using for a graphic or if short on space, please cite as 'data from the SCAR Antarctic Digital Database, accessed [year]'

United Kingdom UK: Rural Population Living in Areas Where Elevation is Below 5 Meters: % of Total Population data was reported at 1.110 % in 2010. This records a decrease from the previous number of 1.120 % for 2000. United Kingdom UK: Rural Population Living in Areas Where Elevation is Below 5 Meters: % of Total Population data is updated yearly, averaging 1.110 % from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 1.120 % in 2000 and a record low of 1.062 % in 1990. United Kingdom UK: Rural Population Living in Areas Where Elevation is Below 5 Meters: % of Total Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Land Use, Protected Areas and National Wealth. Rural population below 5m is the percentage of the total population, living in areas where the elevation is 5 meters or less.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Weighted average;

United Kingdom UK: Rural Land Area Where Elevation is Below 5 Meters: % of Total Land Area data was reported at 3.043 % in 2010. This stayed constant from the previous number of 3.043 % for 2000. United Kingdom UK: Rural Land Area Where Elevation is Below 5 Meters: % of Total Land Area data is updated yearly, averaging 3.043 % from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 3.043 % in 2010 and a record low of 3.043 % in 2010. United Kingdom UK: Rural Land Area Where Elevation is Below 5 Meters: % of Total Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Land Use, Protected Areas and National Wealth. Rural land area below 5m is the percentage of total land where the rural land elevation is 5 meters or less.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Weighted average;

'''DEFINITION'''

The OMI_EXTREME_SL_IBI_slev_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea level measured by tide gauges along the coast. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset omi_extreme_sl_ibi_slev_mean_and_anomaly_obs, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (Pérez Gómez et al 2018).

'''CONTEXT''' Sea level (SLEV) is one of the Essential Ocean Variables most affected by climate change. Global mean sea level rise has accelerated since the 1990’s (Abram et al., 2019, Legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (WCRP, 2018). Basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one meter by the end of the century (Vousdoukas et al., 2020, Tebaldi et al., 2021). This will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves (Boumis et al., 2023). The increase in extreme sea levels over recent decades is, therefore, primarily due to the rise in mean sea level. Note, however, that the methodology used to compute this OMI removes the annual 50th percentile, thereby discarding the mean sea level trend to isolate changes in storminess.
The Iberian Biscay Ireland region shows positive sea level trend modulated by decadal-to-multidecadal variations driven by ocean dynamics and superposed to the long-term trend (Chafik et al., 2019).

'''COPERNICUS MARINE SERVICE KEY FINDINGS''' The completeness index criteria is fulfilled by 62 stations in 2023, five more than those available in 2022 (57), recently added to the multi-year product INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053. The mean 99th percentiles reflect the great tide spatial variability around the UK and the north of France. Minimum values are observed in the Irish eastern coast (e.g.: 0.66 m above mean sea level in Arklow Harbour) and the Canary Islands (e.g.: 0.93 and 0.96 m above mean sea level in Gomera and Hierro, respectively). Maximum values are observed in the Bristol Channel (e.g.: 6.25 and 5.78 m above mean sea level in Newport and Hinkley, respectively), and in the English Channel (e.g.: 5.16 m above mean sea level in St. Helier). The annual 99th percentiles standard deviation reflects the south-north increase of storminess, ranging between 1-3 cm in the Canary Islands to 15 cm in Hinkley (Bristol Channel). Negative or close to zero anomalies of 2023 99th percentile prevail throughout the region this year, reaching < -20 cm in several stations of the UK western coast and the English Channel (e.g.: -22 cm in Newport; -21 cm in St.Helier). Significantly positive anomaly of 2023 99th percentile is only found in Arcklow Harbour, in the eastern Irish coast.

