The Reading Palaeofire Database: an expanded global resource to document changes in fire regimes from sedimentary charcoal records

Earth Syst. Sci. Data, 14, 1109–1124, 2022 https://doi.org/10.5194/essd-14-1109-2022 © Author(s) 2022. This work is distributed under the Creative Commons Attribution 4.0 License. The Reading Palaeofire Database: an expanded global resource to document changes in fire regimes from sedimentary charcoal records Sandy P. Harrison1,2, Roberto Villegas-Diaz1, Esmeralda Cruz-Silva1, Daniel Gallagher2,3, David Kesner1,2, Paul Lincoln1,2, Yicheng Shen1, Luke Sweeney1,2, Daniele Colombaroli2,3, Adam Ali4, Chéïma Barhoumi5, Yves Bergeron6,7, Tatiana Blyakharchuk8, Pˇ remysl Bobek9, Richard Bradshaw10, Jennifer L. Clear11, Sambor Czerwi´ nski12, Anne-Laure Daniau13, John Dodson14,15, Kevin J. Edwards16,17,18,19, Mary E. Edwards20, Angelica Feurdean21, David Foster22, Konrad Gajewski23, Mariusz Gałka24, Michelle Garneau25, Thomas Giesecke26, Graciela Gil Romera27,28, Martin P. Girardin29, Dana Hoefer30, Kangyou Huang31, Jun Inoue32, Eva Jamrichová9, Nauris Jasiunas33, Wenying Jiang34, Gonzalo Jiménez-Moreno35, Monika Karpi´ nska-Kołaczek12, Piotr Kołaczek12, Niina Kuosmanen36, Mariusz Lamentowicz37, Martin Lavoie38, Fang Li39, Jianyong Li40, Olga Lisitsyna41,42, José Antonio López-Sáez43, Reyes Luelmo-Lautenschlaeger43, Gabriel Magnan25, Eniko Katalin Magyari44, Alekss Maksims45, Katarzyna Marcisz12, Elena Marinova46, Jenn Marlon47, Scott Mensing48, Joanna Miroslaw-Grabowska49, Wyatt Oswald22,50, Sebastián Pérez-Díaz51, Ramón Pérez-Obiol52, Sanna Piilo53, Anneli Poska41,54, Xiaoguang Qin34, Cécile C. Remy55, Pierre J. H. Richard56, Sakari Salonen36, Naoko Sasaki57, Hieke Schneider58, William Shotyk59, Migle Stancikaite60, Dace Šteinberga45, Normunds Stivrins33,41,61, Hikaru Takahara62, Zhihai Tan63, Liva Trasune33,36, Charles E. Umbanhowar64,65, Minna Väliranta53, Jüri Vassiljev41, Xiayun Xiao66, Qinghai Xu67, Xin Xu39, Edyta Zawisza49, Yan Zhao68, Zheng Zhou31, and Jordan Paillard56 1School of Archaeology, Geography and Environmental Science, University of Reading, Whiteknights, Reading, RG6 6AH, UK 2Leverhulme Centre for Wildfires, Environment and Society, Imperial College London, South Kensington, London, SW7 2BW, UK 3Department of Geography, Royal Holloway, University of London, Egham, TW20 0SS, UK 4Institut des Sciences de l’Evolution de Montpellier (CNRS, IRD, EPHE), Université de Montpellier, 34090 Montpellier, France 5Department of Palynology and Climate Dynamics, Albrecht-von-Haller-Institute for Plant Sciences, University of Göttingen, Untere Karspüle 2, 37073 Göttingen, Germany 6Forest Research Institute (IRF), Université du Québec en Abitibi-Témiscamingue (UQAT), Rouyn-Noranda, QC, J9X 5E4, Canada 7Department of Biological Sciences, Université du Québec à Montréal (UQAM), Montréal, QC, H3C 3P8, Canada 8Institute of Monitoring of Climatic and Ecological Systems of Siberian branch of the Russian Academy of Sciences (IMCES SB RAS), 634055, Tomsk, Russia 9Institute of Botany, Czech Academy of Sciences, Lidická 25/27, 602 00 Brno, Czech Republic 10Geography and Planning, University of Liverpool, Liverpool, L69 7ZT, UK 11Department of Geography and Environmental Science, Liverpool Hope University, Taggart Street, Childwall, Liverpool, L16 9JD, UK 12Climate Change Ecology Research Unit, Faculty of Geographical and Geological Sciences, Adam Mickiewicz University Pozna´ n, Bogumiła Krygowskiego 10, 61-680 Pozna´ n, Poland Published by Copernicus Publications. 1110 S. P. Harrison et al.: The Reading Palaeofire Database 13Environnements et Paléoenvironnements Océaniques et Continentaux (EPOC), Unité Mixte de Recherche (UMR) 5805, Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux, 33615 Pessac, France 14Institute of Earth Environment, Chinese Academy of Sciences, Keji 1st Rd, Yanta District, Xi’an, Shaanxi, 710061, Shaanxi Province, China 15School of Earth, Atmospheric and Life Sciences, University of Wollongong, Wollongong, NSW 2500, Australia 16Department of Geography and Environment, University of Aberdeen, Aberdeen, AB24 3UX, UK 17Department of Archaeology, University of Aberdeen, AB24 3UX, UK 18McDonald Institute for Archaeological Research, University of Cambridge, Cambridge, CB2 1TN, UK 19Scott Polar Research Institute, University of Cambridge, Cambridge, CB2 1TN, UK 20School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK 21Institute of Physical Geography, Goethe University Frankfurt, Altenhöferallee 1, 60438 Frankfurt am Main, Germany 22Harvard Forest, Harvard University, Petersham, MA 01366, USA 23Département de