Dynamic Query of SNOTEL data¶

SnowEx Hackweek
July 13, 2021

Author: David Shean

Introduction¶

This tutorial will demonstrate a subset of basic concepts of geospatial data processing and analysis using data from the SNOTEL sensor network. We will demonstrate dynamic query of a public API to fetch point location data, review coordinate system transformations with GeoPandas, Geometry objects, and Pandas time series analysis/visualization.

Read a bit about SNOTEL data for the Western U.S.¶

https://www.wcc.nrcs.usda.gov/snow/

This is actually a nice web interface, with some advanced querying and interactive visualization. You can also download formatted ASCII files (csv) for analysis. This is great for one-time projects, but it’s nice to have reproducible code that can be updated as new data appear, without manual steps. That’s what we’re going to do here.

About SNOTEL sites and data:¶

Sample plots for SNOTEL site at Paradise, WA (south side of Mt. Rainier)¶

https://www.nwrfc.noaa.gov/snow/snowplot.cgi?AFSW1
We will reproduce some of these plots/metrics in this tutorial

Interactive dashboard¶

https://climate.washington.edu/climate-data/snowdepth/

Snow today¶

https://nsidc.org/reports/snow-today

CUAHSI WOF server and automated Python data queries¶

We are going to use a server set up by CUAHSI to serve the SNOTEL data, using a standardized database storage format and query structure. You don’t need to worry about this, but can quickly review the following:

#This is the latest CUAHSI API endpoint
wsdlurl = 'https://hydroportal.cuahsi.org/Snotel/cuahsi_1_1.asmx?WSDL'

Acronym soup¶

SNOTEL = Snow Telemetry
CUAHSI = Consortium of Universities for the Advancement of Hydrologic Science, Inc
WOF = WaterOneFlow
WSDL = Web Services Description Language
USDA = United States Department of Agriculture
NRCS = National Resources Conservation Service
AWDB = Air-Water Database

Python options¶

There are a few packages out there that offer convenience functions to query the online SNOTEL databases and unpack the results.

climata (https://pypi.org/project/climata/) - last commit Sept 2017 (not a good sign)
ulmo (https://github.com/ulmo-dev/ulmo) - maintained by Emilio Mayorga over at UW APL)

You can also write your own queries using the Python requests module and some built-in XML parsing libraries.

Hopefully not overwhelming amount of information - let’s just go with ulmo for now. I’ve done most of the work to prepare functions for querying and processing the data. Once you wrap your head around all of the acronyms, it’s pretty simple, basically running a few functions here: https://ulmo.readthedocs.io/en/latest/api.html#module-ulmo.cuahsi.wof

We will use ulmo with daily data for this exercise, but please feel free to experiment with hourly data, other variables or other approaches to fetch SNOTEL data.

import os
from datetime import datetime
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import geopandas as gpd
from shapely.geometry import Point
import contextily as ctx
import ulmo

Part 1: Spatial Query SNOTEL sites¶

Use the ulmo cuahsi interface and the get_sites function to fetch available site metadata from server
This will return a Python dictionary

sites = ulmo.cuahsi.wof.get_sites(wsdlurl)

#Preview first item in dictionary
next(iter(sites.items()))

('SNOTEL:301_CA_SNTL',
 {'code': '301_CA_SNTL',
  'name': 'Adin Mtn',
  'network': 'SNOTEL',
  'location': {'latitude': '41.2358283996582',
   'longitude': '-120.79192352294922'},
  'elevation_m': '1886.7120361328125',
  'site_property': {'county': 'Modoc',
   'state': 'California',
   'site_comments': 'beginDate=10/1/1983 12:00:00 AM|endDate=1/1/2100 12:00:00 AM|HUC=180200021403|HUD=18020002|TimeZone=-8.0|actonId=20H13S|shefId=ADMC1|stationTriplet=301:CA:SNTL|isActive=True',
   'pos_accuracy_m': '0'}})

