2_balance_de_masa.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img 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\" align=\"left\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Balance de masa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ya hemos utilizado un modelo de balance de masa para agregar hielo a nuestro glaciar en [cuaderno 1](1_inclinacion_de_la_base.ipynb).  En este cuaderno, estudiaremos el balance de masa con más detalle. Aprenderemos cómo describir el clima que experimenta el glaciar en el modelo, y veremos cómo el clima determina cuánta agua se almacena como hielo glaciar."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Primero, importamos todos los módulos necesarios:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The commands below are just importing the necessary modules and functions\n",
    "# Plot defaults\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = (9, 6)  # Default plot size\n",
    "# Scientific packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Constants\n",
    "from oggm import cfg\n",
    "cfg.initialize()\n",
    "# OGGM models\n",
    "from oggm.core.massbalance import LinearMassBalance\n",
    "from oggm.core.flowline import FluxBasedModel, RectangularBedFlowline\n",
    "# There are several solvers in OGGM core. We use the default one for this experiment\n",
    "from functools import partial\n",
    "FlowlineModel = partial(FluxBasedModel, min_dt=0, cfl_number=0.01)\n",
    "\n",
    "# OGGM Edu helper functions\n",
    "import oggm_edu as edu\n",
    "import CdeC as cdec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seguimos pasos similares a los que hicimos en [inclinacion_de_la_base](1_inclinacion_de_la_base.ipynb) para configurar nuestro modelo simple."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define horizontal resolution of the model:\n",
    "# nx: number of grid points\n",
    "# map_dx: grid point spacing in meters\n",
    "nx = 200\n",
    "map_dx = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define glacier top and bottom altitudes in meters (instead of slope in the earlier notebook)\n",
    "top = 3400\n",
    "bottom = 1400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is the bed rock, linearily decreasing from top altitude to bottom altitude, in nx steps\n",
    "bed_h = np.linspace(top, bottom, nx)\n",
    "# At the begining, there is no glacier so our glacier surface is at the bed altitude\n",
    "surface_h = bed_h\n",
    "# calculate the corresponding distance along the glacier (from the top)\n",
    "distance_along_glacier = np.linspace(0,nx, nx) *0.1 # in km \n",
    "# Define the glacier width as we did in flowline_model\n",
    "initial_width = 300 #width in meters\n",
    "# Now describe the widths in \"grid points\" for the model, based on grid point spacing map_dx\n",
    "widths = np.zeros(nx) + initial_width/map_dx\n",
    "# Define our bed\n",
    "init_flowline = RectangularBedFlowline(surface_h=surface_h, bed_h=bed_h, widths=widths, map_dx=map_dx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cambiando la altitud de la línea de equilibrio\n",
    "\n",
    "En la mayor parte de la superficie del glaciar, se está agregando o perdiendo masa. Definimos la línea de equilibrio donde los procesos de acumulación (por ejemplo, nieve) y ablación (por ejemplo, fusión) se equilibran, de modo que la superficie está en \"equilibrio\". Este es el mismo diagrama que vimos antes, ahora con la altitud de la línea de equilibrio (\"ELA\" en inglés) marcada en rojo.\n",
    "\n",
    "![ELA-cartoon](https://raw.githubusercontent.com/OGGM/glacier-graphics/master/glacier_intro/thumbnails/glacier_07.png \"ELA diagram, Anne Maussion, Atelier les grox yeux\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discusión: Cómo influye el clima a la altitud de la línea de equilibrio?\n",
    "- Cuándo hace más calor, la ELA va...\n",
    "- Cuándo hace frío, la ELA va...\n",
    "- Cuándo hay más nieve, la ELA va...\n",
