diff --git a/pyproject.toml b/pyproject.toml index c83b1ed9..94f13179 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -57,6 +57,9 @@ build-backend = "setuptools.build_meta" where = ["src"] include = ["physrisk*"] + + + [tool.pdm.dev-dependencies] test = [ "pdm[pytest]", @@ -78,14 +81,12 @@ lint = [ "pre-commit", "tox" ] - -[project.urls] -Homepage = "https://github.com/os-climate/physrisk" -Repository = "https://github.com/os-climate/physrisk" -Downloads = "https://github.com/os-climate/physrisk/releases" -"Bug Tracker" = "https://github.com/os-climate/physrisk/issues" -Documentation = "https://github.com/os-climate/physrisk/tree/main/docs" -"Source Code" = "https://github.com/os-climate/physrisk" +"black[jupyter]" = [] +pandas = [] +dev = [ + "pandas>=2.0.3", + "geopandas>=0.13.2", +] [tool.pdm.scripts] pre_release = "scripts/dev-versioning.sh" @@ -96,6 +97,15 @@ docs = { shell = "cd docs && mkdocs serve", help = "Start the dev server for doc lint = "pre-commit run --all-files" complete = { call = "tasks.complete:main", help = "Create autocomplete files for bash and fish" } + +[project.urls] +Homepage = "https://github.com/os-climate/physrisk" +Repository = "https://github.com/os-climate/physrisk" +Downloads = "https://github.com/os-climate/physrisk/releases" +"Bug Tracker" = "https://github.com/os-climate/physrisk/issues" +Documentation = "https://github.com/os-climate/physrisk/tree/main/docs" +"Source Code" = "https://github.com/os-climate/physrisk" + [tool.pytest.ini_options] testpaths = "tests" addopts = "-v" diff --git a/src/physrisk/data/static/hazard/inventory.json b/src/physrisk/data/static/hazard/inventory.json index c7a1f688..9d0b5726 100644 --- a/src/physrisk/data/static/hazard/inventory.json +++ b/src/physrisk/data/static/hazard/inventory.json @@ -10,7 +10,7 @@ "params": {}, "display_name": "Flood depth/baseline (WRI)", "display_groups": [], - "description": "\nWorld Resources Institute Aqueduct Floods baseline riverine model using historical data.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resources Institute Aqueduct Floods baseline riverine model using historical data.\n\n \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 \u00c3\u2014 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, @@ -63,7 +63,7 @@ "params": {}, "display_name": "Flood depth/NorESM1-M (WRI)", "display_groups": [], - "description": "\nWorld Resources Institute Aqueduct Floods riverine model using GCM model from\nBjerknes Centre for Climate Research, Norwegian Meteorological Institute.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resources Institute Aqueduct Floods riverine model using GCM model from\nBjerknes Centre for Climate Research, Norwegian Meteorological Institute.\n\n \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 \u00c3\u2014 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, @@ -128,7 +128,7 @@ "params": {}, "display_name": "Flood depth/GFDL-ESM2M (WRI)", "display_groups": [], - "description": "\nWorld Resource Institute Aqueduct Floods riverine model using GCM model from\nGeophysical Fluid Dynamics Laboratory (NOAA).\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resource Institute Aqueduct Floods riverine model using GCM model from\nGeophysical Fluid Dynamics Laboratory (NOAA).\n\n \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 \u00c3\u2014 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, @@ -193,7 +193,7 @@ "params": {}, "display_name": "Flood depth/HadGEM2-ES (WRI)", "display_groups": [], - "description": "\nWorld Resource Institute Aqueduct Floods riverine model using GCM model:\nMet Office Hadley Centre.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resource Institute Aqueduct Floods riverine model using GCM model:\nMet Office Hadley Centre.\n\n \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 \u00c3\u2014 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, @@ -258,7 +258,7 @@ "params": {}, "display_name": "Flood depth/IPSL-CM5A-LR (WRI)", "display_groups": [], - "description": "\nWorld Resource Institute Aqueduct Floods riverine model using GCM model from\nInstitut Pierre Simon Laplace\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resource Institute Aqueduct Floods riverine model using GCM model from\nInstitut Pierre Simon Laplace\n\n \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 \u00c3\u2014 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, @@ -323,7 +323,7 @@ "params": {}, "display_name": "Flood depth/MIROC-ESM-CHEM (WRI)", "display_groups": [], - "description": "World Resource Institute Aqueduct Floods riverine model using\n GCM model from Atmosphere and Ocean Research Institute\n (The University of Tokyo), National Institute for Environmental Studies, and Japan Agency\n for Marine-Earth Science and Technology.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "World Resource Institute Aqueduct Floods riverine model using\n GCM model from Atmosphere and Ocean Research Institute\n (The University of Tokyo), National Institute for Environmental Studies, and Japan Agency\n for Marine-Earth Science and Technology.