diff --git a/oggm/sandbox/Redistribute_ice_after_simulation.ipynb b/oggm/sandbox/Redistribute_ice_after_simulation.ipynb new file mode 100644 index 000000000..4571fd043 --- /dev/null +++ b/oggm/sandbox/Redistribute_ice_after_simulation.ipynb @@ -0,0 +1,41104 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "eb728ce4", + "metadata": {}, + "outputs": [], + "source": [ + "import oggm.cfg as cfg\n", + "from oggm import tasks, graphics, utils, workflow\n", + "from oggm.core import flowline\n", + "import numpy as np\n", + "import geopandas as gpd\n", + "import pandas as pd\n", + "import xarray as xr\n", + "import scipy\n", + "import os\n", + "import matplotlib.pyplot as plt\n", + "import salem\n", + "from scipy.stats.mstats import rankdata\n", + "# This is needed to display graphics calculated outside of jupyter notebook\n", + "from IPython.display import HTML, display\n", + "from oggm.core.gis import gaussian_blur\n", + "\n", + "import shapely.geometry as shpg\n", + "from scipy.ndimage import binary_erosion,distance_transform_edt\n", + "from oggm.utils import ncDataset" + ] + }, + { + "cell_type": "markdown", + "id": "729da606", + "metadata": {}, + "source": [ + "This notebook is a proposal on how to redistribute the glacier ice that has been simulated allong the flowline after a simulation where the glacier has been retreating. Extrapolating the glacier ice onto a map comes with some assumptions and trade-offs. Depending on the purpose one might like to make different choices. e.g. for visualisation, one might like to use more smoothing, than when using the output to put in for instance a hydrological model. It would offcourse be possible to add different option to the final function, so one can select the option that is best for their needs, however that hasn't been included yet. \n", + "\n", + "This notebook uses one glacier to show how this redistribution can be done. It has as purpose to discuss it further, before it enters the OGGM code base. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "504b03c3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-10-04 16:30:54: oggm.cfg: Reading default parameters from the OGGM `params.cfg` configuration file.\n", + "2022-10-04 16:30:54: oggm.cfg: Multiprocessing switched OFF according to the parameter file.\n", + "2022-10-04 16:30:54: oggm.cfg: Multiprocessing: using all available processors (N=16)\n", + "2022-10-04 16:30:54: oggm.cfg: WARNING: adding an unknown parameter `prcp_scaling_factor`:`2.5` to PARAMS.\n", + "2022-10-04 16:30:54: oggm.cfg: PARAMS['climate_qc_months'] changed from `0` to `3`.\n" + ] + } + ], + "source": [ + "# Initialize OGGM and set up the default run parameters\n", + "cfg.initialize(logging_level='WARNING')\n", + "rgi_region = '11' # Region Central Europe\n", + "\n", + "# Local working directory (where OGGM will write its output)\n", + "WORKING_DIR = utils.gettempdir('OGGM_distr4')\n", + "cfg.PATHS['working_dir'] = WORKING_DIR\n", + "cfg.PARAMS['prcp_scaling_factor'] = 2.5\n", + "cfg.PARAMS['climate_qc_months']=3" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e50ec256", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/tmp/OGGM/OGGM_distr4\n" + ] + } + ], + "source": [ + "print(WORKING_DIR)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "4c86bebf", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-10-04 16:30:55: oggm.workflow: init_glacier_directories from prepro level 3 on 1 glaciers.