From 4f1769a035d752af2bf32be6b7fbd864be185452 Mon Sep 17 00:00:00 2001 From: Joe Moorhouse <5102656+joemoorhouse@users.noreply.github.com> Date: Fri, 14 Jun 2024 16:59:31 +0100 Subject: [PATCH] Document update and refactor (#303) * Doc update; standard protection handling Signed-off-by: Joe Moorhouse <5102656+joemoorhouse@users.noreply.github.com> * Allow empty environment variables for inventory reader Signed-off-by: Joe Moorhouse <5102656+joemoorhouse@users.noreply.github.com> * Tidy Signed-off-by: Joe Moorhouse <5102656+joemoorhouse@users.noreply.github.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Joe Moorhouse <5102656+joemoorhouse@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- .gitignore | 11 + docs/user-guide.rst | 43 +++ src/physrisk/api/v1/common.py | 4 +- src/physrisk/data/hazard_data_provider.py | 103 ++---- src/physrisk/data/inventory_reader.py | 8 +- .../data/pregenerated_hazard_model.py | 44 +-- .../data/static/hazard/inventory.json | 322 ++++++++++++++++-- src/physrisk/kernel/hazard_model.py | 5 +- src/physrisk/kernel/hazards.py | 53 +-- 9 files changed, 432 insertions(+), 161 deletions(-) diff --git a/.gitignore b/.gitignore index 39315b99..8a5814f8 100644 --- a/.gitignore +++ b/.gitignore @@ -96,3 +96,14 @@ target/ src/test/data/coords.json credentials.env .pdm-python + +# Latex +methodology/PhysicalRiskMethodology.aux +methodology/PhysicalRiskMethodology.blg +methodology/PhysicalRiskMethodology.fdb_latexmk +methodology/PhysicalRiskMethodology.fls +methodology/PhysicalRiskMethodology.glo +methodology/PhysicalRiskMethodology.glsdefs +methodology/PhysicalRiskMethodology.ist +methodology/PhysicalRiskMethodology.out +methodology/PhysicalRiskMethodology.synctex.gz diff --git a/docs/user-guide.rst b/docs/user-guide.rst index 7dd041b5..4b0e20d7 100644 --- a/docs/user-guide.rst +++ b/docs/user-guide.rst @@ -14,3 +14,46 @@ Physic comprises: * Financial models that use the impacts calculated by the :code:`VulnerabilityModels` to calculate risk measures and scores. :code:`VulnerabilityModels` request hazard indicators using an :code:`indicator_id` (e.g. 'flood_depth' for inundation, 'max_speed' for wind). It is the responsibility of the :code:`HazardModel` to select the source of the hazard indicator data. + +Note that units of the quantity are provided to the :code:`VulnerabilityModel` by the :code:`HazardModel`. + +Hazard indicator data sets +------------------------- +The :code:`HazardModel` retrieves hazard indicators in a number of ways and can be made composite in order to combine different ways of accessing the data. At time of writing the common cases are that: + +1. Hazard indicator data is stored in `Zarr `_ format (in an arbitrary Zarr store, although S3 is a popular choice). +2. Hazard indicator data is retrieved via call to an external API. This is mainly used when combining commercial data to the public-domain. + +In case 1, hazard indicators are stored as three dimensional arrays. The array is ordered :math:`(z, y, x)` where :math:`y` is the spatial :math:`y` coordinate, :math:`x` is the spatial :math:`x` coordinate and :math:`z` is an *index* coordinate. The *index* takes on different meanings according to the type of data being stored. + +Indicators can be either: + +* Acute (A): the data comprises a set of hazard intensities for different return periods. In this case *index* refers to the different return periods. +* Parametric (P): the data comprises a set of parameters. Here *index* refers to the different parameters. The parameters may be single values, or *index* might refer to a set of thresholds. Parametric indicators are used for chronic hazards. + +As mentioned above, :code:`VulnerabilityModels` only specify the identifier of the hazard indicator that is required, as well as the climate scenario ID and the year of the future projection. This means that hazard indicator ID uniquely defines the data. For example, a vulnerability model requesting 'flood depth' could have data returned from a variety of data sets, depending on how the :code:`HazardModel` is configured. But + ++-----------------------+-------------------------------+---------------------------------------+ +| Hazard class | Indicator ID (type) | Description | ++=======================+===============================+=======================================+ +| CoastalInundation, | flood_depth (A) | Flood depth (m) for available | +| PluvialInundation, | | return periods. This is unprotected | +| RiverineInundation | | depth. | +| +-------------------------------+---------------------------------------+ +| | sop (P) | Standard of protection | +| | | (as return period in years). | ++-----------------------+-------------------------------+---------------------------------------+ +| Fire | fire_probability (P) | Annual probability that location | +| | | is in a wildfire zone. | ++-----------------------+-------------------------------+---------------------------------------+ +| Heat | mean_degree_days/above/index | Mean mean-temperature degree days per | +| | (P) | year above a set of temperature | +| | | threshold indices. | ++-----------------------+-------------------------------+---------------------------------------+ +| Drought | months/spei/12m/below/index | Mean months per year where the 12 | +| | (P) | month SPEI index is below a set of | +| | | indices. | ++-----------------------+-------------------------------+---------------------------------------+ +| Wind | max_speed | Maximum 1 minute sustained wind speed | +| | (A) | for available return periods. | ++-----------------------+-------------------------------+---------------------------------------+ diff --git a/src/physrisk/api/v1/common.py b/src/physrisk/api/v1/common.py index 0e813aaf..5915cd7e 100644 --- a/src/physrisk/api/v1/common.py +++ b/src/physrisk/api/v1/common.py @@ -1,4 +1,4 @@ -from typing import Dict, List, Optional +from typing import Dict, List, Optional, Union import numpy as np from pydantic import BaseModel, Field @@ -83,7 +83,7 @@ class IntensityCurve(BaseModel): return_periods: Optional[List[float]] = Field( [], description="[Deprecated] Return period in years in the case of an acute hazard." ) - index_values: Optional[List[float]] = Field( + index_values: Optional[Union[List[float], List[str]]] = Field( [], description="Set of index values. \ This is return period in years in the case of an acute hazard or \ diff --git a/src/physrisk/data/hazard_data_provider.py b/src/physrisk/data/hazard_data_provider.py index 8eb6f689..77f029a2 100644 --- a/src/physrisk/data/hazard_data_provider.py +++ b/src/physrisk/data/hazard_data_provider.py @@ -44,10 +44,16 @@ def __init__( zarr_reader: Optional[ZarrReader] = None, interpolation: Optional[str] = "floor", ): - """Create an EventProvider. + """Provides hazard data. Args: - get_source_path: provides the path to the hazard event data source depending on year/scenario/model. + get_source_path (SourcePath): Provides the source path mappings. + store (Optional[MutableMapping], optional): Zarr store instance. Defaults to None. + zarr_reader (Optional[ZarrReader], optional): ZarrReader instance. Defaults to None. + interpolation (Optional[str], optional): Interpolation type. Defaults to "floor". + + Raises: + ValueError: If interpolation not in permitted list. """ self._get_source_path = get_source_path self._reader = zarr_reader if zarr_reader is not None else ZarrReader(store=store) @@ -55,21 +61,7 @@ def __init__( raise ValueError("interpolation must be 'floor', 'linear', 'max' or 'min'") self._interpolation = interpolation - -class AcuteHazardDataProvider(HazardDataProvider): - """Provides hazard event intensities for a single Hazard (type of hazard event).""" - - def __init__( - self, - get_source_path: SourcePath, - *, - store: Optional[MutableMapping] = None, - zarr_reader: Optional[ZarrReader] = None, - interpolation: Optional[str] = "floor", - ): - super().__init__(get_source_path, store=store, zarr_reader=zarr_reader, interpolation=interpolation) - - def get_intensity_curves( + def get_data( self, longitudes: List[float], latitudes: List[float], @@ -80,32 +72,37 @@ def get_intensity_curves( hint: Optional[HazardDataHint] = None, buffer: Optional[int] = None, ): - """Get intensity curve for each latitude and longitude coordinate pair. + """Returns data for set of latitude and longitudes. Args: - longitudes: list of longitudes. - latitudes: list of latitudes. - model: model identifier. - scenario: identifier of scenario, e.g. rcp8p5 (RCP 8.5). - year: projection year, e.g. 2080. - buffer: delimitation of the area for the hazard data expressed in metres (within [0,1000]). + longitudes (List[float]): List of longitudes. + latitudes (List[float]): List of latitudes. + indicator_id (str): Hazard Indicator ID. + scenario (str): Identifier of scenario, e.g. ssp585 (SSP 585), rcp8p5 (RCP 8.5). + year (int): Projection year, e.g. 2080. + hint (Optional[HazardDataHint], optional): Hint. Defaults to None. + buffer (Optional[int], optional): Buffer around each point + expressed in metres (within [0, 1000]). Defaults to None. + + Raises: + Exception: _description_ Returns: - curves: numpy array of intensity (no. coordinate pairs, no. return periods). - return_periods: return periods in years. - units: units. - path: path to the hazard indicator data source. + values (np.ndarray): Hazard indicator values. + indices (np.ndarray): Index values. + units (str). Units + path: Path to the hazard indicator data source. """ path = self._get_source_path(indicator_id=indicator_id, scenario=scenario, year=year, hint=hint) if buffer is None: - curves, return_periods, units = self._reader.get_curves( + values, indices, units = self._reader.get_curves( path, longitudes, latitudes, self._interpolation ) # type: ignore else: if buffer < 0 or 1000 < buffer: raise Exception("The buffer must be an integer between 0 and 1000 metres.") - curves, return_periods, units = self._reader.get_max_curves( + values, indices, units = self._reader.get_max_curves( path, [ ( @@ -119,48 +116,4 @@ def get_intensity_curves( ], self._interpolation, ) # type: ignore - return curves, return_periods, units, path - - -class ChronicHazardDataProvider(HazardDataProvider): - """Provides hazard parameters for a single type of chronic hazard.""" - - def __init__( - self, - get_source_path: SourcePath, - *, - store: Optional[MutableMapping] = None, - zarr_reader: Optional[ZarrReader] = None, - interpolation: Optional[str] = "floor", - ): - super().