1_Preprocessing.ipynb

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   "source": [
    "# Part I: Producing a dataset\n",
    "\n",
    "Authors: Jordi Bolibar & Facundo Sapienza\n",
    "\n",
    "\n",
    "This notebook is dedicated to obtaining all necessary data to train the machine learning models of this project. For that, we need climate, topographical and mass balance data for all glaciers in Scandinavia. The retrieval of the climate and topographic data is processed using the [Open Global Glacier Model](https://oggm.org). \n",
    "First, we import the dependencies and we configure OGGM. \n",
    "\n",
    "```{note}\n",
    "Before running this notebook, be sure you Jupyter kernel (top left corner of the notebook) has been configure to work with the `MB_Finse` conda environment.\n",
    "```"
   ]
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    {
     "data": {
      "text/plain": [
       "'1.6.0'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import oggm\n",
    "oggm.__version__"
   ]
  },
  {
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   "id": "69e4c771-00cc-4628-9d8a-4e3d5affedc7",
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   "source": [
    "We import all the necessary dependencies to retrieve the data with OGGM and to postprocess it. We use multiprocessing with OGGM to make things faster."
   ]
  },
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     "text": [
      "2023-04-16 13:47:02: oggm.cfg: Reading default parameters from the OGGM `params.cfg` configuration file.\n",
      "2023-04-16 13:47:02: oggm.cfg: Multiprocessing switched OFF according to the parameter file.\n",
      "2023-04-16 13:47:02: oggm.cfg: Multiprocessing: using all available processors (N=4)\n",
      "2023-04-16 13:47:02: oggm.cfg: PARAMS['border'] changed from `80` to `10`.\n",
      "2023-04-16 13:47:02: oggm.cfg: Multiprocessing switched ON after user settings.\n"
     ]
    }
   ],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "import rioxarray\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "import oggm\n",
    "from oggm import cfg, utils, workflow, tasks, graphics\n",
    "from oggm import entity_task, global_tasks\n",
    "from oggm.utils import compile_climate_input\n",
    "from oggm.core import gis\n",
    "from oggm.utils import DEM_SOURCES\n",
    "from pathlib import Path\n",
    "import os\n",
    "import logging\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "pd.set_option('display.max_columns',None)\n",
    "\n",
    "cfg.initialize(logging_level='WARNING')\n",
    "cfg.PARAMS['border'] = 10\n",
    "cfg.PARAMS['use_multiprocessing'] = True \n",
    "# Module logger\n",
    "log = logging.getLogger('.'.join(__name__.split('.')[:-1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "861fdc09-d050-4cae-9d68-e78bc112ad9d",
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   },
   "source": [
    "Choose your OGGM path where you want to store all the data. Since we are all working in the same JupyterHub, we can store the information in the same shared folder. "
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "parent_path = os.path.dirname(Path().resolve())\n",
    "workspace_path = os.path.join(parent_path, 'OGGM_data_Finse')\n",
    "#workspace_path = '/home/jovyan/shared/glacier-ml-2022/Mass_Balance_ML_Modelling/Data'\n",
    "\n",
    "if not os.path.exists(workspace_path):\n",
    "    os.mkdir(workspace_path)\n",
    "else:\n",
    "    cfg.PATHS['working_dir'] = workspace_path"
   ]
  },
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   "source": [
    "Entity tasks are custom functions that can be run in OGGM to perform specific tasks. OGGM is centered around `gdir`s, which are Glacier Directories. These are Python classes representing glaciers individually, containing all the necessary information for their simulation. Here, we declare all the custom entity tasks (which will be used later on), since they need to be declared before initializing the glacier directories. This one is used to get all the topographic data for all gdirs. "
   ]
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   "source": [
    "@entity_task(log)\n",
    "def get_topo_predictors(gdir):\n",
    "    \"\"\"Mandatory docstring\n",
    "    \"\"\"\n",
    "        \n",
    "    training_data = {'zmed': 0.0,\n",
    "                 'zmax': 0.0,\n",
    "                 'zmin': 0.0,\n",
    "                 'area': 0.0,\n",
    "                 'slope': 0.0,\n",
    "                 'lat': 0.0,\n",
    "                 'lon': 0.0,\n",
    "                 'icecap': 0.0,\n",
    "                 'ID': \"\"\n",
    "        }\n",
    "    \n",
    "    gl_shp = gdir.read_shapefile('outlines')\n",
    "    \n",
    "    training_data['zmed'] = float(gl_shp.Zmed.values[0])\n",
    "    training_data['zmax'] = float(gl_shp.Zmax.values[0])\n",
    "    training_data['zmin'] = float(gl_shp.Zmin.values[0])\n",
    "    training_data['area'] = gdir.rgi_area_km2\n",
    "    training_data['lat'] = gdir.cenlat\n",
    "    training_data['lon'] = gdir.cenlon\n",
    "    training_data['icecap'] = int(gdir.is_icecap)\n",
    "    training_data['slope'] = float(gl_shp.Slope.values[0])\n",
    "    training_data['ID'] = gdir.rgi_id\n",
    "        \n",
    "    return training_data"
   ]
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   "source": [
    "Download all data from glaciers in Scandinavia (RGI region '08') using the Randolph Glacier Inventory (RGI v6). This might fail if data has already been downloaded (even partially). This is done by initializing the glacier directories. "
   ]
  },
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   "execution_count": 5,
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     "text": [
      "2023-04-16 13:47:44: oggm.workflow: init_glacier_directories from prepro level 3 on 3417 glaciers.\n",
      "2023-04-16 13:47:44: oggm.workflow: Execute entity tasks [gdir_from_prepro] on 3417 glaciers\n",
      "/home/jovyan/envs/oggm_env/lib/python3.11/site-packages/oggm/utils/_workflow.py:2828: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
      "  return np.round(float(_area), decimals=3)\n",
      "/home/jovyan/envs/oggm_env/lib/python3.11/site-packages/oggm/utils/_workflow.py:2828: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
      "  return np.round(float(_area), decimals=3)\n",
      "/home/jovyan/envs/oggm_env/lib/python3.11/site-packages/oggm/utils/_workflow.py:2828: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
      "  return np.round(float(_area), decimals=3)\n",
      "/home/jovyan/envs/oggm_env/lib/python3.11/site-packages/oggm/utils/_workflow.py:2828: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
      "  return np.round(float(_area), decimals=3)\n"
     ]
    }
   ],
   "source": [
    "rgi_region = '08' # Scandinavia\n",
    "rgi_version = '6'\n",
    "rgi_dir = utils.get_rgi_dir(version=rgi_version)\n",
    "path = utils.get_rgi_region_file(region=rgi_region, version=rgi_version)\n",
    "rgidf = gpd.read_file(path)\n",
    "base_url = 'https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.6/L3-L5_files/2023.1/elev_bands/W5E5_w_data/'\n",
    "gdirs = workflow.init_glacier_directories(rgidf, from_prepro_level=3, prepro_base_url=base_url, prepro_border=10)"
   ]
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   "source": [
    "## 1.1. Get geodetic glacier mass balance data\n",
    "\n",
    "In order to train our machine learning models, we need target data, in this case geodetic mass balance. For that, we get the geodetic MB for all glaciers in Scandinavia from [Hugonnet et al. (2021)](https://www.nature.com/articles/s41586-021-03436-z). The [OGGM Shop](https://docs.oggm.org/en/stable/shop.html) allows us to easily have access to a wide range of interesting datasets. "
   ]
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       "                rgi_id                 period        area    dmdtda   \n",
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mb_df = utils.get_geodetic_mb_dataframe()\n",
    "mb_df.drop(columns=['area', 'reg', 'is_cor'])\n",
    "mb_df.index.names = ['rgi_id']\n",
    "mb_df = mb_df.reset_index()\n",
    "\n",
    "# We want to filer just the right region #def extract_region_id(name):\n",
    "mb_df['region'] = mb_df.apply(lambda row: row.rgi_id.split('-')[1][:2], axis=1)\n",
    "mb_df = mb_df[mb_df.region==rgi_region]\n",
    "mb_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "342c539b-28d1-4bf0-b26c-291e4d9f5c80",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "We save this dataset in a CSV file to use it later on. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a7dbc7a7-8a39-4140-9a87-6a49febf7ef0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mb_df.to_csv('./Data/mb_df.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af2f80ad-5795-492d-91d2-9c0a124ef7e5",
   "metadata": {
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   },
   "source": [
    "## 1.2. Get glacier topographical data\n",
    "\n",
    "Now we get the topographical data for all glacier to be used in the training. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "81e98fe3-d9aa-4dc2-a40c-5bf42bb022ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: COMPUTE AVERAGE ICE THICKNESS PER GLACIER"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fd94e7f-20b5-4576-a252-5d27e2d75f87",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "We pick one glacier as an example to display the available data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6b4fc715-f733-44e8-ab63-25399c1bb37e",
   "metadata": {
    "tags": []
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "'8'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdir = gdirs[101]\n",
    "\n",
    "glshp = gdir.read_shapefile('outlines')\n",
    "glshp.O1Region.values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d326d3bf-d9c7-45ff-b2d9-768f437be774",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "dem_path = gdir.get_filepath('dem')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46a80871-1458-4249-b1d9-c983a08ad257",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "We display the topography and outlines of the glacier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f560f2c6-d0ee-4849-ad1e-580866cc3f5e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "da = rioxarray.open_rasterio(dem_path)\n",
    "f, ax = plt.subplots()\n",
    "da.plot(cmap='terrain', ax=ax);\n",
    "# Add the outlines\n",
    "gdir.read_shapefile('outlines').plot(ax=ax, color='none', edgecolor='black');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bb4b65f-ea59-45cb-aa4c-e062799fa442",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "In order to get the topographical features for our machine learning models, we now make use of the entity task that we declared previously. In order to run this more efficiently, we parallelize this using the function as an entity task in OGGM. The topographical data is already available in the Hub. Since this is computationally expensive, only uncomment the code and run it again if you need to re-run it for any reason. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6afaa9c2-3ddc-43e4-ac9f-b6929e0af68c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-16 13:54:22: oggm.workflow: Execute entity tasks [get_topo_predictors] on 3417 glaciers\n"
     ]
    }
   ],
   "source": [
    "topo_dicts = workflow.execute_entity_task(get_topo_predictors, gdirs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "db2d132e-8053-429a-acd4-2da55fae8638",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "## Uncomment if you want to update the topo data\n",
    "# topo_df = pd.DataFrame(topo_dicts)\n",
    "# topo_df.index = topo_df.ID\n",
    "# topo_df.index.name = 'RGI_ID'\n",
    "# topo_df.drop(columns='ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9b7d8cb0-c6d9-4aef-8502-7c55c780d742",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# topo_df.to_csv('topo_df.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92ec3a35-3f65-46bd-ab61-a9ded83a5147",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "To avoid computing all topographical predictors each time, just load the previously stored file. This function provides us with all the necessary topographical data for each glacier in Scandinavia:\n",
    "- `zmed`: Median altitude\n",
    "- `zmax`: Maximum altitude\n",
    "- `area`: Area of the glacier in the RGI (ca 2003)\n",
    "- `slope`: Average slope of the glacier (in degrees)\n",
    "- `lat`: Latitude (in degrees)\n",
    "- `icecap`: If the glacier is an icecap or a valley glacier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "647a70b8-a792-494e-a279-15e3043380bc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RGI_ID</th>\n",
       "      <th>zmed</th>\n",
       "      <th>zmax</th>\n",
       "      <th>zmin</th>\n",
       "      <th>area</th>\n",
       "      <th>slope</th>\n",
       "      <th>lat</th>\n",