\n\n \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 \u00c3\u2014 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, @@ -388,7 +388,7 @@ "params": {}, "display_name": "Flood depth/baseline, no subsidence (WRI)", "display_groups": [], - "description": "\nWorld Resources Institute Aqueduct Floods baseline coastal model using historical data. Model excludes subsidence.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resources Institute Aqueduct Floods baseline coastal model using historical data. Model excludes subsidence.\n\n \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 \u00c3\u2014 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, @@ -443,7 +443,7 @@ "params": {}, "display_name": "Flood depth/95%, no subsidence (WRI)", "display_groups": [], - "description": "\nWorld Resource Institute Aqueduct Floods coastal model, excluding subsidence; 95th percentile sea level rise.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resource Institute Aqueduct Floods coastal model, excluding subsidence; 95th percentile sea level rise.\n\n \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 \u00c3\u2014 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, @@ -508,7 +508,7 @@ "params": {}, "display_name": "Flood depth/5%, no subsidence (WRI)", "display_groups": [], - "description": "\nWorld Resource Institute Aqueduct Floods coastal model, excluding subsidence; 5th percentile sea level rise.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resource Institute Aqueduct Floods coastal model, excluding subsidence; 5th percentile sea level rise.\n\n \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 \u00c3\u2014 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, @@ -573,7 +573,7 @@ "params": {}, "display_name": "Flood depth/50%, no subsidence (WRI)", "display_groups": [], - "description": "\nWorld Resource Institute Aqueduct Floods model, excluding subsidence; 50th percentile sea level rise.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resource Institute Aqueduct Floods model, excluding subsidence; 50th percentile sea level rise.\n\n \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 \u00c3\u2014 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, @@ -638,7 +638,7 @@ "params": {}, "display_name": "Flood depth/baseline, with subsidence (WRI)", "display_groups": [], - "description": "\nWorld Resource Institute Aqueduct Floods model, excluding subsidence; baseline (based on historical data).\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resource Institute Aqueduct Floods model, excluding subsidence; baseline (based on historical data).\n\n \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 \u00c3\u2014 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, @@ -693,7 +693,7 @@ "params": {}, "display_name": "Flood depth/95%, with subsidence (WRI)", "display_groups": [], - "description": "\nWorld Resource Institute Aqueduct Floods model, including subsidence; 95th percentile sea level rise.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resource Institute Aqueduct Floods model, including subsidence; 95th percentile sea level rise.\n\n \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 \u00c3\u2014 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, @@ -758,7 +758,7 @@ "params": {}, "display_name": "Flood depth/5%, with subsidence (WRI)", "display_groups": [], - "description": "\nWorld Resource Institute Aqueduct Floods model, including subsidence; 5th percentile sea level rise.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resource Institute Aqueduct Floods model, including subsidence; 5th percentile sea level rise.\n\n \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 \u00c3\u2014 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, @@ -823,7 +823,7 @@ "params": {}, "display_name": "Flood depth/50%, with subsidence (WRI)", "display_groups": [], - "description": "\nWorld Resource Institute Aqueduct Floods model, including subsidence; 50th percentile sea level rise.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resource Institute Aqueduct Floods model, including subsidence; 50th percentile sea level rise.