diff --git a/.env b/.env index 5f2c25abf..1957bf793 100644 --- a/.env +++ b/.env @@ -28,4 +28,7 @@ GOOGLE_ANALYTICS_ID='G-CQ3WLED121' FEATURE_NEW_EXPLORATION = 'TRUE' -SHOW_CONFIGURABLE_COLOR_MAP = 'TRUE' \ No newline at end of file +SHOW_CONFIGURABLE_COLOR_MAP = 'TRUE' + +CUSTOM_SCRIPT_SRC='https://dap.digitalgov.gov/Universal-Federated-Analytics-Min.js?agency=NASA&subagency=HQ' +CUSTOM_SCRIPT_ID='_fed_an_ua_tag' diff --git a/.veda/ui b/.veda/ui index 7839e8d42..179976ec2 160000 --- a/.veda/ui +++ b/.veda/ui @@ -1 +1 @@ -Subproject commit 7839e8d425dae7dde41676571e92a4b7d3937a88 +Subproject commit 179976ec2da0fff8c9ba3e46a59a69ac5c2aa568 diff --git a/datasets/CMIP-winter-median-pr.data.mdx b/datasets/CMIP-winter-median-pr.data.mdx index c1efdc2b7..3b684c201 100644 --- a/datasets/CMIP-winter-median-pr.data.mdx +++ b/datasets/CMIP-winter-median-pr.data.mdx @@ -11,7 +11,14 @@ media: taxonomy: - name: Topics values: - - EIS + - Precipitation + - Snow + - Climate + - Climate Model + - name: Source + values: + - NASA EIS + - CMIP6 infoDescription: | ::markdown Future changes to precipitation are expected to alter the volume and timing of snow water resources. Here, we present the projected percent-change to Western US cumulative winter precipitation at quarter-degree spatial resoutions across 20-year time periods between 2016 and 2095. Projections are averaged from an ensemble of 23 downscaled climate models from the CMIP6 NASA Earth Exchange Global Daily Downscaled Projections. diff --git a/datasets/CMIP-winter-median-ta.data.mdx b/datasets/CMIP-winter-median-ta.data.mdx index 5a1c52249..c4b1f8104 100644 --- a/datasets/CMIP-winter-median-ta.data.mdx +++ b/datasets/CMIP-winter-median-ta.data.mdx @@ -11,7 +11,15 @@ media: taxonomy: - name: Topics values: - - EIS + - Temperature + - Precipitation + - Snow + - Climate + - Climate Model + - name: Source + values: + - NASA EIS + - CMIP6 infoDescription: | ::markdown Future changes to air temperature are expected to influence the phase of winter precipitation (snowfall or rainfall) and the timing and amount of snowmelt and streamflow. Here, we present the projected percent-change to Western US average winter temperature at quarter-degree spatial resoutions across 20-year time periods between 2016 and 2095. Projections are averaged from an ensemble of 23 downscaled climate models from the [CMIP6 NASA Earth Exchange Global Daily Downscaled Projections](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6). diff --git a/datasets/aerosol-difference.data.mdx b/datasets/aerosol-difference.data.mdx index 7e82eed78..3924be981 100644 --- a/datasets/aerosol-difference.data.mdx +++ b/datasets/aerosol-difference.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - Air Quality + - name: Source + values: + - MODIS infoDescription: | ::markdown This dataset comes from the two decadal COGs that displayed mean Aerosol Optical Depth for 2000-2009 and for 2010-2019. Those tiffs were subtracted to display the differences between the two decades. diff --git a/datasets/bangladesh-landcover-2001-2020.data.mdx b/datasets/bangladesh-landcover-2001-2020.data.mdx index 1ed510d48..02c55f8fc 100644 --- a/datasets/bangladesh-landcover-2001-2020.data.mdx +++ b/datasets/bangladesh-landcover-2001-2020.data.mdx @@ -11,10 +11,13 @@ media: taxonomy: - name: Topics values: - - EIS + - Land Cover + - name: Source + values: + - MODIS infoDescription: | ::markdown - The annual land cover maps of 2001 and 2021 were captured using combined Moderate Resolution Imaging Spectroradiometer (MODIS) Annual Land Cover Type dataset (MCD12Q1 V6, dataset link: [https://lpdaac.usgs.gov/products/mcd12q1v006/](https://lpdaac.usgs.gov/products/mcd12q1v006/)). The actual data product provides global land cover types at yearly intervals (2001-2020) at 500 meters with six different types of land cover classification. Among six different schemes, The International Geosphere–Biosphere Programme (IGBP) land cover classification selected and further simplified to dominant land cover classes (water, urban, cropland, native vegetation) for two different years to illustrate the changes in land use and land cover of the country. + The annual land use - land cover maps for 2001 and 2021 were captured using the combined Moderate Resolution Imaging Spectroradiometer (MODIS) Annual Land Cover Type dataset ([MCD12Q1 V6](https://lpdaac.usgs.gov/products/mcd12q1v006/)). The actual data product provides global land cover types at yearly intervals (2001-2020) at 500 meters with six different types of land cover classification. Among six different schemes, The International Geosphere–Biosphere Programme (IGBP) land cover classification selected and further simplified to dominant land cover classes (water, urban, cropland, native vegetation) for two different years to illustrate the changes in land use and land cover of the country. layers: - id: bangladesh-landcover-2001-2020 stacCol: bangladesh-landcover-2001-2020 diff --git a/datasets/barc-thomasfire.data.mdx b/datasets/barc-thomasfire.data.mdx index 83dfe414c..ef12c1de6 100644 --- a/datasets/barc-thomasfire.data.mdx +++ b/datasets/barc-thomasfire.data.mdx @@ -11,7 +11,10 @@ media: taxonomy: - name: Topics values: - - EIS + - Wildfire + - name: Source + values: + - NASA EIS infoDescription: | ::markdown Burn Area Reflectance Classification (BARC) from the Burned Area Emergency Response (BAER) program for the Thomas Fire of 2017. diff --git a/datasets/caldor-fire-characteristics-burn-severity.data.mdx b/datasets/caldor-fire-characteristics-burn-severity.data.mdx index e4cbec8d4..c2c820b73 100644 --- a/datasets/caldor-fire-characteristics-burn-severity.data.mdx +++ b/datasets/caldor-fire-characteristics-burn-severity.data.mdx @@ -11,7 +11,10 @@ media: taxonomy: - name: Topics values: - - EIS + - Wildfire + - name: Source + values: + - NASA EIS infoDescription: | ::markdown This dataset describes the progression and active fire behavior of the 2021 Caldor Fire in California, as recorded by the algorithm detailed in https://www.nature.com/articles/s41597-022-01343-0. It includes an extra layer detailing the soil burn severity (SBS) conditions provided by the [Burned Area Emergency Response](https://burnseverity.cr.usgs.gov/baer/) team. diff --git a/datasets/camp-fire-albedo-wsa-diff.data.mdx b/datasets/camp-fire-albedo-wsa-diff.data.mdx index 343d37803..e2e13d7d7 100644 --- a/datasets/camp-fire-albedo-wsa-diff.data.mdx +++ b/datasets/camp-fire-albedo-wsa-diff.data.mdx @@ -11,7 +11,12 @@ media: taxonomy: - name: Topics values: - - EIS + - Wildfire + - Disasters + - Land Cover + - name: Source + values: + - MODIS infoDescription: | ::markdown In order to examine how the fire event affected the changes in surface properties, we utilized the MODIS-derived Normalized Difference Vegetation Index (NDVI), albedo, and land surface temperature (LST) products for a six-year period centered on the Camp Fire event (2015-2022). We used these products which are available at 16-day intervals to compute monthly averaged spatial maps of NDVI, albedo, and LST. The monthly average spatial maps were then averaged over the areas affected by the Camp Fire to compute monthly mean values. This dataset is the Albedo WSA difference portion of that analysis. diff --git a/datasets/camp-fire-lst-day-diff.data.mdx b/datasets/camp-fire-lst-day-diff.data.mdx index 6baa80d77..c2bc6a159 100644 --- a/datasets/camp-fire-lst-day-diff.data.mdx +++ b/datasets/camp-fire-lst-day-diff.data.mdx @@ -11,7 +11,12 @@ media: taxonomy: - name: Topics values: - - EIS + - Wildfire + - Disasters + - Temperature + - name: Source + values: + - MODIS infoDescription: | ::markdown In order to examine how the fire event affected the changes in surface properties, we utilized the MODIS-derived Normalized Difference Vegetation Index (NDVI), albedo, and land surface temperature (LST) products for a six-year period centered on the Camp Fire event (2015-2022). We used these products which are available at 16-day intervals to compute monthly averaged spatial maps of NDVI, albedo, and LST. The monthly average spatial maps were then averaged over the areas affected by the Camp Fire to compute monthly mean values. This dataset is the LST Day difference portion of that analysis. diff --git a/datasets/camp-fire-lst-night-diff.data.mdx b/datasets/camp-fire-lst-night-diff.data.mdx index 8995f3d9a..2d31f4ab9 100644 --- a/datasets/camp-fire-lst-night-diff.data.mdx +++ b/datasets/camp-fire-lst-night-diff.data.mdx @@ -11,7 +11,12 @@ media: taxonomy: - name: Topics values: - - EIS + - Wildfire + - Disasters + - Temperature + - name: Source + values: + - MODIS infoDescription: | ::markdown In order to examine how the fire event affected the changes in surface properties, we utilized the MODIS-derived Normalized Difference Vegetation Index (NDVI), albedo, and land surface temperature (LST) products for a six-year period centered on the Camp Fire event (2015-2022). We used these products which are available at 16-day intervals to compute monthly averaged spatial maps of NDVI, albedo, and LST. The monthly average spatial maps were then averaged over the areas affected by the Camp Fire to compute monthly mean values. This dataset is the LST Night difference portion of that analysis. diff --git a/datasets/camp-fire-ndvi-diff.data.mdx b/datasets/camp-fire-ndvi-diff.data.mdx index 5b7e3237e..cbf83d3fa 100644 --- a/datasets/camp-fire-ndvi-diff.data.mdx +++ b/datasets/camp-fire-ndvi-diff.data.mdx @@ -11,7 +11,12 @@ media: taxonomy: - name: Topics values: - - EIS + - Wildfire + - Disasters + - Land Cover + - name: Source + values: + - MODIS infoDescription: | ::markdown In order to examine how the fire event affected the changes in surface properties, we utilized the MODIS-derived