Géographie, Environnement et Géomatique, Université d’Ottawa, Ottawa, ON, K1N 6N5, Canada 24Department of Biogeography, Paleoecology and Nature Protection, Faculty of Biology and Environmental Protection, University of Lodz, 1/3 Banacha St., 90-237 Łód´z, Poland 25Geotop, Université du Québec à Montréal, Montréal, QC, H2X 3Y7, Canada 26Department of Physical Geography, Faculty of Geosciences, Utrecht University, 2584 CS Utrecht, the Netherlands 27Instituto Pirenaico de Ecología – CSIC, Avda. Montañana 1005, 50059 Zaragoza, Spain 28Plant Ecology and Geobotany, Philipps University of Marburg, Karl-Von-Frisch-Straße 8, 35037 Marburg, Germany 29Laurentian Forestry Centre, Canadian Forest Service, Natural Resources Canada, Québec City, QC, G1V V4C, Canada 30Senckenberg Research Station of Quaternary Palaeontology, Am Jakobskirchhof 4, 99423 Weimar, Germany 31School of Earth Sciences and Engineering, Sun Yat-sen University, Zhuhai 519082, China 32Department of Geosciences, Graduate School of Science, Osaka City University, Osaka 558-8585, Japan 33Department of Geography, University of Latvia, Jelgavas iela 1, Riga, 1004, Latvia 34Key Laboratory of Cenozoic Geology and Environment, Institute of Geology and Geophysics, Chinese Academy of Sciences, No. 19 Beitucheng West Rd, Beijing, 100029, China 35Departamento de Estratigrafía y Paleontología, Facultad de Ciencias, Universidad de Granada, Avda. Fuente Nueva S/N, 18002 Granada, Spain 36Department of Geosciences and Geography, University of Helsinki, P.O. Box 64, 00014, Helsinki, Finland 37Faculty of Geographical and Geological Sciences, Adam Mickiewicz University Pozna´ n, Bogumiła Krygowskiego 10, 61-680 Pozna´ n, Poland 38Département de géographie, Université Laval, Québec City, QC, G1V 0A6, Canada 39International Center for Climate and Environment Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China 40Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China 41Department of Geology, Tallinn University of Technology, Ehitajate tee 5, 19086 Tallinn, Estonia 42Russian State Agrarian University, Timiryazevskaya St., 49, 127550, Moscow, Russia 43Environmental Archaeology Research Group, Institute of History, CSIC, 28037 Madrid, Spain 44ELKH-MTM-ELTE Research Group for Paleontology, Department of Environmental and Landscape Geography, Eötvös Loránd university, Pazmany Peter stny 1/c, 1117 Budapest, Hungary 45Department of Geology, University of Latvia, Jelgavas iela 1, Riga, 1004, Latvia 46Laboratory for Archaeobotany, State Office for Cultural Heritage Baden-Württemberg, Fischersteig 9, 78343 Gaienhofen-Hemmenhofen, Germany 47Yale School of the Environment, New Haven, CT 06511, USA 48Department of Geography, University of Nevada, Reno, 1664 N Virginia St., Reno, NV 89557, USA 49Institute of Geological Sciences, Polish Academy of Sciences, Twarda 51/55, 00-818 Warsaw, Poland Earth Syst. Sci. Data, 14, 1109–1124, 2022 https://doi.org/10.5194/essd-14-1109-2022 S. P. Harrison et al.: The Reading Palaeofire Database 1111 50Marlboro Institute for Liberal Arts and Interdisciplinary Studies, Emerson College, Boston, MA 02116, USA 51Department of Geography, Urban and Regional Planning, University of Cantabria, 39005 Santander, Spain 52Unitat de Botànica, Facultat de Biociències, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, 08193 Barcelona, Spain 53Ecosystems, Environment Research Programme, Environmental Change Research Unit (ECRU), Faculty of Biological and Environmental Sciences, University of Helsinki, Viikinkaari 1, P.O. Box 65, 00014, Helsinki, Finland 54Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden 55Institut für Geographie, Universität Augsburg, 86135 Augsburg, Germany 56Département de Géographie, Université de Montréal, Montréal, QC, H2V 0B3, Canada 57Graduate School of Life and Environmental Sciences, Kyoto Prefectural University, Shimogamo, Sakyo-ku, 1-5 Hangi-cho, 606-8522 Kyoto, Japan 58Institut für Geographie, Friedrich-Schiller-Universität Jena, Löbdergraben 32, 07743 Jena, Germany 59Department of Renewable Resources, University of Alberta, 348B South Academic Building, Edmonton, AB, T6G 2H1, Canada 60Institute of Geology and Geography, Nature Research Centre, Akademijos St. 2, 08412, Vilnius, Lithuania 61Institute of Latvian History, University of Latvia, Kalpaka blv. 4, Riga, 1050, Latvia 62Graduate School of Agriculture, Kyoto Prefectural University, Shimogamo, Sakyo-ku, 1-5, Hangi-cho, 606-8522 Kyoto, Japan 63School of Environment and Chemistry Engineering, Xi’an Polytechnic University, Xi’an, Shaanxi 710048, China 64Department of Biology, St Olaf College, 1520 St Olaf Ave, Northfield, MN 