__init__(get_source_path, store=store, zarr_reader=zarr_reader, interpolation=interpolation) - - def get_parameters( - self, - longitudes: List[float], - latitudes: List[float], - *, - indicator_id: str, - scenario: str, - year: int, - hint: Optional[HazardDataHint] = None, - ): - """Get hazard parameters for each latitude and longitude coordinate pair. - - Args: - longitudes: list of longitudes. - latitudes: list of latitudes. - model: model identifier. - scenario: identifier of scenario, e.g. rcp8p5 (RCP 8.5). - year: projection year, e.g. 2080. - - Returns: - parameters: numpy array of parameters. - defns: numpy array defining the parameters (e.g. provides thresholds). - units: units of the parameters. - path: path to the hazard indicator data source. - """ - - path = self._get_source_path(indicator_id=indicator_id, scenario=scenario, year=year, hint=hint) - parameters, defns, units = self._reader.get_curves(path, longitudes, latitudes, self._interpolation) - return parameters, defns, units, path + return values, indices, units, path diff --git a/src/physrisk/data/inventory_reader.py b/src/physrisk/data/inventory_reader.py index 4cf0a6dc..c566f0c0 100644 --- a/src/physrisk/data/inventory_reader.py +++ b/src/physrisk/data/inventory_reader.py @@ -19,7 +19,7 @@ class InventoryReader: # environment variable names: __access_key = "OSC_S3_ACCESS_KEY" __secret_key = "OSC_S3_SECRET_KEY" - __S3_bucket = "OSC_S3_BUCKET" # e.g. redhat-osc-physical-landing-647521352890 + __S3_bucket = "OSC_S3_BUCKET" def __init__( self, @@ -35,11 +35,11 @@ def __init__( fs (Optional[AbstractFileSystem], optional): AbstractFileSystem. Defaults to None in which case S3FileSystem will be created. # noqa: E501 """ if fs is None: - access_key = get_env(self.__access_key, None) - secret_key = get_env(self.__secret_key, None) + access_key = get_env(self.__access_key, "") + secret_key = get_env(self.__secret_key, "") fs = s3fs.S3FileSystem(anon=False, key=access_key, secret=secret_key) - bucket = get_env(self.__S3_bucket, "redhat-osc-physical-landing-647521352890") + bucket = get_env(self.__S3_bucket, "physrisk-hazard-indicators") self._base_path = bucket if base_path is None else base_path self._fs = fs diff --git a/src/physrisk/data/pregenerated_hazard_model.py b/src/physrisk/data/pregenerated_hazard_model.py index b8468072..d813394b 100644 --- a/src/physrisk/data/pregenerated_hazard_model.py +++ b/src/physrisk/data/pregenerated_hazard_model.py @@ -1,11 +1,11 @@ import concurrent.futures from collections import defaultdict -from typing import Dict, List, Mapping, MutableMapping, Optional, cast +from typing import Dict, List, Mapping, MutableMapping, Optional, Type import numpy as np from physrisk.data.zarr_reader import ZarrReader -from physrisk.kernel.hazards import Hazard, HazardKind +from physrisk.kernel.hazards import Hazard, IndicatorData, indicator_data from ..kernel.hazard_model import ( HazardDataFailedResponse, @@ -15,7 +15,7 @@ HazardModel, HazardParameterDataResponse, ) -from .hazard_data_provider import AcuteHazardDataProvider, ChronicHazardDataProvider, HazardDataProvider, SourcePath +from .hazard_data_provider import HazardDataProvider, SourcePath class PregeneratedHazardModel(HazardModel): @@ -23,18 +23,9 @@ class PregeneratedHazardModel(HazardModel): def __init__( self, - hazard_data_providers: Dict[type, HazardDataProvider], + hazard_data_providers: Dict[Type[Hazard], HazardDataProvider], ): - self.acute_hazard_data_providers = dict( - (k, cast(AcuteHazardDataProvider, v)) - for (k, v) in hazard_data_providers.items() - if Hazard.kind(k) == HazardKind.acute - ) - self.chronic_hazard_data_providers = dict( - (k, cast(ChronicHazardDataProvider, v)) - for (k, v) in hazard_data_providers.items() - if Hazard.kind(k) == HazardKind.chronic - ) + self.hazard_data_providers = hazard_data_providers def get_hazard_events( # noqa: C901 self, requests: List[HazardDataRequest] @@ -83,10 +74,8 @@ def _get_hazard_events_batch( ) longitudes = [req.longitude for req in batch] latitudes = [req.latitude for req in batch] - if hazard_type.kind == HazardKind.acute: # type: ignore - intensities, return_periods, units, path = self.acute_hazard_data_providers[ - hazard_type - ].get_intensity_curves( + if indicator_data(hazard_type, indicator_id) == IndicatorData.EVENT: + intensities, return_periods, units, path = self.hazard_data_providers[hazard_type].get_data( longitudes, latitudes, indicator_id=indicator_id, @@ -102,8 +91,8 @@ def _get_hazard_events_batch( if len(valid_periods) == 0: valid_periods, valid_intensities = np.array([100]), np.array([0]) responses[req] = HazardEventDataResponse(valid_periods, valid_intensities, units, path) - elif hazard_type.kind == HazardKind.chronic: # type: ignore - parameters, defns, units, path = self.chronic_hazard_data_providers[hazard_type].get_parameters( + else: # type: ignore + parameters, defns, units, path = self.hazard_data_providers[hazard_type].get_data( longitudes, latitudes, indicator_id=indicator_id, scenario=scenario, year=year, hint=hint ) @@ -121,7 +110,7 @@ class ZarrHazardModel(PregeneratedHazardModel): def __init__( self, *, - source_paths: Dict[type, SourcePath], + source_paths: Dict[Type[Hazard], SourcePath], reader: Optional[ZarrReader] = None, store=None, interpolation="floor", @@ -130,15 +119,8 @@ def __init__( zarr_reader = ZarrReader(store=store) if reader is None else reader super().__init__( - dict( - ( - t, - ( - AcuteHazardDataProvider(sp, zarr_reader=zarr_reader, interpolation=interpolation) - if Hazard.kind(t) == HazardKind.acute - else ChronicHazardDataProvider(sp, zarr_reader=zarr_reader, interpolation=interpolation) - ), - ) + { + t: HazardDataProvider(sp, zarr_reader=zarr_reader, interpolation=interpolation) for t, sp in source_paths.items() - ) + } ) diff --git a/src/physrisk/data/static/hazard/inventory.json b/src/physrisk/data/static/hazard/inventory.json index 04007dc7..5f5b7153 100644 --- a/src/physrisk/data/static/hazard/inventory.json +++ b/src/physrisk/data/static/hazard/inventory.json @@ -10,7 +10,7 @@ "params": {}, "display_name": "Flood depth/baseline (WRI)", "display_groups": [], - "description": "\nWorld Resources Institute Aqueduct Floods baseline riverine model using historical data.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resources Institute Aqueduct Floods baseline riverine model using historical data.\n\n The World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", "map": { "colormap": { "min_index": 1, @@ -40,6 +40,12 @@ -85.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": null, "source": "map_array_pyramid" }, @@ -63,7 +69,7 @@ "params": {}, "display_name": "Flood depth/NorESM1-M (WRI)", "display_groups": [], - "description": "\nWorld Resources Institute Aqueduct Floods riverine model using GCM model from\nBjerknes Centre for Climate Research, Norwegian Meteorological Institute.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resources Institute Aqueduct Floods riverine model using GCM model from\nBjerknes Centre for Climate Research, Norwegian Meteorological Institute.\n\n The World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", "map": { "colormap": { "min_index": 1, @@ -93,6 +99,12 @@ -85.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": [ 8 ], @@ -128,7 +140,7 @@ "params": {}, "display_name": "Flood depth/GFDL-ESM2M (WRI)", "display_groups": [], - "description": "\nWorld Resource Institute Aqueduct Floods riverine model using GCM model from\nGeophysical Fluid Dynamics Laboratory (NOAA).\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resource Institute Aqueduct Floods riverine model using GCM model from\nGeophysical Fluid Dynamics Laboratory (NOAA).\n\n The World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", "map": { "colormap": { "min_index": 1, @@ -158,6 +170,12 @@ -85.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": [ 8 ], @@ -193,7 +211,7 @@ "params": {}, "display_name": "Flood depth/HadGEM2-ES (WRI)", "display_groups": [], - "description": "\nWorld Resource Institute Aqueduct Floods riverine model using GCM model:\nMet Office Hadley Centre.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resource Institute Aqueduct Floods riverine model using GCM model:\nMet Office Hadley Centre.\n\n The World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", "map": { "colormap": { "min_index": 1, @@ -223,6 +241,12 @@ -85.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": [ 8 ], @@ -258,7 +282,7 @@ "params": {}, "display_name": "Flood depth/IPSL-CM5A-LR (WRI)", "display_groups": [], - "description": "\nWorld Resource Institute Aqueduct Floods riverine model using GCM model from\nInstitut Pierre Simon Laplace\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resource Institute Aqueduct Floods riverine model using GCM model from\nInstitut Pierre Simon Laplace\n\n The World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", "map": { "colormap": { "min_index": 1, @@ -288,6 +312,12 @@ -85.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": [ 8 ], @@ -323,7 +353,7 @@ "params": {}, "display_name": "Flood depth/MIROC-ESM-CHEM (WRI)", "display_groups": [], - "description": "World Resource Institute Aqueduct Floods riverine model using\n GCM model from Atmosphere and Ocean Research Institute\n (The University of Tokyo), National Institute for Environmental Studies, and Japan Agency\n for Marine-Earth Science and Technology.