The flow-line model was designed to enable estimation of the age and surface origin for various ice bodies identified within hot-water drilled boreholes on Larsen C Ice Shelf. Surface fluxes are accumulated, converted to thicknesses, and advected down flow from a fixed number of selected points. The model requires input datasets of surface mass balance, surface velocity, vertical strain rates, ice-shelf thickness, and a vertical density profile. This model is part of a larger project. Input datasets such as density profiles and trajectory vectors are available separately. Resolution is dependent on the input datasets. Funding was provided by the NERC grant NE/L005409/1.

The datasets are output from a flow-line model designed to estimate the age and surface origin for various ice bodies identified within hot-water drilled boreholes on Larsen C Ice Shelf (Hubbard et al., 2016, Ashmore et al., 2017). Two trajectories, based on remotely sensed velocities, allow surface fluxes from a regional climate model to be accumulated and advected down flow from selected points on the shelf. Vertical strain rates are taken into account, and surface mass balance is converted to thickness according to density profiles based on borehole data (Ashmore et al., 2017). The model output has a 250m horizontal resolution. These data are part of a larger project. The flow-line model code, the SMB datasets, and the borehole density profiles are also available. Funding was provided by the NERC grant NE/L005409/1.

The dataset contains time series observations of meteorological and soil physics variables logged at one minute time resolution at three Land Surface Stations in India. The three INCOMPASS Land Surface Stations were located at: (1) agricultural land in Southern Karnataka (Berambadi); (2) the University of Agricultural Sciences in Dharwad in northern Karnataka; and (3) a semi-natural grassland at the Indian Institute of Technology in Kanpur (IITK), Uttar Pradesh. Observations were collected under the Interaction of Convective Organization and Monsoon Precipitation, Atmosphere, Surface and Sea (INCOMPASS) Project between January 2016 and January 2019. Full details about this dataset can be found at https://doi.org/10.5285/c5e72461-c61f-4800-8bbf-95c85f74c416