1. HydroShare setup and preparation
2. Re-establish the paths to the mapping file
3. Compute daily, monthly, and annual temperature and precipitation statistics
4. Visualize precipitation results relative to the forcing data
5. Visualize the time-series trends
6. 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
# 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.
For visualization purposes, we will also remap the study site shapefile, which is stored in HydroShare at the following url: https://www.hydroshare.org/resource/c532e0578e974201a0bc40a37ef2d284/. Since the shapefile was previously migrated, we can select 'N' for no overwriting.
In the usecase1 notebook, the treatgeoself function identified the gridded cell centroid coordinates that overlap with our study site. These coordinates were documented within the mapping file, which will be remapped here. In the usecase2 notebook, the downloaded files were cataloged within the mapping file, so we will use the mappingfileSummary function to characterize the files available for Sauk-Suiattle for each gridded data product.
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')
t1 = ogh.mappingfileSummary(listofmappingfiles = [mappingfile1],
listofwatershednames = ['Sauk-Suiattle river'],
meta_file=meta_file)
t1
Out[6]:
This section performs computations and generates plots of the Livneh 2013 and Salathe 2014 mean temperature and mean total monthly precipitation in order to compare them with each other. The generated plots are automatically downloaded and saved as .png files within the "homedir" directory.
Let's compare the Livneh 2013 and Salathe 2014 using the period of overlapping history.
In [7]:
# Livneh et al., 2013
dr1 = meta_file['dailymet_livneh2013']
# Salathe et al., 2014
dr2 = meta_file['dailywrf_salathe2014']
# define overlapping time window
dr = ogh.overlappingDates(date_set1=tuple([dr1['start_date'], dr1['end_date']]),
date_set2=tuple([dr2['start_date'], dr2['end_date']]))
dr
Out[7]:
In [8]:
%%time
ltm = ogh.gridclim_dict(mappingfile=mappingfile1,
metadata=meta_file,
dataset='dailymet_livneh2013',
subset_start_date=dr[0],
subset_end_date=dr[1])
In [9]:
# compute sums and mean monthly an yearly sums
ltm = ogh.aggregate_space_time_sum(df_dict=ltm,
suffix='PRECIP_dailymet_livneh2013',
start_date=dr[0],
end_date=dr[1])
In [10]:
%%time
ltm = ogh.gridclim_dict(mappingfile=mappingfile1,
metadata=meta_file,
dataset='dailywrf_salathe2014',
subset_start_date=dr[0],
subset_end_date=dr[1],
df_dict=ltm)
# compute sums and mean monthly an yearly sums
ltm = ogh.aggregate_space_time_sum(df_dict=ltm,
suffix='PRECIP_dailywrf_salathe2014',
start_date=dr[0],
end_date=dr[1])
In [11]:
# print the name of the Livneh et al. 2013 raw MET and Salathe et al. 2014 raw WRF dataframes and values within ltm
sorted(ltm.keys())
Out[11]:
In [11]:
# initialize list of outputs
files=[]
# create the destination path for the dictionary of dataframes
ltm_sauk=os.path.join(homedir, 'ltm_sauk.json')
ogh.saveDictOfDf(dictionaryObject=ltm, outfilepath=ltm_sauk)
files.append(ltm_sauk)
# append the mapping file for Sauk-Suiattle gridded cell centroids
files.append(mappingfile1)
In [12]:
# # two lowest elevation locations
lowE_ref = ogh.findCentroidCode(mappingfile=mappingfile1, colvar='ELEV', colvalue=164)
# one highest elevation location
highE_ref = ogh.findCentroidCode(mappingfile=mappingfile1, colvar='ELEV', colvalue=2216)
# combine references together
reference_lines = highE_ref + lowE_ref
reference_lines
Out[12]:
In [13]:
# consider value range when comparing Livneh to Salathe
vr = ogh.valueRange([ltm['meanbymonthsum_PRECIP_dailymet_livneh2013'],
ltm['meanbymonthsum_PRECIP_dailywrf_salathe2014']])
print(vr.min(), vr.max())
In [19]:
def in_to_mm(inch):
mm = inch*25.4
return(mm)
def F_to_C(F):
C = (32*F - 32) * (5/9)
return(C)
In [20]:
label = 'Darrington ranger station'
PRECIP_obs_in = {1:11.89, 2:8.84, 3:8.25, 4:5.09, 5:3.50, 6:2.84, 7:1.53, 8:1.71, 9:3.64, 10:7.42, 11:11.65, 12:13.12}
