Discharge Data

11. Discharge Data#

import requests

import pandas as pd
import matplotlib.pyplot as plt

from bs4 import BeautifulSoup

Greenland discharge data is readily available to download from the cloud from Ken Mankoff’s Greenland Ice Sheet solid ice discharge from 1986 through last month dataset.

The most recent paper citation is:

Mankoff, K. D., Solgaard, A., Colgan, W., Ahlstrøm, A. P., Khan, S. A., & Fausto, R. S. (2020). Greenland Ice Sheet solid ice discharge from 1986 through March 2020. Earth System Science Data, 12(2), 1367-1383. https://doi.org/10.5194/essd-12-1367-2020

And the dataset can be found at:

Mankoff, K. D., Solgaard, A., & Larsen, S. (2020). Greenland Ice Sheet solid ice discharge from 1986 through last month: Discharge, GEUS Dataverse https://doi.org/10.22008/promice/data/ice_discharge/d/v02

The dataset updates monthly with new discharge data and a new version. As well as the GEUS dataverse page, there is a specific page that always links to the most recent versions of the dataset, located at https://dataverse.geus.dk/api/datasets/:persistentId/dirindex?persistentId=doi:10.22008/promice/data/ice_discharge/d/v02.

12. Downloading Data#

We can use this page to write a simple function that will get direct links to the most recent CSVs. This uses the standard library requests package, as well as “Beautiful Soup”, a popular library for parsing HTML (i.e. web markup language).

def get_discharge_links():
    url = 'https://dataverse.geus.dk/api/datasets/:persistentId/dirindex?persistentId=doi:10.22008/promice/data/ice_discharge/d/v02'
    html = requests.get(url).text
    soup = BeautifulSoup(html, 'html.parser')
    return {a.text.strip(): "https://dataverse.geus.dk" + a["href"] for a in soup.find_all("a")}

links = get_discharge_links()

We now have a complete dictionary of the links, which I print out below.

links

{'gate.nc': 'https://dataverse.geus.dk/api/access/datafile/93550',
 'gate_coverage.csv': 'https://dataverse.geus.dk/api/access/datafile/93547',
 'gate_D.csv': 'https://dataverse.geus.dk/api/access/datafile/93548',
 'gate_err.csv': 'https://dataverse.geus.dk/api/access/datafile/93549',
 'gate_meta.csv': 'https://dataverse.geus.dk/api/access/datafile/78366',
 'GIS.nc': 'https://dataverse.geus.dk/api/access/datafile/93554',
 'GIS_coverage.csv': 'https://dataverse.geus.dk/api/access/datafile/93551',
 'GIS_D.csv': 'https://dataverse.geus.dk/api/access/datafile/93552',
 'GIS_err.csv': 'https://dataverse.geus.dk/api/access/datafile/93553',
 'README.txt': 'https://dataverse.geus.dk/api/access/datafile/93146',
 'region.nc': 'https://dataverse.geus.dk/api/access/datafile/93558',
 'region_coverage.csv': 'https://dataverse.geus.dk/api/access/datafile/93555',
 'region_D.csv': 'https://dataverse.geus.dk/api/access/datafile/93556',
 'region_err.csv': 'https://dataverse.geus.dk/api/access/datafile/93557',
 'sector.nc': 'https://dataverse.geus.dk/api/access/datafile/93562',
 'sector_coverage.csv': 'https://dataverse.geus.dk/api/access/datafile/93559',
 'sector_D.csv': 'https://dataverse.geus.dk/api/access/datafile/93560',
 'sector_err.csv': 'https://dataverse.geus.dk/api/access/datafile/93561'}

The README.txt outlines what each of the files are:

Filename

Description

GIS_D.csv

Greenland Ice Sheet cumulative discharge by timestamp

GIS_err.csv

Errors for GIS_D.csv

GIS_coverage.csv

Coverage for GIS_D.csv

GIS.nc

Discharge, errors, and coverage for GIS

region_D.csv

Regional discharge

region_err.csv

Errors for region_D.csv

region_coverage.csv

Coverage for region_D.csv

region.nc

Discharge, errors, and coverage for GIS regions

sector_D.csv

Sector discharge

sector_err.csv

Errors for sector_D.csv

sector_coverage.csv

Coverage for sector_D.csv

sector.nc

Discharge, errors, and coverage for GIS sectors

gate_D.csv

Gate discharge

gate_err.csv

Errors for gate_D.csv

gate_coverage.csv

Coverage for gate_D.csv

gate.nc

Discharge, errors, and coverage for GIS gates - including gate metadata

gate_meta.csv

Metadata for each gate

We can load the .csv files directly using Pandas, and begin to play.

12.1. Greenland-wide Discharge#

gis_d_df = pd.read_csv(links['GIS_D.csv'], parse_dates=['Date'])
gis_err_df = pd.read_csv(links['GIS_err.csv'], parse_dates=['Date'])
gis_df = pd.merge(gis_d_df, gis_err_df, on='Date', how='inner')

gis_df
Date Discharge [Gt yr-1] Discharge Error [Gt yr-1]
0 1986-04-15 426.529 39.572
1 1986-05-15 437.313 40.547
2 1986-06-15 443.186 41.209
3 1986-07-15 444.473 41.196
4 1986-08-15 442.602 40.659
... ... ... ...
2920 2025-07-03 494.212 46.926
2921 2025-07-15 499.877 47.389
2922 2025-07-27 491.225 46.367
2923 2025-08-08 486.759 45.928
2924 2025-08-20 491.769 46.459

2925 rows × 3 columns

fig, ax = plt.subplots()

