Run SUMMA models with CAMELS dataset on CyberGIS-Jupyter for Water (CJW)

Introduction

CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) is a large-sample hydrometeorological dataset that provides catchment attributes, forcings and GIS data for 671 small- to medium-sized basins across the CONUS (continental United States). HydroShare hosts a copy of CAMELS and exposes it through different public data access protocols (WMS, WFS and OPeNDAP) for easy visualization, retrieval and subsetting of the dataset in community modeling research. This notebook demostrates how to set up SUMMA models with CAMELS dataset from HydroShare using various tools integrated in the CyberGIS-Jupyter for Water (CJW) environment and execution of ensemble model runs on a High Performance Computing (HPC) resource through CyberGIS-Compute Service.

CAMELS dataset hosted on HydroShare

The CAMELS dataset is currently stored in two HydroShare resources.

  1. Resource "NLDAS Forcing NetCDF..." (Click Here) contains a shapefile "HCDN_nhru_final_671.shp" of the 671 basin boudary and a NetCDF file "nldasForcing1980to2018.nc" of NLDAS forcings from 1980 to 2018.

CAMELS NLDAS resource

  1. Resource "CAMELS Basin Attributes" (Click Here) contains basin attributes in 2 NetCDF files: "attributes.camels.v2.nc" and "trialParams.camels.Oct2020_new.nc".

In HydroShare, shapefiles are represented as GeographicFeatureConetentType and exposed through OGC WMS and WFS services using GeoServer; NetCDF files are represented as MultideimentialContentType and exposed through OPeNDAP protocol using Hyrax Data Server.

To programmatically access or subset the data in Jupyter Notebook environment, the use of one or more client tools that are compatiable to the above protocols are often required, which will be introduced in later sections. For now, we just take advantage of the simple web interfaces built into GeoServer and Hyrax Data Server for quick data preview.

  • Preview of CAMELS Basin shapefile "HCDN_nhru_final_671.shp" in GeoServer: Click Here

CAMELS Basins

  • Preview of CAMELS NLDAS Forcings NetCDF "nldasForcing1980to2018.nc" in Hyrax: Click Here

NLDAS Forcings

Set up SUMMA models with CAMELS dataset

In this section, we will set up SUMMA model for a user-picked CAMELS basin. Several steps are required:

  • Select a CAMELS basin (hru_id) and simulation period (start_datetime and end_datetime);

  • Subset NLDAS hourly forcing data;

  • Subset basin attribute and parameter files;

  • Create initial conditions;

  • Run the single model on Jupyter server;

  • Build ensemble model and run on HPC through CyberGIS Computing Service;

Pick a CAMELS Basin and Simulation Period

You may put the "hru_id" of your interested CAMELS basin below if you already know it. Otherwise we will interactively select a basin on the map. For simulation period, the start_datetime and end_datetime should be in "YYYY-MM-DD HH:MM" format and within "1980-01-01 00:00" to "2018-12-31 23:00" (temporal coverage of NLDAS forcings).

In [1]:
# id of CAMELS basin
hru_id = 13313000
# simulation period (YYYY-MM-DD) within "1980-01-01 00:00" to "2018-12-31 23:00" (39-year)
start_datetime = "1991-01-01 00:00"
end_datetime =   "2000-12-31 23:00"

Select a CAMELS basin on interactive map (optional)

Here we provide an interactive map for you to view the 671 CAMELS basins. By taking advantage of the OGC WFS service that HydroShare has set up on top of the basin shapefile "HCDN_nhru_final_671.shp", we can retrieve the basin geometry in GeoJSON and visualize it using ipyleaflet.

You may hover over a basin to check hru_id shown in the bottom-right corner. You may click on a basin in the map to select it for modelling. A popuop with more info (hru_id, lat, lon, area, elevation and perimeter) about the basin will show up in the upper-left corner. NCAR provides high-resolution basin image and elevation map for some basins located in the western coast. For those basins, the popup shows thumbnails that are clickable and linked to the originals.

Note: Clicking on a basin indicates it is selected for modelling, and this action will overwrite and upate the above "hru_id" variable.

In [2]:
import json
import random
import requests
from ipywidgets import HTML
from ipyleaflet import Map, ZoomControl, GeoJSON, Popup, WidgetControl
from shapely.geometry import shape, GeometryCollection

# A WFS request made to HydroShare-provisioned GeoServer to retrieve basin geometry in geojson format
url = "https://geoserver.hydroshare.org/geoserver/HS-a28685d2dd584fe5885fc368cb76ff2a/wfs?service=wfs&version=2.0.0&request=GetFeature&typeNames=HS-a28685d2dd584fe5885fc368cb76ff2a:HCDN_nhru_final_671&outputFormat=JSON"
r = requests.get(url)
geojson_str = r.content.decode("utf-8")

m = Map(center=(39, -104), zoom=4, zoom_control=False)
m.add_control(ZoomControl(position='topright'))

geo_json = GeoJSON(
    data=json.loads(geojson_str),
    style={'opacity': 0, 'fillOpacity': 0.6, 'weight': 1},
    hover_style={'color': 'cyan', 'opacity': 1, 'weight': 3, 'fillOpacity': 0}, 
    style_callback=lambda feature:  {'color': 'white',
                                     'fillColor': random.choice(['red', 'yellow', 'green', 'orange']),}
)

