hydroflows.methods.climate.climatology#

Method for creating monthly climatology from gridded climate data.

class hydroflows.methods.climate.climatology.MonthlyClimatology(region: ~pathlib.Path, model: str, scenario: str, horizon: ~typing.Annotated[list[list[int]], ~pydantic.functional_validators.BeforeValidator(func=~hydroflows._typing.<lambda>, json_schema_input_type=PydanticUndefined)], catalog_path: ~pathlib.Path | None = None, output_dir: ~pathlib.Path = PosixPath('data/climatology'), **params)[source]#

Create monthly climatology from climate model data.

Parameters:

  • region (Path) – Path to the region vector file.

  • catalog_path (Path) – Path to the data catalog. Should contain the the clim_source data.

  • model (str) – Model name of the climate model (e.g. ‘NOAA-GFDL_GFDL-ESM4’, ‘INM_INM-CM5-0’). Depends on the climate source.

  • scenario (str) – Scenario name of the climate model (e.g. historical, ssp245, ssp585). Depends on the climate source.

  • horizon (ListOfListOfInt) – The horizon(s) of the future scenario (e.g. [[2090, 2100]]).

  • output_dir (Path) – The output directory of the climatology dataset.

  • **params – Additional parameters to pass to the MonthlyClimatology instance. See climatology Params.

See also

climatology Input climatology Output climatology Params

pydantic model hydroflows.methods.climate.climatology.Input[source]#

Input parameters.

This class represents the input data required for the MonthlyClimatology method.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Fields:
field catalog_path: Annotated[Path, AfterValidator(func=filedir_validator)] | None [Required]#

The file path to the data catalog. Should contain the the clim_source data.

field region: Path [Required]#

The Path to the region vector file.

model_post_init(context: Any, /) → None#

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

  • self – The BaseModel instance.

  • context – The context.

pydantic model hydroflows.methods.climate.climatology.Output[source]#

Output parameters.

This class represents the output data generated by the MonthlyClimatology method.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Fields:
field climatology: Path [Required]#

The path to the dataset containing the climate factors.

model_post_init(context: Any, /) → None#

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

  • self – The BaseModel instance.

  • context – The context.

pydantic model hydroflows.methods.climate.climatology.Params[source]#

Parameters for the MonthlyClimatology.

Instances of this class are used in the MonthlyClimatology method to define the required settings.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Fields:
field clim_source: Literal['cmip6', 'cmip5', 'isimip3'] = 'cmip6'#

The source of the climate data (e.g. ‘cmip6’, ‘cmip5’, ‘isimip3’). Must be one of the available sources in the catalog_path.

field horizon: <lambda>, json_schema_input_type=PydanticUndefined)] [Required]#

The horizon of the future scenario (e.g. [[2090, 2100]]).

field members: <lambda>, json_schema_input_type=PydanticUndefined)] = ['r1i1p1f1']#

List of member names of the climate model (e.g. r1i1p1f1). Depends on the climate source.

field model: str [Required]#

Model name of the climate model (e.g. ‘NOAA-GFDL_GFDL-ESM4’, ‘INM_INM-CM5-0’). Depends on the climate source.

field output_dir: Annotated[Path, AfterValidator(func=outputdirpath_validator)] = PosixPath('data/climatology')#

The output directory of the climatology dataset.

field predefined_catalogs: <lambda>, json_schema_input_type=PydanticUndefined)] | None = None#

List of predefined catalogs to use.

field scenario: str [Required]#

Scenario name of the climate model (e.g. historical, ssp245, ssp585). Depends on the climate source.

model_post_init(context: Any, /) → None#

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

  • self – The BaseModel instance.

  • context – The context.