\n", + "2022-10-04 16:30:55: oggm.workflow: Execute entity tasks [gdir_from_prepro] on 1 glaciers\n" + ] + } + ], + "source": [ + "# RGI file\n", + "path = utils.get_rgi_region_file(rgi_region)\n", + "rgidf = gpd.read_file(path)\n", + "\n", + "# Select a glacier\n", + "rgidf = rgidf.loc[rgidf[rgidf.columns[0]] == 'RGI60-11.01450']\n", + "# rgidf = rgidf.loc[rgidf[rgidf.columns[0]] == 'RGI60-11.00897']\n", + "\n", + "base_url = 'https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.4/L3-L5_files/CRU/elev_bands/qc3/pcp2.5/no_match/'\n", + "gdirs = workflow.init_glacier_directories(rgidf, from_prepro_level=3, prepro_border=40, prepro_base_url=base_url)\n", + "gdir = gdirs[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "fd3a1a79", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-10-04 16:30:55: oggm.workflow: Execute entity tasks [distribute_thickness_per_altitude] on 1 glaciers\n" + ] + } + ], + "source": [ + "# Distribute\n", + "workflow.execute_entity_task(tasks.distribute_thickness_per_altitude, gdir);" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "867c8797", + "metadata": {}, + "outputs": [], + "source": [ + "ds = xr.open_dataset(gdirs[0].get_filepath('gridded_data'))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "06a0559e", + "metadata": {}, + "outputs": [], + "source": [ + "tasks.run_constant_climate(gdir, nyears=100, y0=2000, temperature_bias=0.25, store_fl_diagnostics=True)\n", + "f = gdir.get_filepath('fl_diagnostics')\n", + "with xr.open_dataset(f) as di:\n", + " fl_ids = di.flowlines.data" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "40a2ea9a", + "metadata": {}, + "outputs": [], + "source": [ + "# We pick the last flowline (the main one)\n", + "with xr.open_dataset(f, group=f'fl_{fl_ids[-1]}') as dg:\n", + " dg = dg.load() " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "27125639", + "metadata": {}, + "outputs": [], + "source": [ + "# select year 0 from simulation\n", + "dg2 = dg[dict(time=0)]\n", + "# ice covered points along the flowline\n", + "tongue = np.sum([dg2.thickness_m!=0])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "bd014917", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the glacier topo\n", + "ds.where(ds.glacier_mask==1).topo.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "00993b9b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " ...,\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan]])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create an array with only the topo on the glacier and substract X meter from the elevation of glaciers on the outline. \n", + "# This value is arbritary, but seems to result in a more realistic retreat of the glacier tongue than \n", + "# that without this correction. A physical explanation for this might be that there is an influence of steep valley walls \n", + "# along the tongue on the glacier DEM, causing the sides of the glacier to be higher than the center. \n", + "border_correction = 40 \n", + "topo_correction = xr.where(ds.glacier_mask==1, ds.topo, np.nan) - (xr.where(ds.glacier_mask==1, ds.glacier_ext, np.nan) * border_correction)\n", + "topo_correction.values" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "396f1273", + "metadata": {}, + "outputs": [], + "source": [ + "# This smooting function is used to only smooth the topo of the glacier, and excludes the surrounding in the process.\n", + "# (It is therefore different that the smoothing that occurs in the 'process_dem' function, that generates 'topo_smoothed'\n", + "# in the gridded_data.nc file.) This is of importance because the sides on a glacier tongue can other wise be higher that \n", + "# the middle, resulting in an odd shape once the glacier retreats (e.g. the middle of the tongue retreating faster \n", + "# that the edges). \n", + "\n", + "def filter_nan_gaussian_conserving(arr, sigma):\n", + " \"\"\"Apply a gaussian filter to an array with nans.\n", + "\n", + " Intensity is only shifted between not-nan pixels and is hence conserved.\n", + " The intensity redistribution with respect to each single point\n", + " is done by the weights of available pixels according\n", + " to a gaussian distribution.\n", + " All nans in arr, stay nans in gauss.