55057, USA 65Department of Environmental Studies, St Olaf College, 1520 St Olaf Ave, Northfield, MN 55057, USA 66State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China 67College of Resources and Environment Science, Hebei Normal University, Shijiazhuang 050024, China 68Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China Correspondence: Sandy P. Harrison ([email protected]) Received: 12 August 2021 – Discussion started: 19 August 2021 Revised: 12 January 2022 – Accepted: 28 January 2022 – Published: 11 March 2022 Abstract. Sedimentary charcoal records are widely used to reconstruct regional changes in fire regimes through time in the geological past. Existing global compilations are not geographically comprehensive and do not provide consistent metadata for all sites. Furthermore, the age models provided for these records are not harmonised and many are based on older calibrations of the radiocarbon ages. These issues limit the use of existing compilations for research into past fire regimes. Here, we present an expanded database of charcoal records, accompanied by new age models based on recalibration of radiocarbon ages using IntCal20 and Bayesian age-modelling software. We document the structure and contents of the database, the construction of the age models, and the quality control measures applied. We also record the expansion of geographical coverage relative to previous charcoal compilations and the expansion of metadata that can be used to inform analyses. This first version of the Reading Palaeofire Database contains 1676 records (entities) from 1480 sites worldwide. The database (RPDv1b – Harrison et al., 2021) is available at https://doi.org/10.17864/1947.000345. https://doi.org/10.5194/essd-14-1109-2022 Earth Syst. Sci. Data, 14, 1109–1124, 2022 1112 S. P. Harrison et al.: The Reading Palaeofire Database 1 Introduction Wildfires have major impacts on terrestrial ecosystems (Bond et al., 2005; Bowman et al., 2016; He et al., 2019; Lasslop et al., 2020), the global carbon cycle (Li et al., 2014; Arora and Melton, 2018; Pellegrini et al., 2018; Lasslop et al., 2019), atmospheric chemistry (van der Werf et al., 2010; Voulgarakis and Field, 2015; Sokolik et al., 2019), and climate (Randerson et al., 2006; Li et al., 2017; Harrison et al., 2018; Liu et al., 2019). Although the climatic, vegetation, and anthropogenic controls on wildfires are relatively well understood (e.g. Harrison et al., 2010; Bistinas et al., 2014; Knorr et al., 2016; Forkel et al., 2017; Li et al., 2019), recent years have seen wildfires occurring in regions where they were historically rare (e.g. northern Alaska, Greenland, northern Scandinavia – Evangeliou et al., 2019; Hayasaka, 2021) and an increase in fire frequency and severity in more fire-prone regions (e.g. California, the circum-Mediterranean, eastern Australia; e.g. Abatzoglou and Williams, 2016; Dutta et al., 2016; Williams et al., 2019; Nolan et al., 2020). It is useful to look at the preindustrial era (conventionally defined as pre-1850 CE) to understand whether these events are atypical. The pre-industrial past also provides an opportunity to characterise fire regimes before anthropogenic influences, in terms of both ignitions and fire suppression, became important. Ice-core records provide a global picture of changes in wildfire in the geologic past (Rubino et al., 2016). However, wildfires exhibit considerable local to regional variability because of the spatial heterogeneity of the various factors controlling their occurrence and intensity (Bistinas et al., 2014; Andela et al., 2019; Forkel et al., 2019). Thus, it is useful to use information that can provide a picture of regional changes through time. Charcoal, preserved in lake, peat, or marine sediments, can provide a picture of such changes (Clark and Patterson, 1997; Conedera et al., 2009). The wildfire regime can be characterised from sedimentary charcoal records through total charcoal abundance per unit of sediment, which can be considered a measure of the total biomass burned (e.g. Marlon et al., 2006), or by the presence of peaks in charcoal accumulation, which, in records with a sufficiently high temporal resolution, can indicate individual episodes of fire (e.g. Power et al., 2006). The Global Paleofire Working Group (GPWG) was established in 2006 to coordinate the compilation and analysis of charcoal data globally, through the construction of the Global Charcoal Database (GCD – Power et al., 2008). The GPWG was initiated by the International GeosphereBiosphere Programme (IGBP) Fast-Track Initiative on Fire and subsequently recognised