diff --git a/notebooks/tutorials/massbalance_calibration.ipynb b/notebooks/tutorials/massbalance_calibration.ipynb
index 2376910..b7e4d87 100644
--- a/notebooks/tutorials/massbalance_calibration.ipynb
+++ b/notebooks/tutorials/massbalance_calibration.ipynb
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,9 +50,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-11-13 11:26:42: oggm.cfg: Reading default parameters from the OGGM `params.cfg` configuration file.\n",
+      "2025-11-13 11:26:42: oggm.cfg: Multiprocessing switched OFF according to the parameter file.\n",
+      "2025-11-13 11:26:42: oggm.cfg: Multiprocessing: using all available processors (N=22)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-11-13 11:26:42: oggm.cfg: PARAMS['border'] changed from `80` to `10`.\n"
+     ]
+    }
+   ],
    "source": [
     "cfg.initialize(logging_level='WARNING')\n",
     "cfg.PATHS['working_dir'] = utils.gettempdir(dirname='OGGM-calib-mb', reset=True)\n",
@@ -68,9 +85,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-11-13 11:26:42: oggm.workflow: init_glacier_directories from prepro level 3 on 2 glaciers.\n",
+      "2025-11-13 11:26:42: oggm.workflow: Execute entity tasks [gdir_from_prepro] on 2 glaciers\n"
+     ]
+    }
+   ],
    "source": [
     "# we start from preprocessing level 3\n",
     "base_url = 'https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.6/L3-L5_files/2023.3/elev_bands/W5E5/'\n",
@@ -86,9 +112,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "You need to provide a Google API key or set the STATIC_MAP_API_KEY environment variable.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mKeyError\u001b[39m                                  Traceback (most recent call last)",
+      "\u001b[36mFile \u001b[39m\u001b[32m/mnt/c/Users/yg25019/OneDrive - University of Bristol/Documents/OGGM/oggm/oggm/graphics.py:212\u001b[39m, in \u001b[36mplot_googlemap\u001b[39m\u001b[34m(gdirs, ax, figsize, key)\u001b[39m\n\u001b[32m    211\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m212\u001b[39m     key = \u001b[43mos\u001b[49m\u001b[43m.\u001b[49m\u001b[43menviron\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mSTATIC_MAP_API_KEY\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[32m    213\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m<frozen os>:679\u001b[39m, in \u001b[36m__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n",
+      "\u001b[31mKeyError\u001b[39m: 'STATIC_MAP_API_KEY'",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m f, ax = plt.subplots(\u001b[32m1\u001b[39m,\u001b[32m1\u001b[39m,figsize=(\u001b[32m6\u001b[39m, \u001b[32m6\u001b[39m))\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[43mgraphics\u001b[49m\u001b[43m.\u001b[49m\u001b[43mplot_googlemap\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgdirs\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[32;43m2\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[43m=\u001b[49m\u001b[43max\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m      3\u001b[39m ax.set_title(gdirs[\u001b[32m0\u001b[39m].rgi_id + \u001b[33m'\u001b[39m\u001b[33m & \u001b[39m\u001b[33m'\u001b[39m + gdirs[\u001b[32m1\u001b[39m].rgi_id);\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/mnt/c/Users/yg25019/OneDrive - University of Bristol/Documents/OGGM/oggm/oggm/graphics.py:214\u001b[39m, in \u001b[36mplot_googlemap\u001b[39m\u001b[34m(gdirs, ax, figsize, key)\u001b[39m\n\u001b[32m    212\u001b[39m         key = os.environ[\u001b[33m'\u001b[39m\u001b[33mSTATIC_MAP_API_KEY\u001b[39m\u001b[33m'\u001b[39m]\n\u001b[32m    213\u001b[39m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m214\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m'\u001b[39m\u001b[33mYou need to provide a Google API key\u001b[39m\u001b[33m'\u001b[39m\n\u001b[32m    215\u001b[39m                          \u001b[33m'\u001b[39m\u001b[33m or set the STATIC_MAP_API_KEY environment\u001b[39m\u001b[33m'\u001b[39m\n\u001b[32m    216\u001b[39m                          \u001b[33m'\u001b[39m\u001b[33m variable.\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m    218\u001b[39m dofig = \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[32m    219\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "\u001b[31mValueError\u001b[39m: You need to provide a Google API key or set the STATIC_MAP_API_KEY environment variable."
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "f, ax = plt.subplots(1,1,figsize=(6, 6))\n",
     "graphics.plot_googlemap(gdirs[:2], ax=ax)\n",
@@ -111,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {
     "tags": []
    },
@@ -124,11 +178,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'baseline_climate_source': 'GSWP3_W5E5',\n",
+       " 'baseline_yr_0': 1901,\n",
+       " 'baseline_yr_1': 2019,\n",
+       " 'baseline_climate_ref_hgt': 2252.0,\n",
+       " 'baseline_climate_ref_pix_lon': 10.75,\n",
+       " 'baseline_climate_ref_pix_lat': 46.75}"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "gdir_hef.get_climate_info()"
    ]
@@ -142,22 +212,68 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'rgi_id': 'RGI60-11.00897',\n",
+       " 'bias': 0,\n",
+       " 'melt_f': 5.0,\n",
+       " 'prcp_fac': 3.532785225968441,\n",
+       " 'temp_bias': 1.7583130779353044,\n",
+       " 'reference_mb': -1100.3,\n",
+       " 'reference_mb_err': 171.8,\n",
+       " 'reference_period': '2000-01-01_2020-01-01',\n",
+       " 'mb_global_params': {'temp_default_gradient': -0.0065,\n",
+       "  'temp_all_solid': 0.0,\n",
+       "  'temp_all_liq': 2.0,\n",
+       "  'temp_melt': -1.0},\n",
+       " 'baseline_climate_source': 'GSWP3_W5E5'}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "gdir_hef.read_json('mb_calib')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'rgi_id': 'RGI60-11.00787',\n",
+       " 'bias': 0,\n",
+       " 'melt_f': 5.0,\n",
+       " 'prcp_fac': 2.9632765179490517,\n",
+       " 'temp_bias': 1.7583130779353044,\n",
+       " 'reference_mb': -514.8000000000001,\n",
+       " 'reference_mb_err': 136.3,\n",
+       " 'reference_period': '2000-01-01_2020-01-01',\n",
+       " 'mb_global_params': {'temp_default_gradient': -0.0065,\n",
+       "  'temp_all_solid': 0.0,\n",
+       "  'temp_all_liq': 2.0,\n",
+       "  'temp_melt': -1.0},\n",
+       " 'baseline_climate_source': 'GSWP3_W5E5'}"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "gdir_kwf.read_json('mb_calib')"
    ]
@@ -177,9 +293,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RGI60-11.00787 : average MB 2000-2020 = -514.8 kg m-2,  prcp_fac: 2.96\n",
+      "RGI60-11.00897 : average MB 2000-2020 = -1100.3 kg m-2,  prcp_fac: 3.53\n"
+     ]
+    }
+   ],
    "source": [
     "for gdir in gdirs:\n",
     "    mbmod = massbalance.MonthlyTIModel(gdir)\n",
@@ -217,11 +342,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'temp_default_gradient': -0.0065,\n",
+       " 'temp_all_solid': 0.0,\n",
+       " 'temp_all_liq': 2.0,\n",
+       " 'temp_melt': -1.0}"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "gdir_hef.read_json('mb_calib')['mb_global_params']"
    ]
@@ -244,9 +383,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "mod_mb   -1100.3\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "h, w = gdir_hef.get_inversion_flowline_hw()\n",
     "mb_geod = massbalance.MonthlyTIModel(gdir_hef)\n",
@@ -265,9 +416,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "reference MB: -1100.3 kg m-2\n",
+      "reference MB uncertainties: 171.8 kg m-2\n",
+      "reference MB time period: 2000-01-01_2020-01-01\n"
+     ]
+    }
+   ],
    "source": [
     "print('reference MB: ' + str(gdir_hef.read_json('mb_calib')['reference_mb'])+ ' kg m-2')\n",
     "print('reference MB uncertainties: '+ str(gdir_hef.read_json('mb_calib')['reference_mb_err'])+ ' kg m-2')\n",
@@ -283,7 +444,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -328,9 +489,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-1100.3"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# if you use the default calibration data from Hugonnet et al., 2021, \n",
     "# we can get the geodetic data from any glacier from here:\n",
@@ -343,7 +515,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -368,9 +540,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'rgi_id': 'RGI60-11.00897',\n",