       "      <th>icecap</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RGI60-08.00001</td>\n",
       "      <td>242.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>0.030</td>\n",
       "      <td>5.8</td>\n",
       "      <td>67.930</td>\n",
       "      <td>0</td>\n",
       "      <td>RGI60-08.00001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RGI60-08.00002</td>\n",
       "      <td>239.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>0.030</td>\n",
       "      <td>5.7</td>\n",
       "      <td>67.870</td>\n",
       "      <td>0</td>\n",
       "      <td>RGI60-08.00002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RGI60-08.00003</td>\n",
       "      <td>726.0</td>\n",
       "      <td>749.0</td>\n",
       "      <td>701.0</td>\n",
       "      <td>0.020</td>\n",
       "      <td>18.8</td>\n",
       "      <td>67.820</td>\n",
       "      <td>0</td>\n",
       "      <td>RGI60-08.00003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RGI60-08.00004</td>\n",
       "      <td>696.0</td>\n",
       "      <td>725.0</td>\n",
       "      <td>671.0</td>\n",
       "      <td>0.020</td>\n",
       "      <td>20.1</td>\n",
       "      <td>67.830</td>\n",
       "      <td>0</td>\n",
       "      <td>RGI60-08.00004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RGI60-08.00005</td>\n",
       "      <td>1268.0</td>\n",
       "      <td>1807.0</td>\n",
       "      <td>926.0</td>\n",
       "      <td>21.469</td>\n",
       "      <td>8.5</td>\n",
       "      <td>67.139</td>\n",
       "      <td>0</td>\n",
       "      <td>RGI60-08.00005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3412</th>\n",
       "      <td>RGI60-08.03413</td>\n",
       "      <td>980.0</td>\n",
       "      <td>1182.0</td>\n",
       "      <td>887.0</td>\n",
       "      <td>0.098</td>\n",
       "      <td>38.6</td>\n",
       "      <td>68.164</td>\n",
       "      <td>0</td>\n",
       "      <td>RGI60-08.03413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3413</th>\n",
       "      <td>RGI60-08.03414</td>\n",
       "      <td>1377.0</td>\n",
       "      <td>1452.0</td>\n",
       "      <td>1181.0</td>\n",
       "      <td>0.389</td>\n",
       "      <td>17.6</td>\n",
       "      <td>66.590</td>\n",
       "      <td>0</td>\n",
       "      <td>RGI60-08.03414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3414</th>\n",
       "      <td>RGI60-08.03415</td>\n",
       "      <td>1314.0</td>\n",
       "      <td>1330.0</td>\n",
       "      <td>1289.0</td>\n",
       "      <td>0.056</td>\n",
       "      <td>10.7</td>\n",
       "      <td>66.729</td>\n",
       "      <td>0</td>\n",
       "      <td>RGI60-08.03415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3415</th>\n",
       "      <td>RGI60-08.03416</td>\n",
       "      <td>1581.0</td>\n",
       "      <td>1811.0</td>\n",
       "      <td>890.0</td>\n",
       "      <td>14.152</td>\n",
       "      <td>7.7</td>\n",
       "      <td>66.006</td>\n",
       "      <td>0</td>\n",
       "      <td>RGI60-08.03416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3416</th>\n",
       "      <td>RGI60-08.03417</td>\n",
       "      <td>1186.0</td>\n",
       "      <td>1318.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>0.150</td>\n",
       "      <td>32.5</td>\n",
       "      <td>65.923</td>\n",
       "      <td>0</td>\n",
       "      <td>RGI60-08.03417</td>\n",
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       "<p>3417 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "              RGI_ID    zmed    zmax    zmin    area  slope     lat  icecap   \n",
       "0     RGI60-08.00001   242.0   250.0   235.0   0.030    5.8  67.930       0  \\\n",
       "1     RGI60-08.00002   239.0   246.0   228.0   0.030    5.7  67.870       0   \n",
       "2     RGI60-08.00003   726.0   749.0   701.0   0.020   18.8  67.820       0   \n",
       "3     RGI60-08.00004   696.0   725.0   671.0   0.020   20.1  67.830       0   \n",
       "4     RGI60-08.00005  1268.0  1807.0   926.0  21.469    8.5  67.139       0   \n",
       "...              ...     ...     ...     ...     ...    ...     ...     ...   \n",
       "3412  RGI60-08.03413   980.0  1182.0   887.0   0.098   38.6  68.164       0   \n",
       "3413  RGI60-08.03414  1377.0  1452.0  1181.0   0.389   17.6  66.590       0   \n",
       "3414  RGI60-08.03415  1314.0  1330.0  1289.0   0.056   10.7  66.729       0   \n",
       "3415  RGI60-08.03416  1581.0  1811.0   890.0  14.152    7.7  66.006       0   \n",
       "3416  RGI60-08.03417  1186.0  1318.0  1090.0   0.150   32.5  65.923       0   \n",
       "\n",
       "                  ID  \n",
       "0     RGI60-08.00001  \n",
       "1     RGI60-08.00002  \n",
       "2     RGI60-08.00003  \n",
       "3     RGI60-08.00004  \n",
       "4     RGI60-08.00005  \n",
       "...              ...  \n",
       "3412  RGI60-08.03413  \n",
       "3413  RGI60-08.03414  \n",
       "3414  RGI60-08.03415  \n",
       "3415  RGI60-08.03416  \n",
       "3416  RGI60-08.03417  \n",
       "\n",
       "[3417 rows x 9 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topo_df = pd.read_csv('Data/topo_df.csv')\n",
    "topo_df"
   ]
  },
  {
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   "id": "21dfc1ae-d789-405a-b171-3236c423ccbe",
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   "source": [
    "## 1. 3. Get glacier climate data"
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  },
  {
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   "id": "ab58d47e-2351-479c-99f9-645af29841ae",
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   "source": [
    "Finally, we need climate data as input features of our models. We get the climate data from CRU for each glacier. Same as for the topographical data, only uncomment the cell underneath if you really need to re-compute the data for a specific reason. "
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  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7462a171-66e6-4f47-948c-247617db2a16",
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   "outputs": [],
   "source": [
    "#global_tasks.compile_climate_input(gdirs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f1c0731-2594-4fb9-99f3-0adda2172289",
   "metadata": {
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   },
   "source": [
    "We just load the previously computed file using xarray, directly available. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "827182a9-0e07-4845-ad9c-7679f98819f8",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:         (time: 1416, rgi_id: 3417)\n",
       "Coordinates:\n",
       "  * time            (time) float64 1.902e+03 1.902e+03 ... 2.02e+03 2.02e+03\n",
       "  * rgi_id          (rgi_id) object &#x27;RGI60-08.00001&#x27; ... &#x27;RGI60-08.03417&#x27;\n",
       "    hydro_year      (time) int64 ...\n",
       "    hydro_month     (time) int64 ...\n",
       "    calendar_year   (time) int64 ...\n",
       "    calendar_month  (time) int64 ...\n",
       "Data variables:\n",
       "    temp            (time, rgi_id) float32 ...\n",
       "    prcp            (time, rgi_id) float32 ...\n",
       "    ref_hgt         (rgi_id) float64 ...\n",
       "    ref_pix_lon     (rgi_id) float64 ...\n",
       "    ref_pix_lat     (rgi_id) float64 ...\n",
       "Attributes:\n",
       "    description:    OGGM model output\n",
       "    oggm_version:   1.5.3\n",
       "    calendar:       365-day no leap\n",
       "    creation_date:  2022-06-24 13:19:30</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-54d0c30e-d420-4c28-a369-46733143daff' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-54d0c30e-d420-4c28-a369-46733143daff' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1416</li><li><span class='xr-has-index'>rgi_id</span>: 3417</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1f694f5a-e6e3-4240-b935-37c8d51cb596' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1f694f5a-e6e3-4240-b935-37c8d51cb596' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.902e+03 1.902e+03 ... 2.02e+03</div><input id='attrs-604e226d-670f-4468-8501-e673e15bcaa1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-604e226d-670f-4468-8501-e673e15bcaa1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31148edb-fc7d-44ef-9b1e-dc1a0ef27be2' class='xr-var-data-in' type='checkbox'><label for='data-31148edb-fc7d-44ef-9b1e-dc1a0ef27be2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Floating hydrological year</dd></dl></div><div class='xr-var-data'><pre>array([1902.      , 1902.083333, 1902.166667, ..., 2019.75    , 2019.833333,\n",
       "       2019.916667])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>rgi_id</span></div><div class='xr-var-dims'>(rgi_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;RGI60-08.00001&#x27; ... &#x27;RGI60-08.0...</div><input id='attrs-d01869cf-e9b5-4a48-9b63-a00325c2aa98' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d01869cf-e9b5-4a48-9b63-a00325c2aa98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3853d68a-5cb3-4a8d-85d0-0f385bdf3d29' class='xr-var-data-in' type='checkbox'><label for='data-3853d68a-5cb3-4a8d-85d0-0f385bdf3d29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>RGI glacier identifier</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;RGI60-08.00001&#x27;, &#x27;RGI60-08.00002&#x27;, &#x27;RGI60-08.00003&#x27;, ...,\n",
       "       &#x27;RGI60-08.03415&#x27;, &#x27;RGI60-08.03416&#x27;, &#x27;RGI60-08.03417&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hydro_year</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e89d6dda-0166-4361-83bc-34f3dace26d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e89d6dda-0166-4361-83bc-34f3dace26d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e6a82ae-c224-41b5-99f9-8a2a07db1cd7' class='xr-var-data-in' type='checkbox'><label for='data-3e6a82ae-c224-41b5-99f9-8a2a07db1cd7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Hydrological year</dd></dl></div><div class='xr-var-data'><pre>[1416 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hydro_month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1f82cde5-2aee-49ab-9122-d5a4c39ac065' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1f82cde5-2aee-49ab-9122-d5a4c39ac065' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6bd362d0-95d2-4fe9-9c76-8966c97e9971' class='xr-var-data-in' type='checkbox'><label for='data-6bd362d0-95d2-4fe9-9c76-8966c97e9971' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Hydrological month</dd></dl></div><div class='xr-var-data'><pre>[1416 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>calendar_year</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-45b3c920-6931-43f7-ba98-5dff9805fbed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45b3c920-6931-43f7-ba98-5dff9805fbed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-086c92d5-042b-4f03-be01-555c3d728f43' class='xr-var-data-in' type='checkbox'><label for='data-086c92d5-042b-4f03-be01-555c3d728f43' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Calendar year</dd></dl></div><div class='xr-var-data'><pre>[1416 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>calendar_month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d460721d-a49e-4d68-8bc7-ce8ba799227e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d460721d-a49e-4d68-8bc7-ce8ba799227e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c02027f-9a0e-4c59-8c50-a6a760ea0aea' class='xr-var-data-in' type='checkbox'><label for='data-5c02027f-9a0e-4c59-8c50-a6a760ea0aea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Calendar month</dd></dl></div><div class='xr-var-data'><pre>[1416 values with dtype=int64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-db276ac4-564a-4d2c-a3cb-190974f44b8a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-db276ac4-564a-4d2c-a3cb-190974f44b8a' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(time, rgi_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-acdf90ea-439c-4d07-a247-046ed241a535' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-acdf90ea-439c-4d07-a247-046ed241a535' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a50f0372-dda3-4910-b365-34bf39dba12e' class='xr-var-data-in' type='checkbox'><label for='data-a50f0372-dda3-4910-b365-34bf39dba12e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>DegC</dd><dt><span>description :</span></dt><dd>2m Temperature at height ref_hgt</dd></dl></div><div class='xr-var-data'><pre>[4838472 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prcp</span></div><div class='xr-var-dims'>(time, rgi_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-03bf908e-d892-4cb6-a8a2-7563c69d43fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03bf908e-d892-4cb6-a8a2-7563c69d43fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee63e8d9-cc40-4fa9-b6e7-8f7ea9300b7f' class='xr-var-data-in' type='checkbox'><label