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "World Resource Institute Aqueduct Floods riverine model using\n GCM model from Atmosphere and Ocean Research Institute\n (The University of Tokyo), National Institute for Environmental Studies, and Japan Agency\n for Marine-Earth Science and Technology.\n\n The World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", "map": { "colormap": { "min_index": 1, @@ -353,6 +383,12 @@ -85.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": [ 8 ], @@ -388,7 +424,7 @@ "params": {}, "display_name": "Flood depth/baseline, no subsidence (WRI)", "display_groups": [], - "description": "\nWorld Resources Institute Aqueduct Floods baseline coastal model using historical data. Model excludes subsidence.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resources Institute Aqueduct Floods baseline coastal model using historical data. Model excludes subsidence.\n\n The World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", "map": { "colormap": { "min_index": 1, @@ -418,6 +454,12 @@ -85.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": [ 8 ], @@ -443,7 +485,7 @@ "params": {}, "display_name": "Flood depth/95%, no subsidence (WRI)", "display_groups": [], - "description": "\nWorld Resource Institute Aqueduct Floods coastal model, excluding subsidence; 95th percentile sea level rise.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resource Institute Aqueduct Floods coastal model, excluding subsidence; 95th percentile sea level rise.\n\n The World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", "map": { "colormap": { "min_index": 1, @@ -473,6 +515,12 @@ -85.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": [ 8 ], @@ -508,7 +556,7 @@ "params": {}, "display_name": "Flood depth/5%, no subsidence (WRI)", "display_groups": [], - "description": "\nWorld Resource Institute Aqueduct Floods coastal model, excluding subsidence; 5th percentile sea level rise.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resource Institute Aqueduct Floods coastal model, excluding subsidence; 5th percentile sea level rise.\n\n The World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", "map": { "colormap": { "min_index": 1, @@ -538,6 +586,12 @@ -85.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": [ 8 ], @@ -573,7 +627,7 @@ "params": {}, "display_name": "Flood depth/50%, no subsidence (WRI)", "display_groups": [], - "description": "\nWorld Resource Institute Aqueduct Floods model, excluding subsidence; 50th percentile sea level rise.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resource Institute Aqueduct Floods model, excluding subsidence; 50th percentile sea level rise.\n\n The World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", "map": { "colormap": { "min_index": 1, @@ -603,6 +657,12 @@ -85.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": [ 8 ], @@ -638,7 +698,7 @@ "params": {}, "display_name": "Flood depth/baseline, with subsidence (WRI)", "display_groups": [], - "description": "\nWorld Resource Institute Aqueduct Floods model, excluding subsidence; baseline (based on historical data).\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resource Institute Aqueduct Floods model, excluding subsidence; baseline (based on historical data).\n\n The World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", "map": { "colormap": { "min_index": 1, @@ -668,6 +728,12 @@ -85.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": [ 8 ], @@ -693,7 +759,7 @@ "params": {}, "display_name": "Flood depth/95%, with subsidence (WRI)", "display_groups": [], - "description": "\nWorld Resource Institute Aqueduct Floods model, including subsidence; 95th percentile sea level rise.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resource Institute Aqueduct Floods model, including subsidence; 95th percentile sea level rise.\n\n The World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", "map": { "colormap": { "min_index": 1, @@ -723,6 +789,12 @@ -85.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": [ 8 ], @@ -758,7 +830,7 @@ "params": {}, "display_name": "Flood depth/5%, with subsidence (WRI)", "display_groups": [], - "description": "\nWorld Resource Institute Aqueduct Floods model, including subsidence; 5th percentile sea level rise.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resource Institute Aqueduct Floods model, including subsidence; 5th percentile sea level rise.\n\n The World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", "map": { "colormap": { "min_index": 1, @@ -788,6 +860,12 @@ -85.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": [ 8 ], @@ -823,7 +901,7 @@ "params": {}, "display_name": "Flood depth/50%, with subsidence (WRI)", "display_groups": [], - "description": "\nWorld Resource Institute Aqueduct Floods model, including subsidence; 50th percentile sea level rise.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resource Institute Aqueduct Floods model, including subsidence; 50th percentile sea level rise.\n\n The World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", "map": { "colormap": { "min_index": 1, @@ -853,6 +931,12 @@ -85.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": [ 8 ], @@ -929,6 +1013,12 @@ -60.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": null, "source": "map_array" }, @@ -1006,6 +1096,12 @@ -85.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": null, "source": "map_array" }, @@ -1075,6 +1171,12 @@ -85.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": null, "source": "map_array" }, @@ -1144,6 +1246,12 @@ -85.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": null, "source": "map_array" }, @@ -1213,6 +1321,12 @@ -85.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": null, "source": "map_array" }, @@ -1282,6 +1396,12 @@ -85.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": null, "source": "map_array" }, @@ -1351,6 +1471,12 @@ -85.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": null, "source": "map_array" }, @@ -1420,6 +1546,12 @@ -85.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": null, "source": "map_array" }, @@ -1505,6 +1637,12 @@ -60.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": null, "source": "map_array" }, @@ -1602,6 +1740,12 @@ -60.0 ] ], + "bbox": [ + -180.0, + -60.0, + 180.0, + 85.0 + ], "index_values": null, "source": "map_array" }, @@ -1649,7 +1793,7 @@ "params": {}, "display_name": "Max wind speed (IRIS)", "display_groups": [], - "description": "Assessing tropical cyclone risk on a global scale given the infrequency of landfalling tropical cyclones and the short period of reliable observations remains a challenge. Synthetic tropical cyclone datasets can help overcome these problems. Here we present a new global dataset created by IRIS, the ImpeRIal college Storm Model. IRIS is novel because, unlike other synthetic TC models, it only simulates the decay from the point of lifetime maximum intensity. This minimises the bias in the dataset. It takes input from 42 years of observed tropical cyclones and creates a 10,000 year synthetic dataset which is then validated against the observations. IRIS captures important statistical characteristics of the observed data. The return periods of the landfall maximum wind speed (1 minute sustained in m/s) are realistic globally. Climate model projections are used to adjust the life-time maximum intensity.\nhttps://www.imperial.ac.uk/grantham/research/climate-science/modelling-tropical-cyclones/\n", + "description": "Assessing tropical cyclone risk on a global scale given the infrequency of landfalling tropical cyclones and the short period of reliable observations remains a challenge. Synthetic tropical cyclone datasets can help overcome these problems. Here we present a new global dataset created by IRIS, the ImpeRIal college Storm Model. IRIS is novel because, unlike other synthetic TC models, it only simulates the decay from the point of lifetime maximum intensity. This minimises the bias in the dataset. It takes input from 42 years of observed tropical cyclones and creates a 10,000 year synthetic dataset which is then validated against the observations. IRIS captures important statistical characteristics of the observed data. The return periods of the landfall maximum wind speed (1 minute sustained in m/s) are realistic globally. Climate model projections are used to adjust the life-time maximum intensity.\n\n", "map": { "colormap": { "min_index": 1, @@ -1679,6 +1823,12 @@ -60.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": null, "source": "map_array_pyramid" }, @@ -1761,6 +1911,12 @@ -60.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": [ 16, 20, @@ -1853,6 +2009,12 @@ -60.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": [ 16, 20, @@ -1944,6 +2106,12 @@ -85.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": [ 5, 7.5, @@ -2067,6 +2235,12 @@ -85.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": [ 5, 7.5, @@ -2117,7 +2291,7 @@ "display_groups": [ "Days with wet-bulb globe temperature above threshold in \u00b0C" ], - "description": "Days per year for which the 'Wet Bulb Globe Temperature' indicator is above a threshold specified in \u00b0C:\n\n$$\nI = \\frac{365}{n_y} \\sum_{i = 1}^{n_y} \\boldsymbol{\\mathbb{1}}_{\\; \\, T^\\text{WBGT}_i > T^\\text{ref}} \\nonumber\n$$\n\n$I$ is the indicator, $n_y$ is the number of days in the sample and $T^\\text{ref}$ is the reference temperature.