+       " 'bias': 0,\n",
+       " 'melt_f': 9.06088881579363,\n",
+       " 'prcp_fac': 3.357136316271367,\n",
+       " 'temp_bias': 0,\n",
+       " 'reference_mb': -1100.3,\n",
+       " 'reference_mb_err': None,\n",
+       " 'reference_period': '2000-01-01_2020-01-01',\n",
+       " 'mb_global_params': {'temp_default_gradient': -0.0065,\n",
+       "  'temp_all_solid': 0.0,\n",
+       "  'temp_all_liq': 2.0,\n",
+       "  'temp_melt': -1.0},\n",
+       " 'baseline_climate_source': 'GSWP3_W5E5'}"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "gdir_hef.read_json('mb_calib')"
    ]
@@ -398,7 +593,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -417,9 +612,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1.6621867397806107, 3.357136316271367, 5.0)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "mb_params = gdir_hef.read_json('mb_calib')\n",
     "mb_params['temp_bias'], mb_params['prcp_fac'], mb_params['melt_f']"
@@ -448,9 +654,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0, 1.0160354839620402, 5.0)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Let's calibrate on the prcp_fac instead\n",
     "# overwrite_gdir has to be set to True,\n",
@@ -478,7 +695,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -496,9 +713,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.plot(mbdf['in_situ_mb'], label='in-situ observations\\n'+f'average 2000-2020: {mbdf.in_situ_mb.mean():.1f} '+ r'kg m$^{-2}$', color='k', lw=3)\n",
     "plt.plot(mbdf['mod_mb'],\n",
@@ -544,9 +772,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'rgi_id': 'RGI60-11.00897',\n",
+       " 'bias': 0,\n",
+       " 'melt_f': 11.27883598160852,\n",
+       " 'prcp_fac': 3.357136316271367,\n",
+       " 'temp_bias': 0,\n",
+       " 'reference_mb': -648.223880597015,\n",
+       " 'reference_mb_err': None,\n",
+       " 'reference_period': 'custom',\n",
+       " 'mb_global_params': {'temp_default_gradient': -0.0065,\n",
+       "  'temp_all_solid': 0.0,\n",
+       "  'temp_all_liq': 2.0,\n",
+       "  'temp_melt': -1.0},\n",
+       " 'baseline_climate_source': 'GSWP3_W5E5'}"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "mb_calibration_from_wgms_mb(gdir_hef, overwrite_gdir=True)"
    ]
@@ -561,22 +812,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-648.223880597015"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "mbdf_in_situ = gdir_hef.get_ref_mb_data()\n",
     "mbdf_in_situ['ref_mb'] = mbdf_in_situ['ANNUAL_BALANCE']\n",
     "# check that every year between the beginning and the end has MB observations \n",
     "assert len(mbdf_in_situ.index) == (mbdf_in_situ.index[-1] - mbdf_in_situ.index[0] + 1)\n",
-    "ref_mb = mbdf_in_situ.ref_mb.mean()"
+    "ref_mb = mbdf_in_situ.ref_mb.mean()\n",
+    "ref_mb"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "mb_in_situ_obs = massbalance.MonthlyTIModel(gdir_hef)\n",
     "mbdf_in_situ['mod_mb'] = mb_in_situ_obs.get_specific_mb(h, w, year=mbdf_in_situ.index)\n",
@@ -1357,7 +1631,7 @@
  "metadata": {
   "hide_input": false,
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "oggm_env",
    "language": "python",
    "name": "python3"
   },
@@ -1371,7 +1645,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.11.13"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,
diff --git a/notebooks/tutorials/runoff_calibration.ipynb b/notebooks/tutorials/runoff_calibration.ipynb
new file mode 100644
index 0000000..b0e28ac
--- /dev/null
+++ b/notebooks/tutorials/runoff_calibration.ipynb
@@ -0,0 +1,1335 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fc4433b4",
+   "metadata": {},
+   "source": [
+    "# Influence of mass-balance parameters on glacier runoff\n",
+    "\n",
+    "In this notebook, we will calabrate the mass-balance parameters in the OGGM, these parameters will then be used to calculate the components of the runoff and the runoff variable itself. These variables will then be investigated to understand sensitvity against the calibrated parameters.\n",
+    "\n",
+    "For more information on the calibration methods, we would recommend the tutorial on mass-balance calibration [massbalance_calibration.ipynb](https://tutorials.oggm.org/stable/notebooks/tutorials/massbalance_calibration.html). Here our focus is how these impact runoff, this work has been motivated by the Wimberly et al paper [Inter-model differences in 21st centry glacier runoff for the world's major river basins](https://tc.copernicus.org/articles/19/1491/2025/).\n",
+    "\n",
+    "In this notebook we will:\n",
+    "1. Use the OGGM scalar calibration method\n",
+    "2. Show how users can calibrate parameters and use these for runoff outputs to understand impact of parameter changes.\n",
+    "3. Investigate variable sensitivity to the calibrations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1abcbfd",
+   "metadata": {},
+   "source": [
+    "## Set Up\n",
+    "\n",
+    "First install required packages to run this tutorial. \n",
+    "\n",
+    "We will be using level 4 data to allow for the dynamical spinup and the calibration capabilities."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "0b6e8193",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "import oggm\n",
+    "from oggm import cfg, utils, workflow, tasks\n",
+    "import xarray as xr\n",
+    "from oggm.core import massbalance\n",
+    "from oggm.core.massbalance import mb_calibration_from_scalar_mb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "85804d49",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-11-18 10:08:08: oggm.cfg: Reading default parameters from the OGGM `params.cfg` configuration file.\n",
+      "2025-11-18 10:08:08: oggm.cfg: Multiprocessing switched OFF according to the parameter file.\n",
+      "2025-11-18 10:08:08: oggm.cfg: Multiprocessing: using all available processors (N=22)\n",
+      "2025-11-18 10:08:09: oggm.cfg: PARAMS['store_model_geometry'] changed from `False` to `True`.\n"
+     ]
+    }
+   ],
+   "source": [
+    "cfg.initialize(logging_level='WARNING')\n",
+    "cfg.PATHS['working_dir'] = utils.gettempdir(dirname='OGGM-calib-ro', reset=True)\n",
+    "cfg.PARAMS['store_model_geometry'] = True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d2efb09",
+   "metadata": {},
+   "source": [
+    "We start from a well known glacier in the Austrian Alps, Hintereisferner. But you can choose any other glacier, e.g. from [this list](https://github.com/OGGM/oggm-sample-data/blob/master/wgms/rgi_wgms_links_20220112.csv)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "402e44d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-11-18 10:08:09: oggm.workflow: init_glacier_directories from prepro level 4 on 1 glaciers.\n",
+      "2025-11-18 10:08:09: oggm.workflow: Execute entity tasks [gdir_from_prepro] on 1 glaciers\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Hintereisferner\n",
+    "rgi_id = 'RGI60-11.00897'\n",
+    "\n",
+    "# We pick the elevation-bands glaciers because they run a bit faster - but they create more step changes in the area outputs\n",
+    "base_url = 'https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.6/L3-L5_files/2023.3/elev_bands/W5E5_spinup'\n",
+    "gdir_hef = workflow.init_glacier_directories([rgi_id], from_prepro_level=4, prepro_border=160, prepro_base_url=base_url)[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78de2a38",
+   "metadata": {},
+   "source": [
+    "## Generating the Hydrological and Glaciological outputs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "041261fe",
+   "metadata": {},
+   "source": [
+    "The pre-processed directories don't automatically have hydrological outputs, so lets run the `run_with_hydro` task below to calculate these! This requires a dynamical spinup, let's choose the time period 2000-2020."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "36a47e5d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# file identifier where the model output is saved\n",
+    "file_id = '_default'\n",
+    "\n",
+    "# We are using the task run_with_hydro to store hydrological outputs along with the usual glaciological outputs\n",
+    "tasks.run_with_hydro(gdir_hef,  # Run on the selected glacier\n",
