We use open source software and datasets for this project.
We have developed a glacier and snow scheme for the DECIPHeR hydrological model. The original DECIPHeR code is available from GitHub at https://github.com/uob-hydrology/DECIPHeR. We will update this link when the glacier and snow version of the code is released.
The ECMWF reanalysis data has a high resolution (0.25 degrees ~25km). A back extension to the data was released in February 2021, so now there is continuous hourly data from 1950-2021 making this a very useful long term dataset for running our model.
GlabTop2-py is an open source Python package that calculates the ice-thickness distribution of individual glaciers. This is useful to calculate past ice thicknesses when no observations are available. GlacTop2 uses glacier outlines and a DEM as input.
GDAL is a translator library for raster and vector geospatial data formats. We use this very handy library to prepare our input datasets.
Our project uses the MERIT DEM. The MERIT DEM was developed by removing multiple error components (absolute bias, stripe noise, speckle noise, and tree height bias) from the existing spaceborne DEMs (SRTM3 v2.1 and AW3D-30m v1). The DEM has a 3sec resolution (~90m at the equator) and ccessible from http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/
The Randolph Glacier Inventory (RGI 6.0) is a global inventory of glacier outlines, thicknesses and debris cover extent. The data represents the present-day state of glaciers and is used as the starting point for future predictions.
This database contains observations of flow intake at the head of irrigation channels and inflow, release, and volume of reservoirs.
The GRDC contains river discharge observations which we use to calibrate and validate our model.
The Paris agreement aims to hold global warming to well below 2 ∘C and to pursue efforts to limit it to 1.5 ∘C relative to the pre-industrial period, however, current estimates based on intended carbon emissions from participant countries suggest global warming may exceed this. We use high-end GCM projections, defined as exceeding +2 ∘C global average warming relative to the pre-industrial period for this study. The GCM data consists of an ensemble of CMIP5 models that were downscaled using HADGEM3 and EC-EARTH3-HR. Unlike the other datasets listed above this is not open source yet.