as a working group of the Past Global Changes (PAGES) project in 2008. There have now been several iterations of the GCD (Power et al., 2008, 2010; Daniau et al., 2012; Blarquez et al., 2014; Marlon et al., 2016), which since 2020 has been managed by the International Paleofire Network as the Global Paleofire Database (GPD; https://paleofire.org, last access: 21 February 2022). The GCD has been used to examine changes in fire regimes over the past 2 millennia (Marlon et al., 2008), during the current interglacial (Marlon et al., 2013), on glacial–interglacial timescales (Power et al., 2008; Daniau et al., 2012; Williams et al., 2015), and in response to rapid climate changes (Marlon et al., 2009; Daniau et al., 2010), as well as to examine regional fire histories (e.g. Mooney et al., 2011; Vannière et al., 2011; Marlon et al., 2012; Power et al., 2013a, b; Feurdean et al., 2020). However, there are a number of limitations to the use of the GCD for analyses of palaeofire regimes. Firstly, the database does not include many recently published records and needs to be updated. Secondly, there are inconsistencies among the various versions of the database including duplicated and/or missing sites, differences in the metadata included for each site or record, and missing metadata and dating information for some sites or records. Perhaps most crucially, the age models included in the database were made at different times, using different radiocarbon calibration curves, and using different age-modelling methods. The disparities between the archived age models preclude a detailed comparison of changes in wildfire regimes across regions. Here, we present an expanded database of charcoal records (the Reading Palaeofire Database, RPD), accompanied by new age models based on recalibration of radiocarbon ages using IntCal20 (Reimer et al., 2020) and using a consistent Bayesian approach (Bacon – Blaauw et al., 2021) to agemodel construction. However, we have retained the original age models for all the sites for comparison and to allow the user to choose a preferred age model. The RPD is designed to facilitate regional analyses of fire history; it is not designed as a permanent repository. We document the structure and contents of the database, the construction of the new age models, the expanded metadata available, and the quality control measures applied to check the data entry. We also document the expansion of the geographic and temporal coverage and the availability of metadata, relative to previous GCD compilations. 2 Data and methods 2.1 Compilation of data The database contains sedimentary charcoal records, metadata to facilitate the interpretation of these records, and information on the dates used to construct the original age model for each record. Some records were obtained from the GCD. There are multiple versions of the GCD which differ in terms of the sites and the types of metadata included. We compared the GCDv3 (Marlon et al., 2016), GCDv4 (Blarquez, 2018), and GCD web page versions (http://paleofire.org, last access: 21 February 2022) and extracted a single unique version of each site and entity across the three versions. Where sites or entities were duplicated in different versions of the Earth Syst. Sci. Data, 14, 1109–1124, 2022 https://doi.org/10.5194/essd-14-1109-2022 S. P. Harrison et al.: The Reading Palaeofire Database 1113 GCD, we used the latest version. Missing metadata and dating information for these records were obtained from the literature or from the original data providers. Some sites in the GCD were represented by both concentration data and the same data expressed as influx (i.e. concentration per year) from the same samples; because influx calculations are time dependent, we have only retained concentration data for such sites to allow for future improvements to age models. Influx can be easily computed using data available in the RPD. We also removed duplicates where the GCD contained both raw data and concentration data from the same entity. We extracted published charcoal records from public repositories, specifically PANGAEA (https://www.pangaea. de/, last access: 21 February 2022), NOAA National Centers for Environmental Information (https://www.ncdc.noaa.gov/ data-access/paleoclimatology-data, last access: 21 February 2022), the Neotoma Paleoecology Database (https://www. neotomadb.org/, last access: 21 February 2022), the European Pollen Database (http://www.europeanpollendatabase. net/index.php, last access: 21 February 2022), and the Arctic Data Center (https://arcticdata.io/catalog/, last access: 21 February 2022); if these records were also in the GCD, we replaced the GCD version. Additional charcoal data, dating information, and metadata were provided directly by the authors. All the records in the current version of the database are listed in the Supplement (Table S1). 