+    "                     run_task=tasks.run_from_climate_data,  # which climate scenario? See following notebook for other examples\n",
+    "                     ys=2000,  # Period which we will average and constantly repeat\n",
+    "                     init_model_filesuffix='_spinup_historical',  # use the previous run as initial state\n",
+    "                     init_model_yr=2000,  # Start from spinup year 2000\n",
+    "                     store_monthly_hydro=True,  # Monthly ouptuts provide additional information\n",
+    "                     output_filesuffix=file_id);  # an identifier for the output file, to read it later\n",
+    "\n",
+    "with xr.open_dataset(gdir_hef.get_filepath('model_diagnostics', filesuffix='_default')) as ds:\n",
+    "    # The last step of hydrological output is NaN (we can't compute it for this year)\n",
+    "    ds = ds.isel(time=slice(0, -1)).load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac645b31",
+   "metadata": {},
+   "source": [
+    "Now that we have the hydrological and glaciological outputs for our desired time period, lets calculate the total runoff for the glacier. There are four key variables that form the total runoff when summed together, and the process to calculate the runoff can be seen below.\n",
+    "\n",
+    "The total runoff from the glacier is:\n",
+    "`runoff` = `melt_off_glacier` + `melt_on_glacier` + `liq_prcp_off_glacier` + `liq_prcp_on_glacier`\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d20120d",
+   "metadata": {},
+   "source": [
+    "The figure below shows the runoff for the default calibrated parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d27a8a7d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sel_vars = [v for v in ds.variables if 'month_2d' not in ds[v].dims]\n",
+    "df_annual = ds[sel_vars].to_dataframe()\n",
+    "\n",
+    "# These summed variabels give the total runoff from the glacier\n",
+    "runoff_vars = ['melt_off_glacier', 'melt_on_glacier','liq_prcp_off_glacier', 'liq_prcp_on_glacier']\n",
+    "\n",
+    "# Convert them to megatonnes (instead of kg)\n",
+    "df_runoff = df_annual[runoff_vars] * 1e-9\n",
+    "fig, ax = plt.subplots(figsize=(10, 3.5), sharex=True)\n",
+    "df_runoff.sum(axis=1).plot(ax=ax);\n",
+    "plt.ylabel('Runoff'); plt.xlabel('Years'); plt.title(f'Total annual runoff for {rgi_id}');\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92501cfa",
+   "metadata": {},
+   "source": [
+    "This was done with the following parameter set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "0a217ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'rgi_id': 'RGI60-11.00897',\n",
+       " 'bias': 0,\n",
+       " 'melt_f': 4.9206879441503215,\n",
+       " 'prcp_fac': 3.533570332820593,\n",
+       " 'temp_bias': 1.7583130779353044,\n",
+       " 'reference_mb': -1100.3,\n",
+       " 'reference_mb_err': 171.8,\n",
+       " 'reference_period': '2000-01-01_2020-01-01',\n",
+       " 'mb_global_params': {'temp_default_gradient': -0.0065,\n",
+       "  'temp_all_solid': 0.0,\n",
+       "  'temp_all_liq': 2.0,\n",
+       "  'temp_melt': -1.0},\n",
+       " 'baseline_climate_source': 'GSWP3_W5E5'}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdir_hef.read_json('mb_calib')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33a812b6",
+   "metadata": {},
+   "source": [
+    "## Parameter Calibration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d96ff15",
+   "metadata": {},
+   "source": [
+    "The mass-balance parameters at Level 4 are pre-calibrated automatically by the OGGM and stored in the dataframe. However there are flexible mass-balance calibration schemes in the OGGM.\n",
+    "\n",
+    "In this tutorial, our aim is to calibrate the mass-balance parameters and investigate the impact that this calibration has on the run-off. In the OGGM we can currently (at the time of writing this tutorial October 2025) only calibrate the melt_f, prcp_fac and temp_bias parameters. We will calibrate these parameters using the **scalar mass balance** method, and investigate the relationship with the runoff components and the total runoff output."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac6c6eaa",
+   "metadata": {},
+   "source": [
+    "### **Calibration:** Scalar Mass Balance Calibration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5f9d1ac",
+   "metadata": {},
+   "source": [
+    "Firstly comparing the mass balances below for in-situ observations and the default calibrated parameters. To understand the current behaviour of the mass balance output.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1bd5be",
+   "metadata": {},
+   "source": [
+    "Now let's experiment by calibrating the parameters: these parameters are the melt factor (`melt_f`), the precipitation factor (`prcp_fac`) and the temperature bias (`temp_bias`). We will calibrate each parameter and permutations of the calibrations:\n",
+    "1. Just calibrating `melt_f`\n",
+    "2. Just calibrating `prcp_fac`\n",
+    "3. Just calibrating `temp_bias`\n",
+    "4. Calibrating `melt_f` and `prcp_fac`\n",
+    "5. Calibrating `prcp_fac` and `temp_bias`\n",
+    "6. Calibrating `melt_f` and `temp_bias`\n",
+    "7. Calibrating all parameters; `melt_f`, `prcp_fac` and `temp_bias`\n",
+    "\n",
+    "The next calibration section will be relatively repetative, but take note of the parameters being calibrated and the inputs being used in the calibration process.\n",
+    "\n",
+    "We will start the calibration by just calibrating the model the `melt_f` paramater."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33360649",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\">\n",
+    "    <b>\n",
+    "        Note: In the OGGM Scalar Calibration method, even when all parameters are nominated for calibration, they may not reach the calibration stage. The OGGM works by calibrating one parameter at a time, only moving onto the next when the previous parameter calibration cannot reach the desired target. In the tutorial below, we will see the result of this in action with usually only the first parameter being calibrated, even if all parameters are nominated for calibration.\n",
+    "    </b>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f47b6995",
+   "metadata": {},
+   "source": [
+    "### Single Parameter Calibration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d28b4228",
+   "metadata": {},
+   "source": [
+    "Fetch the reference mass balance we want to calibrate to from the geodetic mass-balance data, this is the data from Hugonnet et al 2021:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0e675125",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-1100.3"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ref_mb_df = utils.get_geodetic_mb_dataframe().loc[gdir_hef.rgi_id]\n",
+    "ref_mb_df = ref_mb_df.loc[ref_mb_df['period'] == cfg.PARAMS['geodetic_mb_period']].iloc[0]\n",
+    "# dmdtda: in meters water-equivalent per year -> we convert to kg m-2 yr-1\n",
+    "ref_mb = ref_mb_df['dmdtda'] * 1000\n",
+    "ref_mb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ec4b550",
+   "metadata": {},
+   "source": [
+    "Now we will calibrate with the scalar mass balance and use its default calibration setting. This is just calibrating the `melt_f` parameter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "5b5dab26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'rgi_id': 'RGI60-11.00897',\n",
+       " 'bias': 0,\n",
+       " 'melt_f': 9.059478188653333,\n",
+       " 'prcp_fac': 3.357136316271367,\n",
+       " 'temp_bias': 0,\n",
+       " 'reference_mb': -1100.3,\n",
+       " 'reference_mb_err': None,\n",
+       " 'reference_period': '2000-01-01_2020-01-01',\n",
+       " 'mb_global_params': {'temp_default_gradient': -0.0065,\n",
+       "  'temp_all_solid': 0.0,\n",
+       "  'temp_all_liq': 2.0,\n",
+       "  'temp_melt': -1.0},\n",
+       " 'baseline_climate_source': 'GSWP3_W5E5'}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Just calibrate the melt_f parameter\n",
+    "calib_param_melt_f = mb_calibration_from_scalar_mb(gdir_hef,\n",
+    "                                                   ref_mb = ref_mb,\n",