\n", + " \"\"\"\n", + " nan_msk = np.isnan(arr)\n", + "\n", + " loss = np.zeros(arr.shape)\n", + " loss[nan_msk] = 1\n", + " loss = scipy.ndimage.gaussian_filter(\n", + " loss, sigma=sigma, mode='constant', cval=1)\n", + "\n", + " gauss = arr.copy()\n", + " gauss[nan_msk] = 0\n", + " gauss = scipy.ndimage.gaussian_filter(\n", + " gauss, sigma=sigma, mode='constant', cval=0)\n", + " gauss[nan_msk] = np.nan\n", + "\n", + " gauss += loss * arr\n", + "\n", + " return gauss" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "0cbc09b6", + "metadata": {}, + "outputs": [], + "source": [ + "check2 = filter_nan_gaussian_conserving(topo_correction.values, 1)\n", + "\n", + "ds[\"checkje\"] = xr.full_like(ds.topo, fill_value=np.nan)\n", + "\n", + "for ki in np.arange(len(ds.x)): \n", + " for kj in np.arange(len(ds.y)): \n", + " ds[\"checkje\"].loc[dict(y=ds.y[kj], x=ds.x[ki])] = check2[kj,ki]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "3759967f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 4., 20., 99., 252., 294., 319., 387., 293., 214., 175., 192.,\n", + " 219., 177., 111., 85., 71., 59., 52., 55., 53., 57., 60.,\n", + " 60., 57., 54., 43., 34., 26., 19., 18., 19., 20., 21.,\n", + " 21., 21., 22., 24., 26., 25., 26., 28., 28., 28., 27.,\n", + " 26., 28., 27., 23., 27., 27., 22., 23., 28., 28., 26.,\n", + " 26., 24., 20., 13., 14., 13., 12., 14., 14., 12., 18.,\n", + " 9., 7., 4., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0.])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#number of pixels in section along flowline\n", + "npix1 = dg2.area_m2/(gdir.grid.dx**2)\n", + "npix_rcumsum = np.around(npix1.values[::-1].cumsum()[::-1])\n", + "npix = npix_rcumsum[0:-2] - npix_rcumsum[1:-1]\n", + "npix" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "e8ddabcb", + "metadata": {}, + "outputs": [], + "source": [ + "ds['topo_smoothed'] = ds.checkje" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "bdd4cf57", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#optie 1\n", + "rank_hl1 = rankdata(ds.where(ds.glacier_mask==1).topo_smoothed)\n", + "# optie 2\n", + "# rank_hl1 = rankdata(ds.where(ds.glacier_mask==1).topo_smoothed + ds.distributed_thickness)\n", + "\n", + "# optie 3\n", + "# rank_hl1 = rankdata(ds.where(ds.glacier_mask==1).topo_smoothed + (ds.distributed_thickness/2))\n", + "\n", + "# optie 4\n", + "# rank_hl1 = rankdata(ds.where(ds.glacier_mask==1).topo_smoothed + (ds.distributed_thickness/2)+ (ds.dis_from_border/gdir.grid.dx))\n", + "\n", + "# optie 4\n", + "# rank_hl1 = rankdata(ds.where(ds.glacier_mask==1).topo_smoothed + ((ds.dis_from_border/4)**0.6)) # (ds.distributed_thickness/20)+ \n", + "\n", + "# optie 5\n", + "# rank_hl1 = rankdata(ds.where(ds.glacier_mask==1).topo + ((ds.dis_from_border**0.25)*2))\n", + "# rank_hl1 = rankdata(ds.where(ds.glacier_mask==1).topo)\n", + "\n", + "ds['rank_hl1'] = (('y', 'x'), rank_hl1)\n", + "# ds['rank_hl1'] = ds['rank_hl1'][ds['rank_hl1'].values>1562]\n", + "ds['rank_hl1'] = ds.where(ds.glacier_mask==1).rank_hl1\n", + "ds.where(ds.glacier_mask==1).rank_hl1.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "b61078a3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "137.0" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdir.grid.dx" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "a59e57b4", + "metadata": {}, + "outputs": [], + "source": [ + "ds[\"fl_att\"] = xr.full_like(ds.rank_hl1, fill_value=np.nan)\n", + "\n", + "for ki in ds.x: \n", + " for kj in ds.y:\n", + " check = ds.sel(y=kj, x=ki).rank_hl1.values\n", + " if not np.isnan(check):\n", + " kn = np.sum(npix_rcumsum >= check) \n", + " ds[\"fl_att\"].loc[dict(y=kj, x=ki)] = kn" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "9582d84b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ds[\"fl_att_sub\"] = xr.full_like(ds.rank_hl1, fill_value=np.nan)\n", + "ds[\"rank_hl2\"] = xr.full_like(ds.rank_hl1, fill_value=0)\n", + "\n", + "\n", + "# for ki in np.arange(18):\n", + "for