This dataset contains the Lakes Essential Climate Variable, which is comprised of processed satellite observations at the global scale, over the period 1992-2020, for over 2000 inland water bodies. This dataset was produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. For more information about the Lakes_cci please visit the project website. This is version 2.0.2 of the dataset. The five thematic climate variables included in this dataset are: • Lake Water Level (LWL), derived from satellite altimetry, is fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate change. • Lake Water Extent (LWE), modelled from the relation between LWL and high-resolution spatial extent observed at set time-points, describes the areal extent of the water body. This allows the observation of drought in arid environments, expansion in high Asia, or impact of large-scale atmospheric oscillations on lakes in tropical regions for example. . • Lake Surface Water temperature (LSWT), derived from optical and thermal satellite observations, is correlated with regional air temperatures and is informative about vertical mixing regimes, driving biogeochemical cycling and seasonality. • Lake Ice Cover (LIC), determined from optical observations, describes the freeze-up in autumn and break-up of ice in spring, which are proxies for gradually changing climate patterns and seasonality. • Lake Water-Leaving Reflectance (LWLR), derived from optical satellite observations, is a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions). Data generated in the Lakes_cci are derived from multiple satellite sensors including: TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel 2-3, Landsat OLI, ERS, MODIS Terra/Aqua and Metop. Detailed information about the generation and validation of this dataset is available from the Lakes_cci documentation available on the project website and in Carrea, L., Crétaux, JF., Liu, X. et al. Satellite-derived multivariate world-wide lake physical variable timeseries for climate studies. Sci Data 10, 30 (2023). https://doi.org/10.1038/s41597-022-01889-z

This dataset contains the Lakes Essential Climate Variable, which is comprised of processed satellite observations at the global scale, over the period 1992-2020, for over 2000 inland water bodies. This dataset was produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. For more information about the Lakes_cci please visit the project website. This is version 2.0.1 of the dataset. The five thematic climate variables included in this dataset are: • Lake Water Level (LWL), derived from satellite altimetry, is fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate change. • Lake Water Extent (LWE), modelled from the relation between LWL and high-resolution spatial extent observed at set time-points, describes the areal extent of the water body. This allows the observation of drought in arid environments, expansion in high Asia, or impact of large-scale atmospheric oscillations on lakes in tropical regions for example. . • Lake Surface Water temperature (LSWT), derived from optical and thermal satellite observations, is correlated with regional air temperatures and is informative about vertical mixing regimes, driving biogeochemical cycling and seasonality. • Lake Ice Cover (LIC), determined from optical observations, describes the freeze-up in autumn and break-up of ice in spring, which are proxies for gradually changing climate patterns and seasonality. • Lake Water-Leaving Reflectance (LWLR), derived from optical satellite observations, is a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions). Data generated in the Lakes_cci are derived from multiple satellite sensors including: TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel 2-3, Landsat OLI, ERS, MODIS Terra/Aqua and Metop. Detailed information about the generation and validation of this dataset is available from the Lakes_cci documentation available on the project website.

Concentration and accumulation palaeoenvironmental proxy data derived from a 6.3m sedimentary core drilled at Marcacocha, a present-day wetland (formerly a small lake) located at 3355m above sea-level in the Cordillera Oriental of the Peruvian Andes. Multi-proxy analysis of the sediments at decadal to sub-decadal temporal resolution has provided detailed datasets that include sedimentology, palynology, geochemistry, plant macrofossils, diatoms and oribatid mite remains (Chepstow-Lusty et al., 2003, 2007, 2009; Sterken et al., 2006). Select data are presented here relating to the uppermost 1.9m of the sequence (ca. the last 1200 years). The data relate specifically to: Chepstow-Lusty, A., Frogley, M.R., Baker, A.S. Comparison of Sporormiella dung fungal spores and oribatid mites as indicators of large herbivore presence: evidence from the Cuzco region of Peru. J. Arch. Sci. https://doi.org/10.1016/j.jas.2018.12.006 Chepstow-Lusty, A., Bennett, K., Fjeldså, J., Kendall, A., Galiano, W., Tupayachi Herrera, A., 1998. Tracing 4000 years of environmental history in the Cuzco area, Peru, from the pollen record. Mt. Res. Dev. 18, 159–172. Chepstow-Lusty, A., Frogley, M.R., Bauer, B.S., Bush, M.B., Tupayachi Herrera, A., 2003. A late Holocene record of arid events from the Cuzco region, Peru. J. Quat. Sci. 18, 491–502. Chepstow-Lusty, A., Frogley, M.R., Bauer, B.S., Leng, M., Cundy, A., Boessenkool, K.P., Gioda, A., 2007. Evaluating socio-economic change in the Andes using oribatid mite abundances as indicators of domestic animal densities. J. Arch. Sci. 34, 1178–1186. Chepstow-Lusty, A.J., Frogley, M.R., Bauer, B., Leng, M.J., Boessenkool, K.P., Carcaillet, C., Ali, A.A., Gioda, A., 2009. Putting the rise of the Inca empire within a climatic and land management context. Clim. Past 5, 1–14. Sterken, M., Sabbe, K., Chepstow-Lusty, A., Frogley, M., Vanhoutte, K., Verleyen, E., Cundy, A., Vyverman, W., 2006. Climate and land-use changes in the Cuzco region (Cordillera Oriental, South East Peru) during the last 1200 years: a diatom based reconstruction. Arch. Hydrobiol. 165, 289–312.

