1. HydroShare setup and preparation
2. Read in the json file containing the spatial-temporal computations
3. Visualize the spatial patterns
4. Visualize the paired temporal patterns
To run this notebook, we must import several libaries. These are listed in order of 1) Python standard libraries, 2) hs_utils library provides functions for interacting with HydroShare, including resource querying, dowloading and creation, and 3) the observatory_gridded_hydromet library that is downloaded with this notebook.
In [ ]:
#conda install -c conda-forge basemap-data-hires --yes
In [1]:
# data processing
import os
import pandas as pd, numpy as np, dask, json
import ogh
import geopandas as gpd
# data migration library
from utilities import hydroshare
# plotting and shape libraries
%matplotlib inline
# silencing warning
import warnings
warnings.filterwarnings("ignore")
In [2]:
# initialize ogh_meta
meta_file = dict(ogh.ogh_meta())
sorted(meta_file.keys())
Out[2]:
In [3]:
sorted(meta_file['dailymet_livneh2013'].keys())
Out[3]:
Establish a secure connection with HydroShare by instantiating the hydroshare class that is defined within hs_utils. In addition to connecting with HydroShare, this command also sets and prints environment variables for several parameters that will be useful for saving work back to HydroShare.
In [4]:
notebookdir = os.getcwd()
hs=hydroshare.hydroshare()
homedir = hs.getContentPath(os.environ["HS_RES_ID"])
os.chdir(homedir)
print('Data will be loaded from and save to:'+homedir)
If you are curious about where the data is being downloaded, click on the Jupyter Notebook dashboard icon to return to the File System view. The homedir directory location printed above is where you can find the data and contents you will download to a HydroShare JupyterHub server. At the end of this work session, you can migrate this data to the HydroShare iRods server as a Generic Resource.
This example uses a shapefile with the watershed boundary of the Sauk-Suiattle Basin, which is stored in HydroShare at the following url: https://www.hydroshare.org/resource/c532e0578e974201a0bc40a37ef2d284/.
The data for our processing routines can be retrieved using the getResourceFromHydroShare function by passing in the global identifier from the url above. In the next cell, we download this resource from HydroShare, and identify that the points in this resource are available for downloading gridded hydrometeorology data, based on the point shapefile at https://www.hydroshare.org/resource/ef2d82bf960144b4bfb1bae6242bcc7f/, which is for the extent of North America and includes the average elevation for each 1/16 degree grid cell. The file must include columns with station numbers, latitude, longitude, and elevation. The header of these columns must be FID, LAT, LONG_, and ELEV or RASTERVALU, respectively. The station numbers will be used for the remainder of the code to uniquely reference data from each climate station, as well as to identify minimum, maximum, and average elevation of all of the climate stations. The webserice is currently set to a URL for the smallest geographic overlapping extent - e.g. WRF for Columbia River Basin (to use a limit using data from a FTP service, treatgeoself() would need to be edited in observatory_gridded_hydrometeorology utility).
In [5]:
"""
Sauk
"""
# Watershed extent
hs.getResourceFromHydroShare('c532e0578e974201a0bc40a37ef2d284')
sauk = hs.content['wbdhub12_17110006_WGS84_Basin.shp']
# reproject the shapefile into WGS84
ogh.reprojShapefile(sourcepath=sauk)
In [6]:
# map the mappingfiles from usecase1
mappingfile1 = os.path.join(homedir,'Sauk_mappingfile.csv')
mappingfile2 = os.path.join(homedir,'Elwha_mappingfile.csv')
mappingfile3 = os.path.join(homedir,'RioSalado_mappingfile.csv')
t1 = ogh.mappingfileSummary(listofmappingfiles = [mappingfile1, mappingfile2, mappingfile3],
listofwatershednames = ['Sauk-Suiattle river','Elwha river','Upper Rio Salado'],
meta_file=meta_file)
t1
Out[6]:
This section performs computations and generates plots of the Livneh 2013, Livneh 2016, and WRF 2014 temperature and precipitation data in order to compare them with each other and observations. The generated plots are automatically downloaded and saved as .png files in the "plots" folder of the user's home directory and inline in the notebook.
In [7]:
# readDictOfDF
ltm = ogh.readDictOfDf()
Data frames for each set of data are stored in a dictionary. The inputs to gridclim_dict() include the folder location and name of the hydrometeorology data, the file start and end, the analysis start and end, and the elevation band to be included in the analsyis (max and min elevation).