Normalized Difference Vegetation Index (NDVI), albedo, and land surface temperature (LST) products for a six-year period centered on the Camp Fire event (2015-2022). We used these products which are available at 16-day intervals to compute monthly averaged spatial maps of NDVI, albedo, and LST. The monthly average spatial maps were then averaged over the areas affected by the Camp Fire to compute monthly mean values. This dataset is the NDVI difference portion of that analysis. diff --git a/datasets/camp-fire-nlcd.data.mdx b/datasets/camp-fire-nlcd.data.mdx index b82e63c9d..d66aebf0c 100644 --- a/datasets/camp-fire-nlcd.data.mdx +++ b/datasets/camp-fire-nlcd.data.mdx @@ -11,7 +11,10 @@ media: taxonomy: - name: Topics values: - - EIS + - Land Cover + - name: Source + values: + - Landsat infoDescription: | ::markdown We utilized the National Land Cover Database (NLCD), which provides a classification of land cover categories at 30m spatial resolution over geographical locations within the Continental United States (CONUS). The NLCD is derived from Landsat satellite sensors data and is available at approximately three-year time intervals. We used the NLCD maps for the years 2016 and 2019 to examine changes in land cover type resulting from the Camp Fire event, to examine LULC before and after the Camp Fire. This analysis shows that the dominant vegetation cover type that was present within the region per-wildfire are evergreen forest and shrub/scrub cover, while post-wildfire are grasslands and herbaceous vegetation. diff --git a/datasets/cmip6-tas.data.mdx b/datasets/cmip6-tas.data.mdx index 18f9963b9..fad61c30e 100644 --- a/datasets/cmip6-tas.data.mdx +++ b/datasets/cmip6-tas.data.mdx @@ -1,6 +1,6 @@ --- id: combined_CMIP6_daily_GISS-E2-1-G_tas_kerchunk_DEMO -name: 'CMIP6 Daily GISS-E2-1-G Near-Surface Air Temperature (demo subset)' +name: 'Historic CMIP6 Daily GISS-E2-1-G Near-Surface Air Temperature (1950-2014)' featured: false description: "Daily near-surface air temperature from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) Project." media: @@ -13,11 +13,16 @@ taxonomy: - name: Topics values: - Climate + - Temperature + - name: Source + values: + - CMIP6 + infoDescription: | ::markdown * Format: [kerchunk (metadata)](https://fsspec.github.io/kerchunk/) for netCDF4 * Spatial Coverage: 180° W to 180° E, 60° S to 90° N - * Temporal: 1950-01-01 to 1951-12-31 + * Temporal: 1950-01-01 to 2014-12-31 * _As noted below, this dataset is a subset all available data. The full dataset includes data from 1950 to 2100._ * Data Resolution: * Latitude Resolution: 0.25 degrees (25 km) @@ -26,10 +31,10 @@ infoDescription: | layers: - id: combined_CMIP6_daily_GISS-E2-1-G_tas_kerchunk_DEMO stacCol: combined_CMIP6_daily_GISS-E2-1-G_tas_kerchunk_DEMO - name: CMIP6 Daily GISS-E2-1-G Near-Surface Air Temperature (demo subset) + name: Historic CMIP6 Daily GISS-E2-1-G Near-Surface Air Temperature (1950-2014) type: zarr tileApiEndpoint: 'https://prod-titiler-xarray.delta-backend.com/tilejson.json' - description: "Historical (1950-2014) daily-mean near-surface (usually, 2 meter) air temperature in Kelvin." + description: "Historical CMIP6 (1950-2014) daily-mean near-surface (2 meter) air temperature in Kelvin." zoomExtent: - 0 - 20 @@ -72,7 +77,7 @@ NEX-GDDP-CMIP6 is comprised of global downscaled climate scenarios derived from * Format: [kerchunk (metadata)](https://fsspec.github.io/kerchunk/) for netCDF4 * Spatial Coverage: 180° W to 180° E, 60° S to 90° N -* Temporal: 1950-01-01 to 1951-12-31 +* Temporal: 1950-01-01 to 2014-12-31 * _As noted below, this dataset is a subset all available data. The full dataset includes data from 1950 to 2100._ * Data Resolution: * Latitude Resolution: 0.25 degrees (25 km) diff --git a/datasets/co2.data.mdx b/datasets/co2.data.mdx index 5050b4440..cc62f3f02 100644 --- a/datasets/co2.data.mdx +++ b/datasets/co2.data.mdx @@ -12,7 +12,11 @@ taxonomy: - name: Topics values: - Air Quality - - EIS + - COVID 19 + - name: Source + values: + - GOSAT + infoDescription: | ::markdown The Impact of the COVID-19 Pandemic on Atmospheric CO2 diff --git a/datasets/conus-reach.data.mdx b/datasets/conus-reach.data.mdx index a66c77dd1..17faa68f9 100644 --- a/datasets/conus-reach.data.mdx +++ b/datasets/conus-reach.data.mdx @@ -10,9 +10,9 @@ media: url: https://www.nasa.gov pubDate: 2023-03-03 taxonomy: - - name: Topics + - name: Source values: - - EIS + - NASA EIS infoDescription: | ::markdown This dataset describes the Stream network across the Contiguous United States delineated using Soil and Water Assessment Tool diff --git a/datasets/damage-probability-ian.data.mdx b/datasets/damage-probability-ian.data.mdx index 6717d40e1..d9d4e9d62 100644 --- a/datasets/damage-probability-ian.data.mdx +++ b/datasets/damage-probability-ian.data.mdx @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - Disasters + - name: Source + values: + - HLS layers: - id: damage_probability_2022-10-03 stacCol: damage_probability_2022-10-03 diff --git a/datasets/darnah-flood.data.mdx b/datasets/darnah-flood.data.mdx index d0f03ac57..5febdb17e 100644 --- a/datasets/darnah-flood.data.mdx +++ b/datasets/darnah-flood.data.mdx @@ -11,10 +11,12 @@ media: taxonomy: - name: Topics values: - - EIS + - Disasters + - Precipitation - name: Source values: - - UAH + - HLS + - GPM layers: - id: darnah-flood @@ -37,6 +39,8 @@ layers: ::js ({ dateFns, datetime, compareDatetime }) => { return `${dateFns.format(datetime, 'DD LLL yyyy')}`; } + metadata: + source: HLS - id: darnah-gpm-daily stacCol: darnah-gpm-daily @@ -67,7 +71,9 @@ layers: - '#52076c' - '#f57c16' - '#f7cf39' - + + metadata: + source: GPM --- @@ -143,4 +149,4 @@ Environmental Aspects: When interpreting the data, it is crucial to consider the * [The Deadliest Flood of the 21st Century](https://www.earthdata.nasa.gov/dashboard/stories/darnah-flood) - \ No newline at end of file + diff --git a/datasets/disalexi-etsuppression.data.mdx b/datasets/disalexi-etsuppression.data.mdx index d68c6d8b3..0351e7ce3 100644 --- a/datasets/disalexi-etsuppression.data.mdx +++ b/datasets/disalexi-etsuppression.data.mdx @@ -9,9 +9,9 @@ media: name: Mike Newbry url: https://unsplash.com/photos/DwtX9mMHBJ0 taxonomy: - - name: Topics + - name: Source values: - - EIS + - NASA EIS infoDescription: | ::markdown Impact of fires on changes in evapotranspiration, obtained OpenET observations (DisALEXI model) for 2017-20 fires diff --git a/datasets/ecco-surface-height-change.data.mdx b/datasets/ecco-surface-height-change.data.mdx index d2ed42f41..3a2feaa88 100644 --- a/datasets/ecco-surface-height-change.data.mdx +++ b/datasets/ecco-surface-height-change.data.mdx @@ -9,9 +9,9 @@ media: name: Lance Asper url: https://unsplash.com/photos/3P3NHLZGCp8 taxonomy: - - name: Topics + - name: Source values: - - EIS + - NASA EIS infoDescription: | ::markdown Gridded global sea-surface height change from 1992 to 2017 from the Estimating the Circulation and Climate of the Ocean (ECCO) ocean state estimate. The dataset was calculated as the difference between the annual means over 2017 and 1992, from the 0.5 degree, gridded monthly mean data product available on [PO.DAAC](https://podaac.jpl.nasa.gov/dataset/ECCO_L4_SSH_05DEG_MONTHLY_V4R4). diff --git a/datasets/entropy-difference-ian.data.mdx b/datasets/entropy-difference-ian.data.mdx index 2df23b35c..ed64c776c 100644 --- a/datasets/entropy-difference-ian.data.mdx +++ b/datasets/entropy-difference-ian.data.mdx @@ -11,7 +11,10 @@ media: taxonomy: - name: Topics values: - - EIS + - Disasters + - name: Source + values: + - HLS layers: - id: hls-entropy-difference stacCol: hls-entropy-difference @@ -79,4 +82,4 @@ This work has been supported by the USGS-NASA Landsat Science Team (LST) Program [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0) - \ No newline at end of file + diff --git a/datasets/epa-agriculture--cover.jpg b/datasets/epa-agriculture--cover.jpg deleted file mode 100644 index 9582329c8..000000000 Binary files a/datasets/epa-agriculture--cover.jpg and /dev/null differ diff --git a/datasets/epa-agriculture.data.mdx b/datasets/epa-agriculture.data.mdx deleted file mode 100644 index 89f871f62..000000000 --- a/datasets/epa-agriculture.data.mdx +++ /dev/null @@ -1,254 +0,0 @@ ---- -id: epa-agriculture -name: EPA - Agriculture -description: Emissions from agriculture include enteric fermentation, manure management, rice cultivation, and field burning of agricultural residues -media: - src: ::file ./epa-agriculture--cover.jpg - alt: Tractors tending a corn field - author: - name: James Baltz - url: https://unsplash.com/photos/jAt6cN6zl8M -taxonomy: - - name: Topics - values: - - EIS - - name: Source - values: - - EPA GHG -infoDescription: | - ::markdown - A team at Harvard University along with EPA and other coauthors developed a gridded inventory of U.S. anthropogenic methane emissions with 0.1° x 0.1° spatial resolution, monthly temporal resolution, and detailed scale-dependent error characterization. The inventory is designed to be consistent with the 2016 U.S. [EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks](https://www.epa.gov/ghgemissions/us-greenhouse-gas-inventory-report-1990-2014) estimates for the year 2012, which presents national totals for different source types. The gridded inventory was developed using a wide range of databases at the state, county, local, and point source level to allocate the spatial and temporal distribution of emissions for individual source types. -layers: - - id: epa-annual-emissions_4b_manure_management - stacCol: EPA-annual-emissions_4B_Manure_Management - name: Manure Management - type: raster - description: Emissions from sector 4B from manure management. - zoomExtent: - - 0 - - 20 - sourceParams: - colormap_name: rainbow - rescale: - - 0 - - 46428019752 - nodata: 0 - legend: - type: gradient - min: 0 - max: 46428019752 - stops: - - "#60007d" - - "#30137d" - - "#1960ae" - - "#7ac300" - - "#f2ce00" - - "#ef6a01" - - "#cc0019" - info: - source: EPA - spatialExtent: United States - temporalResolution: Annual - unit: Mg 1/a km² - - id: epa-monthly-emissions_4b_manure_management - stacCol: EPA-monthly-emissions_4B_Manure_Management - name: Manure Management (monthly) - type: raster - description: Emissions from sector 4B from manure management (monthly). - zoomExtent: - - 0 - - 20 - sourceParams: - colormap_name: rainbow - rescale: - - 0 - - 46428019752 - nodata: 0 - legend: - type: gradient - min: 0 - max: 46428019752 - stops: - - "#60007d" - - "#30137d" - - "#1960ae" - - "#7ac300" - - "#f2ce00" - - "#ef6a01" - - "#cc0019" - info: - source: EPA - spatialExtent: United States - temporalResolution: Monthly - unit: Mg 1/a km² - - id: epa-annual-emissions_4c_rice_cultivation - stacCol: EPA-annual-emissions_4C_Rice_Cultivation - name: Rice Cultivation - type: raster - description: Emissions from sector 4C from rice cultivation. - zoomExtent: - - 0 - - 20 - sourceParams: - colormap_name: rainbow - rescale: - - 0 - - 38327875010 - nodata: 0 - legend: - type: gradient - min: 0 - max: 38327875010 - stops: - - "#60007d" - - "#30137d" - - "#1960ae" - - "#7ac300" - - "#f2ce00" - - "#ef6a01" - - "#cc0019" - info: - source: EPA - spatialExtent: United States - temporalResolution: Annual - unit: Mg 1/a km² - - id: epa-monthly-emissions_4c_rice_cultivation - stacCol: EPA-monthly-emissions_4C_Rice_Cultivation - name: Rice Cultivation (monthly) - type: raster - description: Emissions from sector 4C from rice cultivation (monthly). - zoomExtent: - - 0 - - 20 - sourceParams: - colormap_name: rainbow - rescale: - - 0 - - 54815427133 - nodata: 0 - legend: - type: gradient - min: 0 - max: 54815427133 - stops: - - "#60007d" - - "#30137d" - - "#1960ae" - - "#7ac300" - - "#f2ce00" - - "#ef6a01" - - "#cc0019" - info: - source: EPA - spatialExtent: United States - temporalResolution: Monthly - unit: Mg 1/a km² - - id: epa-annual-emissions_4a_enteric_fermentation - stacCol: EPA-annual-emissions_4A_Enteric_Fermentation - name: Enteric Fermentation - type: raster - description: >- - Emissions from sector 4A from enteric fermentation (fermentation that - takes place in the digestive systems of animals). - zoomExtent: - - 0 - - 20 - sourceParams: - colormap_name: rainbow - rescale: - - 0 - - 43784598420 - nodata: 0 - legend: - type: gradient - min: 0 - max: 43784598420 - stops: - - "#60007d" - - "#30137d" - - "#1960ae" - - "#7ac300" - - "#f2ce00" - - "#ef6a01" - - "#cc0019" - info: - source: EPA - spatialExtent: United States - temporalResolution: Annual - unit: Mg 1/a km² - - id: epa-annual-emissions_4f_field_burning - stacCol: EPA-annual-emissions_4F_Field_Burning - name: Field Burning - type: raster - description: Emissions from sector 4F from agricultural field burning. - zoomExtent: - - 0 - - 20 - sourceParams: - colormap_name: rainbow - rescale: - - 0 - - 800788398 - nodata: 0 - legend: - type: gradient - min: 0 - max: 800788398 - stops: - - "#60007d" - - "#30137d" - - "#1960ae" - - "#7ac300" - - "#f2ce00" - - "#ef6a01" - - "#cc0019" - info: - source: EPA - spatialExtent: United States - temporalResolution: Annual - unit: Mg 1/a km² - - id: epa-monthly-emissions_4f_field_burning - stacCol: EPA-monthly-emissions_4F_Field_Burning - name: Field Burning (monthly) - type: raster - description: Emissions from sector 4F from agricultural field burning (monthly). - zoomExtent: - - 0 - - 20 - sourceParams: - colormap_name: rainbow - rescale: - - 0 - - 4510689853 - nodata: 0 - legend: - type: gradient - min: 0 - max: 4510689853 - stops: - - "#60007d" - - "#30137d" - - "#1960ae" - - "#7ac300" - - "#f2ce00" - - "#ef6a01" - - "#cc0019" - info: - source: EPA - spatialExtent: United States - temporalResolution: Monthly - unit: Mg 1/a km² ---- - - - - ## Gridded 2012 Methane Emissions - - A team at Harvard University along with EPA and other coauthors developed a gridded inventory of U.S. anthropogenic methane emissions with 0.1° x 0.1° spatial resolution, monthly temporal resolution, and detailed scale-dependent error characterization. The inventory is designed to be consistent with the 2016 U.S. [EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks](https://www.epa.gov/ghgemissions/us-greenhouse-gas-inventory-report-1990-2014) estimates for the year 2012, which presents national totals for different source types. The gridded inventory was developed using a wide range of databases at the state, county, local, and point source level to allocate the spatial and temporal distribution of emissions for individual source types. - - This data can be used by researchers to better compare the national-level inventory with measurement results that may be at other scales. Users of this gridded inventory are asked to cite the original reference (Maasakkers et al., 2016) in their publications. Error estimates are given in that reference. - - Paper: [Maasakkers et. al. 2016, A Gridded National Inventory of U.S. Methane Emissions](https://www.epa.gov/ghgemissions/gridded-2012-methane-emissions#paper) - - - diff --git a/datasets/epa-annual--cover.jpg b/datasets/epa-annual--cover.jpg new file mode 100644 index 000000000..77997537c Binary files /dev/null and b/datasets/epa-annual--cover.jpg differ diff --git a/datasets/epa-anthropogenic-methane.data.mdx b/datasets/epa-anthropogenic-methane.data.mdx new file mode 100644 index 000000000..14ce5bd0e --- /dev/null +++ b/datasets/epa-anthropogenic-methane.data.mdx @@ -0,0 +1,2724 @@ +--- +id: epa-ch4emission-yeargrid-v2express-manure +name: U.S. Gridded Anthropogenic Methane Emissions Inventory +description: Spatially disaggregated 0.1°x 0.1° maps of annual U.S. anthropogenic methane emissions from over 25 emission sources, consistent with the U.S. Inventory of Greenhouse Gas Emissions and Sinks. +media: + src: ::file ./epa-annual--cover.jpg + alt: Total Gridded Methane Emissions from the U.S. Inventory of Greenhouse Gas Emissions and Sinks + author: + name: EPA +taxonomy: + - name: Topics + values: + - Air Quality + - name: Source + values: + - EPA +sourceExclusive: EPA +infoDescription: | + ::markdown + - Temporal Extent: 2012 - 2020 + - Temporal Resolution: Annual + - Spatial Extent: Contiguous United States + - Spatial Resolution: 0.1° x 0.1° + - Data Units: Megagrams of methane per square kilometer per year (Mg CH₄/km²/yr) + - Data type: Research (v2 express extension) + - Data Latency: N/A + +layers: + - id: total-methane + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Total Methane (annual) + type: raster + description: Total annual methane emission fluxes from all Agriculture, Energy, Waste, and ‘Other’ sources included in this dataset. + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: total-methane + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: total-methane + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.total.methane.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Total Methane (annual) + + - id: total-agriculture + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Total Agriculture (annual) + type: raster + description: Total annual methane emission fluxes from Agriculture sources (sum of inventory categories 3A, 3B, 3C, 3F) + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: total-agriculture + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: total-agriculture + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.total.agriculture.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Total Agriculture (annual) + - id: enteric-fermentation + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Agriculture - Enteric Fermentation (annual) + type: raster + description: Annual methane emission fluxes from livestock Enteric Fermentation (inventory Agriculture category 3A) + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: enteric-fermentation + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: enteric-fermentation + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.enteric.fermentation.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Agriculture - Enteric Fermentation (annual) + - id: manure-management + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Agriculture - Manure Management (annual) + type: raster + description: Annual methane emission fluxes from Manure Management (inventory Agriculture category 3B) + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: manure-management + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: svi-overall + layerId: social-vulnerability-index-overall + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.agriculture.manure.management.anual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Agriculture - Manure Management (annual) + - id: rice-cultivation-l + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Agriculture - Rice Cultivation (annual) + type: raster + description: Annual methane emission fluxes from Rice Cultivation (inventory Agriculture category 3C) + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: rice-cultivation + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: rice-cultivation-l + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.agriculture.rice.cultivation.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Agriculture - Rice Cultivation (annual) + - id: field-burning-l + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Agriculture - Field Burning (annual) + type: raster + description: Annual methane emission fluxes from Field Burning of Agriculture Residues (inventory Agriculture category 3F) + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: field-burning + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: field-burning-l + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.agriculture.field.burning.