{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Lab 5-2: Flood Probability\n", "---\n", "For multiple applications (flood insurance, risk analysis), we need to know the probability of a given location flooding in any year. To do this we look at historic floods, assume a probability distribution, and then use both to estimate the magnitude of a flood with a certain return period or the return period of a certain flood magnitude." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The probability that discharge will exceed a certain amount is called the exceedance probability:\n", "$ P(Q \\geq x) $.\n", "This is defined as one over the return period $T_r$, such that\n", "$$P(Q \\geq x) = \\frac{1}{T_r}$$\n", "---\n", "Thus, a discharge with a 1% chance of being that large is a 100 year flood, and a discharge with a 50% chance of being that large is a 2 year flood.\n", "\n", "In other words, a 2 year return period discharge occurs half of the time (in half of the years) and is often called bank-full flow." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Why care?**\n", "In the U.S., flood insurance is generally required for all houses that would be innundated by a 100 year flood, as exaplained by [FEMA](https://www.fema.gov/glossary/flood-zones). Therefore, a lot of hydrologic work is focused on determining how large the 100 year flood is. This lab demonstrates two ways to do this with data. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import scipy.stats as stats\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "For this lab, we will practice with the Skykomish River in Western Washington.\n", "Load the annual peak flow data from the Skykomish River at Gold Bar. For info on the gauge and real time data, see [here](https://waterdata.usgs.gov/monitoring-location/12134500/#parameterCode=00065&period=P7D).\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.9/site-packages/openpyxl/worksheet/_reader.py:312: UserWarning: Unknown extension is not supported and will be removed\n", " warn(msg)\n" ] }, { "data": { "text/html": [ "
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date of peakwater yearpeak value (cfs)gage_ht (feet)
01928-10-0919291880010.55
11930-02-0519301580010.44
21931-01-2819313510014.08
\n", "
" ], "text/plain": [ " date of peak water year peak value (cfs) gage_ht (feet)\n", "0 1928-10-09 1929 18800 10.55\n", "1 1930-02-05 1930 15800 10.44\n", "2 1931-01-28 1931 35100 14.08" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# load the Skykomish River peak flow data\n", "skykomish_data_file = 'Skykomish_peak_flow_12134500_skykomish_river_near_gold_bar.xlsx'\n", "skykomish_data = pd.read_excel(skykomish_data_file)\n", "# preview the dataframe\n", "skykomish_data.head(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, let's look at this data. We will repeat what you did in lab2-1, where you practiced plotting." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Create a new figure.\n", "plt.figure(figsize=(10,4))\n", "\n", "# Use the plot() function to plot the year on the x-axis, peak flow values on\n", "# the y-axis with an open circle representing each peak flow value.\n", "plt.plot(skykomish_data['water year'], # our x value\n", " skykomish_data['peak value (cfs)'], # our y value\n", " linestyle='-', # plot a solid line\n", " color='blue', # make the line color blue\n", " marker='o', # also plot a circle for each data point\n", " markerfacecolor='white', # make the circle face color white\n", " markeredgecolor='blue') # make the circle edge color blue\n", "\n", "# Label the axes and title.\n", "plt.xlabel('Water Years')\n", "plt.ylabel('Peak Flows (cfs)')\n", "plt.title('(Fig. 1) Skykomish River Peak Flows');" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Define the number of bins for the histogram. Try changing this number and running this cell again\n", "nbins = 20\n", "\n", "# Create a new figure.