This dataset contains the Lakes Essential Climate Variable, which is comprised of processed satellite observations at the global scale, over the period 1992-2020, for over 2000 inland water bodies. This dataset was produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. For more information about the Lakes_cci please visit the project website. This is version 2.0 of the dataset. The five thematic climate variables included in this dataset are: • Lake Water Level (LWL), derived from satellite altimetry, is fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate change. • Lake Water Extent (LWE), modelled from the relation between LWL and high-resolution spatial extent observed at set time-points, describes the areal extent of the water body. This allows the observation of drought in arid environments, expansion in high Asia, or impact of large-scale atmospheric oscillations on lakes in tropical regions for example. . • Lake Surface Water temperature (LSWT), derived from optical and thermal satellite observations, is correlated with regional air temperatures and is informative about vertical mixing regimes, driving biogeochemical cycling and seasonality. • Lake Ice Cover (LIC), determined from optical observations, describes the freeze-up in autumn and break-up of ice in spring, which are proxies for gradually changing climate patterns and seasonality. • Lake Water-Leaving Reflectance (LWLR), derived from optical satellite observations, is a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions). Data generated in the Lakes_cci are derived from multiple satellite sensors including: TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel 2-3, Landsat OLI, ERS, MODIS Terra/Aqua and Metop. Detailed information about the generation and validation of this dataset is available from the Lakes_cci documentation available on the project website.

A very incomplete dataset of surface lakes in Antarctica. Data have been prepared from various map and remotely sensed datasets.

A very incomplete dataset of surface lakes in Antarctica. Data have been prepared from various map and remotely sensed datasets.

Surface waters and shallow groundwater samples were collected by completely filling 30 mL polyethylene bottles, which were then sealed with electrical tape to minimise the risk of evaporative loss. Rainwater samples were integrated samples of total monthly rainfall collected in a specially-adapted rainfall collector following IAEA protocols (IAEA http://www-naweb.iaea.org/napc/ih/documents/userupdate/sampling.pdf [accessed 22 June 2012). Mexico, State of Yucatan. Yaal Chac (lake) (lake centre is Lat: 20.595274 degrees; Long: -89.711301 degrees), Abala Well (Lat: 20.649044 degrees; Long: -89.679814 degrees) and Xanil ha Cave (Lat: 20.650809 degrees; Long: -89.697426 degrees) Rainwater sampler was located adjacent to the lake. Refer to accompanying map for the precise location of the sampling sites.

A very incomplete dataset of surface lakes in Antarctica. Data have been prepared from various map and remotely sensed datasets. This dataset has been generalised from the high resolution version.

This dataset contains genotypes (in three digit-format) for unique clones of the freshwater bryozoan species Cristatella mucedo and Fredericella sultana at microsatellite loci and representing sampling sites across the UK. Cristatella mucedo data additionally covers Northern Ireland. Full details about this nonGeographicDataset can be found at https://doi.org/10.5285/9d63e68b-2499-40fb-b05b-4fa2a55399fc

The dataset is the lake polygons from the UK Lakes Portal (https://eip.ceh.ac.uk/apps/lakes/), originally based on OS PANORAMA but this dataset includes data from a number of sources. It has a basic set of attributes including the water body ID (WBID) as well as the computed area and perimeter of each lake. The WBID is commonly used across research institutions and is the same ID as used on the UK Lakes Portal, where more information can be found on each lake in this dataset. This is v3.6, which follows the same versioning as the underlying database. Although the database has seen the majority of the changes since version 1, the polygons have also been changed and improved over that time, mostly fixing issues with lake outlines, but also some new sites being added. Full details about this dataset can be found at https://doi.org/10.5285/b6b92ce3-dcd7-4f0b-8e43-e937ddf1d4eb