for='data-ee63e8d9-cc40-4fa9-b6e7-8f7ea9300b7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2</dd><dt><span>description :</span></dt><dd>total monthly precipitation amount</dd></dl></div><div class='xr-var-data'><pre>[4838472 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ref_hgt</span></div><div class='xr-var-dims'>(rgi_id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a08ab28a-4b47-4756-abda-955dcda7834e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a08ab28a-4b47-4756-abda-955dcda7834e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18786dc9-6f57-4d15-9ce5-f756ccba1d00' class='xr-var-data-in' type='checkbox'><label for='data-18786dc9-6f57-4d15-9ce5-f756ccba1d00' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>description :</span></dt><dd>reference height</dd></dl></div><div class='xr-var-data'><pre>[3417 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ref_pix_lon</span></div><div class='xr-var-dims'>(rgi_id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4c48de14-172f-49db-a1ee-91a2013f0f2e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c48de14-172f-49db-a1ee-91a2013f0f2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46d13154-8682-4763-b0fd-e714a8ee3a5b' class='xr-var-data-in' type='checkbox'><label for='data-46d13154-8682-4763-b0fd-e714a8ee3a5b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>[3417 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ref_pix_lat</span></div><div class='xr-var-dims'>(rgi_id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-743ffd20-0591-459e-b5b4-f0d400958561' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-743ffd20-0591-459e-b5b4-f0d400958561' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3769af75-19aa-41a9-b8cd-eec11b6fe569' class='xr-var-data-in' type='checkbox'><label for='data-3769af75-19aa-41a9-b8cd-eec11b6fe569' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>[3417 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9ff62a6a-8a9c-4a54-a3cf-b7fc3f58ae82' class='xr-section-summary-in' type='checkbox'  ><label for='section-9ff62a6a-8a9c-4a54-a3cf-b7fc3f58ae82' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c96710df-7835-4b9b-aea4-d5909f5780a4' class='xr-index-data-in' type='checkbox'/><label for='index-c96710df-7835-4b9b-aea4-d5909f5780a4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([            1902.0, 1902.0833333333333, 1902.1666666666667,\n",
       "                  1902.25, 1902.3333333333333, 1902.4166666666667,\n",
       "                   1902.5, 1902.5833333333333, 1902.6666666666667,\n",
       "                  1902.75,\n",
       "       ...\n",
       "       2019.1666666666667,            2019.25, 2019.3333333333333,\n",
       "       2019.4166666666667,             2019.5, 2019.5833333333333,\n",
       "       2019.6666666666667,            2019.75, 2019.8333333333333,\n",
       "       2019.9166666666667],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;, length=1416))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>rgi_id</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a64fe532-9c74-4a40-9fa8-59ed82daacf0' class='xr-index-data-in' type='checkbox'/><label for='index-a64fe532-9c74-4a40-9fa8-59ed82daacf0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;RGI60-08.00001&#x27;, &#x27;RGI60-08.00002&#x27;, &#x27;RGI60-08.00003&#x27;, &#x27;RGI60-08.00004&#x27;,\n",
       "       &#x27;RGI60-08.00005&#x27;, &#x27;RGI60-08.00006&#x27;, &#x27;RGI60-08.00007&#x27;, &#x27;RGI60-08.00008&#x27;,\n",
       "       &#x27;RGI60-08.00009&#x27;, &#x27;RGI60-08.00010&#x27;,\n",
       "       ...\n",
       "       &#x27;RGI60-08.03408&#x27;, &#x27;RGI60-08.03409&#x27;, &#x27;RGI60-08.03410&#x27;, &#x27;RGI60-08.03411&#x27;,\n",
       "       &#x27;RGI60-08.03412&#x27;, &#x27;RGI60-08.03413&#x27;, &#x27;RGI60-08.03414&#x27;, &#x27;RGI60-08.03415&#x27;,\n",
       "       &#x27;RGI60-08.03416&#x27;, &#x27;RGI60-08.03417&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;rgi_id&#x27;, length=3417))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ba4af831-600e-40be-905d-5e3e75e164ca' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ba4af831-600e-40be-905d-5e3e75e164ca' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>OGGM model output</dd><dt><span>oggm_version :</span></dt><dd>1.5.3</dd><dt><span>calendar :</span></dt><dd>365-day no leap</dd><dt><span>creation_date :</span></dt><dd>2022-06-24 13:19:30</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:         (time: 1416, rgi_id: 3417)\n",
       "Coordinates:\n",
       "  * time            (time) float64 1.902e+03 1.902e+03 ... 2.02e+03 2.02e+03\n",
       "  * rgi_id          (rgi_id) object 'RGI60-08.00001' ... 'RGI60-08.03417'\n",
       "    hydro_year      (time) int64 ...\n",
       "    hydro_month     (time) int64 ...\n",
       "    calendar_year   (time) int64 ...\n",
       "    calendar_month  (time) int64 ...\n",
       "Data variables:\n",
       "    temp            (time, rgi_id) float32 ...\n",
       "    prcp            (time, rgi_id) float32 ...\n",
       "    ref_hgt         (rgi_id) float64 ...\n",
       "    ref_pix_lon     (rgi_id) float64 ...\n",
       "    ref_pix_lat     (rgi_id) float64 ...\n",
       "Attributes:\n",
       "    description:    OGGM model output\n",
       "    oggm_version:   1.5.3\n",
       "    calendar:       365-day no leap\n",
       "    creation_date:  2022-06-24 13:19:30"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "climate_ds = xr.open_dataset('Data/climate_input.nc')\n",
    "climate_ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "512aabe9-3145-4166-a4dc-5da348db57e7",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "This xarray `Dataset` provides us with temperature and precipitation for each glacier in the region. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "421594dc-00e5-4096-b349-8dd68ef26d45",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "Let's add the median glacier altitude to the xarray `Dataset` in order to compute a temperature lapse rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bbc5b9a5-6193-418e-8bad-ea8c6d3f7428",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "climate_ds['zmed'] = ('rgi_id', topo_df['zmed'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02294842-00fd-461c-a665-01ee65e35448",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "The compiled climate dataset has time coordinates in decimal years, which is useless. Let's convert them to `datetime`. For this, we create a custom function to do that. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "53ed3b36-bb18-4777-911a-fcdee74149e4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def decimal_to_datetime(ds):\n",
    "    datetimes = []\n",
    "    for dec_year in ds.time.data:\n",
    "        year = int(dec_year)\n",
    "        rem = dec_year - year\n",
    "\n",
    "        base = datetime(year, 1, 1)\n",
    "        calendar_time = base + timedelta(seconds=(base.replace(year=base.year + 1) - base).total_seconds() * rem)\n",
    "        datetimes.append(calendar_time)\n",
    "        \n",
    "    climate_ds['time'] = datetimes\n",
    "        \n",
    "    return climate_ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f48ec891-5bf7-49f1-9176-dacd2fb50d98",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "We trim the data for the desired period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "81bec34c-6477-4b14-867d-1004fea7bc38",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "climate_ds = climate_ds.sel(time=slice(2000.0,2020.0)) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "130c1909-a997-4e2d-a13f-8ed2bd2b3b10",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "Let's convert decimal years to `datetime`. As previously done, this is computationally expensive, so only uncomment this if you need to re-run it for a specific reason. The xarray Dataset `climate_ds` that you have loaded already has all this processing in place. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6da1fb6f-d1f1-4ea0-bd9c-44b0de408975",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#climate_ds = decimal_to_datetime(climate_ds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f859ad1e-fc09-4bae-ac51-6340198f9758",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "The final step is to convert the dataset into a DataFrame in order to have a data science ready format."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d6be676-77d4-471a-a60c-dbea23a1365b",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "First we export the dataset as is, which gives as monthly data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cfc45768-5318-4095-9c91-d389e96b157b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mon_climate_df = climate_ds.to_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "23c1648b-8203-4ca0-b0df-34f1629b58fa",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# mon_climate_df.reset_index(level='year', drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d1abbe42-549f-4956-ad29-6cfa49bb8ceb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#mon_climate_df.set_index('hydro_year', append=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec3937c0-fd88-4f3b-857f-4be0076ca9b4",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "Then, we resample everything with an annual timestep in order to have annual variables to be included in the ML training dataset. Moreover, we also apply the lapse rates to roughly downscale temperature and the rain/snow fraction at each glacier. For this, we also create a custom function to perform this task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8bb0aa34-acd3-4d33-a21b-79f5247cffc4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_annual_dataset(climate_ds, period=np.arange(2000.0, 2020.0)):\n",
    "    \"\"\"\n",
    "    Mandatory docstring\n",
    "    \"\"\"    \n",
    "    # Temperature\n",
    "    climate_ds.temp.data = climate_ds.temp.data + 6.0/1000.0*(climate_ds.zmed.data - climate_ds.ref_hgt.data) # Super rough temperature lapse rate  \n",
    "    yr_temp_ds = climate_ds.temp.where(climate_ds.temp > 0.0).groupby('hydro_year').sum()\n",
    "    yr_temp_ds = yr_temp_ds.rename('PDD')\n",
    "    \n",
    "    # Snowfall\n",
    "    yr_snow_ds = climate_ds.prcp.where(climate_ds.temp <= 0.0).groupby('hydro_year').sum()\n",
    "    yr_snow_ds = yr_snow_ds.rename('snow')\n",
    "    # Rainfall\n",
    "    yr_rain_ds = climate_ds.prcp.where(climate_ds.temp > 0.0).groupby('hydro_year').sum()\n",
    "    yr_rain_ds = yr_rain_ds.rename('rain')\n",
    "    \n",
    "    return yr_temp_ds, yr_snow_ds, yr_rain_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a46333d2-cab4-430e-b269-9cdfff96bb87",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "yr_temp_ds, yr_snow_ds, yr_rain_ds = get_annual_dataset(climate_ds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0315db62-a6b5-4f39-a3b7-c56c61bca8b1",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "And we finally export the 3 annual datasets as `Dataframes.yr_temp_ds`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d357e7b3-3cfb-41d7-9583-325611518fd6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "yr_temp_df = yr_temp_ds.to_dataframe()\n",
    "yr_snow_df = yr_snow_ds.to_dataframe()\n",
    "yr_rain_df = yr_rain_ds.to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0aad9ef-2081-45e7-af5a-682cbbc9010a",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "## 1.4. Creating the ML dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b8a1bd6-bdd7-4612-ad96-985572138b20",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "Now we need a function that fetches the raw data from these different Dataframes and produces the right list of predictors for the ML models.\n",
    "\n",
    "The ML models will take predictors for a given hydrological year comprising:\n",
    "\n",
    "Climate data:\n",
    "\n",
    "- Annual cumulative snowfall\n",
    "- Annual cumulative rainfall\n",
    "- Annual cumulative PDDs\n",
    "- Monthly average temperature\n",
    "- Monthly snowfall\n",
    "\n",
    "Topographical data (around 2003):\n",
    "\n",
    "- Median glacier altitude\n",
    "- Maximum glacier altitude\n",
    "- Minimum glacier altitude\n",
    "- Glacier area\n",
    "- Glacier average slope\n",
    "\n",
    "To be added in order to better constrain ice dynamics (not yet available):\n",
    "\n",
    "- Average ice thickness\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75972b3c-c2d7-476c-973f-32cea6970565",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "We start by merging the yearly climate Dataframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4125dd43-0704-43a5-abda-a455d366a1f9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>PDD</th>\n",
       "      <th>snow</th>\n",
       "      <th>rain</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hydro_year</th>\n",
       "      <th>rgi_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2000</th>\n",
       "      <th>RGI60-08.00001</th>\n",