Store the dictionary as a Pandas DataFrame called `sites_df`¶

See the Pandas from_dict function
Use orient option so the sites comprise the DataFrame index, with columns for ‘name’, ‘elevation_m’, etc
Use the dropna method to remove any empty records

sites_df = pd.DataFrame.from_dict(sites, orient='index').dropna()
sites_df.head()

	code	name	network	location	elevation_m	site_property
SNOTEL:301_CA_SNTL	301_CA_SNTL	Adin Mtn	SNOTEL	{'latitude': '41.2358283996582', 'longitude': ...	1886.7120361328125	{'county': 'Modoc', 'state': 'California', 'si...
SNOTEL:907_UT_SNTL	907_UT_SNTL	Agua Canyon	SNOTEL	{'latitude': '37.522171020507813', 'longitude'...	2712.719970703125	{'county': 'Kane', 'state': 'Utah', 'site_comm...
SNOTEL:916_MT_SNTL	916_MT_SNTL	Albro Lake	SNOTEL	{'latitude': '45.59722900390625', 'longitude':...	2529.840087890625	{'county': 'Madison', 'state': 'Montana', 'sit...
SNOTEL:1267_AK_SNTL	1267_AK_SNTL	Alexander Lake	SNOTEL	{'latitude': '61.749668121337891', 'longitude'...	48.768001556396484	{'county': 'Matanuska-Susitna', 'state': 'Alas...
SNOTEL:908_WA_SNTL	908_WA_SNTL	Alpine Meadows	SNOTEL	{'latitude': '47.779571533203125', 'longitude'...	1066.800048828125	{'county': 'King', 'state': 'Washington', 'sit...

Clean up the DataFrame and prepare Point geometry objects¶

Convert 'location' column (contains dictionary with 'latitude' and 'longitude' values) to Shapely Point objects
Store as a new 'geometry' column (needed by GeoPandas)
Drop the 'location' column, as this is no longer needed
Update the dtype of the 'elevation_m' column to float

sites_df['geometry'] = [Point(float(loc['longitude']), float(loc['latitude'])) for loc in sites_df['location']]

sites_df = sites_df.drop(columns='location')
sites_df = sites_df.astype({"elevation_m":float})

Review output¶

Take a moment to familiarize yourself with the DataFrame structure and different columns.
Note that the index is a set of strings with format ‘SNOTEL:1000_OR_SNTL’
Extract the first record with loc
- Review the 'site_property' dictionary - could parse this and store as separate fields in the DataFrame if desired

sites_df.head()

	code	name	network	elevation_m	site_property	geometry
SNOTEL:301_CA_SNTL	301_CA_SNTL	Adin Mtn	SNOTEL	1886.712036	{'county': 'Modoc', 'state': 'California', 'si...	POINT (-120.7919235229492 41.2358283996582)
SNOTEL:907_UT_SNTL	907_UT_SNTL	Agua Canyon	SNOTEL	2712.719971	{'county': 'Kane', 'state': 'Utah', 'site_comm...	POINT (-112.2711791992188 37.52217102050781)
SNOTEL:916_MT_SNTL	916_MT_SNTL	Albro Lake	SNOTEL	2529.840088	{'county': 'Madison', 'state': 'Montana', 'sit...	POINT (-111.9590225219727 45.59722900390625)
SNOTEL:1267_AK_SNTL	1267_AK_SNTL	Alexander Lake	SNOTEL	48.768002	{'county': 'Matanuska-Susitna', 'state': 'Alas...	POINT (-150.8896636962891 61.74966812133789)
SNOTEL:908_WA_SNTL	908_WA_SNTL	Alpine Meadows	SNOTEL	1066.800049	{'county': 'King', 'state': 'Washington', 'sit...	POINT (-121.6984710693359 47.77957153320312)

sites_df.loc['SNOTEL:301_CA_SNTL']

code                                                   301_CA_SNTL
name                                                      Adin Mtn
network                                                     SNOTEL
elevation_m                                            1886.712036
site_property    {'county': 'Modoc', 'state': 'California', 'si...
geometry               POINT (-120.7919235229492 41.2358283996582)
Name: SNOTEL:301_CA_SNTL, dtype: object

sites_df.loc['SNOTEL:301_CA_SNTL']['site_property']