In [22]:
sorted(ltm.keys())
Out[22]:
In [26]:
# loop through even months in the water year
for ind, eachmonth in enumerate([10, 1, 4, 7]):
monthlabel = pd.datetime.strptime(str(eachmonth), '%m')
ax1 = ogh.renderValuesInPoints(vardf=ltm['month_PRECIP_dailymet_livneh2013'],
vardf_dateindex=eachmonth,
shapefile=sauk,
outfilepath=os.path.join(homedir, 'SaukPrecip{0}.png'.format(monthlabel.strftime('%b'))),
plottitle='Sauk-Suiattle watershed'+'\nPrecipitation in '+ monthlabel.strftime('%B'),
colorbar_label='Average monthly precipitation (meters)',
spatial_resolution=1/16, margin=0.5, epsg=3857,
basemap_image='ESRI_Imagery_World_2D',
cmap='seismic_r')
In [29]:
# visualize the annual precipitation within years of historic lows
ogh.renderValuesInPoints(vardf=ltm['yearsum_PRECIP_dailymet_livneh2013'],
vardf_dateindex=1952,
shapefile=sauk,
outfilepath=os.path.join(homedir, 'SaukPrecip{0}.png'.format(1952)),
plottitle='Sauk-Suiattle watershed'+'\nTotal annual precipitation in '+ str(1952),
colorbar_label='Total annual precipitation (mm)',
spatial_resolution=1/16, margin=0.5, epsg=3857,
basemap_image='ESRI_Imagery_World_2D',
cmap='seismic_r')
# loop through even months in the water year
ogh.renderValuesInPoints(vardf=ltm['yearsum_PRECIP_dailymet_livneh2013'],
vardf_dateindex=1999,
shapefile=sauk,
outfilepath=os.path.join(homedir, 'SaukPrecip{0}.png'.format(1999)),
plottitle='Sauk-Suiattle watershed'+'\nTotal annual precipitation in '+ str(1999),
colorbar_label='Total annual precipitation (mm)',
spatial_resolution=1/16, margin=0.5, epsg=3857,
basemap_image='ESRI_Imagery_World_2D',
cmap='seismic_r')
In [11]:
test = ogh.renderWatershed(shapefile=sauk,
outfilepath=os.path.join(homedir, 'SaukPrecipSVI{0}.png'.format(monthlabel.strftime('%b'))),
epsg=3857, margin=1,
basemap_image='Demographics/USA_Social_Vulnerability_Index')
In [ ]:
ogh.renderValuesInPoints(vardf=ltm['month_PRECIP_dailymet_livneh2013'],
vardf_dateindex=eachmonth,
shapefile=sauk,
outfilepath=os.path.join(homedir, 'SaukPrecipSVI{0}.png'.format(monthlabel.strftime('%b'))),
plottitle='Sauk-Suiattle watershed'+'\nPrecipitation in '+ monthlabel.strftime('%B'),
colorbar_label='Average monthly precipitation (meters)',
spatial_resolution=1/16,
margin=0,
epsg=3857,
basemap_image='Demographics/USA_Social_Vulnerability_Index',
cmap='gray_r')
In [8]:
%%time
for eachmonth in [6,12]:
monthlabel = pd.datetime.strptime(str(eachmonth), '%m')
ogh.renderValuesInPoints(vardf=ltm['month_PRECIP_dailymet_livneh2013'],
vardf_dateindex=eachmonth,
shapefile=sauk,
outfilepath=os.path.join(homedir, 'SaukPrecipSVI{0}.png'.format(monthlabel.strftime('%b'))),
plottitle='Sauk-Suiattle watershed'+'\nPrecipitation in '+ monthlabel.strftime('%B'),
colorbar_label='Average monthly precipitation (meters)',
spatial_resolution=1/16,
margin=0,
epsg=3857,
basemap_image='Demographics/USA_Social_Vulnerability_Index',
cmap='gray_r')
ogh.renderValuesInPoints(vardf=ltm['month_PRECIP_dailymet_livneh2013'],
vardf_dateindex=eachmonth,
shapefile=sauk,
outfilepath=os.path.join(homedir, 'SaukPrecipStMap{0}.png'.format(monthlabel.strftime('%b'))),
plottitle='Sauk-Suiattle watershed'+'\nPrecipitation in '+ monthlabel.strftime('%B'),
colorbar_label='Average monthly precipitation (meters)',
spatial_resolution=1/16,
margin=0.5,
epsg=3857,
basemap_image='ESRI_StreetMap_World_2D',
cmap='gray_r')
ogh.renderValuesInPoints(vardf=ltm['month_PRECIP_dailymet_livneh2013'],
vardf_dateindex=eachmonth,
shapefile=sauk,
outfilepath=os.path.join(homedir, 'SaukPrecipESRIImagery{0}.png'.format(monthlabel.strftime('%b'))),