# ax.errorbar(gis_df['Date'], gis_df['Discharge [Gt yr-1]'], yerr=gis_df['Discharge Error [Gt yr-1]'], capsize=5)

ax.plot(gis_df['Date'], gis_df['Discharge [Gt yr-1]'], color='black')
ax.fill_between(
    gis_df['Date'],
    gis_df['Discharge [Gt yr-1]'] - gis_df['Discharge Error [Gt yr-1]'],
    gis_df['Discharge [Gt yr-1]'] + gis_df['Discharge Error [Gt yr-1]'],
    color='grey', alpha=0.3
)

ax.grid()
ax.set_xlabel('Date')
ax.set_ylabel('Discharge [Gt yr-1]')
ax.set_title('Greenland Ice Sheet Discharge')

Text(0.5, 1.0, 'Greenland Ice Sheet Discharge')
../_images/680dc6f934b98b4fa645eea1307e4136d8ee913e7c8097f061abf7a87259f719.png

12.2. Region Discharge#

region_d_df = pd.read_csv(links['region_D.csv'], parse_dates=['Date'])
region_d_df
Date CE CW NE NO NW SE SW
0 1986-04-15 62.137103 68.260368 22.420875 21.088441 90.888020 141.065906 20.667821
1 1986-05-15 62.681844 77.211197 22.551619 20.375301 91.876267 141.948529 20.667821
2 1986-06-15 65.678520 76.699118 22.784760 23.760039 93.772264 139.823480 20.667821
3 1986-07-15 65.633344 76.246051 20.908024 24.905824 95.793717 140.318272 20.667821
4 1986-08-15 64.001971 74.264191 22.652118 23.707009 95.469182 141.839283 20.667821
... ... ... ... ... ... ... ... ...
2920 2025-07-03 77.428632 83.229713 30.684348 27.458049 117.777602 137.209408 20.424536
2921 2025-07-15 76.549233 86.581371 31.374898 28.080070 119.337322 137.679265 20.274452
2922 2025-07-27 74.588720 85.175539 31.497871 28.159406 114.859978 137.485715 19.457482
2923 2025-08-08 73.874844 84.292667 31.709303 28.196223 114.246686 135.567358 18.872074
2924 2025-08-20 75.351757 86.121670 31.249736 28.125413 115.976991 136.081063 18.862323

2925 rows × 8 columns

fig, ax = plt.subplots()

ax = region_d_df.plot(x='Date', y=['CW','CE','NW','NE','SW','SE'], ax=ax)

ax.grid()
ax.set_ylabel('Discharge [Gt yr-1]')
ax.set_ylim(0, 160)
ax.set_title('Regional Greenland Ice Sheet Discharge')

Text(0.5, 1.0, 'Regional Greenland Ice Sheet Discharge')
../_images/ccd1a4a95ad3ddaf5eb67634b1f4d0106a0d74af66c4d2b409fe64d524737362.png

12.3. Specific Basins#

Divided into basins from Mouginot and Rignot (2019).

sector_d_df = pd.read_csv(links['sector_D.csv'], parse_dates=['Date'])
sector_err_df = pd.read_csv(links['sector_err.csv'], parse_dates=['Date'])

We can see what basin names are available by printing the columns (or, in practice, probably by consulting the Mouginot and Rignot vector dataset in GIS software). I will leave this commented out for now so as now to produce a wall of text.

# for c in sector_d_df.columns:
#     print(c)

Let’s get an example glaciers - KIV Steenstrup Norde Bræ (I have a vested interest).

# KIV_STEENSTRUP_NODRE_BRAE
kiv_d_df = sector_d_df[['Date', 'KIV_STEENSTRUP_NODRE_BRAE']].rename(columns={"KIV_STEENSTRUP_NODRE_BRAE": "Discharge [Gt yr-1]"})
kiv_err_df = sector_err_df[['Date', 'KIV_STEENSTRUP_NODRE_BRAE']].rename(columns={"KIV_STEENSTRUP_NODRE_BRAE": "Discharge Error [Gt yr-1]"})
kiv_df = pd.merge(kiv_d_df, kiv_err_df, on='Date', how='inner')
kiv_df
Date Discharge [Gt yr-1] Discharge Error [Gt yr-1]
0 1986-04-15 5.528626 0.172375
1 1986-05-15 5.442344 0.169099
2 1986-06-15 5.883271 0.197025
3 1986-07-15 6.258195 0.204638
4 1986-08-15 5.526404 0.176612
... ... ... ...
2920 2025-07-03 10.241080 0.364027
2921 2025-07-15 10.143594 0.358980
2922 2025-07-27 9.781145 0.348199
2923 2025-08-08 9.675594 0.343677
2924 2025-08-20 9.700606 0.343281

2925 rows × 3 columns

fig, ax = plt.subplots()

ax.plot(kiv_df['Date'], kiv_df['Discharge [Gt yr-1]'], color='black')
ax.fill_between(
    kiv_df['Date'],
    kiv_df['Discharge [Gt yr-1]'] - kiv_df['Discharge Error [Gt yr-1]'],
    kiv_df['Discharge [Gt yr-1]'] + kiv_df['Discharge Error [Gt yr-1]'],
    color='grey', alpha=0.3
)

ax.grid()
ax.set_ylim(0, 14)
ax.set_xlabel('Date')
ax.set_ylabel('Discharge [Gt yr-1]')
ax.set_title('KIV Steenstrup Norde Bræ Discharge')

Text(0.5, 1.0, 'KIV Steenstrup Norde Bræ Discharge')
../_images/56f19a786c79c8e8e5853108e0a6881eb0667ab5fac90333125d11164ac0334c.png
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