PROMICE - A Python interface to PROMICE data

Table of contents

• WARNING
• Installation
• Quickstart
  • Ice Discharge
    • Find the 2020 discharge for the top 3 discharging glaciers
  • Watson River discharge

WARNING

• This is alpha level software under development.
• Please contact Ken Mankoff <kdm@geus.dk> before using.

Installation

pip install git+https://gitlab.com/mankoff/PROMICE.git

Can be run in new environment, for example:

Conda:

conda create -n PROMICE python=3.8
conda activate PROMICE
pip install --upgrade git+https://gitlab.com/mankoff/PROMICE.git

Test the installation

conda activate PROMICE
python

Then,

import promice
promice.ice_discharge.discharge(resolution="GIS").to_dataframe().tail()

time	discharge	err	coverage
2020-06-11	508.739014	52.574001	0.805
2020-06-23	509.875000	52.702000	0.718
2020-07-05	509.695007	52.209000	0.668
2020-07-17	506.665985	51.771999	0.713
2020-07-29	502.263000	51.325001	0.714

Quickstart

Ice Discharge

from promice import ice_discharge as ice
ds = ice.discharge(resolution="GIS")
ds.to_dataframe().tail()

time	discharge	err	coverage
2020-06-11 00:00:00	508.739	52.574	0.805
2020-06-23 00:00:00	509.875	52.702	0.718
2020-07-05 00:00:00	509.695	52.209	0.668
2020-07-17 00:00:00	506.666	51.772	0.713
2020-07-29 00:00:00	502.263	51.325	0.714

Find the 2020 discharge for the top 3 discharging glaciers

Load an xarray dataset of all discharge

from promice import ice_discharge as ice
ds = ice.discharge(resolution="gate")
print(ds)

<xarray.Dataset>
Dimensions:        (gate: 267, time: 449)
Coordinates:
  * time           (time) datetime64[ns] 1986-04-15 1986-05-15 ... 2020-07-29
  * gate           (gate) int64 1 3 4 5 6 7 8 10 ... 350 352 353 354 355 356 357
Data variables:
    discharge      (gate, time) float32 ...
    err            (gate, time) float32 ...
    coverage       (gate, time) float32 ...
    mean_x         (gate) int32 ...
    mean_y         (gate) int32 ...
    mean_lon       (gate) float32 ...
    mean_lat       (gate) float32 ...
    sector         (gate) int32 ...
    region         (gate) object ...
    name_Bjørk     (gate) object ...
    name_Mouginot  (gate) object ...
Attributes:
    featureType:      timeSeries
    title:            Greenland discharge
    summary:          Greenland discharge per gate
    keywords:         Greenland; Ice Discharge; Calving; Submarine Melt
    source:           git commit: b4f3e88
    creator_name:     Ken Mankoff
    creator_email:    kdm@geus.dk
    creator_url:      http://kenmankoff.com
    institution:      GEUS
    references:       10.22008/promice/ice_discharge
    product_version:  [2.]

top3 = ds.resample(time='A')\
         .mean()['discharge']\
         .to_dataframe()['discharge']\
         .unstack()\
         .iloc[-1]\
         .sort_values(ascending=False)[0:3]
top3

gate
184    34.403828
231    33.083332
190    27.318832
Name: 2020-12-31 00:00:00, dtype: float32

df_3 = ds.sel(gate=top3.index)\
         .to_dataframe()['discharge']\
         .unstack()\
         .T

df_3.head()

time	184	190	231
1986-04-15 00:00:00	23.174	15.267	29.657
1986-05-15 00:00:00	30.07	15.883	33.243
1986-06-15 00:00:00	30.006	16.714	31.41
1986-07-15 00:00:00	29.944	16.85	33.052
1986-08-15 00:00:00	29.88	16.148	31.726

• Convert the gate ID to named glacier
• Subset to just 2020
• Monthly average

df_3.rename(inplace=True, columns=dict([(i, str(ds.sel({'gate':i})['name_Bjørk'].values)) for i in df_3.columns]))
df_3[df_3.index.year >= 2020].resample('M').mean()

time	Sermeq Kujalleq	Kangerlussuaq Gletsjer	Helheim Gletsjer
2020-01-31 00:00:00	32.6853	27.941	35.2233
2020-02-29 00:00:00	31.465	27.523	35.2225
2020-03-31 00:00:00	31.1303	27.3773	34.3623
2020-04-30 00:00:00	31.8335	28.2875	34.657
2020-05-31 00:00:00	34.5737	27.0317	31.8193
2020-06-30 00:00:00	39.9095	26.9235	30.3435
2020-07-31 00:00:00	39.2283	26.407	30.2797

Watson River discharge

from promice import watson
df = watson.discharge()
df['2012-06-01':].head(3)

Date	Q	err
2012-06-01 00:00:00	0.17	0.04
2012-06-02 00:00:00	0.21	0.05
2012-06-03 00:00:00	0.26	0.06