2.1.1 Structure of the database The data are stored in a relational database (MySQL), which consists of 10 linked tables, specifically “site”, “entity”, “sample”, “date info”, “unit”, “entity link publication”, “publication”, “chronology”, “age model”, and “model name”. Figure 1 shows the relationships between these tables. A description of the structure and content of each of the tables is given below, and more detailed information about individual fields is given in the Supplement (Table S2). 2.1.2 Site metadata (table name – site) A site is defined as the hydrological basin from which charcoal records have been obtained (Table 1). There may be several charcoal records from the same site, for example where charcoal records have been obtained on central and marginal cores from the same lake or where there is a lake core and additional cores from peatlands and/or terrestrial deposits (e.g. small hollows, soils) within the same hydrological basin. A site may therefore be linked to several charcoal records, where each record is treated as a separate entity. The site table contains basic metadata about the basin, including site ID, site name, latitude, longitude, elevation, site type, and maximum water depth. The site names are expressed without diacritics to facilitate database querying and subsequent analyses in programming languages that do not handle these characters. Latitude and longitude are given in decimal degrees, truncated to six decimal places since this gives an accuracy of <1 m at the Equator. Broad categories of site type are differentiated (e.g. terrestrial, lacustrine, marine), with subdivisions according to geomorphic origin (e.g. lakes are recorded according to whether they are, for example, fluvial, glacial, or volcanic in origin). In addition to coastal salt marshes and estuaries, we include a generic coastal category for all types of sites that lie within the coastal zone and whose hydrology may therefore have been affected by changes in sea level. Wherever possible, the size of the basin and the catchment are recorded (in km2), but if accurate quantified information is not available, the basin and catchment size are recorded by size classes. The site table also contains information on whether the lake or peatland is hydrologically closed or has inflows and outflows, which can affect the source, quantity, and preservation of charcoal in the sediments. A complete listing of the sites and entities in the RPD is given in Table S1. A list of the valid choices for fields that are selected from a pre-defined list (e.g. site type) is given in Table S2. 2.1.3 Entity metadata (table name – entity) This table provides metadata for each individual entity (Table 2). In addition to distinguishing multiple cores from the same basin as separate entities, we also distinguish different size classes of charcoal from the same core when these data are available. Different charcoal size classes from the same core are also treated as separate entities in the database. However, we have removed duplicates where the same record was expressed in different ways (e.g. as both raw counts and concentration or as concentration and influx) to avoid confusion and mistakes when subsequently processing these data. The RPD contains raw data wherever possible and concentration data when the raw data are not available, and it only includes influx data if neither raw nor concentration data are available. When specific cores were given distinctive names in the original publication or by the original author, we include this information in the entity name for ease of cross-referencing. The entity metadata include information that can be used to interpret the charcoal records, including depositional context, core location, measurement method, and measurement unit. There is no standard measurement unit for charcoal, and in fact, there are >100 different units employed in the database. For convenience, there is a link table to the measurement units (table name – unit). In addition, the entity table provides the source from which the charcoal data were obtained, including whether these data are from a version of the GCD or a data repository or were provided by the original author, and an indication of when the record was last updated. A list of the valid choices for fields that are selected from a predefined list (e.g. depositional