\n\nThe 'Wet-Bulb Globe Temperature' (WBGT) indicator is calculated from both the average daily near-surface surface temperature in \u00b0C denoted $T^\\text{avg}$ and the water vapour partial pressure in kPa denoted $p^\\text{vapour}$:\n\n$$\nT^\\text{WBGT}_i = 0.567 \\times T^\\text{avg}_i + 0.393 \\times p^\\text{vapour}_i + 3.94\n$$\n\nThe water vapour partial pressure $p^\\text{vapour}$ is calculated from relative humidity $h^\\text{relative}$:\n\n$$\np^\\text{vapour}_i = \\frac{h^\\text{relative}_i}{100} \\times 6.105 \\times \\exp \\left( \\frac{17.27 \\times T^\\text{avg}_i}{237.7 + T^\\text{avg}_i} \\right)\n$$\n\nThe OS-Climate-generated indicators are inferred from downscaled CMIP6 data, averaged over for 6 Global Circulation Models: ACCESS-CM2, CMCC-ESM2, CNRM-CM6-1, MPI-ESM1-2-LR, MIROC6 and NorESM2-MM.\nThe downscaled data is sourced from the [NASA Earth Exchange Global Daily Downscaled Projections](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6).\nIndicators are generated for periods: 'historical' (averaged over 1995-2014), 2030 (2021-2040), 2040 (2031-2050), 2050 (2041-2060), 2060 (2051-2070), 2070 (2061-2080), 2080 (2071-2090) and 2090 (2081-2100).\n", + "description": "Days per year for which the 'Wet Bulb Globe Temperature' indicator is above a threshold specified in \u00b0C:\n\n$$\nI = \\frac{365}{n_y} \\sum_{i = 1}^{n_y} \\boldsymbol{\\mathbb{1}}_{\\; \\, T^\\text{WBGT}_i > T^\\text{ref}} \\nonumber\n$$\n\n$I$ is the indicator, $n_y$ is the number of days in the sample and $T^\\text{ref}$ is the reference temperature.\n\nThe 'Wet-Bulb Globe Temperature' (WBGT) indicator is calculated from both the average daily near-surface surface temperature in \u00b0C denoted $T^\\text{avg}$ and the water vapour partial pressure in kPa denoted $p^\\text{vapour}$:\n\n$$\nT^\\text{WBGT}_i = 0.567 \\times T^\\text{avg}_i + 0.393 \\times p^\\text{vapour}_i + 3.94\n$$\n\nThe water vapour partial pressure $p^\\text{vapour}$ is calculated from relative humidity $h^\\text{relative}$:\n\n$$\np^\\text{vapour}_i = \\frac{h^\\text{relative}_i}{100} \\times 6.105 \\times \\exp \\left( \\frac{17.27 \\times T^\\text{avg}_i}{237.7 + T^\\text{avg}_i} \\right)\n$$\n\nThe OS-Climate-generated indicators are inferred from downscaled CMIP6 data, averaged over for 6 Global Circulation Models: ACCESS-CM2, CMCC-ESM2, CNRM-CM6-1, MPI-ESM1-2-LR, MIROC6 and NorESM2-MM.\nThe downscaled data is sourced from the [NASA Earth Exchange Global Daily Downscaled Projections](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6).\nIndicators are generated for periods: 'historical' (averaged over 1995-2014), 2030 (2021-2040), 2040 (2031-2050), 2050 (2041-2060), 2060 (2051-2070), 2070 (2061-2080), 2080 (2071-2090) and 2090 (2081-2100).\n", "map": { "colormap": { "min_index": 1, @@ -2147,6 +2321,12 @@ -85.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": [ 5, 10, @@ -2233,7 +2413,7 @@ "display_groups": [ "Water demand in centimeters/year (Aqueduct 4.0)" ], - "description": "The World Resources Institute (WRI) [Aqueduct 4.0](https://www.wri.org/data/aqueduct-global-maps-40-data) is the latest iteration of [WRI\u2019s water risk framework](https://www.wri.org/data/aqueduct-water-risk-atlas) designed to translate complex\nhydrological data into intuitive indicators of water-related risk:\n\n* **Water demand**: gross demand is the maximum potential water required to meet sectoral demands. Sectoral water demand includes: domestic, industrial, irrigation, and livestock. Demand is displayed as a flux (centimeters/year).\n\n* **Water supply**: available blue water, the total amount of renewable freshwater available to a sub-basin with upstream consumption removed, includes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (centimeters/year).\n\n* **Water stress**: an indicator of competition for water resources defined informally as the ratio of demand for water by human society divided by available water. It can be classified into six categories: -1: Arid and low water use, 0: Low (<10%), 1: Low-medium (10-20%), 2: Medium-high (20-40%), 3: High (40-80%), 4: Extremely high (>80%).\n\n* **Water depletion**: the ratio of total water consumption to available renewable water supplies. Total water consumption includes domestic, industrial, irrigation, and livestock consumptive uses. Available renewable water supplies include the impact of upstream consumptive water users and large dams on downstream water availability. Higher values indicate larger impact on the local water supply and decreased water availability for downstream users. Water depletion is similar to water stress; however, instead of looking at total water demand, water depletion is calculated using consumptive withdrawal only. It can be classified into six categories: -1: Arid and low water use, 0 : Low (<5%), 1: Low-medium (5-25%), 2 : Medium-high (25-50%), 3: High (50-75%), 4 : Extremely high (>75%).\n\n[Aqueduct 4.0 FAQ](https://github.com/wri/Aqueduct40/blob/master/data_FAQ.md) explains why the water supply and demand values are measured as fluxes instead of volumes. Volumes (cubic meters) can vary significantly based on the size of each sub-basin, potentially misleading as they might primarily reflect a larger geographical area rather than indicating a higher rate of water flow. On the other hand, fluxes (centimeters/year), which measure the rate of water flow, offer a more direct and equitable means of comparing water availability between different sub-basins. Volume = Flux x Area.\n\nThe spatial resolution is 5 \u00d7 5 arc minutes which equates roughly to 10 kilometer (km) \u00d7 10 km pixels.\nThe future projections were created using CMIP6 climate forcings based on three future scenarios: optimistic (ssp126), business-as-usual (ssp370), and pessimistic (ssp585) available at [HYPFLOWSCI6](https://public.yoda.uu.nl/geo/UU01/YM7A5H.html). WRI's original data are presented at the HydroBASINS Level 6 scale. Indicators are available for periods: 'historical' (averaged over 1979-2019), 2030 (2015-2045), 2050 (2035-2065) and 2080 (2065-2095).\n", + "description": "The World Resources Institute (WRI) [Aqueduct 4.0](https://www.wri.org/data/aqueduct-global-maps-40-data) is the latest iteration of [WRI\u2019s water risk framework](https://www.wri.org/data/aqueduct-water-risk-atlas) designed to translate complex\nhydrological data into intuitive indicators of water-related risk:\n\n- **Water demand**: gross demand is the maximum potential water required to meet sectoral demands. Sectoral water demand includes: domestic, industrial, irrigation, and livestock. Demand is displayed as a flux (centimeters/year).\n\n- **Water supply**: available blue water, the total amount of renewable freshwater available to a sub-basin with upstream consumption removed, includes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (centimeters/year).\n\n- **Water stress**: an indicator of competition for water resources defined informally as the ratio of demand for water by human society divided by available water. It can be classified into six categories: -1: Arid and low water use, 0: Low (<10%), 1: Low-medium (10-20%), 2: Medium-high (20-40%), 3: High (40-80%), 4: Extremely high (>80%).\n\n- **Water depletion**: the ratio of total water consumption to available renewable water supplies. Total water consumption includes domestic, industrial, irrigation, and livestock consumptive uses. Available renewable water supplies include the impact of upstream consumptive water users and large dams on downstream water availability. Higher values indicate larger impact on the local water supply and decreased water availability for downstream users. Water depletion is similar to water stress; however, instead of looking at total water demand, water depletion is calculated using consumptive withdrawal only. It can be classified into six categories: -1: Arid and low water use, 0 : Low (<5%), 1: Low-medium (5-25%), 2 : Medium-high (25-50%), 3: High (50-75%), 4 : Extremely high (>75%).\n\n[Aqueduct 4.0 FAQ](https://github.com/wri/Aqueduct40/blob/master/data_FAQ.md) explains why the water supply and demand values are measured as fluxes instead of volumes. Volumes (cubic meters) can vary significantly based on the size of each sub-basin, potentially misleading as they might primarily reflect a larger geographical area rather than indicating a higher rate of water flow. On the other hand, fluxes (centimeters/year), which measure the rate of water flow, offer a more direct and equitable means of comparing water availability between different sub-basins. Volume = Flux x Area.\n\nThe spatial resolution is 5 \u00d7 5 arc minutes which equates roughly to 10 kilometer (km) \u00d7 10 km pixels.\nThe future projections were created using CMIP6 climate forcings based on three future scenarios: optimistic (ssp126), business-as-usual (ssp370), and pessimistic (ssp585) available at [HYPFLOWSCI6](https://public.yoda.uu.nl/geo/UU01/YM7A5H.html). WRI's original data are presented at the HydroBASINS Level 6 scale. Indicators are available for periods: 'historical' (averaged over 1979-2019), 2030 (2015-2045), 2050 (2035-2065) and 2080 (2065-2095).\n", "map": { "colormap": { "min_index": 1, @@ -2263,6 +2443,12 @@ -85.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": null, "source": "map_array_pyramid" }, @@ -2312,7 +2498,7 @@ "display_groups": [ "Water supply in centimeters/year (Aqueduct 4.0)" ], - "description": "The World Resources Institute (WRI) [Aqueduct 4.0](https://www.wri.org/data/aqueduct-global-maps-40-data) is the latest iteration of [WRI\u2019s water risk framework](https://www.wri.org/data/aqueduct-water-risk-atlas) designed to translate complex\nhydrological data into intuitive indicators of water-related risk:\n\n* **Water demand**: gross demand is the maximum potential water required to meet sectoral demands. Sectoral water demand includes: domestic, industrial, irrigation, and livestock. Demand is displayed as a flux (centimeters/year).