diff --git a/notebooks/02_collision_protection_volume.ipynb b/notebooks/02_collision_protection_volume.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Investigate \"collision protection volume\" concept"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-03T00:37:28.874517Z",
+     "start_time": "2018-12-03T00:37:28.528594Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "%reload_ext autoreload\n",
+    "%autoreload"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-03T00:37:30.229243Z",
+     "start_time": "2018-12-03T00:37:28.875519Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.core.debugger import set_trace\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "\n",
+    "import plotly\n",
+    "import plotly.graph_objs as go\n",
+    "import plotly.plotly as py\n",
+    "import plotly.tools as tls\n",
+    "import plotly.figure_factory as ff\n",
+    "import plotly.io as pio"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load data\n",
+    "Load data from the `./data/interim/` folder and parse into `pandas` dataframes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-03T02:20:29.132099Z",
+     "start_time": "2018-12-03T02:20:22.217681Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Importing profiles.csv\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\numpy\\lib\\arraysetops.py:472: FutureWarning:\n",
+      "\n",
+      "elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Importing profile_features.csv\n",
+      "Importing impacts_forecasted_foreshore_slope_sto06.csv\n",
+      "Importing impacts_forecasted_mean_slope_sto06.csv\n",
+      "Importing impacts_observed.csv\n",
+      "Importing twl_foreshore_slope_sto06.csv\n",
+      "Importing twl_mean_slope_sto06.csv\n",
+      "Done!\n"
+     ]
+    }
+   ],
+   "source": [
+    "def df_from_csv(csv, index_col, data_folder='../data/interim'):\n",
+    "    print('Importing {}'.format(csv))\n",
+    "    return pd.read_csv(os.path.join(data_folder,csv), index_col=index_col)\n",
+    "\n",
+    "df_profiles = df_from_csv('profiles.csv', index_col=[0, 1, 2])\n",
+    "df_profile_features = df_from_csv('profile_features.csv', index_col=[0])\n",
+    "\n",
+    "impacts = {\n",
+    "    'forecasted': {\n",
+    "        'foreshore_slope_sto06': df_from_csv('impacts_forecasted_foreshore_slope_sto06.csv', index_col=[0]),\n",
+    "        'mean_slope_sto06': df_from_csv('impacts_forecasted_mean_slope_sto06.csv', index_col=[0]),\n",
+    "        },\n",
+    "    'observed': df_from_csv('impacts_observed.csv', index_col=[0])\n",
+    "    }\n",
+    "\n",
+    "twls = {\n",
+    "    'forecasted': {\n",
+    "        'foreshore_slope_sto06': df_from_csv('twl_foreshore_slope_sto06.csv', index_col=[0, 1]),\n",
+    "        'mean_slope_sto06':df_from_csv('twl_mean_slope_sto06.csv', index_col=[0, 1]),\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "print('Done!')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lets define a function to calculate the \"collision protection volume\" based on prestorm profiles."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-03T00:37:37.244970Z",
+     "start_time": "2018-12-03T00:37:36.924017Z"
+    },
+    "code_folding": []
+   },
+   "outputs": [],
+   "source": [
+    "from shapely.geometry import Point, LineString, Polygon\n",
+    "\n",
+    "def collision_protection_vol(x,z, d_low_x, d_low_z, lower_z, angle):\n",
+    "    # First, get the bounding line strings of our protection volume\n",
+    "    lower_line = LineString([Point(min(x), lower_z), Point(max(x), lower_z)])\n",
+    "    profile_line = LineString([Point(x_coord, z_coord) for x_coord, z_coord in zip(x,z) \n",
+    "                               if all([not np.isnan(x_coord), not np.isnan(z_coord)])])\n",
+    "    slope_line = LineString([Point(d_low_x, d_low_z), \n",
+    "                             Point(max(x), d_low_z - max(x) * np.sin(np.deg2rad(angle)))])\n",
+    "\n",
+    "    # Work out where our lower line and slope line intersect\n",
+    "    lower_profile_intersection = lower_line.intersection(profile_line)\n",
+    "    d_protected_intersection = lower_line.intersection(slope_line)\n",
+    "    \n",
+    "    # Define the perimeter of the protection area\n",
+    "    profile_protected = LineString([Point(x_coord, z_coord) for x_coord, z_coord \n",
+    "                                   in zip(profile_line.xy[0],profile_line.xy[1]) \n",
+    "                                   if d_low_x < x_coord < lower_profile_intersection.xy[0][0]] \n",
+    "                                   + [lower_profile_intersection]\n",
+    "                                   + [d_protected_intersection]\n",
+    "                                   + [Point(d_low_x, d_low_z)])\n",
+    "    \n",
+    "    # Convert to polygon and return the area (m3/m)\n",
+    "    protection_area_poly = Polygon(profile_protected)\n",
+    "    protection_area_vol = protection_area_poly.area\n",
+    "    return protection_area_vol\n",
+    "\n",
