{ "cells": [ { "cell_type": "markdown", "id": "de6fe43b", "metadata": { "tags": [] }, "source": [ "# Convert \"raw\" Hugonnet et al (2021) geodetic per glacier dataset to a corrected dataset without outliers and missing glaciers\n", "**Workflow:**\n", "- reindex glaciers with GLIMS-ids to RGI-ids\n", "- missing glaciers from the RGI are added to the dataset with their area and filled up with NaN-values\n", "- Glaciers with connectivity level 2 are removed from the dataset (these are those glaciers that are strongly connected to the Greenland Ice Sheet). We don't want to do projections with them because they are already included in the GIS projections!\n", "- the specific MB and std of all glaciers with MB standard deviations larger than a threshold (i.e. mean error plus three standard deviations of the error distribution) are also replaced by NaN-values (=outliers, in total 1532 glaciers). \n", " - First an overall global threshold over all regions is computed, then this threshold is computed as well for every RGI region.\n", " - If the regional threshold is smaller than the global threshold, we use the global threshold (to not penalize regions that are well-measured), otherwise we use the regional threshold\n", "- For each region a mean specific MB and a mean specific MB standard deviation is estimated. All RGI glaciers (connectivity level < 2) that have NaN-values (no measurements or outlier) are filled up with the regional means according to their RGI region. \n", "\n", "**In total, 8597 of 215547 glaciers are filled up with regional mean data, hence 4.0% of all RGI glaciers. This corresponds to only 0.25% of the total glacier area as mostly small glaciers had no geodetic data or are outliers. 1532 from them are \"outlier\" glaciers!** ([click here to get to a figure visualizing this](#id-dmdtda-stats-plot))\n", "\n", "This method only fills up the dmdtda and err_dmdtda values. We are working at the moment to also fill up dhdt, err_dhdt, dvoldt and err_dvoldt ([see discussions at the end of this notebook](#id-analysis-problems-dhdt-dvoldt)), but this is not yet incorporated!\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 1, "id": "74981034", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import oggm\n", "from oggm import utils\n", "import pickle\n", "pickle.HIGHEST_PROTOCOL = 4" ] }, { "cell_type": "markdown", "id": "d4a736fe", "metadata": {}, "source": [ "## Convert csv to hdf file" ] }, { "cell_type": "code", "execution_count": 3, "id": "fa61bd0d", "metadata": {}, "outputs": [], "source": [ "fpath = 'https://cluster.klima.uni-bremen.de/~oggm/geodetic_ref_mb/hugonnet_2021_ds_rgi60_pergla_rates_10_20_worldwide.csv'\n", "fpath = 'hugonnet_2021_ds_rgi60_pergla_rates_10_20_worldwide.csv'\n", "df = pd.read_csv(fpath, index_col=0)" ] }, { "cell_type": "code", "execution_count": 4, "id": "58b89297", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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periodareadhdterr_dhdtdvoldterr_dvoldtdmdterr_dmdtdmdtdaerr_dmdtdaperc_area_measperc_area_resvalid_obsvalid_obs_pyreg
rgiid
RGI60-01.000012000-01-01_2020-01-01360000.0-0.01500.2559-5414.092139.0-0.0000050.000078-0.01280.21761.0001.00026.4111.111
RGI60-01.000012010-01-01_2020-01-01360000.0-0.05560.4645-20003.0167248.0-0.0000170.000142-0.04720.39491.0001.00018.306.321
RGI60-01.000012000-01-01_2010-01-01360000.00.02550.50599175.0182132.00.0000080.0001550.02170.43001.0001.0008.114.781
RGI60-01.000022000-01-01_2020-01-01558000.0-0.26950.1653-150361.093741.0-0.0001280.000080-0.22900.14601.0001.00026.2116.231
RGI60-01.000022010-01-01_2020-01-01558000.0-0.34100.3234-190257.0181714.0-0.0001620.000155-0.28980.27941.0001.00015.187.951
................................................
RGI60-19.027512000-01-01_2020-01-0111000.0-2.16110.8691-23772.021979.0-0.0000200.000019-1.83692.28911.0001.0004.004.0019
RGI60-19.027512000-01-01_2010-01-0111000.0-2.13171.7586-23449.027483.0-0.0000200.000023-1.81192.60801.0001.0000.000.0019
RGI60-19.027522000-01-01_2010-01-01528000.00.14270.637175344.0336536.00.0000640.0002860.12130.54210.9810.9812.741.7019
RGI60-19.027522000-01-01_2020-01-01528000.0-0.04540.3407-23981.0179916.0-0.0000200.000153-0.03860.28970.9810.9816.915.1519
RGI60-19.027522010-01-01_2020-01-01528000.0-0.23350.5643-123306.0298432.0-0.0001050.000254-0.19850.48140.9810.9814.173.4419
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654390 rows × 15 columns

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" ], "text/plain": [ " period area dhdt err_dhdt dvoldt \\\n", "rgiid \n", "RGI60-01.00001 2000-01-01_2020-01-01 360000.0 -0.0150 0.2559 -5414.0 \n", "RGI60-01.00001 2010-01-01_2020-01-01 360000.0 -0.0556 0.4645 -20003.0 \n", "RGI60-01.00001 2000-01-01_2010-01-01 360000.0 0.0255 0.5059 9175.0 \n", "RGI60-01.00002 2000-01-01_2020-01-01 558000.0 -0.2695 0.1653 -150361.0 \n", "RGI60-01.00002 2010-01-01_2020-01-01 558000.0 -0.3410 0.3234 -190257.0 \n", "... ... ... ... ... ... \n", "RGI60-19.02751 2000-01-01_2020-01-01 11000.0 -2.1611 0.8691 -23772.0 \n", "RGI60-19.02751 2000-01-01_2010-01-01 11000.0 -2.1317 1.7586 -23449.0 \n", "RGI60-19.02752 2000-01-01_2010-01-01 528000.0 0.1427 0.6371 75344.0 \n", "RGI60-19.02752 2000-01-01_2020-01-01 528000.0 -0.0454 0.3407 -23981.0 \n", "RGI60-19.02752 2010-01-01_2020-01-01 528000.0 -0.2335 0.5643 -123306.0 \n", "\n", " err_dvoldt dmdt err_dmdt dmdtda err_dmdtda \\\n", "rgiid \n", "RGI60-01.00001 92139.0 -0.000005 0.000078 -0.0128 0.2176 \n", "RGI60-01.00001 167248.0 -0.000017 0.000142 -0.0472 0.3949 \n", "RGI60-01.00001 182132.0 0.000008 0.000155 0.0217 0.4300 \n", "RGI60-01.00002 93741.0 -0.000128 0.000080 -0.2290 0.1460 \n", "RGI60-01.00002 181714.0 -0.000162 0.000155 -0.2898 0.2794 \n", "... ... ... ... ... ... \n", "RGI60-19.02751 21979.0 -0.000020 0.000019 -1.8369 2.2891 \n", "RGI60-19.02751 27483.0 -0.000020 0.000023 -1.8119 2.6080 \n", "RGI60-19.02752 336536.0 0.000064 0.000286 0.1213 0.5421 \n", "RGI60-19.02752 179916.0 -0.000020 0.000153 -0.0386 0.2897 \n", "RGI60-19.02752 298432.0 -0.000105 0.000254 -0.1985 0.4814 \n", "\n", " perc_area_meas perc_area_res valid_obs valid_obs_py reg \n", "rgiid \n", "RGI60-01.00001 1.000 1.000 26.41 11.11 1 \n", "RGI60-01.00001 1.000 1.000 18.30 6.32 1 \n", "RGI60-01.00001 1.000 1.000 8.11 4.78 1 \n", "RGI60-01.00002 1.000 1.000 26.21 16.23 1 \n", "RGI60-01.00002 1.000 1.000 15.18 7.95 1 \n", "... ... ... ... ... ... \n", "RGI60-19.02751 1.000 1.000 4.00 4.00 19 \n", "RGI60-19.02751 1.000 1.000 0.00 0.00 19 \n", "RGI60-19.02752 0.981 0.981 2.74 1.70 19 \n", "RGI60-19.02752 0.981 0.981 6.91 5.15 19 \n", "RGI60-19.02752 0.981 0.981 4.17 3.44 19 \n", "\n", "[654390 rows x 15 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 5, "id": "9f004612", "metadata": {}, "outputs": [], "source": [ "df.to_hdf('hugonnet_2021_ds_rgi60_pergla_rates_10_20_worldwide.hdf', key='df')" ] }, { "cell_type": "markdown", "id": "43fd3799", "metadata": {}, "source": [ "## Convert Caucasus glaciers to RGI" ] }, { "cell_type": "code", "execution_count": 6, "id": "cd112e8d", "metadata": {}, "outputs": [], "source": [ "df = pd.read_hdf('hugonnet_2021_ds_rgi60_pergla_rates_10_20_worldwide.hdf')" ] }, { "cell_type": "code", "execution_count": 7, "id": "02fe4139", "metadata": {}, "outputs": [], "source": [ "dfs = df.loc[df['period'] == '2000-01-01_2020-01-01']" ] }, { "cell_type": "code", "execution_count": 8, "id": "6776ecb7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "218130" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(dfs)" ] }, { "cell_type": "markdown", "id": "e13271fc", "metadata": {}, "source": [ "the Caucasus glaciers (RGI region 12) have a GLIMS index instead of the RGI index:\n" ] }, { "cell_type": "code", "execution_count": 9, "id": "f36335fc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(3516, 214614)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "not_rgi = np.array(['RGI60' not in s for s in dfs.index])\n", "len(dfs.loc[not_rgi]), len(dfs.loc[~not_rgi])" ] }, { "cell_type": "markdown", "id": "4d14c757", "metadata": {}, "source": [ "get dictionary that relates GLIMS to RGI for region 12 and then reindex them with the actual RGI id" ] }, { "cell_type": "code", "execution_count": 10, "id": "f4ae8a73", "metadata": {}, "outputs": [], "source": [ "url_glimsid = 'https://cluster.klima.uni-bremen.de/~oggm/geodetic_ref_mb/12_GLIMSId_RGIId_dict.csv'\n", "path_glimsid = utils.file_downloader(url_glimsid)\n", "lookup = pd.read_csv(path_glimsid, header=None, index_col=0)[1].to_dict()" ] }, { "cell_type": "code", "execution_count": 11, "id": "cc854477", "metadata": {}, "outputs": [], "source": [ "new_index = df.index.values.copy()\n", "for i, rid in enumerate(new_index):\n", " if rid in lookup:\n", " new_index[i] = lookup[rid]" ] }, { "cell_type": "code", "execution_count": 12, "id": "e6651661", "metadata": {}, "outputs": [], "source": [ "df_new = df.set_index(new_index)" ] }, { "cell_type": "markdown", "id": "62589aac", "metadata": {}, "source": [ "there are still glaciers with GLIMS id from region 12. We will remove them!" ] }, { "cell_type": "code", "execution_count": 13, "id": "1a751f0d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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periodareadhdterr_dhdtdvoldterr_dvoldtdmdterr_dmdtdmdtdaerr_dmdtdaperc_area_measperc_area_resvalid_obsvalid_obs_pyreg
G039564E39446N2010-01-01_2020-01-018000.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.012
G039564E39446N2000-01-01_2020-01-018000.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.012
G039564E39446N2000-01-01_2010-01-018000.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.012
G039579E39451N2000-01-01_2020-01-018000.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.012
G039579E39451N2000-01-01_2010-01-018000.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.012
................................................
G052226E35769N2010-01-01_2020-01-010.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.012
G052226E35769N2000-01-01_2020-01-010.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.012
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G052227E35770N2010-01-01_2020-01-010.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.012
G052227E35770N2000-01-01_2020-01-010.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.012
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6330 rows × 15 columns

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" ], "text/plain": [ " period area dhdt err_dhdt dvoldt \\\n", "G039564E39446N 2010-01-01_2020-01-01 8000.0 NaN NaN NaN \n", "G039564E39446N 2000-01-01_2020-01-01 8000.0 NaN NaN NaN \n", "G039564E39446N 2000-01-01_2010-01-01 8000.0 NaN NaN NaN \n", "G039579E39451N 2000-01-01_2020-01-01 8000.0 NaN NaN NaN \n", "G039579E39451N 2000-01-01_2010-01-01 8000.0 NaN NaN NaN \n", "... ... ... ... ... ... \n", "G052226E35769N 2010-01-01_2020-01-01 0.0 NaN NaN NaN \n", "G052226E35769N 2000-01-01_2020-01-01 0.0 NaN NaN NaN \n", "G052227E35770N 2000-01-01_2010-01-01 0.0 NaN NaN NaN \n", "G052227E35770N 2010-01-01_2020-01-01 0.0 NaN NaN NaN \n", "G052227E35770N 2000-01-01_2020-01-01 0.0 NaN NaN NaN \n", "\n", " err_dvoldt dmdt err_dmdt dmdtda err_dmdtda \\\n", "G039564E39446N NaN NaN NaN NaN NaN \n", "G039564E39446N NaN NaN NaN NaN NaN \n", "G039564E39446N NaN NaN NaN NaN NaN \n", "G039579E39451N NaN NaN NaN NaN NaN \n", "G039579E39451N NaN NaN NaN NaN NaN \n", "... ... ... ... ... ... \n", "G052226E35769N NaN NaN NaN NaN NaN \n", "G052226E35769N NaN NaN NaN NaN NaN \n", "G052227E35770N NaN NaN NaN NaN NaN \n", "G052227E35770N NaN NaN NaN NaN NaN \n", "G052227E35770N NaN NaN NaN NaN NaN \n", "\n", " perc_area_meas perc_area_res valid_obs valid_obs_py reg \n", "G039564E39446N NaN NaN 0.0 0.0 12 \n", "G039564E39446N NaN NaN 0.0 0.0 12 \n", "G039564E39446N NaN NaN 0.0 0.0 12 \n", "G039579E39451N NaN NaN 0.0 0.0 12 \n", "G039579E39451N NaN NaN 0.0 0.0 12 \n", "... ... ... ... ... ... \n", "G052226E35769N NaN NaN 0.0 0.0 12 \n", "G052226E35769N NaN NaN 0.0 0.0 12 \n", "G052227E35770N NaN NaN 0.0 0.0 12 \n", "G052227E35770N NaN NaN 0.0 0.0 12 \n", "G052227E35770N NaN NaN 0.0 0.0 12 \n", "\n", "[6330 rows x 15 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "not_rgi = np.array(['RGI60' not in s for s in df_new.index])\n", "df_new.loc[not_rgi]" ] }, { "cell_type": "markdown", "id": "a3249b3e", "metadata": {}, "source": [ "those glaciers only come from RGI region 12" ] }, { "cell_type": "code", "execution_count": 14, "id": "61072484", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([12])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_new.loc[not_rgi].reg.unique()" ] }, { "cell_type": "markdown", "id": "0043e983", "metadata": {}, "source": [ "Let's only use glaciers with RGI-index:" ] }, { "cell_type": "code", "execution_count": 15, "id": "47804fd4", "metadata": {}, "outputs": [], "source": [ "df_new = df_new.loc[~ not_rgi]" ] }, { "cell_type": "markdown", "id": "760f86c5", "metadata": {}, "source": [ "We create a template for the missing glaciers" ] }, { "cell_type": "code", "execution_count": 16, "id": "9ac38d9b", "metadata": {}, "outputs": [], "source": [ "template = df_new.copy(deep=True).iloc[0:3]" ] }, { "cell_type": "code", "execution_count": 17, "id": "a2e7dc73", "metadata": {}, "outputs": [], "source": [ "template[template.columns[1:]] = np.NaN\n", "template['reg'] = 12" ] }, { "cell_type": "code", "execution_count": 18, "id": "11721685", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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periodareadhdterr_dhdtdvoldterr_dvoldtdmdterr_dmdtdmdtdaerr_dmdtdaperc_area_measperc_area_resvalid_obsvalid_obs_pyreg
RGI60-01.000012000-01-01_2020-01-01NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN12
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RGI60-01.000012000-01-01_2010-01-01NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN12
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" ], "text/plain": [ " period area dhdt err_dhdt dvoldt \\\n", "RGI60-01.00001 2000-01-01_2020-01-01 NaN NaN NaN NaN \n", "RGI60-01.00001 2010-01-01_2020-01-01 NaN NaN NaN NaN \n", "RGI60-01.00001 2000-01-01_2010-01-01 NaN NaN NaN NaN \n", "\n", " err_dvoldt dmdt err_dmdt dmdtda err_dmdtda \\\n", "RGI60-01.00001 NaN NaN NaN NaN NaN \n", "RGI60-01.00001 NaN NaN NaN NaN NaN \n", "RGI60-01.00001 NaN NaN NaN NaN NaN \n", "\n", " perc_area_meas perc_area_res valid_obs valid_obs_py reg \n", "RGI60-01.00001 NaN NaN NaN NaN 12 \n", "RGI60-01.00001 NaN NaN NaN NaN 12 \n", "RGI60-01.00001 NaN NaN NaN NaN 12 " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "template" ] }, { "cell_type": "code", "execution_count": 19, "id": "46eaccfb", "metadata": {}, "outputs": [], "source": [ "path_rgi = utils.file_downloader('https://cluster.klima.uni-bremen.de/~oggm/rgi/rgi62_stats.h5')\n", "rgi = pd.read_hdf(path_rgi)" ] }, { "cell_type": "code", "execution_count": 21, "id": "de12d1d1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "glaciers outside RGI region 12 inside geodetic dataset : 214614\n", "glaciers outside RGI region 12 in total from rgi : 214614\n", "glaciers in total in rgi : 216502\n" ] } ], "source": [ "n = len(df_new.loc[rgi['O1Region']!='12'][df_new.loc[rgi['O1Region']!='12'].period == '2000-01-01_2020-01-01'])\n", "print('glaciers outside RGI region 12 inside geodetic dataset : {}'.format(n))\n", "print('glaciers outside RGI region 12 in total from rgi : {}'.format(len(rgi.loc[rgi['O1Region']!='12'])))\n", "print('glaciers in total in rgi : {}'.format(len(rgi)))" ] }, { "cell_type": "markdown", "id": "256c1567", "metadata": {}, "source": [ "**OK so all glaciers are already in the data except the ones without lookup in region 12.**\n", "\n", "First, add missing glaciers and the area of them to the geodetic dataset. " ] }, { "cell_type": "code", "execution_count": 22, "id": "a0c879ee", "metadata": {}, "outputs": [], "source": [ "to_add = []\n", "for rid, s in rgi.loc[rgi['O1Region']=='12'].iterrows():\n", " if rid not in df_new.index:\n", " new_id = template.copy(deep=True)\n", " new_id.index = [rid]*3\n", " new_id['area'] = s['Area'] * 1e6\n", " to_add.append(new_id)\n", "to_add = pd.concat(to_add)" ] }, { "cell_type": "code", "execution_count": 23, "id": "4e1ded8e", "metadata": {}, "outputs": [], "source": [ "df_new = pd.concat([df_new, to_add]).sort_index()" ] }, { "cell_type": "code", "execution_count": 24, "id": "dbef2a0a", "metadata": {}, "outputs": [], "source": [ "assert len(df_new) / 3 == len(rgi)" ] }, { "cell_type": "code", "execution_count": 25, "id": "044b730c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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periodareadhdterr_dhdtdvoldterr_dvoldtdmdterr_dmdtdmdtdaerr_dmdtdaperc_area_measperc_area_resvalid_obsvalid_obs_pyreg
rgiid
RGI60-01.000012000-01-01_2010-01-01360000.00.02550.50599175.0182132.00.0000080.0001550.02170.43001.0001.0008.114.781
RGI60-01.000012000-01-01_2020-01-01360000.0-0.01500.2559-5414.092139.0-0.0000050.000078-0.01280.21761.0001.00026.4111.111
RGI60-01.000012010-01-01_2020-01-01360000.0-0.05560.4645-20003.0167248.0-0.0000170.000142-0.04720.39491.0001.00018.306.321
RGI60-01.000022000-01-01_2010-01-01558000.0-0.19800.3267-110465.0182728.0-0.0000940.000155-0.16830.27921.0001.00011.048.281
RGI60-01.000022000-01-01_2020-01-01558000.0-0.26950.1653-150361.093741.0-0.0001280.000080-0.22900.14601.0001.00026.2116.231
................................................