\n\n* **Water supply**: available blue water, the total amount of renewable freshwater available to a sub-basin with upstream consumption removed, includes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (centimeters/year).\n\n* **Water stress**: an indicator of competition for water resources defined informally as the ratio of demand for water by human society divided by available water. It can be classified into six categories: -1: Arid and low water use, 0: Low (<10%), 1: Low-medium (10-20%), 2: Medium-high (20-40%), 3: High (40-80%), 4: Extremely high (>80%).\n\n* **Water depletion**: the ratio of total water consumption to available renewable water supplies. Total water consumption includes domestic, industrial, irrigation, and livestock consumptive uses. Available renewable water supplies include the impact of upstream consumptive water users and large dams on downstream water availability. Higher values indicate larger impact on the local water supply and decreased water availability for downstream users. Water depletion is similar to water stress; however, instead of looking at total water demand, water depletion is calculated using consumptive withdrawal only. It can be classified into six categories: -1: Arid and low water use, 0 : Low (<5%), 1: Low-medium (5-25%), 2 : Medium-high (25-50%), 3: High (50-75%), 4 : Extremely high (>75%).\n\n[Aqueduct 4.0 FAQ](https://github.com/wri/Aqueduct40/blob/master/data_FAQ.md) explains why the water supply and demand values are measured as fluxes instead of volumes. Volumes (cubic meters) can vary significantly based on the size of each sub-basin, potentially misleading as they might primarily reflect a larger geographical area rather than indicating a higher rate of water flow. On the other hand, fluxes (centimeters/year), which measure the rate of water flow, offer a more direct and equitable means of comparing water availability between different sub-basins. Volume = Flux x Area.\n\nThe spatial resolution is 5 \u00d7 5 arc minutes which equates roughly to 10 kilometer (km) \u00d7 10 km pixels.\nThe future projections were created using CMIP6 climate forcings based on three future scenarios: optimistic (ssp126), business-as-usual (ssp370), and pessimistic (ssp585) available at [HYPFLOWSCI6](https://public.yoda.uu.nl/geo/UU01/YM7A5H.html). WRI's original data are presented at the HydroBASINS Level 6 scale. Indicators are available for periods: 'historical' (averaged over 1979-2019), 2030 (2015-2045), 2050 (2035-2065) and 2080 (2065-2095).\n", + "description": "The World Resources Institute (WRI) [Aqueduct 4.0](https://www.wri.org/data/aqueduct-global-maps-40-data) is the latest iteration of [WRI\u2019s water risk framework](https://www.wri.org/data/aqueduct-water-risk-atlas) designed to translate complex\nhydrological data into intuitive indicators of water-related risk:\n\n- **Water demand**: gross demand is the maximum potential water required to meet sectoral demands. Sectoral water demand includes: domestic, industrial, irrigation, and livestock. Demand is displayed as a flux (centimeters/year).\n\n- **Water supply**: available blue water, the total amount of renewable freshwater available to a sub-basin with upstream consumption removed, includes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (centimeters/year).\n\n- **Water stress**: an indicator of competition for water resources defined informally as the ratio of demand for water by human society divided by available water. It can be classified into six categories: -1: Arid and low water use, 0: Low (<10%), 1: Low-medium (10-20%), 2: Medium-high (20-40%), 3: High (40-80%), 4: Extremely high (>80%).\n\n- **Water depletion**: the ratio of total water consumption to available renewable water supplies. Total water consumption includes domestic, industrial, irrigation, and livestock consumptive uses. Available renewable water supplies include the impact of upstream consumptive water users and large dams on downstream water availability. Higher values indicate larger impact on the local water supply and decreased water availability for downstream users. Water depletion is similar to water stress; however, instead of looking at total water demand, water depletion is calculated using consumptive withdrawal only. It can be classified into six categories: -1: Arid and low water use, 0 : Low (<5%), 1: Low-medium (5-25%), 2 : Medium-high (25-50%), 3: High (50-75%), 4 : Extremely high (>75%).\n\n[Aqueduct 4.0 FAQ](https://github.com/wri/Aqueduct40/blob/master/data_FAQ.md) explains why the water supply and demand values are measured as fluxes instead of volumes. Volumes (cubic meters) can vary significantly based on the size of each sub-basin, potentially misleading as they might primarily reflect a larger geographical area rather than indicating a higher rate of water flow. On the other hand, fluxes (centimeters/year), which measure the rate of water flow, offer a more direct and equitable means of comparing water availability between different sub-basins. Volume = Flux x Area.\n\nThe spatial resolution is 5 \u00d7 5 arc minutes which equates roughly to 10 kilometer (km) \u00d7 10 km pixels.\nThe future projections were created using CMIP6 climate forcings based on three future scenarios: optimistic (ssp126), business-as-usual (ssp370), and pessimistic (ssp585) available at [HYPFLOWSCI6](https://public.yoda.uu.nl/geo/UU01/YM7A5H.html). WRI's original data are presented at the HydroBASINS Level 6 scale. Indicators are available for periods: 'historical' (averaged over 1979-2019), 2030 (2015-2045), 2050 (2035-2065) and 2080 (2065-2095).\n", "map": { "colormap": { "min_index": 1, @@ -2342,6 +2528,12 @@ -85.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": null, "source": "map_array_pyramid" }, @@ -2391,7 +2583,7 @@ "display_groups": [ "Water stress (Aqueduct 4.0)" ], - "description": "The World Resources Institute (WRI) [Aqueduct 4.0](https://www.wri.org/data/aqueduct-global-maps-40-data) is the latest iteration of [WRI\u2019s water risk framework](https://www.wri.org/data/aqueduct-water-risk-atlas) designed to translate complex\nhydrological data into intuitive indicators of water-related risk:\n\n* **Water demand**: gross demand is the maximum potential water required to meet sectoral demands. Sectoral water demand includes: domestic, industrial, irrigation, and livestock. Demand is displayed as a flux (centimeters/year).\n\n* **Water supply**: available blue water, the total amount of renewable freshwater available to a sub-basin with upstream consumption removed, includes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (centimeters/year).\n\n* **Water stress**: an indicator of competition for water resources defined informally as the ratio of demand for water by human society divided by available water. It can be classified into six categories: -1: Arid and low water use, 0: Low (<10%), 1: Low-medium (10-20%), 2: Medium-high (20-40%), 3: High (40-80%), 4: Extremely high (>80%).\n\n* **Water depletion**: the ratio of total water consumption to available renewable water supplies. Total water consumption includes domestic, industrial, irrigation, and livestock consumptive uses. Available renewable water supplies include the impact of upstream consumptive water users and large dams on downstream water availability. Higher values indicate larger impact on the local water supply and decreased water availability for downstream users. Water depletion is similar to water stress; however, instead of looking at total water demand, water depletion is calculated using consumptive withdrawal only. It can be classified into six categories: -1: Arid and low water use, 0 : Low (<5%), 1: Low-medium (5-25%), 2 : Medium-high (25-50%), 3: High (50-75%), 4 : Extremely high (>75%).\n\n[Aqueduct 4.0 FAQ](https://github.com/wri/Aqueduct40/blob/master/data_FAQ.md) explains why the water supply and demand values are measured as fluxes instead of volumes. Volumes (cubic meters) can vary significantly based on the size of each sub-basin, potentially misleading as they might primarily reflect a larger geographical area rather than indicating a higher rate of water flow. On the other hand, fluxes (centimeters/year), which measure the rate of water flow, offer a more direct and equitable means of comparing water availability between different sub-basins. Volume = Flux x Area.\n\nThe spatial resolution is 5 \u00d7 5 arc minutes which equates roughly to 10 kilometer (km) \u00d7 10 km pixels.\nThe future projections were created using CMIP6 climate forcings based on three future scenarios: optimistic (ssp126), business-as-usual (ssp370), and pessimistic (ssp585) available at [HYPFLOWSCI6](https://public.yoda.uu.nl/geo/UU01/YM7A5H.html). WRI's original data are presented at the HydroBASINS Level 6 scale. Indicators are available for periods: 'historical' (averaged over 1979-2019), 2030 (2015-2045), 2050 (2035-2065) and 2080 (2065-2095).\n", + "description": "The World Resources Institute (WRI) [Aqueduct 4.0](https://www.wri.org/data/aqueduct-global-maps-40-data) is the latest iteration of [WRI\u2019s water risk framework](https://www.wri.org/data/aqueduct-water-risk-atlas) designed to translate complex\nhydrological data into intuitive indicators of water-related risk:\n\n- **Water demand**: gross demand is the maximum potential water required to meet sectoral demands. Sectoral water demand includes: domestic, industrial, irrigation, and livestock. Demand is displayed as a flux (centimeters/year).\n\n- **Water supply**: available blue water, the total amount of renewable freshwater available to a sub-basin with upstream consumption removed, includes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (centimeters/year).\n\n- **Water stress**: an indicator of competition for water resources defined informally as the ratio of demand for water by human society divided by available water. It can be classified into six categories: -1: Arid and low water use, 0: Low (<10%), 1: Low-medium (10-20%), 2: Medium-high (20-40%), 3: High (40-80%), 4: Extremely high (>80%).