bearecinos/Greenland_calving_notebook.ipynb

## Greenland_calving_notebook.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img 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\" align=\"left\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calibration of OGGM's frontal ablation parameterisation with velocity observations:\n",
    "## A case study in Greenland"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook we are illustrating how we use OGGM and velocity observations to estimate a calving constant of proporcionality $k$ per glacier. \n",
    "\n",
    "We only take into account glaciers and ice caps that are weakly connected or not connected to the Ice Sheet and are classified as Marine-terminating. \n",
    "\n",
    "The satellite velocity observations were provided by Marco Möller. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set_context('notebook')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import oggm\n",
    "from oggm import cfg, utils, workflow, tasks, graphics\n",
    "from oggm.core.inversion import find_inversion_calving, calving_flux_from_depth, compute_velocities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We load the velocity toolbox created for this experiment. Check the following [repository](https://github.com/bearecinos/k_calibration/tree/master/velocity_tools)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "MAIN_PATH = os.path.expanduser('~/k_calibration/')\n",
    "import sys\n",
    "sys.path.append(MAIN_PATH)\n",
    "# velocity module\n",
    "from velocity_tools import utils_velocity as utils_vel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-10-04 14:09:25: oggm.cfg: Using configuration file: /home/bea/oggm-bea/oggm/oggm/params.cfg\n"
     ]
    }
   ],
   "source": [
    "cfg.initialize(logging_level='WORKFLOW')\n",
    "cfg.PATHS['working_dir'] = utils.gettempdir(dirname='OGGM-k-calibration', reset=True)\n",
    "cfg.PARAMS['border'] = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pick a glacier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we experiment with the following glacier (RGI60-05.04298)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-10-04 14:09:27: oggm.workflow: init_glacier_regions from prepro level 3 on 1 glaciers.\n",
      "2019-10-04 14:09:27: oggm.workflow: Execute entity task gdir_from_prepro on 1 glaciers\n",
      "2019-10-04 14:09:27: oggm.workflow: Multiprocessing: using all available processors (N=4)\n"
     ]
    }
   ],
   "source": [
    "gdir = workflow.init_glacier_regions(['RGI60-05.04298'], from_prepro_level=3)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "graphics.plot_centerlines(gdir, use_flowlines=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "graphics.plot_catchment_width(gdir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The basic principles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to find a calving flux for this glacier by using OGGM and the frontal ablation parametrisation \n",
    "developed in [Recinos et al. (accepted)](https://www.the-cryosphere-discuss.net/tc-2018-254/).\n",
    "But for Greenland, we do not have previous estimates of frontal ablation that could be use to \n",
    "calibrate the calving law of OGGM.\n",
    "\n",
    "We have now developed a method that uses velocity observations to find the best parameter set up \n",
    "for the calving law in OGGM (the best value for the calving constant of proporcionality $k$). We do this by estimating\n",
    "model surface ice velocities and comparing them to satellite derived values. \n",
    "\n",
    "We pick the parameter set up that gives us results close to the observed velocities. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. First we read the velocity observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RGI-ID</th>\n",
       "      <th>Elevation</th>\n",
       "      <th>Velocity 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>RGI60-05.04298</td>\n",
       "      <td>307</td>\n",
       "      <td>74.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "            RGI-ID  Elevation  Velocity 3\n",
       "61  RGI60-05.04298        307       74.82"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read  marcos data \n",
    "obs = os.path.join(MAIN_PATH, 'input_data/greenland_data.xlsx')\n",
    "d_obs = pd.read_excel(obs, na_values=-9999.0)\n",
    "\n",
    "index = d_obs.index[d_obs['RGI-ID'] == gdir.rgi_id].tolist()\n",
    "data_obs = d_obs.iloc[index]\n",
    "data_obs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Velocity 3:** is the mean surface velocity over all pixels that lie below the elevation treshold.\n",
    "    Units of the velocity are m/yr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. We now estimate model surface velocities for different values of the $k$ parameter: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The basic principles "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OGGM already assumes serveral approximations when estimating the [ice flow](https://oggm.readthedocs.io/en/latest/ice-dynamics.html#ice-flow):\n",
    "\n",
    "- we apply the [Shallow Ice Approximation](http://www.antarcticglaciers.org/glaciers-and-climate/numerical-ice-sheet-models/hierarchy-ice-sheet-models-introduction/)\n",
    "- assume mass conservation\n",
    "- we use an integrated form of Glen's flow law\n",