+    "site_id = 'NARRA0018'\n",
+    "profile_type = 'prestorm'\n",
+    "query = \"site_id == '{}' and profile_type == '{}'\".format(site_id, profile_type)\n",
+    "prestorm_profile = df_profiles.query(query)\n",
+    "profile_features = df_profile_features.query(query)\n",
+    "\n",
+    "x = prestorm_profile.index.get_level_values('x')\n",
+    "z = prestorm_profile.z\n",
+    "d_low_x = profile_features.dune_toe_x.tolist()[0]\n",
+    "d_low_z = profile_features.dune_toe_z.tolist()[0]\n",
+    "angle = 60  # degrees from the horizontal\n",
+    "lower_z = 0.5 # from mhw\n",
+    "\n",
+    "vol = collision_protection_vol(x,z, d_low_x, d_low_z, lower_z, angle)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-03T01:24:18.270476Z",
+     "start_time": "2018-12-03T01:24:18.119986Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from datetime import timedelta\n",
+    "\n",
+    "def wl_time(t, wl, z_lower, z_upper):\n",
+    "    \"\"\"\n",
+    "    Returns the amount of time the water level is between two elevations.\n",
+    "    \"\"\"\n",
+    "    df_wl = pd.DataFrame.from_records([(t_val, R2_val) for t_val, R2_val in zip(t,R2)], columns=['datetime','wl'])\n",
+    "    df_wl.set_index(pd.DatetimeIndex(df_wl['datetime']),inplace=True)\n",
+    "    df_wl.drop(columns=['datetime'], inplace=True)\n",
+    "    \n",
+    "    # Assumes that each record is one hour... probably need to check this\n",
+    "    hours = len(df_wl.query('{} < wl < {}'.format(z_lower, z_upper)))\n",
+    "    return timedelta(hours=hours)\n",
+    "\n",
+    "def wave_power(t, wl, z_lower, z_upper, Hs0, Tp):\n",
+    "    \"\"\"\n",
+    "    Returns the cumulative wave power when the water level is between two elevations.\n",
+    "    \"\"\"\n",
+    "    df_wl = pd.DataFrame.from_records([(t_val, R2_val,Hs0_val,Tp_val) for t_val, R2_val,Hs0_val,Tp_val in zip(t,R2,Hs0,Tp)], columns=['datetime','wl', 'Hs0','Tp'])\n",
+    "    df_wl.set_index(pd.DatetimeIndex(df_wl['datetime']),inplace=True)\n",
+    "    df_wl.drop(columns=['datetime'], inplace=True)\n",
+    "    \n",
+    "    # Assumes that each record is one hour... probably need to check this\n",
+    "    rho = 1025 # kg/m3\n",
+    "    g = 9.8 # m/s2\n",
+    "    df_wl_times = df_wl.query('{} < wl < {}'.format(z_lower, z_upper))\n",
+    "    power = rho * g ** 2 / 64 / np.pi * df_wl_times.Hs0 ** 2 * df_wl_times.Tp\n",
+    "    return power.sum()\n",
+    "\n",
+    "df_twl = twls['forecasted']['mean_slope_sto06']\n",
+    "df_twl_site = df_twl.query(\"site_id == '{}'\".format(site_id))\n",
+    "\n",
+    "R2 = df_twl_site.R2.tolist()\n",
+    "t = df_twl_site.index.get_level_values('datetime')\n",
+    "z_lower = 0.5\n",
+    "z_upper = d_low_z\n",
+    "\n",
+    "exposed_time = wl_time(t, R2, z_lower,z_upper)\n",
+    "\n",
+    "Hs0 = df_twl.Hs0.tolist()\n",
+    "Tp = df_twl.Tp.tolist()\n",
+    "wave_p = wave_power(t, R2, z_lower,z_upper,Hs0, Tp)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-03T01:24:24.215223Z",
+     "start_time": "2018-12-03T01:24:24.210209Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8554715.596323118"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wave_p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-03T00:54:57.439956Z",
+     "start_time": "2018-12-03T00:54:57.425899Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def dune_toe_elevation_change(site_id, df_profile_features):\n",
+    "    query = \"site_id == '{}'\".format(site_id)\n",
+    "    profile_features = df_profile_features.query(query)\n",
+    "    prestorm_dune_toe_z = profile_features.query(\"profile_type=='prestorm'\").dune_toe_z.tolist()[0]\n",
+    "    poststorm_dune_toe_z = profile_features.query(\"profile_type=='poststorm'\").dune_toe_z.tolist()[0]\n",
+    "    return prestorm_dune_toe_z - poststorm_dune_toe_z\n",
+    "\n",
+    "toe_ele_change = dune_toe_elevation_change(\"NARRA0018\", df_profile_features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-03T03:07:48.328428Z",
+     "start_time": "2018-12-03T03:06:54.182343Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 of 204\n",
+      "20 of 204\n",
+      "40 of 204\n",
+      "60 of 204\n",
+      "80 of 204\n",
+      "100 of 204\n",
+      "120 of 204\n",
+      "140 of 204\n",
+      "160 of 204\n",
+      "180 of 204\n",
+      "200 of 204\n"
+     ]
+    }
+   ],
+   "source": [
+    "vols = []\n",
+    "exposed_times = []\n",
+    "toe_ele_changes = []\n",
+    "wave_powers = []\n",
+    "\n",
+    "# Get site ids where we observed collision\n",
+    "observed_site_ids = impacts['observed'].query(\"storm_regime=='collision'\").index.get_level_values('site_id').unique()\n",
+    "\n",
+    "# Get site ids where we forecast swash\n",
+    "forecasted_site_ids = impacts['forecasted']['mean_slope_sto06'].query(\"storm_regime=='swash'\").index.get_level_values('site_id').unique()\n",
+    "\n",
+    "site_ids = set(observed_site_ids).intersection(set(forecasted_site_ids))\n",
+    "\n",
