Volume Variation Time Series

Download as ASCII

# Import package
from dahitiapi.DAHITI import DAHITI

# Initialize DAHITI Class
dahiti = DAHITI()

# Select DAHITI target by DAHITI id
dahiti_id = 10146

# Download Volume Variation Time Series in ASCII format
response = dahiti.download_volume_variation_ascii(dahiti_id)

# Download Volume Variation Time Series in ASCII format and write response to file
response = dahiti.download_volume_variation_ascii_to_file(dahiti_id,'/tmp/10146_wsc.txt')

Output of ASCII response:

1989-06-10 00:00:00 0.02103 0.06865
1989-07-28 00:00:00 0.08202 0.02538
...
2020-02-25 00:00:00 0.33687 0.00074
2020-03-06 00:00:00 0.33516 0.00382

Download as JSON

# Import package
from dahitiapi.DAHITI import DAHITI

# Initialize DAHITI Class
dahiti = DAHITI()

# Select DAHITI target by DAHITI id
dahiti_id = 10146

# Download Volume Variation Time Series in JSON format
response = dahiti.download_volume_variation_json(dahiti_id)

# Download Volume Variation Time Series in JSON format and write response to file
response = dahiti.download_volume_variation_json_to_file(dahiti_id,'/tmp/10146_wsc.json')

Output of JSON response:

[
   {'datetime': '1989-06-10 00:00:00', 'wsc': 0.02103, 'wsc_u': 0.06865},
   {'datetime': '1989-07-28 00:00:00', 'wsc': 0.08202, 'wsc_u': 0.02538},
   ...
   {'datetime': '2020-02-25 00:00:00', 'wsc': 0.33687, 'wsc_u': 0.00074},
   {'datetime': '2020-03-06 00:00:00', 'wsc': 0.33516, 'wsc_u': 0.00382}
]

Download as CSV

# Import package
from dahitiapi.DAHITI import DAHITI

# Initialize DAHITI Class
dahiti = DAHITI()

# Select DAHITI target by DAHITI id
dahiti_id = 10146

# Download Volume Variation Time Series in CSV format
response = dahiti.download_volume_variation_csv(dahiti_id)

# Download Volume Variation Time Series in CSV format and write response to file
response = dahiti.download_volume_variation_csv_to_file(dahiti_id,'/tmp/10146_wsc.csv')

Output of CSV response:

datetime;wsc;wsc_u
1989-06-10 00:00:00;0.02103;0.06865
1989-07-28 00:00:00;0.08202;0.02538
...
2020-02-25 00:00:00;0.33687;0.00074
2020-03-06 00:00:00;0.33516;0.00382

Download as NetCDF

# Import package
from dahitiapi.DAHITI import DAHITI

# Initialize DAHITI Class
dahiti = DAHITI()

# Select DAHITI target by DAHITI id
dahiti_id = 10146

# Download Volume Variation Time Series in NetCDF format and write to file
response = dahiti.download_volume_variation_netcdf_to_file(dahiti_id,'/tmp/10146_wsc.nc')

Content of NetCDF file:

netcdf \10146_wsc {
dimensions:
   time = 874 ;
variables:
string datetime(time) ;
   datetime:standard_name = "UTC Date (YYYY-MM-DD HH:MM:SS)" ;
   datetime:valid_min = "1989-06-10 00:00:00" ;
   datetime:valid_max = "2020-03-06 00:00:00" ;
float wsc(time) ;
   wsc:standard_name = "Volume Variation derived from satellite altimetry and optical imagery" ;
   wsc:unit = "km3" ;
   wsc:valid_min = 0.f ;
   wsc:valid_max = 0.70035 ;
float wsc_u(time) ;
   wsc_u:standard_name = "Volume variation uncertainty" ;
   wsc_u:unit = "km3" ;
   wsc_u:valid_min = 0.f ;
   wsc_u:valid_max = 0.78389 ;

// global attributes:
   :dahiti_id = "10146" ;
   :dataset = "volume-variation" ;
   :target_name = "Ray Roberts, Reservoir" ;
   :location = "None" ;
   :country = "United States of America" ;
   :continent = "North America" ;
   :longitude = -97.0557 ;
   :latitude = 33.3615 ;
   :software = "1.0" ;
   :institution = "DGFI-TUM" ;
   :source = "DAHITI" ;
   :url = "https://dahiti.dgfi.tum.de/10146/" ;
   :creation_date = "2025-10-24 10:37:18" ;
data:

datetime = "1989-06-10 00:00:00", "1989-07-28 00:00:00",
   "1989-08-29 00:00:00", "1989-09-30 00:00:00", "1989-12-03 00:00:00",
   ...
   "2020-02-25 00:00:00", "2020-03-06 00:00:00" ;

wsc = 0.02103, 0.08202, 0.0871, 0.0871, 0.10171, 0.24303, 0.46177, 0.27001,
   0.22256, 0.22093, 0.26844, 0.27858, 0.28338, 0.33099, 0.30765, 0.3154,
   ...
   0.33687, 0.33516 ;

wsc_u = 0.06865, 0.02538, 0.02193, 0.01954, 0.01518, 0.02989, 0.096,
   0.05094, 0.11563, 0.02952, 0.0241, 0.03133, 0.04614, 0.05581, 0.0849,
   ...
   0.00074, 0.00382 ;
}