Section_6b.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<figure>\n",
    "  <IMG SRC=\"Logo.png\"  WIDTH=150 ALIGN=\"right\">\n",
    "</figure>\n",
    "\n",
    "## Projections of mean sea level and tides<br>\n",
    "### Paper in Journal of Coastal and Hydraulic Structures\n",
    "<b>Prepared by: H.G. Voortman</b>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<figure>\n",
    "    <IMG SRC=\"https://www.python.org/static/community_logos/python-powered-w-200x80.png\"  WIDTH=100 ALIGN=\"right\">\n",
    "</figure>\n",
    "\n",
    "#### Description\n",
    "- Supporting work section 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from hvec_support import sqlite as sq\n",
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Woodworth (2012)\n",
    "Nodal amplitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eq_nodal(lat):\n",
    "    return 0.69 * 20 * (3 * (np.sin(np.radians(lat))) ** 2 - 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.625272627125035"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eq_nodal(52.4) * 1.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Comparable to my empirical result around 15 mm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Theoretical acceleration by combined nodal and perigean cycles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7099644414892188, 2.5202475408954914)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# theoretical acceleration by Perigean cycle\n",
    "T = 8.85 # in years\n",
    "omega = 2 * np.pi / T\n",
    "A = 5 # mm\n",
    "a = A * omega ** 2\n",
    "omega, a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.33762414331969837, 2.2798012430472046)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# theoretical acceleration by Nodal cycle\n",
    "T = 18.61 # in years\n",
    "omega = 2 * np.pi / T\n",
    "A = 20 # mm\n",
    "a = A * omega ** 2\n",
    "omega, a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- When in phase, an acceleration of close to 5 mm/yr^2 occurs\n",
    "- What is the theoretical rise if sustained for a century?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23.999999999999996"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.5 * 4.8e-3 * 100**2 # in meter"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wind drag from Pugh and Woodworth (2014, page 156)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def CD(W10):\n",
    "    return (1e-3) * (0.8 + 0.065 * W10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Frederikse et al (2018) omits the factor 1e-3; probably gone unnoticed <i>or</i> reported incorrectly in the paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "rho_a = 1.29\n",
    "\n",
    "def tau_wind(W10):\n",
    "    return rho_a * CD(W10) * W10**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "W10 = np.linspace(1, 30, 1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspect the effect of Steffelbauers simplification of constant drag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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(W10, tau_wind(W10) / (rho_a * 0.8e-3 * W10**2));"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.11.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}