+    "# Calculate for each site\n",
+    "\n",
+    "for n, site_id in enumerate(site_ids):\n",
+    "    \n",
+    "    if n%20 ==0:\n",
+    "        print('{} of {}'.format(n, len(site_ids)))\n",
+    "    \n",
+    "    try:\n",
+    "        query = \"site_id == '{}' and profile_type == '{}'\".format(site_id, 'prestorm')\n",
+    "        prestorm_profile = df_profiles.query(query)\n",
+    "        profile_features = df_profile_features.query(query)\n",
+    "\n",
+    "        vol = collision_protection_vol(x = prestorm_profile.index.get_level_values('x'),\n",
+    "                                       z = prestorm_profile.z,\n",
+    "                                       d_low_x = profile_features.dune_toe_x.tolist()[0],\n",
+    "                                       d_low_z = profile_features.dune_toe_z.tolist()[0],\n",
+    "                                       lower_z = profile_features.dune_toe_z.tolist()[0] - 2, # from mhw\n",
+    "                                       angle = 60,  # degrees from the horizontal\n",
+    "                                      )\n",
+    "\n",
+    "        df_twl = twls['forecasted']['mean_slope_sto06']\n",
+    "        df_twl_site = df_twl.query(\"site_id == '{}'\".format(site_id))\n",
+    "\n",
+    "        exposed_time = wl_time(t = df_twl_site.index.get_level_values('datetime'),\n",
+    "                               wl = df_twl_site.R2.tolist(),\n",
+    "                               z_lower = profile_features.dune_toe_z.tolist()[0] -2,\n",
+    "                               z_upper = profile_features.dune_toe_z.tolist()[0],\n",
+    "                              )\n",
+    "\n",
+    "        \n",
+    "        power = wave_power(t = df_twl_site.index.get_level_values('datetime'),\n",
+    "                           wl = df_twl_site.R2.tolist(),\n",
+    "                           z_lower = profile_features.dune_toe_z.tolist()[0] -2,\n",
+    "                           z_upper = profile_features.dune_toe_z.tolist()[0],\n",
+    "                           Hs0=df_twl_site.Hs0.tolist(),\n",
+    "                           Tp=df_twl_site.Tp.tolist())\n",
+    "        \n",
+    "        toe_ele_change = dune_toe_elevation_change(site_id, df_profile_features)\n",
+    "    except:\n",
+    "        continue\n",
+    "\n",
+    "    vols.append(vol)\n",
+    "    exposed_times.append(exposed_time)\n",
+    "    toe_ele_changes.append(toe_ele_change)\n",
+    "    wave_powers.append(power)\n",
+    "#     if n>100:\n",
+    "#         break\n",
+    "\n",
+    "    \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-03T03:12:11.598150Z",
+     "start_time": "2018-12-03T03:12:11.590128Z"
+    }
+   },
+   "outputs": [
+    {
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+       " 30.0]"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-03T03:31:19.534072Z",
+     "start_time": "2018-12-03T03:31:19.439822Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "01b5e4786fd44414a38db9266df25a12",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>FigureWidget</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "FigureWidget({\n",
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+       "                                   0.7320000000000002, -0.238,\n",
+       "                                   -0.08900000000000041, 0.20699999999999985,\n",
+       "                                   0.9870000000000001, 1.9829999999999997,\n",
+       "                                   0.6460000000000004, 0.06100000000000039,\n",
+       "                                   0.06300000000000017, -0.03500000000000014,\n",
+       "                                   0.5730000000000004, 0.7640000000000002,\n",
+       "                                   -0.7690000000000006, 0.5750000000000002,\n",
+       "                                   1.4529999999999998, 0.07500000000000018, 0.649],\n",
+       "                         'colorscale': 'Viridis',\n",
+       "                         'showscale': True,\n",
+       "                         'size': 4},\n",
+       "              'mode': 'markers',\n",
+       "              'text': [0.6740000000000004<br>STOCS0025, -0.262<br>NINEMn0049,\n",
+       "                       0.5449999999999999<br>NINEMn0043,\n",
+       "                       0.35399999999999965<br>LHOUSEn0056,\n",
+       "                       0.3929999999999998<br>CRESn0120,\n",
+       "                       0.35399999999999965<br>BOOM0008,\n",
+       "                       0.6699999999999999<br>NINEMn0028,\n",
+       "                       -0.22799999999999976<br>DIAMONDn0028,\n",
+       "                       1.0739999999999998<br>NINEMs0019, -0.125<br>DUNBn0032,\n",
+       "                       0.35199999999999987<br>DUNBn0048,\n",
+       "                       0.5299999999999998<br>MANNING0076,\n",
+       "                       0.4109999999999996<br>DIAMONDn0014,\n",
+       "                       -0.1120000000000001<br>NARRA0008,\n",