plottitle='Sauk-Suiattle watershed'+'\nPrecipitation in '+ monthlabel.strftime('%B'),
colorbar_label='Average monthly precipitation (meters)',
spatial_resolution=1/16,
margin=0.5,
epsg=3857,
basemap_image='ESRI_Imagery_World_2D',
cmap='gray_r')
ogh.renderValuesInPoints(vardf=ltm['month_PRECIP_dailymet_livneh2013'],
vardf_dateindex=eachmonth,
shapefile=sauk,
outfilepath=os.path.join(homedir, 'SaukPrecipWhiteHills{0}.png'.format(monthlabel.strftime('%b'))),
plottitle='Sauk-Suiattle watershed'+'\nPrecipitation in '+ monthlabel.strftime('%B'),
colorbar_label='Average monthly precipitation (meters)',
spatial_resolution=1/16,
margin=0.5,
epsg=3857,
basemap_image='Elevation/World_Hillshade',
cmap='seismic_r')
In [33]:
%%time
ltm = ogh.gridclim_dict(mappingfile=mappingfile1,
metadata=meta_file,
dataset='dailywrf_salathe2014',
colvar=None,
file_start_date=dr2['start_date'],
file_end_date=dr2['end_date'],
file_time_step=dr2['temporal_resolution'],
subset_start_date=dr[0],
subset_end_date=dr[1],
df_dict=ltm)
In [34]:
for month in [10, 1, 4, 7]:
monthlabel = pd.datetime.strptime(str(month), '%m')
outfile='SaukSalathePrecip{0}.png'.format(monthlabel.strftime('%b'))
ax1 = ogh.renderValuesInPoints(vardf=ltm['month_PRECIP_dailywrf_salathe2014'],
vardf_dateindex=month,
shapefile=sauk,
basemap_image='ESRI_Imagery_World_2D',
cmap='seismic_r',
plottitle='Sauk-Suiattle watershed'+'\nPrecipitation in '+monthlabel.strftime('%B'),
colorbar_label='Average monthly precipitation (meters)',
outfilepath=os.path.join(homedir, outfile))
In [ ]:
t3 = ltm['month_PRECIP_dailywrf_salathe2014'].copy()
t3
station = ogh.findStationCode(mappingfile=mappingfile1, colvalue=11, colvar='FID')
station[0]
elev = sorted(ogh.mappingfileToDF(mappingfile1, colvar=None)[0]['ELEV'].unique())
colors=pd.qcut(np.array(elev), 5, labels=['red','orange','yellow','green','blue'])
If you want to plot observed snotel point precipitation or temperature with the gridded climate data, set to 'Y' Give name of Snotel file and name to be used in figure legends. File format: Daily SNOTEL Data Report - Historic - By individual SNOTEL site, standard sensors (https://www.wcc.nrcs.usda.gov/snow/snotel-data.html)
Using the hs_utils library, the results of the Geoprocessing steps above can be saved back into HydroShare. First, define all of the required metadata for resource creation, i.e. title, abstract, keywords, content files. In addition, we must define the type of resource that will be created, in this case genericresource.
Note: Make sure you save the notebook at this point, so that all notebook changes will be saved into the new HydroShare resource.
Create list of files to save to HydroShare. Verify location and names.
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ThisNotebook='Observatory_Sauk_TreatGeoSelf.ipynb' #check name for consistency
climate2013_tar = 'livneh2013.tar.gz'
climate2015_tar = 'livneh2015.tar.gz'
wrf_tar = 'salathe2014.tar.gz'
mappingfile = 'Sauk_mappingfile.csv'
files=[ThisNotebook, mappingfile, climate2013_tar, climate2015_tar, wrf_tar]
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# for each file downloaded onto the server folder, move to a new HydroShare Generic Resource
title = 'Results from testing out the TreatGeoSelf utility'
abstract = 'This the output from the TreatGeoSelf utility integration notebook.'
keywords = ['Sauk', 'climate', 'Landlab','hydromet','watershed']
rtype = 'genericresource'
# create the new resource
resource_id = hs.createHydroShareResource(abstract,
title,
keywords=keywords,
resource_type=rtype,
content_files=files,
public=False)