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.

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.

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 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. Tair or air temperature is the 2m air temperature (instantaneous) measured in K at 6 hourly resolution and 0.5 x 0.5 degrees spatial resolution.

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

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

This dataset includes polygons representing ecosystem types (ET) and their respective ecosystem services (ES) and disservices (EDS) in the Luanhe River Basin, with attributes recording 14 ecosystem types (ET), 11 provisioning services (PS), ten regulating services (RS), five cultural services (CS), 7 Ecological integrity indicators (EI), and 11 ecosystem disservices (EDS). Full details about this dataset can be found at https://doi.org/10.5285/2252d8a4-0ef3-403f-b2c3-3f7acbcac1d5

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 data comprises river section, zone and test site delineation, winter Season average NDVI by section and zone 1989-2020, land cover maps seasonally 1989-2020, and derived land cover fractions by section and zone 1989-2020. The data was produced as part of a study to determine how changes in geomorphic form and dynamics due to human alteration to river flows and riparian land management relate to changes in vegetation communities in the Sutlej and Beas Rivers, India. Vegetated and other land cover, including water area, were quantified by winter season NDVI trends (in the plains of Punjab) and seasonal supervised classification of Landsat data for over a 30-year period. The work was supported by the Natural Environment Research Council (Grant NE/S01232X/1). Full details about this dataset can be found at https://doi.org/10.5285/9a96e199-34d0-46f9-9a64-140d300a2531

This dataset contains maximum water depth and maximum water velocity for 12 different Glacial Lake outburst floods (GLOFs) scenarios of the Tsho Rolpa Lake, Nepal. Also included is the water depth of dam breach flow and discharge of dam breach flow under each scenario. The GLOFs scenarios were created using a simple dam breach model. A high-performance hydrodynamic model was then used to simulate the resulting flood hydrodynamics. Full details about this dataset can be found at https://doi.org/10.5285/f4292d99-de93-4a28-a821-b2a6a826df4c