From 8718ecd3b1e27219002c13b8eb5457fe55ac1d2a Mon Sep 17 00:00:00 2001 From: Joe Moorhouse <5102656+joemoorhouse@users.noreply.github.com> Date: Fri, 1 Nov 2024 13:35:41 +0000 Subject: [PATCH] Implement nodata as zero option for map tile generation. (#128) * Add in nodata_as_zero option for maptiles. Signed-off-by: Joe Moorhouse <5102656+joemoorhouse@users.noreply.github.com> * Fix meta-data error. Signed-off-by: Joe Moorhouse <5102656+joemoorhouse@users.noreply.github.com> * Chore: pre-commit autoupdate --------- 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> --- src/hazard/onboard/jupiter.py | 6 +- src/hazard/onboard/tudelft_flood.py | 10 +- src/hazard/utilities/s3_utilities.py | 3 + src/hazard/utilities/tiles.py | 83 +- src/inventories/hazard/inventory.json | 4935 ++++++++++++++++--------- 5 files changed, 3174 insertions(+), 1863 deletions(-) diff --git a/src/hazard/onboard/jupiter.py b/src/hazard/onboard/jupiter.py index 50dbedd..c9163dc 100644 --- a/src/hazard/onboard/jupiter.py +++ b/src/hazard/onboard/jupiter.py @@ -349,7 +349,7 @@ def inventory(self) -> Iterable[HazardResource]: display_name="Max 1 minute sustained wind speed (Jupiter)", description=self._jupiter_description + """ -This wind speed model computes the maximum 1-minute sustained wind speed (in km/hr) experienced over a +This wind speed model computes the maximum 1-minute sustained wind speed (in m/s) experienced over a 100 year return period based on mean wind speed distributions from multiple Global Climate Models (GCMs). """, # noqa:W503 @@ -370,13 +370,13 @@ def inventory(self) -> Iterable[HazardResource]: min_value=0.0, max_index=255, max_value=120.0, - units="km/hour", + units="m/s", ), index_values=None, path="max_1min_{scenario}_{year}_map", source="map_array", ), - units="km/hour", + units="m/s", scenarios=[ Scenario(id="ssp126", years=[2020, 2030, 2040, 2050, 2075, 2100]), Scenario(id="ssp585", years=[2020, 2030, 2040, 2050, 2075, 2100]), diff --git a/src/hazard/onboard/tudelft_flood.py b/src/hazard/onboard/tudelft_flood.py index 4475a92..2d8bfe2 100644 --- a/src/hazard/onboard/tudelft_flood.py +++ b/src/hazard/onboard/tudelft_flood.py @@ -257,7 +257,15 @@ def create_maps(self, source: OscZarr, target: OscZarr): Create map images. """ ... - create_tiles_for_resource(source, target, self._depth_resource, max_zoom=10) + # for TUDelft data, zero risk of flooding seems to be NaN + # this presents a problem when creating lower resolution images where we might want to see an + create_tiles_for_resource( + source, + target, + self._depth_resource, + max_zoom=10, + nodata_as_zero_coarsening=True, + ) def inventory(self) -> Iterable[HazardResource]: """Get the (unexpanded) HazardModel(s) that comprise the inventory.""" diff --git a/src/hazard/utilities/s3_utilities.py b/src/hazard/utilities/s3_utilities.py index 44c8dd4..40ac8b3 100644 --- a/src/hazard/utilities/s3_utilities.py +++ b/src/hazard/utilities/s3_utilities.py @@ -304,6 +304,9 @@ def sync_buckets( ) missing = set(key for key in source_etags if key not in target_etags) different = set(key for key in all_diffs if key not in missing) + logger.info( + f"Checked {len(source_etags)} files from {source_bucket_name} against {target_bucket_name}" # noqa:W503 + ) logger.info( f"Copying {len(missing)} missing files from {source_bucket_name} to {target_bucket_name}: " + _first_5_last_5(list(missing)) # noqa:W503 diff --git a/src/hazard/utilities/tiles.py b/src/hazard/utilities/tiles.py index 232bf7f..72ff227 100644 --- a/src/hazard/utilities/tiles.py +++ b/src/hazard/utilities/tiles.py @@ -27,7 +27,29 @@ def create_tiles_for_resource( target: OscZarr, resource: HazardResource, max_zoom: Optional[int] = None, + nodata=None, + nodata_as_zero=False, + nodata_as_zero_coarsening=False, + check_fill=False, ): + """Create a set of EPSG:3857 (i.e. Web Mercator) tiles according to the + Slippy Map standard. + + Args: + source (OscZarr): OSCZarr source or arrays. Arrays are (z, y, x) where x and y are + spatial coordinates and z is index coordinate (often return period). + target (OscZarr): OSCZarr array target. + resource (HazardResource): Resource for which map tiles should be calculated. + max_zoom (Optional[int], optional): Maximum zoom level; inferred if not specified. Defaults to None. + nodata (Optional[float], optional): If specified, set the nodata value. Defaults to None in which case + this is inferred from array. + nodata_as_zero (bool, optional): If True, nodata is set to zero for purposes of reprojection to highest zoom + level image. Defaults to False. + nodata_as_zero_coarsening (bool, optional): If True, nodata is set to zero for generating coarser tiles from + the highest zoom level image. Use, e.g. for flood maps if nodata is NaN and represents no flood risk. + Defaults to False. + check_fill (bool, optional): If True treat infinity values as nodata. Defaults to False. + """ if resource.map is None or resource.map.source != "map_array_pyramid": raise ValueError("resource does not specify 'map_array_pyramid' map source.") indices = None @@ -43,7 +65,16 @@ def create_tiles_for_resource( indexes = list(da["index"].values) indices = [indexes.index(v) for v in resource.map.index_values] create_tile_set( - source, path, target, map_path, indices=indices, max_zoom=max_zoom + source, + path, + target, + map_path, + indices=indices, + max_zoom=max_zoom, + nodata=nodata, + nodata_as_zero=nodata_as_zero, + nodata_as_zero_coarsening=nodata_as_zero_coarsening, + check_fill=check_fill, ) @@ -133,30 +164,40 @@ def create_tile_set( target: OscZarr, target_path: str, indices: Optional[Sequence[int]] = None, - max_tile_batch_size=32, - reprojection_threads=8, + max_tile_batch_size: int = 32, + reprojection_threads: int = 8, max_zoom: Optional[int] = None, - nodata=None, - nodata_as_zero=False, - check_fill=False, + nodata: Optional[float] = None, + nodata_as_zero: bool = False, + nodata_as_zero_coarsening: bool = False, + check_fill: bool = False, ): """Create a set of EPSG:3857 (i.e. Web Mercator) tiles according to the Slippy Map standard. Args: - source (OscZarr): OSC Zarr array source. Array is (z, y, x) where x and y are - spatial coordinates and z is index coordinate (often return period). - source_path (str): OSC Zarr source path. - target (OscZarr): OSC Zarr array target. - target_path (str): OSC Zarr target path. Arrays are stored in {target_path}/{level}, - e.g. my_hazard_indicator/2 for level 2. + source (OscZarr): OSCZarr source or arrays. Arrays are (z, y, x) where x and y are + spatial coordinates and z is index coordinate (often return period). + source_path (str): Path to the array accessed via the OSCZarr instance. + target (OscZarr): OSCZarr array target. + target_path (str): OSCZarr target path. Arrays are stored in {target_path}/{level}, + e.g. my_hazard_indicator/2 for level 2. indices (Sequence[int], optional): Indices for which map should be generated. max_tile_batch_size (int, optional): Maximum number of tiles in x and y direction that can be processed simultaneously. - Defaults to 64 i.e. max image size is 256 * 64 x 256 * 64 pixels. + Defaults to 64 i.e. max image size is 256 * 64 x 256 * 64 pixels. reprojection_threads (int, optional): Number of threads to use to perform reprojection to EPSG:3857. - Defaults to 8. + Defaults to 8. + max_zoom (Optional[int], optional): Maximum zoom level; inferred if not specified. Defaults to None. + nodata (Optional[float], optional): If specified, set the nodata value. Defaults to None in which case + this is inferred from array. + nodata_as_zero (bool, optional): If True, nodata is set to zero for purposes of reprojection to highest zoom + level image. Defaults to False. + nodata_as_zero_coarsening (bool, optional): If True, nodata is set to zero for generating coarser tiles from + the highest zoom level image. Use, e.g. for flood maps if nodata is NaN and represents no flood risk. + Defaults to False. + check_fill (bool, optional): If True treat infinity values as nodata. Defaults to False. """ if not target_path.endswith("map"): # for safety; should end with 'map' to avoid clash @@ -219,7 +260,14 @@ def create_tile_set( ) # and then progressively coarsen each level and write until we reach level 0" - _coarsen(target, target_path, max_zoom, (left, bottom, right, top), indices) + _coarsen( + target, + target_path, + max_zoom, + (left, bottom, right, top), + indices, + nodata_as_zero_coarsening=nodata_as_zero_coarsening, + ) def _create_empty_tile_pyramid( @@ -375,6 +423,7 @@ def _coarsen( bounds: Tuple[float, float, float, float], indices: Optional[Sequence[int]], max_tile_batch_size: int = 16, + nodata_as_zero_coarsening: bool = False, ): pixels_per_tile = 256 bbox = rasterio.coords.BoundingBox(*bounds) @@ -414,6 +463,10 @@ def _coarsen( # use Zarr array directly, as opposed to e.g.: # da_slice = da_slice.coarsen(x=2, y=2).mean() # type:ignore + if nodata_as_zero_coarsening: + if not np.all(np.isnan(zslice)): + zslice[np.isnan(zslice)] = 0 + zslice = ( zslice[::2, ::2] + zslice[1::2, ::2] diff --git a/src/inventories/hazard/inventory.json b/src/inventories/hazard/inventory.json index 0236e27..ca263c2 100644 --- a/src/inventories/hazard/inventory.json +++ b/src/inventories/hazard/inventory.json @@ -1,1847 +1,3094 @@ { - "resources": [ - { - "hazard_type": "RiverineInundation", - "group_id": "public", - "path": "inundation/wri/v2/inunriver_{scenario}_000000000WATCH_{year}", - "indicator_id": "flood_depth", - "indicator_model_id": null, - "indicator_model_gcm": "historical", - "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", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 2.0, - "name": "flare", - "nodata_index": 0, - "units": "m" - }, - "path": "inundation/wri/v2/inunriver_{scenario}_000000000WATCH_{year}_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -85.0], - [-180.0, -85.0] - ], - "index_values": null, - "source": "map_array_pyramid" - }, - "scenarios": [ - { - "id": "historical", - "years": [1980] - } - ], - "units": "metres" - }, - { - "hazard_type": "RiverineInundation", - "group_id": "public", - "path": "inundation/wri/v2/inunriver_{scenario}_00000NorESM1-M_{year}", - "indicator_id": "flood_depth", - "indicator_model_id": null, - "indicator_model_gcm": "NorESM1-M", - "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", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 2.0, - "name": "flare", - "nodata_index": 0, - "units": "m" - }, - "path": "inundation/wri/v2/inunriver_{scenario}_00000NorESM1-M_{year}_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -85.0], - [-180.0, -85.0] - ], - "index_values": [8], - "source": "map_array_pyramid" - }, - "scenarios": [ - { - "id": "rcp4p5", - "years": [2030, 2050, 2080] - }, - { - "id": "rcp8p5", - "years": [2030, 2050, 2080] - } - ], - "units": "metres" - }, - { - "hazard_type": "RiverineInundation", - "group_id": "public", - "path": "inundation/wri/v2/inunriver_{scenario}_0000GFDL-ESM2M_{year}", - "indicator_id": "flood_depth", - "indicator_model_id": null, - "indicator_model_gcm": "GFDL-ESM2M", - "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", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 2.0, - "name": "flare", - "nodata_index": 0, - "units": "m" - }, - "path": "inundation/wri/v2/inunriver_{scenario}_0000GFDL-ESM2M_{year}_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -85.0], - [-180.0, -85.0] - ], - "index_values": [8], - "source": "map_array_pyramid" - }, - "scenarios": [ - { - "id": "rcp4p5", - "years": [2030, 2050, 2080] - }, - { - "id": "rcp8p5", - "years": [2030, 2050, 2080] - } - ], - "units": "metres" - }, - { - "hazard_type": "RiverineInundation", - "group_id": "public", - "path": "inundation/wri/v2/inunriver_{scenario}_0000HadGEM2-ES_{year}", - "indicator_id": "flood_depth", - "indicator_model_id": null, - "indicator_model_gcm": "HadGEM2-ES", - "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", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 2.0, - "name": "flare", - "nodata_index": 0, - "units": "m" - }, - "path": "inundation/wri/v2/inunriver_{scenario}_0000HadGEM2-ES_{year}_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -85.0], - [-180.0, -85.0] - ], - "index_values": [8], - "source": "map_array_pyramid" - }, - "scenarios": [ - { - "id": "rcp4p5", - "years": [2030, 2050, 2080] - }, - { - "id": "rcp8p5", - "years": [2030, 2050, 2080] - } - ], - "units": "metres" - }, - { - "hazard_type": "RiverineInundation", - "group_id": "public", - "path": "inundation/wri/v2/inunriver_{scenario}_00IPSL-CM5A-LR_{year}", - "indicator_id": "flood_depth", - "indicator_model_id": null, - "indicator_model_gcm": "IPSL-CM5A-LR", - "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", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 2.0, - "name": "flare", - "nodata_index": 0, - "units": "m" - }, - "path": "inundation/wri/v2/inunriver_{scenario}_00IPSL-CM5A-LR_{year}_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -85.0], - [-180.0, -85.0] - ], - "index_values": [8], - "source": "map_array_pyramid" - }, - "scenarios": [ - { - "id": "rcp4p5", - "years": [2030, 2050, 2080] - }, - { - "id": "rcp8p5", - "years": [2030, 2050, 2080] - } - ], - "units": "metres" - }, - { - "hazard_type": "RiverineInundation", - "group_id": "public", - "path": "inundation/wri/v2/inunriver_{scenario}_MIROC-ESM-CHEM_{year}", - "indicator_id": "flood_depth", - "indicator_model_id": null, - "indicator_model_gcm": "MIROC-ESM-CHEM", - "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", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 