{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## USGS STN Flood Event Data: High Water Marks\n", "\n", "The United States Geological Survey (USGS) operates a comprehensive flood event database known as the [Short-Term Network (STN)](https://stn.wim.usgs.gov/stnweb/#/). The STN offers a user-friendly [web front-end](https://stn.wim.usgs.gov/FEV/) for easy access. For developers and scientists, there's a [RESTFul API](https://stn.wim.usgs.gov/STNServices/Documentation/home) available for more advanced queries and integrations. The STN offers access to a variety of data types including instruments, sites, high water marks, and peak summaries.\n", "\n", "### Focus: High Water Marks (HWMs)\n", "\n", "In this notebook, we'll delve into the specifics of high water marks (HWMs) within the STN database. Here's what you can expect:\n", "\n", "1. Data Dictionaries: Understand the structure and meaning of the data.\n", " \n", "2. Data Retrieval: Learn how to fetch all available data by type.\n", " \n", "3. Filtered Queries: Dive deeper with specific, filtered data requests.\n", " \n", "4. Data Limitations: We'll also touch upon some of the known limitations including inconsistent field names and possibility for user introduced errors.\n", "\n", "### Additional Resources:\n", "\n", "For those interested in the methodology behind HWM collection, the USGS provides detailed resources:\n", "- [Technical Guide on HWMs](https://doi.org/10.3133/tm3A24)\n", "- [High Water Marks & Flooding Overview](https://www.usgs.gov/special-topics/water-science-school/science/high-water-marks-and-flooding)\n", "- [Video Guide: Interpreting High Water Marks](https://www.usgs.gov/media/videos/a-usgs-guide-finding-and-interpreting-high-water-marks)\n", "\n", "Let's begin by importing necessary dependencies." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from pathlib import Path\n", "\n", "import matplotlib.lines as mlines\n", "import matplotlib.markers as mmarkers\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "import pygeohydro as gh\n", "from pygeohydro import STNFloodEventData" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After importing, we can start with how we can obtain all the HWM data available in the database as a `GeoDataFrame`. We have two options for get STN data: `STNFloodEventData` class or `stn_flood_event` function. The `stn_flood_event` function can only pull either the information dataset about the supported `data_type`s by STN as a `pandas.DataFrame` or a subset of the actual data for the supported STN `data_type`s as a `geopandas.GeoDataFrame`. Moreover, the `STNFloodEventData` class provides access to some other data about the STN service such as data dictionary.\n", "\n", "For example, we can get information about HWMS data either using `STNFloodEventData.get_all_data(\"hwms\")` or `gh.stn_flood_event(\"hwms\")`." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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hwm_idwaterbodysite_idevent_idhwm_type_idhwm_quality_idhwm_locationdescriptionlatitude_ddlongitude_ddsurvey_date...survey_member_idhwm_labelfilesstillwaterpeak_summary_idlast_updatedlast_updated_byuncertaintyhwm_uncertaintygeometry
013922Swatara Creek1610612311HWM is located on inside of pavillion two40.192896-76.7230802012-04-26T04:00:00...202.0no_labelNaNNaNNaNNaNNaNNaNNaNPOINT (-76.72308 40.19290)
117897Atlantic Ocean191482413Mud line on bench rocks and plants near IBA Cl...41.894148-70.5366292017-06-05T04:00:00...2.0HWMMAPLY-402NaN0.0NaNNaNNaNNaNNaNPOINT (-70.53663 41.89415)
219685East Nishnabotna River2000516856U.S. Highway 34, seed line on flood wall (1 of...41.026290-95.2431991998-07-28T05:00:00...1757.0HWM U/SNaN1.03337.0NaNNaNNaNNaNPOINT (-95.24320 41.02629)