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Agriculture - Field Burning (annual) + - id: total-natural-gas + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Total Natural Gas Systems (annual) + type: raster + description: Total annual methane emission fluxes from Natural Gas Systems (sum of inventory Energy 1B2b sub-categories) + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: total-natural-gas-systems + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: total-natural-gas + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.total.natural.gas.systems.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Total Natural Gas Systems (annual) + - id: exploration-ngs-l + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Natural Gas - Exploration (annual) + type: raster + description: Annual methane emission fluxes from Natural Gas Exploration (inventory Energy 1B2b sub-category) + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: exploration-ngs + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: exploration-ngs-l + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.natural.gas.exploration.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Natural Gas - Exploration (annual) + - id: production-ngs-l + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Natural Gas - Production (annual) + type: raster + description: Annual methane emission fluxes from Natural Gas Production (inventory Energy 1B2b sub-category) + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: production-ngs + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: production-ngs-l + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.natural.gas.production.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Natural Gas - Production (annual) + - id: 1B2b-transmission-storage-ngs + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Natural Gas - Transmission and Storage (annual) + type: raster + description: Annual methane emission fluxes from Natural Gas Transmission and Storage (inventory Energy 1B2b sub-category) + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: transmission-storage-ngs + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: 1B2b-transmission-storage-ngs + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.natural.gas.transmission.and.storage.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Natural Gas - Transmission and Storage (annual) + - id: 1B2b-processing-ngs + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Natural Gas - Processing (annual) + type: raster + description: Annual methane emission fluxes from Natural Gas Processing (inventory Energy 1B2b sub-category) + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: processing-ngs + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: 1B2b-processing-ngs + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.natural.gas.processing.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Natural Gas - Processing (annual) + - id: 1B2b-distribution-ngs + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Natural Gas - Distribution (annual) + type: raster + description: Annual methane emission fluxes from Natural Gas Distribution (inventory Energy 1B2b sub-category). + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: distribution-ngs + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: 1B2b-distribution-ngs + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.natural.gas.distribution.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Natural Gas - Distribution (annual) + - id: post-meter-ng + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Natural Gas - Post-Meter (annual) + type: raster + description: Annual methane emission fluxes from Natural Gas Post-Meter sources (inventory Energy 1B2b sub-category) + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: post-meter + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: post-meter-ng + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.natural.gas.post-meter.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Natural Gas - Post-Meter (annual) + - id: total-petroleum + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Total Petroleum Systems (annual) + type: raster + description: Total annual methane emission fluxes from Petroleum Systems (sum of inventory Energy 1B2a sub-categories) + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: total-petroleum-systems + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: total-petroleum + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.total.petroleum.systems.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Total Petroleum Systems (annual) + - id: 1B2a-exploration-ps + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Petroleum - Exploration (annual) + type: raster + description: Annual methane emission fluxes from Petroleum Exploration (inventory Energy 1B2a sub-category). + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: exploration-ps + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: 1B2a-exploration-ps + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.petroleum.exploration.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Petroleum - Exploration (annual) + - id: 1B2a-production-ps + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Petroleum - Production (annual) + type: raster + description: Annual methane emission fluxes from Petroleum Production (inventory Energy 1B2a sub-category). + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: production-ps + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: 1B2a-production-ps + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.petroleum.production.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Petroleum - Production (annual) + - id: 1B2a-transport-ps + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Petroleum - Transportation (annual) + type: raster + description: Annual methane emission fluxes from Petroleum Transportation (inventory Energy 1B2a sub-category). + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: transport-ps + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: 1B2a-transport-ps + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.petroleum.transportation.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Petroleum - Transportation (annual) + - id: 1B2a-refining-ps + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Petroleum - Refining (annual) + type: raster + description: Annual methane emission fluxes from Petroleum Refining (inventory Energy 1B2a sub-category). + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: refining-ps + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: 1B2a-refining-ps + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.petroleum.refining.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Petroleum - Refining (annual) + - id: total-waste + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Total Waste (annual) + type: raster + description: Total annual methane emission fluxes from Waste (sum of inventory Waste categories 5A1, 5B1, 5D) + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: total-waste + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: total-waste + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.total.waste.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Total Waste (annual) + - id: 5A1-msw-landfill-waste + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Waste - Municipal Solid Waste (MSW) Landfills (annual) + type: raster + description: Annual methane emissions fluxes from Municipal Solid Waste Landfills (inventory Waste 5A1 sub-category). + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: msw-landfill-waste + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: 5A1-msw-landfill-waste + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.waste.municipal.landfills.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Waste - Municipal Solid Waste (MSW) Landfills (annual) + - id: 5A1-industrial-landfill-waste + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Waste - Industrial Landfills (annual) + type: raster + description: Annual methane emissions fluxes from Industrial Landfills (inventory Waste 5A1 sub-category). + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: industrial-landfill-waste + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: 5A1-industrial-landfill-waste + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.waste.industrial.landfills.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Waste - Industrial Landfills (annual) + - id: 5A1-dwtd-waste + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Waste - Domestic Wastewater Treatment & Discharge (annual) + type: raster + description: Annual methane emissions fluxes from Domestic Wastewater Treatment and Discharge (inventory Waste 5D sub-category). + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: dwtd-waste + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: 5A1-dwtd-waste + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.waste.domestic.wastewater.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Waste - Domestic Wastewater Treatment & Discharge (annual) + - id: 5A1-iwtd-waste + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Waste - Industrial Wastewater Treatment & Discharge (annual) + type: raster + description: Annual methane emissions fluxes from Industrial Wastewater Treatment and Discharge (inventory Waste 5D sub-category). + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: iwtd-waste + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: 5A1-iwtd-waste + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.waste.industrial.wastewater.