       "      <td>40.322890</td>\n",
       "      <td>295.120697</td>\n",
       "      <td>372.512909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI60-08.00002</th>\n",
       "      <td>45.640445</td>\n",
       "      <td>295.188782</td>\n",
       "      <td>373.351196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI60-08.00003</th>\n",
       "      <td>35.414001</td>\n",
       "      <td>338.535095</td>\n",
       "      <td>399.014038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI60-08.00004</th>\n",
       "      <td>34.334001</td>\n",
       "      <td>338.535095</td>\n",
       "      <td>399.014038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI60-08.00005</th>\n",
       "      <td>37.163999</td>\n",
       "      <td>522.478821</td>\n",
       "      <td>538.000427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2019</th>\n",
       "      <th>RGI60-08.03413</th>\n",
       "      <td>60.530295</td>\n",
       "      <td>543.243530</td>\n",
       "      <td>655.152649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI60-08.03414</th>\n",
       "      <td>42.606446</td>\n",
       "      <td>659.431519</td>\n",
       "      <td>610.665527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI60-08.03415</th>\n",
       "      <td>32.761335</td>\n",
       "      <td>758.787720</td>\n",
       "      <td>584.368164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI60-08.03416</th>\n",
       "      <td>68.155333</td>\n",
       "      <td>761.559143</td>\n",
       "      <td>951.634644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RGI60-08.03417</th>\n",
       "      <td>54.643332</td>\n",
       "      <td>727.541016</td>\n",
       "      <td>903.703247</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68340 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 PDD        snow        rain\n",
       "hydro_year rgi_id                                           \n",
       "2000       RGI60-08.00001  40.322890  295.120697  372.512909\n",
       "           RGI60-08.00002  45.640445  295.188782  373.351196\n",
       "           RGI60-08.00003  35.414001  338.535095  399.014038\n",
       "           RGI60-08.00004  34.334001  338.535095  399.014038\n",
       "           RGI60-08.00005  37.163999  522.478821  538.000427\n",
       "...                              ...         ...         ...\n",
       "2019       RGI60-08.03413  60.530295  543.243530  655.152649\n",
       "           RGI60-08.03414  42.606446  659.431519  610.665527\n",
       "           RGI60-08.03415  32.761335  758.787720  584.368164\n",
       "           RGI60-08.03416  68.155333  761.559143  951.634644\n",
       "           RGI60-08.03417  54.643332  727.541016  903.703247\n",
       "\n",
       "[68340 rows x 3 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
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    }
   ],
   "source": [
    "yr_climate_df = pd.concat([yr_temp_df, yr_snow_df, yr_rain_df], axis=1)\n",
    "yr_climate_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77ba0a39-6f5a-4a60-b3c0-eb1385818b72",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "We now need to reformat the dataset in order to have each monthly climate series as a dedicated column. This is some funky `DataFrame` formatting using Pandas. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c851298c-32be-4d54-806f-bb15f0aec897",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def sel_month(df, idx):\n",
    "    '''\n",
    "    It takes groupby object and selects one single element of the group, creating a pandas data frames \n",
    "    just with that element.\n",
    "    '''\n",
    "    df_ = pd.DataFrame(df.groupby(['rgi_id', 'hydro_year'], as_index=False).nth(idx)).reset_index()\n",
    "    # Check we have the rigth hydromonth\n",
    "    assert np.unique(df_.hydro_month) == np.array([idx+1])\n",
    "\n",
    "    df_ = df_.set_index(['rgi_id', 'hydro_year'])\n",
    "    df_ = df_[['temp', 'prcp']]\n",
    "    #display(df_)\n",
    "    col_id = '0'+str(idx+1) if idx < 9 else str(idx+1)\n",
    "    df_ = df_.rename(columns={'temp': 'temp_'+col_id,\n",
    "                              'prcp': 'prcp_'+col_id})\n",
    "    \n",
    "    return df_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa996814-12c8-448b-bd25-58ad11d7eaa3",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "We first sort the dataframe to be sure is in increasing hydro year/month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "43bb79e8-e76b-487f-9a71-87d7b64fadeb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mon_climate_df = mon_climate_df.sort_values(['rgi_id', 'hydro_year', 'hydro_month'], ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92c25444-8c9c-4546-9815-60671a965c74",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "Combine information of temperature and precipitation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cb81be08-d5bd-4a56-b10f-a48c1734743d",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "      <td>-11.143332</td>\n",
       "      <td>50.980053</td>\n",
       "      <td>-10.580002</td>\n",
       "      <td>29.002270</td>\n",
       "      <td>-6.611111</td>\n",
       "      <td>59.659908</td>\n",
       "      <td>-1.732222</td>\n",
       "      <td>63.919777</td>\n",
       "      <td>3.233333</td>\n",
       "      <td>43.892872</td>\n",
       "      <td>9.202221</td>\n",
       "      <td>82.060509</td>\n",
       "      <td>13.494446</td>\n",
       "      <td>58.451614</td>\n",
       "      <td>10.450000</td>\n",
       "      <td>50.218761</td>\n",
       "      <td>5.927779</td>\n",
       "      <td>47.939716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>3.411111</td>\n",
       "      <td>50.515018</td>\n",
       "      <td>-3.147778</td>\n",
       "      <td>43.414459</td>\n",
       "      <td>-9.341112</td>\n",
       "      <td>49.670944</td>\n",
       "      <td>-7.543333</td>\n",
       "      <td>27.386324</td>\n",
       "      <td>-15.480002</td>\n",
       "      <td>35.375156</td>\n",
       "      <td>-12.011110</td>\n",
       "      <td>28.706146</td>\n",
       "      <td>-1.865555</td>\n",
       "      <td>43.481659</td>\n",
       "      <td>1.533333</td>\n",
       "      <td>37.501221</td>\n",
       "      <td>10.002221</td>\n",
       "      <td>21.494608</td>\n",
       "      <td>13.594445</td>\n",
       "      <td>83.403297</td>\n",
       "      <td>10.316667</td>\n",
       "      <td>62.819103</td>\n",
       "      <td>7.627779</td>\n",
       "      <td>35.580780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>-0.488889</td>\n",
       "      <td>59.345169</td>\n",
       "      <td>-7.447778</td>\n",
       "      <td>29.263451</td>\n",
       "      <td>-12.507779</td>\n",
       "      <td>24.280972</td>\n",
       "      <td>-12.309999</td>\n",
       "      <td>44.494129</td>\n",
       "      <td>-10.313335</td>\n",
       "      <td>44.540123</td>\n",
       "      <td>-8.044444</td>\n",
       "      <td>37.876644</td>\n",
       "      <td>-0.898889</td>\n",
       "      <td>24.116472</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>40.426228</td>\n",
       "      <td>9.202221</td>\n",
       "      <td>53.544838</td>\n",
       "      <td>13.794446</td>\n",
       "      <td>103.911064</td>\n",
       "      <td>10.316667</td>\n",
       "      <td>55.259472</td>\n",
       "      <td>4.727778</td>\n",
       "      <td>67.236656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>-2.455556</td>\n",
       "      <td>48.070133</td>\n",
       "      <td>-9.747778</td>\n",
       "      <td>34.243984</td>\n",
       "      <td>-13.707779</td>\n",
       "      <td>27.780344</td>\n",
       "      <td>-17.709999</td>\n",
       "      <td>46.093716</td>\n",
       "      <td>-9.480001</td>\n",
       "      <td>26.073019</td>\n",
       "      <td>-5.611111</td>\n",
       "      <td>28.461843</td>\n",
       "      <td>-3.232222</td>\n",
       "      <td>17.521786</td>\n",
       "      <td>4.133333</td>\n",
       "      <td>72.449425</td>\n",
       "      <td>6.135554</td>\n",
       "      <td>23.149651</td>\n",
       "      <td>15.094445</td>\n",
       "      <td>58.197781</td>\n",
       "      <td>11.316667</td>\n",
       "      <td>80.069733</td>\n",
       "      <td>6.494445</td>\n",
       "      <td>48.463291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>0.311111</td>\n",
       "      <td>78.174042</td>\n",
       "      <td>-3.414444</td>\n",
       "      <td>31.993261</td>\n",
       "      <td>-8.741112</td>\n",
       "      <td>62.266918</td>\n",
       "      <td>-10.909999</td>\n",
       "      <td>28.106590</td>\n",
       "      <td>-12.780002</td>\n",
       "      <td>40.665600</td>\n",
       "      <td>-7.111110</td>\n",
       "      <td>24.079779</td>\n",
       "      <td>-2.298889</td>\n",
       "      <td>28.318407</td>\n",
       "      <td>3.033333</td>\n",
       "      <td>55.463512</td>\n",
       "      <td>8.102221</td>\n",
       "      <td>57.078709</td>\n",
       "      <td>14.727779</td>\n",
       "      <td>57.060844</td>\n",
       "      <td>11.216666</td>\n",
       "      <td>97.000999</td>\n",
       "      <td>6.827778</td>\n",
       "      <td>105.222382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">RGI60-08.03417</th>\n",
       "      <th>2015</th>\n",
       "      <td>1.494445</td>\n",
       "      <td>104.149033</td>\n",
       "      <td>-3.284444</td>\n",
       "      <td>111.409660</td>\n",
       "      <td>-5.712222</td>\n",
       "      <td>266.413971</td>\n",
       "      <td>-7.793335</td>\n",
       "      <td>155.734222</td>\n",
       "      <td>-4.908890</td>\n",
       "      <td>189.870132</td>\n",
       "      <td>-2.700000</td>\n",
       "      <td>95.728027</td>\n",
       "      <td>-1.488889</td>\n",
       "      <td>118.604233</td>\n",
       "      <td>2.322222</td>\n",
       "      <td>73.268768</td>\n",
       "      <td>5.307778</td>\n",
       "      <td>99.347084</td>\n",
       "      <td>8.482221</td>\n",
       "      <td>90.682648</td>\n",
       "      <td>10.999999</td>\n",
       "      <td>88.481743</td>\n",
       "      <td>6.890000</td>\n",
       "      <td>135.403824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>1.894444</td>\n",
       "      <td>217.098801</td>\n",
       "      <td>-1.984444</td>\n",
       "      <td>211.801880</td>\n",
       "      <td>-3.912222</td>\n",
       "      <td>242.586319</td>\n",
       "      <td>-10.393335</td>\n",
       "      <td>101.253311</td>\n",
       "      <td>-6.008890</td>\n",
       "      <td>108.876656</td>\n",
       "      <td>-3.799999</td>\n",
       "      <td>145.192505</td>\n",
       "      <td>-1.455556</td>\n",
       "      <td>58.738297</td>\n",
       "      <td>4.088889</td>\n",
       "      <td>61.192421</td>\n",
       "      <td>6.907778</td>\n",
       "      <td>74.471062</td>\n",
       "      <td>10.382221</td>\n",
       "      <td>70.880188</td>\n",
       "      <td>8.599999</td>\n",
       "      <td>119.976753</td>\n",
       "      <td>7.290001</td>\n",
       "      <td>147.315826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>1.027778</td>\n",
       "      <td>89.045914</td>\n",
       "      <td>-4.484444</td>\n",
       "      <td>149.197235</td>\n",
       "      <td>-3.812222</td>\n",
       "      <td>371.887756</td>\n",
       "      <td>-5.493335</td>\n",
       "      <td>299.347626</td>\n",
       "      <td>-6.708890</td>\n",
       "      <td>121.731079</td>\n",
       "      <td>-5.200000</td>\n",
       "      <td>139.568573</td>\n",
       "      <td>-2.988889</td>\n",
       "      <td>149.403473</td>\n",
       "      <td>1.355556</td>\n",
       "      <td>55.090389</td>\n",
       "      <td>7.507778</td>\n",
       "      <td>67.357719</td>\n",
       "      <td>9.482221</td>\n",
       "      <td>117.627174</td>\n",
       "      <td>8.799999</td>\n",
       "      <td>156.433319</td>\n",
       "      <td>6.890000</td>\n",
       "      <td>33.727993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1.494445</td>\n",
       "      <td>176.113358</td>\n",
       "      <td>-4.384444</td>\n",
       "      <td>158.276733</td>\n",
       "      <td>-6.412222</td>\n",
       "      <td>192.674561</td>\n",
       "      <td>-8.093335</td>\n",
       "      <td>64.232956</td>\n",
       "      <td>-9.608891</td>\n",
       "      <td>24.806946</td>\n",
       "      <td>-8.999999</td>\n",
       "      <td>93.973999</td>\n",
       "      <td>-1.488889</td>\n",
       "      <td>65.463066</td>\n",
       "      <td>5.588889</td>\n",
       "      <td>72.653397</td>\n",
       "      <td>6.007778</td>\n",
       "      <td>123.401054</td>\n",
       "      <td>12.582221</td>\n",
       "      <td>25.543489</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>177.151459</td>\n",
       "      <td>5.990001</td>\n",