318530Maquoketa River1943615161County Road X29/220th Avenue, southwest of Del...42.410092-91.3634812010-07-30T05:00:00...1755.0USGS HWM D/SNaN1.02710.0NaNNaNNaNNaNPOINT (-91.36348 42.41009)
419687East Nishnabotna River2000516826U.S. Highway 34, debris line on guardrail (2 o...41.026290-95.2431991998-07-28T05:00:00...1757.0HWM U/SNaN1.03337.0NaNNaNNaNNaNPOINT (-95.24320 41.02629)
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5 rows × 33 columns

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" ], "text/plain": [ " hwm_id waterbody site_id event_id hwm_type_id \\\n", "0 13922 Swatara Creek 16106 123 1 \n", "1 17897 Atlantic Ocean 19148 24 1 \n", "2 19685 East Nishnabotna River 20005 168 5 \n", "3 18530 Maquoketa River 19436 151 6 \n", "4 19687 East Nishnabotna River 20005 168 2 \n", "\n", " hwm_quality_id hwm_locationdescription \\\n", "0 1 HWM is located on inside of pavillion two \n", "1 3 Mud line on bench rocks and plants near IBA Cl... \n", "2 6 U.S. Highway 34, seed line on flood wall (1 of... \n", "3 1 County Road X29/220th Avenue, southwest of Del... \n", "4 6 U.S. Highway 34, debris line on guardrail (2 o... \n", "\n", " latitude_dd longitude_dd survey_date ... survey_member_id \\\n", "0 40.192896 -76.723080 2012-04-26T04:00:00 ... 202.0 \n", "1 41.894148 -70.536629 2017-06-05T04:00:00 ... 2.0 \n", "2 41.026290 -95.243199 1998-07-28T05:00:00 ... 1757.0 \n", "3 42.410092 -91.363481 2010-07-30T05:00:00 ... 1755.0 \n", "4 41.026290 -95.243199 1998-07-28T05:00:00 ... 1757.0 \n", "\n", " hwm_label files stillwater peak_summary_id last_updated \\\n", "0 no_label NaN NaN NaN NaN \n", "1 HWMMAPLY-402 NaN 0.0 NaN NaN \n", "2 HWM U/S NaN 1.0 3337.0 NaN \n", "3 USGS HWM D/S NaN 1.0 2710.0 NaN \n", "4 HWM U/S NaN 1.0 3337.0 NaN \n", "\n", " last_updated_by uncertainty hwm_uncertainty geometry \n", "0 NaN NaN NaN POINT (-76.72308 40.19290) \n", "1 NaN NaN NaN POINT (-70.53663 41.89415) \n", "2 NaN NaN NaN POINT (-95.24320 41.02629) \n", "3 NaN NaN NaN POINT (-91.36348 42.41009) \n", "4 NaN NaN NaN POINT (-95.24320 41.02629) \n", "\n", "[5 rows x 33 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hwm_all = STNFloodEventData.get_all_data(\n", " \"hwms\", as_list=False, async_retriever_kwargs={\"disable\": True, \"max_workers\": 6}\n", ")\n", "hwm_all.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "hwm_all = gh.stn_flood_event(\"hwms\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "There are 34722 HWMs in the database.\n" ] } ], "source": [ "print(f\"There are {len(hwm_all)} HWMs in the database.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For an interactive map, we can use the explore method. There are at least 34,000 HWMs in the STN database scattered throughout the country. It's important to note the possibility of outliers as this data is collected by people and liable to errors. Here, we plot a sample of 1000 HWMs." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hwm_all.sample(1000).explore(\n", " marker_kwds={\"radius\": 2},\n", " style_kwds={\"stroke\": False},\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we illustrate how a filtered query can be completed with the same HWM data. First we want to present what parameters are available to query. We can use the `STNFloodEventData.hwms_query_params` attribute for that." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'County',\n", " 'EndDate',\n", " 'Event',\n", " 'EventStatus',\n", " 'EventType',\n", " 'StartDate',\n", " 'States'}" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "STNFloodEventData.hwms_query_params" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can a subset of HWMS data either using `STNFloodEventData.get_filtered_data(\"hwms\", query_params=query_params)` or `gh.stn_flood_event(\"hwms\", query_params=query_params)`." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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latitudelongitudeeventNamehwmTypeNamehwmQualityNameverticalDatumNameverticalMethodNameapprovalMembermarkerNamehorizontalMethodName...hdatum_idflag_member_idsurvey_member_iduncertaintyhwm_labelfilesheight_above_gndhwm_uncertaintypeak_summary_idgeometry