geo_json_clicked = None

def geojson_onclick_handler(event=None, id=None, properties=None, feature=None):
    global geo_json_clicked
    global hru_id
    if geo_json_clicked is not None:
        geo_json_clicked.on_hover(basin_hover_html, remove=True)
        m.remove_layer(geo_json_clicked)
    _hru_id = feature["properties"]["hru_id"]
    geom = GeometryCollection([shape(feature["geometry"])])
    bounds = geom.bounds
    swne = [(bounds[1], bounds[0]), (bounds[3], bounds[2])]
    m.fit_bounds(swne)
    geo_json_clicked = GeoJSON(
    data=feature,
    style={'color': 'black', 'opacity': 1, 'fillColor': 'cyan', 'fillOpacity': 1},
    )
    m.add_layer(geo_json_clicked)
    geo_json_clicked.on_hover(basin_hover_html)
    basin_click_html(feature)
    # update global hru_id
    hru_id = _hru_id
    
geo_json.on_click(geojson_onclick_handler)
m.add_layer(geo_json)
html_click = HTML('''Click on a basin to start modeling''')
html_click.layout.margin = '0px 20px 20px 20px'
control_click = WidgetControl(widget=html_click, position='topleft')
m.add_control(control_click)

html_hover = HTML('''Hover over to check hru_id''')
html_hover.layout.margin = '0px 20px 0px 20px'
control_hover = WidgetControl(widget=html_hover, position='bottomright')
m.add_control(control_hover)

def get_feature_by(geojson_layer, key, value, first=False):
    out = []
    for fea in geojson_layer.data['features']:
        if fea["properties"][key] == value:           
            if first == True:
                return fea
            out.append(fea)
    return out

def check_url(url):
    # check if a url is reachable
    return requests.head(url).status_code == 200

def img_html(url, alt, link=None):
    img_value = '<img src="{url}"  alt="{alt}" width="100" height="600">'.format(url=url, alt=alt)
    if link is not None:
        img_value = '<a href="{link}" target="_blank"> {img_value} </a>'.format(link=link, img_value=img_value)
    return img_value

def basin_click_html(feature,  **kwargs):
    hru_id = feature['properties']["hru_id"]
    watershed_url = "https://ral.ucar.edu/staff/wood/watersheds/basin_figs/{hru_id}.watershed.png".format(hru_id=hru_id)
    dem_url = "https://ral.ucar.edu/staff/wood/watersheds/dem_figs//{hru_id}.dem.png".format(hru_id=hru_id)
    fields = ["hru_id", "lon_cen", "lat_cen", "AREA", "elev_mean", "Perimeter"]
    table_row_tmpl = '''<tr><th scopt="row">{}</td><td>{}</td></tr>'''
    tbody_value = ''
    for field in fields:
        row = table_row_tmpl.format(field, feature['properties'][field])
        tbody_value = tbody_value + row
    
    html_value = '''<h4><b>Selected Basin</b></h4>
    <table class="table table-striped"><tbody>{tbody_value}</tbody></table>'''.format(tbody_value=tbody_value)
    if check_url(watershed_url):
        html_value = html_value + img_html(watershed_url, "Watershed Boundary", link=watershed_url)
    if check_url(dem_url):
        html_value = html_value + img_html(dem_url, "DEM", link=dem_url)
    html_click.value = html_value

def basin_hover_html(feature,  **kwargs):
    hru_id = feature['properties']["hru_id"]
    html_value = '''<h5><b>{hru_id}</b></h5>'''.format(hru_id=hru_id)
    html_hover.value = html_value

geo_json.on_hover(basin_hover_html)
m
In [3]:
# if user didn't click on a basin, use default basin and zoom to it on map
if geo_json_clicked is None:
    hru_fea = get_feature_by(geo_json, "hru_id", hru_id, first=True)
    geojson_onclick_handler(feature=hru_fea)
    
# the selected basin   
hru_id_selected = hru_id

# display selected hru_id and simulation period as header as a confirmation
from IPython.display import Markdown as md
md("## Prepare NLDAS forcings for CAMELS Basin '{hru_id}' over {start_datetime} to {end_datetime}".format(hru_id=hru_id_selected, start_datetime=start_datetime, end_datetime=end_datetime))
Out[3]:

Prepare NLDAS forcings for CAMELS Basin '13313000' over 1991-01-01 00:00 to 2000-12-31 23:00

In this section, we will subset NLDAS forcing data to the above user-selected basin over the requested simulation period. The orginal forcing contains data for 671 CAMELS basins from 1980-2018 (39 years) in a single 6GB NetCDF file "nldasForcing1980to2018.nc". HydroShare exposes the NetCDF file through OpenDAP protocol using Hydrax, which enables users to directly retrieve a portion of the data without having to download the whole file to Jupyter environment. The public OpenDAP access url for a specific NetCDF file hosted on HydroShare follows the following pattern:

http://hyrax.hydroshare.org/opendap/hyrax/{RESOURCE_ID}/data/contents/{NETCDF_FILE_NAME}

In the case of NLDAS CAMELS forcing file "nldasForcing1980to2018.nc", the access url is:

http://hyrax.hydroshare.org/opendap/hyrax/a28685d2dd584fe5885fc368cb76ff2a/data/contents/nldasForcing1980to2018.nc

We would need to use a OpenDAP-complaint client tool to open the url (putting it in browser directly only results in a warning message). Here we chose to use XArray for this purpose.

The following cell shows the metadata of the remote NLDAS NetCDF file through OpenDAP. Under the Demensions tab, you can see it has 671 basins and 341880 timesteps (14245 days * 24 timesteps/day).