\n\n \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 \u00c3\u2014 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, @@ -899,7 +899,7 @@ "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", + "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\u00c2\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, @@ -1572,7 +1572,7 @@ "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", + "description": "Days per year for which the average near-surface temperature 'tas' is above a threshold specified in \u00c2\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 \u00c2\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, @@ -1731,7 +1731,7 @@ "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", + "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\u00c2\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, @@ -1823,7 +1823,7 @@ "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", + "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\u00c2\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, @@ -1914,7 +1914,7 @@ "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$$I = \\frac{52}{n_y} \\sum_{i = 1}^{n_y} \\boldsymbol{\\mathbb{1}}_{T^{avg}_i > T^\\text{ref}}$$\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", + "description": "Weeks per year for which the average water temperature is above a threshold specified in \u00c2\u00b0C:\n\n$$I = \\frac{52}{n_y} \\sum_{i = 1}^{n_y} \\boldsymbol{\\mathbb{1}}_{T^{avg}_i > T^\\text{ref}}$$\n\n$I$ is the indicator, $T^\\text{avg}_i$ is the weekly average water temperature for week index $i$ in \u00c2\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, @@ -2037,7 +2037,7 @@ "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$$I = \\frac{52}{n_y} \\sum_{i = 1}^{n_y} \\boldsymbol{\\mathbb{1}}_{T^{avg}_i > T^\\text{ref}}$$\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", + "description": "Weeks per year for which the average water temperature is above a threshold specified in \u00c2\u00b0C:\n\n$$I = \\frac{52}{n_y} \\sum_{i = 1}^{n_y} \\boldsymbol{\\mathbb{1}}_{T^{avg}_i > T^\\text{ref}}$$\n\n$I$ is the indicator, $T^\\text{avg}_i$ is the weekly average water temperature for week index $i$ in \u00c2\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, @@ -2117,7 +2117,7 @@ "display_groups": [ "Days with wet-bulb globe temperature above threshold in \u00b0C" ], - "description": "Days per year for which the 'Wet Bulb Globe Temperature' indicator is above a threshold specified in \u00b0C:\n\n$$I = \\frac{365}{n_y} \\sum_{i = 1}^{n_y} \\boldsymbol{\\mathbb{1}}_{T^\\text{WBGT}_i > T^\\text{ref}}$$\n\n$I$ is the indicator, $n_y$ is the number of days in the sample and $T^\\text{ref}$ is the reference temperature. \n\nThe 'Wet-Bulb Globe Temperature' (WBGT) indicator is calculated from both the average daily near-surface surface temperature in \u00b0C denoted $T^\\text{avg}$ and the water vapour partial pressure in kPa denoted $p^\\text{vapour}$:\n\n$$\nT^\\text{WBGT}_i = 0.567 \\times T^\\text{avg}_i + 0.393 \\times p^\\text{vapour}_i + 3.94\n$$\n\nThe water vapour partial pressure $p^\\text{vapour}$ is calculated from relative humidity $h^\\text{relative}$:\n\n$$\np^\\text{vapour}_i = \\frac{h^\\text{relative}_i}{100} \\times 6.105 \\times \\exp \\left( \\frac{17.27 \\times T^\\text{avg}_i}{237.7 + T^\\text{avg}_i} \\right)\n$$\n\nThe OS-Climate-generated indicators are inferred from downscaled CMIP6 data, averaged over for 6 Global Circulation Models: ACCESS-CM2, CMCC-ESM2, CNRM-CM6-1, MPI-ESM1-2-LR, MIROC6 and NorESM2-MM.\nThe downscaled data is sourced from the [NASA Earth Exchange Global Daily Downscaled Projections](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6).\nIndicators are generated for periods: 'historical' (averaged over 1995-2014), 2030 (2021-2040), 2040 (2031-2050), 2050 (2041-2060), 2060 (2051-2070), 2070 (2061-2080), 2080 (2071-2090) and 2090 (2081-2100).\n", + "description": "Days per year for which the 'Wet Bulb Globe Temperature' indicator is above a threshold specified in \u00c2\u00b0C:\n\n$$I = \\frac{365}{n_y} \\sum_{i = 1}^{n_y} \\boldsymbol{\\mathbb{1}}_{T^\\text{WBGT}_i > T^\\text{ref}}$$\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 \u00c2\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, @@ -2229,11 +2229,11 @@ "indicator_model_id": null, "indicator_model_gcm": "combined", "params": {}, - "display_name": "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 (cm/year).", + "display_name": "Water demand in centimeters/year (Aqueduct 4.0)", "display_groups": [ - "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 (cm/year)." + "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 (cm/year).\n\n* **Water supply**: available blue water\u2014the total amount of renewable freshwater available to a sub-basin with upstream consumption removed\u2014includes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (cm/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.\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.