hydroflows.methods.rainfall.pluvial_design_events

Pluvial design events method.

class hydroflows.methods.rainfall.pluvial_design_events.PluvialDesignEvents(precip_nc: ~pathlib.Path, event_root: ~pathlib.Path = PosixPath('data/events/rainfall'), rps: ~typing.Annotated[list[float], ~pydantic.functional_validators.BeforeValidator(func=~hydroflows._typing.<lambda>, json_schema_input_type=PydanticUndefined)] | None = None, event_names: ~typing.List[str] | None = None, ev_type: ~typing.Literal['BM', 'POT'] = 'BM', distribution: str | None = None, wildcard: str = 'event', duration: int = 48, **params)

Method for generating pluvial design events from rainfall timeseries.

Parameters:

• precip_nc (Path) – The file path to the rainfall time series in NetCDF format.

• event_root (Path, optional) – The root folder to save the derived design events, by default “data/events/rainfall”.

• rps (List[float], optional) – Return periods of design events, by default [1, 2, 5, 10, 20, 50, 100].

• event_names (List[str], optional) – List of event names for the design events, by “p_event{i}”, where i is the event number.

• ev_type (Literal["BM", "POT"]) – Method to select events/peaks. Valid options are ‘BM’ (default) for block maxima or ‘POT’ for Peak over threshold.

• distribution (str, optional) – Type of extreme value distribution corresponding with ev_type. Options are “gumb” or “gev” for BM, and “exp” or “gpd” for POT. If None (default) is used, “gumb” is selected for BM and “exp” for POT.

• wildcard (str, optional) – The wildcard key for expansion over the design events, by default “event”.

• duration (int) – Duration of the produced design event, by default 48 hours.

• **params – Additional parameters to pass to the PluvialDesignEvents Params instance.

See also

PluvialDesignEvents Input PluvialDesignEvents Output PluvialDesignEvents Params

Create a new expand method instance.

pydantic model hydroflows.methods.rainfall.pluvial_design_events.Input

Input parameters for PluvialDesignEvents 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:

• precip_nc (pathlib.Path)

field precip_nc: Path [Required]

The file path to the rainfall time series in NetCDF format which are used to apply EVA and derive design events. This file should contain a time dimension This time series can be derived either by the hydroflows.methods.rainfall.get_ERA5_rainfall.GetERA5Rainfall or can be directly supplied by the user.

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.rainfall.pluvial_design_events.Output

Output parameters for PluvialDesignEvents.

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:

• event_csv (pathlib.Path)

• event_set_yaml (pathlib.Path)

• event_yaml (pathlib.Path)

field event_csv: Path [Required]

The path to the event csv timeseries file

field event_set_yaml: Annotated[Path, AfterValidator(func=filedir_validator)] [Required]

The path to the event set yml file, see also hydroflows.methods.events.EventSet.

field event_yaml: Annotated[Path, AfterValidator(func=filedir_validator)] [Required]

The path to the event description file, see also hydroflows.methods.events.Event.

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.rainfall.pluvial_design_events.Params

Parameters for PluvialDesignEvents 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:

• distribution (str | None)

• duration (int)

• ev_type (Literal['BM', 'POT'])

• event_names (list[str] | None)

• event_root (pathlib.Path)

• fig_name_hyeto (str)

• fig_name_idf (str)

• min_dist_days (int)

• min_sample_perc (int)

• plot_fig (bool)

• qthresh (float)

• rps (list[int])

• save_idf_csv (bool)

• t0 (str)

• time_dim (str)

• timesteps (list[int])

• wildcard (str)

field distribution: str | None = None

Type of extreme value distribution corresponding with ev_type. Options are “gumb” or “gev” for BM, and “exp” or “gpd” for POT. If None (default) is used, “gumb” is selected for BM and “exp” for POT.

field duration: int = 48

Duration of the produced design event.

field ev_type: Literal['BM', 'POT'] = 'BM'

Method to select events/peaks. Valid options are ‘BM’ for block maxima or ‘POT’ for Peak over threshold.

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

List of event names associated with return periods.

field event_root: Annotated[Path, AfterValidator(func=outputdirpath_validator)] [Required]

Root folder to save the derived design events.

field fig_name_hyeto: str = 'rainfall_hyetographs'

Name of the design hyetographs figure.

field fig_name_idf: str = 'rainfall_idf_curves'

Name of the idf figure.

field min_dist_days: int = 0

Minimum distance between events/peaks measured in days.

field min_sample_perc: int = 80

Minimum sample percentage in a valid block. Peaks of invalid bins are set to NaN.

field plot_fig: bool = True

Determines whether to plot figures, including the derived design hyetographs as well as the calculated IDF curves per return period.

field qthresh: float = 0.95

Quantile threshold used with peaks over threshold method.

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

Return periods of interest.

field save_idf_csv: bool = True

Determines whether to save the calculated IDF curve values per return period in a csv format.

field t0: str = '2020-01-01'

Random initial date for the design events.

field time_dim: str = 'time'

Time dimension of the input time series provided in Input class.

field timesteps: <lambda>, json_schema_input_type=PydanticUndefined)] = [1, 2, 3, 6, 12, 24, 36, 48]

Intensity Duration Frequencies provided as multiply of the data time step.

field wildcard: str = 'event'

The wildcard key for expansion over the design events.

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.