In [4]:
import os
import sys
import time
import pandas as pd
import numpy as np
import xarray as xr
In [5]:
opendap_access_url = 'http://hyrax.hydroshare.org/opendap/hyrax/a28685d2dd584fe5885fc368cb76ff2a/data/contents/nldasForcing1980to2018.nc'

opendap_access_url = 'https://thredds.hydroshare.org/thredds/dodsC/hydroshare/resources/a28685d2dd584fe5885fc368cb76ff2a/data/contents/nldasForcing1980to2018.nc'
forcing_all = xr.open_dataset(opendap_access_url)
forcing_all
Out[5]:
<xarray.Dataset>
Dimensions:    (hru: 671, time: 341880)
Coordinates:
  * time       (time) datetime64[ns] 1980-01-01 ... 2018-12-31T23:00:00
  * hru        (hru) int32 1013500 1022500 1030500 ... 14362250 14400000
Data variables:
    data_step  timedelta64[ns] 01:00:00
    hruId      (hru) int32 1013500 1022500 1030500 ... 14362250 14400000
    LWRadAtm   (time, hru) float32 ...
    SWRadAtm   (time, hru) float32 ...
    airpres    (time, hru) float32 ...
    airtemp    (time, hru) float32 ...
    pptrate    (time, hru) float32 ...
    spechum    (time, hru) float32 ...
    windspd    (time, hru) float32 ...
Attributes:
    history:                         Wed Nov  4 14:25:03 2020: ncrcat nldasFo...
    NCO:                             netCDF Operators version 4.9.5 (Homepage...
    DODS_EXTRA.Unlimited_Dimension:  time

Here we use hru_id, start_datetime and end_datetime to subset the NLDAS forcing NetCDF. The new Dimensions should show only 1 basin and whatever timesteps that matches the selected simulation period (num of days * 24 timesteps/day). Note that we also tweaked the resulting file a bit (set variable "data_step" and remove attribute "_NCProperties") to make it compatible with SUMMA model.

In [6]:
# subset by basin
the_hru = np.array([hru_id_selected])
forcing = forcing_all.sel(hru=the_hru)
# subset by simulation period
forcing = forcing.loc[dict(time=slice(start_datetime, end_datetime))]
# tweak the resulting netcdf for summa model
forcing['data_step'] = 3600
#del forcing.attrs['_NCProperties']
forcing
Out[6]:
<xarray.Dataset>
Dimensions:    (hru: 1, time: 87672)
Coordinates:
  * time       (time) datetime64[ns] 1991-01-01 ... 2000-12-31T23:00:00
  * hru        (hru) int32 13313000
Data variables:
    data_step  int64 3600
    hruId      (hru) int32 13313000
    LWRadAtm   (time, hru) float32 267.6 267.6 267.6 266.9 ... 243.6 243.5 243.5
    SWRadAtm   (time, hru) float32 5.494 0.0 0.0 0.0 ... 334.9 280.0 218.4 126.5
    airpres    (time, hru) float32 7.794e+04 7.795e+04 ... 7.796e+04 7.802e+04
    airtemp    (time, hru) float32 264.4 264.7 264.9 265.2 ... 271.6 271.0 270.4
    pptrate    (time, hru) float32 0.0 0.0 1.666e-07 1.666e-07 ... 0.0 0.0 0.0
    spechum    (time, hru) float32 0.002008 0.002077 ... 0.003593 0.003469
    windspd    (time, hru) float32 7.609 7.75 7.89 8.033 ... 4.797 4.196 3.596
Attributes:
    history:                         Wed Nov  4 14:25:03 2020: ncrcat nldasFo...
    NCO:                             netCDF Operators version 4.9.5 (Homepage...
    DODS_EXTRA.Unlimited_Dimension:  time

Save forcing subset to local SUMMA model folder

In [7]:
top_folder = os.path.join(os.getcwd(), 'summa_camels')
settings_folder = os.path.join(top_folder, 'settings')
output_folder = os.path.join(top_folder, 'output')
In [8]:
%%time
truth = forcing
t0 = truth['time'].values[0] 
tl = truth['time'].values[-1]
t0_s = pd.to_datetime(str(t0))
t0_sf =t0_s.strftime('%Y%m%d')
tl_s = pd.to_datetime(str(tl))
tl_sf =tl_s.strftime('%Y%m%d')
!mkdir -p {top_folder}/data/forcing
ffname ='NLDAS_' + str(hru_id_selected) + "_" + t0_sf +'-' + tl_sf +'.nc'
truth.to_netcdf(top_folder+'/data/forcing/'+ffname)
truth.close()
fflistname = settings_folder+'/forcingFileList.txt' 
file =open(fflistname,"w")
file.write(ffname)
file.close()
CPU times: user 6.27 s, sys: 1.38 s, total: 7.64 s
Wall time: 8.74 s

Plot forcing subset

In [9]:
import matplotlib.pyplot as plt

%matplotlib inline

#Plot hourly
constant_vars=['airpres','airtemp','LWRadAtm','pptrate','spechum','SWRadAtm','windspd']
fig, axes = plt.subplots(nrows=7, ncols=1, figsize=(20, 20))
axes = axes.flatten()
axes[0].set_title('Hourly')
unit_str = ['($ ^o K$)', '($kg/m/s$)', '($W/m^2$)','($w/m^2$)','($g/g$)','($Pa$)', '($m/s$)',]
forcing_plt= forcing
for idx, var in enumerate(constant_vars):
    forcing_plt[var].plot(ax=axes[idx],label='NLDAS')
    axes[idx].set_title('') 
    axes[idx].set_ylabel('{} {}'.format(var, unit_str[idx]))
    axes[idx].set_xlabel('Date')
plt.tight_layout()
plt.legend()
Out[9]:
<matplotlib.legend.Legend at 0x7fcaa8fe4b50>

Prepare Attribute File for selected CAMELS Basin

SUMMA uses a number of files to specify model attributes and parameters. Although SUMMA's distinction between attributes and parameters is somewhat arbitrary, attributes generally describe characteristics of the model domain that are time-invariant during the simulation, such as GRU and HRU identifiers, spatial organization, an topography. The important part for understanding the organization of the SUMMA input files is that the values specified in the local attributes file do not overlap with those in the various parameter files. Thus, these values do not overwrite any attributes specified elsewhere. In contrast, the various parameter file are read in sequence (as explained in the next paragraph) and parameter values that are read in from the input files successively overwrite values that have been specified earlier.

