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KMarkert/nwm_bq_flood_analysis.ipynb

Created April 8, 2024 02:43
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nwm_bq_flood_analysis.ipynb
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"source": [
"# Copyright 2024 Google LLC\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "markdown",
"id": "e92c5ad5-eab6-4392-a093-7637b60e6550",
"metadata": {
"id": "e92c5ad5-eab6-4392-a093-7637b60e6550"
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"source": [
"# National Water Model on Big Query\n",
"\n",
"<table align=\"left\">\n",
" <td>\n",
" <a href=https://colab.research.google.com/gist/KMarkert/bdb4596518ff0093aff6e116aa24c127/nwm_bq_flood_analysis.ipynb>\n",
" <img src=https://cloud.google.com/ml-engine/images/colab-logo-32px.png alt=\"Colab logo\">\n",
" Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://gist.githubusercontent.com/KMarkert/bdb4596518ff0093aff6e116aa24c127/raw/ef533b7748af9f5b9e234818260a65183797c0ba/nwm_bq_flood_analysis.ipynb>\n",
" <img src=https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32 alt=\\\"Vertex AI logo\\\">\n",
" Open in Vertex AI Workbench\n",
" </a>\n",
" </td>\n",
"</table>\n",
"<br/><br/><br/>"
]
},
{
"cell_type": "markdown",
"id": "0b0f3b36",
"metadata": {
"id": "0b0f3b36"
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"source": [
"## Overview\n",
"This tutorial demonstrates how to use the BigQuery Python client library to select National Water Model (NWM) data and use for analysis.\n",
"\n",
"### Objective\n",
"\n",
"In this tutorial, you learn how to use the `Big Query Python Client` to request NWM data and analyze for a river reach along the Sacramento River in California.\n",
"\n",
"The steps performed include:\n",
"* Run a job to get the historical (2018 to present) streamflow data from the analysis_assim dataset\n",
"* Access gauge observations and compare historical data with the observations\n",
"* Derive flood levels from historical data\n",
"* Run a job to get a forecast for the March 2023 flooding and compare with observations\n",
"* Plot different forecasted streamflow statistics against observed data\n"
]
},
{
"cell_type": "markdown",
"id": "f2ea2fee-acfb-4979-a21c-b1dfefa714bb",
"metadata": {
"id": "f2ea2fee-acfb-4979-a21c-b1dfefa714bb"
},
"source": [
"## Installation\n",
"\n",
"Install the following packages required to execute this notebook.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "25ab9885",
"metadata": {
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"source": [
"# install the USGS package for acquiring gauge data\n",
"!pip install --upgrade dataretrieval --quiet"
]
},
{
"cell_type": "markdown",
"id": "fa6da301-6dd2-4989-b2c2-ec5adad4f8e2",
"metadata": {
"tags": [],
"id": "fa6da301-6dd2-4989-b2c2-ec5adad4f8e2"
},
"source": [
"## Authenticate your Google Cloud account\n",
"**If you are using Vertex AI Workbench Notebook or Colab Enterprise**, your environment is already authenticated. Skip this step.\n",
"\n",
"**If you are using the consumer Colab** (i.e. https://colab.research.google.com), run the cell below and follow the instructions when prompted to authenticate your account via oAuth."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2ac80acb-1e82-41a7-a96a-7cd74d291bf0",
"metadata": {
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"source": [
"# If you are running this notebook in Colab, run this cell and follow the\n",
"# instructions to authenticate your GCP account. This allows for you to\n",
"# submit Big Query jobs and access the NWM dataset.\n",
"\n",
"import os\n",
"import sys\n",
"\n",
"# If on Vertex AI Workbench, then don't execute this code\n",
"IS_COLAB = \"google.colab\" in sys.modules\n",
"if not os.path.exists(\"/opt/deeplearning/metadata/env_version\") and not os.getenv(\n",
" \"DL_ANACONDA_HOME\"\n",
"):\n",
" if \"google.colab\" in sys.modules:\n",
" from google.colab import auth as google_auth\n",
"\n",
" google_auth.authenticate_user()"
]
},
{
"cell_type": "markdown",
"id": "b2e6ea92",
"metadata": {
"id": "b2e6ea92"
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"source": [
"## Import libraries"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "74ab1db1-9a4b-4f97-b03c-72cbfd8d7e72",