2.0, - "name": "flare", - "nodata_index": 0, - "units": "m" - }, - "path": "inundation/wri/v2/inunriver_{scenario}_MIROC-ESM-CHEM_{year}_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -85.0], - [-180.0, -85.0] - ], - "index_values": [8], - "source": "map_array_pyramid" - }, - "scenarios": [ - { - "id": "rcp4p5", - "years": [2030, 2050, 2080] - }, - { - "id": "rcp8p5", - "years": [2030, 2050, 2080] - } - ], - "units": "metres" - }, - { - "hazard_type": "CoastalInundation", - "group_id": "public", - "path": "inundation/wri/v2/inuncoast_historical_nosub_hist_0", - "indicator_id": "flood_depth", - "indicator_model_id": "nosub", - "indicator_model_gcm": "unknown", - "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", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 2.0, - "name": "flare", - "nodata_index": 0, - "units": "m" - }, - "path": "inundation/wri/v2/inuncoast_historical_nosub_hist_0_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -85.0], - [-180.0, -85.0] - ], - "index_values": [8], - "source": "map_array_pyramid" - }, - "scenarios": [ - { - "id": "historical", - "years": [1980] - } - ], - "units": "metres" - }, - { - "hazard_type": "CoastalInundation", - "group_id": "public", - "path": "inundation/wri/v2/inuncoast_{scenario}_nosub_{year}_0", - "indicator_id": "flood_depth", - "indicator_model_id": "nosub/95", - "indicator_model_gcm": "unknown", - "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", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 2.0, - "name": "flare", - "nodata_index": 0, - "units": "m" - }, - "path": "inundation/wri/v2/inuncoast_{scenario}_nosub_{year}_0_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -85.0], - [-180.0, -85.0] - ], - "index_values": [8], - "source": "map_array_pyramid" - }, - "scenarios": [ - { - "id": "rcp4p5", - "years": [2030, 2050, 2080] - }, - { - "id": "rcp8p5", - "years": [2030, 2050, 2080] - } - ], - "units": "metres" - }, - { - "hazard_type": "CoastalInundation", - "group_id": "public", - "path": "inundation/wri/v2/inuncoast_{scenario}_nosub_{year}_0_perc_05", - "indicator_id": "flood_depth/nosub/5", - "indicator_model_id": "nosub/5", - "indicator_model_gcm": "unknown", - "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", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 2.0, - "name": "flare", - "nodata_index": 0, - "units": "m" - }, - "path": "inundation/wri/v2/inuncoast_{scenario}_nosub_{year}_0_perc_05_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -85.0], - [-180.0, -85.0] - ], - "index_values": [8], - "source": "map_array_pyramid" - }, - "scenarios": [ - { - "id": "rcp4p5", - "years": [2030, 2050, 2080] - }, - { - "id": "rcp8p5", - "years": [2030, 2050, 2080] - } - ], - "units": "metres" - }, - { - "hazard_type": "CoastalInundation", - "group_id": "public", - "path": "inundation/wri/v2/inuncoast_{scenario}_nosub_{year}_0_perc_50", - "indicator_id": "flood_depth", - "indicator_model_id": "nosub/50", - "indicator_model_gcm": "unknown", - "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", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 2.0, - "name": "flare", - "nodata_index": 0, - "units": "m" - }, - "path": "inundation/wri/v2/inuncoast_{scenario}_nosub_{year}_0_perc_50_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -85.0], - [-180.0, -85.0] - ], - "index_values": [8], - "source": "map_array_pyramid" - }, - "scenarios": [ - { - "id": "rcp4p5", - "years": [2030, 2050, 2080] - }, - { - "id": "rcp8p5", - "years": [2030, 2050, 2080] - } - ], - "units": "metres" - }, - { - "hazard_type": "CoastalInundation", - "group_id": "public", - "path": "inundation/wri/v2/inuncoast_historical_wtsub_hist_0", - "indicator_id": "flood_depth", - "indicator_model_id": "wtsub", - "indicator_model_gcm": "unknown", - "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", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 2.0, - "name": "flare", - "nodata_index": 0, - "units": "m" - }, - "path": "inundation/wri/v2/inuncoast_historical_wtsub_hist_0_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -85.0], - [-180.0, -85.0] - ], - "index_values": [8], - "source": "map_array_pyramid" - }, - "scenarios": [ - { - "id": "historical", - "years": [1980] - } - ], - "units": "metres" - }, - { - "hazard_type": "CoastalInundation", - "group_id": "public", - "path": "inundation/wri/v2/inuncoast_{scenario}_wtsub_{year}_0", - "indicator_id": "flood_depth", - "indicator_model_id": "wtsub/95", - "indicator_model_gcm": "unknown", - "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", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 2.0, - "name": "flare", - "nodata_index": 0, - "units": "m" - }, - "path": "inundation/wri/v2/inuncoast_{scenario}_wtsub_{year}_0_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -85.0], - [-180.0, -85.0] - ], - "index_values": [8], - "source": "map_array_pyramid" - }, - "scenarios": [ - { - "id": "rcp4p5", - "years": [2030, 2050, 2080] - }, - { - "id": "rcp8p5", - "years": [2030, 2050, 2080] - } - ], - "units": "metres" - }, - { - "hazard_type": "CoastalInundation", - "group_id": "public", - "path": "inundation/wri/v2/inuncoast_{scenario}_wtsub_{year}_0_perc_05", - "indicator_id": "flood_depth", - "indicator_model_id": "wtsub/5", - "indicator_model_gcm": "unknown", - "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", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 2.0, - "name": "flare", - "nodata_index": 0, - "units": "m" - }, - "path": "inundation/wri/v2/inuncoast_{scenario}_wtsub_{year}_0_perc_05_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -85.0], - [-180.0, -85.0] - ], - "index_values": [8], - "source": "map_array_pyramid" - }, - "scenarios": [ - { - "id": "rcp4p5", - "years": [2030, 2050, 2080] - }, - { - "id": "rcp8p5", - "years": [2030, 2050, 2080] - } - ], - "units": "metres" - }, - { - "hazard_type": "CoastalInundation", - "group_id": "public", - "path": "inundation/wri/v2/inuncoast_{scenario}_wtsub_{year}_0_perc_50", - "indicator_id": "flood_depth", - "indicator_model_id": "wtsub/50", - "indicator_model_gcm": "unknown", - "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", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 2.0, - "name": "flare", - "nodata_index": 0, - "units": "m" - }, - "path": "inundation/wri/v2/inuncoast_{scenario}_wtsub_{year}_0_perc_50_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -85.0], - [-180.0, -85.0] - ], - "index_values": [8], - "source": "map_array_pyramid" - }, - "scenarios": [ - { - "id": "rcp4p5", - "years": [2030, 2050, 2080] - }, - { - "id": "rcp8p5", - "years": [2030, 2050, 2080] - } - ], - "units": "metres" - }, - { - "hazard_type": "ChronicHeat", - "group_id": "", - "path": "chronic_heat/osc/v2/mean_degree_days_v2_above_32c_{gcm}_{scenario}_{year}", - "indicator_id": "mean_degree_days/above/32c", - "indicator_model_id": null, - "indicator_model_gcm": "{gcm}", - "params": { - "gcm": [ - "ACCESS-CM2", - "CMCC-ESM2", - "CNRM-CM6-1", - "MIROC6", - "MPI-ESM1-2-LR", - "NorESM2-MM" - ] - }, - "display_name": "Mean degree days above 32\u00b0C/{gcm}", - "display_groups": ["Mean degree days"], - "description": "Degree days indicators are calculated by integrating over time the absolute difference in temperature\nof the medium over a reference temperature. The exact method of calculation may vary;\nhere the daily maximum near-surface temperature 'tasmax' is used to calculate an annual indicator:\n\n$$\nI^\\text{dd} = \\frac{365}{n_y} \\sum_{i = 1}^{n_y} | T^\\text{max}_i - T^\\text{ref} | \\nonumber\n$$\n\n$I^\\text{dd}$ is the indicator, $T^\\text{max}$ is the daily maximum near-surface temperature, $n_y$ is the number of days in the year and $i$ is the day index.\nand $T^\\text{ref}$ is the reference temperature of 32\u00b0C. The OS-Climate-generated indicators are inferred\nfrom downscaled CMIP6 data, averaged over 6 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).\nThe indicators are generated for periods: 'historical' (averaged over 1995-2014), 2030 (2021-2040), 2040 (2031-2050)\nand 2050 (2041-2060).\n", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 4000.0, - "name": "heating", - "nodata_index": 0, - "units": "degree days" - }, - "path": "mean_degree_days_v2_above_32c_{gcm}_{scenario}_{year}_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -60.0], - [-180.0, -60.0] - ], - "index_values": null, - "source": "map_array" - }, - "scenarios": [ - { - "id": "historical", - "years": [2005] - }, - { - "id": "ssp126", - "years": [2030, 2040, 2050] - }, - { - "id": "ssp245", - "years": [2030, 2040, 2050] - }, - { - "id": "ssp585", - "years": [2030, 2040, 2050] - } - ], - "units": "degree days" - }, - { - "hazard_type": "Fire", - "group_id": "jupiter_osc", - "path": "fire/jupiter/v1/fire_probability_{scenario}_{year}", - "indicator_id": "fire_probability", - "indicator_model_id": null, - "indicator_model_gcm": "unknown", - "params": {}, - "display_name": "Fire probability (Jupiter)", - "display_groups": [], - "description": "\nThese data should not be used in any manner relating to emergency management or planning, public safety,\nphysical safety or property endangerment. For higher-resolution data based on up-to-date methods,\nsubject to greater validation, and suitable for bottom-up risk analysis please contact\n[Jupiter Intelligence](https://www.jupiterintel.com).\n\nThis fire model computes the maximum monthly probability per annum of a wildfire within 100 km of\na given location based on several parameters from multiple bias corrected and downscaled Global Climate Models (GCMs).\nFor example, if the probability of occurrence of a wildfire is 5% in July, 20% in August, 10% in September\nand 0% for other months, the hazard indicator value is 20%.\n ", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 0.7, - "name": "heating", - "nodata_index": 0, - "units": "" - }, - "path": "fire_probability_{scenario}_{year}_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -85.0], - [-180.0, -85.0] - ], - "index_values": null, - "source": "map_array" - }, - "scenarios": [ - { - "id": "ssp126", - "years": [2020, 2030, 2040, 2050, 2075, 2100] - }, - { - "id": "ssp585", - "years": [2020, 2030, 2040, 2050, 2075, 2100] - } - ], - "units": "" - }, - { - "hazard_type": "Drought", - "group_id": "jupiter_osc", - "path": "drought/jupiter/v1/months_spei3m_below_-2_{scenario}_{year}", - "indicator_id": "months/spei3m/below/-2", - "indicator_model_id": null, - "indicator_model_gcm": "unknown", - "params": {}, - "display_name": "Drought (Jupiter)", - "display_groups": [], - "description": "\nThese data should not be used in any manner relating to emergency management or planning, public safety,\nphysical safety or property endangerment. For higher-resolution data based on up-to-date methods,\nsubject to greater validation, and suitable for bottom-up risk analysis please contact\n[Jupiter Intelligence](https://www.jupiterintel.com).\n\nThis drought model is based on the Standardized Precipitation-Evapotranspiration Index (SPEI).\nThe SPEl is an extension of the Standardized Precipitation Index which also considers Potential Evapotranspiration (PET)\nin determining drought events.\nThe SPEl is calculated from a log-logistic probability distribution function of climatic water balance\n(precipitation minus evapotranspiration) over a given time scale.\nThe SPEI itself is a standardized variable with a mean value 0 and standard deviation 1.\nThis drought model computes the number of months per annum where the 3-month rolling average\nof SPEI is below -2 based on the mean values of several parameters from\nbias-corrected and downscaled multiple Global Climate Models (GCMs).\n ", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 12.0, - "name": "heating", - "nodata_index": 0, - "units": "months/year" - }, - "path": "months_spei3m_below_-2_{scenario}_{year}_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -85.0], - [-180.0, -85.0] - ], - "index_values": null, - "source": "map_array" - }, - "scenarios": [ - { - "id": "ssp126", - "years": [2020, 2030, 2040, 2050, 2075, 2100] - }, - { - "id": "ssp585", - "years": [2020, 2030, 2040, 2050, 2075, 2100] - } - ], - "units": "months/year" - }, - { - "hazard_type": "Precipitation", - "group_id": "jupiter_osc", - "path": "precipitation/jupiter/v1/max_daily_water_equivalent_{scenario}_{year}", - "indicator_id": "max/daily/water_equivalent", - "indicator_model_id": null, - "indicator_model_gcm": "unknown", - "params": {}, - "display_name": "Precipitation (Jupiter)", - "display_groups": [], - "description": "\nThese data should not be used in any manner relating to emergency management or planning, public safety,\nphysical safety or property endangerment. For higher-resolution data based on up-to-date methods,\nsubject to greater validation, and suitable for bottom-up risk analysis please contact\n[Jupiter Intelligence](https://www.jupiterintel.com).\n\nThis model computes the maximum daily water equivalent precipitation (in mm) measured at the 100 year\nreturn period based on the mean of the precipitation distribution from multiple bias corrected and\ndownscaled Global Climate Models (GCMs).