I am passionate about creating and sharing data.
SurgeWatch: I initiated and lead SurgeWatch – www.surgewatch.org. SurgeWatch is an innovative database to systematic document and improve understanding of coastal flooding mechanisms and consequences. Integrating a variety of ‘hard’ (e.g. meteorological, sea level and wave records) and ‘soft’ (e.g. newspapers, reports and social media) data sources we have identified 330 distinct coastal flooding events from 1915 to 2017 for the UK. We have ranked of each of the flood events using a multi-level categorisation based on inundation, transport disruption, costs, and fatalities: from 1 (Nuisance) to 6 (Disaster). For the most severe events, an accompanying event description (based upon the Source-Pathway-Receptor-Consequence framework) has been produced. SurgeWatch is now the most comprehensive database on coastal flooding anywhere in the world. We are now expanding the database back before 1915. Version 1 and 2 of the database are described in Haigh et al. (2015) and Haigh et al. (2017), respectively.
GESLA: I help to maintain and expand the GESLA database – http://www.gesla.org. GESLA, the Global Extreme Sea Level Analysis, is a quasi-global set of ‘high frequency’ (i.e. hourly or more frequent) measurements of sea level from tide gauges aroundthe world. The first formal GESLA data set (denoted GESLA-1) was assembled by Philip Woodworth (National Oceanography Centre Liverpool), Melisa Menendez (University of Cantabria) and John Hunter (University of Tasmania) around 2009. Marta Marco and I joined the team to help great GESLA-2. GESLA-2 contains 1355 records and 39151 station-years. The GESLA-2 data set is described in Woodworth et al. (2017).
Probabilistic sea level projections: Using an innovative approach we have developed probabilistic projections of global mean sea level rise out to 2100. We developed a simple but clever global climate model that we call WASP. This stands for the Warming Acidification and Sea-level Projector. Itrepresents the whole earth using 8 boxes that capture the interaction between the atmosphere, vegetation, soil and the different layers of the ocean. Using WASP, we can accurately predict future changes in climate, for different carbon emissions, on a global scale. Because of the simple way we represent the earth, we can relatively quickly run the model out to the year 2300, many millions of times; something that is simply not possible using regular climate models. WASP is so efficient that you can run it on a smart phone in seconds (give it a go – click here). Our approach is described in Goodwin et al. (2017) and the data can be downloaded from here: ObsHIst_annual_GMSL_projections. The excel spreadsheet contains probabilistic sea level projections, for 25 percentile levels from 1% to 99%, for four Research Concentration Pathways (RCPs): RCP2.6, RCP4.5, RCP6.0 and RCP8.5.
Historic sea level data for the UK South Coast: As part of my PhD (from 2005-2009) I undertook a data archaeology exercise and digitised sea level records for several sites on the UK south coast: Devonport (1961-1986, 1988-1990), Newhaven (1942-1948, 1950-1951, 1953-1957, 1964-1965, 1973, 1988), Portsmouth (1961-1990), Southampton (1935-1979, 1982-1990), St. Marys (1968-1969, 1973, 1975, 1977-1978, 1987-1989), and Weymouth (1967-1971, 1983-1987). The data can be downloaded here and is described in Haigh et al. (2009). There are raw data files and cleaned data files. The cleaned files have been corrected for datum changes which are recorded in the readme files for each site.