033.681111-78.8916672017 IrmaSeed lineGood: +/- 0.10 ftNAVD88Tape measureMatt PetkewichMarkerRT-GNSS...21381.01381.00.02HWMNaNNaNNaNNaNPOINT (1566648.913 -286040.618)
133.681111-78.8916672020 IsaiasMudGood: +/- 0.10 ftNAVD88RT-GNSSAndy CaldwellMarkerPhone/Car GPS...21381.01381.00.02HWM1NaN3.00.1NaNPOINT (1566648.913 -286040.618)
233.681111-78.8916672020 IsaiasMudGood: +/- 0.10 ftNAVD88RT-GNSSAndy CaldwellMarkerPhone/Car GPS...21381.01381.00.02HWM2NaN3.00.1NaNPOINT (1566648.913 -286040.618)
335.281051-76.6625852011 IreneSeed lineExcellent: +/- 0.05 ftNAVD88Map (digital or paper)...236.0NaNNaNno_labelNaNNaNNaNNaNPOINT (1731958.461 -71714.640)
434.311221-77.7330772018 FlorenceDebrisPoor: +/- 0.40 ftNAVD88RT-GNSSSteve HardenStakeMap (digital or paper)...2761.01919.00.06HWM-01NaN0.0NaNNaNPOINT (1658023.273 -197179.233)
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5 rows × 54 columns

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" ], "text/plain": [ " latitude longitude eventName hwmTypeName hwmQualityName \\\n", "0 33.681111 -78.891667 2017 Irma Seed line Good: +/- 0.10 ft \n", "1 33.681111 -78.891667 2020 Isaias Mud Good: +/- 0.10 ft \n", "2 33.681111 -78.891667 2020 Isaias Mud Good: +/- 0.10 ft \n", "3 35.281051 -76.662585 2011 Irene Seed line Excellent: +/- 0.05 ft \n", "4 34.311221 -77.733077 2018 Florence Debris Poor: +/- 0.40 ft \n", "\n", " verticalDatumName verticalMethodName approvalMember markerName \\\n", "0 NAVD88 Tape measure Matt Petkewich Marker \n", "1 NAVD88 RT-GNSS Andy Caldwell Marker \n", "2 NAVD88 RT-GNSS Andy Caldwell Marker \n", "3 NAVD88 \n", "4 NAVD88 RT-GNSS Steve Harden Stake \n", "\n", " horizontalMethodName ... hdatum_id flag_member_id survey_member_id \\\n", "0 RT-GNSS ... 2 1381.0 1381.0 \n", "1 Phone/Car GPS ... 2 1381.0 1381.0 \n", "2 Phone/Car GPS ... 2 1381.0 1381.0 \n", "3 Map (digital or paper) ... 2 36.0 NaN \n", "4 Map (digital or paper) ... 2 761.0 1919.0 \n", "\n", " uncertainty hwm_label files height_above_gnd hwm_uncertainty \\\n", "0 0.02 HWM NaN NaN NaN \n", "1 0.02 HWM1 NaN 3.0 0.1 \n", "2 0.02 HWM2 NaN 3.0 0.1 \n", "3 NaN no_label NaN NaN NaN \n", "4 0.06 HWM-01 NaN 0.0 NaN \n", "\n", " peak_summary_id geometry \n", "0 NaN POINT (1566648.913 -286040.618) \n", "1 NaN POINT (1566648.913 -286040.618) \n", "2 NaN POINT (1566648.913 -286040.618) \n", "3 NaN POINT (1731958.461 -71714.640) \n", "4 NaN POINT (1658023.273 -197179.233) \n", "\n", "[5 rows x 54 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hwm_filtered = STNFloodEventData.get_filtered_data(\n", " \"hwms\",\n", " crs=\"ESRI:102003\",\n", " async_retriever_kwargs={\"disable\": True},\n", " query_params={\"States\": \"SC,NC\"},\n", ")\n", "hwm_filtered.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first step involves retrieving the data dictionary for HWMs. We can use the `as_dict` argument to return the data as a dictionary but will prefer the default GeoDataFrame for this example. We can also pass keyword arguments to the async retriever as shown here where the caching is disabled." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
FieldDefinition
0latitude_ddHorizontal location of HWM in decimal degrees ...