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Waste - Industrial Wastewater Treatment & Discharge (annual) + - id: 5A1-composting-waste + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Waste - Composting (annual) + type: raster + description: Annual methane emissions fluxes from Composting (inventory Waste category 5B1). + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: composting-waste + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: 5A1-composting-waste + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.composting.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Waste - Composting (annual) + - id: total-coal-mines + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Total Coal Mines (annual) + type: raster + description: Total annual methane emission fluxes from Coal Mines (sum of inventory 1B1a sub-categories) + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: total-coal-mines + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: total-coal-mines + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.total.coal.mines.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Total Coal Mines (annual) + - id: 1B1a-underground-coal + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Coal Mining - Underground Mining (annual) + type: raster + description: Annual methane emission fluxes from active Underground Coal Mining (inventory Energy 1B1a sub-category) + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: underground-coal + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: 1B1a-underground-coal + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.coal.mining.underground.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Coal Mining - Underground Mining (annual) + - id: 1B1a-abn-underground-coal + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Coal Mining - Abandoned Underground Mines (annual) + type: raster + description: Annual methane emission fluxes from Abandoned Underground Coal Mines (inventory Energy 1B1a sub-category) + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: abn-underground-coal + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: 1B1a-abn-underground-coal + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.coal.mining.abandoned.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Coal Mining - Abandoned Underground Mines (annual) + - id: 1B1a-surface-coal + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Coal Mining - Surface Mining (annual) + type: raster + description: Annual methane emission fluxes from active Surface Coal Mining (inventory Energy 1B1a sub-category) + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: surface-coal + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: 1B1a-surface-coal + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.coal.mining.surface.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Coal Mining - Surface Mining (annual) + - id: total-other + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Total Other (annual) + type: raster + description: Total annual methane emission fluxes from ‘other’ remaining sources (sum of inventory categories 1A (energy combustion), 2B8 & 2C2 (petrochemical & ferroalloy production) and 1B2a & 1B2b (abandoned O&G well emissions)) + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: total-other + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: total-other + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.total.other.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Total Other (annual) + - id: 1A-stationary-combustion-other + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Other - Stationary Combustion (annual) + type: raster + description: Annual methane emission fluxes from Stationary Combustion (inventory Energy 1A sub-category) + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: stationary-combustion-other + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: 1A-stationary-combustion-other + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.other.stationary.combustion.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Other - Stationary combustion (annual) + - id: 1A-mobile-combustion-othe + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Other - Mobile Combustion (annual) + type: raster + description: Annual methane emission fluxes from Mobile Combustion (inventory Energy 1A sub-category) + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: mobile-combustion-other + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: 1A-mobile-combustion-othe + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.other.mobile.combustion.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Other - Mobile combustion (annual) + - id: 1A-abn-ong-other + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Other - Abandoned Oil and Gas Wells (annual) + type: raster + description: Annual methane emission fluxes from Abandoned Oil and Gas Wells (inventory Energy 1B2a and 1B2b sub-categories) + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: abn-ong-other + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: 1A-abn-ong-other + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.other.abandoned.oil.gas.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Other - Abandoned Oil and Gas Wells (annual) + - id: 1A-petro-production-other + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Other - Petrochemical Production (annual) + type: raster + description: Annual methane emission fluxes from Petrochemical Production (inventory Industrial Processes and Product Use category 2B8) + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: petro-production-other + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + minzoom: 0 + maxzoom: 5 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: 1A-petro-production-other + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.other.petrochemical.production.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Other - Petrochemical Production (annual) + - id: 1A-ferroalloy-production-other + stacApiEndpoint: https://earth.gov/ghgcenter/api/stac + tileApiEndpoint: https://earth.gov/ghgcenter/api/raster + stacCol: epa-ch4emission-yeargrid-v2express + name: Other - Ferroalloy Production (annual) + type: raster + description: Annual methane emission fluxes from Ferroalloy Production (inventory Industrial Processes and Product Use category 2C2) + initialDatetime: newest + projection: + id: "equirectangular" + basemapId: "light" + zoomExtent: + - 0 + - 20 + sourceParams: + assets: ferroalloy-production-other + colormap_name: epa-ghgi-ch4 + rescale: + - 0 + - 20 + compare: + datasetId: epa-ch4emission-yeargrid-v2express + layerId: 1A-ferroalloy-production-other + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`; + } + analysis: + exclude: true + metrics: + - mean + sourceParams: + dst_crs: "+proj=cea" + info: + source: EPA + spatialExtent: Contiguous United States + temporalResolution: Annual + unit: Mg CH₄/km²/yr + legend: + unit: + label: Mg CH₄/km²/yr + type: gradient + min: 0 + max: 20 + stops: + - "#FFFFFF" + - "#6F4C9B" + - "#6059A9" + - "#5568B8" + - "#4E79C5" + - "#4D8AC6" + - "#4E96BC" + - "#549EB3" + - "#59A5A9" + - "#60AB9E" + - "#69B190" + - "#77B77D" + - "#8CBC68" + - "#A6BE54" + - "#BEBC48" + - "#D1B541" + - "#DDAA3C" + - "#E49C39" + - "#E78C35" + - "#E67932" + - "#E4632D" + - "#DF4828" + - "#DA2222" + - "#B8221E" + - "#95211B" + - "#721E17" + - "#521A13" + media: + src: ::file ./epa-ch4emission-yeargrid-v2express.thumbnails.other.ferroalloy.production.annual.png + alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Other - Ferroalloy Production (annual) + +--- + + + + **Temporal Extent:** 2012 - 2020 + **Temporal Resolution:** Annual + **Spatial Extent:** Contiguous United States + **Spatial Resolution:** 0.1° x 0.1° + **Data Units:** Megagrams of methane per square kilometer per year (Mg CH₄/km²/yr) + **Data type:** Research (v2 express extension)
+ **Data Latency:** N/A + + The gridded EPA U.S. anthropogenic methane greenhouse gas inventory (gridded GHGI) includes spatially disaggregated (0.1 deg x 0.1 deg or approximately 10 x 10 km resolution) maps of annual anthropogenic methane emissions for the contiguous United States (CONUS), consistent with national annual U.S. anthropogenic methane emissions reported in the U.S. EPA [Inventory of U.S. Greenhouse Gas Emissions and Sinks](https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks) (U.S. GHGI). This dataset contains methane emissions provided as fluxes, in units of molecules of methane per square cm per second, for manure management. The data have been converted from their original NetCDF format to Cloud-Optimized GeoTIFF (COG) and scaled to Megagrams of CH4 per km2 per year (Mg/km²/yr). + + ## Source Data Product Citation + Gridded GHGI Version 2 & Express Extension **(this dataset in US GHG Center)**: + McDuffie, E. E., Maasakkers, J. D., Sulprizio, M. P., Chen, C., Schultz, M., Brunelle, L., Thrush, R., Steller, J., Sherry, C., Jacob, Daniel, J., Jeong, S., Irving, B., & Weitz, M. (2023). Gridded EPA U.S. Anthropogenic Methane Greenhouse Gas Inventory (gridded GHGI) (v1.0) [Data set]. Zenodo. [https://doi.org/10.5281/zenodo.8367082](https://doi.org/10.5281/zenodo.8367082) + + Gridded GHGI Version 1: + Maasakkers, J. D., Jacob, D. J., Sulprizio, M. P., Turner, A. J., Weitz, M., Wirth, T., Hight, C., DeFigueiredo, M., Desai, M., Schmeltz, R., Hockstad, L., Bloom, A. A., Bowman, K. W., Jeong, S., Fischer, M. L. (2016) A Gridded National Inventory of U.S. Methane Emissions [Data set]. Available at: [https://www.epa.gov/ghgemissions/gridded-2012-methane-emissions#data](https://www.epa.gov/ghgemissions/gridded-2012-methane-emissions#data) + + ## Version History + The gridded methane GHGI is continually updated to capture ongoing improvements and updates to the U.S. GHG Inventory. The gridded methane GHGI currently includes 2 versions, which reflect sectoral methane emissions that are consistent with different versions of the U.S. GHGI. Versions include: + + Current Version(s) + - Gridded methane GHGI Version 2 - Express Extension (0.1°×0.1° annual emission maps for 2012-2020, consistent with the 2022 U.S. GHGI) + + Previous Versions + - Gridded methane GHGI Version 1 (0.1°×0.1° annual emission maps for 2012, consistent with the 2016 U.S. GHGI) + + **Data available on the Data Exploration page correspond to the V2 Express Extension dataset.