    "\n",
    "I we assume that our ice moves as a laminar flow, meaning that the *z*-component of the velocity equals zero, the creep relation gives: \n",
    "\n",
    " $$\\frac{1}{2} \\frac{du}{dz}  = A \\tau^{n}  \\; (EQ. \\: 1)$$ \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With $\\tau$ being the basal shear stress equal to:\n",
    "    \n",
    "$$ \\tau = \\rho g h_{0} \\alpha \\; (EQ. \\: 2)$$ \n",
    "\n",
    "in OGGM [(See documentation)](https://oggm.readthedocs.io/en/latest/ice-dynamics.html#ice-flow).\n",
    "\n",
    "And for a constant ice density, the driving stress $\\tau$ acting on a depth H - h(z) within the glacier, increases linearly from zero at the surface to $\\tau_{basal}$ at the base. Then Eq.1 can be written as:\n",
    "\n",
    "$$ u(z) = u_{b} + \\frac{2A}{n+1} \\tau^{n} h \\; [1 - [1 - \\frac{z}{H}]^{n+1} ]  \\; (EQ. \\: 3)$$ \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For velocities at the surface, EQ. 3 becomes:\n",
    "\n",
    "$$ u_{s} = u_{b} + \\frac{2A}{n+1} \\tau^{n} h \\; (EQ. \\: 4)$$ \n",
    "    \n",
    "where in OGGM $u_{b}$ is the basal sliding velocity estimated as: \n",
    "    \n",
    "$$ u_{b} =  f_{s} \\frac{\\tau^{n}}{h} \\; (EQ. \\: 5)$$ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By substituting EQ. 2 and EQ. 5 in EQ. 4 we have:\n",
    "\n",
    "$$ u_{s} = f_{s} \\frac{(\\rho g h_{0} \\alpha)^{n}}{h} + \\frac{2A}{n+1} (\\rho g h_{0} \\alpha)^{n} h \\; (EQ. \\: 4)$$ \n",
    "\n",
    "From the methods described in Cuffey and Paterson, (2010). For parallel flowlines of velocity (laminar flow). Eq. 8.35, pp 310.\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By using to EQ. 4. we then estimate surface velocities and cross-sectional velocities along all glacier flowlines.\n",
    "The details of the Phyton code can be found \n",
    "[here.](https://github.com/OGGM/oggm/blob/45f6c772cf6c1a09706819f83a63b1fa2b403749/oggm/core/inversion.py#L444)\n",
    "\n",
    "We estimate velocities after computing a calving flux with different ranges of the $k$ parameter.\n",
    "\n",
    "However, to be able to compare these velocities to the observations we need to estimate an average surface velocity below\n",
    "an altitude treshold. For the glacier in this tutorial that is 307 m. \n",
    "The resultant average velocity is computed with following [Python code](https://github.com/bearecinos/k_calibration/blob/74f971f5e50572d8d1bc4d497cd255dead29fe3f/velocity_tools/utils_velocity.py#L195)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "k_factors = np.arange(0.01,2.5,0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "ele_threshold = data_obs.Elevation.values\n",
    "\n",
    "cross = []\n",
    "surface = []\n",
    "flux = []\n",
    "mu_star = []\n",
    "k_used = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For most k factors we will have a **MassBalanceError** in OGGM, this implies that the $k$ parameter is too big \n",
    "for this glacier, resulting in an overestimation of the calving flux and an unbalanced glacier.\n",
    "\n",
    "Whe this happens OGGM clips the **temperature sensitivity ($\\mu_{*}$) to zero** and relies on OGGM [ice dynamic's \n",
    "module](https://oggm.readthedocs.io/en/latest/ice-dynamics.html) to compute a calving flux,\n",
    "instead of using the calving law.\n",
    "From this point onwards any change on $k$ is irrelevant. \n",
    "\n",
    "We will also see that when calving does not occur in the experiment (e.g. $k$ too small), \n",
    "the terminus of the glacier has zero thickness. This will lead to errors in the velocity estimation, as shown in EQ.4, a velocity term is divided by the ice thickness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bea/oggm-bea/oggm/oggm/core/inversion.py:510: RuntimeWarning: invalid value encountered in true_divide\n",
      "  velocity = flux / section\n",
      "2019-10-04 14:12:58: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -55.34\n",
      "2019-10-04 14:12:58: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -128.24\n",
      "2019-10-04 14:12:59: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -206.91\n",
      "2019-10-04 14:12:59: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -291.15\n",
      "2019-10-04 14:13:00: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -380.81\n",
      "2019-10-04 14:13:00: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -475.74\n",
      "2019-10-04 14:13:00: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -575.81\n",
      "2019-10-04 14:13:01: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -680.90\n",
      "2019-10-04 14:13:01: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -790.90\n",
      "2019-10-04 14:13:02: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -905.71\n",
      "2019-10-04 14:13:02: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -1025.24\n",
      "2019-10-04 14:13:03: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -1149.40\n",
      "2019-10-04 14:13:03: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -1278.12\n",
      "2019-10-04 14:13:04: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -1411.33\n",
      "2019-10-04 14:13:04: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -1548.94\n",
      "2019-10-04 14:13:05: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -1690.91\n",
