src.dynamic_boundary_conditions.tide.tide_slr_combine

Generates combined tide and sea level rise (SLR) data for a specific projection year, taking into account the provided confidence level, SSP scenario, inclusion of Vertical Land Motion (VLM), percentile, and more.

Attributes

log

Functions

get_slr_scenario_data(→ geopandas.GeoDataFrame)

Get sea level rise scenario data based on the specified confidence_level, ssp_scenario, add_vlm, and percentile.

get_interpolated_slr_scenario_data(...)

Interpolates sea level rise scenario data based on the specified year interval and interpolation method.

add_slr_to_tide(→ pandas.DataFrame)

Add sea level rise (SLR) data to the tide data for a specific projection year and

get_combined_tide_slr_data(→ pandas.DataFrame)

Generate the combined tide and sea level rise (SLR) data for a specific projection year, considering the given

Module Contents

src.dynamic_boundary_conditions.tide.tide_slr_combine.log
src.dynamic_boundary_conditions.tide.tide_slr_combine.get_slr_scenario_data(slr_data: geopandas.GeoDataFrame, confidence_level: str, ssp_scenario: str, add_vlm: bool, percentile: int) → geopandas.GeoDataFrame

Get sea level rise scenario data based on the specified confidence_level, ssp_scenario, add_vlm, and percentile.

Parameters:

  • slr_data (gpd.GeoDataFrame) – A GeoDataFrame containing the sea level rise data.

  • confidence_level (str) – The desired confidence level for the scenario data. Valid values are ‘low’ or ‘medium’.

  • ssp_scenario (str) – The desired Shared Socioeconomic Pathways (SSP) scenario for the scenario data. Valid options for both low and medium confidence are: ‘SSP1-2.6’, ‘SSP2-4.5’, or ‘SSP5-8.5’. Additional options for medium confidence are: ‘SSP1-1.9’ or ‘SSP3-7.0’.

  • add_vlm (bool) – Indicates whether to include Vertical Land Motion (VLM) in the scenario data. Set to True if VLM should be included, False otherwise.

  • percentile (int) – The desired percentile for the scenario data. Valid values are 17, 50, or 83.

Returns:

A GeoDataFrame containing the sea level rise scenario data based on the specified confidence_level, ssp_scenario, add_vlm, and percentile.

Return type:

gpd.GeoDataFrame

Raises:

ValueError –

  • If an invalid ‘confidence_level’ value is provided.

  • If an invalid ‘ssp_scenario’ value is provided.

  • If an invalid ‘add_vlm’ value is provided.

  • If an invalid ‘percentile’ value is provided.

src.dynamic_boundary_conditions.tide.tide_slr_combine.get_interpolated_slr_scenario_data(slr_scenario_data: geopandas.GeoDataFrame, increment_year: int = 1, interp_method: str = 'linear') → geopandas.GeoDataFrame

Interpolates sea level rise scenario data based on the specified year interval and interpolation method.

Parameters:

  • slr_scenario_data (gpd.GeoDataFrame) – A GeoDataFrame containing the sea level rise scenario data.

  • increment_year (int = 1) – The year interval used for interpolation. Defaults to 1 year.

  • interp_method (str = "linear") – Temporal interpolation method to be used. Defaults to ‘linear’. Available methods: ‘linear’, ‘nearest’, ‘nearest-up’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘previous’, ‘next’. Refer to ‘scipy.interpolate.interp1d()’ for more details.

Returns:

A GeoDataFrame containing the interpolated sea level rise scenario data.

Return type:

gpd.GeoDataFrame

Raises:

ValueError –

  • If the specified ‘increment_year’ is out of range.

  • If the specified ‘interp_method’ is not supported.

src.dynamic_boundary_conditions.tide.tide_slr_combine.add_slr_to_tide(tide_data: geopandas.GeoDataFrame, slr_interp_scenario: geopandas.GeoDataFrame, proj_year: int) → pandas.DataFrame

Add sea level rise (SLR) data to the tide data for a specific projection year and return the combined tide and sea level rise value.

Parameters:

  • tide_data (gpd.GeoDataFrame) – A GeoDataFrame containing tide data with added time information (seconds, minutes, hours) and location details.

  • slr_interp_scenario (gpd.GeoDataFrame) – A GeoDataFrame containing the interpolated sea level rise scenario data.

  • proj_year (int) – The projection year for which sea level rise data should be added to the tide data.

Returns:

A DataFrame that contains the combined tide and sea level rise data for the specified projection year.

Return type:

pd.DataFrame

Raises:

ValueError – If an invalid ‘proj_year’ value is provided.

src.dynamic_boundary_conditions.tide.tide_slr_combine.get_combined_tide_slr_data(tide_data: geopandas.GeoDataFrame, slr_data: geopandas.GeoDataFrame, proj_year: int, confidence_level: str, ssp_scenario: str, add_vlm: bool, percentile: int, increment_year: int = 1, interp_method: str = 'linear') → pandas.DataFrame

Generate the combined tide and sea level rise (SLR) data for a specific projection year, considering the given confidence_level, ssp_scenario, add_vlm, percentile, and more.

Parameters:

  • tide_data (gpd.GeoDataFrame) – A GeoDataFrame containing tide data with added time information (seconds, minutes, hours) and location details.

  • slr_data (gpd.GeoDataFrame) – A GeoDataFrame containing the sea level rise data.

  • proj_year (int) – The projection year for which the combined tide and sea level rise data should be generated.

  • confidence_level (str) – The desired confidence level for the sea level rise data.

  • ssp_scenario (str) – The desired Shared Socioeconomic Pathways (SSP) scenario for the sea level rise data.

  • add_vlm (bool) – Indicates whether Vertical Land Motion (VLM) should be included in the sea level rise data.

  • percentile (int) – The desired percentile for the sea level rise data.

  • increment_year (int = 1) – The year interval used for interpolating the sea level rise data. Defaults to 1 year.

  • interp_method (str = "linear") – Temporal interpolation method used for interpolating the sea level rise data. Defaults to ‘linear’. Available methods: ‘linear’, ‘nearest’, ‘nearest-up’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘previous’, ‘next’. Refer to ‘scipy.interpolate.interp1d()’ for more details.

Returns:

A DataFrame containing the combined tide and sea level rise data for the specified projection year, taking into account the provided confidence_level, ssp_scenario, add_vlm, percentile, and more.

Return type:

pd.DataFrame