1_inclinacion_de_la_base.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img 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\" align=\"left\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulación simple: hielo sobre una inclinación"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En el laboratorio, investigamos cómo se desplazó nuestro \"glacier goo\" sobre varias inclinaciones.  En este cuaderno, vamos a estudiar la misma cosa con una simulación de computadora."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Primero, configuramos las herramientas que usaremos. No necesitarás modificar estos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import oggm-edu helper package\n",
    "import oggm_edu as edu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import modules, constants and set plotting defaults\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = (9, 6)  # Default plot size\n",
    "\n",
    "# Scientific packages\n",
    "import numpy as np\n",
    "\n",
    "# Constants\n",
    "from oggm import cfg\n",
    "cfg.initialize()\n",
    "\n",
    "# OGGM models\n",
    "from oggm.core.massbalance import LinearMassBalance\n",
    "from oggm.core.flowline import FluxBasedModel, RectangularBedFlowline\n",
    "\n",
    "# There are several numerical implementations in OGGM core. \n",
    "# We use the \"FluxBasedModel\" with some default options\n",
    "from functools import partial\n",
    "FlowlineModel = partial(FluxBasedModel, min_dt=0, cfl_number=0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Salga una caja roja?  No te preocupes, esa caja presenta información sobre la configuración del modelo, `oggm.cfg`--no es un error."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lo esencial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora configuramos nuestra primera inclinación y le decimos al modelo que queremos estudiar un glaciar allí."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Base del glaciar"
   ]
  },
  {
   "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 slope and how high up it is\n",
    "slope = 0.1\n",
    "top = 3400 # m, the peak elevation\n",
    "\n",
    "# create a linear bedrock profile from top to bottom\n",
    "bottom = top - nx * map_dx * slope # m, elevation of the bottom of the incline based on the slope we defined\n",
    "bed_h, surface_h = edu.define_linear_bed(top, bottom, nx)\n",
    "\n",
    "# ask the model to calculate the distance from the top to the bottom of the glacier in km\n",
    "distance_along_glacier = edu.distance_along_glacier(nx, map_dx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot the glacier bedrock profile and the initial glacier surface\n",
    "plt.plot(distance_along_glacier, surface_h, label='Initial surface')\n",
    "edu.plot_xz_bed(x=distance_along_glacier, bed=bed_h);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tenemos que decidir qué tan ancho es nuestro glaciar. Por ahora, utilizamos en la celda abajo la forma `RectangularBedFlowline` con un ancho `initial_width` de 300 m. La forma transversal \"rectangular\" significa que las paredes del \"valle\" glacial son rectas y el ancho *w* es el mismo por todo:\n",
    "\n",
    "![OGGM-rectbed](http://docs.oggm.org/en/latest/_images/bed_vertical.png \"Rectangular bed illustration from OGGM documentation\")\n",
    "\n",
    "En este imagen, las lineas negras significan las paredes del \"valle\". La linea azul significa la superficie del glaciar.\n",
    "**Qué significa *h* en el imagen--qué opinas?**\n",
    "\n",
    "Si estás interesado/a, puedes leer más sobre formas de la base en la [documentación de OGGM](http://docs.oggm.org/en/latest/ice-dynamics.html#rectangular) (en inglés)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": [
    "Definimos un objeto llamado `init_flowline`, que almacena toda la información sobre la inclinación que usa el modelo. También nos puede dar información sobre el glaciar que tenemos allí. Por ejemplo, `volume_km3` puede decirnos cuánto hielo está almacenado actualmente en el glaciar.\n",
    "\n",
    "**¿Cuánto volumen de hielo crees que hay ahora?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Glacier length:', init_flowline.length_m)\n",
    "print('Glacier area:', init_flowline.area_km2)\n",