** + + For more information on the current data set versions, see the associated publication: [Massakkers et al., 2023.](https://pubs.acs.org/doi/full/10.1021/acs.est.3c05138) or visit the [EPA gridded inventory webpage](https://www.epa.gov/ghgemissions/us-gridded-methane-emissions). For more information on the previous version, see the associated publication: [Massakkers et al., 2016.](https://pubs.acs.org/doi/10.1021/acs.est.6b02878) + + + + + + ## Dataset Accuracy + Uncertainties underlying the development of national methane emission estimates are discussed in each annual U.S. GHGI Report. Additional characterization of resolution-dependent uncertainties are discussed in [Maasakkers et al. (2023)](https://pubs.acs.org/doi/full/10.1021/acs.est.3c05138). + + ## Disclaimer + This dataset has been transformed from its original format (NetCDF) into Cloud Optimized GeoTIFF ([COG](https://www.cogeo.org/)). Careful quality checks are used to ensure data transformation has been performed correctly. The manuscript describing the gridded methane GHGI has been peer-reviewed, but is not part of the same annual expert and public review processes as the U.S. EPA National and State-level Inventory. + + Users of these datasets are asked to cite the original references [Maasakkers et al. (2023)](https://pubs.acs.org/doi/full/10.1021/acs.est.3c05138) or [Maasakkers, et al., (2016)](https://pubs.acs.org/doi/10.1021/acs.est.6b02878) in their publications and are encouraged to reach out to the development team with further questions. + + ## Scientific Details + The gridded methane GHGI is developed by spatially allocating national annual methane emissions from individual source categories from the Inventory of U.S. Greenhouse Gas Emissions and Sinks (U.S. GHGI) to a 0.1 deg x 0.1 deg (~10 x 10 km) grid using a series of spatial and temporal proxy datasets at the state, county, and grid levels. Where possible, the proxy data are the same as those used to develop the GHGI so that the gridded emissions can be, as closely as possible, a spatial and temporal representation of those in the national-level U.S. GHGI Report. + + The development of the gridded GHGI enables more direct comparisons between the methane emissions reported in the annual U.S. GHGI and those derived from atmospheric methane observations, such as through inverse analyses, with the aim of improving national inventory estimates and better understanding uncertain sources of methane emissions. + + Details of the methodological development of this dataset are described in the paper Maasakkers et al., 2023: [https://pubs.acs.org/doi/full/10.1021/acs.est.3c05138](https://pubs.acs.org/doi/full/10.1021/acs.est.3c05138) + + ## Key Publications + Maasakkers, J. D., McDuffie, E. E.,, Sulprizio, M. P., Chen, C., Schultz, M., Brunelle, L., Thrush, R., Steller, J., Sherry, C., Jacob, D. J., Jeong, S., Irving, B., & Weitz, M. (2023). A gridded inventory of annual 2012-2018 U.S. anthropogenic methane emissions. Environmental Science & Technology, 57(43), 16276-16288. https://pubs.acs.org/doi/full/10.1021/acs.est.3c05138 + + ## Other Relevant Publications + Maasakkers, J., Jacob, D., Sulprizio, M., Turner, A., Weitz, M., Wirth, T., Hight, C., DeFigueiredo, M., Desai, M., Schmeltz, R., Hockstad, L., Bloom, A., Bowman, K., Jeong, S., Fischer, M. (2016). Gridded National Inventory of U.S. Methane Emissions. *Environmental Science & Technology*, 50(23), 13123-13133. https://doi.org/10.1021/acs.est.6b02878 + + ## Learn More + - Learn more about how this data helps identify trends in U.S. methane emissions in the U.S. Gridded Anthropogenic Greenhouse Gas Emissions Data Insight + - Check out other GHG data [from the EPA](https://www.epa.gov/ghgemissions) + - Check out the [data interpretation notes](https://drive.google.com/file/d/1_c6SrKr4z2SNs4fCy3QQMlX92G09Yf6R/view?usp=drive_link) for more information when viewing this dataset in the US GHG Center Exploration environment + + ## Acknowledgment + This dataset was developed in collaboration between researchers at the U.S. EPA, Netherlands Institute for Space Research (SRON), Harvard University, and Lawrence Berkeley National Laboratory. + + ## License + [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode) (CC BY 4.0) + + ## Data Stewardship + The EPA gridded emissions for manure management in the VEDA platform are served by the U.S. Greenhouse Gas Center data catalog. For information on data stewardship within the U.S. Greenhouse Gas Center please refer to the documentation below. + - [Data Workflow](https://github.com/US-GHG-Center/ghgc-docs/blob/main/data_workflow/media/epa-ch4emission-grid-v2express_Data_Flow.png) + - [Data Transformation Code](https://us-ghg-center.github.io/ghgc-docs/cog_transformation/epa-ch4emission-grid-v2express.html) + - [US GHG Center Data Intake Processing and Verification Report](https://us-ghg-center.github.io/ghgc-docs/processing_and_verification_reports/epa-ch4emission-grid-v2express_Processing%20and%20Verification%20Report.html) + + + diff --git a/datasets/epa-ch4emission-yeargrid-v2express.thumbnails.agriculture.field.burning.annual.png b/datasets/epa-ch4emission-yeargrid-v2express.thumbnails.agriculture.field.burning.annual.png new file mode 100644 index 000000000..dc4972119 Binary files /dev/null and b/datasets/epa-ch4emission-yeargrid-v2express.thumbnails.agriculture.field.burning.annual.png differ diff --git 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a/datasets/epa-coal-mines.data.mdx b/datasets/epa-coal-mines.data.mdx deleted file mode 100644 index f867717a9..000000000 --- a/datasets/epa-coal-mines.data.mdx +++ /dev/null @@ -1,128 +0,0 @@ ---- -id: epa-coal-mines -name: EPA - Coal Mines -description: Coal mining emissions include state-level emission estimates for underground mines and surface mines -media: - src: ::file ./epa-coal-mines--cover.jpg - alt: Machinery working a very large coal mine - author: - name: Dominik Vanyi - url: https://unsplash.com/photos/Mk2ls9UBO2E -taxonomy: - - name: Topics - values: - - EIS - - name: Source - values: - - EPA GHG -infoDescription: | - ::markdown - A team at Harvard University along with EPA and other coauthors developed a gridded inventory of U.S. anthropogenic methane emissions with 0.1° x 0.1° spatial resolution, monthly temporal resolution, and detailed scale-dependent error characterization. The inventory is designed to be consistent with the 2016 U.S. [EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks](https://www.epa.gov/ghgemissions/us-greenhouse-gas-inventory-report-1990-2014) estimates for the year 2012, which presents national totals for different source types. The gridded inventory was developed using a wide range of databases at the state, county, local, and point source level to allocate the spatial and temporal distribution of emissions for individual source types. -layers: - - id: epa-annual-emissions_1b1a_coal_mining_underground - stacCol: EPA-annual-emissions_1B1a_Coal_Mining_Underground - name: Underground Coal Mines - type: raster - description: Emissions from sector 1B1a from underground coal mining. - zoomExtent: - - 0 - - 20 - sourceParams: - colormap_name: rainbow - rescale: - - 0 - - 2022634652958 - nodata: 0 - legend: - type: gradient - min: 0 - max: 2022634652958 - stops: - - '#60007d' - - '#30137d' - - '#1960ae' - - '#7ac300' - - '#f2ce00' - - '#ef6a01' - - '#cc0019' - info: - source: EPA - spatialExtent: United States - temporalResolution: Annual - unit: Mg 1/a km² - - id: epa-annual-emissions_1b1a_coal_mining_surface - stacCol: EPA-annual-emissions_1B1a_Coal_Mining_Surface - name: Surface Coal Mines - type: raster - description: Emissions from sector 1B1a from surface coal mining. - zoomExtent: - - 0 - - 20 - sourceParams: - colormap_name: rainbow - rescale: - - 0 - - 1002466086748 - nodata: 0 - legend: - type: gradient - min: 0 - max: 1002466086748 - stops: - - '#60007d' - - '#30137d' - - '#1960ae' - - '#7ac300' - - '#f2ce00' - - '#ef6a01' - - '#cc0019' - info: - source: EPA - spatialExtent: United States - temporalResolution: Annual - unit: Mg 1/a km² - - id: epa-annual-emissions_1b1a_abandoned_coal - stacCol: EPA-annual-emissions_1B1a_Abandoned_Coal - name: Abandoned Coal Mines - type: raster - description: Emissions from sector 1B1a from abandoned coal mines. - zoomExtent: - - 0 - - 20 - sourceParams: - colormap_name: rainbow - rescale: - - 0 - - 90163894026 - nodata: 0 - legend: - type: gradient - min: 0 - max: 90163894026 - stops: - - '#60007d' - - '#30137d' - - '#1960ae' - - '#7ac300' - - '#f2ce00' - - '#ef6a01' - - '#cc0019' - info: - source: EPA - spatialExtent: United States - temporalResolution: Annual - unit: Mg 1/a km² ---- - - - - ## Gridded 2012 Methane Emissions - - A team at Harvard University along with EPA and other coauthors developed a gridded inventory of U.S. anthropogenic methane emissions with 0.1° x 0.1° spatial resolution, monthly temporal resolution, and detailed scale-dependent error characterization. The inventory is designed to be consistent with the 2016 U.S. [EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks](https://www.epa.gov/ghgemissions/us-greenhouse-gas-inventory-report-1990-2014) estimates