\n\n- **Water depletion**: the ratio of total water consumption to available renewable water supplies. Total water consumption includes domestic, industrial, irrigation, and livestock consumptive uses. Available renewable water supplies include the impact of upstream consumptive water users and large dams on downstream water availability. Higher values indicate larger impact on the local water supply and decreased water availability for downstream users. Water depletion is similar to water stress; however, instead of looking at total water demand, water depletion is calculated using consumptive withdrawal only. It can be classified into six categories: -1: Arid and low water use, 0 : Low (<5%), 1: Low-medium (5-25%), 2 : Medium-high (25-50%), 3: High (50-75%), 4 : Extremely high (>75%).\n\n[Aqueduct 4.0 FAQ](https://github.com/wri/Aqueduct40/blob/master/data_FAQ.md) explains why the water supply and demand values are measured as fluxes instead of volumes. Volumes (cubic meters) can vary significantly based on the size of each sub-basin, potentially misleading as they might primarily reflect a larger geographical area rather than indicating a higher rate of water flow. On the other hand, fluxes (centimeters/year), which measure the rate of water flow, offer a more direct and equitable means of comparing water availability between different sub-basins. Volume = Flux x Area.\n\nThe spatial resolution is 5 \u00d7 5 arc minutes which equates roughly to 10 kilometer (km) \u00d7 10 km pixels.\nThe future projections were created using CMIP6 climate forcings based on three future scenarios: optimistic (ssp126), business-as-usual (ssp370), and pessimistic (ssp585) available at [HYPFLOWSCI6](https://public.yoda.uu.nl/geo/UU01/YM7A5H.html). WRI's original data are presented at the HydroBASINS Level 6 scale. Indicators are available for periods: 'historical' (averaged over 1979-2019), 2030 (2015-2045), 2050 (2035-2065) and 2080 (2065-2095).\n", "map": { "colormap": { "min_index": 1, @@ -2421,6 +2613,12 @@ -85.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": null, "source": "map_array_pyramid" }, @@ -2470,7 +2668,7 @@ "display_groups": [ "Water depletion (Aqueduct 4.0)" ], - "description": "The World Resources Institute (WRI) [Aqueduct 4.0](https://www.wri.org/data/aqueduct-global-maps-40-data) is the latest iteration of [WRI\u2019s water risk framework](https://www.wri.org/data/aqueduct-water-risk-atlas) designed to translate complex\nhydrological data into intuitive indicators of water-related risk:\n\n* **Water demand**: gross demand is the maximum potential water required to meet sectoral demands. Sectoral water demand includes: domestic, industrial, irrigation, and livestock. Demand is displayed as a flux (centimeters/year).\n\n* **Water supply**: available blue water, the total amount of renewable freshwater available to a sub-basin with upstream consumption removed, includes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (centimeters/year).\n\n* **Water stress**: an indicator of competition for water resources defined informally as the ratio of demand for water by human society divided by available water. It can be classified into six categories: -1: Arid and low water use, 0: Low (<10%), 1: Low-medium (10-20%), 2: Medium-high (20-40%), 3: High (40-80%), 4: Extremely high (>80%).\n\n* **Water depletion**: the ratio of total water consumption to available renewable water supplies. Total water consumption includes domestic, industrial, irrigation, and livestock consumptive uses. Available renewable water supplies include the impact of upstream consumptive water users and large dams on downstream water availability. Higher values indicate larger impact on the local water supply and decreased water availability for downstream users. Water depletion is similar to water stress; however, instead of looking at total water demand, water depletion is calculated using consumptive withdrawal only. It can be classified into six categories: -1: Arid and low water use, 0 : Low (<5%), 1: Low-medium (5-25%), 2 : Medium-high (25-50%), 3: High (50-75%), 4 : Extremely high (>75%).\n\n[Aqueduct 4.0 FAQ](https://github.com/wri/Aqueduct40/blob/master/data_FAQ.md) explains why the water supply and demand values are measured as fluxes instead of volumes. Volumes (cubic meters) can vary significantly based on the size of each sub-basin, potentially misleading as they might primarily reflect a larger geographical area rather than indicating a higher rate of water flow. On the other hand, fluxes (centimeters/year), which measure the rate of water flow, offer a more direct and equitable means of comparing water availability between different sub-basins. Volume = Flux x Area.\n\nThe spatial resolution is 5 \u00d7 5 arc minutes which equates roughly to 10 kilometer (km) \u00d7 10 km pixels.\nThe future projections were created using CMIP6 climate forcings based on three future scenarios: optimistic (ssp126), business-as-usual (ssp370), and pessimistic (ssp585) available at [HYPFLOWSCI6](https://public.yoda.uu.nl/geo/UU01/YM7A5H.html). WRI's original data are presented at the HydroBASINS Level 6 scale. Indicators are available for periods: 'historical' (averaged over 1979-2019), 2030 (2015-2045), 2050 (2035-2065) and 2080 (2065-2095).\n", + "description": "The World Resources Institute (WRI) [Aqueduct 4.0](https://www.wri.org/data/aqueduct-global-maps-40-data) is the latest iteration of [WRI\u2019s water risk framework](https://www.wri.org/data/aqueduct-water-risk-atlas) designed to translate complex\nhydrological data into intuitive indicators of water-related risk:\n\n- **Water demand**: gross demand is the maximum potential water required to meet sectoral demands. Sectoral water demand includes: domestic, industrial, irrigation, and livestock. Demand is displayed as a flux (centimeters/year).\n\n- **Water supply**: available blue water, the total amount of renewable freshwater available to a sub-basin with upstream consumption removed, includes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (centimeters/year).\n\n- **Water stress**: an indicator of competition for water resources defined informally as the ratio of demand for water by human society divided by available water. It can be classified into six categories: -1: Arid and low water use, 0: Low (<10%), 1: Low-medium (10-20%), 2: Medium-high (20-40%), 3: High (40-80%), 4: Extremely high (>80%).\n\n- **Water depletion**: the ratio of total water consumption to available renewable water supplies. Total water consumption includes domestic, industrial, irrigation, and livestock consumptive uses. Available renewable water supplies include the impact of upstream consumptive water users and large dams on downstream water availability. Higher values indicate larger impact on the local water supply and decreased water availability for downstream users. Water depletion is similar to water stress; however, instead of looking at total water demand, water depletion is calculated using consumptive withdrawal only. It can be classified into six categories: -1: Arid and low water use, 0 : Low (<5%), 1: Low-medium (5-25%), 2 : Medium-high (25-50%), 3: High (50-75%), 4 : Extremely high (>75%).\n\n[Aqueduct 4.0 FAQ](https://github.com/wri/Aqueduct40/blob/master/data_FAQ.md) explains why the water supply and demand values are measured as fluxes instead of volumes. Volumes (cubic meters) can vary significantly based on the size of each sub-basin, potentially misleading as they might primarily reflect a larger geographical area rather than indicating a higher rate of water flow. On the other hand, fluxes (centimeters/year), which measure the rate of water flow, offer a more direct and equitable means of comparing water availability between different sub-basins. Volume = Flux x Area.\n\nThe spatial resolution is 5 \u00d7 5 arc minutes which equates roughly to 10 kilometer (km) \u00d7 10 km pixels.\nThe future projections were created using CMIP6 climate forcings based on three future scenarios: optimistic (ssp126), business-as-usual (ssp370), and pessimistic (ssp585) available at [HYPFLOWSCI6](https://public.yoda.uu.nl/geo/UU01/YM7A5H.html). WRI's original data are presented at the HydroBASINS Level 6 scale. Indicators are available for periods: 'historical' (averaged over 1979-2019), 2030 (2015-2045), 2050 (2035-2065) and 2080 (2065-2095).\n", "map": { "colormap": { "min_index": 1, @@ -2500,6 +2698,12 @@ -85.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": null, "source": "map_array_pyramid" }, @@ -2549,7 +2753,7 @@ "display_groups": [ "Water stress category (Aqueduct 4.0)" ], - "description": "The World Resources Institute (WRI) [Aqueduct 4.0](https://www.wri.org/data/aqueduct-global-maps-40-data) is the latest iteration of [WRI\u2019s water risk framework](https://www.wri.org/data/aqueduct-water-risk-atlas) designed to translate complex\nhydrological data into intuitive indicators of water-related risk:\n\n* **Water demand**: gross demand is the maximum potential water required to meet sectoral demands. Sectoral water demand includes: domestic, industrial, irrigation, and livestock. Demand is displayed as a flux (centimeters/year).\n\n* **Water supply**: available blue water, the total amount of renewable freshwater available to a sub-basin with upstream consumption removed, includes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (centimeters/year).\n\n* **Water stress**: an indicator of competition for water resources defined informally as the ratio of demand for water by human society divided by available water. It can be classified into six categories: -1: Arid and low water use, 0: Low (<10%), 1: Low-medium (10-20%), 2: Medium-high (20-40%), 3: High (40-80%), 4: Extremely high (>80%).