RGI60-19.027512000-01-01_2020-01-0111000.0-2.16110.8691-23772.021979.0-0.0000200.000019-1.83692.28911.0001.0004.004.0019
RGI60-19.027512010-01-01_2020-01-0111000.0-2.19041.5831-24095.026564.0-0.0000200.000023-1.86192.57551.0001.0004.004.0019
RGI60-19.027522000-01-01_2010-01-01528000.00.14270.637175344.0336536.00.0000640.0002860.12130.54210.9810.9812.741.7019
RGI60-19.027522000-01-01_2020-01-01528000.0-0.04540.3407-23981.0179916.0-0.0000200.000153-0.03860.28970.9810.9816.915.1519
RGI60-19.027522010-01-01_2020-01-01528000.0-0.23350.5643-123306.0298432.0-0.0001050.000254-0.19850.48140.9810.9814.173.4419
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649506 rows × 15 columns

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" ], "text/plain": [ " period area dhdt err_dhdt dvoldt \\\n", "rgiid \n", "RGI60-01.00001 2000-01-01_2010-01-01 360000.0 0.0255 0.5059 9175.0 \n", "RGI60-01.00001 2000-01-01_2020-01-01 360000.0 -0.0150 0.2559 -5414.0 \n", "RGI60-01.00001 2010-01-01_2020-01-01 360000.0 -0.0556 0.4645 -20003.0 \n", "RGI60-01.00002 2000-01-01_2010-01-01 558000.0 -0.1980 0.3267 -110465.0 \n", "RGI60-01.00002 2000-01-01_2020-01-01 558000.0 -0.2695 0.1653 -150361.0 \n", "... ... ... ... ... ... \n", "RGI60-19.02751 2000-01-01_2020-01-01 11000.0 -2.1611 0.8691 -23772.0 \n", "RGI60-19.02751 2010-01-01_2020-01-01 11000.0 -2.1904 1.5831 -24095.0 \n", "RGI60-19.02752 2000-01-01_2010-01-01 528000.0 0.1427 0.6371 75344.0 \n", "RGI60-19.02752 2000-01-01_2020-01-01 528000.0 -0.0454 0.3407 -23981.0 \n", "RGI60-19.02752 2010-01-01_2020-01-01 528000.0 -0.2335 0.5643 -123306.0 \n", "\n", " err_dvoldt dmdt err_dmdt dmdtda err_dmdtda \\\n", "rgiid \n", "RGI60-01.00001 182132.0 0.000008 0.000155 0.0217 0.4300 \n", "RGI60-01.00001 92139.0 -0.000005 0.000078 -0.0128 0.2176 \n", "RGI60-01.00001 167248.0 -0.000017 0.000142 -0.0472 0.3949 \n", "RGI60-01.00002 182728.0 -0.000094 0.000155 -0.1683 0.2792 \n", "RGI60-01.00002 93741.0 -0.000128 0.000080 -0.2290 0.1460 \n", "... ... ... ... ... ... \n", "RGI60-19.02751 21979.0 -0.000020 0.000019 -1.8369 2.2891 \n", "RGI60-19.02751 26564.0 -0.000020 0.000023 -1.8619 2.5755 \n", "RGI60-19.02752 336536.0 0.000064 0.000286 0.1213 0.5421 \n", "RGI60-19.02752 179916.0 -0.000020 0.000153 -0.0386 0.2897 \n", "RGI60-19.02752 298432.0 -0.000105 0.000254 -0.1985 0.4814 \n", "\n", " perc_area_meas perc_area_res valid_obs valid_obs_py reg \n", "rgiid \n", "RGI60-01.00001 1.000 1.000 8.11 4.78 1 \n", "RGI60-01.00001 1.000 1.000 26.41 11.11 1 \n", "RGI60-01.00001 1.000 1.000 18.30 6.32 1 \n", "RGI60-01.00002 1.000 1.000 11.04 8.28 1 \n", "RGI60-01.00002 1.000 1.000 26.21 16.23 1 \n", "... ... ... ... ... ... \n", "RGI60-19.02751 1.000 1.000 4.00 4.00 19 \n", "RGI60-19.02751 1.000 1.000 4.00 4.00 19 \n", "RGI60-19.02752 0.981 0.981 2.74 1.70 19 \n", "RGI60-19.02752 0.981 0.981 6.91 5.15 19 \n", "RGI60-19.02752 0.981 0.981 4.17 3.44 19 \n", "\n", "[649506 rows x 15 columns]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_new = df_new.rename_axis('rgiid').sort_values(by = ['rgiid', 'period'])\n", "df_new" ] }, { "cell_type": "code", "execution_count": 26, "id": "32d4ffd1", "metadata": {}, "outputs": [], "source": [ "df_new.to_hdf('hugonnet_2021_ds_rgi60_pergla_rates_10_20_worldwide_reg12cor.hdf', key='df')" ] }, { "cell_type": "markdown", "id": "efab9a60", "metadata": {}, "source": [ "## Remove glaciers with connectivity level 2\n", "\n", "Glaciers with connectivity level 2 are those glaciers that are strongly connected to the Greenland Ice Sheet. We don't want to do projections with them because they are already included in the GIS projections!" ] }, { "cell_type": "code", "execution_count": 27, "id": "30827edf", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "955" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# there are some glaciers on connectivity level 2\n", "len(rgi.loc[rgi['Connect'] == 2])" ] }, { "cell_type": "code", "execution_count": 28, "id": "7bd89766", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['05'], dtype=object)" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# check: they are only in RGI region 05 (Greenland)\n", "rgi.loc[rgi['Connect'] == 2]['O1Region'].unique()" ] }, { "cell_type": "code", "execution_count": 29, "id": "81e21879", "metadata": {}, "outputs": [], "source": [ "valid_ids = rgi.loc[rgi['Connect'] != 2].index\n", "c2_ids = rgi.loc[rgi['Connect'] == 2].index\n", "df_new = df_new.loc[valid_ids].copy(deep=True)\n", "rgi = rgi.loc[valid_ids].copy(deep=True)" ] }, { "cell_type": "code", "execution_count": 30, "id": "17507f49", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "215547" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(rgi)" ] }, { "cell_type": "markdown", "id": "f6a15d7f", "metadata": {}, "source": [ "## Remove outliers and fill missing " ] }, { "cell_type": "code", "execution_count": 31, "id": "88ea4ac0", "metadata": {}, "outputs": [], "source": [ "dfs = df_new.loc[df_new.period == '2000-01-01_2020-01-01'].copy(deep=True)[['area', 'dmdtda', 'err_dmdtda', 'reg']]" ] }, { "cell_type": "markdown", "id": "977e3aa3", "metadata": {}, "source": [ "statistics on the error of the specific mass balance from the geodetic data:" ] }, { "cell_type": "code", "execution_count": 32, "id": "7d92450c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.001 0.075294\n", "0.010 0.103500\n", "0.500 0.222300\n", "0.990 0.879962\n", "0.999 2.051393\n", "Name: err_dmdtda, dtype: float64" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfs.err_dmdtda.quantile([0.001, 0.01, 0.5, 0.99, 0.999])" ] }, { "cell_type": "code", "execution_count": 33, "id": "5f7937d5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "13.7913" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfs.err_dmdtda.max()" ] }, { "cell_type": "markdown", "id": "d12f0172", "metadata": {}, "source": [ "**we remove all glaciers where the error is larger than the mean error plus three standard deviations