\n\n* **Interannual variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\n\n* **Seasonal variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\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).", + "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\u00e2\u20ac\u2122s 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 \u00c3\u2014 5 arc minutes which equates roughly to 10 kilometer (km) \u00c3\u2014 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).", "map": { "colormap": { "min_index": 1, @@ -2242,7 +2242,7 @@ "max_value": 100.0, "name": "heating", "nodata_index": 0, - "units": "cm/year" + "units": "centimeters/year" }, "path": "maps/water_risk/wri/v2/water_demand_{scenario}_{year}_map", "bounds": [ @@ -2298,7 +2298,7 @@ ] } ], - "units": "cm/year" + "units": "centimeters/year" }, { "hazard_type": "WaterRisk", @@ -2308,11 +2308,11 @@ "indicator_model_id": null, "indicator_model_gcm": "combined", "params": {}, - "display_name": "Available blue water \u2014 the total amount of renewable freshwater available to a sub-basin with upstream consumption removed \u2014 includes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (cm/year).", + "display_name": "Water supply in centimeters/year (Aqueduct 4.0)", "display_groups": [ - "Available blue water \u2014 the total amount of renewable freshwater available to a sub-basin with upstream consumption removed \u2014 includes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (cm/year)." + "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 (cm/year).\n\n* **Water supply**: available blue water\u2014the total amount of renewable freshwater available to a sub-basin with upstream consumption removed\u2014includes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (cm/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.\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.\n\n* **Interannual variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\n\n* **Seasonal variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\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).", + "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\u00e2\u20ac\u2122s 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 \u00c3\u2014 5 arc minutes which equates roughly to 10 kilometer (km) \u00c3\u2014 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).", "map": { "colormap": { "min_index": 1, @@ -2321,7 +2321,7 @@ "max_value": 2000.0, "name": "heating", "nodata_index": 0, - "units": "cm/year" + "units": "centimeters/year" }, "path": "maps/water_risk/wri/v2/water_supply_{scenario}_{year}_map", "bounds": [ @@ -2377,7 +2377,7 @@ ] } ], - "units": "cm/year" + "units": "centimeters/year" }, { "hazard_type": "WaterRisk", @@ -2387,11 +2387,11 @@ "indicator_model_id": null, "indicator_model_gcm": "combined", "params": {}, - "display_name": "Water stress is an indicator of competition for water resources and is defined informally as the ratio of demand for water by human society divided by available water.", + "display_name": "Water stress (Aqueduct 4.0)", "display_groups": [ - "Water stress is an indicator of competition for water resources and is defined informally as the ratio of demand for water by human society divided by available water." + "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 (cm/year).\n\n* **Water supply**: available blue water\u2014the total amount of renewable freshwater available to a sub-basin with upstream consumption removed\u2014includes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (cm/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.\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.\n\n* **Interannual variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\n\n* **Seasonal variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\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).", + "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\u00e2\u20ac\u2122s 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 \u00c3\u2014 5 arc minutes which equates roughly to 10 kilometer (km) \u00c3\u2014 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).", "map": { "colormap": { "min_index": 1, @@ -2466,11 +2466,11 @@ "indicator_model_id": null, "indicator_model_gcm": "combined", "params": {}, - "display_name": "Water depletion measures 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.", + "display_name": "Water depletion (Aqueduct 4.0)", "display_groups": [ - "Water depletion measures 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." + "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 (cm/year).\n\n* **Water supply**: available blue water\u2014the total amount of renewable freshwater available to a sub-basin with upstream consumption removed\u2014includes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (cm/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.\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.\n\n* **Interannual variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\n\n* **Seasonal variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\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).", + "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\u00e2\u20ac\u2122s 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 \u00c3\u2014 5 arc minutes which equates roughly to 10 kilometer (km) \u00c3\u2014 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).", "map": { "colormap": { "min_index": 1, @@ -2545,11 +2545,11 @@ "indicator_model_id": null, "indicator_model_gcm": "combined", "params": {}, - "display_name": "Discrete measure of the ratio of total water withdrawals to available renewable surface and ground water supplies:\n-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%).", + "display_name": "Water stress category (Aqueduct 4.0)", "display_groups": [ - "Discrete measure of the ratio of total water withdrawals to available renewable surface and ground water supplies:\n-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%)." + "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 (cm/year).