\n\n* **Water depletion**: the ratio of total water consumption to available renewable water supplies. Total water consumption includes domestic, industrial, irrigation, and livestock consumptive uses. Available renewable water supplies include the impact of upstream consumptive water users and large dams on downstream water availability. Higher values indicate larger impact on the local water supply and decreased water availability for downstream users. Water depletion is similar to water stress; however, instead of looking at total water demand, water depletion is calculated using consumptive withdrawal only. It can be classified into six categories: -1: Arid and low water use, 0 : Low (<5%), 1: Low-medium (5-25%), 2 : Medium-high (25-50%), 3: High (50-75%), 4 : Extremely high (>75%).\n\n[Aqueduct 4.0 FAQ](https://github.com/wri/Aqueduct40/blob/master/data_FAQ.md) explains why the water supply and demand values are measured as fluxes instead of volumes. Volumes (cubic meters) can vary significantly based on the size of each sub-basin, potentially misleading as they might primarily reflect a larger geographical area rather than indicating a higher rate of water flow. On the other hand, fluxes (centimeters/year), which measure the rate of water flow, offer a more direct and equitable means of comparing water availability between different sub-basins. Volume = Flux x Area.\n\nThe spatial resolution is 5 \u00d7 5 arc minutes which equates roughly to 10 kilometer (km) \u00d7 10 km pixels.\nThe future projections were created using CMIP6 climate forcings based on three future scenarios: optimistic (ssp126), business-as-usual (ssp370), and pessimistic (ssp585) available at [HYPFLOWSCI6](https://public.yoda.uu.nl/geo/UU01/YM7A5H.html). WRI's original data are presented at the HydroBASINS Level 6 scale. Indicators are available for periods: 'historical' (averaged over 1979-2019), 2030 (2015-2045), 2050 (2035-2065) and 2080 (2065-2095).\n", + "description": "The World Resources Institute (WRI) [Aqueduct 4.0](https://www.wri.org/data/aqueduct-global-maps-40-data) is the latest iteration of [WRI\u2019s water risk framework](https://www.wri.org/data/aqueduct-water-risk-atlas) designed to translate complex\nhydrological data into intuitive indicators of water-related risk:\n\n- **Water demand**: gross demand is the maximum potential water required to meet sectoral demands. Sectoral water demand includes: domestic, industrial, irrigation, and livestock. Demand is displayed as a flux (centimeters/year).\n\n- **Water supply**: available blue water, the total amount of renewable freshwater available to a sub-basin with upstream consumption removed, includes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (centimeters/year).\n\n- **Water stress**: an indicator of competition for water resources defined informally as the ratio of demand for water by human society divided by available water. It can be classified into six categories: -1: Arid and low water use, 0: Low (<10%), 1: Low-medium (10-20%), 2: Medium-high (20-40%), 3: High (40-80%), 4: Extremely high (>80%).\n\n- **Water depletion**: the ratio of total water consumption to available renewable water supplies. Total water consumption includes domestic, industrial, irrigation, and livestock consumptive uses. Available renewable water supplies include the impact of upstream consumptive water users and large dams on downstream water availability. Higher values indicate larger impact on the local water supply and decreased water availability for downstream users. Water depletion is similar to water stress; however, instead of looking at total water demand, water depletion is calculated using consumptive withdrawal only. It can be classified into six categories: -1: Arid and low water use, 0 : Low (<5%), 1: Low-medium (5-25%), 2 : Medium-high (25-50%), 3: High (50-75%), 4 : Extremely high (>75%).\n\n[Aqueduct 4.0 FAQ](https://github.com/wri/Aqueduct40/blob/master/data_FAQ.md) explains why the water supply and demand values are measured as fluxes instead of volumes. Volumes (cubic meters) can vary significantly based on the size of each sub-basin, potentially misleading as they might primarily reflect a larger geographical area rather than indicating a higher rate of water flow. On the other hand, fluxes (centimeters/year), which measure the rate of water flow, offer a more direct and equitable means of comparing water availability between different sub-basins. Volume = Flux x Area.\n\nThe spatial resolution is 5 \u00d7 5 arc minutes which equates roughly to 10 kilometer (km) \u00d7 10 km pixels.\nThe future projections were created using CMIP6 climate forcings based on three future scenarios: optimistic (ssp126), business-as-usual (ssp370), and pessimistic (ssp585) available at [HYPFLOWSCI6](https://public.yoda.uu.nl/geo/UU01/YM7A5H.html). WRI's original data are presented at the HydroBASINS Level 6 scale. Indicators are available for periods: 'historical' (averaged over 1979-2019), 2030 (2015-2045), 2050 (2035-2065) and 2080 (2065-2095).\n", "map": { "colormap": { "min_index": 1, @@ -2579,6 +2783,12 @@ -85.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": null, "source": "map_array_pyramid" }, @@ -2628,7 +2838,7 @@ "display_groups": [ "Water depletion category (Aqueduct 4.0)" ], - "description": "The World Resources Institute (WRI) [Aqueduct 4.0](https://www.wri.org/data/aqueduct-global-maps-40-data) is the latest iteration of [WRI\u2019s water risk framework](https://www.wri.org/data/aqueduct-water-risk-atlas) designed to translate complex\nhydrological data into intuitive indicators of water-related risk:\n\n* **Water demand**: gross demand is the maximum potential water required to meet sectoral demands. Sectoral water demand includes: domestic, industrial, irrigation, and livestock. Demand is displayed as a flux (centimeters/year).\n\n* **Water supply**: available blue water, the total amount of renewable freshwater available to a sub-basin with upstream consumption removed, includes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (centimeters/year).\n\n* **Water stress**: an indicator of competition for water resources defined informally as the ratio of demand for water by human society divided by available water. It can be classified into six categories: -1: Arid and low water use, 0: Low (<10%), 1: Low-medium (10-20%), 2: Medium-high (20-40%), 3: High (40-80%), 4: Extremely high (>80%).\n\n* **Water depletion**: the ratio of total water consumption to available renewable water supplies. Total water consumption includes domestic, industrial, irrigation, and livestock consumptive uses. Available renewable water supplies include the impact of upstream consumptive water users and large dams on downstream water availability. Higher values indicate larger impact on the local water supply and decreased water availability for downstream users. Water depletion is similar to water stress; however, instead of looking at total water demand, water depletion is calculated using consumptive withdrawal only. It can be classified into six categories: -1: Arid and low water use, 0 : Low (<5%), 1: Low-medium (5-25%), 2 : Medium-high (25-50%), 3: High (50-75%), 4 : Extremely high (>75%).\n\n[Aqueduct 4.0 FAQ](https://github.com/wri/Aqueduct40/blob/master/data_FAQ.md) explains why the water supply and demand values are measured as fluxes instead of volumes. Volumes (cubic meters) can vary significantly based on the size of each sub-basin, potentially misleading as they might primarily reflect a larger geographical area rather than indicating a higher rate of water flow. On the other hand, fluxes (centimeters/year), which measure the rate of water flow, offer a more direct and equitable means of comparing water availability between different sub-basins. Volume = Flux x Area.\n\nThe spatial resolution is 5 \u00d7 5 arc minutes which equates roughly to 10 kilometer (km) \u00d7 10 km pixels.\nThe future projections were created using CMIP6 climate forcings based on three future scenarios: optimistic (ssp126), business-as-usual (ssp370), and pessimistic (ssp585) available at [HYPFLOWSCI6](https://public.yoda.uu.nl/geo/UU01/YM7A5H.html). WRI's original data are presented at the HydroBASINS Level 6 scale. Indicators are available for periods: 'historical' (averaged over 1979-2019), 2030 (2015-2045), 2050 (2035-2065) and 2080 (2065-2095).\n", + "description": "The World Resources Institute (WRI) [Aqueduct 4.0](https://www.wri.org/data/aqueduct-global-maps-40-data) is the latest iteration of [WRI\u2019s water risk framework](https://www.wri.org/data/aqueduct-water-risk-atlas) designed to translate complex\nhydrological data into intuitive indicators of water-related risk:\n\n- **Water demand**: gross demand is the maximum potential water required to meet sectoral demands. Sectoral water demand includes: domestic, industrial, irrigation, and livestock. Demand is displayed as a flux (centimeters/year).\n\n- **Water supply**: available blue water, the total amount of renewable freshwater available to a sub-basin with upstream consumption removed, includes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (centimeters/year).\n\n- **Water stress**: an indicator of competition for water resources defined informally as the ratio of demand for water by human society divided by available water. It can be classified into six categories: -1: Arid and low water use, 0: Low (<10%), 1: Low-medium (10-20%), 2: Medium-high (20-40%), 3: High (40-80%), 4: Extremely high (>80%).