ki in np.arange(len(npix)):\n", + " ds[\"rank_hl2_\" + str(ki)] = xr.full_like(ds.rank_hl1, fill_value=0)\n", + " rank_hl2 = rankdata(ds.distributed_thickness.where(ds.fl_att==ki))\n", + " ds[\"rank_hl2_\" + str(ki)] = (('y', 'x'), rank_hl2)\n", + " # ds.where(ds.fl_att==1).rank_hl2.plot()\n", + " ds[\"rank_hl2_\" + str(ki)] = ds[\"rank_hl2_\" + str(ki)].where(ds.fl_att==ki, 0)\n", + "\n", + "rank_hl3 = ds.rank_hl2_0.values\n", + "\n", + "for ki in np.arange(1,np.max(ds.fl_att)):\n", + " rank_hl3 = rank_hl3 + (ds['rank_hl2_' + str(int(ki))]).values\n", + " \n", + "ds[\"rank_hl3\"] = xr.full_like(ds.rank_hl1, fill_value=np.nan)\n", + "ds[\"rank_hl3\"] = (('y', 'x'), rank_hl3)\n", + "ds.rank_hl3.plot()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "de9afc8d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ds['fl_att_sub']=ds.where(ds.glacier_mask==1).rank_hl3\n", + "ds.fl_att_sub.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "ea2cffaa", + "metadata": {}, + "outputs": [], + "source": [ + "def distribute_thick(gdir, yr=0, mask=None, topo=None):\n", + "\n", + " # start of implementing distributing glacier thickness\n", + " # Variables\n", + "\n", + " dis_from_border_exp=0.25 # pas op aangepast!\n", + " smooth_radius=None\n", + " grids_file = gdir.get_filepath('gridded_data')\n", + " # See if we have the masks, else compute them\n", + " with utils.ncDataset(grids_file) as nc:\n", + " has_masks = 'glacier_ext_erosion' in nc.variables\n", + "\n", + " with utils.ncDataset(grids_file) as nc:\n", + " topo_smoothed = nc.variables['topo_smoothed'][:]\n", + " slope_factor = nc.variables['slope_factor'][:]\n", + "# topo_smoothed = nc.variables['topo_smoothed'][:]\n", + " topo_smoothed = nc.variables['topo'][:]\n", + " if yr==0:\n", + " glacier_mask = nc.variables['glacier_mask'][:]\n", + " dis_from_border = nc.variables['dis_from_border'][:]\n", + " else:\n", + " glacier_mask = mask\n", + "# topo_smoothed = topo\n", + " # Glacier exterior including nunataks\n", + " gdfi = gpd.GeoDataFrame(columns=['geometry'])\n", + " erode = binary_erosion(glacier_mask)\n", + " glacier_ext = glacier_mask ^ erode\n", + " glacier_ext = np.where(glacier_mask == 1, glacier_ext, 0)\n", + "\n", + " dist = np.array([])\n", + " jj, ii = np.where(glacier_ext)\n", + " for j, i in zip(jj, ii):\n", + " dist = np.append(dist, np.min(gdfi.distance(shpg.Point(i, j))))\n", + "\n", + " pok = np.where(dist <= 1)\n", + " glacier_ext_intersect = glacier_ext * 0\n", + " glacier_ext_intersect[jj[pok], ii[pok]] = 1\n", + "\n", + " # Distance from border mask - Scipy does the job\n", + " dx = gdir.grid.dx\n", + " dis_from_border = 1 + glacier_ext_intersect - glacier_ext\n", + " dis_from_border = distance_transform_edt(dis_from_border) * dx\n", + "# topo_smoothed = ds.topo_smoothed[:].values - np.nan_to_num(ds[\"new_thick_\" + str(int(yr-1))][:].values - ds['distributed_thickness'][:].values)\n", + " topo_smoothed = ds.topo[:].values - np.nan_to_num(ds[\"new_thick_\" + str(int(yr-1))][:].values - ds['distributed_thickness'][:].values)\n", + "\n", + "# topo_smoothed = topo_smoothed - ds['distributed_thickness'][:].values + ds[\"new_thick_\" + str(int(yr-1))][:].values\n", + "\n", + " # Along the lines\n", + " hs, ts, vs, xs, ys = [], [], [], [], []\n", + " hs = dg.thickness_m[dict(time=yr)].values + dg.bed_h.values\n", + " ts = dg.thickness_m[dict(time=yr)]\n", + " vs = dg.volume_m3[dict(time=yr)]\n", + " # Squeezed flowlines, dummy coords\n", + " x = hs * 0 - 1\n", + " y = hs * 0 - 1\n", + " xs = np.append(xs, x)\n", + " ys = np.append(ys, y)\n", + " init_vol = np.sum(vs)\n", + "\n", + " # Assign a first order thickness to the points\n", + " # very inefficient inverse distance stuff\n", + " thick = glacier_mask * np.NaN\n", + " for y in range(thick.shape[0]):\n", + " for x in range(thick.shape[1]):\n", + " phgt = topo_smoothed[y, x]\n", + " # take the ones in a 100m range\n", + " starth = 100.