context) is given in Table S2. A list of the charcoal measurement units currently in use in the RPD is given in Table S3. https://doi.org/10.5194/essd-14-1109-2022 Earth Syst. Sci. Data, 14, 1109–1124, 2022 1114 S. P. Harrison et al.: The Reading Palaeofire Database Figure 1. Diagram showing the structure of the database, individual tables and their contents, and the nature of the relationships between the component tables. One-to-many linkages indicate that it is possible to have several entries in one table linked to a single entry in another table. The database uses both primary and foreign keys. The primary key ensures that data included in a specific field are unique. The foreign key refers to the field in a table which is the primary key of another table and ensures that there is a link between these tables. 2.1.4 Sample metadata and data (table name – sample) The sample table provides information on the average depth in the core or profile and the thickness of the sample on which charcoal was measured (Table 3). The thickness measurements relate to the total thickness of the charcoal sample and provide an indication of whether the sampling was contiguous downcore. The sample table also provides information on the sample size and units and the quantity of charcoal present. The charcoal measurement units have been standardised by converting units expressed as multiples (e.g. fragments ×100) back to the whole numbers and by converting units expressed in milligrams or kilograms to grams. As a result, the values in the RPD may apparently differ from published values. 2.1.5 Dating information (table name – date_info) This table provides information about the dates available for each entity that can be used to construct an age model (Table 4). We include information about the age of the core top for records that were known to be actively accumulating sediment at the time of collection. In addition to radiometric dates, we include information about the presence of tephras (either dated at the site or independently dated elsewhere) and stratigraphic events that can be used to establish correlative ages (e.g. changes in the pollen assemblage that are dated in other cores from the region or evidence of known fires in the catchment). Wherever possible the name of a tephra is given to facilitate the use of subsequent and more accurate estimates of its age. Similarly, the basis for correlative dates is given, again to facilitate the use of updated estimates of the age of the event. Radiocarbon ages are given in radiocarbon years, but all other ages are given in calendar years before present (BP) using 1950 CE as the reference zero date. Error estimates are given for radiometric ages and wherever possible for calendar ages. We provide an indication of whether a specific date was used in the original age model for the entity and an explanation for why specific dates were rejected, since this can be a guide as to whether the dates should be incorporated in the construction of new age models. A list of the valid choices for fields that are selected from a pre-defined list (e.g. material dated) is given in Table S2. 2.1.6 Publication information (table name – publication) This table provides full bibliographic citations for the original references documenting the charcoal records and/or their age models. There may be multiple publications for a single charcoal record, and all of these references are listed. Conversely, there may be a single publication for multiple Earth Syst. Sci. Data, 14, 1109–1124, 2022 https://doi.org/10.5194/essd-14-1109-2022 S. P. Harrison et al.: The Reading Palaeofire Database 1115 Table 1. Definition of the site table. Field name Definition Data type Constraints/notes ID_SITE Unique identifier for each site Unsigned integer Positive integer site_name Site name as given by original authors or as defined by us where there was no unique name given to the site Text Required latitude Latitude of the sampling site, given in decimal degrees, where N is positive and S is negative Double Numeric value between −90 and 90 longitude Longitude of the sampling site in decimal degrees, where E is positive and W is negative Double Numeric value between −180 and 180 elevation Elevation of the sampling site in metres above (+) or below (−) sea level Double None site_type Information about the type of site (e.g. lake, peatland, terrestrial) Text Selected from pre-defined list water_depth Water depth of the sampling site in metres Double None flow_type Indication of whether there is inflow and/or outflow from the sampled site Text Selected from pre-defined list basin_size_km2 Size of sampled site (e.g. lake or bog) in km2Double None catch_size_km2 Size of hydrological catchment in km2Double None basin_size_class Categorical estimate of basin size Text Selected from pre-defined list catch_size_class Categorical estimate of hydrological catchment size Text Selected from pre-defined list charcoal records. There is also a table (table name – entity_link_publication) that links the publications to the specific entity. 