1latituderepeat of latitude_dd
2longituderepeat of longitude_dd
3eventNameEvent name
4hwmTypeNameType of HWM evience. See hwm_type_id for curr...
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" ], "text/plain": [ " Field Definition\n", "0 latitude_dd Horizontal location of HWM in decimal degrees ...\n", "1 latitude repeat of latitude_dd\n", "2 longitude repeat of longitude_dd\n", "3 eventName Event name\n", "4 hwmTypeName Type of HWM evience. See hwm_type_id for curr..." ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hwm_dd = STNFloodEventData.data_dictionary(\n", " \"hwms\", as_dict=False, async_retriever_kwargs={\"disable\": True}\n", ")\n", "hwm_dd.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's important to note that the schemas for the three requests: all data, filtered data, and data dictionaries don't necessarily agree. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Do the columns have the same length?: False\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
All HWM FieldsFiltered HWM FieldsHWM Data Dictionary Fields
0hwm_idlatitudelatitude_dd
1waterbodylongitudelatitude
2site_ideventNamelongitude
3event_idhwmTypeNameeventName
4hwm_type_idhwmQualityNamehwmTypeName
\n", "
" ], "text/plain": [ " All HWM Fields Filtered HWM Fields HWM Data Dictionary Fields\n", "0 hwm_id latitude latitude_dd\n", "1 waterbody longitude latitude\n", "2 site_id eventName longitude\n", "3 event_id hwmTypeName eventName\n", "4 hwm_type_id hwmQualityName hwmTypeName" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# compares the columns\n", "print(\n", " f\"Do the columns have the same length?: {set(hwm_all.columns) == set(hwm_filtered.columns) == set(hwm_dd.columns)}\"\n", ")\n", "\n", "# compare columns\n", "pd.concat(\n", " [\n", " pd.Series(hwm_all.columns, name=\"All HWM Fields\"),\n", " pd.Series(hwm_filtered.columns, name=\"Filtered HWM Fields\"),\n", " pd.Series(hwm_dd[\"Field\"], name=\"HWM Data Dictionary Fields\"),\n", " ],\n", " axis=1,\n", ").head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While many of the differences can be inferred, some of the discrepancies could lead to columns with ambiguous information. The USGS is working on an updated RESTFul API that should address this. These differences are available for the other data types, \"instruments\", \"peaks\", and \"sites\", as well." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we will plot some of the HWMs. First we retrieve some state lines and project those as well as the filtered HWMs to the 'EPSG:4329' CRS." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "carolina_lines = gh.get_us_states([\"NC\", \"SC\"]).to_crs(\"EPSG:4329\")\n", "hwm_filtered = hwm_filtered.to_crs(\"EPSG:4329\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(6, 4), dpi=150)\n", "\n", "event_names = hwm_filtered.loc[:, \"eventName\"].unique()\n", "markers = dict(zip(event_names, mmarkers.MarkerStyle.filled_markers[: len(event_names)]))\n", "\n", "ax.set_title(\"HWMs - Height Above Ground (ft)\", fontsize=9)\n", "ax.set_xlabel(\"Longitude (deg)\", fontsize=8)\n", "ax.set_ylabel(\"Latitude (deg)\", fontsize=8)\n", "\n", "ax.tick_params(axis=\"both\", which=\"major\", labelsize=8)\n", "\n", "vmin, vmax = (\n", " hwm_filtered.loc[:, \"height_above_gnd\"].min(),\n", " hwm_filtered.loc[:, \"height_above_gnd\"].max(),\n", ")\n", "\n", "legend = True\n", "for i, (event_name, data) in enumerate(hwm_filtered.groupby(\"eventName\")):\n", " if i == 1:\n", " legend = False\n", "\n", " data.plot(\n", " ax=ax,\n", " column=\"height_above_gnd\",\n", " alpha=0.7,\n", " legend=legend,\n", " markersize=3,\n", " marker=markers[event_name],\n", " vmin=0,\n", " vmax=10,\n", " )\n", "\n", "# Create a list of Line2D objects to use for the legend\n", "legend_elements = [\n", " mlines.Line2D([0], [0], color=\"black\", marker=markers[event_name], linestyle=\"None\")\n", " for event_name in event_names\n", "]\n", "\n", "# Add the legend to the plot\n", "ax.legend(\n", " legend_elements,\n", " event_names,\n", " loc=\"lower right\",\n", " title=\"Event Names\",\n", " bbox_to_anchor=(1, 0),\n", " prop={\"size\": 5},\n", ")\n", "\n", "colorbar = plt.gcf().get_axes()[-1]\n", "colorbar.tick_params(labelsize=8)\n", "\n", "carolina_lines.plot(ax=ax, facecolor=\"none\", edgecolor=\"black\", linewidth=0.2)\n", "fig.savefig(Path(\"_static\", \"hmws.png\"), bbox_inches=\"tight\", dpi=150)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data Quality Issues\n", "\n", "Inspecting the figure above reveals a HWM in the Atlantic Ocean. Trying to pick that one out, we get the following information about the outlier. We only display a few of the fields that may contain the problem." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "siteDescription US Route 17 crossing of the Pee Dee and Waccam...\n", "waterbody Pee Dee / Waccamaw River\n", "stateName SC\n", "countyName Georgetown County\n", "latitude_dd 30.36694\n", "longitude_dd -79.26778\n", "site_latitude 33.365582\n", "site_longitude -79.25334\n", "height_above_gnd 2.68\n", "Name: 1127, dtype: object\n" ] } ], "source": [ "outlier = hwm_filtered.loc[hwm_filtered.latitude < 31, :].squeeze()\n", "print(\n", " outlier.loc[\n", " [\n", " \"siteDescription\",\n", " \"waterbody\",\n", " \"stateName\",\n", " \"countyName\",\n", " \"latitude_dd\",\n", " \"longitude_dd\",\n", " \"site_latitude\",\n", " \"site_longitude\",\n", " \"height_above_gnd\",\n", " ]\n", " ]\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Inspecting the fields above reveals that this potential outlier should be in Georgetown County which is on the coast of South Carolina just south of Myrtle Beach. Additionally, the fields show two different entries for latitude and longitudes. We look at the definitions for latitudes below." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "'site_latitude' : Horizontal location of Site of common water surface (not location of HWM)\n", "'latitude_dd' : Horizontal location of HWM in decimal degrees (not location of associated site)\n" ] } ], "source": [ "print(\n", " f\"'site_latitude' : {hwm_dd.loc[hwm_dd.loc[:,'Field'] == 'site_latitude','Definition'].iloc[0]}\"\n", ")\n", "print(f\"'latitude_dd' : {hwm_dd.loc[hwm_dd.loc[:,'Field'] == 'latitude_dd','Definition'].iloc[0]}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From this, we can say that the 'site_latitude' field reveals horizontal locations of the common water surface while the 'latitude_dd' field refers to that of the HWM. This distinction indicates why these two fields are expected to differ. Nevertheless, the location of the HWM all but impossibly collected so far from the Pee Dee and Waccamaw Rivers. It's likely that this was a typo. It's important to note that this service is fed by data by real people who are liable to make simple mistakes. It's advised to take a look at your data and inspect for any inconsistencies prior to using for analysis." ] } ], "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": "3.11.5" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }