src/inventories/hazard/inventory.json

{
  "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": {
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          "min_value": 0.0,
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          "units": "m"
        },
        "path": "inundation/wri/v2/inunriver_{scenario}_000000000WATCH_{year}_map",
        "bounds": [
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        "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",
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        "path": "inundation/wri/v2/inunriver_{scenario}_00000NorESM1-M_{year}_map",
        "bounds": [
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          [180.0, 85.0],
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      "scenarios": [
        {
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          "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",
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          "units": "m"
        },
        "path": "inundation/wri/v2/inunriver_{scenario}_0000GFDL-ESM2M_{year}_map",
        "bounds": [
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          [180.0, 85.0],
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        ],
        "index_values": [8],
        "source": "map_array_pyramid"
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      "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": {
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          "units": "m"
        },
        "path": "inundation/wri/v2/inunriver_{scenario}_0000HadGEM2-ES_{year}_map",
        "bounds": [
          [-180.0, 85.0],
          [180.0, 85.0],
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        "index_values": [8],
        "source": "map_array_pyramid"
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      "scenarios": [
        {
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          "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": {
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          "units": "m"
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        "path": "inundation/wri/v2/inunriver_{scenario}_00IPSL-CM5A-LR_{year}_map",
        "bounds": [
          [-180.0, 85.0],
          [180.0, 85.0],
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        ],
        "index_values": [8],
        "source": "map_array_pyramid"
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      "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": {
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        },
        "path": "inundation/wri/v2/inunriver_{scenario}_MIROC-ESM-CHEM_{year}_map",
        "bounds": [
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          [180.0, 85.0],
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      "scenarios": [
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          "id": "rcp4p5",
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        },
        {
          "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": {
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          "units": "m"
        },
        "path": "inundation/wri/v2/inuncoast_historical_nosub_hist_0_map",
        "bounds": [
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          [180.0, 85.0],
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        "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": {
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        },
        "path": "inundation/wri/v2/inuncoast_{scenario}_nosub_{year}_0_map",
        "bounds": [
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          [180.0, 85.0],
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        },
        {
          "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": {
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        "path": "inundation/wri/v2/inuncoast_{scenario}_nosub_{year}_0_perc_05_map",
        "bounds": [
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        {
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      ],
      "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": {
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        "path": "inundation/wri/v2/inuncoast_{scenario}_nosub_{year}_0_perc_50_map",
        "bounds": [
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        {
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      ],
      "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": {
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          "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). <https://doi.org/10.1038/s41597-024-03250-y>\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)"],
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        {
          "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]
        }
      ],
      "units": "millimetres/year"
    }
  ]
}