    "- Cuándo hay sequía, la ELA va...\n",
    "\n",
    "**Primero, piensa sólo. Después, discutir con un compañero.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora vamos a probar tres ELA diferentes para ver cómo cambia nuestro glaciar. Basado en nuestra discusión, ¿qué ELA crees que corresponderá al glaciar más grande?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the ELAs we want to compare: (we worked with ELA=3000 m in the previous two notebooks)\n",
    "# We will calculate models with the ELAs: 2700, 3000 and 3200 mm/m. These numbers can be found for real glaciers. \n",
    "ELAs = [2700, 3000, 3200]\n",
    "# Define the time period for which the glacier should be calculated:\n",
    "years = 600"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In the following lists intermediate steps of the models will be saved\n",
    "mb_models = []\n",
    "annual_mbs = []\n",
    "# here the \"final\" models will be saved\n",
    "models_ELA = []\n",
    "# Colors and styles for the graphs\n",
    "colors = ['C1', 'C3', 'C5']\n",
    "linestyles = ['--', '-.', ':']\n",
    "# altitude gradient\n",
    "altgrad = 4\n",
    "\n",
    "for ELA in ELAs:\n",
    "    # Calculation of the mass balance models for each gradient \n",
    "    mb_model = LinearMassBalance(ELA, grad=altgrad)\n",
    "    mb_models.append(mb_model)\n",
    "    # Calculation of the annual mass balance along the glacier profile\n",
    "    annual_mbs.append(mb_model.get_annual_mb(surface_h) * cfg.SEC_IN_YEAR)\n",
    "    # The models require the initial glacier bed, a mass balance model, and an initial time (the year y0)\n",
    "    model = FlowlineModel(init_flowline, mb_model=mb_model, y0=0.)\n",
    "    # First, look at the state of the glacier models after the chosen amount of years\n",
    "    model.run_until(years)\n",
    "    # Store for later analyses\n",
    "    models_ELA.append(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the results\n",
    "plt.figure(figsize=[14,6])\n",
    "\n",
    "plt.subplot(121)\n",
    "# Annual mass balance\n",
    "for color, ELA, annual_mb in zip(colors, ELAs, annual_mbs):\n",
    "    plt.plot(annual_mb, bed_h, color=color, label='Mass balance, ELA='+ str(ELA))\n",
    "# Add ELA, where mass balance = 0:\n",
    "# plt.axhline(y=ELA, color='k', linestyle='--', linewidth=0.8, label='Equilibrium line altitude')\n",
    "plt.axvline(x=0, color='k', linestyle='--', linewidth=0.8)\n",
    "plt.xlabel('Annual mass balance (m yr-1)')\n",
    "plt.ylabel('Altitude (m)')\n",
    "plt.legend(loc='best');\n",
    "\n",
    "plt.subplot(122)\n",
    "# Plot the initial conditions first:\n",
    "plt.plot(distance_along_glacier, init_flowline.surface_h, label='Initial glacier')\n",
    "# Get the modelled flowline (model.fls[-1]) and plot its new surface\n",
    "for ELA, color, linestyle, model in zip(ELAs, colors, linestyles, models_ELA):\n",
    "    plt.plot(distance_along_glacier, model.fls[-1].surface_h, label='Glacier after {} years, ELA={}'.format(model.yr, ELA), color=color)\n",
    "    # Add ELA:\n",
    "    plt.axhline(y=ELA, color='k', linestyle=linestyle, linewidth=0.8, label='Equilibrium line altitude')\n",
    "# Add the bedrock:\n",
    "edu.plot_xz_bed(distance_along_glacier, bed_h)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Primero, piensa sólo. Después, discutir con un compañero y en clase:**\n",
    "- **¿Que nos enseña estos dos gráficos? ¿Que hay en la eje de abscisas (x)? ¿de ordenadas (y)?**\n",
    "- **En el gráfico derecho, los tres glaciares son muy diferentes, pero ¿que nos enseña la posición de la ELA por cada glaciar? ¿Que característica es la misma por cada glaciar?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cambiando el gradiente de balance de masa (MBG)\n",
    "\n",
    "El gradiente de balance de masa (MBG, o `grad` en la celda de abajo) es lo que dice: el cambio en el balance de masa del glaciar con la altitud. Veamos un ejemplo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grad = 4 # mm/m\n",