       "      <td>250.382614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>0.694444</td>\n",
       "      <td>217.587555</td>\n",
       "      <td>-1.051111</td>\n",
       "      <td>137.044174</td>\n",
       "      <td>-4.612222</td>\n",
       "      <td>171.008667</td>\n",
       "      <td>-8.993335</td>\n",
       "      <td>167.764053</td>\n",
       "      <td>-6.508890</td>\n",
       "      <td>227.375122</td>\n",
       "      <td>-6.400000</td>\n",
       "      <td>161.393158</td>\n",
       "      <td>0.211111</td>\n",
       "      <td>49.349823</td>\n",
       "      <td>2.022222</td>\n",
       "      <td>85.042221</td>\n",
       "      <td>7.674444</td>\n",
       "      <td>145.225189</td>\n",
       "      <td>10.182220</td>\n",
       "      <td>47.727448</td>\n",
       "      <td>9.999999</td>\n",
       "      <td>84.718292</td>\n",
       "      <td>4.990001</td>\n",
       "      <td>137.008652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68340 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            temp_01     prcp_01   temp_02     prcp_02   \n",
       "rgi_id         hydro_year                                               \n",
       "RGI60-08.00001 2000        2.011111   89.949440 -4.847777   39.799580  \\\n",
       "               2001        3.411111   50.515018 -3.147778   43.414459   \n",
       "               2002       -0.488889   59.345169 -7.447778   29.263451   \n",
       "               2003       -2.455556   48.070133 -9.747778   34.243984   \n",
       "               2004        0.311111   78.174042 -3.414444   31.993261   \n",
       "...                             ...         ...       ...         ...   \n",
       "RGI60-08.03417 2015        1.494445  104.149033 -3.284444  111.409660   \n",
       "               2016        1.894444  217.098801 -1.984444  211.801880   \n",
       "               2017        1.027778   89.045914 -4.484444  149.197235   \n",
       "               2018        1.494445  176.113358 -4.384444  158.276733   \n",
       "               2019        0.694444  217.587555 -1.051111  137.044174   \n",
       "\n",
       "                             temp_03     prcp_03    temp_04     prcp_04   \n",
       "rgi_id         hydro_year                                                 \n",
       "RGI60-08.00001 2000       -10.641112   51.759113 -11.143332   50.980053  \\\n",
       "               2001        -9.341112   49.670944  -7.543333   27.386324   \n",
       "               2002       -12.507779   24.280972 -12.309999   44.494129   \n",
       "               2003       -13.707779   27.780344 -17.709999   46.093716   \n",
       "               2004        -8.741112   62.266918 -10.909999   28.106590   \n",
       "...                              ...         ...        ...         ...   \n",
       "RGI60-08.03417 2015        -5.712222  266.413971  -7.793335  155.734222   \n",
       "               2016        -3.912222  242.586319 -10.393335  101.253311   \n",
       "               2017        -3.812222  371.887756  -5.493335  299.347626   \n",
       "               2018        -6.412222  192.674561  -8.093335   64.232956   \n",
       "               2019        -4.612222  171.008667  -8.993335  167.764053   \n",
       "\n",
       "                             temp_05     prcp_05    temp_06     prcp_06   \n",
       "rgi_id         hydro_year                                                 \n",
       "RGI60-08.00001 2000       -10.580002   29.002270  -6.611111   59.659908  \\\n",
       "               2001       -15.480002   35.375156 -12.011110   28.706146   \n",
       "               2002       -10.313335   44.540123  -8.044444   37.876644   \n",
       "               2003        -9.480001   26.073019  -5.611111   28.461843   \n",
       "               2004       -12.780002   40.665600  -7.111110   24.079779   \n",
       "...                              ...         ...        ...         ...   \n",
       "RGI60-08.03417 2015        -4.908890  189.870132  -2.700000   95.728027   \n",
       "               2016        -6.008890  108.876656  -3.799999  145.192505   \n",
       "               2017        -6.708890  121.731079  -5.200000  139.568573   \n",
       "               2018        -9.608891   24.806946  -8.999999   93.973999   \n",
       "               2019        -6.508890  227.375122  -6.400000  161.393158   \n",
       "\n",
       "                            temp_07     prcp_07   temp_08    prcp_08   \n",
       "rgi_id         hydro_year                                              \n",
       "RGI60-08.00001 2000       -1.732222   63.919777  3.233333  43.892872  \\\n",
       "               2001       -1.865555   43.481659  1.533333  37.501221   \n",
       "               2002       -0.898889   24.116472  3.333333  40.426228   \n",
       "               2003       -3.232222   17.521786  4.133333  72.449425   \n",
       "               2004       -2.298889   28.318407  3.033333  55.463512   \n",
       "...                             ...         ...       ...        ...   \n",
       "RGI60-08.03417 2015       -1.488889  118.604233  2.322222  73.268768   \n",
       "               2016       -1.455556   58.738297  4.088889  61.192421   \n",
       "               2017       -2.988889  149.403473  1.355556  55.090389   \n",
       "               2018       -1.488889   65.463066  5.588889  72.653397   \n",
       "               2019        0.211111   49.349823  2.022222  85.042221   \n",
       "\n",
       "                             temp_09     prcp_09    temp_10     prcp_10   \n",
       "rgi_id         hydro_year                                                 \n",
       "RGI60-08.00001 2000         9.202221   82.060509  13.494446   58.451614  \\\n",
       "               2001        10.002221   21.494608  13.594445   83.403297   \n",
       "               2002         9.202221   53.544838  13.794446  103.911064   \n",
       "               2003         6.135554   23.149651  15.094445   58.197781   \n",
       "               2004         8.102221   57.078709  14.727779   57.060844   \n",
       "...                              ...         ...        ...         ...   \n",
       "RGI60-08.03417 2015         5.307778   99.347084   8.482221   90.682648   \n",
       "               2016         6.907778   74.471062  10.382221   70.880188   \n",
       "               2017         7.507778   67.357719   9.482221  117.627174   \n",
       "               2018         6.007778  123.401054  12.582221   25.543489   \n",
       "               2019         7.674444  145.225189  10.182220   47.727448   \n",
       "\n",
       "                             temp_11     prcp_11   temp_12     prcp_12  \n",
       "rgi_id         hydro_year                                               \n",
       "RGI60-08.00001 2000        10.450000   50.218761  5.927779   47.939716  \n",
       "               2001        10.316667   62.819103  7.627779   35.580780  \n",
       "               2002        10.316667   55.259472  4.727778   67.236656  \n",
       "               2003        11.316667   80.069733  6.494445   48.463291  \n",
       "               2004        11.216666   97.000999  6.827778  105.222382  \n",
       "...                              ...         ...       ...         ...  \n",
       "RGI60-08.03417 2015        10.999999   88.481743  6.890000  135.403824  \n",
       "               2016         8.599999  119.976753  7.290001  147.315826  \n",
       "               2017         8.799999  156.433319  6.890000   33.727993  \n",
       "               2018         9.400000  177.151459  5.990001  250.382614  \n",
       "               2019         9.999999   84.718292  4.990001  137.008652  \n",
       "\n",
       "[68340 rows x 24 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mon_training_df = pd.concat([sel_month(mon_climate_df, i) for i in range(12)], axis=1, ignore_index=False)\n",
    "mon_training_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b20e0ef8-f6ca-4d86-8cbe-0332ef080ada",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "And merge the monthly dataset with the annual climate dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "08084aef-27d9-478c-b8c7-c82c7901c5a3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>temp_01</th>\n",
       "      <th>prcp_01</th>\n",
       "      <th>temp_02</th>\n",
       "      <th>prcp_02</th>\n",
       "      <th>temp_03</th>\n",
       "      <th>prcp_03</th>\n",
       "      <th>temp_04</th>\n",
       "      <th>prcp_04</th>\n",
       "      <th>temp_05</th>\n",
       "      <th>prcp_05</th>\n",
       "      <th>temp_06</th>\n",
       "      <th>prcp_06</th>\n",
       "      <th>temp_07</th>\n",
       "      <th>prcp_07</th>\n",
       "      <th>temp_08</th>\n",
       "      <th>prcp_08</th>\n",
       "      <th>temp_09</th>\n",
       "      <th>prcp_09</th>\n",
       "      <th>temp_10</th>\n",
       "      <th>prcp_10</th>\n",
       "      <th>temp_11</th>\n",
       "      <th>prcp_11</th>\n",
       "      <th>temp_12</th>\n",
       "      <th>prcp_12</th>\n",
       "      <th>PDD</th>\n",
       "      <th>snow</th>\n",
       "      <th>rain</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rgi_id</th>\n",
       "      <th>hydro_year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">RGI60-08.00001</th>\n",
       "      <th>2000</th>\n",
       "      <td>2.011111</td>\n",
       "      <td>89.949440</td>\n",
       "      <td>-4.847777</td>\n",
       "      <td>39.799580</td>\n",
       "      <td>-10.641112</td>\n",
       "      <td>51.759113</td>\n",
       "      <td>-11.143332</td>\n",
       "      <td>50.980053</td>\n",
       "      <td>-10.580002</td>\n",
       "      <td>29.002270</td>\n",
       "      <td>-6.611111</td>\n",
       "      <td>59.659908</td>\n",
       "      <td>-1.732222</td>\n",
       "      <td>63.919777</td>\n",
       "      <td>3.233333</td>\n",
       "      <td>43.892872</td>\n",
       "      <td>9.202221</td>\n",
       "      <td>82.060509</td>\n",
       "      <td>13.494446</td>\n",
       "      <td>58.451614</td>\n",
       "      <td>10.450000</td>\n",
       "      <td>50.218761</td>\n",
       "      <td>5.927779</td>\n",
       "      <td>47.939716</td>\n",
       "      <td>40.322890</td>\n",
       "      <td>295.120697</td>\n",
       "      <td>372.512909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>3.411111</td>\n",
       "      <td>50.515018</td>\n",
       "      <td>-3.147778</td>\n",
       "      <td>43.414459</td>\n",
       "      <td>-9.341112</td>\n",
       "      <td>49.670944</td>\n",
       "      <td>-7.543333</td>\n",
       "      <td>27.386324</td>\n",
       "      <td>-15.480002</td>\n",
       "      <td>35.375156</td>\n",
       "      <td>-12.011110</td>\n",
       "      <td>28.706146</td>\n",
       "      <td>-1.865555</td>\n",
       "      <td>43.481659</td>\n",
       "      <td>1.533333</td>\n",
       "      <td>37.501221</td>\n",
       "      <td>10.002221</td>\n",
       "      <td>21.494608</td>\n",
       "      <td>13.594445</td>\n",
       "      <td>83.403297</td>\n",
       "      <td>10.316667</td>\n",
       "      <td>62.819103</td>\n",
       "      <td>7.627779</td>\n",
       "      <td>35.580780</td>\n",
       "      <td>42.489556</td>\n",
       "      <td>228.034683</td>\n",
       "      <td>291.314026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>-0.488889</td>\n",
       "      <td>59.345169</td>\n",
       "      <td>-7.447778</td>\n",
       "      <td>29.263451</td>\n",
       "      <td>-12.507779</td>\n",
       "      <td>24.280972</td>\n",
       "      <td>-12.309999</td>\n",
       "      <td>44.494129</td>\n",
       "      <td>-10.313335</td>\n",
       "      <td>44.540123</td>\n",
       "      <td>-8.044444</td>\n",
       "      <td>37.876644</td>\n",
       "      <td>-0.898889</td>\n",
       "      <td>24.116472</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>40.426228</td>\n",
       "      <td>9.202221</td>\n",
       "      <td>53.544838</td>\n",
       "      <td>13.794446</td>\n",
       "      <td>103.911064</td>\n",
       "      <td>10.316667</td>\n",
       "      <td>55.259472</td>\n",
       "      <td>4.727778</td>\n",
       "      <td>67.236656</td>\n",
       "      <td>38.044445</td>\n",
       "      <td>263.916962</td>\n",
       "      <td>320.378265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>-2.455556</td>\n",
       "      <td>48.070133</td>\n",
       "      <td>-9.747778</td>\n",
       "      <td>34.243984</td>\n",
       "      <td>-13.707779</td>\n",
       "      <td>27.780344</td>\n",
       "      <td>-17.709999</td>\n",
       "      <td>46.093716</td>\n",
       "      <td>-9.480001</td>\n",
       "      <td>26.073019</td>\n",
       "      <td>-5.611111</td>\n",
       "      <td>28.461843</td>\n",
       "      <td>-3.232222</td>\n",
       "      <td>17.521786</td>\n",
       "      <td>4.133333</td>\n",
       "      <td>72.449425</td>\n",