+       "                       0.9180000000000001<br>NINEMn0036, 0.621<br>BILG0005,\n",
+       "                       -0.3579999999999992<br>CRESn0085,\n",
+       "                       -0.20699999999999985<br>SWRO0009,\n",
+       "                       -0.48400000000000043<br>NINEMs0027,\n",
+       "                       0.5430000000000001<br>NINEMs0012,\n",
+       "                       -0.09600000000000053<br>ONEMILE0006,\n",
+       "                       -0.41700000000000026<br>CRESn0066,\n",
+       "                       0.4340000000000006<br>NINEMn0018,\n",
+       "                       0.7559999999999998<br>DUNBn0033,\n",
+       "                       0.8720000000000003<br>NINEMs0036,\n",
+       "                       -0.5759999999999996<br>ENTRA0074,\n",
+       "                       0.5119999999999996<br>NINEMn0037,\n",
+       "                       0.6440000000000001<br>DUNBn0035, 0.395<br>CRESn0103,\n",
+       "                       0.09300000000000042<br>NINEMs0038, 0.923<br>MONA0011,\n",
+       "                       0.4339999999999997<br>STUART0041,\n",
+       "                       0.5429999999999993<br>NAMB0070,\n",
+       "                       -0.09699999999999998<br>NINEMs0013,\n",
+       "                       -0.20899999999999963<br>NINEMs0047,\n",
+       "                       -0.8130000000000002<br>NINEMs0057, 0.706<br>NINEMn0053,\n",
+       "                       0.36399999999999944<br>NINEMn0052,\n",
+       "                       0.34099999999999975<br>ENTRA0067,\n",
+       "                       -0.08499999999999996<br>NINEMs0014,\n",
+       "                       0.21799999999999953<br>STOCNn0047,\n",
+       "                       0.6240000000000006<br>ENTRA0073,\n",
+       "                       -0.16700000000000026<br>NINEMs0001, -0.278<br>NINEMn0042,\n",
+       "                       -0.5110000000000001<br>ONEMILE0003, 0.726<br>GRANTSs0002,\n",
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+       "                       -0.3440000000000003<br>ENTRA0069,\n",
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+       "                       -0.5719999999999996<br>NINEMs0048,\n",
+       "                       -0.4980000000000002<br>CRESn0105,\n",
+       "                       -0.2020000000000004<br>NINEMs0031,\n",
+       "                       0.34199999999999964<br>DUNBn0047,\n",
+       "                       0.41999999999999993<br>STUART0050,\n",
+       "                       0.5800000000000001<br>BLUEYS0004,\n",
+       "                       0.7200000000000002<br>SWRO0006,\n",
+       "                       0.6230000000000002<br>NINEMs0044,\n",
+       "                       -0.49100000000000055<br>NINEMs0020,\n",
+       "                       0.11499999999999977<br>NINEMs0039, -0.242<br>SWRO0010,\n",
+       "                       -0.6240000000000006<br>NINEMn0047,\n",
+       "                       0.9500000000000002<br>NAMB0068,\n",
+       "                       1.2479999999999998<br>OLDBAR0025,\n",
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+       "                       0.021999999999999797<br>NINEMs0005,\n",
+       "                       -0.3200000000000003<br>ONEMILE0010,\n",
+       "                       0.7829999999999999<br>GRANTSn0018,\n",
+       "                       -0.32899999999999974<br>NINEMs0028,\n",
+       "                       0.3389999999999995<br>NSHORE_n0031,\n",
+       "                       -0.01599999999999957<br>WAMBE0012,\n",
+       "                       0.38399999999999945<br>NARRA0024,\n",
+       "                       -0.04599999999999982<br>ONEMILE0002,\n",
+       "                       0.4849999999999999<br>LHOUSE0006,\n",
+       "                       0.02499999999999991<br>WAMBE0014,\n",
+       "                       -0.7749999999999999<br>DIAMONDn0013, 0.617<br>DIAMONDn0034,\n",
+       "                       -0.06500000000000039<br>ELIZA0005,\n",
+       "                       0.5589999999999997<br>OLDBAR0023,\n",
+       "                       -0.1299999999999999<br>ENTRA0046,\n",
+       "                       -0.0040000000000000036<br>DIAMONDn0039, 1.298<br>NINEMs0040,\n",
+       "                       0.05500000000000016<br>NINEMs0017,\n",
+       "                       0.9689999999999999<br>NINEMn0051,\n",
+       "                       -0.28800000000000026<br>CATHIE0012,\n",
+       "                       -0.09700000000000042<br>NAMB0059,\n",
+       "                       -0.03100000000000014<br>CRESn0112,\n",
+       "                       0.7960000000000003<br>STOCS0015,\n",
+       "                       0.33599999999999985<br>LHOUSEn0028,\n",
+       "                       0.28900000000000015<br>NINEMs0003, 0.798<br>STUART0045,\n",
+       "                       -0.05900000000000016<br>WAMBE0013,\n",
+       "                       -0.0029999999999996696<br>NINEMs0018,\n",
+       "                       -0.15399999999999991<br>BLUEYS0002,\n",
+       "                       0.31999999999999984<br>NINEMs0011, -0.613<br>STUART0036,\n",
+       "                       0.2650000000000001<br>ELIZA0003, 0.258<br>NARRA0020,\n",
+       "                       1.513<br>NSHORE_n0028, 0.36199999999999966<br>ELIZA0007,\n",
+       "                       0.7849999999999997<br>STOCNn0010,\n",
+       "                       -0.28900000000000015<br>STUART0058,\n",
+       "                       1.0290000000000004<br>STOCNn0008,\n",
+       "                       0.8260000000000005<br>STOCS0002,\n",
+       "                       -0.03799999999999981<br>NINEMs0026,\n",
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+       "                       1.6029999999999998<br>NARRA0022,\n",
+       "                       0.5699999999999998<br>OLDBAR0021,\n",
+       "                       0.9300000000000002<br>NSHORE_n0029,\n",
+       "                       0.0389999999999997<br>ONEMILE0008,\n",
+       "                       -0.3110000000000004<br>LHOUSEn0042,\n",
+       "                       1.0070000000000001<br>NARRA0021,\n",
+       "                       0.11500000000000021<br>STOCS0029,\n",
+       "                       0.7269999999999999<br>BLUEYS0001,\n",
+       "                       0.30100000000000016<br>BOOM0009,\n",
+       "                       -0.5180000000000002<br>MANNING0042,\n",
+       "                       0.40600000000000014<br>NINEMn0046,\n",
+       "                       0.1389999999999998<br>LHOUSEn0069,\n",
+       "                       -0.08299999999999974<br>STOCNs0192, 1.174<br>ENTRA0065,\n",
+       "                       -0.29300000000000015<br>LHOUSEn0025,\n",
+       "                       0.036000000000000476<br>NINEMs0007, 0.702<br>GRANTSn0013,\n",
+       "                       0.49099999999999966<br>MANNING0095,\n",
+       "                       0.27400000000000047<br>NINEMs0043,\n",
+       "                       0.6890000000000005<br>ENTRA0070,\n",
+       "                       0.46199999999999974<br>LHOUSEn0059,\n",
+       "                       0.7839999999999998<br>LHOUSEn0057,\n",
+       "                       0.2939999999999996<br>NINEMs0029,\n",
+       "                       0.013000000000000345<br>LHOUSEn0030,\n",
+       "                       -0.09400000000000075<br>NINEMs0008,\n",
+       "                       0.022999999999999687<br>MANNING0092,\n",
+       "                       -0.6230000000000002<br>MONA0012,\n",
+       "                       0.9040000000000004<br>NSHORE_n0026,\n",
+       "                       0.027000000000000135<br>DIAMONDn0030,\n",
+       "                       -0.1280000000000001<br>MANNING0091, 0.25<br>STUART0043,\n",
+       "                       0.488<br>NSHORE_n0020, -0.1200000000000001<br>ELIZA0004,\n",
+       "                       0.43599999999999994<br>SWRO0005,\n",
+       "                       0.04999999999999982<br>HARR0050,\n",
+       "                       0.11599999999999966<br>FOST0001,\n",
+       "                       1.5290000000000001<br>STOCS0008,\n",
+       "                       1.0189999999999997<br>NARRA0009,\n",
+       "                       0.5780000000000003<br>STUART0037,\n",
+       "                       -0.4969999999999999<br>STUART0033,\n",
+       "                       -0.39800000000000013<br>STUART0046,\n",
+       "                       0.6430000000000002<br>FOST0004,\n",
+       "                       -0.04500000000000037<br>SWRO0001,\n",
+       "                       -0.40700000000000003<br>NINEMn0054,\n",
+       "                       0.07000000000000028<br>STOCNs0069,\n",
+       "                       -0.18599999999999994<br>STOCNn0004,\n",
+       "                       1.4900000000000002<br>MANNING0084,\n",
+       "                       0.2889999999999997<br>STUART0040,\n",
+       "                       -0.3719999999999999<br>NAMB0048,\n",
+       "                       0.1519999999999997<br>BOOM0007,\n",
+       "                       0.10899999999999999<br>NINEMn0034,\n",
+       "                       -0.3700000000000001<br>ONEMILE0001,\n",
+       "                       0.7320000000000002<br>ENTRA0075, -0.238<br>STOCNn0011,\n",
+       "                       -0.08900000000000041<br>DIAMONDn0021,\n",
+       "                       0.20699999999999985<br>LHOUSEn0060,\n",
+       "                       0.9870000000000001<br>CRESn0064,\n",
+       "                       1.9829999999999997<br>NARRA0007,\n",
+       "                       0.6460000000000004<br>MANNING0073,\n",
+       "                       0.06100000000000039<br>ONEMILE0005,\n",
+       "                       0.06300000000000017<br>NINEMs0037,\n",
+       "                       -0.03500000000000014<br>LHOUSEn0061,\n",
+       "                       0.5730000000000004<br>NINEMn0040,\n",
+       "                       0.7640000000000002<br>AVOCAn0005,\n",
+       "                       -0.7690000000000006<br>STOCNn0041,\n",
+       "                       0.5750000000000002<br>ELIZA0006,\n",
+       "                       1.4529999999999998<br>ENTRA0072,\n",
+       "                       0.07500000000000018<br>MANNING0082, 0.649<br>LHOUSEn0029],\n",
+       "              'type': 'scatter',\n",
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+       "                    80.0, 49.0, 29.0, 17.0, 82.0, 91.0, 1.0, 75.0, 46.0, 210.0,\n",
+       "                    205.0, 16.0, 35.0, 82.0, 49.0, 0.0, 29.0, 58.0, 57.0, 103.0,\n",
+       "                    29.0, 0.0, 46.0, 48.0, 1.0, 17.0, 48.0, 29.0, 17.0, 165.0,\n",
+       "                    45.0, 17.0, 426.0, 30.0],\n",
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+       "                    77.02236536546766, 46.43886428194846, 74.93504091748079,\n",
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+       "                    54.53170249189508, 53.70339057156003, 22.441638077872113,\n",
+       "                    54.791537795661974, 62.13997820476877, 29.40727911432272,\n",
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+       "                    89.42388399872287, 53.47096959758166]}],\n",
+       "    'layout': {'title': 'Dune Collision Protection',\n",
+       "               'xaxis': {'autorange': True, 'showgrid': True, 'showline': True, 'title': 'Exposed time', 'zeroline': True},\n",
+       "               'yaxis': {'autorange': True,\n",
+       "                         'showgrid': True,\n",
+       "                         'showline': True,\n",
+       "                         'title': 'Collision protection volume',\n",
+       "                         'zeroline': True}}\n",
+       "})"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "trace1 = go.Scatter(\n",
+    "    x=[x.total_seconds() / 60 / 60 for x in exposed_times],\n",
+    "    y=vols,\n",
+    "    text = ['{}<br>{}'.format(ele, site_id) for ele,site_id in zip(toe_ele_changes,site_ids)],\n",
+    "    mode='markers',\n",
+    "    marker=dict(\n",
+    "        size=4,\n",
+    "#         color = [-1 if x<0 else 1 for x in toe_ele_changes],\n",
+    "        color =  toe_ele_changes,\n",
+    "#         color = wave_powers,\n",
+    "#         color = [x.total_seconds() / 60 / 60 for x in exposed_times],\n",
+    "        colorscale='Viridis',\n",
+    "        showscale=True\n",
+    "    ))\n",
+    "\n",
+    "layout = go.Layout(\n",
+    "    title='Dune Collision Protection',\n",
+    "#     height=300,\n",
+    "#     legend=dict(font={'size': 10}),\n",
+    "#     margin=dict(t=50, b=50, l=50, r=20),\n",
+    "    xaxis=dict(\n",
+    "        title='Exposed time',\n",
+    "        autorange=True,\n",
+    "        showgrid=True,\n",
+    "        zeroline=True,\n",
+    "        showline=True,\n",
+    "    ),\n",
+    "    yaxis=dict(\n",
+    "        title='Collision protection volume',\n",
+    "        autorange=True,\n",
+    "        showgrid=True,\n",
+    "        zeroline=True,\n",
+    "        showline=True,\n",
+    "    ))\n",
+    "\n",
+    "g_plot = go.FigureWidget(data=[trace1], layout=layout)\n",
+    "g_plot"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.6.4"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
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+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/03_dune_to_vs_runup.ipynb b/notebooks/03_dune_to_vs_runup.ipynb
new file mode 100644
index 0000000..9673fcb
--- /dev/null
+++ b/notebooks/03_dune_to_vs_runup.ipynb
@@ -0,0 +1,554 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Investigate how dune toe compares to R_high"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-03T03:38:44.538853Z",
+     "start_time": "2018-12-03T03:38:44.189514Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "%reload_ext autoreload\n",
+    "%autoreload"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-03T03:38:46.213387Z",
+     "start_time": "2018-12-03T03:38:44.781382Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.core.debugger import set_trace\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "\n",
+    "import plotly\n",
+    "import plotly.graph_objs as go\n",
+    "import plotly.plotly as py\n",
+    "import plotly.tools as tls\n",
+    "import plotly.figure_factory as ff\n",
+    "import plotly.io as pio"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load data\n",
+    "Load data from the `./data/interim/` folder and parse into `pandas` dataframes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-03T03:38:53.297184Z",
+     "start_time": "2018-12-03T03:38:46.365829Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Importing profiles.csv\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\numpy\\lib\\arraysetops.py:472: FutureWarning:\n",
+      "\n",
+      "elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Importing profile_features.csv\n",
+      "Importing impacts_forecasted_foreshore_slope_sto06.csv\n",
+      "Importing impacts_forecasted_mean_slope_sto06.csv\n",