Since the CAMELS basin attribute NetCDF file attributes.camels.v2.nc is pretty small (70KB), a copy is included in the summa model folder. We just need to subset it to our selected basin.

In [10]:
# remove existing attributes.nc if any
! rm -rf {settings_folder}/attributes.nc
In [11]:
# Attributes
the_gru = the_hru
attrib_orig = xr.open_dataset(settings_folder+'/attributes.camels.v2.nc')
attrib = attrib_orig.copy()
attrib_orig.close()
attrib = attrib.assign_coords(hru=attrib['hruId'])
attrib = attrib.assign_coords(gru=attrib['gruId'])
gg = attrib['gruId'] # save because gruId was missing from the parameter file
attrib = attrib.sel(hru=the_hru)
attrib = attrib.sel(gru=the_gru)
attrib = attrib.drop(['hru','gru']) #summa doesn't like these to have coordinates
attrib.to_netcdf(settings_folder+'/attributes.nc')
attrib.close()

Prepare Parameter File for selected CAMELS Basin

The trial parameters file is a NetCDF file that specifies model parameters for GRUs and individual HRUs. This enables the user to overwrite the default and/or Noah-MP parameter values with local-specific ones.

Since the CAMELS parameter NetCDF file trialParams.camels.Oct2020.nc is pretty small (250KB), a copy is included in the summa model folder. We just need to subset it to our selected basin.

In [12]:
# remove existing parameters.nc if any
! rm -rf {settings_folder}/parameters.nc
In [13]:
# Parameters
param_orig = xr.open_dataset(settings_folder+'/trialParams.camels.Oct2020.nc')
param = param_orig.copy()
param_orig.close()
param = param.assign_coords(hru=param['hruId'])
param = param.assign_coords(gru=gg) # there should be a gruId in here, but there wasn't
param = param.sel(hru=the_hru)
param = param.sel(gru=the_gru)
param = param.drop(['hru','gru']) #summa doesn't like these to have coordinates
param.to_netcdf(settings_folder+'/parameters.nc')
param.close()

Create Initial Conditions

In [14]:
!cd {settings_folder}; rm -rf init_cond.nc; {sys.executable} gen_coldstate.py attributes.nc init_cond.nc int
gen_coldstate.py:35: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  data = f.variables[varname][:]
read output outPolyIds ('hruId') from example domain file
writing output file
adding data
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Run SUMMA model locally