\n\n* **Water supply**: available blue water\u2014the total amount of renewable freshwater available to a sub-basin with upstream consumption removed\u2014includes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (cm/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.\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.\n\n* **Interannual variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\n\n* **Seasonal variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\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).", + "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\u00e2\u20ac\u2122s 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 \u00c3\u2014 5 arc minutes which equates roughly to 10 kilometer (km) \u00c3\u2014 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).", "map": { "colormap": { "min_index": 1, @@ -2624,11 +2624,11 @@ "indicator_model_id": null, "indicator_model_gcm": "combined", "params": {}, - "display_name": "Discrete measure of the ratio of total water consumption to available renewable water supplies:\n-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%).", + "display_name": "Water depletion category (Aqueduct 4.0)", "display_groups": [ - "Discrete measure of the ratio of total water consumption to available renewable water supplies:\n-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%)." + "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 (cm/year).\n\n* **Water supply**: available blue water\u2014the total amount of renewable freshwater available to a sub-basin with upstream consumption removed\u2014includes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (cm/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.\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.\n\n* **Interannual variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\n\n* **Seasonal variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\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).", + "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\u00e2\u20ac\u2122s 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 \u00c3\u2014 5 arc minutes which equates roughly to 10 kilometer (km) \u00c3\u2014 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).", "map": { "colormap": { "min_index": 1, diff --git a/src/physrisk/vulnerability_models/thermal_power_generation_models.py b/src/physrisk/vulnerability_models/thermal_power_generation_models.py index 6ee6fea1..98731856 100644 --- a/src/physrisk/vulnerability_models/thermal_power_generation_models.py +++ b/src/physrisk/vulnerability_models/thermal_power_generation_models.py @@ -189,13 +189,20 @@ def __init__( class ThermalPowerGenerationDroughtModel(VulnerabilityModelBase): # Number of disrupted days per year _default_resource = "WRI thermal power plant physical climate vulnerability factors" + _impact_based_on_a_single_point = False - def __init__(self, *, resource: str = _default_resource): + def __init__( + self, + *, + resource: str = _default_resource, + impact_based_on_a_single_point: bool = _impact_based_on_a_single_point, + ): """ Drought vulnerability model for thermal power generation. Args: resource (str): embedded resource identifier used to infer vulnerability table. + impact_based_on_a_single_point (str): calculation based on a single point instead of a curve. """ hazard_type = Drought @@ -206,26 +213,9 @@ def __init__(self, *, resource: str = _default_resource): (c.asset_type, c) for c in curve_set.items if c.event_type == hazard_type.__name__ ) - denominator = norm.cdf(-2.0) - self.impact_by_asset_type = defaultdict(list) - for key in self.vulnerability_curves: - probabilities = np.array( - [norm.cdf(intensity) / denominator for intensity in self.vulnerability_curves[key].intensity] - ) - probabilities[:-1] -= probabilities[1:] - self.impact_by_asset_type[key] = sum( - [ - probability * impact - for probability, impact in zip(probabilities, self.vulnerability_curves[key].impact_mean) - ] - ) - - self.impact_by_type = defaultdict(list) self.vuln_curves_by_type = defaultdict(list) for key in self.vulnerability_curves: - turbine_kind = TurbineKind[key.split("/")[0]] - self.impact_by_type[turbine_kind].append(self.impact_by_asset_type[key]) - self.vuln_curves_by_type[turbine_kind].append(self.vulnerability_curves[key]) + self.vuln_curves_by_type[TurbineKind[key.split("/")[0]]].append(self.vulnerability_curves[key]) impact_type = ( ImpactType.disruption @@ -236,7 +226,11 @@ def __init__(self, *, resource: str = _default_resource): ) # global circulation parameter 'model' is a hint; can be overriden by hazard model - super().