The world population surpassed eight billion people in 2022, having doubled from its figure less than 50 years previously. Looking forward, it is projected that the world population will reach nine billion in 2038, and 10 billion in 2060, but it will peak around 10.3 billion in the 2080s before it then goes into decline. Regional variations The global population has seen rapid growth since the early 1800s, due to advances in areas such as food production, healthcare, water safety, education, and infrastructure, however, these changes did not occur at a uniform time or pace across the world. Broadly speaking, the first regions to undergo their demographic transitions were Europe, North America, and Oceania, followed by Latin America and Asia (although Asia's development saw the greatest variation due to its size), while Africa was the last continent to undergo this transformation. Because of these differences, many so-called "advanced" countries are now experiencing population decline, particularly in Europe and East Asia, while the fastest population growth rates are found in Sub-Saharan Africa. In fact, the roughly two billion difference in population between now and the 2080s' peak will be found in Sub-Saharan Africa, which will rise from 1.2 billion to 3.2 billion in this time (although populations in other continents will also fluctuate). Changing projections The United Nations releases their World Population Prospects report every 1-2 years, and this is widely considered the foremost demographic dataset in the world. However, recent years have seen a notable decline in projections when the global population will peak, and at what number. Previous reports in the 2010s had suggested a peak of over 11 billion people, and that population growth would continue into the 2100s, however a sooner and shorter peak is now projected. Reasons for this include a more rapid population decline in East Asia and Europe, particularly China, as well as a prolonged development arc in Sub-Saharan Africa.

This database, and the accompanying website called ‘SurgeWatch’ (http://surgewatch.stg.rlp.io), provides a systematic UK-wide record of high sea level and coastal flood events over the last 100 years (1915-2014). Derived using records from the National Tide Gauge Network, a dataset of exceedence probabilities from the Environment Agency and meteorological fields from the 20th Century Reanalysis, the database captures information of 96 storm events that generated the highest sea levels around the UK since 1915. For each event, the database contains information about: (1) the storm that generated that event; (2) the sea levels recorded around the UK during the event; and (3) the occurrence and severity of coastal flooding as consequence of the event. The data are presented to be easily assessable and understandable to a wide range of interested parties. The database contains 100 files; four CSV files and 96 PDF files. Two CSV files contain the meteorological and sea level data for each of the 96 events. A third file contains the list of the top 20 largest skew surges at each of the 40 study tide gauge site. In the file containing the sea level and skew surge data, the tide gauge sites are numbered 1 to 40. A fourth accompanying CSV file lists, for reference, the site name and location (longitude and latitude). A description of the parameters in each of the four CSV files is given in the table below. There are also 96 separate PDF files containing the event commentaries. For each event these contain a concise narrative of the meteorological and sea level conditions experienced during the event, and a succinct description of the evidence available in support of coastal flooding, with a brief account of the recorded consequences to people and property. In addition, these contain graphical representation of the storm track and mean sea level pressure and wind fields at the time of maximum high water, the return period and skew surge magnitudes at sites around the UK, and a table of the date and time, offset return period, water level, predicted tide and skew surge for each site where the 1 in 5 year threshold was reached or exceeded for each event. A detailed description of how the database was created is given in Haigh et al. (2015). Coastal flooding caused by extreme sea levels can be devastating, with long-lasting and diverse consequences. The UK has a long history of severe coastal flooding. The recent 2013-14 winter in particular, produced a sequence of some of the worst coastal flooding the UK has experienced in the last 100 years. At present 2.5 million properties and £150 billion of assets are potentially exposed to coastal flooding. Yet despite these concerns, there is no formal, national framework in the UK to record flood severity and consequences and thus benefit an understanding of coastal flooding mechanisms and consequences. Without a systematic record of flood events, assessment of coastal flooding around the UK coast is limited. The database was created at the School of Ocean and Earth Science, National Oceanography Centre, University of Southampton with help from the Faculty of Engineering and the Environment, University of Southampton, the National Oceanography Centre and the British Oceanographic Data Centre. Collation of the database and the development of the website was funded through a Natural Environment Research Council (NERC) impact acceleration grant. The database contributes to the objectives of UK Engineering and Physical Sciences Research Council (EPSRC) consortium project FLOOD Memory (EP/K013513/1).

1. Summary

Global estimates of reach-level bankfull river width generated in the article by Peirong Lin, Ming Pan, George H. Allen, Renato Frasson, Zhenzhong Zeng, Dai Yamazaki, Eric F. Wood entitled "Global reach-level bankfull river width leveraging big-data geospatial analysis", Geophysical Research Letters (accepted).

2. File Description

Shapefile storing machine learning-derived bankfull river width, and environmental covariates used to predict the width (~1.4GB). The polylines were vectorized by Lin et al. (2019) based on the Multi-Error Removed Improved-Terrain (MERIT) DEM and MERIT Hydro (Yamazaki et al., 2017, 2019), under a channelization threshold of 25 km². Only rivers wider than 30 m are shown here; these locations were determined by jointly using the Global River Widths from Landsat (GRWL) database (Allen & Pavelsky, 2018) and the MERIT Hydro width estimates (Yamazaki et al., 2019).