In [15]:
! cd {top_folder}; chmod +x installTestCases_local.sh; ./installTestCases_local.sh
In [16]:
# get full path to summa executable
executable =  os.popen('which summa.exe').read().split("\n")[0]
file_manager = top_folder+'/settings/file_manager.txt'
!mkdir -p {output_folder}
In [17]:
import pysumma as ps
camels_summa = ps.Simulation(executable, file_manager)
camels_summa.manager['simStartTime'] = start_datetime
camels_summa.manager['simEndTime'] = end_datetime
camels_summa.manager.write()
print(camels_summa.manager)
controlVersion                       'SUMMA_FILE_MANAGER_V3.0.0'
simStartTime                         '1991-01-01 00:00'
simEndTime                           '2000-12-31 23:00'
tmZoneInfo                           'utcTime'
settingsPath                         '/home/jovyan/work/Downloads/17bc4f0031554944b8ec7558fd9ee3c2/17bc4f0031554944b8ec7558fd9ee3c2/data/contents/summa_camels/settings/'
forcingPath                          '/home/jovyan/work/Downloads/17bc4f0031554944b8ec7558fd9ee3c2/17bc4f0031554944b8ec7558fd9ee3c2/data/contents/summa_camels/data/forcing/'
outputPath                           '/home/jovyan/work/Downloads/17bc4f0031554944b8ec7558fd9ee3c2/17bc4f0031554944b8ec7558fd9ee3c2/data/contents/summa_camels/output/'
decisionsFile                        'modelDecisions.txt'
outputControlFile                    'output_control.txt'
globalHruParamFile                   'localParamInfo.txt'
globalGruParamFile                   'basinParamInfo.txt'
attributeFile                        'attributes.nc'
trialParamFile                       'parameters.nc'
forcingListFile                      'forcingFileList.txt'
initConditionFile                    'init_cond.nc'
outFilePrefix                        'camels'
vegTableFile                         'VEGPARM.TBL'
soilTableFile                        'SOILPARM.TBL'
generalTableFile                     'GENPARM.TBL'
noahmpTableFile                      'MPTABLE.TBL'
In [18]:
# remove old output files if any
! rm -rf {output_folder}/*.*
In [19]:
%%time
camels_summa.run('local')
CPU times: user 420 ms, sys: 123 ms, total: 544 ms
Wall time: 1min 12s
In [20]:
print(camels_summa.stdout)
file_suffix is 'pysumma_run'.
file_master is '/home/jovyan/work/Downloads/17bc4f0031554944b8ec7558fd9ee3c2/17bc4f0031554944b8ec7558fd9ee3c2/data/contents/summa_camels/settings/.pysumma/pysumma_run/file_manager.txt'.
   1 controlVersion: SUMMA_FILE_MANAGER_V3.0.0
   2 simStartTime: 1991-01-01 00:00
   3 simEndTime: 2000-12-31 23:00
   4 tmZoneInfo: utcTime
   5 settingsPath: /home/jovyan/work/Downloads/17bc4f0031554944b8ec7558fd9ee3c2/17bc4f0031554944b8ec7558fd9ee3c2/data/contents/summa_camels/settings/.pysumma/pysumma_run/
   6 forcingPath: /home/jovyan/work/Downloads/17bc4f0031554944b8ec7558fd9ee3c2/17bc4f0031554944b8ec7558fd9ee3c2/data/contents/summa_camels/data/forcing/
   7 outputPath: /home/jovyan/work/Downloads/17bc4f0031554944b8ec7558fd9ee3c2/17bc4f0031554944b8ec7558fd9ee3c2/data/contents/summa_camels/output/
   8 decisionsFile: modelDecisions.txt
   9 outputControlFile: output_control.txt
  10 globalHruParamFile: localParamInfo.txt
  11 globalGruParamFile: basinParamInfo.txt
  12 attributeFile: attributes.nc
  13 trialParamFile: parameters.nc
  14 forcingListFile: forcingFileList.txt
  15 initConditionFile: init_cond.nc
  16 outFilePrefix: camels
  17 vegTableFile: VEGPARM.TBL
  18 soilTableFile: SOILPARM.TBL
  19 generalTableFile: GENPARM.TBL
  20 noahmpTableFile: MPTABLE.TBL
decisions file =  /home/jovyan/work/Downloads/17bc4f0031554944b8ec7558fd9ee3c2/17bc4f0031554944b8ec7558fd9ee3c2/data/contents/summa_camels/settings/.pysumma/pysumma_run/modelDecisions.txt
   1 soilCatTbl: STAS
   2 vegeParTbl: MODIFIED_IGBP_MODIS_NOAH
   3 soilStress: NoahType
   4 stomResist: BallBerry
   5 num_method: itertive
   6 fDerivMeth: analytic
   7 LAI_method: specified
   8 f_Richards: mixdform
   9 groundwatr: bigBuckt
  10 hc_profile: constant
  11 bcUpprTdyn: nrg_flux
  12 bcLowrTdyn: zeroFlux
  13 bcUpprSoiH: liq_flux
  14 bcLowrSoiH: drainage
  15 veg_traits: Raupach_BLM1994
  16 canopyEmis: difTrans
  17 snowIncept: lightSnow
  18 windPrfile: logBelowCanopy
  19 astability: louisinv
  20 canopySrad: BeersLaw
  21 alb_method: conDecay
  22 compaction: anderson
  23 snowLayers: CLM_2010
  24 thCondSnow: jrdn1991
  25 thCondSoil: funcSoilWet
  26 spatial_gw: localColumn
  27 subRouting: timeDlay
startTime: iyyy, im, id, ih, imin = 1991  1  1  0  0
finshTime: iyyy, im, id, ih, imin = 2000 12 31 23  0
number of time steps =       87672
Skipping over LUTYPE = USGS
 WARNING: routingRunoffFuture is not in the initial conditions file ... using zeros
 Created output file: /home/jovyan/work/Downloads/17bc4f0031554944b8ec7558fd9ee3c2/17bc4f0031554944b8ec7558fd9ee3c2/data/contents/summa_camels/output/camels_pysumma_run_day.nc
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initial date/time = 2021-08-30  15:47:22.155
  final date/time = 2021-08-30  15:48:34.286

     elapsed init =   0.1350000     s
    fraction init =   1.8715947E-03

    elapsed setup =   6.4000000E-02 s
   fraction setup =   8.8727454E-04

  elapsed restart =   8.0000000E-03 s
 fraction restart =   1.1090932E-04

     elapsed read =    21.89300     s
    fraction read =   0.3035172    

    elapsed write =    2.769000     s
   fraction write =   3.8388488E-02

  elapsed physics =    46.89600     s
 fraction physics =   0.6501504    

     elapsed time =    72.13100     s
       or              1.202183     m
       or             2.0036389E-02 h
       or             8.3484954E-04 d

   number threads =          1

 FORTRAN STOP: finished simulation successfully.

In [21]:
camels_summa.output
Out[21]:
<xarray.Dataset>
Dimensions:                (gru: 1, hru: 1, time: 3652)
Coordinates:
  * time                   (time) datetime64[ns] 1991-01-02 ... 2000-12-31
  * hru                    (hru) int64 13313000
  * gru                    (gru) int64 13313000
Data variables:
    pptrate                (time, hru) float64 0.0 0.0 0.0 ... 0.0 0.0 1.268e-05
    airtemp                (time, hru) float64 264.2 264.3 265.5 ... 269.9 269.3
    spechum                (time, hru) float64 0.001973 0.00188 ... 0.003438
    windspd                (time, hru) float64 4.727 2.96 2.83 ... 3.323 1.772
    SWRadAtm               (time, hru) float64 7.986 6.299 7.14 ... 2.079 3.473
    LWRadAtm               (time, hru) float64 204.3 177.2 182.3 ... 271.0 228.7
    airpres                (time, hru) float64 7.792e+04 7.816e+04 ... 7.806e+04
    scalarCanopyWat        (time, hru) float64 0.003413 0.003411 ... 0.1014
    scalarSWE              (time, hru) float64 1.578e-06 1.578e-06 ... 76.58
    scalarTotalSoilWat     (time, hru) float64 764.7 763.2 761.6 ... 615.2 614.9
    scalarSenHeatTotal     (time, hru) float64 -70.53 -22.28 ... 6.6 22.01
    scalarLatHeatTotal     (time, hru) float64 -2.793 -2.126 ... -0.771 -0.06717
    scalarSnowSublimation  (time, hru) float64 0.0 0.0 ... -4.171e-08 3.989e-09
    scalarRainPlusMelt     (time, hru) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0
    scalarInfiltration     (time, hru) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0
    scalarSurfaceRunoff    (time, hru) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0
    scalarSoilDrainage     (time, hru) float64 1.695e-08 1.695e-08 ... 4.075e-09
    scalarAquiferBaseflow  (time, hru) float64 1.695e-08 1.695e-08 ... 4.079e-09
    scalarTotalET          (time, hru) float64 -1.117e-06 -8.499e-07 ... 0.0 0.0
    scalarTotalRunoff      (time, hru) float64 1.695e-08 1.695e-08 ... 4.079e-09
    scalarNetRadiation     (time, hru) float64 -76.55 -100.7 ... -20.52 -56.76
    hruId                  (hru) int64 13313000
    gruId                  (gru) int64 13313000
Attributes:
    summaVersion:     v3.0.3
    buildTime:        Thu Aug 19 14:07:32 EDT 2021
    gitBranch:        tags/v3.0.3-0-g4ee457d
    gitHash:          4ee457df3d3c0779696c6388c67962ba76736df9
    soilCatTbl:       STAS
    vegeParTbl:       MODIFIED_IGBP_MODIS_NOAH
    soilStress:       NoahType
    stomResist:       BallBerry
    num_method:       itertive
    fDerivMeth:       analytic
    LAI_method:       specified
    f_Richards:       mixdform
    groundwatr:       bigBuckt
    hc_profile:       constant
    bcUpprTdyn:       nrg_flux
    bcLowrTdyn:       zeroFlux
    bcUpprSoiH:       liq_flux
    bcLowrSoiH:       drainage
    veg_traits:       Raupach_BLM1994
    canopyEmis:       difTrans
    snowIncept:       lightSnow
    windPrfile:       logBelowCanopy
    astability:       louisinv
    canopySrad:       BeersLaw
    alb_method:       conDecay
    snowLayers:       CLM_2010
    compaction:       anderson
    thCondSnow:       jrdn1991
    thCondSoil:       funcSoilWet
    spatial_gw:       localColumn
    subRouting:       timeDlay
    notPopulatedYet:  notPopulatedYet

Plot model outputs

In [22]:
output_variable = ['pptrate', 'airtemp', 'spechum', 'windspd', 'SWRadAtm', 'LWRadAtm', 'airpres', 'scalarCanopyWat', 'scalarSWE',
                   'scalarTotalSoilWat', 'scalarSenHeatTotal', 'scalarLatHeatTotal', 'scalarSnowSublimation', 'scalarRainPlusMelt', 
                   'scalarInfiltration', 'scalarSurfaceRunoff', 'scalarSoilDrainage', 'scalarAquiferBaseflow', 'scalarTotalET', 
                   'scalarTotalRunoff', 'scalarNetRadiation' ]
fig = plt.figure(figsize=(18,18))
for i in range(len(output_variable)):
    fig.add_subplot(7, 3, i+1)
    plt.plot(camels_summa.output['time'], camels_summa.output[output_variable[i]], label=output_variable[i])
    plt.legend(fontsize=11, loc=2)
camels_summa.output.close()

Run Ensemble SUMMA model on HPC through CyberGIS-Compute Service

Build Ensemble

In [23]:
decisions = {
    'stomResist': ['Jarvis', 'BallBerry', 'simpleResistance'],
}
In [24]:
parameters = {
    'aquiferBaseflowExp': [1.0, 5.0, 10.0],
    'qSurfScale': [1.0, 100.0],
}
In [25]:
config = ps.ensemble.total_product(dec_conf=decisions, param_trial_conf=parameters)
print(len(config))
18

Create folders for job submission

In [26]:
import tempfile
import shutil, os
workspace_dir = os.path.join(os.getcwd(), 'workspace')
!mkdir -p {workspace_dir}
unzip_dir = tempfile.mkdtemp(dir=workspace_dir)
model_folder_name = "summa_camels"
model_folder = os.path.join(unzip_dir, model_folder_name)
shutil.make_archive(model_folder_name, 'zip', os.getcwd()+"/summa_camels")
!unzip -o {model_folder_name}.zip -d {model_folder}
shutil.rmtree(os.path.join(model_folder, "output"))
!mkdir -p {os.path.join(model_folder, "output")}
Archive:  summa_camels.zip
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In [27]:
import json
with open(os.path.join(model_folder, 'summa_options.json'), 'w') as outfile:
    json.dump(config, outfile)

# check ensemble parameters    
print("Number of ensemble runs: {}".format(len(config)))
print(json.dumps(config, indent=4, sort_keys=True)[:800])
print("...")
Number of ensemble runs: 18
{
    "++BallBerry++aquiferBaseflowExp=1.0++qSurfScale=1.0++": {
        "attributes": {},
        "decisions": {
            "stomResist": "BallBerry"
        },
        "parameters": {},
        "trial_parameters": {
            "aquiferBaseflowExp": 1.0,
            "qSurfScale": 1.0
        }
    },
    "++BallBerry++aquiferBaseflowExp=1.0++qSurfScale=100.0++": {
        "attributes": {},
        "decisions": {
            "stomResist": "BallBerry"
        },
        "parameters": {},
        "trial_parameters": {
            "aquiferBaseflowExp": 1.0,
            "qSurfScale": 100.0
        }
    },
    "++BallBerry++aquiferBaseflowExp=10.0++qSurfScale=1.0++": {
        "attributes": {},
        "decisions": {
            "stomResist": "BallBerry"
        },
        "parameters": {},

...