2.1.7 Original age-model information (table name – chronology) This table provides information about the original age model for each record and the ages assigned to individual samples. There can be many records that use the same type of age model (e.g. linear interpolation, spline, regression), and for convenience, there is a table that links the records to the agemodel name (table name – model_name). 2.1.8 New age-model information (table name – age_model) This table contains information about the age models that have been constructed for this version of the database using the IntCal20 calibration curve (Reimer et al., 2020) and the Bacon (Blaauw et al., 2021) age-modelling R package (see Sect. 2.3) (Table 5). We preserve information on the mean and median ages, as well as the quantile ranges for each sample. 2.2 Construction of new age models The original age models for the charcoal records were made at different times, using different radiocarbon calibration curves, and using different age-modelling methods. We standardised the age modelling, using rbacon (Blaauw and Christen, 2011; Blaauw et al., 2021) to construct new Bayesian age–depth models in the ageR package (Villegas-Diaz et al., 2021). The ageR package provides functions that facilitate the supervised creation of multiple age models for many cores and different data sources, including databases and commaand tab-separated files. The IntCal20 Northern Hemisphere calibration curve (Reimer et al., 2020) and the SHCal20 Southern Hemisphere calibration curve (Hogg et al., 2020) were used for entities between the latitudes of 90 and 15◦N and 15 to 90◦S respectively. Entities in equatorial latitudes (15◦N to 15◦S) used a 50 :50 mixed calibration curve to account for north–south air mass mixing following Hogg et al. (2020), and radiocarbon ages from marine entities were calibrated using the Marine20 calibration curve (Heaton et al., 2020). To estimate the optimum age-modelling scenarios based upon the date and sample information for each entity, multiple rbacon age models were run using different prior accumulation rate (acc.mean) and thickness values. Prior accumulation rate values were selected using an initial linear regression of the ages in each entity, which was then increased https://doi.org/10.5194/essd-14-1109-2022 Earth Syst. Sci. Data, 14, 1109–1124, 2022 1116 S. P. Harrison et al.: The Reading Palaeofire Database Table 2. Definition of the entity table. Field name Definition Data type Constraints/notes ID_ENTITY Unique identifier for each entity Unsigned integer Positive integer ID_SITE Refers to unique identifier for each site (as given in site table) Unsigned integer Auto-numeric, foreign key of the site table, a positive integer entity_name Name of entity, where an entity may be a separate core from the site or a separate type of measurement on the same core Text Required latitude Latitude of the entity, given in decimal degrees, where N is positive and S is negative Double A numeric value between −90 and 90 longitude Longitude of the entity, given in decimal degrees, where E is positive and W is negative Double A numeric value between −180 and 180 elevation Elevation of the sampling site, in metres above (+) or below (−) sea level Double None depositional_context Type of sediment sampled for charcoal Text Selected from pre-defined list measurement_method Method used to measure the amount of charcoal Text Selected from pre-defined list TYPE The unit type of the measured charcoal values (e.g. concentration, influx) Text Selected from pre-defined list source Source of charcoal data Text Selected from pre-defined list core_location Location of the entity within the site (e.g. central core or marginal core) Text Selected from pre-defined list last_updated Date when the entity or its linked data were last updated Date In format YYYY/mm/dd ID_UNIT Unique identifier for measurement unit (as in unit table) Unsigned integer Auto-numeric, foreign key of the unit table, a positive integer Table 3. Definition of the sample table. Field name Definition Data type Constraints/notes ID_SAMPLE Unique identifier for each charcoal sample Unsigned integer