__init__(indicator_id="months/spei3m/below/-2", hazard_type=hazard_type, impact_type=impact_type) + super().__init__( + indicator_id="months/spei3m/below/-2" if impact_based_on_a_single_point else "months/spei12m/below/index", + hazard_type=hazard_type, + impact_type=impact_type, + ) def get_data_requests( self, asset: Asset, *, scenario: str, year: int @@ -254,24 +248,49 @@ def get_impact(self, asset: Asset, data_responses: List[HazardDataResponse]) -> assert isinstance(asset, ThermalPowerGeneratingAsset) # The unit being number of months per year, we divide by 12 to express the result as a year fraction. - spei3m_below_minus_2 = cast(HazardParameterDataResponse, data_responses[0]).parameter / 12.0 + intensities = np.array(cast(HazardParameterDataResponse, data_responses[0]).parameters / 12.0) + if len(intensities) == 1: + thresholds = np.array([-2.0]) # hard-coded + else: + thresholds = np.array(cast(HazardParameterDataResponse, data_responses[0]).param_defns) + intensities[:-1] -= intensities[1:] - impact = 0.0 + curves: List[VulnerabilityCurve] = [] if asset.turbine is None: - impact = np.max([self.impact_by_asset_type[key] for key in self.impact_by_asset_type]) + curves = [self.vulnerability_curves[key] for key in self.vulnerability_curves] elif asset.cooling is not None: key = "/".join([asset.turbine.name, asset.cooling.name]) - if key in self.impact_by_asset_type: - impact = self.impact_by_asset_type[key] + if key in self.vulnerability_curves: + curves = [self.vulnerability_curves[key]] elif asset.turbine in self.vuln_curves_by_type: - impact = np.max(self.impact_by_type[asset.turbine]) + curves = self.vuln_curves_by_type[asset.turbine] + + if 0 < len(curves): + if len(intensities) == 1: + impact = 0.0 + denominator = norm.cdf(thresholds[0]) + for curve in curves: + probabilities = np.array([norm.cdf(intensity) / denominator for intensity in curve.intensity]) + probabilities[:-1] -= probabilities[1:] + impact = max( + impact, + sum([probability * impact for probability, impact in zip(probabilities, curve.impact_mean)]), + ) + impacts = [impact] + else: + impacts = [ + np.max([np.interp(threshold, curve.intensity[::-1], curve.impact_mean[::-1]) for curve in curves]) + for threshold in thresholds + ] + else: + impacts = [0.0 for _ in thresholds] # The point injected at the beginning of impacts/intensities # allows to successfully call to_exceedance() in the get_impact API: impact_distrib = ImpactDistrib( self.hazard_type, - np.array([0.0, impact]), - np.array([1.0 - spei3m_below_minus_2, spei3m_below_minus_2]), + [0.0] + impacts, + np.concatenate((np.array([1.0 - sum(intensities)]), intensities)), self.impact_type, ) return impact_distrib diff --git a/tests/api/impact_requests_test.py b/tests/api/impact_requests_test.py index a792728a..6bb884af 100644 --- a/tests/api/impact_requests_test.py +++ b/tests/api/impact_requests_test.py @@ -278,21 +278,33 @@ def test_thermal_power_generation(self): t, ) - return_periods = [0.0] - shape, t = shape_transform_21600_43200(return_periods=return_periods) - # Add mock drought data: + return_periods = [0.0, -1.0, -1.5, -2.0, -2.5, -3.0, -3.6] + shape, t = shape_transform_21600_43200(return_periods=return_periods) add_curves( root, longitudes, latitudes, - "drought/jupiter/v1/months_spei3m_below_-2_ssp585_2050", + "drought/osc/v1/months_spei12m_below_index_MIROC6_ssp585_2050", shape, - np.array([0.16958899796009064]), + np.array( + [ + 6.900000095367432, + 1.7999999523162842, + 0.44999998807907104, + 0.06584064255906408, + 0.06584064255906408, + 0.0, + 0.0, + ] + ), return_periods, t, ) + return_periods = [0.0] + shape, t = shape_transform_21600_43200(return_periods=return_periods) + # Add mock water-related risk data: add_curves( root, @@ -576,12 +588,12 @@ def test_thermal_power_generation(self): self.assertEqual(response.asset_impacts[5].impacts[0].impact_mean, 0.0) # Drought - self.assertEqual(response.asset_impacts[0].impacts[1].impact_mean, 0.0005486720213255343) + self.assertEqual(response.asset_impacts[0].impacts[1].impact_mean, 0.0005486719775944949) self.assertEqual(response.asset_impacts[1].impacts[1].impact_mean, 0.0) - self.assertEqual(response.asset_impacts[2].impacts[1].impact_mean, 0.0005486720213255343) + self.assertEqual(response.asset_impacts[2].impacts[1].impact_mean, 0.0005486719775944949) self.assertEqual(response.asset_impacts[3].impacts[1].impact_mean, 0.0) - self.assertEqual(response.asset_impacts[4].impacts[1].impact_mean, 0.0005486720213255343) - self.assertEqual(response.asset_impacts[5].impacts[1].impact_mean, 0.0005486720213255343) + self.assertEqual(response.asset_impacts[4].impacts[1].impact_mean, 0.0005486719775944949) + self.assertEqual(response.asset_impacts[5].impacts[1].impact_mean, 0.0005486719775944949) # Riverine Inundation self.assertEqual(response.asset_impacts[0].impacts[2].impact_mean, 0.005372887389199415)