{'county': 'Modoc',
 'state': 'California',
 'site_comments': 'beginDate=10/1/1983 12:00:00 AM|endDate=1/1/2100 12:00:00 AM|HUC=180200021403|HUD=18020002|TimeZone=-8.0|actonId=20H13S|shefId=ADMC1|stationTriplet=301:CA:SNTL|isActive=True',
 'pos_accuracy_m': '0'}

Convert to a Geopandas GeoDataFrame¶

We already have 'geometry' column, but still need to define the crs of the point coordinates
Note the number of records

sites_gdf_all = gpd.GeoDataFrame(sites_df, crs='EPSG:4326')
sites_gdf_all.head()

	code	name	network	elevation_m	site_property	geometry
SNOTEL:301_CA_SNTL	301_CA_SNTL	Adin Mtn	SNOTEL	1886.712036	{'county': 'Modoc', 'state': 'California', 'si...	POINT (-120.79192 41.23583)
SNOTEL:907_UT_SNTL	907_UT_SNTL	Agua Canyon	SNOTEL	2712.719971	{'county': 'Kane', 'state': 'Utah', 'site_comm...	POINT (-112.27118 37.52217)
SNOTEL:916_MT_SNTL	916_MT_SNTL	Albro Lake	SNOTEL	2529.840088	{'county': 'Madison', 'state': 'Montana', 'sit...	POINT (-111.95902 45.59723)
SNOTEL:1267_AK_SNTL	1267_AK_SNTL	Alexander Lake	SNOTEL	48.768002	{'county': 'Matanuska-Susitna', 'state': 'Alas...	POINT (-150.88966 61.74967)
SNOTEL:908_WA_SNTL	908_WA_SNTL	Alpine Meadows	SNOTEL	1066.800049	{'county': 'King', 'state': 'Washington', 'sit...	POINT (-121.69847 47.77957)

sites_gdf_all.shape

(930, 6)

Create a scatterplot showing elevation values for all sites¶

#geojson of state polygons
states_url = 'http://eric.clst.org/assets/wiki/uploads/Stuff/gz_2010_us_040_00_5m.json'
states_gdf = gpd.read_file(states_url)

f, ax = plt.subplots(figsize=(10,6))
sites_gdf_all.plot(ax=ax, column='elevation_m', markersize=3, cmap='inferno', legend=True, legend_kwds={'label': "Elevation (m)"})
#This prevents matplotlib from updating the axes extent (states polygons cover larger area than SNOTEL points)
ax.autoscale(False)
states_gdf.plot(ax=ax, facecolor='none', edgecolor='k', alpha=0.3);

Exclude the Alaska (AK) points to isolate points over Western U.S.¶

Simple appraoch is to remove points where the site name contains ‘AK’ with attribute filter
Note the number of records

sites_gdf_conus = sites_gdf_all[~(sites_gdf_all.index.str.contains('AK'))]

Alternatively, can use a spatial filter (see GeoPandas cx indexer functionality for a bounding box)

#xmin, xmax, ymin, ymax = [-126, 102, 30, 50]
#sites_gdf_conus = sites_gdf_all.cx[xmin:xmax, ymin:ymax]

sites_gdf_conus.shape

(865, 6)

Update your scatterplot as sanity check¶

Should look something like the Western U.S.

f, ax = plt.subplots(figsize=(10,6))
sites_gdf_conus.plot(ax=ax, column='elevation_m', markersize=3, cmap='inferno', legend=True, legend_kwds={'label': "Elevation (m)"})
ax.autoscale(False)
states_gdf.plot(ax=ax, facecolor='none', edgecolor='k', alpha=0.3);

Export SNOTEL site GeoDataFrame as a geojson¶

Maybe useful for other purposes, and can avoid all of the above processing, just load directly with geopandas read_file

sites_gdf_conus.to_file?

sites_fn = 'snotel_conus_sites.json'
if not os.path.exists(sites_fn):
    sites_gdf_conus.to_file(sites_fn, driver='GeoJSON')

Part 2: Spatial filter points by polygon¶

Load Grand Mesa Polygon¶

gm_poly_fn = 'grand_mesa_poly.geojson'

gm_poly_gdf = gpd.read_file(gm_poly_fn)

gm_poly_gdf.plot()