+    "                                                   ref_period=cfg.PARAMS['geodetic_mb_period'],\n",
+    "                                                   overwrite_gdir=True)\n",
+    "\n",
+    "# Creating a mass balance data frame to store the results\n",
+    "mbdf= pd.DataFrame(index = np.arange(2000,2020,1))\n",
+    "\n",
+    "# Adding the mass balance model with the new calibration\n",
+    "mbmod = massbalance.MonthlyTIModel(gdir_hef)\n",
+    "fls = gdir_hef.read_pickle('inversion_flowlines')\n",
+    "mbdf['melt_f_mb'] = mbmod.get_specific_mb(fls=fls, year=mbdf.index)\n",
+    "\n",
+    "calib_param_melt_f"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2f0fcc5",
+   "metadata": {},
+   "source": [
+    "We can see the parameters above, we will use the run_with_hydro task to gather the hydrological outputs. We will now calculate the runoff from the calabrated parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "97847d11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We are using the task run_with_hydro to store hydrological outputs along with the usual glaciological outputs\n",
+    "# Run this again with the calibrated parameters\n",
+    "file_id = '_melt_f'\n",
+    "\n",
+    "tasks.run_with_hydro(gdir_hef,  # Run on the selected glacier\n",
+    "                    run_task=tasks.run_from_climate_data,  # running from observed climate data\n",
+    "                    ys=2000,  # Period which we will average and constantly repeat\n",
+    "                    init_model_filesuffix='_spinup_historical',  # use the previous run as initial state\n",
+    "                    init_model_yr=2000, # Start from spinup year 2000\n",
+    "                    store_monthly_hydro=True,  # Monthly ouptuts provide additional information\n",
+    "                    output_filesuffix=file_id);  # an identifier for the output file, to read it later\n",
+    "\n",
+    "with xr.open_dataset(gdir_hef.get_filepath('model_diagnostics', filesuffix=file_id)) as ds_melt_f:\n",
+    "    # The last step of hydrological output is NaN (we can't compute it for this year)\n",
+    "    ds_melt_f = ds_melt_f.isel(time=slice(0, -1)).load()\n",
+    "\n",
+    "# Plot the runoff again for the calibrated melt_f parameter\n",
+    "sel_vars = [v for v in ds_melt_f.variables if 'month_2d' not in ds_melt_f[v].dims]\n",
+    "df_annual_melt_f = ds_melt_f[sel_vars].to_dataframe()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f0173ff",
+   "metadata": {},
+   "source": [
+    "Now calibrating the precipitation factor variable, `prcp_fac`. Here we fix the `melt_f` and the `temp_bias` and then calibrate the precipitation factor accordingly, this will ensure that the reference average mass balance will be matched."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f4182717",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'rgi_id': 'RGI60-11.00897',\n",
+       " 'bias': 0,\n",
+       " 'melt_f': 5.0,\n",
+       " 'prcp_fac': 1.016447639835006,\n",
+       " 'temp_bias': 0,\n",
+       " 'reference_mb': -1100.3,\n",
+       " 'reference_mb_err': None,\n",
+       " 'reference_period': '2000-01-01_2020-01-01',\n",
+       " 'mb_global_params': {'temp_default_gradient': -0.0065,\n",
+       "  'temp_all_solid': 0.0,\n",
+       "  'temp_all_liq': 2.0,\n",
+       "  'temp_melt': -1.0},\n",
+       " 'baseline_climate_source': 'GSWP3_W5E5'}"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Just calibrate the prcp_fac parameter\n",
+    "calib_param_prcp_fac = mb_calibration_from_scalar_mb(gdir_hef,\n",
+    "                              ref_mb = ref_mb,\n",
+    "                              ref_period=cfg.PARAMS['geodetic_mb_period'],\n",
+    "                              calibrate_param1='prcp_fac',\n",
+    "                              overwrite_gdir=True)\n",
+    "\n",
+    "file_id = '_prcp_fac'\n",
+    "\n",
+    "# Adding the mass balance model with the new calibration\n",
+    "mbmod = massbalance.MonthlyTIModel(gdir_hef)\n",
+    "fls = gdir_hef.read_pickle('inversion_flowlines')\n",
+    "mbdf['prcp_fac_mb'] = mbmod.get_specific_mb(fls=fls, year=mbdf.index)\n",
+    "\n",
+    "# We are using the task run_with_hydro to store hydrological outputs along with the usual glaciological outputs\n",
+    "# Run this again with the calibrated parameters\n",
+    "tasks.run_with_hydro(gdir_hef,  # Run on the selected glacier\n",
+    "                     run_task=tasks.run_from_climate_data,  # running from observed climate data\n",
+    "                     ys=2000,  # Period which we will average and constantly repeat\n",
+    "                     init_model_yr=2000, # Start from spinup year 2000\n",
+    "                     init_model_filesuffix='_spinup_historical',  # use the previous run as initial state\n",
+    "                     store_monthly_hydro=True,  # Monthly ouptuts provide additional information\n",
+    "                     output_filesuffix=file_id);  # an identifier for the output file, to read it later\n",
+    "\n",
+    "with xr.open_dataset(gdir_hef.get_filepath('model_diagnostics', filesuffix=file_id)) as ds_prcp_fac:\n",
+    "    # The last step of hydrological output is NaN (we can't compute it for this year)\n",
+    "    ds_prcp_fac = ds_prcp_fac.isel(time=slice(0, -1)).load()\n",
+    "\n",
+    "# Plot the runoff again for the calibrated melt_f parameter\n",
+    "sel_vars = [v for v in ds_prcp_fac.variables if 'month_2d' not in ds_prcp_fac[v].dims]\n",
+    "df_annual_prcp_fac = ds_prcp_fac[sel_vars].to_dataframe()\n",
+    "\n",
+    "calib_param_prcp_fac"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86551587",
+   "metadata": {},
+   "source": [
+    "Note here that now the `melt_f` is lower than before, and the `prcp_fac` is now lowered in calibration so the average reference mass-balance can be matched."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30085bd7",
+   "metadata": {},
+   "source": [
+    "Now calibrating the temperature bias parameter, `temp_bias`. Here we now fix the `melt_f` and `prcp_fac` to the default values, and calibrate the `temp_bias` to match the average reference mass-balance:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "0279da19",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'rgi_id': 'RGI60-11.00897',\n",
+       " 'bias': 0,\n",
+       " 'melt_f': 5.0,\n",
+       " 'prcp_fac': 3.357136316271367,\n",
+       " 'temp_bias': 1.6617639899008794,\n",
+       " 'reference_mb': -1100.3,\n",
+       " 'reference_mb_err': None,\n",
+       " 'reference_period': '2000-01-01_2020-01-01',\n",
+       " 'mb_global_params': {'temp_default_gradient': -0.0065,\n",
+       "  'temp_all_solid': 0.0,\n",
+       "  'temp_all_liq': 2.0,\n",
+       "  'temp_melt': -1.0},\n",
+       " 'baseline_climate_source': 'GSWP3_W5E5'}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Just calibrate the temp_bias parameters\n",
+    "temp_bias_calib = mb_calibration_from_scalar_mb(gdir_hef,\n",
+    "                              ref_mb = ref_mb,\n",
+    "                              ref_period=cfg.PARAMS['geodetic_mb_period'],\n",
+    "                              calibrate_param1='temp_bias',\n",
+    "                              overwrite_gdir=True)\n",
+    "\n",
+    "file_id = '_temp_bias'\n",
+    "\n",
+    "# Adding the mass balance model with the new calibration\n",
+    "mbmod = massbalance.MonthlyTIModel(gdir_hef)\n",
+    "fls = gdir_hef.read_pickle('inversion_flowlines')\n",
+    "mbdf['temp_bias_mb'] = mbmod.get_specific_mb(fls=fls, year=mbdf.index)\n",
+    "\n",
+    "# We are using the task run_with_hydro to store hydrological outputs along with the usual glaciological outputs\n",
+    "# Run this again with the calibrated parameters\n",
+    "tasks.run_with_hydro(gdir_hef,  # Run on the selected glacier\n",
+    "                     run_task=tasks.run_from_climate_data,  # running from observed climate data\n",
+    "                     ys=2000,  # Period which we will average and constantly repeat\n",
+    "                     init_model_yr=2000, # Start from spinup year 2000\n",
+    "                     init_model_filesuffix='_spinup_historical',  # use the previous run as initial state\n",
+    "                     store_monthly_hydro=True,  # Monthly ouptuts provide additional information\n",
+    "                     output_filesuffix=file_id);  # an identifier for the output file, to read it later\n",
+    "\n",
+    "with xr.open_dataset(gdir_hef.get_filepath('model_diagnostics', filesuffix=file_id)) as ds_temp_bias:\n",
+    "    # The last step of hydrological output is NaN (we can't compute it for this year)\n",
+    "    ds_temp_bias = ds_temp_bias.isel(time=slice(0, -1)).load()\n",
+    "\n",
+    "# Plot the runoff again for the calibrated melt_f parameter\n",
+    "sel_vars = [v for v in ds_temp_bias.variables if 'month_2d' not in ds_temp_bias[v].dims]\n",
+    "df_annual_temp_bias = ds_temp_bias[sel_vars].to_dataframe()\n",
+    "\n",
+    "temp_bias_calib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a48b2676",
+   "metadata": {},
+   "source": [
+    "Now both `melt_f` and `prcp_fac` are fixed to their default values. These are both on the lower-end to what they have been calibrated to previously when `temp_bias` is fixed. Since both of these parameters are slightly lower, the `temp_bias` is now calibrated to be higher than 0 to match the average reference mass-balance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b1b8ddf",
+   "metadata": {},
+   "source": [
+    "### Calibrating permutations of two parameters:\n",
+    "\n",
+    "In `mb_calibration_from_scalar_mb` in the OGGM, calibrating two parameters works as the following:\n",
+    "1. Set calibrate_param1 and calibrate_param2.\n",
+    "2. The method fixes the third parameter that has not been set.\n",
+    "3. The method then fixes the parameter set to calibrate_param2 and calibrates calibrate_param1.\n",
+    "4. If the parameter set to calibrate_param1 can find a solution with just the one parameter, the calibration will stop.\n",
+    "5. If the parameter set to calibrate_param1 cannot find a solution with just one parameter alone, the method will fix the parameter to either its upper or lower limit (whichever is closer to a solution) and then move onto calibrating the parameter set to calibrate_param2.\n",
+    "6. If a solution is found with the second calibration attempt, the calibration will stop.\n",
+    "7. If no solution is found with either parameter calibrated, an error will raise and the calibration will stop with no solution found."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aabbc505",
+   "metadata": {},
+   "source": [
+    "Now we will be using this technique to calibrate two parameters.\n",
+    "\n",
+    "Now calibrating `melt_f` and `prcp_fac`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "15e7b69f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'rgi_id': 'RGI60-11.00897',\n",
+       " 'bias': 0,\n",
+       " 'melt_f': 9.059478188653333,\n",
+       " 'prcp_fac': 3.357136316271367,\n",
+       " 'temp_bias': 0,\n",
+       " 'reference_mb': -1100.3,\n",
+       " 'reference_mb_err': None,\n",
+       " 'reference_period': '2000-01-01_2020-01-01',\n",
+       " 'mb_global_params': {'temp_default_gradient': -0.0065,\n",
+       "  'temp_all_solid': 0.0,\n",
+       "  'temp_all_liq': 2.0,\n",
+       "  'temp_melt': -1.0},\n",
+       " 'baseline_climate_source': 'GSWP3_W5E5'}"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Calibrating the melt_f and prcp_fac parameters together\n",
+    "mf_pf_calibration = mb_calibration_from_scalar_mb(gdir_hef,\n",
+    "                              ref_mb = ref_mb,\n",
+    "                              ref_period=cfg.PARAMS['geodetic_mb_period'],\n",
+    "                              calibrate_param1='melt_f',\n",
+    "                              calibrate_param2='prcp_fac',\n",
+    "                              overwrite_gdir=True)\n",
+    "\n",
+    "\n",
+    "file_id = '_mf_pf'\n",
+    "\n",
+    "# Adding the mass balance model with the new calibration\n",
+    "mbmod = massbalance.MonthlyTIModel(gdir_hef)\n",
+    "fls = gdir_hef.read_pickle('inversion_flowlines')\n",
+    "mbdf['mf_pf_mb'] = mbmod.get_specific_mb(fls=fls, year=mbdf.index)\n",
+    "\n",
+    "# We are using the task run_with_hydro to store hydrological outputs along with the usual glaciological outputs\n",
+    "# Run this again with the calibrated parameters\n",
+    "tasks.run_with_hydro(gdir_hef,  # Run on the selected glacier\n",
+    "                     run_task=tasks.run_from_climate_data,  # running from observed climate data\n",
+    "                     ys=2000,  # Period which we will average and constantly repeat\n",
+    "                     init_model_yr=2000, # Start from spinup year 2000\n",
+    "                     init_model_filesuffix='_spinup_historical',  # use the previous run as initial state\n",
+    "                     store_monthly_hydro=True,  # Monthly ouptuts provide additional information\n",
+    "                     output_filesuffix=file_id);  # an identifier for the output file, to read it later\n",
+    "\n",
+    "with xr.open_dataset(gdir_hef.get_filepath('model_diagnostics', filesuffix=file_id)) as ds_mf_pf:\n",
+    "    # The last step of hydrological output is NaN (we can't compute it for this year)\n",
+    "    ds_mf_pf = ds_mf_pf.isel(time=slice(0, -1)).load()\n",
+    "\n",
+    "# Plot the runoff again for the calibrated melt_f parameter\n",
+    "sel_vars = [v for v in ds_mf_pf.variables if 'month_2d' not in ds_mf_pf[v].dims]\n",
+    "df_annual_mf_pf = ds_mf_pf[sel_vars].to_dataframe()\n",
+    "\n",
+    "mf_pf_calibration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d011eaae",
+   "metadata": {},
+   "source": [
+    "Notice here that we fix `temp_bias` to the default value of 0. The way that scalar calibration works, `melt_f` is calibrated first to see if a solution can be found. Therefore when the parameters are calibrated, the `prcp_fac` is firstly fixed while `melt_f` is calibrated. Since a solution can be found by just calibrating `melt_f`, as we saw at the start of this tutorial. This is why this result is the same as the calibration from just calibrating the `melt_f`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7377e76a",
+   "metadata": {},
+   "source": [
+    "Now calibrating `prcp_fac` and `temp_bias`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "f6a6ef2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'rgi_id': 'RGI60-11.00897',\n",
+       " 'bias': 0,\n",
+       " 'melt_f': 5.0,\n",
+       " 'prcp_fac': 1.016447639835006,\n",
+       " 'temp_bias': 0,\n",
+       " 'reference_mb': -1100.3,\n",
+       " 'reference_mb_err': None,\n",
+       " 'reference_period': '2000-01-01_2020-01-01',\n",
+       " 'mb_global_params': {'temp_default_gradient': -0.0065,\n",
+       "  'temp_all_solid': 0.0,\n",
+       "  'temp_all_liq': 2.0,\n",
+       "  'temp_melt': -1.0},\n",
+       " 'baseline_climate_source': 'GSWP3_W5E5'}"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Calibrating the prcp_fac and temp_bias parameters together\n",
+    "pf_tb_calibration = mb_calibration_from_scalar_mb(gdir_hef,\n",
+    "                              ref_mb = ref_mb,\n",
+    "                              ref_period=cfg.PARAMS['geodetic_mb_period'],\n",
+    "                              calibrate_param1='prcp_fac',\n",
+    "                              calibrate_param2='temp_bias',\n",
+    "                              overwrite_gdir=True)\n",
+    "\n",
+    "file_id = '_pf_tb'\n",
+    "\n",
+    "# Adding the mass balance model with the new calibration\n",
+    "mbmod = massbalance.MonthlyTIModel(gdir_hef)\n",
+    "fls = gdir_hef.read_pickle('inversion_flowlines')\n",
+    "mbdf['pf_tb_mb'] = mbmod.get_specific_mb(fls=fls, year=mbdf.index)\n",
+    "\n",
+    "# We are using the task run_with_hydro to store hydrological outputs along with the usual glaciological outputs\n",
+    "# Run this again with the calibrated parameters\n",
+    "tasks.run_with_hydro(gdir_hef,  # Run on the selected glacier\n",
+    "                     run_task=tasks.run_from_climate_data,  # running from observed climate data\n",
+    "                     ys=2000,  # Period which we will average and constantly repeat\n",
+    "                     init_model_yr=2000, # Start from spinup year 2000\n",
+    "                     init_model_filesuffix='_spinup_historical',  # use the previous run as initial state\n",
+    "                     store_monthly_hydro=True,  # Monthly ouptuts provide additional information\n",
+    "                     output_filesuffix=file_id);  # an identifier for the output file, to read it later\n",
+    "\n",
+    "with xr.open_dataset(gdir_hef.get_filepath('model_diagnostics', filesuffix=file_id)) as ds_pf_tb:\n",
+    "    # The last step of hydrological output is NaN (we can't compute it for this year)\n",
+    "    ds_pf_tb = ds_pf_tb.isel(time=slice(0, -1)).load()\n",
+    "\n",
+    "# Plot the runoff again for the calibrated prcp_fac parameter\n",
+    "sel_vars = [v for v in ds_pf_tb.variables if 'month_2d' not in ds_pf_tb[v].dims]\n",
+    "df_annual_pf_tb = ds_pf_tb[sel_vars].to_dataframe()\n",
+    "\n",
+    "pf_tb_calibration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd9d286c",
+   "metadata": {},
+   "source": [
+    "Notice here that we fix `melt_f` to the default value of 5.0. To maintain the same average mass balance as before, `prcp_fac` is reduced to compensate. The parameters `melt_f` and `prcp_fac` are showing mass leaving and entering the glacier respectively."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40aaa9a3",
+   "metadata": {},
+   "source": [
+    "Now calibrating `melt_f` and `temp_bias`, we fix the `prcp_fac`. To calibrate we firstly fix the `temp_bias` and calibrate the `melt_f`, then again since a solution can be found by calibrating `melt_f` alone, the `temp_bias` does not get calibrated here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "6cae2e59",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'rgi_id': 'RGI60-11.00897',\n",
+       " 'bias': 0,\n",
+       " 'melt_f': 9.059478188653333,\n",
+       " 'prcp_fac': 3.357136316271367,\n",
+       " 'temp_bias': 0,\n",
+       " 'reference_mb': -1100.3,\n",
+       " 'reference_mb_err': None,\n",
+       " 'reference_period': '2000-01-01_2020-01-01',\n",
+       " 'mb_global_params': {'temp_default_gradient': -0.0065,\n",
+       "  'temp_all_solid': 0.0,\n",
+       "  'temp_all_liq': 2.0,\n",
+       "  'temp_melt': -1.0},\n",
+       " 'baseline_climate_source': 'GSWP3_W5E5'}"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Calibrating the melt_f and temp_bias parameters together\n",
+    "mf_tb_calib = mb_calibration_from_scalar_mb(gdir_hef,\n",
+    "                              ref_mb = ref_mb,\n",
+    "                              ref_period=cfg.PARAMS['geodetic_mb_period'],\n",
+    "                              calibrate_param1='melt_f',\n",
+    "                              calibrate_param2='temp_bias',\n",
+    "                              overwrite_gdir=True)\n",
+    "\n",
+    "file_id = '_mf_tb'\n",
+    "\n",
+    "# Adding the mass balance model with the new calibration\n",
+    "mbmod = massbalance.MonthlyTIModel(gdir_hef)\n",
+    "fls = gdir_hef.read_pickle('inversion_flowlines')\n",
+    "mbdf['mf_tb_mb'] = mbmod.get_specific_mb(fls=fls, year=mbdf.index)\n",
+    "\n",
+    "# We are using the task run_with_hydro to store hydrological outputs along with the usual glaciological outputs\n",
+    "# Run this again with the calibrated parameters\n",
+    "tasks.run_with_hydro(gdir_hef,  # Run on the selected glacier\n",
+    "                     run_task=tasks.run_from_climate_data,  # running from observed climate data\n",
+    "                     ys=2000,  # Period which we will average and constantly repeat\n",
+    "                     init_model_yr=2000, # Start from spinup year 2000\n",
+    "                     init_model_filesuffix='_spinup_historical',  # use the previous run as initial state\n",
+    "                     store_monthly_hydro=True,  # Monthly ouptuts provide additional information\n",
+    "                     output_filesuffix=file_id);  # an identifier for the output file, to read it later\n",
+    "\n",
+    "with xr.open_dataset(gdir_hef.get_filepath('model_diagnostics', filesuffix=file_id)) as ds_mf_tb:\n",
+    "    # The last step of hydrological output is NaN (we can't compute it for this year)\n",
+    "    ds_mf_tb = ds_mf_tb.isel(time=slice(0, -1)).load()\n",
+    "\n",
+    "# Plot the runoff again for the calibrated melt_f parameter\n",
+    "sel_vars = [v for v in ds_mf_tb.variables if 'month_2d' not in ds_mf_tb[v].dims]\n",
+    "df_annual_mf_tb = ds_mf_tb[sel_vars].to_dataframe()\n",
+    "\n",
+    "mf_tb_calib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246378e3",
+   "metadata": {},
+   "source": [
+    "Now calibrating `melt_f`, `prcp_fac` and `temp_bias`, we firstly calibrate `melt_f` and fix the remaining two parameters to the default values. Since we know from previous calibrations, with the values chosen for our calibration, a solution can be found with just calibrating `melt_f` alone and therefore `prcp_fac` and `temp_bias` do not reach calibration. Therefore returning the same results as before in this tutorial where `melt_f` is calibrated first:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d58ddcbf",
+   "metadata": {},
+   "source": [
+    "### Calibrating all three parameters:\n",
+    "\n",
+    "Now we will use `mb_calibration_from_scalar_mb` to calibrate the three parameters. This works as the following:\n",
+    "1. Set calibrate_param1, calibrate_param2 and calibrate_param3.\n",
+    "2. Fix the parameters assigned to calibrate_param2 and calibrate_param3.\n",
+    "3. Calibrate calibrate_param1.\n",
+    "4. If a solution is found, stop calibration here.\n",
+    "5. If no solution can be found, move onto calibrate the next parameter.\n",
+    "6. Repeat steps 4 and 5, therefore either reaching a calibrated solution or, if no solution can be found for any parameter, an error is raised and calibration stops with no solution.\n",
+    "\n",
+    "Here we can see that the chosen order to calibrate parameters can affect the outcomes of the calibration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7077bbef",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'rgi_id': 'RGI60-11.00897',\n",
+       " 'bias': 0,\n",
+       " 'melt_f': 9.059478188653333,\n",
+       " 'prcp_fac': 3.357136316271367,\n",
+       " 'temp_bias': 0,\n",
+       " 'reference_mb': -1100.3,\n",
+       " 'reference_mb_err': None,\n",
+       " 'reference_period': '2000-01-01_2020-01-01',\n",
+       " 'mb_global_params': {'temp_default_gradient': -0.0065,\n",
+       "  'temp_all_solid': 0.0,\n",
+       "  'temp_all_liq': 2.0,\n",
+       "  'temp_melt': -1.0},\n",
+       " 'baseline_climate_source': 'GSWP3_W5E5'}"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Calibrate all of the parameters\n",
+    "calib_all = mb_calibration_from_scalar_mb(gdir_hef,\n",
+    "                              ref_mb = ref_mb,\n",
+    "                              ref_period=cfg.PARAMS['geodetic_mb_period'],\n",
+    "                              calibrate_param1='melt_f',\n",
+    "                              calibrate_param2='prcp_fac',\n",
+    "                              calibrate_param3='temp_bias',\n",
+    "                              overwrite_gdir=True)\n",
+    "\n",
+    "file_id = '_calib_all'\n",
+    "\n",
+    "# Adding the mass balance model with the new calibration\n",
+    "mbmod = massbalance.MonthlyTIModel(gdir_hef)\n",
+    "fls = gdir_hef.read_pickle('inversion_flowlines')\n",
+    "mbdf['calib_all_mb'] = mbmod.get_specific_mb(fls=fls, year=mbdf.index)\n",
+    "\n",
+    "# We are using the task run_with_hydro to store hydrological outputs along with the usual glaciological outputs\n",
+    "# Run this again with the calibrated parameters\n",
+    "tasks.run_with_hydro(gdir_hef,  # Run on the selected glacier\n",
+    "                     run_task=tasks.run_from_climate_data,  # running from observed climate data\n",
+    "                     ys=2000,  # Period which we will average and constantly repeat\n",
+    "                     init_model_yr=2000, # Start from spinup year 2000\n",
+    "                     init_model_filesuffix='_spinup_historical',  # use the previous run as initial state\n",
+    "                     store_monthly_hydro=True,  # Monthly ouptuts provide additional information\n",
+    "                     output_filesuffix=file_id);  # an identifier for the output file, to read it later\n",
+    "\n",
+    "with xr.open_dataset(gdir_hef.get_filepath('model_diagnostics', filesuffix=file_id)) as ds_calib:\n",
+    "    # The last step of hydrological output is NaN (we can't compute it for this year)\n",
+    "    ds_calib = ds_calib.isel(time=slice(0, -1)).load()\n",
+    "\n",
+    "# Plot the runoff again for the calibrated melt_f parameter\n",
+    "sel_vars = [v for v in ds_calib.variables if 'month_2d' not in ds_calib[v].dims]\n",
+    "df_annual_calib = ds_calib[sel_vars].to_dataframe()\n",
+    "\n",
+    "calib_all"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7e6bb8f",
+   "metadata": {},
+   "source": [
+    "We can see above that the calibrated parameters are now the same as in cases where we calibrate the `melt_f` first, and when we calibrate `melt_f` and `prcp_fac`. This is due to the behaviour of the scalar calibration function, which calibrates one parameter at a time in the order that we pass into the calibration function.\n",
+    "\n",
+    "This also means that the order in which we calibrate the parameters will change the outcome of our calibration, so keep this in mind when using the `mb_calibration_from_scalar_mb` function. In this tutorial we have kept to the same order of calibration in the permutation:\n",
+    "1. `melt_f`\n",
+    "2. `prcp_fac`\n",
+    "3. `temp_bias`\n",
+    "\n",
+    "But this order can be changed and is up to the discretion of the user to decide what they would like to calibrate initially."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31fa073d",
+   "metadata": {},
+   "source": [
+    "Now lets investigate the effect of the parameter calibration on the runoff, the aim of this tutorial!\n",
+    "\n",
+    "First we will convert the runoff values to megatonnes for readability."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "0288677b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert them to megatonnes (instead of kg)\n",
+    "df_runoff_melt_f = df_annual_melt_f[runoff_vars] * 1e-9\n",
+    "df_runoff_prcp_fac = df_annual_prcp_fac[runoff_vars] * 1e-9\n",
+    "df_runoff_temp_bias = df_annual_temp_bias[runoff_vars] * 1e-9\n",
+    "df_calib = df_annual_calib[runoff_vars] * 1e-9\n",
+    "df_runoff_mf_pf = df_annual_mf_pf[runoff_vars] * 1e-9\n",
+    "df_runoff_pf_tb = df_annual_pf_tb[runoff_vars] * 1e-9\n",
+    "df_runoff_mf_tb = df_annual_mf_tb[runoff_vars] * 1e-9"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c34f50d",
+   "metadata": {},
+   "source": [
+    "And plotting runoff output values resulting from all calibration of parameters..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "87e51a4a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10, 3.5), sharex=True)\n",
+    "df_calib.sum(axis=1).plot(ax=ax, label='all params calibrated');\n",
+    "df_runoff_melt_f.sum(axis=1).plot(ax=ax, label='calibrated melt_f');\n",
+    "df_runoff.sum(axis=1).plot(ax=ax, label='default calib');\n",
+    "df_runoff_prcp_fac.sum(axis=1).plot(ax=ax, label='calibrated prcp_fac');\n",
+    "df_runoff_temp_bias.sum(axis=1).plot(ax=ax, label='calibrated temp_bias');\n",
+    "df_runoff_mf_pf.sum(axis=1).plot(ax=ax, label='melt_f & prcp_fac calibrated', linestyle='dashed');\n",
+    "df_runoff_pf_tb.sum(axis=1).plot(ax=ax, label='prcp_fac & temp_bias calibrated', linestyle='dashed');\n",
+    "df_runoff_mf_tb.sum(axis=1).plot(ax=ax, label='melt_f & temp_bias calibrated', linestyle='dashed');\n",
+    "plt.ylabel('Runoff in Megatonnes'); plt.xlabel('Years'); plt.title(f'Total annual runoff for {rgi_id}');\n",
+    "plt.legend();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5955278f",
+   "metadata": {},
+   "source": [
+    "The impact of calibrating different permutations of parameters can be seen above, this seems to impact the runoff result quite significantly depending on the calibration choices made! It can be seen that the rough shape is similar for all calibrations, but that different permutation of calibrating parameters can have a large impact on the amount of runoff predicted by the OGGM.\n",
+    "\n",
+    "We can see that some of these runoff values are the same for certain calibrations, note that here that this is due to the style of calibration of our parameters, as we can see above the calibrations that calibrate `prcp_fac` first have a significantly lower runoff than the rest."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8bad322a",
+   "metadata": {},
+   "source": [
+    "Now plotting the mass-balance results together and comparing this to the runoff outputs:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "c9455a05",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Now plotting all the mass balance results together\n",
+    "\n",
+    "# Adding the in-situ mass balance observations for comparison\n",
+    "mbdf['in_situ_mb'] = gdir_hef.get_ref_mb_data().loc[2000:2019]['ANNUAL_BALANCE']\n",
+    "\n",
+    "# The mass-balance from diffrent calibration runs throughout the tutorial\n",
+    "plt.plot(mbdf['in_situ_mb'], label='In-situ MB')\n",
+    "plt.plot(mbdf['melt_f_mb'], label='melt_f')\n",
+    "plt.plot(mbdf['prcp_fac_mb'], label='prcp_fac')\n",
+    "plt.plot(mbdf['temp_bias_mb'], label='temp_bias')\n",
+    "plt.plot(mbdf['calib_all_mb'], label='All params', linestyle='dashed')\n",
+    "plt.plot(mbdf['mf_pf_mb'], label='melt_f & prcp_fac', linestyle='dashed')\n",
+    "plt.plot(mbdf['pf_tb_mb'], label='prcp_fac & temp_bias', linestyle='dashed')\n",
+    "plt.plot(mbdf['mf_tb_mb'], label='melt_f & temp_bias', linestyle='dashed')\n",
+    "plt.legend()\n",
+    "plt.xlabel(\"Year\")\n",
+    "plt.ylabel(\"Mass Balance\");\n",
+    "plt.title(f'Mass Balance time series for {rgi_id}');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4aa15ef",
+   "metadata": {},
+   "source": [
+    "In this graph we can now see that our different calibrations also have an impact on the mass-balance\n",
+    "\n",
+    "Here, we can see that there are a few overlaps again for different permutations of calibrations, if we look closely we can see that these are the same as the above for runoff.\n",
+    "\n",
+    "However, from initial inspection we can see that for the runoff there is quite a significant difference based off of choices of calibrations. Here we can see that the differences between the mass-balance don't seem quite as dramatic.\n",
+    "\n",
+    "From the runoff graph, the calibration of the `prcp_fac` appears to be impactful on the runoff. We can now explore a bit further, and investigate the relationship between `prcp_fac` and `melt_on_glacier`, which is a component of the `runoff`. We will do this by fixing the `prcp_fac` to a range of values and calibrating the `melt_f` to obtain a range of calibrated values. This will allow us to verify the sensitivity of these parameters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eef65f7e",
+   "metadata": {},
+   "source": [
+    "## Sensitivity Analysis of runoff parameters in calibration\n",
+    "\n",
+    "**Sensitivity Analysis** is a technique used to determine how a model output is affected by the changes in parameters. We will perform a simple sensitvity analysis in this tutorial to investigate the affects of parameter calibration on some of the runoff components.\n",
+    "\n",
+    "We will now investigate the sensitivity of the runoff parameters to the calibration of the mass-balance parameters below. This will allow us to understand the relationship between parameters further, for example if one parameter is changed, how much does this affect the other parameter?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af1a9529",
+   "metadata": {},
+   "source": [
+    "**First let's start with some definitions!**\n",
+    "\n",
+    "We will be investigating the melt contribution, which is:\n",
+    "$ \\frac{\\text{melt on glacier}}{\\text{runoff`}}$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "003110aa",
+   "metadata": {},
+   "source": [
+    "In this tutorial we will investigate the sensitivities of the `melt_on_glacier` and melt contribution to the `prcp_fac`.\n",
+    "\n",