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"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from scipy import stats\n",
"import dataretrieval.nwis as nwis\n",
"\n",
"import plotly.express as px\n",
"import plotly.graph_objects as go\n",
"\n",
"from google.cloud.bigquery import Client, QueryJobConfig"
]
},
{
"cell_type": "markdown",
"id": "9b34ae03",
"metadata": {
"id": "9b34ae03"
},
"source": [
"## Visualize streamflow data\n",
"\n",
"In this section you will request data using Big Query for a reach for the `analysis_assim` dataset and visualize the time series"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ec6171c0-d88d-41ad-91a5-8b9aa0a96539",
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"outputs": [],
"source": [
"# initialize the Big Query client for submitting jobs\n",
"client = Client()"
]
},
{
"cell_type": "markdown",
"source": [
"> **NOTE:** If you the consumer Colab environment, you will need to provide a cloud project to run the jobs (`client = Client(project=PROJECT)`). If you are using Vertex AI Workbench or Colab Enterprise then it will use your currently active cloud project."
],
"metadata": {
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"id": "98bc752b",
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"source": [
"# define the reach feature id to run the queries for\n",
"nwm_fid = 15039097"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "155100d7-e063-45f9-9ff3-0be413ec98f8",
"metadata": {
"id": "155100d7-e063-45f9-9ff3-0be413ec98f8"
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"source": [
"# define the BQ query\n",
"query = f\"\"\"\n",
"SELECT\n",
" reference_time,\n",
" time,\n",
" streamflow\n",
"FROM\n",
" `bigquery-public-data.national_water_model.analysis_assim_channel_rt`\n",
"WHERE\n",
" feature_id = {nwm_fid}\n",
" AND forecast_offset = 1\n",
"ORDER BY\n",
" time\n",
"\"\"\""
]
},
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},
"outputs": [],
"source": [
"# submit the BQ job and load as a pandas dataframe\n",
"job = client.query(query)\n",
"df = job.to_dataframe()"
]
},
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" reference_time time streamflow\n",
"0 2022-07-31 21:00:00 2022-07-31 22:00:00 215.779999\n",
"1 2022-07-31 22:00:00 2022-07-31 23:00:00 215.489990\n",
"2 2022-07-31 23:00:00 2022-08-01 00:00:00 213.509995\n",
"3 2022-08-01 00:00:00 2022-08-01 01:00:00 212.659988\n",
"4 2022-08-01 01:00:00 2022-08-01 02:00:00 211.239990"
],
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"metadata": {},
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"source": [
"# inspect the resulting dataframe\n",
"df.head()"
]
},
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"source": [
"# plot the time series\n",
"fig = go.Figure()\n",
"fig.add_trace(go.Scatter(x=df[\"time\"], y=df[\"streamflow\"], name='NWM analysis_assim'))\n",
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"## Compare data with observed gauge\n",
"\n",
"It is common practice to visualize the simulated streamflow with the observed data to compare how well the model performs. Here you will request the observed data from the USGS and plot with the `analysis_assim` time series"
]
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"# specify the USGS site code for which we want data.\n",
"# this site corresponds with the NWM feature id we are using\n",
"site = '11425500'"
]
},
{
"cell_type": "code",
"execution_count": null,
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"# get the daily values for the last 40 years from the site with the parameter code 00060 (discharge)\n",
"gauge = nwis.get_record(sites=site, service='dv', start='1980-01-01',parameterCd='00060')\n",
"gauge.mask(gauge['00060_Mean'] < 0, inplace=True)"
]
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"# convert the cubic feet per second to cubic meters per second\n",
"# and add a new column for cms discharge\n",
"gauge['streamflow'] = gauge['00060_Mean'] * 0.02832\n"
]
},
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"# select the time frame from the NWM data\n",
"gauge_sel = gauge.loc[gauge.index >= '2022-01-01']"
]
},
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