      "2019-10-04 14:13:05: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -1837.16\n",
      "2019-10-04 14:13:05: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -1987.64\n",
      "2019-10-04 14:13:06: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -2142.30\n",
      "2019-10-04 14:13:07: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -2301.08\n",
      "2019-10-04 14:13:07: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -2463.93\n",
      "2019-10-04 14:13:07: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -2630.80\n",
      "2019-10-04 14:13:08: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -2801.66\n",
      "2019-10-04 14:13:08: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -2976.45\n",
      "2019-10-04 14:13:09: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -3155.14\n",
      "2019-10-04 14:13:09: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -3337.69\n",
      "2019-10-04 14:13:10: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -3524.06\n",
      "2019-10-04 14:13:10: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -3714.20\n",
      "2019-10-04 14:13:10: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -3908.10\n",
      "2019-10-04 14:13:11: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -4105.71\n",
      "2019-10-04 14:13:11: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -4307.00\n",
      "2019-10-04 14:13:12: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -4511.93\n",
      "2019-10-04 14:13:12: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -4720.49\n",
      "2019-10-04 14:13:13: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -4932.64\n",
      "2019-10-04 14:13:13: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -5148.34\n",
      "2019-10-04 14:13:14: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -5367.58\n",
      "2019-10-04 14:13:14: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -5590.33\n",
      "2019-10-04 14:13:14: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -5816.56\n",
      "2019-10-04 14:13:15: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -6046.24\n",
      "2019-10-04 14:13:15: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -6279.35\n",
      "2019-10-04 14:13:16: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -6515.87\n",
      "2019-10-04 14:13:16: oggm.core.climate: MassBalanceCalibrationError occurred during task local_t_star on RGI60-05.04298: mu* out of specified bounds: -6755.78\n"
     ]
    }
   ],
   "source": [
    "for k in k_factors:\n",
    "    \n",
    "    # Find a calving flux.\n",
    "    cfg.PARAMS['k_calving'] = k\n",
    "    out = find_inversion_calving(gdir)\n",
    "    if out is None:\n",
    "        continue\n",
    "    calving_flux = out['calving_flux']\n",
    "    calving_mu_star = out['calving_mu_star']\n",
    "\n",
    "    compute_velocities(gdir)\n",
    "\n",
    "    vel_surface, vel_cross = utils_vel.velocity_average_from_elevation(gdir,\n",
    "                                                     ele=ele_threshold)\n",
    "\n",
    "    cross = np.append(cross, vel_cross)\n",
    "    surface = np.append(surface, vel_surface)\n",
    "    flux = np.append(flux, calving_flux)\n",
    "    mu_star = np.append(mu_star, calving_mu_star)\n",
    "    k_used = np.append(k_used, k)\n",
    "\n",
    "d = {'k_values': k_used,\n",
    "     'velocity_cross': cross,\n",
    "     'velocity_surf': surface,\n",
    "     'calving_flux': flux,\n",
    "     'mu_star': mu_star}\n",
    "\n",
    "df = pd.DataFrame(data=d)\n",
    "\n",
    "df.to_csv(os.path.join(cfg.PATHS['working_dir'], gdir.rgi_id + '.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's read the result of the k experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_to_output_file = os.path.join(cfg.PATHS['working_dir'], gdir.rgi_id + '.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <td>441.628316</td>\n",
       "      <td>552.035394</td>\n",
       "      <td>0.396125</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>0.56</td>\n",
       "      <td>441.628316</td>\n",
       "      <td>552.035394</td>\n",
       "      <td>0.396125</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0  k_values  velocity_cross  velocity_surf  calving_flux  \\\n",
       "0            0      0.01             NaN            NaN      0.000658   \n",
       "1            1      0.06       44.478698      55.598372      0.017003   \n",
       "2            2      0.11       90.107957     112.634947      0.049115   \n",
       "3            3      0.16      145.256610     181.570763      0.094081   \n",
       "4            4      0.21      207.618276     259.522845      0.150450   \n",
       "5            5      0.26      275.907079     344.883849      0.217280   \n",
       "6            6      0.31      349.291714     436.614642      0.293888   \n",
       "7            7      0.36      427.183537     533.979421      0.379744   \n",
       "8            8      0.41      441.628316     552.035394      0.396125   \n",
       "9            9      0.46      441.628316     552.035394      0.396125   \n",
       "10          10      0.51      441.628316     552.035394      0.396125   \n",
       "11          11      0.56      441.628316     552.035394      0.396125   \n",
       "\n",
       "       mu_star  \n",
       "0   279.533242  \n",