+    "We have chosen these parameters as `melt_on_glacier` is a single variable, defined by a linear relationship from the `prcp_fac`. Here we will just be affecting the  `melt_on_glacier` directly.\n",
+    "\n",
+    "The melt contribution variable is a bit more complicated, as this will be investigating the relationship between the run off which includes `melt_on_glacier` variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "834456bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Varying prcp_fac between a range of values with a step of 1.0\n",
+    "pd_prcp_sens = pd.DataFrame(index=np.arange(0.1, 5.0, 0.3))\n",
+    "file_id = '_sens'\n",
+    "\n",
+    "# Calibrate the melt factor for each precipitation factor\n",
+    "spec_mb_melt_f_sens_dict = {}\n",
+    "for pf in pd_prcp_sens.index:\n",
+    "    calib_param = mb_calibration_from_scalar_mb(gdir_hef,\n",
+    "                                  ref_mb = ref_mb,\n",
+    "                                  prcp_fac = pf,\n",
+    "                                  ref_period=cfg.PARAMS['geodetic_mb_period'],\n",
+    "                                  overwrite_gdir=True)\n",
+    "\n",
+    "    # Fill the dataframe with the calibrated parameters\n",
+    "    pd_prcp_sens.loc[pf, 'melt_f'] = calib_param['melt_f']\n",
+    "\n",
+    "    # We are using the task run_with_hydro to store hydrological outputs along with the usual glaciological outputs\n",
+    "    # Run this again with the calibrated parameters\n",
+    "    tasks.run_with_hydro(gdir_hef,  # Run on the selected glacier\n",
+    "                        run_task=tasks.run_from_climate_data, # running from observed climate data\n",
+    "                        ys=2000,  # Period which we will average and constantly repeat\n",
+    "                        init_model_yr=2000, # Start from spinup year 2000\n",
+    "                        init_model_filesuffix='_spinup_historical',  # use the previous run as initial state\n",
+    "                        store_monthly_hydro=True,  # Monthly ouptuts provide additional information\n",
+    "                        output_filesuffix=file_id);  # an identifier for the output file, to read it later\n",
+    "\n",
+    "    with xr.open_dataset(gdir_hef.get_filepath('model_diagnostics', filesuffix=file_id)) as ds_sens:\n",
+    "        # The last step of hydrological output is NaN (we can't compute it for this year)\n",
+    "        ds_sens = ds_sens.isel(time=slice(0, -1)).load()\n",
+    "\n",
+    "    # Plot the runoff again for the calibrated melt_f parameter\n",
+    "    sel_vars = [v for v in ds_sens.variables if 'month_2d' not in ds_sens[v].dims]\n",
+    "    df_annual_sens = ds_sens[sel_vars].to_dataframe()\n",
+    "\n",
+    "    pd_prcp_sens.loc[pf, 'melt_off_glacier'] = df_annual_sens['melt_off_glacier'].mean()\n",
+    "    pd_prcp_sens.loc[pf, 'melt_on_glacier'] = df_annual_sens['melt_on_glacier'].mean()\n",
+    "    pd_prcp_sens.loc[pf, 'liq_prcp_off_glacier'] = df_annual_sens['liq_prcp_off_glacier'].mean()\n",
+    "    pd_prcp_sens.loc[pf, 'liq_prcp_on_glacier'] = df_annual_sens['liq_prcp_on_glacier'].mean()\n",
+    "    pd_prcp_sens.loc[pf, 'runoff'] = pd_prcp_sens.loc[pf, 'melt_off_glacier'] + pd_prcp_sens.loc[pf, 'melt_on_glacier'] + pd_prcp_sens.loc[pf, 'liq_prcp_off_glacier'] + pd_prcp_sens.loc[pf, 'liq_prcp_on_glacier']\n",
+    "\n",
+    "colors_melt_f = plt.get_cmap('viridis').colors[10::10]\n",
+    "plt.figure()\n",
+    "for j, pf in enumerate(pd_prcp_sens.index):\n",
+    "    plt.plot(pf, pd_prcp_sens.loc[pf, 'melt_on_glacier'], 'o', color=colors_melt_f[j])\n",
+    "    plt.xlabel('Precipitation factor')\n",
+    "    plt.ylabel('Mean annual melt on glacier (kg m-2 yr-1)')\n",
+    "    plt.title('Sensitivity of Melt On Glacier to Precipitation Factor')\n",
+    "\n",
+    "# Here we are varying the precipitation factor and calibrating the melt factor for each value\n",
+    "# Then plotting the mean annual melt_on_glacier against the precipitation factor"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00bed96c",
+   "metadata": {},
+   "source": [
+    "In the above plot, a strong linear positive correlation is shown between the precipitation factor and the mean annual melt on the glacier. \n",
+    "\n",
+    "From the calibration, it can be seen that as the precipitation factor is changed, the melt factor parameter is calibrated accordingly and increases with the precipitation factor to maintain the average annual mass-balance. Therefore as the `prcp_fac` increases, the `melt_on_glacier` increases linearly in a 1:1 ratio.\n",
+    "\n",
+    "In the OGGM, the `melt_on_glacier` is derived from both the `melt_f` parameter and `prcp_fac`. Therefore this sensitivity makes sense."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28875fe5",
+   "metadata": {},
+   "source": [
+    "Now investigating the sensitivity of the Glacier Melt Contribution to the Precipitation Factor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "7ad8fa07",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "colors_melt_f = plt.get_cmap('viridis').colors[10::10]\n",
+    "plt.figure()\n",
+    "for j, pf in enumerate(pd_prcp_sens.index):\n",
+    "    plt.plot(pf, pd_prcp_sens.loc[pf, 'melt_on_glacier']/pd_prcp_sens.loc[pf, 'runoff'], 'o', color=colors_melt_f[j])\n",
+    "    plt.xlabel('Precipitation factor')\n",
+    "    plt.ylabel('Mean Glacial Melt Contribution')\n",
+    "    plt.title('Sensitivity of Glacial Melt Contribution to the Precipitation Factor')\n",
+    "\n",
+    "# Here we are plotting how the glacial melt contribution (melt_on_glacier/runoff) varies with the changing precipitation factor\n",
+    "# This shows how the relative contribution of glacier melt to total runoff changes as we adjust precipitation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75444665",
+   "metadata": {},
+   "source": [
+    "It can be seen that there is a relatively strong negative correlation, between the precipitation factor and the mean glacial melt contribution. And therefore it can be seen that the mean glacial melt contribution is sensitive to the change in precipitation factor. As we increase the `prcp_fac`, we know that the `melt_f` increases along with this. Considering the melt contribution variable contains a numerator of `melt_on_glacier`, which has been shown to increase as the `prcp_fac` parameter increases.\n",
+    "\n",
+    "The denominator is the runoff, which contains 4 summed variables (including `melt_on_glacier`) that are affected by the `prcp_fac`, all of which increase as the `prcp_fac` increases, as can be seen within the OGGM model implementation. The runoff therefore increases at least as much as the `melt_on_glacier`.\n",
+    "\n",
+    "Therefore the increase of the denominator overpowers the increase of the numerator, and the relationship shown above reflects this reaction to the increase in `prcp_fac`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf62afed",
+   "metadata": {},
+   "source": [
+    "## Exercise:\n",
+    "\n",
+    "Try this yourself for sensitivity with the precipitation factor when varying the melt factor as above:\n",
+    "1. The melt_off_glacier parameter.\n",
+    "2. The total liquid precipitation parameters (sum of on and off).\n",
+    "3. Any other interesting combinations you can discover!\n",
+    "\n",
+    "Does anything you discover surprise you?\n",
+    "Are there particularly sensitive parameters compared to others?\n",
+    "\n",
+    "**Stretch:** Can you experiment different combinations of parameters for sensitvity, e.g. such as varying the temp_bias parameters instead? "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e0eae95",
+   "metadata": {},
+   "source": [
+    "## WGMS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "53b9a503",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # We start by fetching the observations\n",
+    "# mbdf = gdir_hef.get_ref_mb_data()[['ANNUAL_BALANCE']]\n",
+    "# mbdf.columns = ['in_situ_mb']\n",
+    "# mbdf = mbdf.loc[2000:2019]\n",
+    "# mbdf.plot();\n",
+    "# mbdf.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "155dbd5a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "oggm_env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}