3. Attribute Description

• COMID: identification number of the river reach, same as that used in global river modeling by Lin et al., (2019);
• Order: Strahler-Horton stream order, with stream order 1 starting from those with an upstream drainage area of 25 km²;
• Area: Upstream drainage basin area in km²;
• Sin: Sinuosity of the river segment (unitless);
• Slp: mean slope of the river segment (unitless);
• Elev: mean elevation of the river segment;
• K: mean bedrock permeability of the unit catchment surrounding the river segment, with data extracted from Huscroft et al. (2018);
• P: mean bedrock porosity of the unit catchment surrounding the river segment, with data extracted from Huscroft et al. (2018);
• AI: mean aridity index of the unit catchment; data extracted from Trabucco & Zomer (2019);
• LAI: mean leaf area index of the unit catchment; data extracted from Zhu et al. (2013);
• SND: mean sand content (mass percentage, %) of the unit catchment; data extracted from Hengl et al. (2017);
• CLY: mean clay content (mass percentage, %) of the unit catchment; data extracted from Hengl et al. (2017);
• SLT: mean silt content (mass percentage, %) of the unit catchment; data extracted from Hengl et al. (2017);
• Urb: mean urban fraction of the unit catchment; data extracted from Liu et al. (2018);
• WTD: mean water table depth (m below surface) of the unit catchment; data extracted from Fan et al. (2013);
• HW: mean human water use (irrigational + industrial + domestic) of the unit catchment; data extracted from Wada et al. (2016)
• DOR: degree of dam regulation for the river segment; the definition of DOR and data were sourced from Grill et al. (2019)
• QMEAN: mean annual discharge (m³/s) for the river segment; the multi-year averaged were calculated from Lin et al. (2019);
• Q2: 2-year return period flood discharge (m³/s) for the river segment; the 35-year data used to calculate the field was sourced from Lin et al. (2019);
• Width_m: bankfull river width (m) estimated by using the optimized machine learning model of this study, applied to Q2 and other environmental covariates;
• Width_DHG: bankfull river width (m) estimated by using the Moody & Troutman (2002) equation applied to Q2 estimated in this study

4. References

Allen, G. H., & Pavelsky, T. M. (2018). Global extent of rivers and streams. Science, 361(6402), 585–588. https://doi.org/10.1126/science.aat0636

Fan, Y., Li, H., & Miguez-Macho, G. (2013). Global Patterns of Groundwater Table Depth. Science, 339(6122), 940–943. https://doi.org/10.1126/science.1229881

Grill, G., Lehner, B., Thieme, M., Geenen, B., Tickner, D., Antonelli, F., et al. (2019). Mapping the world’s free-flowing rivers. Nature, 569(7755), 215. https://doi.org/10.1038/s41586-019-1111-9

Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., et al. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLOS ONE, 12(2), e0169748. https://doi.org/10.1371/journal.pone.0169748

Huscroft, J., Gleeson, T., Hartmann, J., & Börker, J. (2018). Compiling and Mapping Global Permeability of the Unconsolidated and Consolidated Earth: GLobal HYdrogeology MaPS 2.0 (GLHYMPS 2.0). Geophysical Research Letters, 45(4), 1897–1904. https://doi.org/10.1002/2017GL075860

Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., et al. (2019). Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches. Water Resources Research, 0(0). https://doi.org/10.1029/2019WR025287

Liu, X., Hu, G., Chen, Y., Li, X., Xu, X., Li, S., et al. (2018). High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform. Remote Sensing of Environment, 209, 227–239. https://doi.org/10.1016/j.rse.2018.02.055

Trabucco, A., & Zomer, R. (2019, January 18). Global Aridity Index and Potential Evapotranspiration (ET0) Climate Database v2. https://doi.org/10.6084/m9.figshare.7504448.v3

Wada, Y., Graaf, I. E. M. de, & Beek, L. P. H. van. (2016). High-resolution modeling of human and climate impacts on global water resources. Journal of Advances in Modeling Earth Systems, 8(2), 735–763. https://doi.org/10.1002/2015MS000618

Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O’Loughlin, F., Neal, J. C., et al. (2017). A high-accuracy map of global terrain elevations. Geophysical Research Letters, 44(11), 5844–5853. https://doi.org/10.1002/2017GL072874

Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G. H., & Pavelsky, T. M. (2019). MERIT Hydro: A High-Resolution Global Hydrography Map Based on Latest Topography Dataset. Water Resources Research. https://doi.org/10.1029/2019WR024873

Zhu, Z., Bi, J., Pan, Y., Ganguly, S., Anav, A., Xu, L., et al. (2013). Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011. Remote Sensing, 5(2), 927–948. https://doi.org/10.3390/rs5020927