\n ", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 1000.0, - "name": "heating", - "nodata_index": 0, - "units": "mm" - }, - "path": "max_daily_water_equivalent_{scenario}_{year}_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -85.0], - [-180.0, -85.0] - ], - "index_values": null, - "source": "map_array" - }, - "scenarios": [ - { - "id": "ssp126", - "years": [2020, 2030, 2040, 2050, 2075, 2100] - }, - { - "id": "ssp585", - "years": [2020, 2030, 2040, 2050, 2075, 2100] - } - ], - "units": "mm" - }, - { - "hazard_type": "Hail", - "group_id": "jupiter_osc", - "path": "hail/jupiter/v1/days_above_5cm_{scenario}_{year}", - "indicator_id": "days/above/5cm", - "indicator_model_id": null, - "indicator_model_gcm": "unknown", - "params": {}, - "display_name": "Large hail days per year (Jupiter)", - "display_groups": [], - "description": "\nThese data should not be used in any manner relating to emergency management or planning, public safety,\nphysical safety or property endangerment. For higher-resolution data based on up-to-date methods,\nsubject to greater validation, and suitable for bottom-up risk analysis please contact\n[Jupiter Intelligence](https://www.jupiterintel.com).\n\nThis hail model computes the number of days per annum where hail exceeding 5 cm diameter is possible\nbased on the mean distribution of several parameters\nacross multiple bias-corrected and downscaled Global Climate Models (GCMs).\n ", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 10.0, - "name": "heating", - "nodata_index": 0, - "units": "days/year" - }, - "path": "days_above_5cm_{scenario}_{year}_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -85.0], - [-180.0, -85.0] - ], - "index_values": null, - "source": "map_array" - }, - "scenarios": [ - { - "id": "ssp126", - "years": [2020, 2030, 2040, 2050, 2075, 2100] - }, - { - "id": "ssp585", - "years": [2020, 2030, 2040, 2050, 2075, 2100] - } - ], - "units": "days/year" - }, - { - "hazard_type": "ChronicHeat", - "group_id": "jupiter_osc", - "path": "chronic_heat/jupiter/v1/days_above_35c_{scenario}_{year}", - "indicator_id": "days/above/35c", - "indicator_model_id": null, - "indicator_model_gcm": "unknown", - "params": {}, - "display_name": "Days per year above 35\u00b0C (Jupiter)", - "display_groups": [], - "description": "\nThese data should not be used in any manner relating to emergency management or planning, public safety,\nphysical safety or property endangerment. For higher-resolution data based on up-to-date methods,\nsubject to greater validation, and suitable for bottom-up risk analysis please contact\n[Jupiter Intelligence](https://www.jupiterintel.com).\n\nThis heat model computes the number of days exceeding 35\u00b0C per annum based on the mean of distribution fits\nto the bias-corrected and downscaled high temperature distribution\nacross multiple Global Climate Models (GCMs).\n ", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 365.0, - "name": "heating", - "nodata_index": 0, - "units": "days/year" - }, - "path": "days_above_35c_{scenario}_{year}_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -85.0], - [-180.0, -85.0] - ], - "index_values": null, - "source": "map_array" - }, - "scenarios": [ - { - "id": "ssp126", - "years": [2020, 2030, 2040, 2050, 2075, 2100] - }, - { - "id": "ssp585", - "years": [2020, 2030, 2040, 2050, 2075, 2100] - } - ], - "units": "days/year" - }, - { - "hazard_type": "Wind", - "group_id": "jupiter_osc", - "path": "wind/jupiter/v1/max_1min_{scenario}_{year}", - "indicator_id": "max_speed", - "indicator_model_id": "1min", - "indicator_model_gcm": "unknown", - "params": {}, - "display_name": "Max 1 minute sustained wind speed (Jupiter)", - "display_groups": [], - "description": "\nThese data should not be used in any manner relating to emergency management or planning, public safety,\nphysical safety or property endangerment. For higher-resolution data based on up-to-date methods,\nsubject to greater validation, and suitable for bottom-up risk analysis please contact\n[Jupiter Intelligence](https://www.jupiterintel.com).\n\nThis wind speed model computes the maximum 1-minute sustained wind speed (in km/hr) experienced over a\n100 year return period based on mean wind speed distributions\nfrom multiple Global Climate Models (GCMs).\n ", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 120.0, - "name": "heating", - "nodata_index": 0, - "units": "km/hour" - }, - "path": "max_1min_{scenario}_{year}_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -85.0], - [-180.0, -85.0] - ], - "index_values": null, - "source": "map_array" - }, - "scenarios": [ - { - "id": "ssp126", - "years": [2020, 2030, 2040, 2050, 2075, 2100] - }, - { - "id": "ssp585", - "years": [2020, 2030, 2040, 2050, 2075, 2100] - } - ], - "units": "km/hour" - }, - { - "hazard_type": "CombinedInundation", - "group_id": "jupiter_osc", - "path": "combined_flood/jupiter/v1/fraction_{scenario}_{year}", - "indicator_id": "flooded_fraction", - "indicator_model_id": null, - "indicator_model_gcm": "unknown", - "params": {}, - "display_name": "Flooded fraction (Jupiter)", - "display_groups": [], - "description": "\nThese data should not be used in any manner relating to emergency management or planning, public safety,\nphysical safety or property endangerment. For higher-resolution data based on up-to-date methods,\nsubject to greater validation, and suitable for bottom-up risk analysis please contact\n[Jupiter Intelligence](https://www.jupiterintel.com).\n\nFlooded fraction provides the spatial fraction of land flooded in a defined grid.\nIt is derived from higher-resolution flood hazards, and computed directly as the fraction of\ncells within the 30-km cell that have non-zero flooding at that return period.\nThis model uses a 30-km grid that experiences flooding at the 200-year return period.\nOpen oceans are excluded.\n ", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 1.0, - "name": "heating", - "nodata_index": 0, - "units": "" - }, - "path": "fraction_{scenario}_{year}_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -85.0], - [-180.0, -85.0] - ], - "index_values": null, - "source": "map_array" - }, - "scenarios": [ - { - "id": "ssp126", - "years": [2020, 2030, 2040, 2050, 2075, 2100] - }, - { - "id": "ssp585", - "years": [2020, 2030, 2040, 2050, 2075, 2100] - } - ], - "units": "none" - }, - { - "hazard_type": "ChronicHeat", - "group_id": "", - "path": "chronic_heat/osc/v2/mean_work_loss_{intensity}_{gcm}_{scenario}_{year}", - "indicator_id": "mean_work_loss/{intensity}", - "indicator_model_id": null, - "indicator_model_gcm": "{gcm}", - "params": { - "intensity": ["low", "medium", "high"], - "gcm": [ - "ACCESS-CM2", - "CMCC-ESM2", - "CNRM-CM6-1", - "MPI-ESM1-2-LR", - "MIROC6", - "NorESM2-MM" - ] - }, - "display_name": "Mean work loss, {intensity} intensity/{gcm}", - "display_groups": ["Mean work loss"], - "description": "The mean work loss indicator is calculated from the 'Wet Bulb Globe Temperature' (WBGT) indicator:\n\n$$\nI^\\text{WBGT}_i = 0.567 \\times T^\\text{avg}_i + 0.393 \\times p^\\text{vapour}_i + 3.94\n$$\n\n$I^\\text{WBGT}_i$ is the WBGT indicator, $T^\\text{avg}_i$ is the average daily near-surface surface temperature (in degress Celsius) on day index, $i$, and $p^\\text{vapour}$\nis the water vapour partial pressure (in kPa). $p^\\text{vapour}$ is calculated from relative humidity $h_r$ via:\n\n$$\np^\\text{vapour}_i = \\frac{h_r}{100} \\times 6.105 \\times \\exp \\left( \\frac{17.27 \\times T^\\text{avg}_i}{237.7 + T^\\text{avg}_i} \\right)\n$$\n\nThe work ability indicator, $I^{\\text{WA}}$ is finally calculated via:\n\n$$\nI^{\\text{WA}}_i = 0.1 + 0.9 / \\left( 1 + (I^\\text{WBGT}_i / \\alpha_1)^{\\alpha_2} \\right)\n$$\n\nAn annual average work loss indicator, $I^{\\text{WL}}$ is calculated via:\n\n$$\nI^{\\text{WL}} = 1 - \\frac{1}{n_y} \\sum_{i = 1}^{n_y} I^{\\text{WA}}_i,\n$$\n\n$n_y$ being the number of days in the year. The OS-Climate-generated indicators are inferred from CMIP6 data, averaged over 6 models: ACCESS-CM2, CMCC-ESM2, CNRM-CM6-1, MPI-ESM1-2-LR, MIROC6 and NorESM2-MM.\nThe indicators are generated for periods: 'historical' (averaged over 1995-2014), 2030 (2021-2040), 2040 (2031-2050) and 2050 (2041-2060).\n", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 0.8, - "name": "heating", - "nodata_index": 0, - "units": "fractional loss" - }, - "path": "mean_work_loss_{intensity}_{gcm}_{scenario}_{year}_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -60.0], - [-180.0, -60.0] - ], - "index_values": null, - "source": "map_array" - }, - "scenarios": [ - { - "id": "historical", - "years": [2005] - }, - { - "id": "ssp126", - "years": [2030, 2040, 2050] - }, - { - "id": "ssp245", - "years": [2030, 2040, 2050] - }, - { - "id": "ssp585", - "years": [2030, 2040, 2050] - } - ], - "units": "fractional loss" - }, - { - "hazard_type": "ChronicHeat", - "group_id": "", - "path": "chronic_heat/osc/v2/days_tas_above_{temp_c}c_{gcm}_{scenario}_{year}", - "indicator_id": "days_tas/above/{temp_c}c", - "indicator_model_id": null, - "indicator_model_gcm": "{gcm}", - "params": { - "temp_c": ["25", "30", "35", "40", "45", "50", "55"], - "gcm": [ - "ACCESS-CM2", - "CMCC-ESM2", - "CNRM-CM6-1", - "MPI-ESM1-2-LR", - "MIROC6", - "NorESM2-MM" - ] - }, - "display_name": "Days with average temperature above {temp_c}\u00b0C/{gcm}", - "display_groups": ["Days with average temperature above"], - "description": "Days per year for which the average near-surface temperature 'tas' is above a threshold specified in \u00b0C.\n\n$$\nI = \\frac{365}{n_y} \\sum_{i = 1}^{n_y} \\boldsymbol{\\mathbb{1}}_{\\; \\, T^{avg}_i > T^\\text{ref}} \\nonumber\n$$\n\n$I$ is the indicator, $T^\\text{avg}_i$ is the daily average near-surface temperature for day index $i$ in \u00b0C, $n_y$ is the number of days in the year\nand $T^\\text{ref}$ is the reference temperature.\nThe OS-Climate-generated indicators are inferred from downscaled CMIP6 data. This is done 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)\nand 2050 (2041-2060).\n", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 100.0, - "name": "heating", - "nodata_index": 0, - "units": "days/year" - }, - "path": "days_tas_above_{temp_c}c_{gcm}_{scenario}_{year}_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -60.0], - [-180.0, -60.0] - ], - "index_values": null, - "source": "map_array" - }, - "scenarios": [ - { - "id": "historical", - "years": [2005] - }, - { - "id": "ssp126", - "years": [2030, 2040, 2050] - }, - { - "id": "ssp245", - "years": [2030, 2040, 2050] - }, - { - "id": "ssp585", - "years": [2030, 2040, 2050] - } - ], - "units": "days/year" - }, - { - "hazard_type": "Wind", - "group_id": "iris_osc", - "path": "wind/iris/v1/max_speed_{scenario}_{year}", - "indicator_id": "max_speed", - "indicator_model_id": null, - "indicator_model_gcm": "combined", - "params": {}, - "display_name": "Max wind speed (IRIS)", - "display_groups": [], - "description": "Sparks, N., Toumi, R. The Imperial College Storm Model (IRIS) Dataset. *Sci Data* **11**, 424 (2024). \n## The Imperial College Storm Model (IRIS) Dataset - Scientific Data\nAssessing 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***Disclaimer***: There have been many improvements on the dataset. Contact Professor Toumi from the Imperial College London for improved data.\n", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 120.0, - "name": "heating", - "nodata_index": 0, - "units": "m/s" - }, - "path": "wind/iris/v1/max_speed_{scenario}_{year}_map", - "bounds": [ - [-180.0, 60.0], - [180.0, 60.0], - [180.0, -60.0], - [-180.0, -60.0] - ], - "index_values": null, - "source": "map_array_pyramid" - }, - "scenarios": [ - { - "id": "historical", - "years": [2010] - }, - { - "id": "ssp119", - "years": [2050] - }, - { - "id": "ssp245", - "years": [2050] - }, - { - "id": "ssp585", - "years": [2050] - } - ], - "units": "m/s" - }, - { - "hazard_type": "ChronicHeat", - "group_id": "", - "path": "chronic_heat/osc/v2/mean_degree_days_above_index_{gcm}_{scenario}_{year}", - "indicator_id": "mean_degree_days/above/index", - "indicator_model_id": null, - "indicator_model_gcm": "{gcm}", - "params": { - "gcm": [ - "ACCESS-CM2", - "CMCC-ESM2", - "CNRM-CM6-1", - "MPI-ESM1-2-LR", - "MIROC6", - "NorESM2-MM" - ] - }, - "display_name": "Mean degree days above index value/{gcm}", - "display_groups": ["Mean degree days"], - "description": "Degree days indicators are calculated by integrating over time the absolute difference in temperature\nof the medium over a reference temperature. The exact method of calculation may vary;\nhere the daily maximum near-surface temperature 'tasmax' is used to calculate an annual indicator:\n\n$$\nI^\\text{dd} = \\frac{365}{n_y} \\sum_{i = 1}^{n_y} | T^\\text{max}_i - T^\\text{ref} | \\nonumber\n$$\n\n$I^\\text{dd}$ is the indicator, $T^\\text{max}$ is the daily maximum near-surface temperature, $n_y$ is the number of days in the year and $i$ is the day index.\nand $T^\\text{ref}$ is the reference temperature of 32\u00b0C. The OS-Climate-generated indicators are inferred\nfrom downscaled CMIP6 data, averaged over 6 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).\nThe indicators are generated for periods: 'historical' (averaged over 1995-2014), 2030 (2021-2040), 2040 (2031-2050)\nand 2050 (2041-2060).