https://doi.org/10.5061/dryad.pk0p2ngxf

Files contain century scale runoff projections for all 75 GRDC major river basins larger than 3000 km^2 with at least 30 km^2 of glacier cover in the year 2000. For each basin there is a time series corresponding to each of the 4 implemented emission scenarios (SSPs) and each of the 12 implemented Global Climate Models (GCMs). The combination of these variables is given by the index column and is formatted as: GCM_SSP_Basin. There are two versions of the data set where the time series (the Date variable) is structured slightly differently (see methods). 

Description of the data and file structure

Data is stored as .csv files and was formatted using a pandas DataFrame. In addition to the GCM_SSP_Basin index column, each DataFrame contains variables: Date, a string of the format YEAR-MO month for which the runoff projections correspond to; GloGEM, which gives the runoff projection given my the Global Glacier Evolution Model in km^3; PyGEM, which gives the runoff projection given my the Python Glacier Evolution Model in km^3; and OGGM, which gives the runoff projection given my the Open Global Glacier Model in km^3.

Note to users: Each data set is a table consisting of more than 4 million rows (this was done as an alternative to uploading separate files for each combination of SSP, GCM, and basin). The below code can be used to read in (one of) the data sets into a dictionary of the form RF_dict[gcm][ssp][basin], where each dictionary entry contains a DataFrame with century-scale projections for a single basin given by a single SSP and GCM.

# Path to the single CSV file
all_rf_file_path = '/Your/File/Path/all_rf_data.csv'
all_rf_aligned_file_path = '/Your/File/Path/all_rf_aligned_data.csv'

# Read the CSV file
df = pd.read_csv(all_rf_file_path)

# Dictionary to store DataFrames
RF_dict = {}

# Iterate over the rows and populate the dictionary
for index, row in df.iterrows():
    # Extract GCM, SSP, and Basin from the source_file column
    gcm_ssp_basin = row['GCM_SSP_Basin'].split('_')
    gcm = gcm_ssp_basin[0]
    ssp = gcm_ssp_basin[1]
    basin = gcm_ssp_basin[2]
    
    # Initialize nested dictionary structure if not already done
    if gcm not in RF_dict:
        RF_dict[gcm] = {}
    if ssp not in RF_dict[gcm]:
        RF_dict[gcm][ssp] = {}
    if basin not in RF_dict[gcm][ssp]:
        RF_dict[gcm][ssp][basin] = []
    
    # Append the row to the corresponding DataFrame
    RF_dict[gcm][ssp][basin].append(row)

# Convert lists of rows to DataFrames
for gcm in RF_dict:
    for ssp in RF_dict[gcm]:
        for basin in RF_dict[gcm][ssp]:
            # Convert list of rows to DataFrame
            temp_df = pd.DataFrame(RF_dict[gcm][ssp][basin])
            # Drop the 'source_file' column
            temp_df.drop(columns=['GCM_SSP_Basin'], inplace=True)
            # Set the 'Date' column as the index
            temp_df.set_index('Date', inplace=True)
            # Store the DataFrame back in the dictionary
            RF_dict[gcm][ssp][basin] = temp_df

Code/Software

All files were generated using Jupyter Notebooks and can be found within the repository https://github.com/finnwimberly/glacial_runoff_intercomparison