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SnowWaterEquivalent_trainingdata

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The dataset generation failed

Error code:   DatasetGenerationError
Exception:    ArrowTypeError
Message:      ("Expected bytes, got a 'int' object", 'Conversion failed for column station_name with type object')
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1854, in _prepare_split_single
                  for _, table in generator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/csv/csv.py", line 191, in _generate_tables
                  pa_table = pa.Table.from_pandas(df)
                File "pyarrow/table.pxi", line 3874, in pyarrow.lib.Table.from_pandas
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/pandas_compat.py", line 611, in dataframe_to_arrays
                  arrays = [convert_column(c, f)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/pandas_compat.py", line 611, in <listcomp>
                  arrays = [convert_column(c, f)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/pandas_compat.py", line 598, in convert_column
                  raise e
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/pandas_compat.py", line 592, in convert_column
                  result = pa.array(col, type=type_, from_pandas=True, safe=safe)
                File "pyarrow/array.pxi", line 339, in pyarrow.lib.array
                File "pyarrow/array.pxi", line 85, in pyarrow.lib._ndarray_to_array
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowTypeError: ("Expected bytes, got a 'int' object", 'Conversion failed for column station_name with type object')
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1420, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1052, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 924, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1000, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1741, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1897, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

date string	lat float64	lon float64	station_name string	Slope float64	water_year int64	cumulative_SWE float64	cumulative_air_temperature_tmmn float64	cumulative_potential_evapotranspiration float64	cumulative_mean_vapor_pressure_deficit float64	cumulative_relative_humidity_rmax float64	cumulative_relative_humidity_rmin float64	cumulative_precipitation_amount float64	cumulative_air_temperature_tmmx float64	cumulative_wind_speed float64	cumulative_ndsi float64
2018-10-01	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-10-02	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-10-03	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-10-04	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-10-05	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-10-06	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-10-07	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-10-08	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-10-09	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-10-10	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-10-11	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-10-12	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-10-13	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-10-16	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-10-19	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-10-20	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-10-21	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-10-22	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-10-23	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-10-24	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-10-27	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-10-28	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-10-29	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-10-30	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-10-31	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-11-01	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-11-02	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-11-03	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-11-04	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-11-05	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-11-06	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-11-07	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-11-08	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-11-09	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-11-10	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-11-11	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-11-12	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-11-13	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-11-14	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-11-15	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-11-16	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-11-17	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-11-18	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-11-19	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-11-20	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-11-21	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-11-22	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-11-23	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-11-24	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-11-25	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-11-26	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-11-27	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-11-28	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-11-29	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-11-30	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-12-01	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-12-02	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-12-03	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-12-04	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-12-05	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-12-06	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-12-07	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-12-08	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-12-09	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-12-10	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-12-11	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1
2018-12-12	29.13346	-103.51433	USC00411524	89.958916	2,019	-1	-1	-1	-1	-1	-1	-1	-1	-1	-1

End of preview.

Snow Water Equivalent Forecasting Training Data

Dataset Summary

Snow Water Equivalent dataset contains information about snow water equivalent (SWE), temperature, precipitation, wind speed, relative humidity, and related climate variables across different locations and time periods. It includes daily observations and derived variables for hydrological and climate analysis. An additional column, source, specifies the origin of each data column.

Authors

Ziheng Sun
Justin Pflug
Nicoleta Cristea
Gokul Prathin Asamani
Keenan Ganz
Millie Spencer
Kehan Yang
Mahboubeh Bousshagh
Juan S. Acero Triana
Bonan Li
Rebecca Gustine

Year

2024

Usage


from datasets import load_dataset

data = load_dataset("Geoweaver/SnowWaterEquivalent_trainingdata", data_files="training_data.csv")

Related Data Sources

SNOTEL: NRCS SNOTEL Website
GridMET: GridMET Website
USGS SRTM: USGS SRTM Website
MODIS 10A1 NDSI_Snow_Cover: MODIS 10A1 Website