+      "Importing impacts_observed.csv\n",
+      "Importing twl_foreshore_slope_sto06.csv\n",
+      "Importing twl_mean_slope_sto06.csv\n",
+      "Done!\n"
+     ]
+    }
+   ],
+   "source": [
+    "def df_from_csv(csv, index_col, data_folder='../data/interim'):\n",
+    "    print('Importing {}'.format(csv))\n",
+    "    return pd.read_csv(os.path.join(data_folder,csv), index_col=index_col)\n",
+    "\n",
+    "df_profiles = df_from_csv('profiles.csv', index_col=[0, 1, 2])\n",
+    "df_profile_features = df_from_csv('profile_features.csv', index_col=[0])\n",
+    "\n",
+    "impacts = {\n",
+    "    'forecasted': {\n",
+    "        'foreshore_slope_sto06': df_from_csv('impacts_forecasted_foreshore_slope_sto06.csv', index_col=[0]),\n",
+    "        'mean_slope_sto06': df_from_csv('impacts_forecasted_mean_slope_sto06.csv', index_col=[0]),\n",
+    "        },\n",
+    "    'observed': df_from_csv('impacts_observed.csv', index_col=[0])\n",
+    "    }\n",
+    "\n",
+    "twls = {\n",
+    "    'forecasted': {\n",
+    "        'foreshore_slope_sto06': df_from_csv('twl_foreshore_slope_sto06.csv', index_col=[0, 1]),\n",
+    "        'mean_slope_sto06':df_from_csv('twl_mean_slope_sto06.csv', index_col=[0, 1]),\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "print('Done!')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compare underpredicted cases"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-03T04:05:30.984007Z",
+     "start_time": "2018-12-03T04:05:30.805508Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>dune_toe_z</th>\n",
+       "      <th>R_high</th>\n",
+       "      <th>diff</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>AVOCAn0005</th>\n",
+       "      <td>3.306</td>\n",
+       "      <td>3.260440</td>\n",
+       "      <td>-0.045560</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AVOCAn0008</th>\n",
+       "      <td>3.507</td>\n",
+       "      <td>3.220084</td>\n",
+       "      <td>-0.286916</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BILG0005</th>\n",
+       "      <td>4.807</td>\n",
+       "      <td>3.293445</td>\n",
+       "      <td>-1.513555</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BLUEYS0001</th>\n",
+       "      <td>3.064</td>\n",
+       "      <td>2.800144</td>\n",
+       "      <td>-0.263856</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BLUEYS0002</th>\n",
+       "      <td>2.929</td>\n",
+       "      <td>2.470641</td>\n",
+       "      <td>-0.458359</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            dune_toe_z    R_high      diff\n",
+       "AVOCAn0005       3.306  3.260440 -0.045560\n",
+       "AVOCAn0008       3.507  3.220084 -0.286916\n",
+       "BILG0005         4.807  3.293445 -1.513555\n",
+       "BLUEYS0001       3.064  2.800144 -0.263856\n",
+       "BLUEYS0002       2.929  2.470641 -0.458359"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Find site_ids where the forecast has been underpredicted\n",
+    "set1 = set(impacts['forecasted']['mean_slope_sto06'].query(\"storm_regime == 'swash'\").index.get_level_values('site_id'))\n",
+    "set2 = set(impacts['observed'].query(\"storm_regime == 'collision'\").index.get_level_values('site_id'))\n",
+    "site_ids = list(set1.intersection(set2))\n",
+    "\n",
+    "# Get dune toes at these sites and predicted max R_high\n",
+    "df_toes = df_profile_features.loc[site_ids].query('profile_type==\"prestorm\"').dune_toe_z\n",
+    "df_R_highs = twls['forecasted']['mean_slope_sto06'].loc[site_ids].groupby('site_id')['R_high'].max()\n",
+    "\n",
+    "# Join into one dataframe\n",
+    "df_twl_toes = pd.concat([df_toes, df_R_highs],axis=1,sort=True)\n",
+    "df_twl_toes['diff'] = df_twl_toes['R_high'] - df_twl_toes['dune_toe_z']\n",
+    "df_twl_toes.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's plot the comparison between our R_high TWL values and the dune toes to see how far off they were."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-03T04:08:15.732169Z",
+     "start_time": "2018-12-03T04:08:15.656966Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "35b9331242af473dba2f91761c307022",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>FigureWidget</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "FigureWidget({\n",
+       "    'data': [{'type': 'histogram',\n",
+       "              'uid': '75f0d11f-9242-4fc7-b433-1f04e1e37ba6',\n",
+       "              'y': [-0.045560088746212646, -0.28691603912686325,\n",
+       "                    -1.5135547360075963, ..., -0.4613631587476821,\n",
+       "                    -0.5212332930925054, -0.3948507473332721]}],\n",
+       "    'layout': {'bargap': 0.2,\n",
+       "               'bargroupgap': 0.1,\n",
+       "               'title': 'D_low - R_high<br>Observed Collision, Forecasted Swash',\n",
+       "               'xaxis': {'title': 'Count'},\n",
+       "               'yaxis': {'title': 'z (m AHD)'}}\n",
