These data encompass land cover data and sediment geochemistry from the Rio Santa catchment of the Peruvian Andes. Sediment samples were collected within the SIGMA: Peru project between 2019-2020. Geochemistry data include major and minor elements in sediment samples determined by X-Ray Fluorescence (XRF), organic matter determined by loss-on-ignition (LOI), and particle size data, and are provided for both the wider Rio Santa catchment and the Ranrahirca sub-catchment. The research was supported by the Natural Environment Research Council and the Newton Fund (grant NE/S013245/1). Full details about this dataset can be found at https://doi.org/10.5285/20837a27-414c-4597-a6d2-3edc1d2e9a98

The data consists of identified exposed objects subject to flooding risk from the Tsho Rolpa Lake. The Tsho Rolpa Lake is the largest moraine-dammed proglacial lake in Nepal and was identified as one of the country’s most dangerous glacier lakes with a high possibility of outburst. Full details about this dataset can be found at https://doi.org/10.5285/3834d477-7a1d-4ad3-8a41-d38fc727dbd8

The dataset consists of long-term vegetation monitoring data from the Hard Hill burning plots sited in the Moor House - Upper Teesdale National Nature Reserve, Cumbria. An experiment to investigate the effects of rotational burning and grazing was initiated in 1954, consisting of a replicated block layout. Initial vegetation recording was carried out in 1961 and 1965 using a quadrat method and DOMIN scale. In 1972 onwards, vegetation was recorded using a pin frame. Data were recorded by staff from the Centre for Ecology & Hydrology and its predecessors. Full details about this dataset can be found at https://doi.org/10.5285/0b931b16-796e-4ce4-8c64-d112f09293f7

This dataset contains Land Cover/Land Use (LCLU) maps for Sindhudurg, Shivamogga and Wayanad, India. LCLU products are state-of-the-art statically stable and area weighted accuracy assessed products. The LCLU product was generated for Kyasanur Forest Disease (KFD), a Zoonotic disease. KFD is an “ecotonal” disease. Diverse forest-plantation mosaics, zone moist evergreen forest and plantation, and low coverage of dry deciduous forest will cause higher risks for KFD. Our LCLU product aimed to separate diverse forest types and plantation and we achieved high accuracy (>90%). The study covers Sindhudurg, Shivamogga, and Wayanad Western Ghats district which belong to Indian state Maharashtra, Karnataka, and Kerala respectively. Full details about this dataset can be found at https://doi.org/10.5285/cacb66de-aea0-41d5-97b3-9eacd4683aaf

Data was collected to look at long-term trends in invertebrate ground predators. This dataset consists of count data (by gender) for all species of spider collected from three habitats (mire, dwarf-shrub heath, pine woodland) at the Cairngorms Environmental Change Network (ECN) site between 2004 and 2021. Spiders were collected in pitfall traps on a two-weekly basis between March and early November. Each habitat contained ten pitfall traps, spaced 10 m apart. Samples were aggregated by habitat and collection date prior to analysis. The number of male and females of each species was recorded by the same expert araneologist for the duration (2004-2021). Full details about this dataset can be found at https://doi.org/10.5285/6077cd88-ad40-44bd-806a-fa3cebaa29d7

The WATCH Forcing data is a twentieth century meteorological forcing dataset for land surface and hydrological models. It consists of three/six-hourly states of the weather for global half-degree land grid points. It was generated as part of the EU FP 6 project "WATCH" (WATer and global CHange") which ran from 2007-2011. The data was generated in 2 tranches with slightly different methodology: 1901-1957 and 1958-2001, but generally the dataset can be considered as continuous. More details regarding the generation process can be found in the associated WATCH technical report and paper in J. Hydrometeorology. To understand how the data grid is formed it is necessary to read the attached WFD-land-long-lat-z files either in NetCDF or DAT formats. The data covers land points only and excludes the Antarctica. Qair or 2m specific humidity (instantaneous) is the instantaneous specific humidity at 2m measured in kg/kg at 6 hourly resolution and 0.5 x 0.5 degrees spatial resolution.

The WATCH Forcing data is a twentieth century meteorological forcing dataset for land surface and hydrological models. It consists of three/six-hourly states of the weather for global half-degree land grid points. It was generated as part of the EU FP 6 project "WATCH" (WATer and global CHange") which ran from 2007-2011. The data was generated in 2 tranches with slightly different methodology: 1901-1957 and 1958-2001, but generally the dataset can be considered as continuous. More details regarding the generation process can be found in the associated WATCH technical report and paper in J. Hydrometeorology. To understand how the data grid is formed it is necessary to read the attached WFD-land-long-lat-z files either in NetCDF or DAT formats. The data covers land points only and excludes the Antarctica. Snowf or snowfall is the snowfall rate based on the GPCC bias corrected, undercatch corrected measured in kg/m2/s at 3 hourly resolution averaged over the next 3 hours and at 0.5 x 0.5 degrees spatial resolution. Please note that there is also a WFD Snowf CRU bias corrected dataset, but as the GPCC dataset is the preferred dataset only this snowfall dataset is available from the EIDC. These snowfall datasets contain snowfall data only and need to be combined with the respective WFD rainfall datasets to obtain precipitation data.

The WATCH Forcing data is a twentieth century meteorological forcing dataset for land surface and hydrological models. It consists of three/six-hourly states of the weather for global half-degree land grid points. It was generated as part of the EU FP 6 project "WATCH" (WATer and global CHange") which ran from 2007-2011. The data was generated in 2 tranches with slightly different methodology: 1901-1957 and 1958-2001, but generally the dataset can be considered as continuous. More details regarding the generation process can be found in the associated WATCH technical report and paper in J. Hydrometeorology. To understand how the data grid is formed it is necessary to read the attached WFD-land-long-lat-z files either in NetCDF or DAT formats. The data covers land points only and excludes the Antarctica. Wind or near surface wind speed at 10m is the near surface wind speed at 10m in m/s-1 at 6 hourly resolution and 0.5 x 0.5 degrees spatial resolution.

This dataset presents predicted soil erosion rates (t ha-1 yr-1) and its impact on topsoils, including lifespans (yr) assuming erosion rates remain constant and there is no replacement of soil; flux rates of soil organic carbon via erosion (t SOC ha-1 yr-1); flux rates of soil nitrogen via erosion (t N ha-1 yr-1); and flux rates of soil phosphorous via erosion (t P ha-1 yr-1). The dataset comes in the form of three multi-band raster GeoTiff files, structured as follows: LC16_Results.tif: Model predictions generated under the 2016 Copernicus Land Cover Map at 30-metre resolution (five bands) Mitigation_scenarios.tif: Predicted reductions in erosion rates in the event of implementing mitigation scenarios described in sixteen different scenarios (sixteen bands). PNV_Results.tif: Same structure as LC16_Results.tif, but stores predictions generated under the Potential Natural Vegetation cover map for East Africa at 30-metre resolution (five bands) Full details about this dataset can be found at https://doi.org/10.5285/86d07d98-2956-4395-8b02-29dd5d98e6be

The dataset contains model output from the CityCAT hydrodynamic model showing maximum water depths in Jakarta, Indonesia, during the January/February 2007 flood. The hourly rainfall and hourly lateral inflow boundary conditions from rivers used to obtain the flooding depths are also included. Full details about this dataset can be found at https://doi.org/10.5285/8e58f0bb-3ff1-41e8-b8f4-380983ec68bc