\n", "plt.figure()\n", "\n", "# Use the hist() function from matplotlib to plot the histogram\n", "plt.hist(skykomish_data['peak value (cfs)'], nbins, ec=\"black\", facecolor='lightblue')\n", "\n", "# Labels and title\n", "plt.title('(Fig. 3) Skykomish River Peak Flow Histogram')\n", "plt.xlabel('Peak Flow (cfs)')\n", "plt.ylabel('Number of Occurences')\n", "plt.ticklabel_format(axis='x', style='sci', scilimits=(0,0)) # formatting the x axis to use scientific notation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Probability distributions**\n", "We seldom have enough data to directly determine the 100-year flood, so we plot our data and assess what distribution we think it came from. Open the probability-distributions.ipynb file and look at the different distributions.\n", "\n", "**Which do you think best matches the histogram plotted above? Could we consider this a normal distribution?**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sample Mean, Variance, Standard Deviation, Skew:\n", "\n", "We just have a sample from the population, and can calculate the mean, variance, standard deviation, and skew as follows:\n", "\n", "**Sample Mean:** [`np.mean()`](https://numpy.org/doc/stable/reference/generated/numpy.mean.html)\n", "\n", "$\\bar{X} = \\displaystyle\\sum_{i=1}^{n} \\frac{X_i}{n}$\n", "\n", "\n", "\n", "**Sample Variance:** [`np.var(...,ddof=1)`](https://numpy.org/doc/stable/reference/generated/numpy.var.html)\n", "\n", "$\\sigma^{2} = \\displaystyle\\sum_{i=1}^{n} \\frac{(X_i - \\bar{X})^2}{(n-1)}$\n", "\n", "\n", "**Sample Standard Deviation:** [`np.std(...,ddof=1)`](https://numpy.org/doc/stable/reference/generated/numpy.var.html)\n", "\n", "$\\sigma = \\sqrt{\\sigma^2}$\n", "\n", "\n", "**Sample Skew:** [`stats.skew(...,ddof=1)`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.skew.html)\n", "\n", "$G_s = n\\displaystyle\\sum_{i=1}^{n} \\frac{(X_i - \\bar{X})^2}{(n-1)(n-2)\\sigma^3}$\n", "\n", "(See Ch. 1 of Helsel et al., 2020)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Method 1: Log-Pearson III\n", "The USGS recommends that the Log-Pearson III (LP3) distribution be used for all official flood analysis.\n", "* Take $log_{10}$ of all of the instantaneous maximum annual flood values.\n", "* Using the log dataset, calculate the mean, standard deviation, and skew\n", "* Look up K as a function of the return period of interest and the calculated skew. (Note that there are a lot of details into how K can also be regionally adjusted based on the skew calculated for other streams in the region. We will neglect this for now and use Appendix 3 from the USGS Bulletin17b.)\n", "* Calculate $log⁡(Q_{Tr})= mean(log⁡Q)+K\\sigma_{logQ}$, where $K$ is a function of the return period and the skew.\n", "* Convert from log back to the predicted Q at that return interval." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4.617478007039956\n", "0.21534507752397467\n", "-0.19468591294950363\n" ] } ], "source": [ "# With out dataset\n", "logdata = np.log10(skykomish_data['peak value (cfs)'])\n", "meanlogdata = np.mean(logdata)\n", "print(meanlogdata)\n", "stdlogdata = np.std(logdata)\n", "print(stdlogdata)\n", "skewlogdata = stats.skew(logdata)\n", "print(skewlogdata)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have to look up K for the 100 year flood and a -0.19 skew. Here is a snapshot for the page relevant to our skew.\n", "![Bulletin2](Bulletin17b_appendix3_K_skykomish.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the 100 year flood, we look up a 1%, 0,.01, exceedance probability. We round -0.19 to -0.2. I get that K = 2.17840." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "122063.4733926666\n" ] } ], "source": [ "# So we plug this into the formula\n", "K=2.17840\n", "logQ100 = meanlogdata + K*stdlogdata\n", "#and convert back from log space\n", "Q100 = 10**(logQ100)\n", "print(Q100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, note that by the Log-Pearson III method, we calculate the 100-year flood at 122,063 cfs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Method 2: Plot the data according to an assumed distribution and extrapolate to the desired value\n", "**Now we need to sort our data and assign a flood return probability.**\n", "For this class, we will use the Weibull plotting position. Note that there are multiple approaches on how to calculate plotting and extreme value probability. More will be covered in Hydrology and Advanced Hydrology classes.\n", "\n", "**Step 1** Rank the data from highest to lowest flows" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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indexdate of peakwater yearpeak value (cfs)gage_ht (feet)
0782006-11-06200712900024.51
1621990-11-24199110200022.49
2862015-11-1720169590021.73
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" ], "text/plain": [ " index date of peak water year peak value (cfs) gage_ht (feet)\n", "0 78 2006-11-06 2007 129000 24.51\n", "1 62 1990-11-24 1991 102000 22.49\n", "2 86 2015-11-17 2016 95900 21.73" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Rank all our values\n", "df = skykomish_data\n", "column_name = 'peak value (cfs)'\n", "ranked_df = df.sort_values(by=[column_name], ascending=False).reset_index()\n", " \n", "# preview the dataframe\n", "ranked_df.head(3) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Step 2** Estimate the probability of exceedence using the Weibull Order\n", "$$ P(Q>x) = \\frac{m}{n+1} $$\n", "where $x$ is the observed flow value, $m$ is the rank order,\n", "and $n$ is the total length of the record" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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indexdate of peakwater yearpeak value (cfs)gage_ht (feet)weibull_plotting_position
0782006-11-06200712900024.510.010870
1621990-11-24199110200022.490.021739
2862015-11-1720169590021.730.032609
\n", "
" ], "text/plain": [ " index date of peak water year peak value (cfs) gage_ht (feet) \\\n", "0 78 2006-11-06 2007 129000 24.51 \n", "1 62 1990-11-24 1991 102000 22.49 \n", "2 86 2015-11-17 2016 95900 21.73 \n", "\n", " weibull_plotting_position \n", "0 0.010870 \n", "1 0.021739 \n", "2 0.032609 " ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ " # Calculate the Weibull plotting position\n", "ranked_df['weibull_plotting_position'] = (ranked_df.index + 1) / (ranked_df[column_name].count() + (1))\n", "# where we add the 1 in the top line because the ranked index starts at 0\n", " \n", "ranked_df.head(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the highest flow observed in our record, 129,000 cfs, is higher than the predicted 100 year flood by method 1." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# create a figure and specify its size\n", "plt.figure(figsize=(6,6))\n", "\n", "plt.plot(ranked_df['peak value (cfs)'],ranked_df['weibull_plotting_position'], label='peak flow')\n", "\n", "plt.legend(loc='lower left') # add a legend to the lower left of the figure\n", "plt.xlabel('peak flow (cfs)') # set the label for the x axis\n", "plt.ylabel('Exceedance probability') # set the label for the y axis\n", "plt.title('Skykomish River Flood Probability'); # give our plot a title" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Re-do this plot as a function of return period on the x-axis with a log scale" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# create a figure and specify its size\n", "plt.figure(figsize=(6,6))\n", "\n", "plt.plot(1/ranked_df['weibull_plotting_position'], ranked_df['peak