1. HydroShare setup and preparation
2. Re-establish the paths to the mapping file
3. Download climate data
4. Summarize the file availability from each watershed mapping file
5. Save results back into HydroShare
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 [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
import warnings
warnings.filterwarnings("ignore")
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 [2]:
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)
Adding the following system variables:
HS_USR_NAME = jphuong
HS_RES_ID = 87dc5742cf164126a11ff45c3307fd9d
HS_RES_TYPE = compositeresource
JUPYTER_HUB_IP = jupyter.cuahsi.org
These can be accessed using the following command:
os.environ[key]
(e.g.)
os.environ["HS_USR_NAME"] => jphuong
The hs_utils library requires a secure connection to your HydroShare account.
Enter the HydroShare password for user 'jphuong': ········
Successfully established a connection with HydroShare
Data will be loaded from and save to:/home/jovyan/work/notebooks/data/87dc5742cf164126a11ff45c3307fd9d/87dc5742cf164126a11ff45c3307fd9d/data/contents
In [3]:
# initialize ogh_meta
meta_file = dict(ogh.ogh_meta())
sorted(meta_file.keys())
Out[3]:
['dailymet_bclivneh2013',
'dailymet_livneh2013',
'dailymet_livneh2015',
'dailyvic_livneh2013',
'dailyvic_livneh2015',
'dailywrf_bcsalathe2014',
'dailywrf_salathe2014']
In [4]:
# map the mapping files generated for Sauk-Suiattle, Elwha, and Upper Rio Salado 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[4]:
Watershed
Sauk-Suiattle river
Elwha river
Upper Rio Salado
Median elevation in meters [range](Number of gridded cells)
1171[164-2216] (n=99)
1120[36-1642] (n=55)
2308[1962-2669] (n=31)
Each function reads in the mapping file table, generates the destination folder, downloads and unzips the files, then catalogs the downloaded files within the mapping file.
Meteorology - MET; Weather Research and Forecasting (WRF); Variable Infiltration Capacity - VIC
1. getDailyMET_livneh2013
2. getDailyMET_bcLivneh2013
3. getDailyMET_livneh2015
4. getDailyVIC_livneh2013
5. getDailyVIC_livneh2015
6. getDailyWRF_salathe2014
7. getDailyWRF_bcsalathe2014
1. Continental United States (CONUS)
Livneh, B. (2017). Gridded climatology locations (1/16th degree): Continental United States extent, HydroShare, http://www.hydroshare.org/resource/14f0a6619c6b45cc90d1f8cabc4129af
2. North America (NAmer)
Livneh, B. (2017). Gridded climatology locations (1/16th degree): North American extent, HydroShare, http://www.hydroshare.org/resource/ef2d82bf960144b4bfb1bae6242bcc7f
3. Pacific Northwest - Columbia River Basin
Bandaragoda, C. (2017). Sauk Suiattle HydroMeteorology (WRF), HydroShare, http://www.hydroshare.org/resource/0db969e4cfb54cb18b4e1a2014a26c82
1. Livneh B., E.A. Rosenberg, C. Lin, B. Nijssen, V. Mishra, K.M. Andreadis, E.P. Maurer, and D.P. Lettenmaier, 2013: A Long-Term Hydrologically Based Dataset of Land Surface Fluxes and States for the Conterminous United States: Update and Extensions, Journal of Climate, 26, 9384–9392.
2. Livneh B., T.J. Bohn, D.S. Pierce, F. Munoz-Ariola, B. Nijssen, R. Vose, D. Cayan, and L.D. Brekke, 2015: A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and southern Canada 1950-2013, Nature Scientific Data, 5:150042, doi:10.1038/sdata.2015.42.
