Section_6c.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": 2,
   "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": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "lat = 51.44\n",
    "lon = 3.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['clean_datasets.ipynb',\n",
       " 'data_single_year_high_freq.db',\n",
       " 'frederikse_2016_nodal_rsl.grd',\n",
       " 'IPCC.db',\n",
       " 'Readme.txt',\n",
       " 'RWS_JCHS.db',\n",
       " 'Steffelbauer_2022_nodal.nc']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = r'./Data'\n",
    "os.chdir(path)\n",
    "os.listdir()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Frederikse et al. (2016)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "fred = xr.open_dataset('frederikse_2016_nodal_rsl.grd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Data variables:\n",
       "    RSL      (y, x) float32 ..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fred.data_vars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "fred_rsl = fred['RSL']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(-6.44829959)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fred_rsl.interp(x = lat, y = lon).data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Correct sign\n",
    "- Amplitude factor 5 too small"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Steffelbauer et al. (2022)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "stef = xr.open_dataset('Steffelbauer_2022_nodal.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Data variables:\n",
       "    rsl_pot  (y, x) float32 ...\n",
       "    gsl_pot  (y, x) float32 ...\n",
       "    rsl_eq   (y, x) float32 ...\n",
       "    gsl_eq   (y, x) float32 ..."
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stef.data_vars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "stef_rsl = stef['rsl_eq']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(-6.43830928)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stef_rsl.interp(x = lat, y = lon).data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Steffelbauer claims to have used Frederikses dataset\n",
    "- Amplitude is virtually equal\n",
    "- But considering digital transfer of data, a difference from the 2nd decimal and further is peculiar\n",
    "- Not furhter investigated; the amplitude is close to a factor 5 too small compared to Woodworth and empirical estimate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x206141b7f90>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stef_rsl.plot.contour()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}