\n", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 4000.0, - "name": "heating", - "nodata_index": 0, - "units": "degree days" - }, - "path": "mean_degree_days_above_index_{gcm}_{scenario}_{year}_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -60.0], - [-180.0, -60.0] - ], - "index_values": [16, 20, 24], - "source": "map_array" - }, - "scenarios": [ - { - "id": "historical", - "years": [2005] - }, - { - "id": "ssp126", - "years": [2030, 2040, 2050] - }, - { - "id": "ssp245", - "years": [2030, 2040, 2050] - }, - { - "id": "ssp585", - "years": [2030, 2040, 2050] - } - ], - "units": "degree days" - }, - { - "hazard_type": "ChronicHeat", - "group_id": "", - "path": "chronic_heat/osc/v2/mean_degree_days_below_index_{gcm}_{scenario}_{year}", - "indicator_id": "mean_degree_days/below/index", - "indicator_model_id": null, - "indicator_model_gcm": "{gcm}", - "params": { - "gcm": [ - "ACCESS-CM2", - "CMCC-ESM2", - "CNRM-CM6-1", - "MPI-ESM1-2-LR", - "MIROC6", - "NorESM2-MM" - ] - }, - "display_name": "Mean degree days below index value/{gcm}", - "display_groups": ["Mean degree days"], - "description": "Degree days indicators are calculated by integrating over time the absolute difference in temperature\nof the medium over a reference temperature. The exact method of calculation may vary;\nhere the daily maximum near-surface temperature 'tasmax' is used to calculate an annual indicator:\n\n$$\nI^\\text{dd} = \\frac{365}{n_y} \\sum_{i = 1}^{n_y} | T^\\text{max}_i - T^\\text{ref} | \\nonumber\n$$\n\n$I^\\text{dd}$ is the indicator, $T^\\text{max}$ is the daily maximum near-surface temperature, $n_y$ is the number of days in the year and $i$ is the day index.\nand $T^\\text{ref}$ is the reference temperature of 32\u00b0C. The OS-Climate-generated indicators are inferred\nfrom downscaled CMIP6 data, averaged over 6 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).\nThe indicators are generated for periods: 'historical' (averaged over 1995-2014), 2030 (2021-2040), 2040 (2031-2050)\nand 2050 (2041-2060).\n", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 4000.0, - "name": "heating", - "nodata_index": 0, - "units": "degree days" - }, - "path": "mean_degree_days_below_index_{gcm}_{scenario}_{year}_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -60.0], - [-180.0, -60.0] - ], - "index_values": [16, 20, 24], - "source": "map_array" - }, - "scenarios": [ - { - "id": "historical", - "years": [2005] - }, - { - "id": "ssp126", - "years": [2030, 2040, 2050] - }, - { - "id": "ssp245", - "years": [2030, 2040, 2050] - }, - { - "id": "ssp585", - "years": [2030, 2040, 2050] - } - ], - "units": "degree days" - }, - { - "hazard_type": "ChronicHeat", - "group_id": "", - "path": "chronic_heat/nluu/v2/weeks_water_temp_above_{gcm}_{scenario}_{year}", - "indicator_id": "weeks_water_temp_above", - "indicator_model_id": null, - "indicator_model_gcm": "{gcm}", - "params": { - "gcm": ["GFDL", "HadGEM", "IPSL", "MIROC", "NorESM"] - }, - "display_name": "Weeks with average water temperature above threshold in \u00b0C/{gcm}", - "display_groups": [ - "Weeks with average water temperature above threshold in \u00b0C" - ], - "description": "Weeks per year for which the average water temperature is above a threshold specified in \u00b0C:\n\n$$\nI = \\frac{52}{n_y} \\sum_{i = 1}^{n_y} \\boldsymbol{\\mathbb{1}}_{\\; \\, T^{avg}_i > T^\\text{ref}} \\nonumber\n$$\n\n$I$ is the indicator, $T^\\text{avg}_i$ is the weekly average water temperature for week index $i$ in \u00b0C, $n_y$ is the number of weeks in the sample\nand $T^\\text{ref}$ is the reference temperature.\n\nThe OS-Climate-generated indicators are inferred from downscaled CMIP5 data. This is done for 5 Global Circulation Models: GFDL-ESM2M, HadGEM2-ES, ISPL-CM5A-LR, MIROC-ESM-CHEM and NorESM1-M.\nThe downscaled data is sourced from the [Futurestreams dataset](https://geo.public.data.uu.nl/vault-futurestreams/research-futurestreams%5B1633685642%5D/original/waterTemp/) on the data publication platform of Utrecht University.\nIndicators are generated for periods: 'historical' (averaged over 1976-2005), 2020 (2006-2030), 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, - "min_value": 0.0, - "max_index": 255, - "max_value": 100.0, - "name": "heating", - "nodata_index": 0, - "units": "weeks/year" - }, - "path": "maps/chronic_heat/nluu/v2/weeks_water_temp_above_{gcm}_{scenario}_{year}_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -85.0], - [-180.0, -85.0] - ], - "index_values": [ - 5, 7.5, 10, 12.5, 15, 17.5, 20, 22.5, 25, 27.5, 30, 32.5, 35, 37.5, 40 - ], - "source": "map_array_pyramid" - }, - "scenarios": [ - { - "id": "historical", - "years": [1991] - }, - { - "id": "rcp2p6", - "years": [2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090] - }, - { - "id": "rcp4p5", - "years": [2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090] - }, - { - "id": "rcp6p0", - "years": [2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090] - }, - { - "id": "rcp8p5", - "years": [2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090] - } - ], - "units": "weeks/year" - }, - { - "hazard_type": "ChronicHeat", - "group_id": "", - "path": "chronic_heat/nluu/v2/weeks_water_temp_above_E2O_{scenario}_{year}", - "indicator_id": "weeks_water_temp_above", - "indicator_model_id": null, - "indicator_model_gcm": "E2O", - "params": {}, - "display_name": "Weeks with average water temperature above threshold in \u00b0C/E2O", - "display_groups": [ - "Weeks with average water temperature above threshold in \u00b0C" - ], - "description": "Weeks per year for which the average water temperature is above a threshold specified in \u00b0C:\n\n$$\nI = \\frac{52}{n_y} \\sum_{i = 1}^{n_y} \\boldsymbol{\\mathbb{1}}_{\\; \\, T^{avg}_i > T^\\text{ref}} \\nonumber\n$$\n\n$I$ is the indicator, $T^\\text{avg}_i$ is the weekly average water temperature for week index $i$ in \u00b0C, $n_y$ is the number of weeks in the sample\nand $T^\\text{ref}$ is the reference temperature.\n\nThe OS-Climate-generated indicators are inferred from downscaled CMIP5 data. This is done for 5 Global Circulation Models: GFDL-ESM2M, HadGEM2-ES, ISPL-CM5A-LR, MIROC-ESM-CHEM and NorESM1-M.\nThe downscaled data is sourced from the [Futurestreams dataset](https://geo.public.data.uu.nl/vault-futurestreams/research-futurestreams%5B1633685642%5D/original/waterTemp/) on the data publication platform of Utrecht University.\nIndicators are generated for periods: 'historical' (averaged over 1979-2005), 2020 (2006-2030), 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, - "min_value": 0.0, - "max_index": 255, - "max_value": 100.0, - "name": "heating", - "nodata_index": 0, - "units": "weeks/year" - }, - "path": "maps/chronic_heat/nluu/v2/weeks_water_temp_above_E2O_{scenario}_{year}_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -85.0], - [-180.0, -85.0] - ], - "index_values": [ - 5, 7.5, 10, 12.5, 15, 17.5, 20, 22.5, 25, 27.5, 30, 32.5, 35, 37.5, 40 - ], - "source": "map_array_pyramid" - }, - "scenarios": [ - { - "id": "historical", - "years": [1991] - } - ], - "units": "weeks/year" - }, - { - "hazard_type": "ChronicHeat", - "group_id": "", - "path": "chronic_heat/osc/v2/days_wbgt_above_{gcm}_{scenario}_{year}", - "indicator_id": "days_wbgt_above", - "indicator_model_id": null, - "indicator_model_gcm": "{gcm}", - "params": { - "gcm": [ - "ACCESS-CM2", - "CMCC-ESM2", - "CNRM-CM6-1", - "MPI-ESM1-2-LR", - "MIROC6", - "NorESM2-MM" - ] - }, - "display_name": "Days with wet-bulb globe temperature above threshold in \u00b0C/{gcm}", - "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", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 100.0, - "name": "heating", - "nodata_index": 0, - "units": "days/year" - }, - "path": "maps/chronic_heat/osc/v2/days_wbgt_above_{gcm}_{scenario}_{year}_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -85.0], - [-180.0, -85.0] - ], - "index_values": [5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60], - "source": "map_array_pyramid" - }, - "scenarios": [ - { - "id": "historical", - "years": [2005] - }, - { - "id": "ssp126", - "years": [2030, 2040, 2050, 2060, 2070, 2080, 2090] - }, - { - "id": "ssp245", - "years": [2030, 2040, 2050, 2060, 2070, 2080, 2090] - }, - { - "id": "ssp370", - "years": [2030, 2040, 2050, 2060, 2070, 2080, 2090] - }, - { - "id": "ssp585", - "years": [2030, 2040, 2050, 2060, 2070, 2080, 2090] - } - ], - "units": "days/year" - }, - { - "hazard_type": "WaterRisk", - "group_id": "", - "path": "water_risk/wri/v2/water_demand_{scenario}_{year}", - "indicator_id": "water_demand", - "indicator_model_id": null, - "indicator_model_gcm": "combined", - "params": {}, - "display_name": "Water demand in centimeters/year (Aqueduct 4.0)", - "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", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 100.0, - "name": "heating", - "nodata_index": 0, - "units": "centimeters/year" - }, - "path": "maps/water_risk/wri/v2/water_demand_{scenario}_{year}_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -85.0], - [-180.0, -85.0] - ], - "index_values": null, - "source": "map_array_pyramid" - }, - "scenarios": [ - { - "id": "historical", - "years": [1999] - }, - { - "id": "ssp126", - "years": [2030, 2050, 2080] - }, - { - "id": "ssp370", - "years": [2030, 2050, 2080] - }, - { - "id": "ssp585", - "years": [2030, 2050, 2080] - } - ], - "units": "centimeters/year" - }, - { - "hazard_type": "WaterRisk", - "group_id": "", - "path": "water_risk/wri/v2/water_supply_{scenario}_{year}", - "indicator_id": "water_supply", - "indicator_model_id": null, - "indicator_model_gcm": "combined", - "params": {}, - "display_name": "Water supply in centimeters/year (Aqueduct 4.0)", - "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", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 2000.0, - "name": "heating", - "nodata_index": 0, - "units": "centimeters/year" - }, - "path": "maps/water_risk/wri/v2/water_supply_{scenario}_{year}_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -85.0], - [-180.0, -85.0] - ], - "index_values": null, - "source": "map_array_pyramid" - }, - "scenarios": [ - { - "id": "historical", - "years": [1999] - }, - { - "id": "ssp126", - "years": [2030, 2050, 2080] - }, - { - "id": "ssp370", - "years": [2030, 2050, 2080] - }, - { - "id": "ssp585", - "years": [2030, 2050, 2080] - } - ], - "units": "centimeters/year" - }, - { - "hazard_type": "WaterRisk", - "group_id": "", - "path": "water_risk/wri/v2/water_stress_{scenario}_{year}", - "indicator_id": "water_stress", - "indicator_model_id": null, - "indicator_model_gcm": "combined", - "params": {}, - "display_name": "Water stress (Aqueduct 4.0)", - "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", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 2.0, - "name": "heating", - "nodata_index": 0, - "units": "" - }, - "path": "maps/water_risk/wri/v2/water_stress_{scenario}_{year}_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -85.0], - [-180.0, -85.0] - ], - "index_values": null, - "source": "map_array_pyramid" - }, - "scenarios": [ - { - "id": "historical", - "years": [1999] - }, - { - "id": "ssp126", - "years": [2030, 2050, 2080] - }, - { - "id": "ssp370", - "years": [2030, 2050, 2080] - }, - { - "id": "ssp585", - "years": [2030, 2050, 2080] - } - ], - "units": "" - }, - { - "hazard_type": "WaterRisk", - "group_id": "", - "path": "water_risk/wri/v2/water_depletion_{scenario}_{year}", - "indicator_id": "water_depletion", - "indicator_model_id": null, - "indicator_model_gcm": "combined", - "params": {}, - "display_name": "Water depletion (Aqueduct 4.0)", - "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", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 2.0, - "name": "heating", - "nodata_index": 0, - "units": "" - }, - "path": "maps/water_risk/wri/v2/water_depletion_{scenario}_{year}_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -85.0], - [-180.0, -85.0] - ], - "index_values": null, - "source": "map_array_pyramid" - }, - "scenarios": [ - { - "id": "historical", - "years": [1999] - }, - { - "id": "ssp126", - "years": [2030, 2050, 2080] - }, - { - "id": "ssp370", - "years": [2030, 2050, 2080] - }, - { - "id": "ssp585", - "years": [2030, 2050, 2080] - } - ], - "units": "" - }, - { - "hazard_type": "WaterRisk", - "group_id": "", - "path": "water_risk/wri/v2/water_stress_category_{scenario}_{year}", - "indicator_id": "water_stress_category", - "indicator_model_id": null, - "indicator_model_gcm": "combined", - "params": {}, - "display_name": "Water stress category (Aqueduct 4.0)", - "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", - "map": { - "colormap": { - "min_index": 1, - "min_value": -5.0, - "max_index": 255, - "max_value": 5.0, - "name": "heating", - "nodata_index": 0, - "units": "" - }, - "path": "maps/water_risk/wri/v2/water_stress_category_{scenario}_{year}_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -85.0], - [-180.0, -85.0] - ], - "index_values": null, - "source": "map_array_pyramid" - }, - "scenarios": [ - { - "id": "historical", - "years": [1999] - }, - { - "id": "ssp126", - "years": [2030, 2050, 2080] - }, - { - "id": "ssp370", - "years": [2030, 2050, 2080] - }, - { - "id": "ssp585", - "years": [2030, 2050, 2080] - } - ], - "units": "" - }, - { - "hazard_type": "WaterRisk", - "group_id": "", - "path": "water_risk/wri/v2/water_depletion_category_{scenario}_{year}", - "indicator_id": "water_depletion_category", - "indicator_model_id": null, - "indicator_model_gcm": "combined", - "params": {}, - "display_name": "Water depletion category (Aqueduct 4.0)", - "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", - "map": { - "colormap": { - "min_index": 1, - "min_value": -5.0, - "max_index": 255, - "max_value": 5.0, - "name": "heating", - "nodata_index": 0, - "units": "" - }, - "path": "maps/water_risk/wri/v2/water_depletion_category_{scenario}_{year}_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -85.0], - [-180.0, -85.0] - ], - "index_values": null, - "source": "map_array_pyramid" - }, - "scenarios": [ - { - "id": "historical", - "years": [1999] - }, - { - "id": "ssp126", - "years": [2030, 