    "print('Glacier volume:', init_flowline.volume_km3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hasta ahora hemos configurado un valle.  Hemos definido su inclinación, su ancho, su forma transversal--la geometría del hábitat en que va a crecer nuestro glaciar."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Añadimos el hielo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora queremos agregar hielo a nuestra inclinación y hacer un glaciar. Nosotros glaciólogos describimos la cantidad de hielo que se agrega o elimina sobre toda la superficie del glaciar como el \"balance de masa\" (en inglés: \"mass balance\"). Aquí abajo ves una ilustración del sitio web de OGGM:\n",
    "\n",
    "![MB-diagram](https://raw.githubusercontent.com/OGGM/glacier-graphics/master/glacier_intro/thumbnails/glacier_05.png \"Mass balance diagram. Author: Anne Maussion, Atelier les gros yeux\")\n",
    "\n",
    "Un balance de masa lineal se define por la altitud de línea de equilibrio (ELA, por el inglés \"Equilibrium Line Altitude\") y un gradiente de altitud (en [mm m$^{-1}$]).  En arriba de la ELA, agregamos hielo (de nieve) y abajo de la linea removemos hielo (derritiéndolo). Aprenderemos más sobre esto en el [cuaderno 2](2_balance_de_masa.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ELA at 3000m a.s.l., gradient 4 mm m-1\n",
    "ELA = 3000 # equilibrium line altitude in meters above sea level\n",
    "altgrad = 4 # altitude gradient in mm/m\n",
    "mb_model = LinearMassBalance(ELA, grad=altgrad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El modelo de balance de masa, \"mb_model\", nos brinda el balance de masa para cualquier altitud que deseamos, en unidades [m s$^{-1}$]. Calculemos el balance de masa *anual* a lo largo del perfil del glaciar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "annual_mb = mb_model.get_annual_mb(surface_h) * cfg.SEC_IN_YEAR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot it\n",
    "plt.plot(annual_mb, bed_h, color='C2', label='Mass balance')\n",
    "plt.xlabel('Annual mass balance (m yr-1)')\n",
    "plt.ylabel('Altitude (m)')\n",
    "plt.legend(loc='best')\n",
    "# Display equilibrium line altitude, where annual mass balance = 0\n",
    "plt.axvline(x=0, color='k', linestyle='--', linewidth=0.8)\n",
    "plt.axhline(y=mb_model.ela_h, color='k', linestyle='--', linewidth=0.8);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Qué notaste en el gráfico?**  Que son los ejes?  En cuales altitudes se agrega hielo al glaciar (balance de masa positivo)?  En cuales altitudes se derrite el hielo?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ejecución del modelo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cuando hicimos nuestros experimentos en el laboratorio, tuvimos una inclinación y algo de material para verter nuestro \"glaciar\". También decidimos cuándo comenzaría nuestro experimento, es decir, cuándo verteríamos el material. Configurar nuestro modelo de glaciar en la computadora es muy similar. Hemos recopilado nuestra inclinación (`init_flowline`), el balance de masa (`mb_model`), y ahora le diremos al modelo que comience en el año 0 (`y0 = 0`, para \"year 0\" en ingles)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The model requires 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.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Primero ejecutemos el modelo por un año:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "runtime = 1\n",
    "model.run_until(runtime)\n",
    "edu.glacier_plot(x=distance_along_glacier, bed=bed_h, model=model, mb_model=mb_model, init_flowline=init_flowline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Year:', model.yr)\n",
    "print('Glacier length (m):', model.length_m)\n",
    "print('Glacier area (km2):', model.area_km2)\n",
    "print('Glacier volume (km3):', model.volume_km3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El \"glaciar\" modelado ya llena toda el fundamento y su longitud sube hasta la ELA (línea discontinua), pero es extremadamente delgada."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora podemos ejecutar el modelo durante 150 años. \n",
    "\n",
    "**En el siguiente código, ¿puedes identificar dónde le decimos al modelo cuántos años queremos estudiar?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "runtime = 150\n",
    "model.run_until(runtime)\n",
    "edu.glacier_plot(x=distance_along_glacier, bed=bed_h, model=model, mb_model=mb_model, init_flowline=init_flowline)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Veamos cómo ha cambiado nuestro glaciar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Current model year:', model.yr)\n",
    "print('Glacier length (m):', model.length_m)\n",
    "print('Glacier area (km2):', model.area_km2)\n",
    "print('Glacier volume (km3):', model.volume_km3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Compara este \"glaciar numérico\" con el \"goo glaciar\" que hicimos en el laboratorio:**\n",
    "- **cuanto tiempo necesita el goo glaciar para \"crecer\" y bajar en la inclinación? Cuanto tiempo necesita el glaciar numérico?**\n",
    "- **bajo de la linea ELA (3000 m) ocurre algo para el glaciar numérico (que ocurre para glaciares reales también) que no ocurro en el goo glaciar: que es?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si queremos calcular más tiempo, tenemos que establecer la fecha deseada.\n",
    "\n",
    "**Intenta editar la celda siguiente para pedirle al modelo que se ejecute durante 500 años en lugar de 150.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "runtime = 150\n",
    "model.run_until(runtime)\n",
    "edu.glacier_plot(x=distance_along_glacier, bed=bed_h, model=model, mb_model=mb_model, init_flowline=init_flowline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Current model year:', model.yr)\n",
    "print('Glacier length (m):', model.length_m)\n",
    "print('Glacier area (km2):', model.area_km2)\n",
    "print('Glacier volume (km3):', model.volume_km3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basado en esta información, ¿crees que modificaste la celda correctamente?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Es importante tener en cuenta que el modelo no se calculará atrás en el tiempo.\n",
    "Una vez calculado para 500 años, el modelo no volverá a la fecha 450 años y se mantiene en año 500. Intenta ejecutar la celda de abajo. ¿La salida coincide con lo que esperabas?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.run_until(450)\n",
    "print('Current model year:', model.yr)\n",
    "print('Glacier length (m):', model.length_m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para estudiar varios puntos en el tiempo, será útil almacenar algunos pasos intermedios de la evolución del glaciar. Hacemos un bucle \"for\" para que el modelo nos informe varias veces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reinitialize the model\n",
    "model = FlowlineModel(init_flowline, mb_model=mb_model, y0=0.)\n",
    "# Year 0 to 600 in 5 years step\n",
    "yrs = np.arange(0, 601, 5) \n",
    "# Array to fill with data\n",
    "nsteps = len(yrs)\n",
    "length = np.zeros(nsteps)\n",
    "vol = np.zeros(nsteps)\n",
    "# Loop\n",
    "for i, yr in enumerate(yrs):\n",
    "    model.run_until(yr)\n",
    "    length[i] = model.length_m\n",
    "    vol[i] = model.volume_km3\n",
    "# I store the final results for later use\n",
    "simple_glacier_h = model.fls[-1].surface_h"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora podemos trazar la evolución de la longitud y el volumen del glaciar en tiempo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n",
    "ax1.plot(yrs, length);\n",
    "ax1.set_xlabel('Years')\n",
    "ax1.set_ylabel('Length (m)');\n",
    "ax2.plot(yrs, vol);\n",
    "ax2.set_xlabel('Years')\n",
    "ax2.set_ylabel('Volume (km3)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Qué paso en este imagen?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La longitud del glaciar es una función escalonada en el primer año de simulación porque, por encima de la altitud de la línea de equilibrio (ELA), solo está pasando la acumulación.\n",
    "Después de eso, al principio la longitud del glaciar permanece constante. El hielo no es muy espeso, por lo que la porción que llega por debajo del ELA no sobrevive a la fusión que se produce allí. Pero no importa qué tan espeso sea el glaciar, la parte sobre el ELA siempre está ganando masa. Eventualmente, hay suficiente hielo para persistir por debajo de la ELA. Aprenderemos más sobre el ELA en el [siguiente cuaderno](2_balance_de_masa.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Glacier length from the top to the equilibrium line altitude ('+str(mb_model.ela_h)+' m) is: ' \n",