'''DOI (product):''' https://doi.org/10.48670/moi-00253

definitionthe omi_extreme_sl_northwestshelf_slev_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). it is computed for the variable sea level measured by tide gauges along the coast. the use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. the annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset northwestshelf_omi_sl_extreme_var_slev_mean_and_anomaly_obs, jointly with the anomaly in the target year. this study of extreme variability was first applied to sea level variable (pérez gómez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (pérez gómez et al 2018).contextsea level (slev) is one of the essential ocean variables most affected by climate change. global mean sea level rise has accelerated since the 1990’s (abram et al., 2019, legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (wcrp, 2018). basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one metre by the end of the century (vousdoukas et al., 2020, tebaldi et al., 2021). this will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves (boumis et al., 2023). the increase in extreme sea levels over recent decades is, therefore, primarily due to the rise in mean sea level. note, however, that the methodology used to compute this omi removes the annual 50th percentile, thereby discarding the mean sea level trend to isolate changes in storminess. the north west shelf area presents positive sea level trends with higher trend estimates in the german bight and around denmark, and lower trends around the southern part of great britain (dettmering et al., 2021).copernicus marine service key findingsthe completeness index criteria is fulfilled in this region by 34 stations, eight more than in 2021 (26), most of them from norway. the mean 99th percentiles present a large spatial variability related to the tidal pattern, with largest values found in east england and at the entrance of the english channel, and lowest values along the danish and swedish coasts, ranging from the 3.08 m above mean sea level in immingan (east england) to 0.57 m above mean sea level in ringhals (sweden) and helgeroa (norway). the standard deviation of annual 99th percentiles ranges between 2-3 cm in the western part of the region (e.g.: 2 cm in harwich, 3 cm in dunkerke) and 7-8 cm in the eastern part and the kattegat (e.g. 8 cm in stenungsund, sweden).. the 99th percentile anomalies for 2022 show positive values in southeast england, with a maximum value of +8 cm in lowestoft, and negative values in the eastern part of the kattegat, reaching -8 cm in oslo. the remaining stations exhibit minor positive or negative values.references:abram, n., gattuso, j.-p., prakash, a., cheng, l., chidichimo, m. p., crate, s., enomoto, h., garschagen, m., gruber, n., harper, s., holland, e., kudela, r. m., rice, j., steffen, k., & von schuckmann, k. (2019). framing and context of the report. in h. o. pörtner, d. c. roberts, v. masson-delmotte, p. zhai, m. tignor, e. poloczanska, k. mintenbeck, a. alegría, m. nicolai, a. okem, j. petzold, b. rama, & n. m. weyer (eds.), ipcc special report on the ocean and cryosphere in a changing climate (pp. 73–129). in press. https://www.ipcc.ch/srocc/.boumis, g., moftakhari, h. r., & moradkhani, h. 2023. coevolution of extreme sea levels and sea-level rise under global warming. earth's future, 11, e2023ef003649. https://doi. org/10.1029/2023ef003649.dettmering d, müller fl, oelsmann j, passaro m, schwatke c, restano m, benveniste j, and seitz f. 2021. north seal: a new dataset of sea level changes in the north sea from satellite altimetry, earth syst sci data, 13, 3733–3753, https://doi.org/10.5194/essd-13-3733-2021. legeais j-f, llovel w, melet a, and meyssignac b. 2020. evidence of the topex-a altimeter instrumental anomaly and acceleration of the global mean sea level, in: copernicus marine service ocean state report, issue 4, journal of operational oceanography, s77–s82, https://doi.org/10.1080/1755876x.2020.1785097.pérez-gómez b, álvarez-fanjul e, she j, pérez-gonzález i, manzano f. 2016. extreme sea level events, section 4.4, p:300. in: von schuckmann k, le traon py, alvarez-fanjul e, axell l, balmaseda m, breivik la, brewin rjw, bricaud c, drevillon m, drillet y, dubois c , embury o, etienne h, garcía-sotillo m, garric[...]

Physical indicators of winter climate variability (coastal upwelling, sea level, precipitation) influenced by the winter North Pacific High. The data are all anomalies (normalized to a mean of 0 and std dev of 1) and thus have no units.

Data include:
North Pacific High: Mean Jan-Mar Hadley Centre HadSLP2 sea level pressure anomaly for the region 25N and 35N by 145W and 125W. Data were acquired through: http://www.metoffice.gov.uk/hadobs/hadslp2/

River Discharge: Water year discharge (Oct 1-Sep 30) anomaly for seven rivers, and the mean of these anomalies. Data acquired from the United States Geological Survey https://waterdata.usgs.gov/nwis/rt

Sea Level: Winter (Jan-Mar) anomaly of sea level at five locations along the west coast of North America as well as their mean. All linear trends have been removed. Sea level data were acquired from the University of Hawaii Sea Level Center. http://uhslc.soest.hawaii.edu/

Upwelling: Winter (Jan-Mar) upwelling anomaly for eight upwelling stations (30N, 33N, 36N, 39N, 42N, 45N, 48N, 51N) along the west coast of North America as well as their mean. Upwelling data were acquired from the NOAA Pacific Fisheries Environmental Laboratory. https://www.pfeg.noaa.gov/products/PFEL/modeled/indices/upwelling/NA/upwell_menu_NA.html

Precipitation: Winter (Jan-Mar) precipitation anomaly for thirteen NOAA climate divisions in western North America, as well as their mean. Precipitation data were acquired from the NOAA Climatic Data Center https://www7.ncdc.noaa.gov/CDO/CDODivisionalSelect.jsp