Data Fields

Key	Unit	Data Type	Description	Source
date	-	Object	Date of the observation.	GridMET
lat	Degrees	Float64	Latitude of the observation location.	GridMET
lon	Degrees	Float64	Longitude of the observation location.	GridMET
SWE	Inches	Float64	Snow water equivalent at the observation point.	GridMET
SWE_1	Inches	Float64	SWE value from one day before.	GridMET
air_temperature_tmmn_1	Fahrenheit	Float64	Minimum air temperature from one day before.	GridMET
potential_evapotranspiration_1	Inches/day	Float64	Potential evapotranspiration from one day before.	GridMET
mean_vapor_pressure_deficit_1	kPa	Float64	Mean vapor pressure deficit from one day before.	GridMET
relative_humidity_rmax_1	Percentage	Float64	Maximum relative humidity from one day before.	GridMET
relative_humidity_rmin_1	Percentage	Float64	Minimum relative humidity from one day before.	GridMET
precipitation_amount_1	Inches	Float64	Precipitation amount from one day before.	GridMET
air_temperature_tmmx_1	Fahrenheit	Float64	Maximum air temperature from one day before.	GridMET
wind_speed_1	m/s	Float64	Wind speed from one day before.	GridMET
ndsi_1	Percentage	Float64	Fractional snow-covered area from one day before.	MODIS
SWE_2	Inches	Float64	SWE value from two days before.	GridMET
air_temperature_tmmn_2	Fahrenheit	Float64	Minimum air temperature from two days before.	GridMET
potential_evapotranspiration_2	Inches/day	Float64	Potential evapotranspiration from two days before.	GridMET
mean_vapor_pressure_deficit_2	kPa	Float64	Mean vapor pressure deficit from two days before.	GridMET
relative_humidity_rmax_2	Percentage	Float64	Maximum relative humidity from two days before.	GridMET
relative_humidity_rmin_2	Percentage	Float64	Minimum relative humidity from two days before.	GridMET
precipitation_amount_2	Inches	Float64	Precipitation amount from two days before.	GridMET
air_temperature_tmmx_2	Fahrenheit	Float64	Maximum air temperature from two days before.	GridMET
wind_speed_2	m/s	Float64	Wind speed from two days before.	MODIS
ndsi_2	Percentage	Float64	snow-covered area from two days before.	MODIS
cumulative_SWE	Inches	Float64	Cumulative SWE over a specific time period starting from October 1.	SNOTEL
cumulative_air_temperature_tmmn	Fahrenheit	Float64	Cumulative minimum air temperature over a specific time period starting from October 1.
cumulative_potential_evapotranspiration	Inches/day	Float64	Cumulative potential evapotranspiration starting from October 1.
cumulative_mean_vapor_pressure_deficit	kPa	Float64	Cumulative mean vapor pressure deficit starting from October 1.
cumulative_relative_humidity_rmax	Percentage	Float64	Cumulative maximum relative humidity starting from October 1.
cumulative_relative_humidity_rmin	Percentage	Float64	Cumulative minimum relative humidity starting from October 1.
cumulative_precipitation_amount	Inches	Float64	Cumulative precipitation amount starting from October 1.
cumulative_air_temperature_tmmx	Fahrenheit	Float64	Cumulative maximum air temperature starting from October 1.
cumulative_wind_speed	m/s	Float64	Cumulative wind speed starting from October 1.
cumulative_ndsi	Percentage	Float64	Cumulative fractional snow-covered area starting from October 1.	MODIS
station_name	-	Object	Name of the observation station.	SNOTEL
swe_value	Inches	Float64	Measured value of SWE.	SNOTEL
change_in_swe_inch	Inches	Float64	Change in SWE in inches.	SNOTEL
snow_depth	Inches	Float64	Depth of the snow at the observation site.	SNOTEL
air_temperature_observed_f	Fahrenheit	Float64	Observed air temperature.	SNOTEL
precipitation_amount	Inches	Float64	Amount of precipitation recorded.	SNOTEL
relative_humidity_rmin	Percentage	Float64	Minimum relative humidity recorded.	GridMET
potential_evapotranspiration	Inches/day	Float64	Potential rate of evaporation and transpiration.	GridMET
air_temperature_tmmx	Fahrenheit	Float64	Maximum air temperature recorded.	GridMET
relative_humidity_rmax	Percentage	Float64	Maximum relative humidity recorded.	GridMET
mean_vapor_pressure_deficit	kPa	Float64	Mean vapor pressure deficit.	GridMET
air_temperature_tmmn	Fahrenheit	Float64	Minimum air temperature recorded.	GridMET
wind_speed	m/s	Float64	Wind speed at the observation location.	GridMET
Elevation	Meters	Float64	Elevation of the observation site.	USGS SRTM
Aspect	Degrees	Float64	Compass direction the slope faces.	USGS SRTM
Curvature	-	Float64	Terrain curvature at the observation site.	USGS SRTM
Northness	-	Float64	Degree of orientation towards the north.	USGS SRTM
Eastness	-	Float64	Degree of orientation towards the east.	USGS SRTM

Data Processing

Cumulative Metrics: Columns with cumulative prefixes (e.g., cumulative_relative_humidity_rmax) start accumulating data from the previous water year, typically beginning on October 1. These metrics are not normalized unless specified.
Smoothing Process: Several columns underwent a smoothing process to fill gaps in missing data. These include:
- SWE
- air_temperature_observed_f
- precipitation_amount
- relative_humidity_rmin
- relative_humidity_rmax
- mean_vapor_pressure_deficit

License

CC-BY-4.0

Citation

If you find our dataset/code/paper helpful, please consider cite our papers 📝:

@misc {geoweaver_2025,
    author       = { {Geoweaver} },
    title        = { SnowWaterEquivalent_trainingdata},
    year         = 2025,
    url          = { https://huggingface.co/datasets/Geoweaver/SnowWaterEquivalent_trainingdata },
    doi          = { 10.57967/hf/4179 },
    publisher    = { Hugging Face }
}

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