2050, 2080] - }, - { - "id": "ssp370", - "years": [2030, 2050, 2080] - }, - { - "id": "ssp585", - "years": [2030, 2050, 2080] - } - ], - "units": "" - }, - { - "hazard_type": "Drought", - "group_id": "", - "path": "drought/osc/v1/months_spei12m_below_index_{gcm}_{scenario}_{year}", - "indicator_id": "months/spei12m/below/index", - "indicator_model_id": null, - "indicator_model_gcm": "{gcm}", - "params": { - "gcm": ["MIROC6"] - }, - "display_name": "Drought SPEI index", - "display_groups": ["Drought SPEI index"], - "description": "", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 12.0, - "name": "heating", - "nodata_index": 0, - "units": "months/year" - }, - "path": "drought/osc/v1/months_spei12m_below_index_{gcm}_{scenario}_{year}_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -60.0], - [-180.0, -60.0] - ], - "index_values": [0, -1, -1.5, -2, -2.5, -3, -3.6], - "source": "map_array_pyramid" - }, - "scenarios": [ - { - "id": "ssp585", - "years": [2005, 2030, 2040, 2050, 2080] - } - ], - "units": "months/year" - }, - { - "hazard_type": "RiverineInundation", - "group_id": "", - "path": "inundation/river_tudelft/v2/flood_depth_{scenario}_{year}", - "indicator_id": "flood_depth", - "indicator_model_id": "tudelft", - "indicator_model_gcm": "CLMcom-CCLM4-8-17-EC-EARTH", - "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.", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 5.0, - "name": "flare", - "nodata_index": 0, - "units": "metres" - }, - "path": "maps/inundation/river_tudelft/v2/flood_depth_{scenario}_{year}_map", - "bounds": [], - "index_values": null, - "source": "map_array_pyramid" - }, - "scenarios": [ - { - "id": "historical", - "years": [1971] - }, - { - "id": "rcp4p5", - "years": [2035, 2085] - }, - { - "id": "rcp8p5", - "years": [2035, 2085] - } - ], - "units": "metres" - }, - { - "hazard_type": "Subsidence", - "group_id": "", - "path": "subsidence/csm/v1/land_subsidence_rate_{scenario}_{year}", - "indicator_id": "land_subsidence_rate", - "indicator_model_id": null, - "indicator_model_gcm": "historical", - "params": {}, - "display_name": "Land subsidence rate (Davydzenka et Al (2024))", - "display_groups": [], - "description": "Land subsidence refers to gradual settlement or rapid sinking of the ground that can occur as a result of natural factors (e.g., volcanic or seismic activity, collapse of subsurface cavities, compaction of loose fine-grained deposits) or anthropogenic activities (e.g., excessive groundwater (GW) abstraction, mining, subsurface energy extraction). It is a destructive phenomenon occurring around the globe causing damage to infrastructure, increased flood risks, and reduction of aquifer storage. However, prediction and quantification of land subsidence rates globally using physics-based methods presents a major challenge. Capitalizing on the robustness of modern deep learning methods and taking advantage of the increasingly available data sets of environmental parameters, the study [here](https://doi.org/10.1029/2023GL104497) introduces the first map of land subsidence rates on a global scale with a spatial resolution of 30 \u00c3\u2014 30 arc seconds (approx. 1 km at the equator).\n\nDavydzenka, T., Tahmasebi, P., & Shokri, N. (2023). [Data](https://doi.org/10.5281/zenodo.10223637) for \"Unveiling the Global Extent of Land Subsidence: The sinking crisis\".", - "map": { - "colormap": { - "min_index": 1, - "min_value": 0.0, - "max_index": 255, - "max_value": 100.0, - "name": "heating", - "nodata_index": 0, - "units": "millimetres/year" - }, - "path": "maps/subsidence/csm/v1/land_subsidence_rate_{scenario}_{year}_map", - "bounds": [ - [-180.0, 85.0], - [180.0, 85.0], - [180.0, -60.0], - [-180.0, -60.0] - ], - "bbox": [-180.0, -60.0, 180.0, 85.0], - "index_values": null, - "source": "map_array_pyramid" - }, - "scenarios": [ - { - "id": "historical", - "years": [2021] + "resources": [ + { + "hazard_type": "RiverineInundation", + "group_id": "public", + "path": "inundation/wri/v2/inunriver_{scenario}_000000000WATCH_{year}", + "indicator_id": "flood_depth", + "indicator_model_id": null, + "indicator_model_gcm": "historical", + "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", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 2.0, + "name": "flare", + "nodata_index": 0, + "units": "m" + }, + "path": "inundation/wri/v2/inunriver_{scenario}_000000000WATCH_{year}_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -85.0 + ], + [ + -180.0, + -85.0 + ] + ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], + "index_values": null, + "source": "map_array_pyramid" + }, + "scenarios": [ + { + "id": "historical", + "years": [ + 1980 + ] + } + ], + "units": "metres" + }, + { + "hazard_type": "RiverineInundation", + "group_id": "public", + "path": "inundation/wri/v2/inunriver_{scenario}_00000NorESM1-M_{year}", + "indicator_id": "flood_depth", + "indicator_model_id": null, + "indicator_model_gcm": "NorESM1-M", + "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", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 2.0, + "name": "flare", + "nodata_index": 0, + "units": "m" + }, + "path": "inundation/wri/v2/inunriver_{scenario}_00000NorESM1-M_{year}_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -85.0 + ], + [ + -180.0, + -85.0 + ] + ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], + "index_values": [ + 8 + ], + "source": "map_array_pyramid" + }, + "scenarios": [ + { + "id": "rcp4p5", + "years": [ + 2030, + 2050, + 2080 + ] + }, + { + "id": "rcp8p5", + "years": [ + 2030, + 2050, + 2080 + ] + } + ], + "units": "metres" + }, + { + "hazard_type": "RiverineInundation", + "group_id": "public", + "path": "inundation/wri/v2/inunriver_{scenario}_0000GFDL-ESM2M_{year}", + "indicator_id": "flood_depth", + "indicator_model_id": null, + "indicator_model_gcm": "GFDL-ESM2M", + "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", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 2.0, + "name": "flare", + "nodata_index": 0, + "units": "m" + }, + "path": "inundation/wri/v2/inunriver_{scenario}_0000GFDL-ESM2M_{year}_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -85.0 + ], + [ + -180.0, + -85.0 + ] + ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], + "index_values": [ + 8 + ], + "source": "map_array_pyramid" + }, + "scenarios": [ + { + "id": "rcp4p5", + "years": [ + 2030, + 2050, + 2080 + ] + }, + { + "id": "rcp8p5", + "years": [ + 2030, + 2050, + 2080 + ] + } + ], + "units": "metres" + }, + { + "hazard_type": "RiverineInundation", + "group_id": "public", + "path": "inundation/wri/v2/inunriver_{scenario}_0000HadGEM2-ES_{year}", + "indicator_id": "flood_depth", + "indicator_model_id": null, + "indicator_model_gcm": "HadGEM2-ES", + "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", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 2.0, + "name": "flare", + "nodata_index": 0, + "units": "m" + }, + "path": "inundation/wri/v2/inunriver_{scenario}_0000HadGEM2-ES_{year}_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -85.0 + ], + [ + -180.0, + -85.0 + ] + ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], + "index_values": [ + 8 + ], + "source": "map_array_pyramid" + }, + "scenarios": [ + { + "id": "rcp4p5", + "years": [ + 2030, + 2050, + 2080 + ] + }, + { + "id": "rcp8p5", + "years": [ + 2030, + 2050, + 2080 + ] + } + ], + "units": "metres" + }, + { + "hazard_type": "RiverineInundation", + "group_id": "public", + "path": "inundation/wri/v2/inunriver_{scenario}_00IPSL-CM5A-LR_{year}", + "indicator_id": "flood_depth", + "indicator_model_id": null, + "indicator_model_gcm": "IPSL-CM5A-LR", + "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", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 2.0, + "name": "flare", + "nodata_index": 0, + "units": "m" + }, + "path": "inundation/wri/v2/inunriver_{scenario}_00IPSL-CM5A-LR_{year}_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -85.0 + ], + [ + -180.0, + -85.0 + ] + ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], + "index_values": [ + 8 + ], + "source": "map_array_pyramid" + }, + "scenarios": [ + { + "id": "rcp4p5", + "years": [ + 2030, + 2050, + 2080 + ] + }, + { + "id": "rcp8p5", + "years": [ + 2030, + 2050, + 2080 + ] + } + ], + "units": "metres" + }, + { + "hazard_type": "RiverineInundation", + "group_id": "public", + "path": "inundation/wri/v2/inunriver_{scenario}_MIROC-ESM-CHEM_{year}", + "indicator_id": "flood_depth", + "indicator_model_id": null, + "indicator_model_gcm": "MIROC-ESM-CHEM", + "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", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 2.0, + "name": "flare", + "nodata_index": 0, + "units": "m" + }, + "path": "inundation/wri/v2/inunriver_{scenario}_MIROC-ESM-CHEM_{year}_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -85.0 + ], + [ + -180.0, + -85.0 + ] + ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], + "index_values": [ + 8 + ], + "source": "map_array_pyramid" + }, + "scenarios": [ + { + "id": "rcp4p5", + "years": [ + 2030, + 2050, + 2080 + ] + }, + { + "id": "rcp8p5", + "years": [ + 2030, + 2050, + 2080 + ] + } + ], + "units": "metres" + }, + { + "hazard_type": "CoastalInundation", + "group_id": "public", + "path": "inundation/wri/v2/inuncoast_historical_nosub_hist_0", + "indicator_id": "flood_depth", + "indicator_model_id": "nosub", + "indicator_model_gcm": "unknown", + "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", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 2.0, + "name": "flare", + "nodata_index": 0, + "units": "m" + }, + "path": "inundation/wri/v2/inuncoast_historical_nosub_hist_0_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -85.0 + ], + [ + -180.0, + -85.0 + ] + ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], + "index_values": [ + 8 + ], + "source": "map_array_pyramid" + }, + "scenarios": [ + { + "id": "historical", + "years": [ + 1980 + ] + } + ], + "units": "metres" + }, + { + "hazard_type": "CoastalInundation", + "group_id": "public", + "path": "inundation/wri/v2/inuncoast_{scenario}_nosub_{year}_0", + "indicator_id": "flood_depth", + "indicator_model_id": "nosub/95", + "indicator_model_gcm": "unknown", + "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", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 2.0, + "name": "flare", + "nodata_index": 0, + "units": "m" + }, + "path": "inundation/wri/v2/inuncoast_{scenario}_nosub_{year}_0_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -85.0 + ], + [ + -180.0, + -85.0 + ] + ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], + "index_values": [ + 8 + ], + "source": "map_array_pyramid" + }, + "scenarios": [ + { + "id": "rcp4p5", + "years": [ + 2030, + 2050, + 2080 + ] + }, + { + "id": "rcp8p5", + "years": [ + 2030, + 2050, + 2080 + ] + } + ], + "units": "metres" + }, + { + "hazard_type": "CoastalInundation", + "group_id": "public", + "path": "inundation/wri/v2/inuncoast_{scenario}_nosub_{year}_0_perc_05", + "indicator_id": "flood_depth/nosub/5", + "indicator_model_id": "nosub/5", + "indicator_model_gcm": "unknown", + "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", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 2.0, + "name": "flare", + "nodata_index": 0, + "units": "m" + }, + "path": "inundation/wri/v2/inuncoast_{scenario}_nosub_{year}_0_perc_05_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -85.0 + ], + [ + -180.0, + -85.0 + ] + ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], + "index_values": [ + 8 + ], + "source": "map_array_pyramid" + }, + "scenarios": [ + { + "id": "rcp4p5", + "years": [ + 2030, + 2050, + 2080 + ] + }, + { + "id": "rcp8p5", + "years": [ + 2030, + 2050, + 2080 + ] + } + ], + "units": "metres" + }, + { + "hazard_type": "CoastalInundation", + "group_id": "public", + "path": "inundation/wri/v2/inuncoast_{scenario}_nosub_{year}_0_perc_50", + "indicator_id": "flood_depth", + "indicator_model_id": "nosub/50", + "indicator_model_gcm": "unknown", + "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", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 2.0, + "name": "flare", + "nodata_index": 0, + "units": "m" + }, + "path": "inundation/wri/v2/inuncoast_{scenario}_nosub_{year}_0_perc_50_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -85.0 + ], + [ + -180.0, + -85.0 + ] + ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], + "index_values": [ + 8 + ], + "source": "map_array_pyramid" + }, + "scenarios": [ + { + "id": "rcp4p5", + "years": [ + 2030, + 2050, + 2080 + ] + }, + { + "id": "rcp8p5", + "years": [ + 2030, + 2050, + 2080 + ] + } + ], + "units": "metres" + }, + { + "hazard_type": "CoastalInundation", + "group_id": "public", + "path": "inundation/wri/v2/inuncoast_historical_wtsub_hist_0", + "indicator_id": "flood_depth", + "indicator_model_id": "wtsub", + "indicator_model_gcm": "unknown", + "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", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 2.0, + "name": "flare", + "nodata_index": 0, + "units": "m" + }, + "path": "inundation/wri/v2/inuncoast_historical_wtsub_hist_0_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -85.0 + ], + [ + -180.0, + -85.0 + ] + ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], + "index_values": [ + 8 + ], + "source": "map_array_pyramid" + }, + "scenarios": [ + { + "id": "historical", + "years": [ + 1980 + ] + } + ], + "units": "metres" + }, + { + "hazard_type": "CoastalInundation", + "group_id": "public", + "path": "inundation/wri/v2/inuncoast_{scenario}_wtsub_{year}_0", + "indicator_id": "flood_depth", + "indicator_model_id": "wtsub/95", + "indicator_model_gcm": "unknown", + "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", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 