Submit ensemble model to HPC for execution through CyberGIS-Compute Service

In [28]:
from job_supervisor_client import *
communitySummaSession = Session('summa', isJupyter=True)
communitySummaJob = communitySummaSession.job() # create new job
communitySummaJob.upload(model_folder)
📃 created session constructor file [job_supervisor_constructor_summa.json]
Out[28]:
{'file': '1630338526geib'}
In [29]:
communitySummaJob.submit(payload={
    "node": 18,
    "machine": "keeling",
    "file_manager_rel_path": "settings/file_manager.txt"
})
✅ job registered with ID: 1630338527zJND
Out[29]:
<job_supervisor_client.Job.Job at 0x7fcaa8546590>

Monitor Job Status

In [30]:
%%time
communitySummaJob.events(liveOutput=True)
📮Job ID: 1630338527zJND
📍Destination: summa

types message time
JOB_QUEUED job [1630338527zJND] is queued, waiting for registration 2021-08-30T15:48:47.062Z
JOB_REGISTERED job [1630338527zJND] is registered with the supervisor, waiting for initialization2021-08-30T15:48:53.805Z
SUMMA_HPC_CONNECTEDconnected to HPC 2021-08-30T15:49:10.498Z
SUMMA_HPC_SUBMITTEDsubmitted SUMMA job to HPC 2021-08-30T15:49:10.498Z
JOB_INITIALIZED initialized SUMMA job in HPC job queue with remote_id 347565 2021-08-30T15:49:10.498Z
JOB_STATUS RUNNING 2021-08-30T15:49:21.529Z
JOB_STATUS RUNNING 2021-08-30T15:49:31.468Z
JOB_STATUS RUNNING 2021-08-30T15:49:40.799Z
JOB_STATUS RUNNING 2021-08-30T15:49:49.441Z
JOB_STATUS RUNNING 2021-08-30T15:49:58.925Z
JOB_STATUS RUNNING 2021-08-30T15:50:13.052Z
JOB_STATUS RUNNING 2021-08-30T15:50:20.636Z
JOB_STATUS RUNNING 2021-08-30T15:50:31.755Z
JOB_STATUS RUNNING 2021-08-30T15:50:39.700Z
JOB_STATUS RUNNING 2021-08-30T15:50:49.393Z
JOB_ENDED SUMMA job with remote_id 347565 completed 2021-08-30T15:51:07.448Z
CPU times: user 2.53 s, sys: 187 ms, total: 2.72 s
Wall time: 2min 21s

Retrieve Ensemble Model Output

In [31]:
%%time
job_dir = os.path.join(model_folder, "{}".format(communitySummaJob.id))
!mkdir -p {job_dir}/output
communitySummaJob.download(job_dir)
file successfully downloaded under: /home/jovyan/work/Downloads/17bc4f0031554944b8ec7558fd9ee3c2/17bc4f0031554944b8ec7558fd9ee3c2/data/contents/workspace/tmpcs8949yt/summa_camels/1630338527zJND/1630338527zJND.zip
CPU times: user 175 ms, sys: 111 ms, total: 286 ms
Wall time: 4.5 s
Out[31]:
'/home/jovyan/work/Downloads/17bc4f0031554944b8ec7558fd9ee3c2/17bc4f0031554944b8ec7558fd9ee3c2/data/contents/workspace/tmpcs8949yt/summa_camels/1630338527zJND/1630338527zJND.zip'
In [32]:
!cd {job_dir} && unzip *.zip -d output

# check output directory
output_path = os.path.join(job_dir, "output")
# check SUMMA output file 
name_list = os.listdir(output_path)
full_list = [os.path.join(output_path,i) for i in name_list if i.endswith(".nc")]
sorted_list = sorted(full_list)
sorted_list = sorted(sorted_list, key=lambda v: v.upper())