of the error distribution (i.e. all sigma threshold)** " ] }, { "cell_type": "code", "execution_count": 34, "id": "19759d52", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "all sigma threshold (global estimate): 0.83\n" ] } ], "source": [ "# SHOULD IT BE AREA WEIGHTED???? Maybe not\n", "all_sigma_mean = dfs['err_dmdtda'].mean()\n", "all_sigma_std = dfs['err_dmdtda'].std()\n", "all_sigma_threshold = all_sigma_mean + 3 * all_sigma_std\n", "print('all sigma threshold (global estimate):', np.round(all_sigma_threshold, 2))" ] }, { "cell_type": "code", "execution_count": 35, "id": "22844156", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 27108 182 0.83\n", "2 18855 190 0.83\n", "3 4556 14 0.83\n", "4 7415 8 0.83\n", "5 19306 27 0.83\n", "6 568 1 0.83\n", "7 1615 1 0.83\n", "8 3417 30 0.83\n", "9 1069 0 0.83\n", "10 5151 77 1.11\n", "11 3927 57 1.19\n", "12 1888 19 0.83\n", "13 54429 200 0.83\n", "14 27988 238 0.83\n", "15 13119 128 0.83\n", "16 2939 46 0.92\n", "17 15908 228 1.53\n", "18 3537 29 1.08\n", "19 2752 47 1.45\n" ] } ], "source": [ "periods = ['2000-01-01_2010-01-01', '2000-01-01_2020-01-01', '2010-01-01_2020-01-01']\n", "# We filter and fill all periods based on the 20 year threshold\n", "df_filled = df_new.copy()[['period', 'area', 'dmdtda', 'err_dmdtda', 'reg']]\n", "df_filled['is_cor'] = False\n", "for reg in range(1, 20):\n", " \n", " dfs_subset = dfs.loc[dfs['reg'] == reg]\n", " \n", " # Too high of sigma causes large issues for model\n", " # compute regional threshold for every region\n", " reg_sigma_mean = dfs_subset['err_dmdtda'].mean()\n", " reg_sigma_std = dfs_subset['err_dmdtda'].std()\n", " reg_sigma_threshold = reg_sigma_mean + 3 * reg_sigma_std\n", " \n", " # Don’t penalize regions that are well-measured, so use all threshold as minimum:\n", " # if the regional threshold is smaller than the global threshold,\n", " # we use the global threshold, otherwise we use the regional threshold\n", " if reg_sigma_threshold < all_sigma_threshold:\n", " reg_sigma_threshold = all_sigma_threshold\n", " \n", " to_replace = dfs_subset.loc[dfs_subset['err_dmdtda'] > reg_sigma_threshold]\n", " to_keep = dfs_subset.loc[dfs_subset['err_dmdtda'] <= reg_sigma_threshold]\n", " \n", " print(reg, len(dfs_subset), len(to_replace), np.round(reg_sigma_threshold, 2))\n", " \n", " df_filled.loc[to_replace.index, 'dmdtda'] = np.NaN\n", " df_filled.loc[to_replace.index, 'err_dmdtda'] = np.NaN\n", " \n", " # Replace nan values - SHOULD BE AREA WEIGHTED????\n", " for period in periods:\n", " \n", " # indices of glaciers without dmdtda data (because missing or outlier)\n", " loc_no = (df_filled['reg'] == reg) & (df_filled['dmdtda'].isnull()) & (df_filled['period'] == period)\n", " # indices of glaciers with dmdtda data\n", " loc_yes = (df_filled['reg'] == reg) & (~ df_filled['dmdtda'].isnull()) & (df_filled['period'] == period)\n", " \n", " # replace the nan-values from the missing and outlier glaciers with the regional mean and standard deviation mean \n", " # Note that every glacier without \"usable\" geodetic data will get the same dmdtda and err_dmdtda \n", " # (independent of the glacier characteristics such as size, elevation) !!!\n", " df_filled.loc[loc_no, 'dmdtda'] = df_filled.loc[loc_yes, 'dmdtda'].mean()\n", " df_filled.loc[loc_no, 'err_dmdtda'] = df_filled.loc[loc_yes, 'err_dmdtda'].mean()\n", " df_filled.loc[loc_no, 'is_cor'] = True" ] }, { "cell_type": "code", "execution_count": 36, "id": "a1e9cfdd", "metadata": {}, "outputs": [], "source": [ "dfs_filled = df_filled.loc[df_new.period == '2000-01-01_2020-01-01']" ] }, { "cell_type": "code", "execution_count": 37, "id": "f6cbd55b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.001 0.075754\n", "0.010 0.104000\n", "0.500 0.226100\n", "0.990 0.739012\n", "0.999 1.154537\n", "Name: err_dmdtda, dtype: float64" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfs_filled.err_dmdtda.quantile([0.001, 0.01, 0.5, 0.99, 0.999])" ] }, { "cell_type": "code", "execution_count": 38, "id": "43ba2497", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.5258" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfs_filled.err_dmdtda.max()" ] }, { "cell_type": "code", "execution_count": 39, "id": "6bacb3cd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "8.034" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfs_filled.dmdtda.max()" ] }, { "cell_type": "code", "execution_count": 40, "id": "fcee7665", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "dfs_filled.dmdtda.plot(kind='hist', bins=100, bottom=0.1);\n", "ax.set_yscale('log')\n", "plt.title(f'Distribution of the specific mass balance after correction for all glaciers (n={len(dfs_filled)})')\n", "plt.xlabel(r'specific mass balance (kg m$^{-2}$)');" ] }, { "cell_type": "code", "execution_count": 41, "id": "e4516368", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.131646168795644" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# relative amount of glacier area with a positive specific mass balance\n", "dfs_filled.loc[dfs_filled.dmdtda > 0].area.sum() / dfs_filled.area.sum()" ] }, { "cell_type": "code", "execution_count": 42, "id": "127a0919", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(ncols=3, figsize=(18,6))\n", "plt.suptitle(f'Distribution of the specific mass balance after correction for all glaciers (n={len(dfs_filled)})')\n", "for k,period in enumerate([periods[1], periods[0], periods[2]]):\n", " for _,period2 in enumerate([periods[1], periods[0], periods[2]]):\n", " dfs_filled = df_filled.loc[df_new.period == period2]\n", " dfs_filled.dmdtda.plot(kind='hist', bins=100, bottom=0.1, alpha=0.3, ax = ax[k], color = 'grey', label='other periods');\n", " dfs_filled = df_filled.loc[df_new.period == period]\n", " dfs_filled.dmdtda.plot(kind='hist', bins=100, bottom=0.1, alpha=1, ax = ax[k], label = 'actual period');\n", " ax[k].set_yscale('log')\n", " ax[k].set_title('period: {},\\n max error: {:0.2f}, max dmdtda: {:0.2f},\\n glacier area with dmdtda above zero : {:0.2f}%'.format(period,\n", " dfs_filled.err_dmdtda.max(),\n", " dfs_filled.dmdtda.max(),\n", " dfs_filled.loc[dfs_filled.dmdtda > 0].area.sum()*100 / dfs_filled.area.sum()))\n", " ax[k].legend()\n", " ax[k].set_xlabel(r'specific mass balance (kg m$^{-2}$)');\n", "plt.tight_layout()" ] }, { "cell_type": "code", "execution_count": 43, "id": "c7305ecb", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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periodareadmdtdaerr_dmdtdaregis_cor