    "mb_ex = LinearMassBalance(3000, grad=grad)\n",
    "annual_mb = mb_ex.get_annual_mb(surface_h) * cfg.SEC_IN_YEAR\n",
    "\n",
    "# Plot the mass balance with altitude\n",
    "fig, ax = plt.subplots(1)\n",
    "ax.plot(annual_mb, bed_h, color='b', label='Mass balance, grad='+ str(grad))\n",
    "ax.axhline(y=3000, color='k', linestyle='--', linewidth=0.8, label='Equilibrium line altitude')\n",
    "ax.axvline(x=0, color='k', linestyle='--', linewidth=0.8)\n",
    "ax.set(xlim=(-7.5, 2.5), xlabel='Annual mass balance (m/yr)', ylabel = 'Altitude (m)')\n",
    "ax.legend(loc='best');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Que pasa si cambiamos el valor de `grad`?  Pruébalo en la celda arriba.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El MBG no es tan facil de interpretar como la ELA. Aqui ves varios ejemplos de MBG medidos en glaciares del mundo:\n",
    "\n",
    "![MBG](https://slideplayer.com/slide/14020519/86/images/11/Glaciology+%E2%80%93+Mass+Balance+Profiles.jpg \"https://slideplayer.com/slide/14020519/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discusión: Cómo influye el clima al gradiente de balance de masa a la altitud?\n",
    "- Cuándo hace más calor, el MBG va...\n",
    "- Cuándo hace frío, el MBG va...\n",
    "- Cuándo hay más nieve, el MBG va...\n",
    "- Cuándo hay sequía, el MBG va..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparemos algunos glaciares con diferentes MBGs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the MBGs we want to compare: (we worked with grad=4mm/m in the \n",
    "# glacier flowline modelling notebook)\n",
    "# We will calculate models with the MBGs: 0.3, 4 and 15 mm/m. These numbers can be found for real glaciers. \n",
    "grads = [0.3, 4, 15]\n",
    "# Define the time period for which the glacier should be calculated:\n",
    "years = 300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In the following lists intermediate steps of the models will be saved\n",
    "mb_models = []\n",
    "annual_mb = []\n",
    "# here the \"final\" models will be saved\n",
    "models_MBG = []\n",
    "# Colors for the graphs\n",
    "colors = ['C1', 'C3', 'C5'] \n",
    "# equilibrium line altitude (ELA)\n",
    "ELA = 3000\n",
    "\n",
    "for grad in grads:\n",
    "    # Calculation of the mass balance models for each gradient \n",
    "    a = LinearMassBalance(ELA, grad=grad)\n",
    "    mb_models.append(a)\n",
    "    # Calculation of the annual mass balance along the glacier profile\n",
    "    annual_mb.append(a.get_annual_mb(surface_h) * cfg.SEC_IN_YEAR)\n",
    "    # The models require the initial glacier bed, a mass balance model, and an initial time (the year y0)\n",
    "    model = FlowlineModel(init_flowline, mb_model=a, y0=0.)\n",
    "    # First, look at the state of the glacier models after the chosen amount of years\n",
    "    model.run_until(years)\n",
    "    # Store the models for later analyses\n",
    "    models_MBG.append(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the results\n",
    "plt.figure(figsize=[14,6])\n",
    "\n",
    "plt.subplot(121)\n",
    "# Annual mass balance\n",
    "for color, grad, ann_mb in zip(colors, grads, annual_mb):\n",
    "    plt.plot(ann_mb, bed_h, color=color, label='Mass balance, grad='+ str(grad))\n",
    "# Add ELA, where mass balance = 0:\n",
    "plt.axhline(y=ELA, color='k', linestyle='--', linewidth=0.8, label='Equilibrium line altitude')\n",
    "plt.axvline(x=0, color='k', linestyle='--', linewidth=0.8)\n",
    "plt.xlabel('Annual mass balance (m yr-1)')\n",
    "plt.ylabel('Altitude (m)')\n",
    "plt.legend(loc='best');\n",
    "\n",
    "plt.subplot(122)\n",
    "# Plot the initial conditions first:\n",
    "plt.plot(distance_along_glacier, init_flowline.surface_h, label='Initial glacier')\n",
    "# Get the modelled flowline (model.fls[-1]) and plot its new surface\n",
    "for color, grad, model in zip(colors, grads, models_MBG):\n",