Auto-numeric, primary key, a positive integer ID_ENTITY Unique identifier for the entity (as in entity table) Unsigned integer Auto-numeric, foreign key of the entity table, a positive integer avg_depth Average sampling depth, in metres Double None sample_thickness Sample thickness, in metres Double None charcoal_measurement Quantity of charcoal measured in the sample Double None analytical_sample_size Total amount of sediment sampled Text 255-character maximum length analytical_sample_size_unit Units used for the sampling Text 255-character maximum length (decreased) sequentially from the default value to up to two times more (less) than the initial value. As an example, if the initial accumulation rate value selected from the linear regression was 20 yr cm−1, age models would also be run using values of 10, 15, 20, 30, and 40 yr cm−1. In cases where the regional accumulation rate was known, the upper and lower values of the accumulation rate scenarios were manually constrained. The range of prior thicknesses used in the models was calculated by increasing and decreasing the rbacon default thickness value (5 cm) to up to a value one-eighth of the Earth Syst. Sci. Data, 14, 1109–1124, 2022 https://doi.org/10.5194/essd-14-1109-2022 S. P. Harrison et al.: The Reading Palaeofire Database 1117 Table 4. Definition of the date info table. Field name Definition Data type Constraints/notes ID_DATE_INFO Unique identifier for the date record Unsigned integer Auto-numeric, primary key, a positive integer ID_ENTITY Unique identifier for the entity (as in entity table) Unsigned integer Auto-numeric, foreign key of the entity table, a positive integer material_dated Material from which the date was obtained if applicable Text Selected from pre-defined list date_type Technique used to obtain the date measurement Text Selected from pre-defined list avg_depth Average depth in the sedimentary sequence where the date was measured, in metres Double None thickness Thickness of the sample used for dating, in metres Double None lab_number Unique identifying code assigned by the dating laboratory Text 65 535-character maximum length age_C14 Uncalibrated radiocarbon age Double None age_calib The calendar age of a date Double None error Analytical or measurement error of the date Double None correlation_info Indication of basis for correlative dating (e.g. pollen, tephra, or stratigraphic correlations) Text Selected from pre-defined list age_used Indicates whether date was used by the author(s) in the construction of the original age model Text Selected from pre-defined list reason_age_not_used Indication of why a date was not used in the original age model, blank if dates were used in original model Text Selected from pre-defined list notes Additional comments regarding a date record Text 65 535-character maximum length overall length of the core. For a 400 cm core for example, the thickness scenarios would be 5, 10, 15, 20, 25, 30, 35, 40, 45, and 50 cm. Thus, the number of scenarios created by possible accumulation rates and thicknesses varies between different entities. Depths of known hiatuses reported in the original publications were included in the date_info table (Sect. 2.1.5) and have also been included in the age models run in ageR. In instances where the sedimentation rates were different above and below a hiatus, separate age models were run before and after the non-deposition period to account for these variations (Blaauw and Christen, 2011). A three-step procedure was used to select the best model for each entity. First, an optimum model was selected by ageR, using the lowest quantified area between the prior and posterior accumulation rate distribution curves (Supplement Fig. S1). This selection was checked manually using comparisons between the distance of the estimated ages and the controls to check the accuracy of the model interpolation. Finally, the age model was visually inspected to ensure that final interpolation accurately represented the date information and did not show abrupt shifts in accumulation rates or changes at the dated depths. If the ageR model selection was deemed to be erroneous or inaccurate, the next suitable model with the lowest area between the prior and posterior curves, which accurately represented the distribution of dates in the sequence, was selected (Supplement Fig. S2). 2.3 Quality control Individual records in the RPD were compiled either by the original authors or from published and open-access material by specialists in the collection and interpretation of charcoal records. 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