rgiid
\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: [period, area, dmdtda, err_dmdtda, reg, is_cor]\n", "Index: []" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# check: are there any glaciers left with nan-values?\n", "df_filled.loc[df_filled['dmdtda'].isnull()]\n", "# No!" ] }, { "cell_type": "markdown", "id": "15b97412", "metadata": {}, "source": [ "save the corrected dataset to use it then to calibrate the mass-balance model" ] }, { "cell_type": "code", "execution_count": 44, "id": "4eb00b51", "metadata": {}, "outputs": [], "source": [ "df_filled.to_hdf('hugonnet_2021_ds_rgi60_pergla_rates_10_20_worldwide_filled.hdf', key='df')" ] }, { "cell_type": "markdown", "id": "42a1b44f", "metadata": {}, "source": [ "## Some more statistics" ] }, { "cell_type": "code", "execution_count": 45, "id": "42eb6c66", "metadata": {}, "outputs": [], "source": [ "# first repeat the filling by using the medians instead of means (just for a check)!\n", "df_filled_med = df_new.copy()[['period', 'area', 'dmdtda', 'err_dmdtda', 'reg']]\n", "df_filled_med['is_cor'] = False\n", "for reg in range(1, 20):\n", " \n", " dfs_subset = dfs.loc[dfs['reg'] == reg]\n", " \n", " # Too high of sigma causes large issues for model\n", " # compute regional threshold for every region\n", " reg_sigma_mean = dfs_subset['err_dmdtda'].mean()\n", " reg_sigma_std = dfs_subset['err_dmdtda'].std()\n", " reg_sigma_threshold = reg_sigma_mean + 3 * reg_sigma_std\n", " \n", " # Don’t penalize regions that are well-measured, so use all threshold as minimum:\n", " # if the regional threshold is smaller than the global threshold,\n", " # we use the global threshold, otherwise we use the regional threshold\n", " if reg_sigma_threshold < all_sigma_threshold:\n", " reg_sigma_threshold = all_sigma_threshold\n", " \n", " to_replace = dfs_subset.loc[dfs_subset['err_dmdtda'] > reg_sigma_threshold]\n", " to_keep = dfs_subset.loc[dfs_subset['err_dmdtda'] <= reg_sigma_threshold]\n", " \n", " #print(reg, len(dfs_subset), len(to_replace), np.round(reg_sigma_threshold, 2))\n", " \n", " df_filled_med.loc[to_replace.index, 'dmdtda'] = np.NaN\n", " df_filled_med.loc[to_replace.index, 'err_dmdtda'] = np.NaN\n", " \n", " # Replace nan values - SHOULD BE AREA WEIGHTED????\n", " for period in periods:\n", " \n", " # indices of glaciers without dmdtda data (because missing or outlier)\n", " loc_no = (df_filled_med['reg'] == reg) & (df_filled_med['dmdtda'].isnull()) & (df_filled_med['period'] == period)\n", " # indices of glaciers with dmdtda data\n", " loc_yes = (df_filled_med['reg'] == reg) & (~ df_filled_med['dmdtda'].isnull()) & (df_filled_med['period'] == period)\n", " \n", " # replace the nan-values from the missing and outlier glaciers with the regional median and standard deviation median \n", " # Note that every glacier without \"usable\" geodetic data will get the same dmdtda and err_dmdtda \n", " # (independent of the glacier characteristics such as size, elevation) !!!\n", " df_filled_med.loc[loc_no, 'dmdtda'] = df_filled_med.loc[loc_yes, 'dmdtda'].median() \n", " df_filled_med.loc[loc_no, 'err_dmdtda'] = df_filled_med.loc[loc_yes, 'err_dmdtda'].median()\n", " df_filled_med.loc[loc_no, 'is_cor'] = True" ] }, { "cell_type": "markdown", "id": "909b0774-2821-4df1-a91d-bbee0d504291", "metadata": {}, "source": [ "" ] }, { "cell_type": "code", "execution_count": 46, "id": "d3775010", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8597 out of 215547 glaciers were filled up with regional mean data, hence 3.99%.\n", "0.25% of the glacier area was refilled with regional mean data\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df_filled_per = df_filled[df_filled['period']=='2000-01-01_2020-01-01']\n", "n_cor = len(df_filled_per.loc[df_filled_per['is_cor']])\n", "n_total = len(df_filled_per)\n", "area_cor = df_filled_per.loc[df_filled_per['is_cor']].area\n", "area_cor_sum = area_cor.sum()\n", "area_cor_med = area_cor.median()\n", "print(f'{n_cor} out of {n_total} glaciers were filled up with regional mean data, hence {np.round(n_cor*100/n_total,2)}%.')\n", "print(f'{np.round(area_cor_sum*100/df_filled_per.area.sum(),2)}% of the glacier area was refilled with regional mean data')\n", "\n", "# same but using instead regional medians for corrections\n", "df_filled_per_med = df_filled_med[df_filled_med['period']=='2000-01-01_2020-01-01']\n", "\n", "plt.figure(figsize=(20,10))\n", "plt.subplot(121)\n", "plt.plot(df_filled_per.loc[~df_filled_per['is_cor']].area,df_filled_per.loc[~df_filled_per['is_cor']].dmdtda, '.', label='no correction necessary')\n", "plt.plot(df_filled_per.loc[df_filled_per['is_cor']].area,df_filled_per.loc[df_filled_per['is_cor']].dmdtda, '.', \n", " label='corrected by regional means')\n", "plt.plot(df_filled_per_med.loc[df_filled_per_med['is_cor']].area,\n", " df_filled_per_med.loc[df_filled_per['is_cor']].dmdtda, 'x',alpha=0.4, ms=4,\n", " label='corrected by regional medians')\n", "plt.legend()\n", "plt.xlabel('area (m2)')\n", "plt.ylabel('dmdtda')\n", "\n", "plt.subplot(122)\n", "plt.plot(df_filled_per.loc[~df_filled_per['is_cor']].area,df_filled_per.loc[~df_filled_per['is_cor']].err_dmdtda, '.', label='no correction necessary')\n", "plt.plot(df_filled_per.loc[df_filled_per['is_cor']].area,df_filled_per.loc[df_filled_per['is_cor']].err_dmdtda, '.', \n", " label='corrected by regional