       "      <td>6.135554</td>\n",
       "      <td>23.149651</td>\n",
       "      <td>15.094445</td>\n",
       "      <td>58.197781</td>\n",
       "      <td>11.316667</td>\n",
       "      <td>80.069733</td>\n",
       "      <td>6.494445</td>\n",
       "      <td>48.463291</td>\n",
       "      <td>39.844445</td>\n",
       "      <td>228.244812</td>\n",
       "      <td>282.329895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>0.311111</td>\n",
       "      <td>78.174042</td>\n",
       "      <td>-3.414444</td>\n",
       "      <td>31.993261</td>\n",
       "      <td>-8.741112</td>\n",
       "      <td>62.266918</td>\n",
       "      <td>-10.909999</td>\n",
       "      <td>28.106590</td>\n",
       "      <td>-12.780002</td>\n",
       "      <td>40.665600</td>\n",
       "      <td>-7.111110</td>\n",
       "      <td>24.079779</td>\n",
       "      <td>-2.298889</td>\n",
       "      <td>28.318407</td>\n",
       "      <td>3.033333</td>\n",
       "      <td>55.463512</td>\n",
       "      <td>8.102221</td>\n",
       "      <td>57.078709</td>\n",
       "      <td>14.727779</td>\n",
       "      <td>57.060844</td>\n",
       "      <td>11.216666</td>\n",
       "      <td>97.000999</td>\n",
       "      <td>6.827778</td>\n",
       "      <td>105.222382</td>\n",
       "      <td>40.577779</td>\n",
       "      <td>293.604614</td>\n",
       "      <td>371.826447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">RGI60-08.03417</th>\n",
       "      <th>2015</th>\n",
       "      <td>1.494445</td>\n",
       "      <td>104.149033</td>\n",
       "      <td>-3.284444</td>\n",
       "      <td>111.409660</td>\n",
       "      <td>-5.712222</td>\n",
       "      <td>266.413971</td>\n",
       "      <td>-7.793335</td>\n",
       "      <td>155.734222</td>\n",
       "      <td>-4.908890</td>\n",
       "      <td>189.870132</td>\n",
       "      <td>-2.700000</td>\n",
       "      <td>95.728027</td>\n",
       "      <td>-1.488889</td>\n",
       "      <td>118.604233</td>\n",
       "      <td>2.322222</td>\n",
       "      <td>73.268768</td>\n",
       "      <td>5.307778</td>\n",
       "      <td>99.347084</td>\n",
       "      <td>8.482221</td>\n",
       "      <td>90.682648</td>\n",
       "      <td>10.999999</td>\n",
       "      <td>88.481743</td>\n",
       "      <td>6.890000</td>\n",
       "      <td>135.403824</td>\n",
       "      <td>51.437776</td>\n",
       "      <td>819.156006</td>\n",
       "      <td>709.937317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>1.894444</td>\n",
       "      <td>217.098801</td>\n",
       "      <td>-1.984444</td>\n",
       "      <td>211.801880</td>\n",
       "      <td>-3.912222</td>\n",
       "      <td>242.586319</td>\n",
       "      <td>-10.393335</td>\n",
       "      <td>101.253311</td>\n",
       "      <td>-6.008890</td>\n",
       "      <td>108.876656</td>\n",
       "      <td>-3.799999</td>\n",
       "      <td>145.192505</td>\n",
       "      <td>-1.455556</td>\n",
       "      <td>58.738297</td>\n",
       "      <td>4.088889</td>\n",
       "      <td>61.192421</td>\n",
       "      <td>6.907778</td>\n",
       "      <td>74.471062</td>\n",
       "      <td>10.382221</td>\n",
       "      <td>70.880188</td>\n",
       "      <td>8.599999</td>\n",
       "      <td>119.976753</td>\n",
       "      <td>7.290001</td>\n",
       "      <td>147.315826</td>\n",
       "      <td>55.643333</td>\n",
       "      <td>597.908813</td>\n",
       "      <td>961.475220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>1.027778</td>\n",
       "      <td>89.045914</td>\n",
       "      <td>-4.484444</td>\n",
       "      <td>149.197235</td>\n",
       "      <td>-3.812222</td>\n",
       "      <td>371.887756</td>\n",
       "      <td>-5.493335</td>\n",
       "      <td>299.347626</td>\n",
       "      <td>-6.708890</td>\n",
       "      <td>121.731079</td>\n",
       "      <td>-5.200000</td>\n",
       "      <td>139.568573</td>\n",
       "      <td>-2.988889</td>\n",
       "      <td>149.403473</td>\n",
       "      <td>1.355556</td>\n",
       "      <td>55.090389</td>\n",
       "      <td>7.507778</td>\n",
       "      <td>67.357719</td>\n",
       "      <td>9.482221</td>\n",
       "      <td>117.627174</td>\n",
       "      <td>8.799999</td>\n",
       "      <td>156.433319</td>\n",
       "      <td>6.890000</td>\n",
       "      <td>33.727993</td>\n",
       "      <td>50.003331</td>\n",
       "      <td>1231.135742</td>\n",
       "      <td>519.282471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1.494445</td>\n",
       "      <td>176.113358</td>\n",
       "      <td>-4.384444</td>\n",
       "      <td>158.276733</td>\n",
       "      <td>-6.412222</td>\n",
       "      <td>192.674561</td>\n",
       "      <td>-8.093335</td>\n",
       "      <td>64.232956</td>\n",
       "      <td>-9.608891</td>\n",
       "      <td>24.806946</td>\n",
       "      <td>-8.999999</td>\n",
       "      <td>93.973999</td>\n",
       "      <td>-1.488889</td>\n",
       "      <td>65.463066</td>\n",
       "      <td>5.588889</td>\n",
       "      <td>72.653397</td>\n",
       "      <td>6.007778</td>\n",
       "      <td>123.401054</td>\n",
       "      <td>12.582221</td>\n",
       "      <td>25.543489</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>177.151459</td>\n",
       "      <td>5.990001</td>\n",
       "      <td>250.382614</td>\n",
       "      <td>57.004444</td>\n",
       "      <td>533.965210</td>\n",
       "      <td>890.708435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>0.694444</td>\n",
       "      <td>217.587555</td>\n",
       "      <td>-1.051111</td>\n",
       "      <td>137.044174</td>\n",
       "      <td>-4.612222</td>\n",
       "      <td>171.008667</td>\n",
       "      <td>-8.993335</td>\n",
       "      <td>167.764053</td>\n",
       "      <td>-6.508890</td>\n",
       "      <td>227.375122</td>\n",
       "      <td>-6.400000</td>\n",
       "      <td>161.393158</td>\n",
       "      <td>0.211111</td>\n",
       "      <td>49.349823</td>\n",
       "      <td>2.022222</td>\n",
       "      <td>85.042221</td>\n",
       "      <td>7.674444</td>\n",
       "      <td>145.225189</td>\n",
       "      <td>10.182220</td>\n",
       "      <td>47.727448</td>\n",
       "      <td>9.999999</td>\n",
       "      <td>84.718292</td>\n",
       "      <td>4.990001</td>\n",
       "      <td>137.008652</td>\n",
       "      <td>54.643332</td>\n",
       "      <td>727.541016</td>\n",
       "      <td>903.703247</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68340 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            temp_01     prcp_01   temp_02     prcp_02   \n",
       "rgi_id         hydro_year                                               \n",
       "RGI60-08.00001 2000        2.011111   89.949440 -4.847777   39.799580  \\\n",
       "               2001        3.411111   50.515018 -3.147778   43.414459   \n",
       "               2002       -0.488889   59.345169 -7.447778   29.263451   \n",
       "               2003       -2.455556   48.070133 -9.747778   34.243984   \n",
       "               2004        0.311111   78.174042 -3.414444   31.993261   \n",
       "...                             ...         ...       ...         ...   \n",
       "RGI60-08.03417 2015        1.494445  104.149033 -3.284444  111.409660   \n",
       "               2016        1.894444  217.098801 -1.984444  211.801880   \n",
       "               2017        1.027778   89.045914 -4.484444  149.197235   \n",
       "               2018        1.494445  176.113358 -4.384444  158.276733   \n",
       "               2019        0.694444  217.587555 -1.051111  137.044174   \n",
       "\n",
       "                             temp_03     prcp_03    temp_04     prcp_04   \n",
       "rgi_id         hydro_year                                                 \n",
       "RGI60-08.00001 2000       -10.641112   51.759113 -11.143332   50.980053  \\\n",
       "               2001        -9.341112   49.670944  -7.543333   27.386324   \n",
       "               2002       -12.507779   24.280972 -12.309999   44.494129   \n",
       "               2003       -13.707779   27.780344 -17.709999   46.093716   \n",
       "               2004        -8.741112   62.266918 -10.909999   28.106590   \n",
       "...                              ...         ...        ...         ...   \n",
       "RGI60-08.03417 2015        -5.712222  266.413971  -7.793335  155.734222   \n",
       "               2016        -3.912222  242.586319 -10.393335  101.253311   \n",
       "               2017        -3.812222  371.887756  -5.493335  299.347626   \n",
       "               2018        -6.412222  192.674561  -8.093335   64.232956   \n",
       "               2019        -4.612222  171.008667  -8.993335  167.764053   \n",
       "\n",
       "                             temp_05     prcp_05    temp_06     prcp_06   \n",
       "rgi_id         hydro_year                                                 \n",
       "RGI60-08.00001 2000       -10.580002   29.002270  -6.611111   59.659908  \\\n",
       "               2001       -15.480002   35.375156 -12.011110   28.706146   \n",
       "               2002       -10.313335   44.540123  -8.044444   37.876644   \n",
       "               2003        -9.480001   26.073019  -5.611111   28.461843   \n",
       "               2004       -12.780002   40.665600  -7.111110   24.079779   \n",
       "...                              ...         ...        ...         ...   \n",
       "RGI60-08.03417 2015        -4.908890  189.870132  -2.700000   95.728027   \n",
       "               2016        -6.008890  108.876656  -3.799999  145.192505   \n",
       "               2017        -6.708890  121.731079  -5.200000  139.568573   \n",
       "               2018        -9.608891   24.806946  -8.999999   93.973999   \n",
       "               2019        -6.508890  227.375122  -6.400000  161.393158   \n",
       "\n",
       "                            temp_07     prcp_07   temp_08    prcp_08   \n",
       "rgi_id         hydro_year                                              \n",
       "RGI60-08.00001 2000       -1.732222   63.919777  3.233333  43.892872  \\\n",
       "               2001       -1.865555   43.481659  1.533333  37.501221   \n",
       "               2002       -0.898889   24.116472  3.333333  40.426228   \n",
       "               2003       -3.232222   17.521786  4.133333  72.449425   \n",
       "               2004       -2.298889   28.318407  3.033333  55.463512   \n",
       "...                             ...         ...       ...        ...   \n",
       "RGI60-08.03417 2015       -1.488889  118.604233  2.322222  73.268768   \n",
       "               2016       -1.455556   58.738297  4.088889  61.192421   \n",
       "               2017       -2.988889  149.403473  1.355556  55.090389   \n",
       "               2018       -1.488889   65.463066  5.588889  72.653397   \n",
       "               2019        0.211111   49.349823  2.022222  85.042221   \n",
       "\n",
       "                             temp_09     prcp_09    temp_10     prcp_10   \n",
       "rgi_id         hydro_year                                                 \n",
       "RGI60-08.00001 2000         9.202221   82.060509  13.494446   58.451614  \\\n",
       "               2001        10.002221   21.494608  13.594445   83.403297   \n",
       "               2002         9.202221   53.544838  13.794446  103.911064   \n",
       "               2003         6.135554   23.149651  15.094445   58.197781   \n",
       "               2004         8.102221   57.078709  14.727779   57.060844   \n",
       "...                              ...         ...        ...         ...   \n",
       "RGI60-08.03417 2015         5.307778   99.347084   8.482221   90.682648   \n",
       "               2016         6.907778   74.471062  10.382221   70.880188   \n",
       "               2017         7.507778   67.357719   9.482221  117.627174   \n",
       "               2018         6.007778  123.401054  12.582221   25.543489   \n",
       "               2019         7.674444  145.225189  10.182220   47.727448   \n",
       "\n",
       "                             temp_11     prcp_11   temp_12     prcp_12   \n",
       "rgi_id         hydro_year                                                \n",
       "RGI60-08.00001 2000        10.450000   50.218761  5.927779   47.939716  \\\n",
       "               2001        10.316667   62.819103  7.627779   35.580780   \n",
       "               2002        10.316667   55.259472  4.727778   67.236656   \n",
       "               2003        11.316667   80.069733  6.494445   48.463291   \n",
       "               2004        11.216666   97.000999  6.827778  105.222382   \n",
       "...                              ...         ...       ...         ...   \n",