<AxesSubplot:>

A quick aside on `geometry` objects¶

Vector data contain `geometry` objects¶

Classes (Point, Line, Polygon) with unique attribute and methods
Polygon vs. MultiPolygon
https://automating-gis-processes.github.io/site/notebooks/L1/geometric-objects.html
https://shapely.readthedocs.io/en/stable/manual.html#geometric-objects

Geometry types Image Source: National Ecological Observatory Network (NEON), from https://datacarpentry.org/organization-geospatial

Isolate Polygon geometry within GeoDataFrame¶

type(gm_poly_gdf)

geopandas.geodataframe.GeoDataFrame

gm_poly_gdf

	geometry
0	POLYGON ((-108.31168 39.13758, -108.34116 39.0...

gm_poly_gdf.total_bounds

array([-108.34115668,   38.82320553, -107.72839859,   39.19563035])

gm_poly_gdf.iloc[0] #Now a GeoSeries

geometry    POLYGON ((-108.31168 39.13758, -108.34116 39.0...
Name: 0, dtype: geometry

gm_poly_geom = gm_poly_gdf.iloc[0].geometry
gm_poly_geom

print(gm_poly_geom)

POLYGON ((-108.3116825655377 39.13757646212944, -108.3411566832522 39.03758987613325, -108.2878686387796 38.89051431295789, -108.2077296878005 38.8232055291981, -108.0746016431103 38.8475137825863, -107.9856051049498 38.9439912011017, -107.7283985875575 39.01510930230633, -107.7872414249099 39.19563034965999, -108.0493948009875 39.13950466335424, -108.1728700097086 39.15920066396116, -108.3116825655377 39.13757646212944))

list(gm_poly_geom.exterior.coords)

[(-108.31168256553767, 39.13757646212944),
 (-108.34115668325224, 39.03758987613325),
 (-108.2878686387796, 38.89051431295789),
 (-108.20772968780051, 38.8232055291981),
 (-108.07460164311031, 38.8475137825863),
 (-107.98560510494981, 38.9439912011017),
 (-107.72839858755752, 39.01510930230633),
 (-107.78724142490994, 39.195630349659986),
 (-108.04939480098754, 39.139504663354245),
 (-108.17287000970857, 39.15920066396116),
 (-108.31168256553767, 39.13757646212944)]

gm_poly_geom.bounds

(-108.34115668325224,
 38.8232055291981,
 -107.72839858755752,
 39.195630349659986)

Generate boolean index for points that intersect the polygon¶

This will return a new GeoDataSeries with True/False values for each record

idx = sites_gdf_all.intersects(gm_poly_geom)

idx.head()

SNOTEL:301_CA_SNTL     False
SNOTEL:907_UT_SNTL     False
SNOTEL:916_MT_SNTL     False
SNOTEL:1267_AK_SNTL    False
SNOTEL:908_WA_SNTL     False
dtype: bool

idx.value_counts()

False    928
True       2
dtype: int64

Use fancy indexing to isolate points and return new GeoDataFrame¶

gm_snotel_sites = sites_gdf_all.loc[idx]

gm_snotel_sites

	code	name	network	elevation_m	site_property	geometry
SNOTEL:622_CO_SNTL	622_CO_SNTL	Mesa Lakes	SNOTEL	3048.000000	{'county': 'Mesa', 'state': 'Colorado', 'site_...	POINT (-108.05835 39.05831)
SNOTEL:682_CO_SNTL	682_CO_SNTL	Park Reservoir	SNOTEL	3035.808105	{'county': 'Delta', 'state': 'Colorado', 'site...	POINT (-107.87414 39.04644)

Quick plot¶

f, ax = plt.subplots(figsize=(10,6))
gm_snotel_sites.plot(ax=ax, column='elevation_m', markersize=20, edgecolor='k', cmap='inferno', \
                  legend=True, legend_kwds={'label':'Elevation (m)'})
#ctx.add_basemap(ax=ax, crs=gm_snotel_sites.crs, source=ctx.providers.Stamen.Terrain)
ax.set_title('Grand Mesa SNOTEL Stations');

import hvplot.pandas
from geoviews import tile_sources as gvts

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