means')\n", "plt.plot(df_filled_per_med.loc[df_filled_per_med['is_cor']].area,df_filled_per_med.loc[df_filled_per_med['is_cor']].err_dmdtda,\n", " 'x', alpha=0.4, ms=4,label='corrected by regional medians')\n", "plt.legend()\n", "plt.xlabel('area (m2)')\n", "plt.ylabel('err_dmdtda');\n" ] }, { "cell_type": "markdown", "id": "13bd72f5", "metadata": {}, "source": [ "- Glaciers that needed to be filled up with regional means are small \n", "- There is no direct relationship between area and dmdtda. Glaciers that are small but do not need any corrections can have both very negative or very positive dmdta. In addition the standard deviation increases with decreasing area of a glacier. However, the variability of the standard deviation is large for small glaciers\n", "- Shouldn't we increase the standard deviation of the corrected small glaciers ?! \n", " - maybe yes, but just using the medians instead even decreases the standard deviation!\n", " - this is because there are much more small glaciers than larger ones. So, as we do not weight over the area while averaging, it is mostly the small glaciers that decide over the regional mean dmdtda and err_dmdtda, which is what we want! So maybe this simple method is just ok. \n" ] }, { "cell_type": "markdown", "id": "a3fcffd6-8abf-4fbb-8b35-a8cadfcf383e", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "id": "a4e9f65a", "metadata": { "tags": [] }, "source": [ "## How can we best fill up dhdt, err_dhdt and dvoldt, err_dvoldt ?!\n", "\n", "- We have not yet found the best solution for that\n", "- in the following there are just some plots and a possible way to interpolate dvoldt and err_dvoldt using an area-dependency. However, this is not yet incorporated anywhere and needs further tuning!" ] }, { "cell_type": "markdown", "id": "f2c80d12-4fe0-40c8-bb22-a1ee5bd2de9d", "metadata": {}, "source": [ "**Some further thoughts about that:**\n", "- for dhdt and its error, probably the same method as used with dmdtda is ok.\n", "- For dvoldt and dvoldt_err, we really should somehow take into account that the missing glaciers are small .\n", " - E.g. take those glaciers with data from each region that are within 95% range of glacier area distribution from the missing glaciers, and then use that mean / median ?\n", " - but if we do this, we could also apply it for all measures to simplify it ...\n", " - Probably this is still bad. So, we rather need to take those glaciers within the area range of the missing glaciers, and use the relation with the area to fit the missing dvoldt & err_dvoldt values (because dVdt depends strongly on the area).\n", " - globally this works more or less, but it would be better to repeat this regionally \n", "- but of course there are other methods. E.g. Loris Compagno (2021) uses estimates from the nearest glacier with similar area to fill up the data. If this is better is not clear. \n", "- For global scale it is probably not important. But when doing smaller-scale studies, it can get important to think about which filling method to apply! ... at the end for all filling stuff, one could do some kind of \"small\"-cross validation to check how well or bad the method is?!" ] }, { "cell_type": "markdown", "id": "d2324ab4-c531-417e-aeb8-4d6d6dd41c94", "metadata": {}, "source": [ "----" ] }, { "cell_type": "code", "execution_count": 47, "id": "dcd9c277", "metadata": {}, "outputs": [], "source": [ "import seaborn as sns\n", "import sklearn\n", "from sklearn.linear_model import LinearRegression" ] }, { "cell_type": "code", "execution_count": 48, "id": "d930e163", "metadata": {}, "outputs": [], "source": [ "# Let's only look at the 20-year period\n", "df_new_per = df_new[df_new['period']=='2000-01-01_2020-01-01']" ] }, { "cell_type": "markdown", "id": "3e0b37e4-1392-4d54-9e09-c9343e1c8122", "metadata": {}, "source": [ "### Let's look at dhdt and err_dhdt first\n", "- it looks quite similar as dmdtda,\n", "- so probably we can apply the same approach as done with dmdtda" ] }, { "cell_type": "code", "execution_count": 49, "id": "d2603950-d73c-4021-a5a2-36199a2818b9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'dhdt')" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(15,3))\n", "plt.plot(df_new_per.area, df_new_per.dhdt, '.')\n", "plt.axvline(np.quantile(df_filled[~df_filled.is_cor].area,0.025), ls='--')\n", "plt.axvline(df_filled[df_filled.is_cor].area.median())\n", "plt.axvline(np.quantile(df_filled[~df_filled.is_cor].area,0.975), ls='--')\n", "plt.xlabel('area (m2)')\n", "plt.ylabel('dhdt')" ] }, { "cell_type": "markdown", "id": "930b0e69-c478-46b3-a0fc-aa93990d90df", "metadata": {}, "source": [ "**Let's look at only the glaciers area range with glaciers that need to be corrected:**" ] }, { "cell_type": "code", "execution_count": 50, "id": "eb8faf8b-c465-4115-8b7f-19e8d2a81ec6", "metadata": {}, "outputs": [], "source": [ "# Let's only look at those glaciers that have are in the same area range as the glaciers that need corrections:\n", "df_new_per_small = df_new_per[(df_new_per.area >= df_filled_per[df_filled_per.is_cor].area.min()) & (df_new_per.area <= df_filled_per[df_filled_per.is_cor].area.max())]\n", "df_new_per_small = df_new_per_small.dropna()" ] }, { "cell_type": "code", "execution_count": 51, "id": "1cbbd8dc-27bc-4c25-bf5c-9c3a68bb2c0f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'dhdt')" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(15,10))\n", "plt.plot(df_new_per_small.area, df_new_per_small.dhdt, '.')\n", "plt.axvline(np.quantile(df_filled[~df_filled.is_cor].area,0.025), ls='--')\n", "plt.axvline(df_filled[df_filled.is_cor].area.median())\n", "plt.axvline(np.quantile(df_filled[~df_filled.is_cor].area,0.975), ls='--')\n", "plt.xlabel('area (m2)')\n", "plt.ylabel('dhdt')" ] }, { "cell_type": "code", "execution_count": 52, "id": "ad181623-0c33-463d-9831-61fc8bde1709", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'dhdt')" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(15,10))\n", "plt.plot(df_new_per_small.area, df_new_per_small.err_dhdt, '.')