       "RGI60-08.03417 2015        10.999999   88.481743  6.890000  135.403824   \n",
       "               2016         8.599999  119.976753  7.290001  147.315826   \n",
       "               2017         8.799999  156.433319  6.890000   33.727993   \n",
       "               2018         9.400000  177.151459  5.990001  250.382614   \n",
       "               2019         9.999999   84.718292  4.990001  137.008652   \n",
       "\n",
       "                                 PDD         snow        rain  \n",
       "rgi_id         hydro_year                                      \n",
       "RGI60-08.00001 2000        40.322890   295.120697  372.512909  \n",
       "               2001        42.489556   228.034683  291.314026  \n",
       "               2002        38.044445   263.916962  320.378265  \n",
       "               2003        39.844445   228.244812  282.329895  \n",
       "               2004        40.577779   293.604614  371.826447  \n",
       "...                              ...          ...         ...  \n",
       "RGI60-08.03417 2015        51.437776   819.156006  709.937317  \n",
       "               2016        55.643333   597.908813  961.475220  \n",
       "               2017        50.003331  1231.135742  519.282471  \n",
       "               2018        57.004444   533.965210  890.708435  \n",
       "               2019        54.643332   727.541016  903.703247  \n",
       "\n",
       "[68340 rows x 27 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_df = pd.merge(mon_training_df, yr_climate_df, on=[\"rgi_id\", \"hydro_year\"])\n",
    "training_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "318a9ad3-ab77-4113-9630-0237fe1972d9",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "And we finally add the topographical data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "dbfc281d-6d41-4da1-ad4d-61f2c6332477",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>PDD</th>\n",
       "      <th>RGI_ID</th>\n",
       "      <th>area</th>\n",
       "      <th>icecap</th>\n",
       "      <th>lat</th>\n",
       "      <th>prcp_01</th>\n",
       "      <th>prcp_02</th>\n",
       "      <th>prcp_03</th>\n",
       "      <th>prcp_04</th>\n",
       "      <th>prcp_05</th>\n",
       "      <th>prcp_06</th>\n",
       "      <th>prcp_07</th>\n",
       "      <th>prcp_08</th>\n",
       "      <th>prcp_09</th>\n",
       "      <th>prcp_10</th>\n",
       "      <th>prcp_11</th>\n",
       "      <th>prcp_12</th>\n",
       "      <th>rain</th>\n",
       "      <th>slope</th>\n",
       "      <th>snow</th>\n",
       "      <th>temp_01</th>\n",
       "      <th>temp_02</th>\n",
       "      <th>temp_03</th>\n",
       "      <th>temp_04</th>\n",
       "      <th>temp_05</th>\n",
       "      <th>temp_06</th>\n",
       "      <th>temp_07</th>\n",
       "      <th>temp_08</th>\n",
       "      <th>temp_09</th>\n",
       "      <th>temp_10</th>\n",
       "      <th>temp_11</th>\n",
       "      <th>temp_12</th>\n",
       "      <th>zmax</th>\n",
       "      <th>zmed</th>\n",
       "      <th>zmin</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rgi_id</th>\n",
       "      <th>hydro_year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">RGI60-08.00001</th>\n",
       "      <th>2000</th>\n",
       "      <td>40.322890</td>\n",
       "      <td>RGI60-08.00001</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0</td>\n",
       "      <td>67.930</td>\n",
       "      <td>89.949440</td>\n",
       "      <td>39.799580</td>\n",
       "      <td>51.759113</td>\n",
       "      <td>50.980053</td>\n",
       "      <td>29.002270</td>\n",
       "      <td>59.659908</td>\n",
       "      <td>63.919777</td>\n",
       "      <td>43.892872</td>\n",
       "      <td>82.060509</td>\n",
       "      <td>58.451614</td>\n",
       "      <td>50.218761</td>\n",
       "      <td>47.939716</td>\n",
       "      <td>372.512909</td>\n",
       "      <td>5.8</td>\n",
       "      <td>295.120697</td>\n",
       "      <td>2.011111</td>\n",
       "      <td>-4.847777</td>\n",
       "      <td>-10.641112</td>\n",
       "      <td>-11.143332</td>\n",
       "      <td>-10.580002</td>\n",
       "      <td>-6.611111</td>\n",
       "      <td>-1.732222</td>\n",
       "      <td>3.233333</td>\n",
       "      <td>9.202221</td>\n",
       "      <td>13.494446</td>\n",
       "      <td>10.450000</td>\n",
       "      <td>5.927779</td>\n",
       "      <td>250.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>42.489556</td>\n",
       "      <td>RGI60-08.00001</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0</td>\n",
       "      <td>67.930</td>\n",
       "      <td>50.515018</td>\n",
       "      <td>43.414459</td>\n",
       "      <td>49.670944</td>\n",
       "      <td>27.386324</td>\n",
       "      <td>35.375156</td>\n",
       "      <td>28.706146</td>\n",
       "      <td>43.481659</td>\n",
       "      <td>37.501221</td>\n",
       "      <td>21.494608</td>\n",
       "      <td>83.403297</td>\n",
       "      <td>62.819103</td>\n",
       "      <td>35.580780</td>\n",
       "      <td>291.314026</td>\n",
       "      <td>5.8</td>\n",
       "      <td>228.034683</td>\n",
       "      <td>3.411111</td>\n",
       "      <td>-3.147778</td>\n",
       "      <td>-9.341112</td>\n",
       "      <td>-7.543333</td>\n",
       "      <td>-15.480002</td>\n",
       "      <td>-12.011110</td>\n",
       "      <td>-1.865555</td>\n",
       "      <td>1.533333</td>\n",
       "      <td>10.002221</td>\n",
       "      <td>13.594445</td>\n",
       "      <td>10.316667</td>\n",
       "      <td>7.627779</td>\n",
       "      <td>250.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>38.044445</td>\n",
       "      <td>RGI60-08.00001</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0</td>\n",
       "      <td>67.930</td>\n",
       "      <td>59.345169</td>\n",
       "      <td>29.263451</td>\n",
       "      <td>24.280972</td>\n",
       "      <td>44.494129</td>\n",
       "      <td>44.540123</td>\n",
       "      <td>37.876644</td>\n",
       "      <td>24.116472</td>\n",
       "      <td>40.426228</td>\n",
       "      <td>53.544838</td>\n",
       "      <td>103.911064</td>\n",
       "      <td>55.259472</td>\n",
       "      <td>67.236656</td>\n",
       "      <td>320.378265</td>\n",
       "      <td>5.8</td>\n",
       "      <td>263.916962</td>\n",
       "      <td>-0.488889</td>\n",
       "      <td>-7.447778</td>\n",
       "      <td>-12.507779</td>\n",
       "      <td>-12.309999</td>\n",
       "      <td>-10.313335</td>\n",
       "      <td>-8.044444</td>\n",
       "      <td>-0.898889</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>9.202221</td>\n",
       "      <td>13.794446</td>\n",
       "      <td>10.316667</td>\n",
       "      <td>4.727778</td>\n",
       "      <td>250.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>39.844445</td>\n",
       "      <td>RGI60-08.00001</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0</td>\n",
       "      <td>67.930</td>\n",
       "      <td>48.070133</td>\n",
       "      <td>34.243984</td>\n",
       "      <td>27.780344</td>\n",
       "      <td>46.093716</td>\n",
       "      <td>26.073019</td>\n",
       "      <td>28.461843</td>\n",
       "      <td>17.521786</td>\n",
       "      <td>72.449425</td>\n",
       "      <td>23.149651</td>\n",
       "      <td>58.197781</td>\n",
       "      <td>80.069733</td>\n",
       "      <td>48.463291</td>\n",
       "      <td>282.329895</td>\n",
       "      <td>5.8</td>\n",
       "      <td>228.244812</td>\n",
       "      <td>-2.455556</td>\n",
       "      <td>-9.747778</td>\n",
       "      <td>-13.707779</td>\n",
       "      <td>-17.709999</td>\n",
       "      <td>-9.480001</td>\n",
       "      <td>-5.611111</td>\n",
       "      <td>-3.232222</td>\n",
       "      <td>4.133333</td>\n",
       "      <td>6.135554</td>\n",
       "      <td>15.094445</td>\n",
       "      <td>11.316667</td>\n",
       "      <td>6.494445</td>\n",
       "      <td>250.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>40.577779</td>\n",
       "      <td>RGI60-08.00001</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0</td>\n",
       "      <td>67.930</td>\n",
       "      <td>78.174042</td>\n",
       "      <td>31.993261</td>\n",
       "      <td>62.266918</td>\n",
       "      <td>28.106590</td>\n",
       "      <td>40.665600</td>\n",
       "      <td>24.079779</td>\n",
       "      <td>28.318407</td>\n",
       "      <td>55.463512</td>\n",
       "      <td>57.078709</td>\n",
       "      <td>57.060844</td>\n",
       "      <td>97.000999</td>\n",
       "      <td>105.222382</td>\n",
       "      <td>371.826447</td>\n",
       "      <td>5.8</td>\n",
       "      <td>293.604614</td>\n",
       "      <td>0.311111</td>\n",
       "      <td>-3.414444</td>\n",
       "      <td>-8.741112</td>\n",
       "      <td>-10.909999</td>\n",
       "      <td>-12.780002</td>\n",
       "      <td>-7.111110</td>\n",
       "      <td>-2.298889</td>\n",
       "      <td>3.033333</td>\n",
       "      <td>8.102221</td>\n",
       "      <td>14.727779</td>\n",
       "      <td>11.216666</td>\n",
       "      <td>6.827778</td>\n",
       "      <td>250.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">RGI60-08.03417</th>\n",
       "      <th>2015</th>\n",
       "      <td>51.437776</td>\n",
       "      <td>RGI60-08.03417</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0</td>\n",
       "      <td>65.923</td>\n",
       "      <td>104.149033</td>\n",
       "      <td>111.409660</td>\n",
       "      <td>266.413971</td>\n",
       "      <td>155.734222</td>\n",
       "      <td>189.870132</td>\n",
       "      <td>95.728027</td>\n",
       "      <td>118.604233</td>\n",
       "      <td>73.268768</td>\n",
       "      <td>99.347084</td>\n",
       "      <td>90.682648</td>\n",
       "      <td>88.481743</td>\n",
       "      <td>135.403824</td>\n",
       "      <td>709.937317</td>\n",
       "      <td>32.5</td>\n",
       "      <td>819.156006</td>\n",
       "      <td>1.494445</td>\n",
       "      <td>-3.284444</td>\n",
       "      <td>-5.712222</td>\n",
       "      <td>-7.793335</td>\n",
       "      <td>-4.908890</td>\n",
       "      <td>-2.700000</td>\n",
       "      <td>-1.488889</td>\n",
       "      <td>2.322222</td>\n",
       "      <td>5.307778</td>\n",
       "      <td>8.482221</td>\n",
       "      <td>10.999999</td>\n",
       "      <td>6.890000</td>\n",
       "      <td>1318.0</td>\n",
       "      <td>1186.0</td>\n",
       "      <td>1090.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>55.643333</td>\n",
       "      <td>RGI60-08.03417</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0</td>\n",
       "      <td>65.923</td>\n",
       "      <td>217.098801</td>\n",
       "      <td>211.801880</td>\n",
       "      <td>242.586319</td>\n",
       "      <td>101.253311</td>\n",
       "      <td>108.876656</td>\n",
       "      <td>145.192505</td>\n",
       "      <td>58.738297</td>\n",
       "      <td>61.192421</td>\n",
       "      <td>74.471062</td>\n",
       "      <td>70.880188</td>\n",
       "      <td>119.976753</td>\n",
       "      <td>147.315826</td>\n",
       "      <td>961.475220</td>\n",
       "      <td>32.5</td>\n",
       "      <td>597.908813</td>\n",
       "      <td>1.894444</td>\n",
       "      <td>-1.984444</td>\n",
       "      <td>-3.912222</td>\n",
       "      <td>-10.393335</td>\n",
       "      <td>-6.008890</td>\n",
       "      <td>-3.799999</td>\n",
       "      <td>-1.455556</td>\n",
       "      <td>4.088889</td>\n",
       "      <td>6.907778</td>\n",
       "      <td>10.382221</td>\n",
       "      <td>8.599999</td>\n",
       "      <td>7.290001</td>\n",
       "      <td>1318.0</td>\n",
       "      <td>1186.0</td>\n",
       "      <td>1090.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>50.003331</td>\n",
       "      <td>RGI60-08.03417</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0</td>\n",
       "      <td>65.923</td>\n",
       "      <td>89.045914</td>\n",
       "      <td>149.197235</td>\n",
       "      <td>371.887756</td>\n",
       "      <td>299.347626</td>\n",
       "      <td>121.731079</td>\n",
       "      <td>139.568573</td>\n",
       "      <td>149.403473</td>\n",
       "      <td>55.090389</td>\n",
       "      <td>67.357719</td>\n",
       "      <td>117.627174</td>\n",
       "      <td>156.433319</td>\n",
       "      <td>33.727993</td>\n",
       "      <td>519.282471</td>\n",
       "      <td>32.5</td>\n",
       "      <td>1231.135742</td>\n",
       "      <td>1.027778</td>\n",
       "      <td>-4.484444</td>\n",
       "      <td>-3.812222</td>\n",
       "      <td>-5.493335</td>\n",
       "      <td>-6.708890</td>\n",
       "      <td>-5.200000</td>\n",
       "      <td>-2.988889</td>\n",
       "      <td>1.355556</td>\n",