2.0, + "name": "flare", + "nodata_index": 0, + "units": "m" + }, + "path": "inundation/wri/v2/inuncoast_{scenario}_wtsub_{year}_0_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -85.0 + ], + [ + -180.0, + -85.0 + ] + ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], + "index_values": [ + 8 + ], + "source": "map_array_pyramid" + }, + "scenarios": [ + { + "id": "rcp4p5", + "years": [ + 2030, + 2050, + 2080 + ] + }, + { + "id": "rcp8p5", + "years": [ + 2030, + 2050, + 2080 + ] + } + ], + "units": "metres" + }, + { + "hazard_type": "CoastalInundation", + "group_id": "public", + "path": "inundation/wri/v2/inuncoast_{scenario}_wtsub_{year}_0_perc_05", + "indicator_id": "flood_depth", + "indicator_model_id": "wtsub/5", + "indicator_model_gcm": "unknown", + "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", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 2.0, + "name": "flare", + "nodata_index": 0, + "units": "m" + }, + "path": "inundation/wri/v2/inuncoast_{scenario}_wtsub_{year}_0_perc_05_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -85.0 + ], + [ + -180.0, + -85.0 + ] + ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], + "index_values": [ + 8 + ], + "source": "map_array_pyramid" + }, + "scenarios": [ + { + "id": "rcp4p5", + "years": [ + 2030, + 2050, + 2080 + ] + }, + { + "id": "rcp8p5", + "years": [ + 2030, + 2050, + 2080 + ] + } + ], + "units": "metres" + }, + { + "hazard_type": "CoastalInundation", + "group_id": "public", + "path": "inundation/wri/v2/inuncoast_{scenario}_wtsub_{year}_0_perc_50", + "indicator_id": "flood_depth", + "indicator_model_id": "wtsub/50", + "indicator_model_gcm": "unknown", + "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", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 2.0, + "name": "flare", + "nodata_index": 0, + "units": "m" + }, + "path": "inundation/wri/v2/inuncoast_{scenario}_wtsub_{year}_0_perc_50_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -85.0 + ], + [ + -180.0, + -85.0 + ] + ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], + "index_values": [ + 8 + ], + "source": "map_array_pyramid" + }, + "scenarios": [ + { + "id": "rcp4p5", + "years": [ + 2030, + 2050, + 2080 + ] + }, + { + "id": "rcp8p5", + "years": [ + 2030, + 2050, + 2080 + ] + } + ], + "units": "metres" + }, + { + "hazard_type": "ChronicHeat", + "group_id": "", + "path": "chronic_heat/osc/v2/mean_degree_days_v2_above_32c_{gcm}_{scenario}_{year}", + "indicator_id": "mean_degree_days/above/32c", + "indicator_model_id": null, + "indicator_model_gcm": "{gcm}", + "params": { + "gcm": [ + "ACCESS-CM2", + "CMCC-ESM2", + "CNRM-CM6-1", + "MIROC6", + "MPI-ESM1-2-LR", + "NorESM2-MM" + ] + }, + "display_name": "Mean degree days above 32\u00b0C/{gcm}", + "display_groups": [ + "Mean degree days" + ], + "description": "Degree days indicators are calculated by integrating over time the absolute difference in temperature\nof the medium over a reference temperature. The exact method of calculation may vary;\nhere the daily maximum near-surface temperature 'tasmax' is used to calculate an annual indicator:\n\n$$\nI^\\text{dd} = \\frac{365}{n_y} \\sum_{i = 1}^{n_y} | T^\\text{max}_i - T^\\text{ref} | \\nonumber\n$$\n\n$I^\\text{dd}$ is the indicator, $T^\\text{max}$ is the daily maximum near-surface temperature, $n_y$ is the number of days in the year and $i$ is the day index.\nand $T^\\text{ref}$ is the reference temperature of 32\u00b0C.\n\nDownscaled climate simulations from the Coupled Model Intercomparison Project (CMIP6), sourced from the [NASA Earth Exchange Global Daily Downscaled Projections](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6).\n\nIndicators are generated for periods: 'historical' (averaged over 1995-2015), 2030 (2020-2040), 2040 (2030-2050), and 2050 (2040-2060).", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 4000.0, + "name": "heating", + "nodata_index": 0, + "units": "degree days" + }, + "path": "mean_degree_days_v2_above_32c_{gcm}_{scenario}_{year}_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -60.0 + ], + [ + -180.0, + -60.0 + ] + ], + "bbox": [ + -180.0, + -60.0, + 180.0, + 85.0 + ], + "index_values": null, + "source": "map_array" + }, + "scenarios": [ + { + "id": "historical", + "years": [ + 2005 + ] + }, + { + "id": "ssp126", + "years": [ + 2030, + 2040, + 2050 + ] + }, + { + "id": "ssp245", + "years": [ + 2030, + 2040, + 2050 + ] + }, + { + "id": "ssp585", + "years": [ + 2030, + 2040, + 2050 + ] + } + ], + "units": "degree days" + }, + { + "hazard_type": "Fire", + "group_id": "jupiter_osc", + "path": "fire/jupiter/v1/fire_probability_{scenario}_{year}", + "indicator_id": "fire_probability", + "indicator_model_id": null, + "indicator_model_gcm": "unknown", + "params": {}, + "display_name": "Fire probability (Jupiter)", + "display_groups": [], + "description": "\nThese data should not be used in any manner relating to emergency management or planning, public safety,\nphysical safety or property endangerment. For higher-resolution data based on up-to-date methods,\nsubject to greater validation, and suitable for bottom-up risk analysis please contact\n[Jupiter Intelligence](https://www.jupiterintel.com).\n\nThis fire model computes the maximum monthly probability per annum of a wildfire within 100 km of\na given location based on several parameters from multiple bias corrected and downscaled Global Climate Models (GCMs).\nFor example, if the probability of occurrence of a wildfire is 5% in July, 20% in August, 10% in September\nand 0% for other months, the hazard indicator value is 20%.\n ", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 0.7, + "name": "heating", + "nodata_index": 0, + "units": "" + }, + "path": "fire_probability_{scenario}_{year}_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -85.0 + ], + [ + -180.0, + -85.0 + ] + ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], + "index_values": null, + "source": "map_array" + }, + "scenarios": [ + { + "id": "ssp126", + "years": [ + 2020, + 2030, + 2040, + 2050, + 2075, + 2100 + ] + }, + { + "id": "ssp585", + "years": [ + 2020, + 2030, + 2040, + 2050, + 2075, + 2100 + ] + } + ], + "units": "" + }, + { + "hazard_type": "Drought", + "group_id": "jupiter_osc", + "path": "drought/jupiter/v1/months_spei3m_below_-2_{scenario}_{year}", + "indicator_id": "months/spei3m/below/-2", + "indicator_model_id": null, + "indicator_model_gcm": "unknown", + "params": {}, + "display_name": "Drought (Jupiter)", + "display_groups": [], + "description": "\nThese data should not be used in any manner relating to emergency management or planning, public safety,\nphysical safety or property endangerment. For higher-resolution data based on up-to-date methods,\nsubject to greater validation, and suitable for bottom-up risk analysis please contact\n[Jupiter Intelligence](https://www.jupiterintel.com).\n\nThis drought model is based on the Standardized Precipitation-Evapotranspiration Index (SPEI).\nThe SPEl is an extension of the Standardized Precipitation Index which also considers Potential Evapotranspiration (PET)\nin determining drought events.\nThe SPEl is calculated from a log-logistic probability distribution function of climatic water balance\n(precipitation minus evapotranspiration) over a given time scale.\nThe SPEI itself is a standardized variable with a mean value 0 and standard deviation 1.\nThis drought model computes the number of months per annum where the 3-month rolling average\nof SPEI is below -2 based on the mean values of several parameters from\nbias-corrected and downscaled multiple Global Climate Models (GCMs).\n ", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 12.0, + "name": "heating", + "nodata_index": 0, + "units": "months/year" + }, + "path": "months_spei3m_below_-2_{scenario}_{year}_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -85.0 + ], + [ + -180.0, + -85.0 + ] + ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], + "index_values": null, + "source": "map_array" + }, + "scenarios": [ + { + "id": "ssp126", + "years": [ + 2020, + 2030, + 2040, + 2050, + 2075, + 2100 + ] + }, + { + "id": "ssp585", + "years": [ + 2020, + 2030, + 2040, + 2050, + 2075, + 2100 + ] + } + ], + "units": "months/year" + }, + { + "hazard_type": "Precipitation", + "group_id": "jupiter_osc", + "path": "precipitation/jupiter/v1/max_daily_water_equivalent_{scenario}_{year}", + "indicator_id": "max/daily/water_equivalent", + "indicator_model_id": null, + "indicator_model_gcm": "unknown", + "params": {}, + "display_name": "Precipitation (Jupiter)", + "display_groups": [], + "description": "\nThese data should not be used in any manner relating to emergency management or planning, public safety,\nphysical safety or property endangerment. For higher-resolution data based on up-to-date methods,\nsubject to greater validation, and suitable for bottom-up risk analysis please contact\n[Jupiter Intelligence](https://www.jupiterintel.com).\n\nThis model computes the maximum daily water equivalent precipitation (in mm) measured at the 100 year\nreturn period based on the mean of the precipitation distribution from multiple bias corrected and\ndownscaled Global Climate Models (GCMs).\n ", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 1000.0, + "name": "heating", + "nodata_index": 0, + "units": "mm" + }, + "path": "max_daily_water_equivalent_{scenario}_{year}_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -85.0 + ], + [ + -180.0, + -85.0 + ] + ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], + "index_values": null, + "source": "map_array" + }, + "scenarios": [ + { + "id": "ssp126", + "years": [ + 2020, + 2030, + 2040, + 2050, + 2075, + 2100 + ] + }, + { + "id": "ssp585", + "years": [ + 2020, + 2030, + 2040, + 2050, + 2075, + 2100 + ] + } + ], + "units": "mm" + }, + { + "hazard_type": "Hail", + "group_id": "jupiter_osc", + "path": "hail/jupiter/v1/days_above_5cm_{scenario}_{year}", + "indicator_id": "days/above/5cm", + "indicator_model_id": null, + "indicator_model_gcm": "unknown", + "params": {}, + "display_name": "Large hail days per year (Jupiter)", + "display_groups": [], + "description": "\nThese data should not be used in any manner relating to emergency management or planning, public safety,\nphysical safety or property endangerment. For higher-resolution data based on up-to-date methods,\nsubject to greater validation, and suitable for bottom-up risk analysis please contact\n[Jupiter Intelligence](https://www.jupiterintel.com).\n\nThis hail model computes the number of days per annum where hail exceeding 5 cm diameter is possible\nbased on the mean distribution of several parameters\nacross multiple bias-corrected and downscaled Global Climate Models (GCMs).\n ", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 10.0, + "name": "heating", + "nodata_index": 0, + "units": "days/year" + }, + "path": "days_above_5cm_{scenario}_{year}_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -85.0 + ], + [ + -180.0, + -85.0 + ] + ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], + "index_values": null, + "source": "map_array" + }, + "scenarios": [ + { + "id": "ssp126", + "years": [ + 2020, + 2030, + 2040, + 2050, + 2075, + 2100 + ] + }, + { + "id": "ssp585", + "years": [ + 2020, + 2030, + 2040, + 2050, + 2075, + 2100 + ] + } + ], + "units": "days/year" + }, + { + "hazard_type": "ChronicHeat", + "group_id": "jupiter_osc", + "path": "chronic_heat/jupiter/v1/days_above_35c_{scenario}_{year}", + "indicator_id": "days/above/35c", + "indicator_model_id": null, + "indicator_model_gcm": "unknown", + "params": {}, + "display_name": "Days per year above 35\u00b0C (Jupiter)", + "display_groups": [], + "description": "\nThese data should not be used in any manner relating to emergency management or planning, public safety,\nphysical safety or property endangerment. For higher-resolution data based on up-to-date methods,\nsubject to greater validation, and suitable for bottom-up risk analysis please contact\n[Jupiter Intelligence](https://www.jupiterintel.com).\n\nThis heat model computes the number of days exceeding 35\u00b0C per annum based on the mean of distribution fits\nto the bias-corrected and downscaled high temperature distribution\nacross multiple Global Climate Models (GCMs).\n ", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 365.0, + "name": "heating", + "nodata_index": 0, + "units": "days/year" + }, + "path": "days_above_35c_{scenario}_{year}_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -85.0 + ], + [ + -180.0, + -85.0 + ] + ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], + "index_values": null, + "source": "map_array" + }, + "scenarios": [ + { + "id": "ssp126", + "years": [ + 2020, + 2030, + 2040, + 2050, + 2075, + 2100 + ] + }, + { + "id": "ssp585", + "years": [ + 2020, + 2030, + 2040, + 2050, + 2075, + 2100 + ] + } + ], + "units": "days/year" + }, + { + "hazard_type": "Wind", + "group_id": "jupiter_osc", + "path": "wind/jupiter/v1/max_1min_{scenario}_{year}", + "indicator_id": "max_speed", + "indicator_model_id": "1min", + "indicator_model_gcm": "unknown", + "params": {}, + "display_name": "Max 1 minute sustained wind speed (Jupiter)", + "display_groups": [], + "description": "\nThese data should not be used in any manner relating to emergency management or planning, public safety,\nphysical safety or property endangerment. For higher-resolution data based on up-to-date methods,\nsubject to greater validation, and suitable for bottom-up risk analysis please contact\n[Jupiter Intelligence](https://www.jupiterintel.com).