\n", "plt.axvline(np.quantile(df_filled[~df_filled.is_cor].area,0.025), ls='--')\n", "plt.axvline(df_filled[df_filled.is_cor].area.median())\n", "plt.axvline(np.quantile(df_filled[~df_filled.is_cor].area,0.975), ls='--')\n", "plt.xlabel('area (m2)')\n", "plt.ylabel('dhdt')" ] }, { "cell_type": "markdown", "id": "f9b58a0e-ae53-487c-9e3d-16b2e69f3c5c", "metadata": {}, "source": [ "### What about dvoldt and err_dvoldt?\n" ] }, { "cell_type": "code", "execution_count": 53, "id": "d23dd577-ccd8-4b68-90c7-59f36a6d3752", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'dvoldt (m3)')" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(15,3))\n", "plt.plot(df_new_per.area, df_new_per.dvoldt, '.')\n", "plt.axvline(np.quantile(df_filled[~df_filled.is_cor].area, 0.025), ls='--')\n", "plt.axvline(df_filled[df_filled.is_cor].area.median())\n", "plt.axvline(np.quantile(df_filled[~df_filled.is_cor].area, 0.975), ls='--')\n", "plt.xlabel('area (m2)')\n", "plt.ylabel('dvoldt (m3)')\n" ] }, { "cell_type": "markdown", "id": "2d15ee37-2604-4a5a-93ed-be67f33384b7", "metadata": {}, "source": [ "- For large glaciers, there is no relationship between dvoldt and area (> 97.5\\%). However, those glaciers that need to be filled up with values are rather smaller\n", "\n", "**Let's look at only the glaciers area range with glaciers that need to be corrected:**" ] }, { "cell_type": "code", "execution_count": 54, "id": "222e3047", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(15,10))\n", "plt.subplot(121)\n", "reg = LinearRegression().fit(np.atleast_2d(df_new_per_small.area.values).T, df_new_per_small.dvoldt)\n", "missing_area_glaciers = np.atleast_2d(df_filled_per[df_filled_per.is_cor].area.values).T\n", "dvoldt_pred_cor = reg.predict(missing_area_glaciers)\n", "dvoldt_pred_test = reg.predict(np.atleast_2d(df_new_per.loc[df_new_per_small.index].area.values).T)\n", "plt.plot(df_new_per_small.area, df_new_per_small.dvoldt, '.', label='world-wide RGI glaciers')\n", "plt.plot(missing_area_glaciers, dvoldt_pred_cor, 'o', label='filled up missing glaciers')\n", "plt.axvline(np.quantile(df_filled_per[df_filled_per.is_cor].area,0.0025), ls='--', label='00.25%-area of missing glaciers')\n", "plt.axvline(df_filled_per[df_filled_per.is_cor].area.median(), label='median of missing glaciers')\n", "plt.axvline(np.quantile(df_filled_per[df_filled_per.is_cor].area,0.9975), ls='--', label='99.75%-area of missing glaciers')\n", "plt.xlabel('area (m2)')\n", "plt.ylabel('dvoldt')\n", "plt.plot(df_new_per.loc[df_new_per_small.index].area.values, dvoldt_pred_test, '.', color = 'grey', ms=3, alpha = 0.2, label='fitted')\n", "plt.title('R2-value: {:0.2f}'.format(sklearn.metrics.r2_score(df_new_per_small.dvoldt,\n", " dvoldt_pred_test)))\n", "plt.legend()\n", "\n", "plt.subplot(122)\n", "reg2 = LinearRegression().fit(np.atleast_2d(df_new_per_small.area.values).T, df_new_per_small.err_dvoldt)\n", "missing_area_glaciers = np.atleast_2d(df_filled_per[df_filled_per.is_cor].area.values).T\n", "err_dvoldt_pred_cor = reg2.predict(missing_area_glaciers)\n", "err_dvoldt_pred_test = reg2.predict(np.atleast_2d(df_new_per.loc[df_new_per_small.index].area.values).T)\n", "\n", "\n", "plt.plot(df_new_per_small.area, df_new_per_small.err_dvoldt, '.', label='world-wide RGI glaciers')\n", "plt.plot(missing_area_glaciers, err_dvoldt_pred_cor, 'o', label='filled up missing glaciers')\n", "plt.axvline(np.quantile(df_filled_per[df_filled_per.is_cor].area,0.0025), ls='--', label='00.25%-area of missing glaciers')\n", "plt.axvline(df_filled_per[df_filled_per.is_cor].area.median(), label='median of missing glaciers')\n", "plt.axvline(np.quantile(df_filled_per[df_filled_per.is_cor].area,0.9975), ls='--', label='99.75%-area of missing glaciers')\n", "plt.xlabel('area (m2)')\n", "plt.ylabel('err_dvoldt')\n", "plt.plot(df_new_per.loc[df_new_per_small.index].area.values, err_dvoldt_pred_test, '.', color = 'grey', ms=3, alpha = 0.2, label='fitted')\n", "plt.title('R2-value: {:0.2f}'.format(sklearn.metrics.r2_score(df_new_per_small.err_dvoldt,\n", " err_dvoldt_pred_test)))\n", "plt.legend()\n", "#plt.savefig('dvoldt_missing_fit_global.png')" ] }, { "cell_type": "markdown", "id": "a3b6200a-1593-4e58-8314-6da99de3e060", "metadata": {}, "source": [ "- we could use a linear relationship to fill up **dvoldt** and **err_dvoldt** (instead of the regional mean/median)\n", " - however, for a small area, there are quite some outliers with larger uncertainties that would be neglected in this case\n", " - maybe we should repeat this for each RGI region?" ] }, { "cell_type": "markdown", "id": "4556323f-0c95-445c-9a34-b59f786b7e33", "metadata": {}, "source": [ "**Problem: what do we do with rather large glaciers that need to be filled up?**\n", "- These are the largest glaciers that need to be corrected!\n", "- Maybe it would be good to treat them separately and to think about another way how to correct them best? " ] }, { "cell_type": "code", "execution_count": 55, "id": "085e1ba4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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periodareadmdtdaerr_dmdtdaregis_cor
rgiid
RGI60-10.000022000-01-01_2020-01-0148144000.0-0.4069830.35320810True
RGI60-19.017532000-01-01_2020-01-0160795000.0-0.1363110.29526519True
\n", "
" ], "text/plain": [ " period area dmdtda err_dmdtda reg \\\n", "rgiid \n", "RGI60-10.00002 2000-01-01_2020-01-01 48144000.0 -0.406983 0.353208 10 \n", "RGI60-19.01753 2000-01-01_2020-01-01 60795000.0 -0.136311 0.295265 19 \n", "\n", " is_cor \n", "rgiid \n", "RGI60-10.00002 True \n", "RGI60-19.01753 True " ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_filled_per[df_filled_per.is_cor][df_filled_per[df_filled_per.is_cor].area > 3e7]" ] }, { "cell_type": "code", "execution_count": null, "id": "c0afe3a1", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 5 }