3. Salathé, EP, AF Hamlet, CF Mass, M Stumbaugh, S-Y Lee, R Steed: 2017. Estimates of 21st Century Flood Risk in the Pacific Northwest Based on Regional Scale Climate Model Simulations. J. Hydrometeorology. DOI: 10.1175/JHM-D-13-0137.1
In [5]:
%%time
ogh.getDailyMET_livneh2013(homedir, mappingfile1)
ogh.getDailyMET_bcLivneh2013(homedir, mappingfile1)
ogh.getDailyMET_livneh2015(homedir, mappingfile1)
ogh.getDailyVIC_livneh2013(homedir, mappingfile1)
ogh.getDailyVIC_livneh2015(homedir, mappingfile1)
ogh.getDailyWRF_salathe2014(homedir, mappingfile1)
ogh.getDailyWRF_bcsalathe2014(homedir, mappingfile1)
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CPU times: user 3.43 s, sys: 994 ms, total: 4.42 s
Wall time: 2min 58s
In [6]:
%%time
ogh.getDailyMET_livneh2013(homedir, mappingfile2)
ogh.getDailyMET_bcLivneh2013(homedir, mappingfile2)
ogh.getDailyMET_livneh2015(homedir, mappingfile2)
ogh.getDailyVIC_livneh2013(homedir, mappingfile2)
ogh.getDailyVIC_livneh2015(homedir, mappingfile2)
ogh.getDailyWRF_salathe2014(homedir, mappingfile2)
ogh.getDailyWRF_bcsalathe2014(homedir, mappingfile2)
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CPU times: user 2.31 s, sys: 804 ms, total: 3.11 s
Wall time: 1min 46s
In [7]:
%%time
ogh.getDailyMET_livneh2013(homedir, mappingfile3)
ogh.getDailyMET_bcLivneh2013(homedir, mappingfile3)
ogh.getDailyMET_livneh2015(homedir, mappingfile3)
ogh.getDailyVIC_livneh2013(homedir, mappingfile3)
ogh.getDailyVIC_livneh2015(homedir, mappingfile3)
ogh.getDailyWRF_salathe2014(homedir, mappingfile3)
ogh.getDailyWRF_bcsalathe2014(homedir, mappingfile3)
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CPU times: user 1.72 s, sys: 712 ms, total: 2.43 s
Wall time: 52.6 s
In [8]:
t1 = ogh.mappingfileSummary(listofmappingfiles = [mappingfile1, mappingfile2, mappingfile3],
listofwatershednames = ['Sauk-Suiattle river','Elwha river','Upper Rio Salado'],
meta_file=meta_file)
t1.to_csv(os.path.join(homedir, 'watershed_table.txt'), sep='\t', header=True, index=True)
t1
Out[8]:
Watershed
Sauk-Suiattle river
Elwha river
Upper Rio Salado
Median elevation in meters [range](Number of gridded cells)
1171[164-2216] (n=99)
1120[36-1642] (n=55)
2308[1962-2669] (n=31)
dailymet_bclivneh2013
1171[164-2216] (n=99)
1120[36-1642] (n=55)
0
dailymet_livneh2013
1171[164-2216] (n=99)
1146[174-1642] (n=52)
2308[1962-2669] (n=31)
dailymet_livneh2015
1171[164-2216] (n=99)
1120[36-1642] (n=55)
2308[1962-2669] (n=31)
dailyvic_livneh2013
1171[164-2216] (n=99)
1146[174-1642] (n=52)
2308[1962-2669] (n=31)
dailyvic_livneh2015
1171[164-2216] (n=99)
1120[36-1642] (n=55)
2308[1962-2669] (n=31)
dailywrf_bcsalathe2014
1171[164-2216] (n=99)
1142[97-1642] (n=53)
0
dailywrf_salathe2014
1171[164-2216] (n=99)
1142[97-1642] (n=53)
0
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.
In [ ]:
%%time
!tar -zcf livneh2013.tar.gz livneh2013
!tar -zcf livneh2015.tar.gz livneh2015
!tar -zcf salathe2014.tar.gz salathe2014
In [ ]:
# the downloaded tar files
climate2013_tar = os.path.join(homedir,'livneh2013.tar.gz')
climate2015_tar = os.path.join(homedir,'livneh2015.tar.gz')
wrf_tar = os.path.join(homedir,'salathe2014.tar.gz')
In [ ]:
# for each file downloaded onto the server folder, move to a new HydroShare Generic Resource
title = 'Downloaded data sets from each study site for each of seven gridded data products'
abstract = 'This resource contains the downloaded data files for each study site and the gridded cell centroids that intersected within the study area. The file availability is described within the watershed_table file, which summarizes each of the three mapping file catalogs.'
keywords = ['Sauk', 'Elwha','Rio Salado','climate','hydromet','watershed']
rtype = 'genericresource'
# files to migrate
files=[mappingfile1, # sauk
mappingfile2, # elwha
mappingfile3, # riosalado
watershed_table, # watershed summary table
climate2013_tar, # Livneh et al. 2013 raw MET, bc MET, and VIC
climate2015_tar, # Livneh et al. 2015 raw MET, and VIC
wrf_tar] # Salathe et al. 2014 raw WRF and bc WRF
# create the new resource
resource_id = hs.createHydroShareResource(title=title,
abstract=abstract,
keywords=keywords,
resource_type=rtype,
content_files=files,
public=False)
Content source: ChristinaB/Observatory
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