\n\nThis wind speed model computes the maximum 1-minute sustained wind speed (in m/s) experienced over a\n100 year return period based on mean wind speed distributions\nfrom multiple Global Climate Models (GCMs).\n ", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 120.0, + "name": "heating", + "nodata_index": 0, + "units": "m/s" + }, + "path": "max_1min_{scenario}_{year}_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -85.0 + ], + [ + -180.0, + -85.0 + ] + ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], + "index_values": null, + "source": "map_array" + }, + "scenarios": [ + { + "id": "ssp126", + "years": [ + 2020, + 2030, + 2040, + 2050, + 2075, + 2100 + ] + }, + { + "id": "ssp585", + "years": [ + 2020, + 2030, + 2040, + 2050, + 2075, + 2100 + ] + } + ], + "units": "m/s" + }, + { + "hazard_type": "CombinedInundation", + "group_id": "jupiter_osc", + "path": "combined_flood/jupiter/v1/fraction_{scenario}_{year}", + "indicator_id": "flooded_fraction", + "indicator_model_id": null, + "indicator_model_gcm": "unknown", + "params": {}, + "display_name": "Flooded fraction (Jupiter)", + "display_groups": [], + "description": "\nThese data should not be used in any manner relating to emergency management or planning, public safety,\nphysical safety or property endangerment. For higher-resolution data based on up-to-date methods,\nsubject to greater validation, and suitable for bottom-up risk analysis please contact\n[Jupiter Intelligence](https://www.jupiterintel.com).\n\nFlooded fraction provides the spatial fraction of land flooded in a defined grid.\nIt is derived from higher-resolution flood hazards, and computed directly as the fraction of\ncells within the 30-km cell that have non-zero flooding at that return period.\nThis model uses a 30-km grid that experiences flooding at the 200-year return period.\nOpen oceans are excluded.\n ", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 1.0, + "name": "heating", + "nodata_index": 0, + "units": "" + }, + "path": "fraction_{scenario}_{year}_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -85.0 + ], + [ + -180.0, + -85.0 + ] + ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], + "index_values": null, + "source": "map_array" + }, + "scenarios": [ + { + "id": "ssp126", + "years": [ + 2020, + 2030, + 2040, + 2050, + 2075, + 2100 + ] + }, + { + "id": "ssp585", + "years": [ + 2020, + 2030, + 2040, + 2050, + 2075, + 2100 + ] + } + ], + "units": "none" + }, + { + "hazard_type": "ChronicHeat", + "group_id": "", + "path": "chronic_heat/osc/v2/mean_work_loss_{intensity}_{gcm}_{scenario}_{year}", + "indicator_id": "mean_work_loss/{intensity}", + "indicator_model_id": null, + "indicator_model_gcm": "{gcm}", + "params": { + "intensity": [ + "low", + "medium", + "high" + ], + "gcm": [ + "ACCESS-CM2", + "CMCC-ESM2", + "CNRM-CM6-1", + "MPI-ESM1-2-LR", + "MIROC6", + "NorESM2-MM" + ] + }, + "display_name": "Mean work loss, {intensity} intensity/{gcm}", + "display_groups": [ + "Mean work loss" + ], + "description": "The mean work loss indicator is calculated from the 'Wet Bulb Globe Temperature' (WBGT) indicator:\n\n$$\nI^\\text{WBGT}_i = 0.567 \\times T^\\text{avg}_i + 0.393 \\times p^\\text{vapour}_i + 3.94\n$$\n\n$I^\\text{WBGT}_i$ is the WBGT indicator, $T^\\text{avg}_i$ is the average daily near-surface surface temperature (in degress Celsius) on day index, $i$, and $p^\\text{vapour}$\nis the water vapour partial pressure (in kPa). $p^\\text{vapour}$ is calculated from relative humidity $h_r$ via:\n\n$$\np^\\text{vapour}_i = \\frac{h_r}{100} \\times 6.105 \\times \\exp \\left( \\frac{17.27 \\times T^\\text{avg}_i}{237.7 + T^\\text{avg}_i} \\right)\n$$\n\nThe work ability indicator, $I^{\\text{WA}}$ is finally calculated via:\n\n$$\nI^{\\text{WA}}_i = 0.1 + 0.9 / \\left( 1 + (I^\\text{WBGT}_i / \\alpha_1)^{\\alpha_2} \\right)\n$$\n\nAn annual average work loss indicator, $I^{\\text{WL}}$ is calculated via:\n\n$$\nI^{\\text{WL}} = 1 - \\frac{1}{n_y} \\sum_{i = 1}^{n_y} I^{\\text{WA}}_i,\n$$\n\n$n_y$ being the number of days in the year. The OS-Climate-generated indicators are inferred from CMIP6 data, averaged over 6 models: ACCESS-CM2, CMCC-ESM2, CNRM-CM6-1, MPI-ESM1-2-LR, MIROC6 and NorESM2-MM.\nThe indicators are generated for periods: 'historical' (averaged over 1995-2014), 2030 (2021-2040), 2040 (2031-2050) and 2050 (2041-2060).\n", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 0.8, + "name": "heating", + "nodata_index": 0, + "units": "fractional loss" + }, + "path": "mean_work_loss_{intensity}_{gcm}_{scenario}_{year}_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -60.0 + ], + [ + -180.0, + -60.0 + ] + ], + "bbox": [ + -180.0, + -60.0, + 180.0, + 85.0 + ], + "index_values": null, + "source": "map_array" + }, + "scenarios": [ + { + "id": "historical", + "years": [ + 2005 + ] + }, + { + "id": "ssp126", + "years": [ + 2030, + 2040, + 2050 + ] + }, + { + "id": "ssp245", + "years": [ + 2030, + 2040, + 2050 + ] + }, + { + "id": "ssp585", + "years": [ + 2030, + 2040, + 2050 + ] + } + ], + "units": "fractional loss" + }, + { + "hazard_type": "ChronicHeat", + "group_id": "", + "path": "chronic_heat/osc/v2/days_tas_above_{temp_c}c_{gcm}_{scenario}_{year}", + "indicator_id": "days_tas/above/{temp_c}c", + "indicator_model_id": null, + "indicator_model_gcm": "{gcm}", + "params": { + "temp_c": [ + "25", + "30", + "35", + "40", + "45", + "50", + "55" + ], + "gcm": [ + "ACCESS-CM2", + "CMCC-ESM2", + "CNRM-CM6-1", + "MPI-ESM1-2-LR", + "MIROC6", + "NorESM2-MM" + ] + }, + "display_name": "Days with average temperature above {temp_c}\u00b0C/{gcm}", + "display_groups": [ + "Days with average temperature above" + ], + "description": "Days per year for which the average near-surface temperature 'tas' is above a threshold specified in \u00b0C.\n\n$$\nI = \\frac{365}{n_y} \\sum_{i = 1}^{n_y} \\boldsymbol{\\mathbb{1}}_{\\; \\, T^{avg}_i > T^\\text{ref}} \\nonumber\n$$\n\n$I$ is the indicator, $T^\\text{avg}_i$ is the daily average near-surface temperature for day index $i$ in \u00b0C, $n_y$ is the number of days in the year\nand $T^\\text{ref}$ is the reference temperature.\n\nDownscaled climate simulations from the Coupled Model Intercomparison Project (CMIP6), sourced from the [NASA Earth Exchange Global Daily Downscaled Projections](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6).\n\nIndicators are generated for periods: 'historical' (averaged over 1995-2015), 2030 (2020-2040), 2040 (2030-2050), and 2050 (2040-2060).", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 100.0, + "name": "heating", + "nodata_index": 0, + "units": "days/year" + }, + "path": "days_tas_above_{temp_c}c_{gcm}_{scenario}_{year}_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -60.0 + ], + [ + -180.0, + -60.0 + ] + ], + "bbox": [ + -180.0, + -60.0, + 180.0, + 85.0 + ], + "index_values": null, + "source": "map_array" + }, + "scenarios": [ + { + "id": "historical", + "years": [ + 2005 + ] + }, + { + "id": "ssp126", + "years": [ + 2030, + 2040, + 2050 + ] + }, + { + "id": "ssp245", + "years": [ + 2030, + 2040, + 2050 + ] + }, + { + "id": "ssp585", + "years": [ + 2030, + 2040, + 2050 + ] + } + ], + "units": "days/year" + }, + { + "hazard_type": "Wind", + "group_id": "iris_osc", + "path": "wind/iris/v1/max_speed_{scenario}_{year}", + "indicator_id": "max_speed", + "indicator_model_id": null, + "indicator_model_gcm": "combined", + "params": {}, + "display_name": "Max wind speed (IRIS)", + "display_groups": [], + "description": "Sparks, N., Toumi, R. The Imperial College Storm Model (IRIS) Dataset. *Sci Data* **11**, 424 (2024). \n\n## The Imperial College Storm Model (IRIS) Dataset - Scientific Data\nAssessing 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***Disclaimer***: There have been many improvements on the dataset. Contact Professor Toumi from the Imperial College London for improved data.\n", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 120.0, + "name": "heating", + "nodata_index": 0, + "units": "m/s" + }, + "path": "wind/iris/v1/max_speed_{scenario}_{year}_map", + "bounds": [ + [ + -180.0, + 60.0 + ], + [ + 180.0, + 60.0 + ], + [ + 180.0, + -60.0 + ], + [ + -180.0, + -60.0 + ] + ], + "bbox": [ + -180.0, + -60.0, + 180.0, + 60.0 + ], + "index_values": null, + "source": "map_array_pyramid" + }, + "scenarios": [ + { + "id": "historical", + "years": [ + 2010 + ] + }, + { + "id": "ssp119", + "years": [ + 2050 + ] + }, + { + "id": "ssp245", + "years": [ + 2050 + ] + }, + { + "id": "ssp585", + "years": [ + 2050 + ] + } + ], + "units": "m/s" + }, + { + "hazard_type": "ChronicHeat", + "group_id": "", + "path": "chronic_heat/osc/v2/mean_degree_days_above_index_{gcm}_{scenario}_{year}", + "indicator_id": "mean_degree_days/above/index", + "indicator_model_id": null, + "indicator_model_gcm": "{gcm}", + "params": { + "gcm": [ + "ACCESS-CM2", + "CMCC-ESM2", + "CNRM-CM6-1", + "MPI-ESM1-2-LR", + "MIROC6", + "NorESM2-MM" + ] + }, + "display_name": "Mean degree days above index value/{gcm}", + "display_groups": [ + "Mean degree days" + ], + "description": "Degree days indicators are calculated by integrating over time the absolute difference in temperature\nof the medium over a reference temperature. The exact method of calculation may vary;\nhere the daily maximum near-surface temperature 'tasmax' is used to calculate an annual indicator:\n\n$$\nI^\\text{dd} = \\frac{365}{n_y} \\sum_{i = 1}^{n_y} | T^\\text{max}_i - T^\\text{ref} | \\nonumber\n$$\n\n$I^\\text{dd}$ is the indicator, $T^\\text{max}$ is the daily maximum near-surface temperature, $n_y$ is the number of days in the year and $i$ is the day index.\nand $T^\\text{ref}$ is the reference temperature of 32\u00b0C.\n", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 4000.0, + "name": "heating", + "nodata_index": 0, + "units": "degree days" + }, + "path": "mean_degree_days_above_index_{gcm}_{scenario}_{year}_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -60.0 + ], + [ + -180.0, + -60.0 + ] + ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], + "index_values": [ + 16, + 20, + 24 + ], + "source": "map_array" + }, + "scenarios": [ + { + "id": "historical", + "years": [ + 2005 + ] + }, + { + "id": "ssp126", + "years": [ + 2030, + 2040, + 2050 + ] + }, + { + "id": "ssp245", + "years": [ + 2030, + 2040, + 2050 + ] + }, + { + "id": "ssp585", + "years": [ + 2030, + 2040, + 2050 + ] + } + ], + "units": "degree days" + }, + { + "hazard_type": "ChronicHeat", + "group_id": "", + "path": "chronic_heat/osc/v2/mean_degree_days_below_index_{gcm}_{scenario}_{year}", + "indicator_id": "mean_degree_days/below/index", + "indicator_model_id": null, + "indicator_model_gcm": "{gcm}", + "params": { + "gcm": [ + "ACCESS-CM2", + "CMCC-ESM2", + "CNRM-CM6-1", + "MPI-ESM1-2-LR", + "MIROC6", + "NorESM2-MM" + ] + }, + "display_name": "Mean degree days below index value/{gcm}", + "display_groups": [ + "Mean degree days" + ], + "description": "Degree days indicators are calculated by integrating over time the absolute difference in temperature\nof the medium over a reference temperature. The exact method of calculation may vary;\nhere the daily maximum near-surface temperature 'tasmax' is used to calculate an annual indicator:\n\n$$\nI^\\text{dd} = \\frac{365}{n_y} \\sum_{i = 1}^{n_y} | T^\\text{max}_i - T^\\text{ref} | \\nonumber\n$$\n\n$I^\\text{dd}$ is the indicator, $T^\\text{max}$ is the daily maximum near-surface temperature, $n_y$ is the number of days in the year and $i$ is the day index.\nand $T^\\text{ref}$ is the reference temperature of 32\u00b0C.\n", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 4000.0, + "name": "heating", + "nodata_index": 0, + "units": "degree days" + }, + "path": "mean_degree_days_below_index_{gcm}_{scenario}_{year}_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -60.0 + ], + [ + -180.0, + -60.0 + ] + ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], + "index_values": [ + 16, + 20, + 24 + ], + "source": "map_array" + }, + "scenarios": [ + { + "id": "historical", + "years": [ + 2005 + ] + }, + { + "id": "ssp126", + "years": [ + 2030, + 2040, + 2050 + ] + }, + { + "id": "ssp245", + "years": [ + 2030, + 2040, + 2050 + ] + }, + { + "id": "ssp585", + "years": [ + 2030, + 2040, + 2050 + ] + } + ], + "units": "degree days" + }, + { + "hazard_type": "ChronicHeat", + "group_id": "", + "path": "chronic_heat/nluu/v2/weeks_water_temp_above_{gcm}_{scenario}_{year}", + "indicator_id": "weeks_water_temp_above", + "indicator_model_id": null, + "indicator_model_gcm": "{gcm}", + "params": { + "gcm": [ + "GFDL", + "HadGEM", + "IPSL", + "MIROC", + "NorESM" + ] + }, + "display_name": "Weeks with average water temperature above threshold in \u00b0C/{gcm}", + "display_groups": [ + "Weeks with average water temperature above threshold in \u00b0C" + ], + "description": "Weeks per year for which the average water temperature is above a threshold specified in \u00b0C:\n\n$$\nI = \\frac{52}{n_y} \\sum_{i = 1}^{n_y} \\boldsymbol{\\mathbb{1}}_{\\; \\, T^{avg}_i > T^\\text{ref}} \\nonumber\n$$\n\n$I$ is the indicator, $T^\\text{avg}_i$ is the weekly average water temperature for week index $i$ in \u00b0C, $n_y$ is the number of weeks in the sample\nand $T^\\text{ref}$ is the reference temperature.\n\nThe OS-Climate-generated indicators are inferred from downscaled CMIP5 data. This is done for 5 Global Circulation Models: GFDL-ESM2M, HadGEM2-ES, ISPL-CM5A-LR, MIROC-ESM-CHEM and NorESM1-M.