    "    plt.plot(distance_along_glacier, model.fls[-1].surface_h, label='Glacier after {} years, '.format(model.yr) + 'grad=' + str(grad), color=color)\n",
    "# Add ELA:\n",
    "plt.axhline(y=ELA, color='k', linestyle='--', linewidth=0.8, label='Equilibrium line altitude')\n",
    "# Add the bedrock:\n",
    "edu.plot_xz_bed(distance_along_glacier, bed_h)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compara los gráficos de la izquierda y la derecha.\n",
    "\n",
    "El modelo con el gradiente más alto (línea púrpura) experimenta la mayor cantidad de ablación *y* la mayor acumulación. Por lo tanto, muestra el glaciar más largo y espeso después de la cantidad dada de años modelo. Con todos los modelos calculados para el mismo intervalo de tiempo, podemos concluir que un alto gradiente de balance de masa conduce a un crecimiento más rápido de un glaciar.\n",
    "\n",
    "**¿Qué piensas: qué tipo de clima produce altos gradientes MBG?**\n",
    "\n",
    "Puede leer más sobre la conexión de MBG y el clima en las referencias abajo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Almacenamiento de agua"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Entonces, hemos visto que el clima influye la forma de nuestro glaciar.  A su parte, la forma del glaciar determina la cantidad de agua que puede almacenar.  Cuánto agua almacenan nuestros glaciares con sus differentes lineas de equilibrio?  Hacemos una tabla."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create lists with different properties of the glacier models\n",
    "length = []\n",
    "vol_ice = []\n",
    "vol_water = []\n",
    "\n",
    "# Add a value to the lists for each model\n",
    "for model in models_ELA:\n",
    "    length.append(model.length_m)\n",
    "    vol_ice.append(model.volume_km3)\n",
    "    vol_water.append(cdec.ice_to_freshwater(model.volume_km3))\n",
    "\n",
    "# Create a table with the properties of the glacier models\n",
    "dic = {'ELA': ELAs, 'length': length, 'volume': vol_ice, 'equivalent water': vol_water}\n",
    "table = pd.DataFrame.from_dict(dic)\n",
    "table # ask Jupyter to show us the result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Imaginamos una ELA \"inicial\" de 3000 m s.n.m.  Qué cambio de clima (entonces qué ELA nueva) reduce la cantidad de agua almacenado por la mitad?**\n",
    "Puedes modificar el experimento de ELAs en cambiando la lista `ELAs` en arriba.\n",
    "\n",
    "También puedes investigar la misma pregunta para un cambio de MBG. Ves cómo? En el cuaderno guardamos una lista de modelos que puede ser util aqui. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Más información\n",
    "El balance de masa es muy importante y estudiado en la glaciología.  Para saber más, puedes leer:\n",
    "\n",
    "- [1](https://www.esa.int/SPECIALS/Eduspace_Global_ES/SEM4RKH2SDH_0.html). Descripción del clima y los glaciares, incluyendo el concepto de balance de masa, por la Agencia Espacial Europa\n",
    "\n",
    "- [2](http://www.glaciologia.cl/web/glaciologia_es/libro.php). Rivera A., F. Bown, F. Napoleoni, C. Muñoz and M. Vuille (2016), Balance de masa glaciar. Ediciones CECs, Valdivia, Chile.\n",
    "\n",
    "- [3](http://www.antarcticglaciers.org/modern-glaciers/introduction-glacier-mass-balance/). [EN] Introduction to glacier mass balance\n",
    "\n",
    "- [4](https://www.cambridge.org/core/journals/journal-of-glaciology/article/seasonal-massbalance-gradients-in-norway/BE541534879036C053F8942BCD7C4BF3). [EN] Rasmussen, L. A., & Andreassen, L. M. (2005). Seasonal mass-balance gradients in Norway. *Journal of Glaciology*, 51(175), 601-606.\n",
    "\n",
    "- [5](https://science.sciencemag.org/content/258/5079/115). [EN] Oerlemans, J., & Fortuin, J. P. F. (1992). Sensitivity of glaciers and small ice caps to greenhouse warming. *Science, 258(5079)*, 115-117.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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