\n", + " while True:\n", + " starth += 10\n", + " pok = np.nonzero(np.abs(phgt - hs) <= starth)[0]\n", + " if len(pok) != 0:\n", + " break\n", + " sqr = np.sqrt((xs[pok]-x)**2 + (ys[pok]-y)**2)\n", + " pzero = np.where(sqr == 0)\n", + " if len(pzero[0]) == 0:\n", + " thick[y, x] = np.average(ts[pok], weights=1 / sqr)\n", + " elif len(pzero[0]) == 1:\n", + " thick[y, x] = ts[pzero]\n", + " else:\n", + " raise RuntimeError('We should not be there')\n", + "\n", + " # Distance from border (normalized)\n", + " dis_from_border = dis_from_border**dis_from_border_exp\n", + " dis_from_border /= np.mean(dis_from_border[glacier_mask == 1])\n", + " thick *= dis_from_border\n", + "\n", + " # Slope\n", + " thick *= slope_factor\n", + "\n", + " # Smooth\n", + " dx = gdir.grid.dx\n", + " if smooth_radius != 0:\n", + " if smooth_radius is None:\n", + " smooth_radius = np.rint(cfg.PARAMS['smooth_window'] / dx)\n", + " thick = gaussian_blur(thick, int(smooth_radius))\n", + " thick = np.where(glacier_mask, thick, 0.)\n", + "\n", + " # Re-mask\n", + " utils.clip_min(thick, 0, out=thick)\n", + " thick[glacier_mask == 0] = np.NaN\n", + " assert np.all(np.isfinite(thick[glacier_mask == 1]))\n", + "\n", + " # Conserve volume\n", + " tmp_vol = np.nansum(thick * dx**2)\n", + " thick *= (init_vol / tmp_vol).values\n", + " \n", + " ds[\"new_thick_\" + str(int(yr))] = xr.full_like(ds.rank_hl1, fill_value=np.nan)\n", + " ds[\"new_thick_\" + str(int(yr))] = (('y', 'x'), thick)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "730cf722", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_extend(yr=0, ds=ds):\n", + " ds[\"fl_att_sub\"] = xr.full_like(ds.rank_hl1, fill_value=np.nan)\n", + " ds[\"rank_hl2\"] = xr.full_like(ds.rank_hl1, fill_value=0)\n", + "\n", + " for ki in np.arange(len(npix)):\n", + " ds[\"rank_hl2_\" + str(ki)] = xr.full_like(ds.rank_hl1, fill_value=0)\n", + " rank_hl2 = rankdata(ds[\"new_thick_\" + str(int(yr))].where(ds[\"new_thick_\" + str(int(yr))]==ki))\n", + " ds[\"rank_hl2_\" + str(ki)] = (('y', 'x'), rank_hl2)\n", + " ds[\"rank_hl2_\" + str(ki)] = ds[\"rank_hl2_\" + str(ki)].where(ds.fl_att==ki, 0)\n", + "\n", + " rank_hl3 = ds.rank_hl2_0.values\n", + "\n", + " for ki in np.arange(1,np.max(ds.fl_att)):\n", + " rank_hl3 = rank_hl3 + (ds['rank_hl2_' + str(int(ki))]).values\n", + "\n", + " ds[\"rank_hl3\"] = xr.full_like(ds.rank_hl1, fill_value=np.nan)\n", + " ds[\"rank_hl3\"] = (('y', 'x'), rank_hl3)\n", + " ds['fl_att_sub']=ds.where(ds.glacier_mask==1).rank_hl3\n", + "\n", + "\n", + " ds['extend_' + str(yr)] = xr.full_like(ds.rank_hl1, fill_value=np.nan)\n", + " for ki in np.arange(len(npix),0,-1):\n", + " if dg.sel(dict(time=yr)).area_m2[ki].values == 0:\n", + " ds[\"extend_\" + str(yr)] = xr.where(ds.fl_att==ki, np.nan, ds[\"extend_\" + str(yr)])\n", + " elif dg.sel(dict(time=yr)).area_m2[ki].values >= np.min([dg[dict(time=0)].area_m2[ki].values, \\\n", + " npix[ki]*(gdir.grid.dx**2)]):\n", + " ds[\"extend_\" + str(yr)] = xr.where(ds.fl_att==ki, ki, ds[\"extend_\" + str(yr)]) \n", + " else:\n", + " pix_cov = dg.sel(dict(time=yr)).area_m2[ki].values/(npix[ki]*(gdir.grid.dx**2))\n", + " \n", + " mask = ((ds.fl_att==ki) & (ds.fl_att_sub>=pix_cov))\n", + " \n", + " ds[\"extend_\" + str(yr)] = xr.where(mask, ki, ds[\"extend_\" + str(yr)]) " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "35e1e48a", + "metadata": {}, + "outputs": [], + "source": [ + "distribute_thick(gdir, yr=0)\n", + "compute_extend(yr=0, ds=ds)\n", + "\n", + "for yr in np.arange(1,100):\n", + " compute_extend(yr=yr-1, ds=ds)\n", + " distribute_thick(gdir, yr=yr, mask=ds.where(ds['extend_' + str(int(yr-1))]>0, 0).glacier_mask[:].values)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "ec1b7f05", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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