\nThe downscaled data is sourced from the [Futurestreams dataset](https://geo.public.data.uu.nl/vault-futurestreams/research-futurestreams%5B1633685642%5D/original/waterTemp/) on the data publication platform of Utrecht University.\nIndicators are generated for periods: 'historical' (averaged over 1976-2005), 2020 (2006-2030), 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, + "min_value": 0.0, + "max_index": 255, + "max_value": 100.0, + "name": "heating", + "nodata_index": 0, + "units": "weeks/year" + }, + "path": "maps/chronic_heat/nluu/v2/weeks_water_temp_above_{gcm}_{scenario}_{year}_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -85.0 + ], + [ + -180.0, + -85.0 + ] + ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], + "index_values": [ + 5, + 7.5, + 10, + 12.5, + 15, + 17.5, + 20, + 22.5, + 25, + 27.5, + 30, + 32.5, + 35, + 37.5, + 40 + ], + "source": "map_array_pyramid" + }, + "scenarios": [ + { + "id": "historical", + "years": [ + 1991 + ] + }, + { + "id": "rcp2p6", + "years": [ + 2020, + 2030, + 2040, + 2050, + 2060, + 2070, + 2080, + 2090 + ] + }, + { + "id": "rcp4p5", + "years": [ + 2020, + 2030, + 2040, + 2050, + 2060, + 2070, + 2080, + 2090 + ] + }, + { + "id": "rcp6p0", + "years": [ + 2020, + 2030, + 2040, + 2050, + 2060, + 2070, + 2080, + 2090 + ] + }, + { + "id": "rcp8p5", + "years": [ + 2020, + 2030, + 2040, + 2050, + 2060, + 2070, + 2080, + 2090 + ] + } + ], + "units": "weeks/year" + }, + { + "hazard_type": "ChronicHeat", + "group_id": "", + "path": "chronic_heat/nluu/v2/weeks_water_temp_above_E2O_{scenario}_{year}", + "indicator_id": "weeks_water_temp_above", + "indicator_model_id": null, + "indicator_model_gcm": "E2O", + "params": {}, + "display_name": "Weeks with average water temperature above threshold in \u00b0C/E2O", + "display_groups": [ + "Weeks with average water temperature above threshold in \u00b0C" + ], + "description": "Weeks per year for which the average water temperature is above a threshold specified in \u00b0C:\n\n$$\nI = \\frac{52}{n_y} \\sum_{i = 1}^{n_y} \\boldsymbol{\\mathbb{1}}_{\\; \\, T^{avg}_i > T^\\text{ref}} \\nonumber\n$$\n\n$I$ is the indicator, $T^\\text{avg}_i$ is the weekly average water temperature for week index $i$ in \u00b0C, $n_y$ is the number of weeks in the sample\nand $T^\\text{ref}$ is the reference temperature.\n\nThe OS-Climate-generated indicators are inferred from downscaled CMIP5 data. This is done for 5 Global Circulation Models: GFDL-ESM2M, HadGEM2-ES, ISPL-CM5A-LR, MIROC-ESM-CHEM and NorESM1-M.\nThe downscaled data is sourced from the [Futurestreams dataset](https://geo.public.data.uu.nl/vault-futurestreams/research-futurestreams%5B1633685642%5D/original/waterTemp/) on the data publication platform of Utrecht University.\nIndicators are generated for periods: 'historical' (averaged over 1979-2005), 2020 (2006-2030), 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, + "min_value": 0.0, + "max_index": 255, + "max_value": 100.0, + "name": "heating", + "nodata_index": 0, + "units": "weeks/year" + }, + "path": "maps/chronic_heat/nluu/v2/weeks_water_temp_above_E2O_{scenario}_{year}_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -85.0 + ], + [ + -180.0, + -85.0 + ] + ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], + "index_values": [ + 5, + 7.5, + 10, + 12.5, + 15, + 17.5, + 20, + 22.5, + 25, + 27.5, + 30, + 32.5, + 35, + 37.5, + 40 + ], + "source": "map_array_pyramid" + }, + "scenarios": [ + { + "id": "historical", + "years": [ + 1991 + ] + } + ], + "units": "weeks/year" + }, + { + "hazard_type": "ChronicHeat", + "group_id": "", + "path": "chronic_heat/osc/v2/days_wbgt_above_{gcm}_{scenario}_{year}", + "indicator_id": "days_wbgt_above", + "indicator_model_id": null, + "indicator_model_gcm": "{gcm}", + "params": { + "gcm": [ + "ACCESS-CM2", + "CMCC-ESM2", + "CNRM-CM6-1", + "MPI-ESM1-2-LR", + "MIROC6", + "NorESM2-MM" + ] + }, + "display_name": "Days with wet-bulb globe temperature above threshold in \u00b0C/{gcm}", + "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", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 100.0, + "name": "heating", + "nodata_index": 0, + "units": "days/year" + }, + "path": "maps/chronic_heat/osc/v2/days_wbgt_above_{gcm}_{scenario}_{year}_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -85.0 + ], + [ + -180.0, + -85.0 + ] + ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], + "index_values": [ + 5, + 10, + 15, + 20, + 25, + 30, + 35, + 40, + 45, + 50, + 55, + 60 + ], + "source": "map_array_pyramid" + }, + "scenarios": [ + { + "id": "historical", + "years": [ + 2005 + ] + }, + { + "id": "ssp126", + "years": [ + 2030, + 2040, + 2050, + 2060, + 2070, + 2080, + 2090 + ] + }, + { + "id": "ssp245", + "years": [ + 2030, + 2040, + 2050, + 2060, + 2070, + 2080, + 2090 + ] + }, + { + "id": "ssp370", + "years": [ + 2030, + 2040, + 2050, + 2060, + 2070, + 2080, + 2090 + ] + }, + { + "id": "ssp585", + "years": [ + 2030, + 2040, + 2050, + 2060, + 2070, + 2080, + 2090 + ] + } + ], + "units": "days/year" + }, + { + "hazard_type": "WaterRisk", + "group_id": "", + "path": "water_risk/wri/v2/water_demand_{scenario}_{year}", + "indicator_id": "water_demand", + "indicator_model_id": null, + "indicator_model_gcm": "combined", + "params": {}, + "display_name": "Water demand in centimeters/year (Aqueduct 4.0)", + "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", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 100.0, + "name": "heating", + "nodata_index": 0, + "units": "centimeters/year" + }, + "path": "maps/water_risk/wri/v2/water_demand_{scenario}_{year}_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -85.0 + ], + [ + -180.0, + -85.0 + ] + ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], + "index_values": null, + "source": "map_array_pyramid" + }, + "scenarios": [ + { + "id": "historical", + "years": [ + 1999 + ] + }, + { + "id": "ssp126", + "years": [ + 2030, + 2050, + 2080 + ] + }, + { + "id": "ssp370", + "years": [ + 2030, + 2050, + 2080 + ] + }, + { + "id": "ssp585", + "years": [ + 2030, + 2050, + 2080 + ] + } + ], + "units": "centimeters/year" + }, + { + "hazard_type": "WaterRisk", + "group_id": "", + "path": "water_risk/wri/v2/water_supply_{scenario}_{year}", + "indicator_id": "water_supply", + "indicator_model_id": null, + "indicator_model_gcm": "combined", + "params": {}, + "display_name": "Water supply in centimeters/year (Aqueduct 4.0)", + "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", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 2000.0, + "name": "heating", + "nodata_index": 0, + "units": "centimeters/year" + }, + "path": "maps/water_risk/wri/v2/water_supply_{scenario}_{year}_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -85.0 + ], + [ + -180.0, + -85.0 + ] + ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], + "index_values": null, + "source": "map_array_pyramid" + }, + "scenarios": [ + { + "id": "historical", + "years": [ + 1999 + ] + }, + { + "id": "ssp126", + "years": [ + 2030, + 2050, + 2080 + ] + }, + { + "id": "ssp370", + "years": [ + 2030, + 2050, + 2080 + ] + }, + { + "id": "ssp585", + "years": [ + 2030, + 2050, + 2080 + ] + } + ], + "units": "centimeters/year" + }, + { + "hazard_type": "WaterRisk", + "group_id": "", + "path": "water_risk/wri/v2/water_stress_{scenario}_{year}", + "indicator_id": "water_stress", + "indicator_model_id": null, + "indicator_model_gcm": "combined", + "params": {}, + "display_name": "Water stress (Aqueduct 4.0)", + "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", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 2.0, + "name": "heating", + "nodata_index": 0, + "units": "" + }, + "path": "maps/water_risk/wri/v2/water_stress_{scenario}_{year}_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -85.0 + ], + [ + -180.0, + -85.0 + ] + ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], + "index_values": null, + "source": "map_array_pyramid" + }, + "scenarios": [ + { + "id": "historical", + "years": [ + 1999 + ] + }, + { + "id": "ssp126", + "years": [ + 2030, + 2050, + 2080 + ] + }, + { + "id": "ssp370", + "years": [ + 2030, + 2050, + 2080 + ] + }, + { + "id": "ssp585", + "years": [ + 2030, + 2050, + 2080 + ] + } + ], + "units": "" + }, + { + "hazard_type": "WaterRisk", + "group_id": "", + "path": "water_risk/wri/v2/water_depletion_{scenario}_{year}", + "indicator_id": "water_depletion", + "indicator_model_id": null, + "indicator_model_gcm": "combined", + "params": {}, + "display_name": "Water depletion (Aqueduct 4.0)", + "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", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 2.0, + "name": "heating", + "nodata_index": 0, + "units": "" + }, + "path": "maps/water_risk/wri/v2/water_depletion_{scenario}_{year}_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -85.0 + ], + [ + -180.0, + -85.0 + ] + ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], + "index_values": null, + "source": "map_array_pyramid" + }, + "scenarios": [ + { + "id": "historical", + "years": [ + 1999 + ] + }, + { + "id": "ssp126", + "years": [ + 2030, + 2050, + 2080 + ] + }, + { + "id": "ssp370", + "years": [ + 2030, + 2050, + 2080 + ] + }, + { + "id": "ssp585", + "years": [ + 2030, + 2050, + 2080 + ] + } + ], + "units": "" + }, + { + "hazard_type": "WaterRisk", + "group_id": "", + "path": "water_risk/wri/v2/water_stress_category_{scenario}_{year}", + "indicator_id": "water_stress_category", + "indicator_model_id": null, + "indicator_model_gcm": "combined", + "params": {}, + "display_name": "Water stress category (Aqueduct 4.0)", + "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", + "map": { + "colormap": { + "min_index": 1, + "min_value": -5.0, + "max_index": 255, + "max_value": 5.0, + "name": "heating", + "nodata_index": 0, + "units": "" + }, + "path": "maps/water_risk/wri/v2/water_stress_category_{scenario}_{year}_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -85.0 + ], + [ + -180.0, + -85.0 + ] + ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], + "index_values": null, + "source": "map_array_pyramid" + }, + "scenarios": [ + { + "id": "historical", + "years": [ + 1999 + ] + }, + { + "id": "ssp126", + "years": [ + 2030, + 2050, + 2080 + ] + }, + { + "id": "ssp370", + "years": [ + 2030, + 2050, + 2080 + ] + }, + { + "id": "ssp585", + "years": [ + 2030, + 2050, + 2080 + ] + } + ], + "units": "" + }, + { + "hazard_type": "WaterRisk", + "group_id": "", + "path": "water_risk/wri/v2/water_depletion_category_{scenario}_{year}", + "indicator_id": "water_depletion_category", + "indicator_model_id": null, + "indicator_model_gcm": "combined", + "params": {}, + "display_name": "Water depletion category (Aqueduct 4.0)", + "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", + "map": { + "colormap": { + "min_index": 1, + "min_value": -5.0, + "max_index": 255, + "max_value": 5.0, + "name": "heating", + "nodata_index": 0, + "units": "" + }, + "path": "maps/water_risk/wri/v2/water_depletion_category_{scenario}_{year}_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -85.0 + ], + [ + -180.0, + -85.0 + ] + ], + "bbox": [ + -180.0, + -85.0, + 180.0, + 85.0 + ], + "index_values": null, + "source": "map_array_pyramid" + }, + "scenarios": [ + { + "id": "historical", + "years": [ + 1999 + ] + }, + { + "id": "ssp126", + "years": [ + 2030, + 2050, + 2080 + ] + }, + { + "id": "ssp370", + "years": [ + 2030, + 2050, + 2080 + ] + }, + { + "id": "ssp585", + "years": [ + 2030, + 2050, + 2080 + ] + } + ], + "units": "" + }, + { + "hazard_type": "Drought", + "group_id": "", + "path": "drought/osc/v1/months_spei12m_below_index_{gcm}_{scenario}_{year}", + "indicator_id": "months/spei12m/below/index", + "indicator_model_id": null, + "indicator_model_gcm": "{gcm}", + "params": { + "gcm": [ + "MIROC6" + ] + }, + "display_name": "Drought SPEI index", + "display_groups": [ + "Drought SPEI index" + ], + "description": "", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 12.0, + "name": "heating", + "nodata_index": 0, + "units": "months/year" + }, + "path": "drought/osc/v1/months_spei12m_below_index_{gcm}_{scenario}_{year}_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -60.0 + ], + [ + -180.0, + -60.0 + ] + ], + "bbox": [ + -180.0, + -60.0, + 180.0, + 85.0 + ], + "index_values": [ + 0, + -1, + -1.5, + -2, + -2.5, + -3, + -3.6 + ], + "source": "map_array_pyramid" + }, + "scenarios": [ + { + "id": "ssp585", + "years": [ + 2005, + 2030, + 2040, + 2050, + 2080 + ] + } + ], + "units": "months/year" + }, + { + "hazard_type": "RiverineInundation", + "group_id": "", + "path": "inundation/river_tudelft/v2/flood_depth_unprot_{scenario}_{year}", + "indicator_id": "flood_depth", + "indicator_model_id": "tudelft", + "indicator_model_gcm": "CLMcom-CCLM4-8-17-EC-EARTH", + "params": {}, + "display_name": "Flood depth (TUDelft)", + "display_groups": [], + "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 100 m 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, + "min_value": 0.0, + "max_index": 255, + "max_value": 5.0, + "name": "flare", + "nodata_index": 0, + "units": "metres" + }, + "path": "maps/inundation/river_tudelft/v2/flood_depth_unprot_{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": "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\u201310 years, 10\u201330 years, 30\u2013100 years, 100\u2013300 years, 300\u20131000 years, 1000\u201310000 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" } - ], - "units": "millimetres/year" - } - ] + ] }