for f in sorted_list:
    print(f)
print("Number of NC files: {}".format(len(sorted_list)))
Archive:  1630338527zJND.zip
  inflating: output/camels_++BallBerry++aquiferBaseflowExp=1.0++qSurfScale=1.0++_day.nc  
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/home/jovyan/work/Downloads/17bc4f0031554944b8ec7558fd9ee3c2/17bc4f0031554944b8ec7558fd9ee3c2/data/contents/workspace/tmpcs8949yt/summa_camels/1630338527zJND/output/camels_++BallBerry++aquiferBaseflowExp=1.0++qSurfScale=1.0++_day.nc
/home/jovyan/work/Downloads/17bc4f0031554944b8ec7558fd9ee3c2/17bc4f0031554944b8ec7558fd9ee3c2/data/contents/workspace/tmpcs8949yt/summa_camels/1630338527zJND/output/camels_++BallBerry++aquiferBaseflowExp=1.0++qSurfScale=100.0++_day.nc
/home/jovyan/work/Downloads/17bc4f0031554944b8ec7558fd9ee3c2/17bc4f0031554944b8ec7558fd9ee3c2/data/contents/workspace/tmpcs8949yt/summa_camels/1630338527zJND/output/camels_++BallBerry++aquiferBaseflowExp=10.0++qSurfScale=1.0++_day.nc
/home/jovyan/work/Downloads/17bc4f0031554944b8ec7558fd9ee3c2/17bc4f0031554944b8ec7558fd9ee3c2/data/contents/workspace/tmpcs8949yt/summa_camels/1630338527zJND/output/camels_++BallBerry++aquiferBaseflowExp=10.0++qSurfScale=100.0++_day.nc
/home/jovyan/work/Downloads/17bc4f0031554944b8ec7558fd9ee3c2/17bc4f0031554944b8ec7558fd9ee3c2/data/contents/workspace/tmpcs8949yt/summa_camels/1630338527zJND/output/camels_++BallBerry++aquiferBaseflowExp=5.0++qSurfScale=1.0++_day.nc
/home/jovyan/work/Downloads/17bc4f0031554944b8ec7558fd9ee3c2/17bc4f0031554944b8ec7558fd9ee3c2/data/contents/workspace/tmpcs8949yt/summa_camels/1630338527zJND/output/camels_++BallBerry++aquiferBaseflowExp=5.0++qSurfScale=100.0++_day.nc
/home/jovyan/work/Downloads/17bc4f0031554944b8ec7558fd9ee3c2/17bc4f0031554944b8ec7558fd9ee3c2/data/contents/workspace/tmpcs8949yt/summa_camels/1630338527zJND/output/camels_++Jarvis++aquiferBaseflowExp=1.0++qSurfScale=1.0++_day.nc
/home/jovyan/work/Downloads/17bc4f0031554944b8ec7558fd9ee3c2/17bc4f0031554944b8ec7558fd9ee3c2/data/contents/workspace/tmpcs8949yt/summa_camels/1630338527zJND/output/camels_++Jarvis++aquiferBaseflowExp=1.0++qSurfScale=100.0++_day.nc
/home/jovyan/work/Downloads/17bc4f0031554944b8ec7558fd9ee3c2/17bc4f0031554944b8ec7558fd9ee3c2/data/contents/workspace/tmpcs8949yt/summa_camels/1630338527zJND/output/camels_++Jarvis++aquiferBaseflowExp=10.0++qSurfScale=1.0++_day.nc
/home/jovyan/work/Downloads/17bc4f0031554944b8ec7558fd9ee3c2/17bc4f0031554944b8ec7558fd9ee3c2/data/contents/workspace/tmpcs8949yt/summa_camels/1630338527zJND/output/camels_++Jarvis++aquiferBaseflowExp=10.0++qSurfScale=100.0++_day.nc
/home/jovyan/work/Downloads/17bc4f0031554944b8ec7558fd9ee3c2/17bc4f0031554944b8ec7558fd9ee3c2/data/contents/workspace/tmpcs8949yt/summa_camels/1630338527zJND/output/camels_++Jarvis++aquiferBaseflowExp=5.0++qSurfScale=1.0++_day.nc
/home/jovyan/work/Downloads/17bc4f0031554944b8ec7558fd9ee3c2/17bc4f0031554944b8ec7558fd9ee3c2/data/contents/workspace/tmpcs8949yt/summa_camels/1630338527zJND/output/camels_++Jarvis++aquiferBaseflowExp=5.0++qSurfScale=100.0++_day.nc
/home/jovyan/work/Downloads/17bc4f0031554944b8ec7558fd9ee3c2/17bc4f0031554944b8ec7558fd9ee3c2/data/contents/workspace/tmpcs8949yt/summa_camels/1630338527zJND/output/camels_++simpleResistance++aquiferBaseflowExp=1.0++qSurfScale=1.0++_day.nc
/home/jovyan/work/Downloads/17bc4f0031554944b8ec7558fd9ee3c2/17bc4f0031554944b8ec7558fd9ee3c2/data/contents/workspace/tmpcs8949yt/summa_camels/1630338527zJND/output/camels_++simpleResistance++aquiferBaseflowExp=1.0++qSurfScale=100.0++_day.nc
/home/jovyan/work/Downloads/17bc4f0031554944b8ec7558fd9ee3c2/17bc4f0031554944b8ec7558fd9ee3c2/data/contents/workspace/tmpcs8949yt/summa_camels/1630338527zJND/output/camels_++simpleResistance++aquiferBaseflowExp=10.0++qSurfScale=1.0++_day.nc
/home/jovyan/work/Downloads/17bc4f0031554944b8ec7558fd9ee3c2/17bc4f0031554944b8ec7558fd9ee3c2/data/contents/workspace/tmpcs8949yt/summa_camels/1630338527zJND/output/camels_++simpleResistance++aquiferBaseflowExp=10.0++qSurfScale=100.0++_day.nc
/home/jovyan/work/Downloads/17bc4f0031554944b8ec7558fd9ee3c2/17bc4f0031554944b8ec7558fd9ee3c2/data/contents/workspace/tmpcs8949yt/summa_camels/1630338527zJND/output/camels_++simpleResistance++aquiferBaseflowExp=5.0++qSurfScale=1.0++_day.nc
/home/jovyan/work/Downloads/17bc4f0031554944b8ec7558fd9ee3c2/17bc4f0031554944b8ec7558fd9ee3c2/data/contents/workspace/tmpcs8949yt/summa_camels/1630338527zJND/output/camels_++simpleResistance++aquiferBaseflowExp=5.0++qSurfScale=100.0++_day.nc
Number of NC files: 18

Plot Ensemble Model Output

In [33]:
import matplotlib.pyplot as plt
%matplotlib inline

def plot_ensemble_output_var(nc_list, var_name):
    fig = plt.figure(figsize=(18, 10))
    for i in nc_list:
        ds = xr.open_dataset(i)
        plt.plot(ds.time.values, ds[var_name].values, label=i.split("_")[-2])
        plt.legend()
        ds.close()
In [34]:
plot_ensemble_output_var(sorted_list, "scalarTotalRunoff")