value (cfs)'], label='peak flow')\n", "\n", "plt.legend(loc='lower left') # add a legend to the lower left of the figure\n", "plt.ylabel('peak flow (cfs)') # set the label for the x axis\n", "plt.xlabel('return period (years)') # set the label for the y axis\n", "plt.title('Skykomish River Flood Probability'); # give our plot a title\n", "plt.xscale('log')" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# And read off the plot the value that corresponds to a 100 year return period (10^2)\n", "fig, ax = plt.subplots(figsize=(6,6))\n", "\n", "\n", "ax.plot(1/ranked_df['weibull_plotting_position'], ranked_df['peak value (cfs)'], label='peak flow')\n", "ax.axvline(100, linestyle='--', color='r', lw=1)\n", "ax.text(100,40000,'T_r = 100 years')\n", "ax.axhline(133000, linestyle='--', color='r', lw=1)\n", "\n", "ax.legend(loc='lower left') # add a legend to the lower left of the figure\n", "ax.set_ylabel('peak flow (cfs)') # set the label for the y axis\n", "ax.set_xlabel('return period (years)') # set the label for the x axis\n", "ax.set_title('Skykomish River Flood Probability'); # give our plot a title\n", "ax.set_xscale('log')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The idea is that we want to extrapolate our data to the 100 year reutrn period, as marked by the red dashed lines above. We would read the peak flow value that corresponds to the 100 year return period on the plot. The problem is that extrapolation that is not linear is tricky. These data do not plot on a line on a log scale. So we try to find a distribution to fit our data to. If we plot our data on special probability paper (or with a special computer program knowing that distribution), then it should plot on a straight line." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Now we want to see if the Weibull distribution is a good fit**\n", "The Weibull distribution is commonly used in exceedence probabilities and risk analysis. If our data fit the Weibull distribution, we can use it to estimate our 100 year flood value.\n" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'Discharge (cfs)')" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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AK2Mgf7fXYRljKmGJPghElJUwZv09dNv3FV90/QMrky/3OiT/Df4pjHsD9myAl86BvM1eR2SMqcASvde0jPM23kdq3hzmdPktK5Kv9Dqi49dzFNz4ERTmO2f2uRu8jsgY48MSvceGbX2Ok/bMZn6n21jWrsabkINX+5PhxhlQVgyTx8Cu1V5HZIxxWaL3UN9d73LKjldZ0fYylrS/wetwaq9Nb7jpY4iIhv+7AHav8ToiYwyW6D3Ted8Czv7+YTYlns6XXX8f1Bdej0urVLhpJkQ1glcvhj2ZXkdkTINnid4DzY9sZcz6P7KnSTdm9ngAleAbQlkrLbrA9R8ACq+OhX1bvI7ImAbNEn09iyo9woXf3UGZRDG912MURzXxOqS6kdQDrnsfig/D/421oZfGeMgSfX1S5dzMB2h5eDMzez5AfmxbryOqW237wLXT4NAemHI5HD3odUTGNEiW6OtR/11vc9KeWXzdcSLbmp/qdTj1o/3JcOWrzoXZt66DkiKvIzKmwbFEX0+SCtYzfPMTbEocxuKUG70Op36lngNjn4FNc+CD20Dt2e/G1KcaE72IvCwiOSJS6cBoERkhIgdEZLm73OuzbpSIrBeRTBG5K5CBh5Ko0qOM3vAnjkQnMiv1PpAG+Pk68Bo4+0+w6i2Y+5DX0RjToPgz3GMy8CzwajV1vlLVC3wLRCQSeA44F8gClojIdFVde4KxhqwztjxNyyNbmNb7WY5GN/c6HO+c8XvYuwnm/MMZhtnnMq8jMqZBqPHUUlXnAXknsO/BQKaqblLVImAqcNEJ7Cekdc5bwIBdb7O03dUNp1++KiJw4ZPQ8TR4/xeQtdTriIxpEALVhzBURFaIyMci0tstaw9s96mT5ZZVSkQmiEiGiGTk5uYGKCxvxRXvZ2Tm/eQ27s6CTr/wOpzgEBULV70GTdvA1PFwYIfXERkT9gKR6L8FOqlqf+AZ4H23vLJbPau8Cqeqk1Q1XVXTk5KSAhCW94ZvfoK4kgN80uN+Tx4BGLSatISr33LmsH/rOigp9DoiY8JarRO9qh5U1QL39UwgWkRa4ZzBd/CpmgJk1/Z4oaLzvgWk5c5kScpN7GmS6nU4waf1SXDxv2DHUpjxOxuJY0wdqnWiF5G2Is5ELSIy2N3nXmAJkCoiXUQkBhgHTK/t8UJBTEkBP8n8B3sbdWFxyk1ehxO80sY6F2iX/ReWvuJ1NMaErRpH3YjIG8AIoJWIZAH3AdEAqvoCcDnwcxEpAY4A41RVgRIRuR2YBUQCL6tqg5jO8PStzxFflMPUfi9TGhHjdTjB7ax7YOcKmHkHtOkLHU7xOiJjwk6NiV5Vq50kXVWfxRl+Wdm6mcDMEwstNCUfXMmAXe/wbfJ4dsX38Tqc4BcRCZe9CP8eDm/fCD+b5/ThG2MCpgHeuVN3REs4e9ND5Me05utOE70OJ3Q0SoQr/w8O5cC7P4WyMq8jMiasWKIPoH673qX1oQ3M7fJbiiMbex1OaGk3EEb9E77/HL56zOtojAkrYTYROjzxqTfPK21ctJfTtv6Lrc1PZWPLsz2JIeSl3wzbFsKcB6HjqdDlTK8jMiYs2Bl9gAzb+gzRZUf5ousfwudpUfVNBC54Alp0g2k/daY3NsbUmiX6AEg+uILeOTPIaH8d+xt18jqc0BbbFK54BY7sg/cmWn+9MQFgib62tIzhm5+gICbJxswHStu+cN4DkPkpLHze62iMCXmW6Gup557ZJBesYUGnX1AS2cjrcMLHKbfCSRfAZ3+BHd96HY0xIc0SfS1Elh5l2JZn2d3kJNYmjfE6nPAiAhc9C01bw7RbobDA64iMCVmW6GthUPYbNCvazdwuv26YDxOpa40S4dJJkLcJZt3tdTTGhCzLTieocdEeBmdNZmOLEexIONnrcMJX52Ew7Dfw7auw9gOvozEmJFmiP0Gnbn+JSC1kfudfeh1K+Btxt3ND1fT/Z/PXG3MCLNGfgIQjWfTd/R6r21zM/kYdvQ4n/EXFwGUvQWkRfPALG3JpzHGyRH8Chm57gTKJYlGHW70OpeFo2Q3OexA2zYElL3odjTEhxRL9cUoqWE+vPbNY1m48h2JaeR1Ow3LyjZB6Hnx6L+Su9zoaY0KGJfrjdNq2f3E0qhkZ7a/3OpSGRwTGPgPRjZ1ZLkuKvI7ImJBgif44tD+wjK77FrCk/Q0URsV7HU7DFN8GLnzKeVjJvEe8jsaYkGCJ/jgM3fZvDkW3ZHnylV6H0rCljYX+453pjHcs9ToaY4JejYleRF4WkRwRWV3F+mtEZKW7fC0i/X3WbRGRVSKyXEQyAhl4fUvZn0GHg0tZknIDJZFxXodjRv0T4ts6E58VH/E6GmOCmj9n9JOBUdWs3wwMV9V+wN+ASRXWn6WqA1Q1/cRCDAKqDN0+iYLoVqxsc4nX0RiARs2dKRL2bIAv/u51NMYEtRoTvarOA/KqWf+1qu5z3y4EUgIUW9DocGAJKQeXsSTlRkrtbD54dDvbmfzsm+dgywKvozEmaAW6j/4W4GOf9wrMFpGlIjKhug1FZIKIZIhIRm5uboDDqgVVhm6bRH5Ma1a1vdjraExF594PiZ2dG6ls4jNjKhWwRC8iZ+Ek+jt9ik9X1UHAaOA2Eany2XCqOklV01U1PSkpKVBh1VrHA4tpn7+CxSk3UhoR63U4pqKYJnDx87BvqzOlsTHmRwKS6EWkH/Af4CJV3VterqrZ7s8c4D1gcCCOV59O3f4S+TGtWdPmIq9DMVXpdBoM+blzx+zmeV5HY0zQqXWiF5GOwLvAdaq6wae8iYjEl78GRgKVjtwJVu0PLCPl4DKWtr+W0ogYr8Mx1Tn7z86zZj+4DQrzvY7GmKDiz/DKN4BvgJ4ikiUit4jIRBGZ6Fa5F2gJPF9hGGUbYL6IrAAWAzNU9ZM6aEOdGZz1MoejE1llI22CX0xjpwtn/3ZnigRjzP9E1VRBVcfXsP5W4Eeze6nqJqD/j7cIDW3y19J5/0Lmd7rNxs2Hio5DYOht8M2zkHYxdB3udUTGBAW7M7YKp2S9wtHIeFa0vdzrUMzxOOuPThfO9NttFI4xLkv0lWh5KJPUvDksb3cVRVFNvQ7HHI+YxnDRc04Xjo3CMQawRF+pU3a8SlFEI5YlX+V1KOZEdBr6wyicLfO9jsYYz1miryD+6E565s5mVdtLOBrd3OtwzIk6+8+Q2MUZhVN0yOtojPGUJfoKBmW/jgp8267aa9Am2MU0dubC2bfF5sIxDV6No24akrji/fTd/T7rW42iILat1+EEvSc+3VBzpTrym3N71Fyp8zA45aew8F+QdpEzKseYBsjO6H303/UO0WVHyWh/rdehmEA55y+Q0MHpwrHpjE0DZYneFVV6lAHZb7IpcRh7m3T3OhwTKLFNYezTsDcTvnzQ62iM8YQleldazoc0LtlPRvvrvA7FBFq3s2DQ9c6NVPZEKtMAWaIHREs5Oft1djbtw45mA70Ox9SFkX+Hpm3h/dugpNDraIypV5boga55X9H8aBZL218DIl6HY+pCXAJc+CTkroN5j3odjTH1yhI9zpDKA7HJZLYc4XUopi71OA/6jYP5j8POlV5HY0y9afCJvk3+GlIOLmNZ8jhUbLRp2Bv1D2jUwhmFU1rsdTTG1IsGn+gHZb9OYWQT1rQZ63Uopj40bgHnPwa7VsKCJ72Oxph60aATfXzhLnrs+ZxVbS62ycsakrSx0PsSmPsw5KzzOhpj6lyDTvQDsqcCsLzdOI8jMfVuzKMQG+924ZR4HY0xdarBJvro0sP02f0BG1udTb5Nd9DwNGkFox92xtUvfN7raIypU/48SvBlEckRkUqf9yqOp0UkU0RWisggn3WjRGS9u+6uQAZeW71yZhBXWsCyZDubb7D6XAYnXQBfPgB7Mr2Oxpg6488Z/WRgVDXrRwOp7jIB+BeAiEQCz7nr04DxIpJWm2ADRssYuPNNdjVNY2d8X6+jMV4RgfMfh6g4pwunrNTriIypEzUmelWdB+RVU+Ui4FV1LASai0gyMBjIVNVNqloETHXreq7T/oW0OLLVOZu3G6Qatvg2MPoh2L4QFv3b62iMqROB6KNvD2z3eZ/lllVVXikRmSAiGSKSkZubG4CwqjYweyoF0a3Y0OqcOj2OCRH9roLU8+Dz+2Hv915HY0zABSLRV3ZKrNWUV0pVJ6lquqqmJyUlBSCsyiUe3kKX/d+wMvkyyiKi6+w4JoSIONMjRMbA9F9CWZnXERkTUIFI9FlAB5/3KUB2NeWeGrDzTUokmpVtLvU6FBNMmrWDUQ/C1gXOs2aNCSOBSPTTgevd0TdDgAOquhNYAqSKSBcRiQHGuXU9E1NSQFrODNYnjeRITAsvQzHBaMA10P1c+OwvkLfJ62iMCRh/hle+AXwD9BSRLBG5RUQmishEt8pMYBOQCbwI/AJAVUuA24FZwDrgLVVdUwdt8FtazkfElB1hefJVXoZhgpUIXPgURETDB7dbF44JGzXO4qWq1T4lW1UVuK2KdTNxPgi8p2UM2Pk22fF9yWnay+toTLBKaO904Xxwm9OFc+rPjlnt1XNy/XpGrjFVaDB3xnbav4jEo9tYnnyl16GYYOfbhWOjcEwYaDCJfsDOtzgU3YKNLX/idSgm2B3ThXObdeGYkNcgEn3C0Sy67FvAqraX2pBK45+E9jD6n7DtG1j0gtfRGFMrDSLR99v5DmUSYUMqzfHpPx56jILP/2pz4ZiQFvaJPqr0KH1yPiSzxVkciq27G7FMGCrvwomKg/cn2lw4JmSFfaLvsWc2cSUHWZF8hdehmFAU39aZuz5rCXz9tNfRGHNCwjvRqzJg5zvsadyVHc0Geh2NCVV9L4deY+HLB2l5yLpwTOgJ60TfpmAtbQ6tY0Xby22WSnPiROCCJyAugVEb7yWizB4qbkJLWCf6/rvepiiiMd8ljfY6FBPqmrSCC5+m9aGNDNn+H6+jMea4hG2ijyveT8/cT1nXerQ9+NsExkljWNP6Ak7Jmkzb/FVeR2OM38I20ffO+ZAoLXK6bYwJkDldfkdBbGtGbbiPqNIjXodjjF/CM9FrGf12TSOr2UD2NunudTQmjBRFNeWT1L/Q/GgWZ255yutwjPFLWCb6TvsX0fzoDjubN3ViR8LJLG13Df13TaNz3gKvwzGmRmGZ6Pvtmsbh6ES+bznC61BMmPq608/JbdydkZn306h4n9fhGFOtsEv0TQt30zXvK1a3HktpRIzX4ZgwVRoRwyc97ie2JJ9zMh8ArfIpmcZ4LuwSfd/d7yMoq9ravDambu1pksqCTrfRPW8ufXa/73U4xlQpvBJ9aTF9dr3P5sTTOBjXzutoTAPwbbvxbE0YzIjNj9P8yFavwzGmUn4lehEZJSLrRSRTRO6qZP0fRGS5u