\n\n- **Water depletion**: the ratio of total water consumption to available renewable water supplies. Total water consumption includes domestic, industrial, irrigation, and livestock consumptive uses. Available renewable water supplies include the impact of upstream consumptive water users and large dams on downstream water availability. Higher values indicate larger impact on the local water supply and decreased water availability for downstream users. Water depletion is similar to water stress; however, instead of looking at total water demand, water depletion is calculated using consumptive withdrawal only. It can be classified into six categories: -1: Arid and low water use, 0 : Low (<5%), 1: Low-medium (5-25%), 2 : Medium-high (25-50%), 3: High (50-75%), 4 : Extremely high (>75%).\n\n[Aqueduct 4.0 FAQ](https://github.com/wri/Aqueduct40/blob/master/data_FAQ.md) explains why the water supply and demand values are measured as fluxes instead of volumes. Volumes (cubic meters) can vary significantly based on the size of each sub-basin, potentially misleading as they might primarily reflect a larger geographical area rather than indicating a higher rate of water flow. On the other hand, fluxes (centimeters/year), which measure the rate of water flow, offer a more direct and equitable means of comparing water availability between different sub-basins. Volume = Flux x Area.\n\nThe spatial resolution is 5 \u00d7 5 arc minutes which equates roughly to 10 kilometer (km) \u00d7 10 km pixels.\nThe future projections were created using CMIP6 climate forcings based on three future scenarios: optimistic (ssp126), business-as-usual (ssp370), and pessimistic (ssp585) available at [HYPFLOWSCI6](https://public.yoda.uu.nl/geo/UU01/YM7A5H.html). WRI's original data are presented at the HydroBASINS Level 6 scale. Indicators are available for periods: 'historical' (averaged over 1979-2019), 2030 (2015-2045), 2050 (2035-2065) and 2080 (2065-2095).\n", "map": { "colormap": { "min_index": 1, @@ -2658,6 +2868,12 @@ -85.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": null, "source": "map_array_pyramid" }, @@ -2741,6 +2957,12 @@ -60.0 ] ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], "index_values": [ 0, -1, @@ -2776,7 +2998,7 @@ "params": {}, "display_name": "Flood depth (TUDelft)", "display_groups": [], - "description": "Flood depth for riverine floods occurring in Europe in the present and future climate, including protection levels\nfrom the FLOPROS database.\nBased on the CLMcom-CCLM4-8-17-EC-EARTH regional climate simulation (EURO-CORDEX), sets are available for RCP 4.5 and RCP 8.5\nfor projected (central) years 2035 and 2085. The set has a spatial resolution of 100m and comprises return periods of\n10, 30, 100, 300 and 1000 years.\n\nThe data set is [here](https://data.4tu.nl/datasets/df7b63b0-1114-4515-a562-117ca165dc5b), part of the \n[RAIN data set](https://data.4tu.nl/collections/1e84bf47-5838-40cb-b381-64d3497b3b36)\n(Risk Analysis of Infrastructure Networks in Response to Extreme Weather). The RAIN report is\n[here](http://rain-project.eu/wp-content/uploads/2016/09/D2.5_REPORT_final.pdf).\n\nTo derive this indicator, 'River_flood_extent_X_Y_Z_with_protection' and 'River_flood_depth_X_Y_Z_R' data sets are combined to\nyield protected flood depths.", + "description": "Unprotected flood depth for riverine floods occurring in Europe in the present and future climate.\nBased on the CLMcom-CCLM4-8-17-EC-EARTH regional climate simulation (EURO-CORDEX), sets are available for RCP 4.5 and RCP 8.5\nfor projected (central) years 2035 and 2085. The set has a spatial resolution of 100m and comprises return periods of\n10, 30, 100, 300 and 1000 years.\n\nThe data set is [here](https://data.4tu.nl/datasets/df7b63b0-1114-4515-a562-117ca165dc5b), part of the\n[RAIN data set](https://data.4tu.nl/collections/1e84bf47-5838-40cb-b381-64d3497b3b36)\n(Risk Analysis of Infrastructure Networks in Response to Extreme Weather). The RAIN report is\n[here](http://rain-project.eu/wp-content/uploads/2016/09/D2.5_REPORT_final.pdf).\n\nUnprotected flood depths are combined from the 'River_flood_depth_X_Y_Z_R' data sets.\nStandard of protection data is separately available using the 'River_flood_extent_X_Y_Z_with_protection' data set\n(which makes use of the FLOPROS database), which provides protected flood extent.\n", "map": { "colormap": { "min_index": 1, @@ -2789,6 +3011,7 @@ }, "path": "maps/inundation/river_tudelft/v2/flood_depth_{scenario}_{year}_map", "bounds": [], + "bbox": [], "index_values": null, "source": "map_array_pyramid" }, @@ -2796,7 +3019,7 @@ { "id": "historical", "years": [ - 1971 + 1985 ] }, { @@ -2815,6 +3038,57 @@ } ], "units": "metres" + }, + { + "hazard_type": "RiverineInundation", + "group_id": "", + "path": "inundation/river_tudelft/v2/flood_sop_{scenario}_{year}", + "indicator_id": "flood_sop", + "indicator_model_id": "tudelft", + "indicator_model_gcm": "CLMcom-CCLM4-8-17-EC-EARTH", + "params": {}, + "display_name": "Standard of protection (TUDelft)", + "display_groups": [], + "description": "Maximum and minimum standards of protection for riverine floods occurring in Europe in the present and future climate. \nThis is derived from a data set of protected flood extent i.e. the minimum return period for which flood depth is non-zero.\n\nThe data set is [here](https://data.4tu.nl/datasets/df7b63b0-1114-4515-a562-117ca165dc5b), part of the\n[RAIN data set](https://data.4tu.nl/collections/1e84bf47-5838-40cb-b381-64d3497b3b36)\n(Risk Analysis of Infrastructure Networks in Response to Extreme Weather). The RAIN report is\n[here](http://rain-project.eu/wp-content/uploads/2016/09/D2.5_REPORT_final.pdf).\n\nThe 'River_flood_extent_X_Y_Z_with_protection' data set is obtained by applying FLOPROS database flood protection standards\non top of a data set of unprotected flood depth. That is, it provides information about flood protection for regions susceptible\nto flood. This data set is not simply based on FLOPROS database therefore.\n\nA minimum and maximum is provided to capture uncertainty in flood protection which vulnerability models may choose to take into\naccount. The protection bands are those of FLOPROS: 2-10 years, 10-30 years, 30-100 years, 100-300 years, 300-1000 years, 1000-10000 years.\n\nAs an example, if a region has a protected extent of 300 years, this is taken to imply that the area is protected against flood within the\nbounds 100-300 years. It is then a modelling choice as to whether protection is taken from the 100 or 300 year flood depth.\n", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 1500.0, + "name": "flare", + "nodata_index": 0, + "units": "years" + }, + "path": "maps/inundation/river_tudelft/v2/flood_sop_{scenario}_{year}_map", + "bounds": [], + "bbox": [], + "index_values": null, + "source": "map_array_pyramid" + }, + "scenarios": [ + { + "id": "historical", + "years": [ + 1985 + ] + }, + { + "id": "rcp4p5", + "years": [ + 2035, + 2085 + ] + }, + { + "id": "rcp8p5", + "years": [ + 2035, + 2085 + ] + } + ], + "units": "years" } ] } diff --git a/src/physrisk/kernel/hazard_model.py b/src/physrisk/kernel/hazard_model.py index 6b456d3d..40bbc958 100644 --- a/src/physrisk/kernel/hazard_model.py +++ b/src/physrisk/kernel/hazard_model.py @@ -1,11 +1,12 @@ import sys from abc import ABC, abstractmethod from collections import defaultdict -from typing import Dict, List, Mapping, Optional, Protocol, Tuple +from typing import Dict, List, Mapping, Optional, Protocol, Tuple, Type import numpy as np from physrisk.data.hazard_data_provider import HazardDataHint +from physrisk.kernel.hazards import Hazard class HazardDataRequest: @@ -16,7 +17,7 @@ class HazardDataRequest: def __init__( self, - hazard_type: type, + hazard_type: Type[Hazard], longitude: float, latitude: float, *, diff --git a/src/physrisk/kernel/hazards.py b/src/physrisk/kernel/hazards.py index c307cc6a..70710e84 100644 --- a/src/physrisk/kernel/hazards.py +++ b/src/physrisk/kernel/hazards.py @@ -1,32 +1,42 @@ import inspect import sys from enum import Enum -from typing import cast +from typing import Dict, Type -class HazardKind(Enum): - acute = (1,) - chronic = 2 +class IndicatorData(Enum): + EVENT = 1 + PARAMETERS = 2 -class InundationType(Enum): - riverine = (1,) - coastal = 2 +class HazardKind(Enum): + ACUTE = 1 + CHRONIC = 2 + UNKNOWN = 3 class Hazard: - @staticmethod - def kind(hazard_type): - return cast(HazardKind, hazard_type.kind) + kind = HazardKind.UNKNOWN + indicator_data: Dict[str, IndicatorData] = {} + + +def hazard_kind(hazard_type: Type[Hazard]): + return hazard_type.kind + + +def indicator_data(hazard_type: Type[Hazard], indicator_id: str): + default = IndicatorData.EVENT if hazard_type.kind == HazardKind.ACUTE else IndicatorData.PARAMETERS + return hazard_type.indicator_data.get(indicator_id, default) class ChronicHeat(Hazard): - kind = HazardKind.chronic + kind = HazardKind.CHRONIC pass class Inundation(Hazard): - kind = HazardKind.acute + kind = HazardKind.ACUTE + indicator_data = {"flood_depth": IndicatorData.EVENT, "flood_sop": IndicatorData.PARAMETERS} pass @@ -35,52 +45,49 @@ class AirTemperature(ChronicHeat): class CoastalInundation(Inundation): - kind = HazardKind.acute pass class ChronicWind(Hazard): - kind = HazardKind.chronic + kind = HazardKind.CHRONIC pass class CombinedInundation(Hazard): - kind = HazardKind.chronic + kind = HazardKind.CHRONIC pass class Drought(Hazard): - kind = HazardKind.chronic + kind = HazardKind.CHRONIC pass class Fire(Hazard): - kind = HazardKind.chronic + kind = HazardKind.CHRONIC pass class Hail(Hazard): - kind = HazardKind.chronic + kind = HazardKind.CHRONIC pass class PluvialInundation(Inundation): - kind = HazardKind.acute pass class Precipitation(Hazard): - kind = HazardKind.chronic + kind = HazardKind.CHRONIC pass class RiverineInundation(Inundation): - kind = HazardKind.acute pass class WaterRisk(Hazard): - kind = HazardKind.chronic + kind = HazardKind.CHRONIC pass @@ -89,7 +96,7 @@ class WaterTemperature(ChronicHeat): class Wind(Hazard): - kind = HazardKind.acute + kind = HazardKind.ACUTE pass