       "      <td>7.507778</td>\n",
       "      <td>9.482221</td>\n",
       "      <td>8.799999</td>\n",
       "      <td>6.890000</td>\n",
       "      <td>1318.0</td>\n",
       "      <td>1186.0</td>\n",
       "      <td>1090.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>57.004444</td>\n",
       "      <td>RGI60-08.03417</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0</td>\n",
       "      <td>65.923</td>\n",
       "      <td>176.113358</td>\n",
       "      <td>158.276733</td>\n",
       "      <td>192.674561</td>\n",
       "      <td>64.232956</td>\n",
       "      <td>24.806946</td>\n",
       "      <td>93.973999</td>\n",
       "      <td>65.463066</td>\n",
       "      <td>72.653397</td>\n",
       "      <td>123.401054</td>\n",
       "      <td>25.543489</td>\n",
       "      <td>177.151459</td>\n",
       "      <td>250.382614</td>\n",
       "      <td>890.708435</td>\n",
       "      <td>32.5</td>\n",
       "      <td>533.965210</td>\n",
       "      <td>1.494445</td>\n",
       "      <td>-4.384444</td>\n",
       "      <td>-6.412222</td>\n",
       "      <td>-8.093335</td>\n",
       "      <td>-9.608891</td>\n",
       "      <td>-8.999999</td>\n",
       "      <td>-1.488889</td>\n",
       "      <td>5.588889</td>\n",
       "      <td>6.007778</td>\n",
       "      <td>12.582221</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>5.990001</td>\n",
       "      <td>1318.0</td>\n",
       "      <td>1186.0</td>\n",
       "      <td>1090.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>54.643332</td>\n",
       "      <td>RGI60-08.03417</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0</td>\n",
       "      <td>65.923</td>\n",
       "      <td>217.587555</td>\n",
       "      <td>137.044174</td>\n",
       "      <td>171.008667</td>\n",
       "      <td>167.764053</td>\n",
       "      <td>227.375122</td>\n",
       "      <td>161.393158</td>\n",
       "      <td>49.349823</td>\n",
       "      <td>85.042221</td>\n",
       "      <td>145.225189</td>\n",
       "      <td>47.727448</td>\n",
       "      <td>84.718292</td>\n",
       "      <td>137.008652</td>\n",
       "      <td>903.703247</td>\n",
       "      <td>32.5</td>\n",
       "      <td>727.541016</td>\n",
       "      <td>0.694444</td>\n",
       "      <td>-1.051111</td>\n",
       "      <td>-4.612222</td>\n",
       "      <td>-8.993335</td>\n",
       "      <td>-6.508890</td>\n",
       "      <td>-6.400000</td>\n",
       "      <td>0.211111</td>\n",
       "      <td>2.022222</td>\n",
       "      <td>7.674444</td>\n",
       "      <td>10.182220</td>\n",
       "      <td>9.999999</td>\n",
       "      <td>4.990001</td>\n",
       "      <td>1318.0</td>\n",
       "      <td>1186.0</td>\n",
       "      <td>1090.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68340 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 PDD          RGI_ID  area  icecap     lat   \n",
       "rgi_id         hydro_year                                                    \n",
       "RGI60-08.00001 2000        40.322890  RGI60-08.00001  0.03       0  67.930  \\\n",
       "               2001        42.489556  RGI60-08.00001  0.03       0  67.930   \n",
       "               2002        38.044445  RGI60-08.00001  0.03       0  67.930   \n",
       "               2003        39.844445  RGI60-08.00001  0.03       0  67.930   \n",
       "               2004        40.577779  RGI60-08.00001  0.03       0  67.930   \n",
       "...                              ...             ...   ...     ...     ...   \n",
       "RGI60-08.03417 2015        51.437776  RGI60-08.03417  0.15       0  65.923   \n",
       "               2016        55.643333  RGI60-08.03417  0.15       0  65.923   \n",
       "               2017        50.003331  RGI60-08.03417  0.15       0  65.923   \n",
       "               2018        57.004444  RGI60-08.03417  0.15       0  65.923   \n",
       "               2019        54.643332  RGI60-08.03417  0.15       0  65.923   \n",
       "\n",
       "                              prcp_01     prcp_02     prcp_03     prcp_04   \n",
       "rgi_id         hydro_year                                                   \n",
       "RGI60-08.00001 2000         89.949440   39.799580   51.759113   50.980053  \\\n",
       "               2001         50.515018   43.414459   49.670944   27.386324   \n",
       "               2002         59.345169   29.263451   24.280972   44.494129   \n",
       "               2003         48.070133   34.243984   27.780344   46.093716   \n",
       "               2004         78.174042   31.993261   62.266918   28.106590   \n",
       "...                               ...         ...         ...         ...   \n",
       "RGI60-08.03417 2015        104.149033  111.409660  266.413971  155.734222   \n",
       "               2016        217.098801  211.801880  242.586319  101.253311   \n",
       "               2017         89.045914  149.197235  371.887756  299.347626   \n",
       "               2018        176.113358  158.276733  192.674561   64.232956   \n",
       "               2019        217.587555  137.044174  171.008667  167.764053   \n",
       "\n",
       "                              prcp_05     prcp_06     prcp_07    prcp_08   \n",
       "rgi_id         hydro_year                                                  \n",
       "RGI60-08.00001 2000         29.002270   59.659908   63.919777  43.892872  \\\n",
       "               2001         35.375156   28.706146   43.481659  37.501221   \n",
       "               2002         44.540123   37.876644   24.116472  40.426228   \n",
       "               2003         26.073019   28.461843   17.521786  72.449425   \n",
       "               2004         40.665600   24.079779   28.318407  55.463512   \n",
       "...                               ...         ...         ...        ...   \n",
       "RGI60-08.03417 2015        189.870132   95.728027  118.604233  73.268768   \n",
       "               2016        108.876656  145.192505   58.738297  61.192421   \n",
       "               2017        121.731079  139.568573  149.403473  55.090389   \n",
       "               2018         24.806946   93.973999   65.463066  72.653397   \n",
       "               2019        227.375122  161.393158   49.349823  85.042221   \n",
       "\n",
       "                              prcp_09     prcp_10     prcp_11     prcp_12   \n",
       "rgi_id         hydro_year                                                   \n",
       "RGI60-08.00001 2000         82.060509   58.451614   50.218761   47.939716  \\\n",
       "               2001         21.494608   83.403297   62.819103   35.580780   \n",
       "               2002         53.544838  103.911064   55.259472   67.236656   \n",
       "               2003         23.149651   58.197781   80.069733   48.463291   \n",
       "               2004         57.078709   57.060844   97.000999  105.222382   \n",
       "...                               ...         ...         ...         ...   \n",
       "RGI60-08.03417 2015         99.347084   90.682648   88.481743  135.403824   \n",
       "               2016         74.471062   70.880188  119.976753  147.315826   \n",
       "               2017         67.357719  117.627174  156.433319   33.727993   \n",
       "               2018        123.401054   25.543489  177.151459  250.382614   \n",
       "               2019        145.225189   47.727448   84.718292  137.008652   \n",
       "\n",
       "                                 rain  slope         snow   temp_01   temp_02   \n",
       "rgi_id         hydro_year                                                       \n",
       "RGI60-08.00001 2000        372.512909    5.8   295.120697  2.011111 -4.847777  \\\n",
       "               2001        291.314026    5.8   228.034683  3.411111 -3.147778   \n",
       "               2002        320.378265    5.8   263.916962 -0.488889 -7.447778   \n",
       "               2003        282.329895    5.8   228.244812 -2.455556 -9.747778   \n",
       "               2004        371.826447    5.8   293.604614  0.311111 -3.414444   \n",
       "...                               ...    ...          ...       ...       ...   \n",
       "RGI60-08.03417 2015        709.937317   32.5   819.156006  1.494445 -3.284444   \n",
       "               2016        961.475220   32.5   597.908813  1.894444 -1.984444   \n",
       "               2017        519.282471   32.5  1231.135742  1.027778 -4.484444   \n",
       "               2018        890.708435   32.5   533.965210  1.494445 -4.384444   \n",
       "               2019        903.703247   32.5   727.541016  0.694444 -1.051111   \n",
       "\n",
       "                             temp_03    temp_04    temp_05    temp_06   \n",
       "rgi_id         hydro_year                                               \n",
       "RGI60-08.00001 2000       -10.641112 -11.143332 -10.580002  -6.611111  \\\n",
       "               2001        -9.341112  -7.543333 -15.480002 -12.011110   \n",
       "               2002       -12.507779 -12.309999 -10.313335  -8.044444   \n",
       "               2003       -13.707779 -17.709999  -9.480001  -5.611111   \n",
       "               2004        -8.741112 -10.909999 -12.780002  -7.111110   \n",
       "...                              ...        ...        ...        ...   \n",
       "RGI60-08.03417 2015        -5.712222  -7.793335  -4.908890  -2.700000   \n",
       "               2016        -3.912222 -10.393335  -6.008890  -3.799999   \n",
       "               2017        -3.812222  -5.493335  -6.708890  -5.200000   \n",
       "               2018        -6.412222  -8.093335  -9.608891  -8.999999   \n",
       "               2019        -4.612222  -8.993335  -6.508890  -6.400000   \n",
       "\n",
       "                            temp_07   temp_08    temp_09    temp_10   \n",
       "rgi_id         hydro_year                                             \n",
       "RGI60-08.00001 2000       -1.732222  3.233333   9.202221  13.494446  \\\n",
       "               2001       -1.865555  1.533333  10.002221  13.594445   \n",
       "               2002       -0.898889  3.333333   9.202221  13.794446   \n",
       "               2003       -3.232222  4.133333   6.135554  15.094445   \n",
       "               2004       -2.298889  3.033333   8.102221  14.727779   \n",
       "...                             ...       ...        ...        ...   \n",
       "RGI60-08.03417 2015       -1.488889  2.322222   5.307778   8.482221   \n",
       "               2016       -1.455556  4.088889   6.907778  10.382221   \n",
       "               2017       -2.988889  1.355556   7.507778   9.482221   \n",
       "               2018       -1.488889  5.588889   6.007778  12.582221   \n",
       "               2019        0.211111  2.022222   7.674444  10.182220   \n",
       "\n",
       "                             temp_11   temp_12    zmax    zmed    zmin  \n",
       "rgi_id         hydro_year                                               \n",
       "RGI60-08.00001 2000        10.450000  5.927779   250.0   242.0   235.0  \n",
       "               2001        10.316667  7.627779   250.0   242.0   235.0  \n",
       "               2002        10.316667  4.727778   250.0   242.0   235.0  \n",
       "               2003        11.316667  6.494445   250.0   242.0   235.0  \n",
       "               2004        11.216666  6.827778   250.0   242.0   235.0  \n",
       "...                              ...       ...     ...     ...     ...  \n",
       "RGI60-08.03417 2015        10.999999  6.890000  1318.0  1186.0  1090.0  \n",
       "               2016         8.599999  7.290001  1318.0  1186.0  1090.0  \n",
       "               2017         8.799999  6.890000  1318.0  1186.0  1090.0  \n",
       "               2018         9.400000  5.990001  1318.0  1186.0  1090.0  \n",
       "               2019         9.999999  4.990001  1318.0  1186.0  1090.0  \n",
       "\n",
       "[68340 rows x 35 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_df = training_df.reset_index().merge(topo_df.set_index('ID'), how='left', left_on='rgi_id', right_index=True)\n",
    "training_df = training_df.set_index(['rgi_id', 'hydro_year'])\n",
    "training_df = training_df.reindex(sorted(training_df.columns), axis=1)\n",
    "training_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3694fd20-0030-4939-a7c7-d5939fb9424a",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "We save the training DataFrame with all the necessary training data to be used in the following notebooks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e349b15a-552d-41f4-9925-35869cd074d8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "training_df.to_csv('Data/training_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8cf55517-79b9-466f-91e4-9ca430c4276a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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