6wWkVIRaeGu2yIiq9x1GYFuwDHWf0zT4j12Nm/qj0QwK/UvlETEMmb9n+2uWROU/HlmbCTwHDAaSAPGi0iabx1VfURVB6jqAOBuYK6q5vlUOctdnx640CuR8RIHY9uyOfH0Oj2MMb4OxSbxafc/0ubQOk7bZnPXm+Djzxn9YCBTVTepahEwFbiomvrjgTcCEdxxKcyHPZmsanMJKpH1fnjTsH3f8ixWtrmE9B3/peP+RV6HY8wx/En07YHtPu+z3LIfEZHGwChgmk+xArNFZKmITKjqICIyQUQyRCQjNzfXj7AqiI2HX63g23ZXH/+2xgTA3C6/Ja9RZ0ZtuM+GXJqg4k+ir2zax6rGkl0ILKjQbXO6qg7C6fq5TUTOrGxDVZ2kqumqmp6UdIIPCImMoiQy7sS2NaaWSiLjmNnzAWJL8hm58a825NIEDX8SfRbQwed9CpBdRd1xVOi2UdVs92cO8B5OV5AxYWlPk1TmdfkVXfctYGB2/fdgGlMZfxL9EiBVRLqISAxOMp9esZKIJADDgQ98ypqISHz5a2AksDoQgRsTrFa0vYLMFsM5Y+sztMlf43U4xtSc6FW1BLgdmAWsA95S1TUiMlFEJvpUvQSYraqHfMraAPNFZAWwGJihqp8ELnxjgpAIs7v/mUMxrTh//T3EluR7HZFp4KL8qaSqM4GZFcpeqPB+MjC5QtkmoH+tIjQmBBVGJzCj54NcueqnnJP5ADN6/sOecmY8E153xhoTRHbF92VBx9vosfdz+u962+twTANmid6YOrS0/TVsShzG8M1PWH+98YwlemPqkkTwSepfKIhJ4oL1dxFXvN/riEwDZInemDrm9Nf/k8ZFexm18T7QMq9DMg2MJXpj6sHu+DTmdvkdXfZ9zalZL3sdjmlgLNEbU09Wtr2UdUmjGbptEp332SMITf2xRG9MfRHhs273kNukO6PX/5mEI1leR2QaCEv0xtSjksg4PjrpYRDhwu/uIKr0qNchmQbAEr0x9exAXAoze/yNVoczOTfz7zb5malzluiN8cDWxNNY0OnnnLRnFuk7XvU6HBPmLNEb45El7W9kfatzGbb1Obrkzfc6HBPGLNEb4xURZne/l5wmPRm94U+0OLzZ64hMmLJEb4yHSiLjmN7rEUoiYhm77nd256ypE5bojfFYQWxbPjzpEeILd3PBd3cSUVbsdUgmzFiiNyYI7GzWj9mpf6bDwW855/sHbSSOCSi/5qM3xtS99UmjSDyyjaHbX2Rfo44sSbnJ65BMmLBEb0wQWdjhpyQe2cawrc9zMDaZ9UmjvA7JhAG/um5EZJSIrBeRTBG5q5L1I0TkgIgsd5d7/d3WGONDhNmp95LVbCDnbfwrKQeWeh2RCQM1JnoRiQSeA0YDacB4EUmrpOpXqjrAXe4/zm2NMa7SiBim93qU/XEduHDd72l5+HuvQzIhzp8z+sFApqpuUtUiYCpwkZ/7r822xjRYhVHNeC/tKUojYrl4za/gwA6vQzIhzJ9E3x7Y7vM+yy2raKiIrBCRj0Wk93Fui4hMEJEMEcnIzc31Iyxjwlt+XDLvpT1FbGkB/PcSOJzndUgmRPmT6Ct7dH3FsV/fAp1UtT/wDPD+cWzrFKpOUtV0VU1PSkryIyxjwl9u05580Osx2LcFplwBhQVeh2RCkD+jbrKADj7vU4Bs3wqqetDn9UwReV5EWvmzrTEn4olPN3gdQr3ZkXAyXP4yvHWds4yfClGxXodlQog/Z/RLgFQR6SIiMcA4YLpvBRFpKyLivh7s7nevP9saY/zQ6wK48Gn4/gt452YoLfE6IhNCajyjV9USEbkdmAVEAi+r6hoRmeiufwG4HPi5iJQAR4BxqqpApdvWUVuMCW+DroOiQ/DJnfD+z+GSFyAi0uuoTAjw64YpVZ0JzKxQ9oLP62eBZ/3d1hhzgoZMhOJD8Pn9EN0ILngSImwmE1M9uzPWmFBzxu+g6DB89ahzRj/mMUv2plqW6I0JRWf/CbQU5j/hvLdkb6phid6YUCQCP7nPeT3/CWe2y/Mft2RvKmWJ3phQ9b9kLzD/cSgtgrHP2AVa8yOW6I0JZSLwk3udcfVz/gHFh+HSFyEy2uvITBCxRG9MqBOBEXdBTBOY/ScoPgpXvOKMyjEGS/TGhAT/7gQ+j35dCzh7w0PseGY003s9RmFUfK2P/Ztze9R6H8ZbduXGmDCyMvkyZvb4O8n5q7hi1QSaFNoEgcYSvTFhZ0PSSN5Pe5KEo9lcteoWWhze7HVIxmOW6I0JQ9uan8rbfV8gqqyQq1beQsr+DK9DMh6yRG9MmMpp2oup/V7hUEwrLl37S9JyPvI6JOMRS/TGhLGDce14s99L7HCfQXv61udAy7wOy9QzS/TGhLnCqHjeS3uKVW0uZnDWZC5a9ztiSuwBJg2JJXpjGoCyiGg+63YPX3T9A533fcO4lTfT/MhWr8My9cQSvTENhQgrkq9kWu9naVycx9UrbqD73i+8jsrUA0v0xjQwWc3TmTLgNfIadebC7+7kjM1PElFmT6wKZ5bojWmA8mPb8nbfSSxvewXp2VO4YvUEmh21xzmHK78SvYiMEpH1IpIpIndVsv4aEVnpLl+LSH+fdVtEZJWILBcRG8xrTJAojYjhy253MKPHA7Q8vIlrll9Djz2feh2WqQM1JnoRiQSeA0YDacB4EUmrUG0zMFxV+wF/AyZVWH+Wqg5Q1fQAxGyMCaANSSN5bcAU9jXqzPnr7+HcjffbqJww488Z/WAgU1U3qWoRMBW4yLeCqn6tqvvctwuBlMCGaYypSwfj2vNW3xdZlHITaTkzuHb51aQcWOp1WCZA/En07YHtPu+z3LKq3AJ87PNegdkislREJlS1kYhMEJEMEcnIzbWJmIypb2URUXzd6Re81fdFyiSKK1ZPZMSmR6HQzu5DnT+JXiop00oripyFk+jv9Ck+XVUH4XT93CYiZ1a2rapOUtV0VU1PSkryIyxjTF3Y2awfrw2YwrLkKxm48014fihs/MzrsEwt+JPos4AOPu9TgB9dnheRfsB/gItUdW95uapmuz9zgPdwuoKMMUGsJLIRc7r+gTf7/gei42DKZTDtVsjf7XVo5gT4k+iXAKki0kVEYoBxwHTfCiLSEXgXuE5VN/iUNxGR+PLXwEhgdaCCN8bUrexm/eFnX8GZd8DaD+DZdFj4ApTauPtQUmOiV9US4HZgFrAOeEtV14jIRBGZ6Fa7F2gJPF9hGGUbYL6IrAAWAzNU9ZOAt8IYU3ei4+DsP8LPv4GUdPjkTpg0HDbN8Toy4ydRrbS73VPp6emakXFiQ+79e+SaMcZfxzxKUBXWTXeeTbt/G/QYDSP/Bq1SvQvQACAiS6sawm7PjDXGVOvHJ0+9iOz1OgOzpzL4+1eIfvZU1rS5gIUdfkpBbJuAHdeeVRs4luiNMcetNCKWjJQbWNP6Qk7Nepm+u96lV87HrEi+nIz213M4pqXXIRofNteNMeaEHYlpwZyuv2fyoGmsTxrJwOyp3LL0IoZvesweTB5E7IzeGFNr+XHJzE69j8UpN3FK1mQG7HybfrumsS5pDN+2v4a8xl28DrFBs0RvjAmY/Y068mnqvSzucDODdkyhT86H9M35gE2Jw1jWbhzbEgaDVHYPpqlLluiNMQF3IC6FL7vdycKOE+i/823673yby9bczt5GnVmRfAXrksZQFNXU6zAbDEv0xpg6cyQ6kYUdJ7Ak5QZ67vmUAdlvcfamRzhjyzNsaHUOq9tcTHZ8PzvLr2OW6I0xda40Ipa1rS9gbdL5tClYS9/d79Mzdza9cz5iX1xH1iWN4rvWozkQZxPf1gVL9MaY+iPC7vje7I7vzdzOv6bH3s/plTOT07ZP4rTtk9jZtA8bWp3DhlY/AWwcfaDYnbHGGM/FF+7ipNxPSN3zGW0OrXcK2w2Ck8ZAz/OhdS/r3qlBdXfGWqI3xgSV5ke2kbrnc4aVLoId7sNPEjpC6jnQ/VzocgbExtdpDF7lkdrcDWxTIBhjQsb+Rh1Z0uEmhp37Dzi4EzZ8DBs/hRVvQsbLEBEF7U+GLsOh8zBIOQViGnsddlCzRG+MCV7NkiH9ZmcpKYJt38DmubBpLnz1KMx72En8yQOg4xAn6aecAgnVPQSv4bFEb4wJDVEx0HW4s/wEOHoAti+GrQtg69eweBJ886xTNz4Zkvs7HwDJ/aFtH0jo0GD7+S3RG2NCU1wCpJ7rLAAlhbBrNWQthuxlzrJhFv978mlcArTuDUk9nYu7rXo40yvHt4OI8J72yxK9MSY8RMVCysnOUq4wH3avhd2r3WUtrHkPlr7yQ53oxtCiKyR2hhZdILELnfZFkh+bzMHYZEoi4+q9KYFmid4YE75i46Hjqc5SThUKciD3O9ibCXu/d37u2eBc9C0t5FKfXRyJSiA/tjUFMW04FNOKgpgkDsW04nB0Cw7FtORwdAuORCdSHNEoaLuG/Er0IjIKeAqIBP6jqv+ssF7c9WOAw8CNqvqtP9saY0y9EoH4Ns7Sdfix68rKIH8nb372NfGFO2l2dCfxRbuJL9xN06Ic2hSspXHxPoQfD0sviYjlSFQCR6ITOBqVwNGo5hyNiqewfImMpzCqCUWRTSmKbEJhVBOKIxtTFNmE4ohGlETE1lmTa0z0IhIJPAecC2QBS0Rkuqqu9ak2Gkh1l1OBfwGn+rmtMcYEh4gISGjvPBSd/pVXKSuhUXEeTYrzaFy0l8bFe2lcvJ9GJftpVLyPuOIDNCo5QKvDG4ktySe2JJ8oLa7x0IrA2g7w61UBbpR/Z/SDgUxV3QQgIlOBiwDfZH0R8Ko6d18tFJHmIpIMdPZjW2OMCRllEVEcim3NodjW/m2gSlRZIbGlBcSUFDg/Sw+5y2GiSw8TXXqE6NIjDEltWycx+5Po2wPbfd5n4Zy111SnvZ/bAiAiE4AJ7tsCEVnvR2x1pRWwx8Pj1wVrU/ALt/ZALdr02wAHEiD18De680Q37FTVCn8SfWVXFyp2UFVVx59tnULVScAkP+KpcyKSUdWtxKHK2hT8wq09EH5tCtX2+JPos4AOPu9TgGw/68T4sa0xxpg65M9dAkuAVBHpIiIxwDhgeoU604HrxTEEOKCqO/3c1hhjTB2q8YxeVUtE5HZgFs4QyZdVdY2ITHTXvwDMxBlamYkzvPKm6ratk5YEVlB0IQWYtSn4hVt7IPzaFJLtCcppio0xxgROeE/wYIwxxhK9McaEu7BN9CLSQUS+FJF1IrJGRH7llrcQkU9FZKP7M9Fnm7tFJFNE1ovIeT7lJ4vIKnfd0+6UD4hIrIi86ZYvEpHO9dS2SBFZJiIfhXqb3Jvr3hGR79y/1dBQbo97zN+4/+ZWi8gbIhIXam0SkZdFJEdEVvuU1UsbROQG9xgbReSGOmzPI+6/u5Ui8p6INA+V9hw3VQ3LBUgGBrmv44ENQBrwMHCXW34X8JD7Og1YAcQCXYDvgUh33WJgKM59AR8Do93yXwAvuK/HAW/WU9t+C7wOfOS+D9k2Af8H3Oq+jgGah3h72gObgUbu+7eAG0OtTcCZwCBgtU9ZnbcBaAFscn8muq8T66g9I4Eo9/VDodSe425/fR/QqwX4AGfOnfVAsluWDKx3X98N3O1Tf5b7B00GvvMpHw/827eO+zoK5445qeN2pACfA2fzQ6IPyTYBzXCSolQoD8n2uMcovxu8hXu8j9yEEnJtwpnCxDcx1nkbfOu46/4NjK+L9lRYdwkwJZTaczxL2Hbd+HK/Rg0EFgFt1Bnjj/uzfMKK6qZxyKqk/JhtVLUEOAC0rJNG/OBJ4A6gzKcsVNvUFcgFXnG7ov4jIk0I3fagqjuAR4FtwE6ce0pmE8Jt8lEfbahqX3XtZpwz9GNiqxBDKLXnGGGf6EWkKTAN+LWqHqyuaiVlNU3j4PcUD4EgIhcAOaq61N9NKikLpjZF4Xyd/peqDgQO4XQJVCXY24Pbb30Rzlf+dkATEbm2uk0qKQuqNvkhkG2o97aJyB+BEmBKeVEVMYREeyoT1oleRKJxkvwUVX3XLd4tzsyauD9z3PKqpnHIcl9XLD9mGxGJAhKAvMC35H9OB8aKyBZgKnC2iLxG6LYpC8hS1UXu+3dwEn+otgfgHGCzquaqajHwLnAaod2mcvXRBn+mXAkY9+LoBcA16vatVBND0LenKmGb6N2r4S8B61T1cZ9V04HyK9834PTdl5ePc6+ed8GZW3+x+xU1X0SGuPu8vsI25fu6HPjC5x9LwKnq3aqaoqqdcS74fKGq14Zqm1R1F7BdRHq6RT/BmcI6JNvj2gYMEZHGbiw/AdaFeJvK1UcbZgEjRSTR/XY00i0LOHEeinQnMFZVD1doZ8i1p1r1fVGgvhZgGM5XpJXAcncZg9Nv9jmw0f3ZwmebP+JcYV+PezXdLU8HVrvrnuWHO4rjgLdxpn5YDHStx/aN4IeLsSHbJmAAkOH+nd7HGZkQsu1xj/lX4Ds3nv/ijN4IqTYBb+BcYyjGOSu9pb7agNNfnukuN9VhezJx+s+Xu8sLodKe411sCgRjjAlzYdt1Y4wxxmGJ3hhjwpwlemOMCXOW6I0xJsxZojfGmDBnid4ELREpFZHl4swEuUJEfisiEe66dBF5+gT2OUdEPH24s4j8WkSur6HOI267H6lifV8RmVwnAZqw48/DwY3xyhFVHQAgIq1xZuxMAO5T1Qyc8ff1RkQiVbW0lvuIwhlXPaiGqj8DklS1sLKVqrpKRFJEpKOqbqtNTCb82Rm9CQmqmgNMAG4Xxwj5YT7+4e6Z/3J3crR4t/wOd+7wFSLyT5/dXSEii0Vkg4ic4dbtLCJfici37nKaWz5CnOcavA6sEpEIEXnePdv+SERmisjlbt2TRWSuiCwVkVnl0wVUcDbwrToTXyEi3UXkMzfGb0Wkm4hMB5oAi0TkKhG5Qpy57VeIyDyffX2Ic4e0MdXz4i4tW2zxZwEKKinbB7Th2DuDPwROd183xfmmOhr4Gmjslrdwf84BHnNfjwE+c183BuLc16lAhvt6BM5ka13c95cDM3FOktq68VwORLvHS3LrXQW8XEn8fwV+6fN+EXCJ+zrOJ94CnzqrgPbu6+Y+5acDH3r9d7Il+BfrujGhprLZABcAj4vIFOBdVc0SkXOAV9Sdw0RVfSf9Kp/gbinOHOXgJOpnRWQAUAr08Km/WFU3u6+HAW+rahmwS0S+dMt7An2AT51pUIjEueW+omScuW9wv3m0V9X33BiPVtHmBcBkEXnLJ3ZwJhVrV8U2xvyPJXoTMkSkK04SzgF6lZer6j9FZAbOGfpCN8kLVU8HW97vXcoP/wd+A+wG+uOcrfsm3UO+YVQVHrBGVYfW0IwjOGfu1e3rGKo6UUROBc4HlovIAFXd6+7niD/7MA2b9dGbkCAiScALwLOqqhXWdVPVVar6EM4F2pOA2cDNItLYrdOihkMkADvdM/XrcM7IKzMfuMztqy/vQgJn8qskERnqHi9aRHpXsv06oDuAOs9HyBKRi91tYsvjraR9i1T1XpwnF5VPe9sDZ4ItY6plZ/QmmDUSkeU43SolODNBPl5JvV+LyFk4Z+hrgY9VtdDthskQkSKcfvV7qjnW88A0EbkC+JJjz+J9TcOZeng1znOIF+E8RarIvSj7tIgk4PzfehJYU2H7j912lLsO+LeI3I8zs+IVOM8V9fWIiKTifAP4HOd5pgBnATOqaZMxADZ7pTHHS0SaqmqBiLTEmZL2dHXm1vd3+/eAO1R1Yy1iiAXmAsPUHcFjTFUs0RtznERkDtAciAEeVtXJx7l9T5znr86rsXLV+0jFuZA750T3YRoOS/TGGBPm7GKsMcaEOUv0xhgT5izRG2NMmLNEb4wxYc4SvTHGhLn/D5XGfN8fzevwAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from