TMAX_obs_F = {1:40.7, 2:46.1, 3:52.0, 4:59.5, 5:66.5, 6:70.6, 7:77.3, 8:77.2, 9:71.1, 10:60.5, 11:48.0, 12:42.2}
TMIN_obs_F = {1:27.7,2:29.9,3:32.9,4:36.7,5:42.6,6:47.7,7:50.4,8:50.8,9:45.6,10:39.3,11:33.3,12:30.2}
# convert the units and rearrange to wateryear month order
wateryear=[10,11,12,1,2,3,4,5,6,7,8,9]
PRECIP_obs_mm = pd.DataFrame([in_to_mm(PRECIP_obs_in[ind]) for ind in wateryear]).rename(columns={0:label})
TMAX_obs_C = pd.DataFrame([F_to_C(TMAX_obs_F[ind]) for ind in wateryear]).rename(columns={0:label})
TMIN_obs_C = pd.DataFrame([F_to_C(TMIN_obs_F[ind]) for ind in wateryear]).rename(columns={0:label})
In [21]:
# plot the distribution of total monthly precipitation by month across stations
outfile=os.path.join(homedir, 'meanbymonthsumprecip_liv_bx.png')
files.append(outfile)
ax1 = ogh.renderValueInBoxplot(vardf=ltm['meanbymonthsum_PRECIP_dailymet_livneh2013'],
vmin=vr.min()-1,
vmax=vr.max()+1,
outfilepath=outfile,
plottitle='Sauk-Suiattle average total precipitation by month',
time_steps='month',
wateryear=True,
value_name='Average monthly precipitation (mm)',
cmap='seismic_r',
figsize=(10,8),
reference_lines = reference_lines,
obs_datavector=[PRECIP_obs_mm.iloc[:,0]],
obs_datalabel=['Darrington ranger stn'])
# plot the distribution of total monthly precipitation by month across stations
outfile=os.path.join(homedir, 'meanbymonthsumprecip_wrf_bx.png')
files.append(outfile)
ax1 = ogh.renderValueInBoxplot(vardf=ltm['meanbymonthsum_PRECIP_dailywrf_salathe2014'],
vmin=0,
vmax=vr.max()+1,
outfilepath=outfile,
plottitle='',
time_steps='month',
wateryear=True,
value_name='',
cmap='seismic_r',
figsize=(10,8),
reference_lines = reference_lines,
obs_datavector=[PRECIP_obs_mm.iloc[:,0]],
obs_datalabel=['Darrington ranger stn'],
ref_legend=False)
In [26]:
%%time
# loop through even months in the water year with Livneh et al. (2013)
for ind, eachmonth in enumerate([10, 1, 4]):
monthlabel = pd.datetime.strptime(str(eachmonth), '%m')
outfile=os.path.join(homedir, 'SaukLivPrecip_{0}.png'.format(monthlabel.strftime('%b')))
files.append(outfile)
ogh.renderValuesInPoints(vardf=ltm['meanbymonthsum_PRECIP_dailymet_livneh2013'],
vardf_dateindex=eachmonth,
shapefile=sauk,
outfilepath=outfile,
plottitle='Sauk-Suiattle watershed\nAverage total monthly Precipitation in '+ monthlabel.strftime('%B'),
colorbar_label='Average monthly precipitation (mm)',
spatial_resolution=1/16, margin=0.25, epsg=3857,
basemap_image='ESRI_Imagery_World_2D',
cmap='seismic_r',
figsize=(2,2))
In [27]:
%%time
"""loop through even months in the water year for Salathe et al. (2014)"""
for ind, eachmonth in enumerate([10, 1, 4]):
monthlabel = pd.datetime.strptime(str(eachmonth), '%m')
outfile=os.path.join(homedir, 'SaukWRFPrecip_{0}.png'.format(monthlabel.strftime('%b')))
files.append(outfile)
ogh.renderValuesInPoints(vardf=ltm['meanbymonthsum_PRECIP_dailywrf_salathe2014'],
vardf_dateindex=eachmonth,
shapefile=sauk,
outfilepath=outfile,
plottitle='',
colorbar_label='Average monthly precipitation (mm)',
spatial_resolution=1/16, margin=0.25, epsg=3857,
basemap_image='ESRI_Imagery_World_2D',
cmap='seismic_r',
figsize=(2,2))
In [19]:
%%time
"""Creating a 2x2 comparison figure of daily TMAX and TMIN by Livneh et al. (2013) versus Salathe et al.(2014)"""
vr = ogh.valueRange([ltm['meanbymonth_TMAX_dailymet_livneh2013'], ltm['meanbymonth_TMIN_dailymet_livneh2013'],
ltm['meanbymonth_TMAX_dailywrf_salathe2014'], ltm['meanbymonth_TMIN_dailywrf_salathe2014']])
# Generate top-left figure with Livneh et al. (2013) TMAX (with legend and yaxis label)
eachdf = 'meanbymonth_TMAX_dailymet_livneh2013'
outfile=os.path.join(homedir, '{0}.png'.format(eachdf))
files.append(outfile)
ax1 = ogh.renderValueInBoxplot(vardf=ltm[eachdf],
vmin=vr.min()-1,
vmax=vr.max()+1,
outfilepath=outfile,
plottitle='',
time_steps='month',
wateryear=True,
value_name='Average daily maximum temperature (C)',
cmap='gnuplot2',
figsize=(8,8),
reference_lines=reference_lines,