+       "})"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "trace1 = go.Histogram(y=df_twl_toes['diff'].tolist())\n",
+    "\n",
+    "layout = go.Layout(\n",
+    "    title='D_low - R_high<br>Observed Collision, Forecasted Swash',\n",
+    "    yaxis=dict(\n",
+    "        title='z (m AHD)'\n",
+    "    ),\n",
+    "    xaxis=dict(\n",
+    "        title='Count'\n",
+    "    ),\n",
+    "    bargap=0.2,\n",
+    "    bargroupgap=0.1\n",
+    ")\n",
+    "\n",
+    "g_plot = go.FigureWidget(data=[trace1], layout=layout)\n",
+    "g_plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above plot shows that the R_high value for most of the incorrectly forecasted collision regimes, was typically underpredicted by less than 0.5 m."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compare overpredicted cases"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-03T04:08:56.128806Z",
+     "start_time": "2018-12-03T04:08:55.894182Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>dune_toe_z</th>\n",
+       "      <th>R_high</th>\n",
+       "      <th>diff</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>AVOCAn0004</th>\n",
+       "      <td>3.178</td>\n",
+       "      <td>3.416988</td>\n",
+       "      <td>0.238988</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BOOM0004</th>\n",
+       "      <td>3.065</td>\n",
+       "      <td>3.074980</td>\n",
+       "      <td>0.009980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BOOM0011</th>\n",
+       "      <td>2.771</td>\n",
+       "      <td>6.491824</td>\n",
+       "      <td>3.720824</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BOOM0012</th>\n",
+       "      <td>2.796</td>\n",
+       "      <td>3.148087</td>\n",
+       "      <td>0.352087</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CATHIE0001</th>\n",
+       "      <td>2.780</td>\n",
+       "      <td>3.522792</td>\n",
+       "      <td>0.742792</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            dune_toe_z    R_high      diff\n",
+       "AVOCAn0004       3.178  3.416988  0.238988\n",
+       "BOOM0004         3.065  3.074980  0.009980\n",
+       "BOOM0011         2.771  6.491824  3.720824\n",
+       "BOOM0012         2.796  3.148087  0.352087\n",
+       "CATHIE0001       2.780  3.522792  0.742792"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Find site_ids where the forecast has been overpredicted\n",
+    "set1 = set(impacts['forecasted']['mean_slope_sto06'].query(\"storm_regime == 'collision'\").index.get_level_values('site_id'))\n",
+    "set2 = set(impacts['observed'].query(\"storm_regime == 'swash'\").index.get_level_values('site_id'))\n",
+    "site_ids = list(set1.intersection(set2))\n",
+    "\n",
+    "# Get dune toes at these sites and predicted max R_high\n",
+    "df_toes = df_profile_features.loc[site_ids].query('profile_type==\"prestorm\"').dune_toe_z\n",
+    "df_R_highs = twls['forecasted']['mean_slope_sto06'].loc[site_ids].groupby('site_id')['R_high'].max()\n",
+    "\n",
+    "# Join into one dataframe\n",
+    "df_twl_toes = pd.concat([df_toes, df_R_highs],axis=1,sort=True)\n",
+    "df_twl_toes['diff'] = df_twl_toes['R_high'] - df_twl_toes['dune_toe_z']\n",
+    "df_twl_toes.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-03T04:14:46.601092Z",
+     "start_time": "2018-12-03T04:14:46.522883Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3ea49a4ac07c4ea19bbb4532326ff94c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>FigureWidget</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "FigureWidget({\n",
+       "    'data': [{'type': 'histogram',\n",
+       "              'uid': '4a284474-2be1-4fd7-87d5-25364cc78df4',\n",
+       "              'y': [0.23898814460475037, 0.009980312001434566, 3.720823710344608,\n",
+       "                    ..., 1.5720238663972683, 0.912998680585452, 1.1419977620500927]}],\n",
+       "    'layout': {'bargap': 0.2,\n",
+       "               'bargroupgap': 0.1,\n",
+       "               'title': 'D_low - R_high<br>Observed Swash, Forecasted Collision',\n",
+       "               'xaxis': {'title': 'Count'},\n",
+       "               'yaxis': {'title': 'z (m AHD)'}}\n",
+       "})"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "trace1 = go.Histogram(y=df_twl_toes['diff'].tolist())\n",
+    "\n",
+    "layout = go.Layout(\n",
+    "    title='D_low - R_high<br>Observed Swash, Forecasted Collision',\n",
+    "    yaxis=dict(\n",
+    "        title='z (m AHD)'\n",
+    "    ),\n",
+    "    xaxis=dict(\n",
+    "        title='Count'\n",
+    "    ),\n",
+    "    bargap=0.2,\n",
+    "    bargroupgap=0.1\n",
+    ")\n",
+    "\n",
+    "g_plot = go.FigureWidget(data=[trace1], layout=layout)\n",
+    "g_plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The errors when we forecast collision but observe swash are much greater than we we forecast swash and observe collision. For this case, errors in excess of 1.0 m common. Why is this?"
+   ]
+  }
+ ],
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