    "      + str(length[1])+'m')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Después de varios siglos, el glaciar se equilibra con su clima. Su longitud y volumen no cambiarán más si el clima se mantiene aproximadamente constante."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Un primer experimento "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, hemos visto lo básico. Ahora, ¿qué tal un glaciar en una inclinación diferente? A veces los glaciares son muy escarpados, como este en Nepal.\n",
    "\n",
    "![khumbu-himal](https://www.swisseduc.ch/glaciers/earth_icy_planet/icons-02/11-hanging-glacier.jpg \"An image of a hanging glacier, Khumbu Himal. Hambrey & Alean\")\n",
    "\n",
    "Ajustemos la inclinación en el modelo para observar un glaciar más inclinado:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**En el siguiente código, ¿puedes identificar dónde le decimos al modelo la nueva inclinación que vamos a usar?**\n",
    "**¿Que inclinación usamos para el primero experimento? Busca esa información al principio del cuaderno.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define new slope\n",
    "new_slope = 0.2\n",
    "top = 3400 #m, the peak elevation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a linear bedrock profile from top to bottom\n",
    "bottom_1 = top - nx*map_dx*new_slope #m, elevation of the bottom of the incline based on the slope we defined\n",
    "bed_1, surface_1 = edu.define_linear_bed(top, bottom_1, nx)\n",
    "# Define new flowline\n",
    "flowline_1 = RectangularBedFlowline(surface_h=surface_1, bed_h=bed_1, widths=widths, map_dx=map_dx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora ejecutaremos nuestro modelo con las nuevas condiciones iniciales (nuevamente durante 600 años), y almacenaremos la salida en una nueva variable para comparación:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reinitialize the model with the new input\n",
    "model_1 = FlowlineModel(flowline_1, mb_model=mb_model, y0=0.)\n",
    "# Array to fill with data\n",
    "nsteps = len(yrs)\n",
    "length_w = np.zeros(nsteps)\n",
    "vol_w = np.zeros(nsteps)\n",
    "# Loop\n",
    "for i, yr in enumerate(yrs):\n",
    "    model_1.run_until(yr)\n",
    "    length_w[i] = model_1.length_m\n",
    "    vol_w[i] = model_1.volume_km3\n",
    "# I store the final results for later use\n",
    "new_glacier_h = model_1.fls[-1].surface_h"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparando nuestros glaciares"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparemos ahora el glaciar que estudiamos primero con nuestro nuevo glaciar en una pendiente diferente. **¿Crees que el nuevo glaciar se verá más espeso o más delgado que el primero?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the final results - first our previous glacier:\n",
    "plt.plot(distance_along_glacier, simple_glacier_h, label='First glacier')\n",
    "edu.plot_xz_bed(x=distance_along_glacier, bed=bed_h);\n",
    "plt.ylim([1200, 3700]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# and now our new glacier: \n",
    "plt.plot(distance_along_glacier, new_glacier_h, label='New glacier')\n",
    "edu.plot_xz_bed(x=distance_along_glacier, bed=bed_1)\n",
    "plt.ylim([1200, 3700]);  # same range as above for comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Probablemente ya puedas adivinar cómo se compara el volumen del nuevo glaciar con el anterior. Grafiquemos cómo el volumen de ambos glaciares crece a tiempo para ver:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n",
    "ax1.plot(yrs, length, label='First glacier');\n",
    "ax1.plot(yrs, length_w, label='New glacier');\n",
    "ax1.legend(loc='best')\n",
    "ax1.set_xlabel('Years')\n",
    "ax1.set_ylabel('Length (m)');\n",
    "ax2.plot(yrs, vol, label='First glacier');\n",
    "ax2.plot(yrs, vol_w, label='New glacier');\n",
    "ax2.legend(loc='best')\n",
    "ax2.set_xlabel('Years')\n",
    "ax2.set_ylabel('Volume (km3)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Actividad\n",
    "\n",
    "**Ahora, trata de cambiar la inclinación a otro valor, y ejecutar el modelo otra vez.  Qué ves?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Qué sigue?\n",
    "Hacia adelante al [cuaderno 2](2_balance_de_masa.ipynb)!"
   ]
  }
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