ref_legend_loc=2,
obs_legend_loc=3,
obs_datavector=[TMAX_obs_C.iloc[:,0]],
obs_datalabel=['Darrington ranger stn'])
# Generate bottom-left figure with Livneh et al. (2013) TMIN (with yaxis label)
eachdf = 'meanbymonth_TMIN_dailymet_livneh2013'
outfile=os.path.join(homedir, '{0}.png'.format(eachdf))
files.append(outfile)
ax1 = ogh.renderValueInBoxplot(vardf=ltm[eachdf],
vmin=vr.min()-1,
vmax=vr.max()+1,
outfilepath=outfile,
plottitle='',
time_steps='month',
wateryear=True,
value_name='Average daily minimum temperature (C)',
cmap='gnuplot2',
figsize=(8,8),
reference_lines=reference_lines,
ref_legend=False,
obs_datavector=[TMIN_obs_C.iloc[:,0]],
obs_datalabel=['Darrington ranger stn'],
obs_legend=False)
# Generate top-and-bottom-right figures with Salathe WRF TMAX and TMIN, respectively (no legend or yaxis labels)
eachdf = 'meanbymonth_TMAX_dailywrf_salathe2014'
outfile=os.path.join(homedir, '{0}.png'.format(eachdf))
files.append(outfile)
ax1 = ogh.renderValueInBoxplot(vardf=ltm[eachdf],
vmin=vr.min()-1,
vmax=vr.max()+1,
outfilepath=outfile,
plottitle='',
time_steps='month',
wateryear=True,
value_name='',
cmap='gnuplot2',
figsize=(8,8),
reference_lines=reference_lines,
ref_legend=False,
obs_datavector=[TMAX_obs_C.iloc[:,0]],
obs_datalabel=['Darrington ranger stn'],
obs_legend=False)
eachdf = 'meanbymonth_TMIN_dailywrf_salathe2014'
outfile=os.path.join(homedir, '{0}.png'.format(eachdf))
files.append(outfile)
ax1 = ogh.renderValueInBoxplot(vardf=ltm[eachdf],
vmin=vr.min()-1,
vmax=vr.max()+1,
outfilepath=outfile,
plottitle='',
time_steps='month',
wateryear=True,
value_name='',
cmap='gnuplot2',
figsize=(8,8),
reference_lines=reference_lines,
ref_legend=False,
obs_datavector=[TMIN_obs_C.iloc[:,0]],
obs_datalabel=['Darrington ranger stn'],
obs_legend=False)
In [20]:
%%time
"""loop through even months in the water year for Livneh et al. (2013) and Salathe et al. (2014)"""
vr = ogh.valueRange([ltm['meanbymonth_TMAX_dailymet_livneh2013'], ltm['meanbymonth_TMIN_dailymet_livneh2013'],
ltm['meanbymonth_TMAX_dailywrf_salathe2014'], ltm['meanbymonth_TMIN_dailywrf_salathe2014']])
for ind, eachmonth in enumerate([11]):
monthlabel = pd.datetime.strptime(str(eachmonth), '%m')
outfile=os.path.join(homedir, 'SaukLIVTMAX_{0}.png'.format(monthlabel.strftime('%b')))
files.append(outfile)
ogh.renderValuesInPoints(vardf=ltm['meanbymonth_TMAX_dailymet_livneh2013'],
vmin=vr.min()-1,
vmax=vr.max()+1,
vardf_dateindex=eachmonth,
shapefile=sauk,
outfilepath=outfile,
plottitle='',
colorbar_label='Air temperature (C)',
spatial_resolution=1/16, margin=1, epsg=3857, gridcell_alpha=0.8,
basemap_image='Canvas/World_Light_Gray_Base',
cmap='gnuplot2',
figsize=(1.5,1.5))
monthlabel = pd.datetime.strptime(str(eachmonth), '%m')
outfile=os.path.join(homedir, 'SaukWRFTMAX_{0}.png'.format(monthlabel.strftime('%b')))
files.append(outfile)
ogh.renderValuesInPoints(vardf=ltm['meanbymonth_TMAX_dailywrf_salathe2014'],
vmin=vr.min()-1,
vmax=vr.max()+1,
vardf_dateindex=eachmonth,
shapefile=sauk,
outfilepath=outfile,
plottitle='',
colorbar_label='Air temperature (C)',
spatial_resolution=1/16, margin=1, epsg=3857, gridcell_alpha=0.8,
basemap_image='Canvas/World_Light_Gray_Base',
cmap='gnuplot2',
figsize=(1.5,1.5))
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 [ ]:
len(files)
In [ ]:
# for each file downloaded onto the server folder, move to a new HydroShare Generic Resource
title = 'Computed spatial-temporal summaries of two gridded data product data sets for Sauk-Suiattle'
abstract = 'This resource contains the computed summaries for the Meteorology data from Livneh et al. 2013 and the WRF data from Salathe et al. 2014.'
keywords = ['Sauk-Suiattle', 'Livneh 2013', 'Salathe 2014','climate','hydromet','watershed', 'visualizations and summaries']
rtype = 'genericresource'
# create the new resource
resource_id = hs.createHydroShareResource(abstract,
title,
keywords=keywords,
resource_type=rtype,
content_files=files,
public=False)