       "1   267.979634  \n",
       "2   245.281120  \n",
       "3   213.497681  \n",
       "4   173.653738  \n",
       "5   126.414922  \n",
       "6    72.264907  \n",
       "7    11.578516  \n",
       "8     0.000000  \n",
       "9     0.000000  \n",
       "10    0.000000  \n",
       "11    0.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glacier = pd.read_csv(path_to_output_file)\n",
    "glacier.head(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. We find a $k$ parameter for which the modeled surface velocities are close to the observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We have to give the function some parameters\n",
    "\n",
    "# The column of the OGGM variable that we are going to use to calibrate k: e.g. Velocity_surf\n",
    "oggm_col_index = 3\n",
    "\n",
    "# The column of the variable in the observational data to which we will compare OGGM: e.g. Velocity 3\n",
    "obs_col_index = 2\n",
    "\n",
    "# The relative tolerance to identify surface velocity close to the observations (e.g. within 20%)\n",
    "tol_r = 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "k, mu_star = utils_vel.k_calibration_with_observations(glacier, data_obs, oggm_col_index, obs_col_index, tol_r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.06000000000000001, 267.9796335334095)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k, mu_star"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. If the velocity estimated by E.Q 4 does not match the observations we calibrate $k$ using $\\mu_{*}$ instead."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the velocities estimated by E.Q. 4 never match the observations for any of the k-values in the experiment range. \n",
    "We pick the $k$ value that leads to the smallest $\\mu_{*}$, before this is clip to cero. \n",
    "\n",
    "For this glacier that is the row No. 7 of the table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>k_values</th>\n",
       "      <th>velocity_cross</th>\n",
       "      <th>velocity_surf</th>\n",
       "      <th>calving_flux</th>\n",
       "      <th>mu_star</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000658</td>\n",
       "      <td>279.533242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.06</td>\n",
       "      <td>44.478698</td>\n",
       "      <td>55.598372</td>\n",
       "      <td>0.017003</td>\n",
       "      <td>267.979634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.11</td>\n",
       "      <td>90.107957</td>\n",
       "      <td>112.634947</td>\n",
       "      <td>0.049115</td>\n",
       "      <td>245.281120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.16</td>\n",
       "      <td>145.256610</td>\n",
       "      <td>181.570763</td>\n",
       "      <td>0.094081</td>\n",
       "      <td>213.497681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.21</td>\n",
       "      <td>207.618276</td>\n",
       "      <td>259.522845</td>\n",
       "      <td>0.150450</td>\n",
       "      <td>173.653738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.26</td>\n",
       "      <td>275.907079</td>\n",
       "      <td>344.883849</td>\n",
       "      <td>0.217280</td>\n",
       "      <td>126.414922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.31</td>\n",
       "      <td>349.291714</td>\n",
       "      <td>436.614642</td>\n",
       "      <td>0.293888</td>\n",
       "      <td>72.264907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0.36</td>\n",
       "      <td>427.183537</td>\n",
       "      <td>533.979421</td>\n",
       "      <td>0.379744</td>\n",
       "      <td>11.578516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>0.41</td>\n",
       "      <td>441.628316</td>\n",
       "      <td>552.035394</td>\n",
       "      <td>0.396125</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  k_values  velocity_cross  velocity_surf  calving_flux  \\\n",
       "0           0      0.01             NaN            NaN      0.000658   \n",
       "1           1      0.06       44.478698      55.598372      0.017003   \n",
       "2           2      0.11       90.107957     112.634947      0.049115   \n",
       "3           3      0.16      145.256610     181.570763      0.094081   \n",
       "4           4      0.21      207.618276     259.522845      0.150450   \n",
       "5           5      0.26      275.907079     344.883849      0.217280   \n",
       "6           6      0.31      349.291714     436.614642      0.293888   \n",
       "7           7      0.36      427.183537     533.979421      0.379744   \n",
       "8           8      0.41      441.628316     552.035394      0.396125   \n",
       "\n",
       "      mu_star  \n",
       "0  279.533242  \n",
       "1  267.979634  \n",
       "2  245.281120  \n",
       "3  213.497681  \n",
       "4  173.653738  \n",
       "5  126.414922  \n",
       "6   72.264907  \n",
       "7   11.578516  \n",
       "8    0.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glacier.head(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "k, mu_star = utils_vel.k_calibration_with_mu_star(glacier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.36000000000000004, 11.578515515782778)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k, mu_star"
   ]
  }
 ],
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