scipy.stats import weibull_min\n", "plt.hist(ranked_df['peak value (cfs)'], density=True, alpha=0.5)\n", "shape, loc, scale = weibull_min.fit(ranked_df['peak value (cfs)'], floc=0)\n", "x = np.linspace(ranked_df['peak value (cfs)'].min(), ranked_df['peak value (cfs)'].max(), 100)\n", "plt.plot(x, weibull_min(shape, loc, scale).pdf(x))\n", "plt.title(\"Weibull fit on Peak Flow data\")\n", "plt.xlabel(\"Discharge (cfs)\")" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Weibull probability plot of peak flow data')" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from scipy.stats import probplot, weibull_min\n", "shape, loc, scale = weibull_min.fit(ranked_df['peak value (cfs)'], floc=0)\n", "probplot(ranked_df['peak value (cfs)'], \\\n", "dist=weibull_min(shape,loc,scale),\\\n", "plot=plt.figure().add_subplot(111))\n", "plt.title(\"Weibull probability plot of peak flow data\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above plot, called a Q-Q plot (which shows observed vs. theoretical quantiles), is a way to determine if our distribution is a good fit. If it is a good fit, the data will plot on a straight line. Here we see that most of our data plot on that line, but our largest flood does not. This indicates that for the most extreme values, the Weibull distribution might not be the best." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "For comparison with the Log-Pearson Method (part 1), we can ask the computer to output the highest 1% of the Weibull distribution. (With modern day computers, this is faster than doing this graphically.)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.9902365744397528\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot a cumulative distribution function of the weibull distribution fitted to our data\n", "x = np.linspace(ranked_df['peak value (cfs)'].min(), ranked_df['peak value (cfs)'].max()+10000, 100)\n", "plt.plot(x, weibull_min(shape, loc, scale).cdf(x))\n", "plt.xlabel('peak flow (cfs)') # set the label for the x axis\n", "plt.ylabel('Non-exceedance probability') # set the label for the y axis\n", "plt.title('Skykomish River Flood Probability');\n", "\n", "# The 99% probability of non-exceedance corresponds to a 1% probability of exceedance\n", "# Through trial and error, we find that a flow of 106,000 cfs corresponds here.\n", "# This is less than the highest flow we observed, which could mean our highest flow was greater than a 100 year\n", "# flood or that we chose the wrong distribution. \n", "output = weibull_min(shape, loc, scale).cdf(106000)\n", "print(output)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Summary and Discussion\n", "Note that assuming a Weibull distribution, we would predict the 100 year flood to be 106,000 cfs.\n", "The Q-Q plot suggested that our highest peak did not fall on a Weibull distribution.\n", "The Log-Pearson III distribution, recommended by the USGS and US government, predicts the 100 year flood to be 122,000 cfs. \n", "Our largest flood of record is 129,000 cfs. It likely has a greater return period than 100 years, suggesting that is has less than 1% probability of occurring any given year." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You will want to use this code to do Homework 5, problem 2. Note that the plotting fits in Method 2 were done by eye. This is fine, you can iterate on values in the code to find what looks right to you." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.10.13" } }, "nbformat": 4, "nbformat_minor": 4 }