for the year 2012, which presents national totals for different source types. The gridded inventory was developed using a wide range of databases at the state, county, local, and point source level to allocate the spatial and temporal distribution of emissions for individual source types. - - This data can be used by researchers to better compare the national-level inventory with measurement results that may be at other scales. Users of this gridded inventory are asked to cite the original reference (Maasakkers et al., 2016) in their publications. Error estimates are given in that reference. - - Paper: [Maasakkers et. al. 2016, A Gridded National Inventory of U.S. Methane Emissions](https://www.epa.gov/ghgemissions/gridded-2012-methane-emissions#paper) - - - diff --git a/datasets/epa-natural-gas--cover.jpg b/datasets/epa-natural-gas--cover.jpg deleted file mode 100644 index 3e7c7691e..000000000 Binary files a/datasets/epa-natural-gas--cover.jpg and /dev/null differ diff --git a/datasets/epa-natural-gas-systems.data.mdx b/datasets/epa-natural-gas-systems.data.mdx deleted file mode 100644 index d241f778c..000000000 --- a/datasets/epa-natural-gas-systems.data.mdx +++ /dev/null @@ -1,192 +0,0 @@ ---- -id: epa-natural-gas-systems -name: EPA - Natural Gas Systems -description: Emissions from Natural Gas Systems include emissions from natural gas production, processing, transmission, and distribution -media: - src: ::file ./epa-natural-gas--cover.jpg - alt: Gas processing plant at dusk - author: - name: American Public Power Association - url: https://unsplash.com/photos/TF-DL_2L1JM -taxonomy: - - name: Topics - values: - - EIS - - name: Source - values: - - EPA GHG -infoDescription: | - ::markdown - A team at Harvard University along with EPA and other coauthors developed a gridded inventory of U.S. anthropogenic methane emissions with 0.1° x 0.1° spatial resolution, monthly temporal resolution, and detailed scale-dependent error characterization. The inventory is designed to be consistent with the 2016 U.S. [EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks](https://www.epa.gov/ghgemissions/us-greenhouse-gas-inventory-report-1990-2014) estimates for the year 2012, which presents national totals for different source types. The gridded inventory was developed using a wide range of databases at the state, county, local, and point source level to allocate the spatial and temporal distribution of emissions for individual source types. -layers: - - id: epa-annual-emissions_1b2b_natural_gas_processing - stacCol: EPA-annual-emissions_1B2b_Natural_Gas_Processing - name: Natural Gas Processing - type: raster - description: Non-combustion emissions from sector 1B2b for natural gas processing. - zoomExtent: - - 0 - - 20 - sourceParams: - colormap_name: rainbow - rescale: - - 0 - - 963867416985 - nodata: 0 - legend: - type: gradient - min: 0 - max: 963867416985 - stops: - - '#60007d' - - '#30137d' - - '#1960ae' - - '#7ac300' - - '#f2ce00' - - '#ef6a01' - - '#cc0019' - info: - source: EPA - spatialExtent: United States - temporalResolution: Annual - unit: Mg 1/a km² - - id: epa-annual-emissions_1b2b_natural_gas_production - stacCol: EPA-annual-emissions_1B2b_Natural_Gas_Production - name: Natural Gas Production - type: raster - description: Non-combustion emissions from sector 1B2b for natural gas production. - zoomExtent: - - 0 - - 20 - sourceParams: - colormap_name: rainbow - rescale: - - 0 - - 194600207646 - nodata: 0 - legend: - type: gradient - min: 0 - max: 194600207646 - stops: - - '#60007d' - - '#30137d' - - '#1960ae' - - '#7ac300' - - '#f2ce00' - - '#ef6a01' - - '#cc0019' - info: - source: EPA - spatialExtent: United States - temporalResolution: Annual - unit: Mg 1/a km² - - id: epa-monthly-emissions_1b2b_natural_gas_production - stacCol: EPA-monthly-emissions_1B2b_Natural_Gas_Production - name: Natural Gas Production (monthly) - type: raster - description: >- - Non-combustion emissions from sector 1B2b for natural gas production - (monthly). - zoomExtent: - - 0 - - 20 - sourceParams: - colormap_name: rainbow - rescale: - - 0 - - 201580808765 - nodata: 0 - legend: - type: gradient - min: 0 - max: 201580808765 - stops: - - '#60007d' - - '#30137d' - - '#1960ae' - - '#7ac300' - - '#f2ce00' - - '#ef6a01' - - '#cc0019' - info: - source: EPA - spatialExtent: United States - temporalResolution: Monthly - unit: Mg 1/a km² - - id: epa-annual-emissions_1b2b_natural_gas_transmission - stacCol: EPA-annual-emissions_1B2b_Natural_Gas_Transmission - name: Natural Gas Transmission - type: raster - description: Non-combustion emissions from sector 1B2b for natural gas transmission. - zoomExtent: - - 0 - - 20 - sourceParams: - colormap_name: rainbow - rescale: - - 0 - - 138826376806 - nodata: 0 - legend: - type: gradient - min: 0 - max: 138826376806 - stops: - - '#60007d' - - '#30137d' - - '#1960ae' - - '#7ac300' - - '#f2ce00' - - '#ef6a01' - - '#cc0019' - info: - source: EPA - spatialExtent: United States - temporalResolution: Annual - unit: Mg 1/a km² - - id: epa-annual-emissions_1b2b_natural_gas_distribution - stacCol: EPA-annual-emissions_1B2b_Natural_Gas_Distribution - name: Natural Gas Distribution - type: raster - description: Non-combustion emissions from sector 1B2b for natural gas distribution. - zoomExtent: - - 0 - - 20 - sourceParams: - colormap_name: rainbow - rescale: - - 0 - - 32621776076 - nodata: 0 - legend: - type: gradient - min: 0 - max: 32621776076 - stops: - - '#60007d' - - '#30137d' - - '#1960ae' - - '#7ac300' - - '#f2ce00' - - '#ef6a01' - - '#cc0019' - info: - source: EPA - spatialExtent: United States - temporalResolution: Annual - unit: Mg 1/a km² ---- - - - - ## Gridded 2012 Methane Emissions - - A team at Harvard University along with EPA and other coauthors developed a gridded inventory of U.S. anthropogenic methane emissions with 0.1° x 0.1° spatial resolution, monthly temporal resolution, and detailed scale-dependent error characterization. The inventory is designed to be consistent with the 2016 U.S. [EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks](https://www.epa.gov/ghgemissions/us-greenhouse-gas-inventory-report-1990-2014) estimates for the year 2012, which presents national totals for different source types. The gridded inventory was developed using a wide range of databases at the state, county, local, and point source level to allocate the spatial and temporal distribution of emissions for individual source types. - - This data can be used by researchers to better compare the national-level inventory with measurement results that may be at other scales. Users of this gridded inventory are asked to cite the original reference (Maasakkers et al., 2016) in their publications. Error estimates are given in that reference. - - Paper: [Maasakkers et. al. 2016, A Gridded National Inventory of U.S. Methane Emissions](https://www.epa.gov/ghgemissions/gridded-2012-methane-emissions#paper) - - - diff --git a/datasets/epa-other--cover.jpg b/datasets/epa-other--cover.jpg deleted file mode 100644 index 630663eea..000000000 Binary files a/datasets/epa-other--cover.jpg and /dev/null differ diff --git a/datasets/epa-other.data.mdx b/datasets/epa-other.data.mdx deleted file mode 100644 index 69f9afab7..000000000 --- a/datasets/epa-other.data.mdx +++ /dev/null @@ -1,262 +0,0 @@ ---- -id: epa-other -name: EPA - Other -description: Others include emissions from Petrochemical Production, Ferroalloy Production, Combustion (Mobile and Stationary), and Forest Fires -media: - src: ::file ./epa-other--cover.jpg - alt: Orange Ford truck leaving a Union Carbide ferro-alloy plant. Shot on May 1975 - author: - name: Harry Schaefer / Documerica - url: https://unsplash.com/photos/EjSw6WdnLRA -taxonomy: - - name: Topics - values: - - EIS - - name: Source - values: - - EPA GHG -infoDescription: | - ::markdown - A team at Harvard University along with EPA and other coauthors developed a gridded inventory of U.S. anthropogenic methane emissions with 0.1° x 0.1° spatial resolution, monthly temporal resolution, and detailed scale-dependent error characterization. The inventory is designed to be consistent with the 2016 U.S. [EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks](https://www.epa.gov/ghgemissions/us-greenhouse-gas-inventory-report-1990-2014) estimates for the year 2012, which presents national totals for different source types. The gridded inventory was developed using a wide range of databases at the state, county, local, and point source level to allocate the spatial and temporal distribution of emissions for individual source types. -layers: - - id: epa-annual-emissions_2b5_petrochemical_production - stacCol: EPA-annual-emissions_2B5_Petrochemical_Production - name: Petrochemical Production - type: raster - description: Emissions from sector 2B5 from petrochemical production. - zoomExtent: - - 0 - - 20 - sourceParams: - colormap_name: rainbow - rescale: - - 0 - - 3715968204 - nodata: 0 - legend: - type: gradient - min: 0 - max: 3715968204 - stops: - - '#60007d' - - '#30137d' - - '#1960ae' - - '#7ac300' - - '#f2ce00' - - '#ef6a01' - - '#cc0019' - info: - source: EPA - spatialExtent: United States - temporalResolution: Annual - unit: Mg 1/a km² - - id: epa-annual-emissions_2c2_ferroalloy_production - stacCol: EPA-annual-emissions_2C2_Ferroalloy_Production - name: Ferroalloy Production - type: raster - description: Emissions from sector 2C2 from ferroalloy production. - zoomExtent: - - 0 - - 20 - sourceParams: - colormap_name: rainbow - rescale: - - 0 - - 2316570132 - nodata: 0 - legend: - type: gradient - min: 0 - max: 2316570132 - stops: - - '#60007d' - - '#30137d' - - '#1960ae' - - '#7ac300' - - '#f2ce00' - - '#ef6a01' - - '#cc0019' - info: - source: EPA - spatialExtent: United States - temporalResolution: Annual - unit: Mg 1/a km² - - id: epa-annual-emissions_1a_combustion_mobile - stacCol: EPA-annual-emissions_1A_Combustion_Mobile - name: Mobile Combustion - type: raster - description: >- - Mobile emissions from sector 1A, including on-road and non-road vehicles, - waterborne, rail, and air. - zoomExtent: - - 0 - - 20 - sourceParams: - colormap_name: rainbow - rescale: - - 0 - - 1126809681 - nodata: 0 - legend: - type: gradient - min: 0 - max: 1126809681 - stops: - - '#60007d' - - '#30137d' - - '#1960ae' - - '#7ac300' - - '#f2ce00' - - '#ef6a01' - - '#cc0019' - info: - source: EPA - spatialExtent: United States - temporalResolution: Annual - unit: Mg 1/a km² - - id: epa-annual-emissions_1a_combustion_stationary - stacCol: EPA-annual-emissions_1A_Combustion_Stationary - name: Stationary Combustion - type: raster - description: >- - Stationary (non-mobile) emissions from sector 1A, including boilers, - heaters, furnaces, kilns, ovens, flares, thermal oxidizers, dryers, and - any other equipment or machinery that combusts carbon bearing fuels or - waste stream materials. - zoomExtent: - - 0 - - 20 - sourceParams: - colormap_name: rainbow - rescale: - - 0 - - 11699984793 - nodata: 0 - legend: - type: gradient - min: 0 - max: 11699984793 - stops: - - '#60007d' - - '#30137d' - - '#1960ae' - - '#7ac300' - - '#f2ce00' - - '#ef6a01' - - '#cc0019' - info: - source: EPA - spatialExtent: United States - temporalResolution: Annual - unit: Mg 1/a km² - - id: epa-monthly-emissions_1a_combustion_stationary - stacCol: EPA-monthly-emissions_1A_Combustion_Stationary - name: Stationary Combustion (monthly) - type: raster - description: >- - Stationary (non-mobile) emissions from sector 1A, including boilers, - heaters, furnaces, kilns, ovens, flares, thermal oxidizers, dryers, and - any other equipment or machinery that combusts carbon bearing fuels or - waste stream materials. - zoomExtent: - - 0 - - 20 - sourceParams: - colormap_name: rainbow - rescale: - - 0 - - 11792563568 - nodata: 0 - legend: - type: gradient - min: 0 - max: 11792563568 - stops: - - '#60007d' - - '#30137d' - - '#1960ae' - - '#7ac300' - - '#f2ce00' - - '#ef6a01' - - '#cc0019' - info: - source: EPA - spatialExtent: United States - temporalResolution: Monthly - unit: Mg 1/a km² - - id: epa-annual-emissions_5_forest_fires - stacCol: EPA-annual-emissions_5_Forest_Fires - name: Forest Fires - type: raster - description: Emissions from sector 5 from forest fires. - zoomExtent: - - 0 - - 20 - sourceParams: - colormap_name: rainbow - rescale: - - 0 - - 23039817809 - nodata: 0 - legend: - type: gradient - min: 0 - max: 23039817809 - stops: - - '#60007d' - - '#30137d' - - '#1960ae' - - '#7ac300' - - '#f2ce00' - - '#ef6a01' - - '#cc0019' - info: - source: EPA - spatialExtent: United States - temporalResolution: Annual - unit: Mg 1/a km² - - id: epa-daily-emissions_5_forest_fires - stacCol: EPA-daily-emissions_5_Forest_Fires - name: Forest Fires (daily) - type: raster - description: Emissions from sector 5 from forest fires (daily). - zoomExtent: - - 0 - - 20 - sourceParams: - colormap_name: rainbow - rescale: - - 0 - - 1690773099642 - nodata: 0 - legend: - type: gradient - min: 0 - max: 1690773099642 - stops: - - '#60007d' - - '#30137d' - - '#1960ae' - - '#7ac300' - - '#f2ce00' - - '#ef6a01' - - '#cc0019' - info: - source: EPA - spatialExtent: United States - temporalResolution: Daily - unit: Mg 1/a km² ---- - - - - ## Gridded 2012 Methane Emissions - - A team at Harvard University along with EPA and other coauthors developed a gridded inventory of U.S. anthropogenic methane emissions with 0.1° x 0.1° spatial resolution, monthly temporal resolution, and detailed scale-dependent error characterization. The inventory is designed to be consistent with the 2016 U.S. [EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks](https://www.epa.gov/ghgemissions/us-greenhouse-gas-inventory-report-1990-2014) estimates for the year 2012, which presents national totals for different source types. The gridded inventory was developed using a wide range of databases at the state, county, local, and point source level to allocate the spatial and temporal distribution of emissions for individual source types. - - This data can be used by researchers to better compare the national-level inventory with measurement results that may be at other scales. Users of this gridded inventory are asked to cite the original reference (Maasakkers et al., 2016) in their publications. Error estimates are given in that reference. - - Paper: [Maasakkers et. al. 2016, A Gridded National Inventory of U.S. Methane Emissions](https://www.epa.gov/ghgemissions/gridded-2012-methane-emissions#paper) - - - diff --git a/datasets/epa-petroleum--cover.jpg b/datasets/epa-petroleum--cover.jpg deleted file mode 100644 index 709740ef4..000000000 Binary files a/datasets/epa-petroleum--cover.jpg and /dev/null differ diff --git a/datasets/epa-petroleum-systems.data.mdx b/datasets/epa-petroleum-systems.data.mdx deleted file mode 100644 index 5273c0552..000000000 --- a/datasets/epa-petroleum-systems.data.mdx +++ /dev/null @@ -1,101 +0,0 @@ ---- -id: epa-petroleum-systems -name: EPA - Petroleum Systems -description: National emissions from different activities and equipment related to petroleum production, refining, and transport -media: - src: ::file ./epa-petroleum--cover.jpg - alt: Punk style picture of a petroleum refinery - author: - name: Patrick Hendry - url: https://unsplash.com/photos/6xeDIZgoPaw -taxonomy: - - name: Topics - values: - - EIS - - name: Source - values: - - EPA GHG -infoDescription: | - ::markdown - A team at Harvard University along with EPA and other coauthors developed a gridded inventory of U.S. anthropogenic methane emissions with 0.1° x 0.1° spatial resolution, monthly temporal resolution, and detailed scale-dependent error characterization. The inventory is designed to be consistent with the 2016 U.S. [EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks](https://www.epa.gov/ghgemissions/us-greenhouse-gas-inventory-report-1990-2014) estimates for the year 2012, which presents national totals for different source types. The gridded inventory was developed using a wide range of databases at the state, county, local, and point source level to allocate the spatial and temporal distribution of emissions for individual source types. -layers: - - id: epa-annual-emissions_1b2a_petroleum - stacCol: EPA-annual-emissions_1B2a_Petroleum - name: Petroleum - type: raster - description: >- - Non-combustion emissions from sector 1B2a for petroleum systems, including - production, transportation, and refining. - zoomExtent: - - 0 - - 20 - sourceParams: - colormap_name: rainbow - rescale: - - 0 - - 282207914557 - nodata: 0 - legend: - type: gradient - min: 0 - max: 282207914557 - stops: - - '#60007d' - - '#30137d' - - '#1960ae' - - '#7ac300' - - '#f2ce00' - - '#ef6a01' - - '#cc0019' - info: - source: EPA - spatialExtent: United States - temporalResolution: Annual - unit: Mg 1/a km² - - id: epa-monthly-emissions_1b2a_petroleum - stacCol: EPA-monthly-emissions_1B2a_Petroleum - name: Petroleum (monthly) - type: raster - description: >- - Non-combustion emissions from sector 1B2a for petroleum systems, including - production, transportation, and refining (monthly). - zoomExtent: - - 0 - - 20 - sourceParams: - colormap_name: rainbow - rescale: - - 0 - - 376365761167 - nodata: 0 - legend: - type: gradient - min: 0 - max: 376365761167 - stops: - - '#60007d' - - '#30137d' - - '#1960ae' - - '#7ac300' - - '#f2ce00' - - '#ef6a01' - - '#cc0019' - info: - source: EPA - spatialExtent: United States - temporalResolution: Monthly - unit: Mg 1/a km² ---- - - - - ## Gridded 2012 Methane Emissions - - A team at Harvard University along with EPA and other coauthors developed a gridded inventory of U.S. anthropogenic methane emissions with 0.1° x 0.1° spatial resolution, monthly temporal resolution, and detailed scale-dependent error characterization. The inventory is designed to be consistent with the 2016 U.S. [EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks](https://www.epa.gov/ghgemissions/us-greenhouse-gas-inventory-report-1990-2014) estimates for the year 2012, which presents national totals for different source types. The gridded inventory was developed using a wide range of databases at the state, county, local, and point source level to allocate the spatial and temporal distribution of emissions for individual source types. - - This data can be used by researchers to better compare the national-level inventory with measurement results that may be at other scales. Users of this gridded inventory are asked to cite the original reference (Maasakkers et al., 2016) in their publications. Error estimates are given in that reference. - - Paper: [Maasakkers et. al. 2016, A Gridded National Inventory of U.S. Methane Emissions](https://www.epa.gov/ghgemissions/gridded-2012-methane-emissions#paper) - - - diff --git a/datasets/epa-waste--cover.jpg b/datasets/epa-waste--cover.jpg deleted file mode 100644 index eb1381600..000000000 Binary files a/datasets/epa-waste--cover.jpg and /dev/null differ diff --git a/datasets/epa-waste.data.mdx b/datasets/epa-waste.data.mdx deleted file mode 100644 index 5e6fd8cab..000000000 --- a/datasets/epa-waste.data.mdx +++ /dev/null @@ -1,196 +0,0 @@ ---- -id: epa-waste -name: EPA - Waste -description: Waste emissions include landfills, wastewater treatment, and composting -media: - src: ::file ./epa-waste--cover.jpg - alt: Mountain of rubbish and garbage on the beach by the sea - author: - name: Antoine GIRET - url: https://unsplash.com/photos/7_TSzqJms4w -taxonomy: - - name: Topics - values: - - EIS - - name: Source - values: - - EPA GHG -infoDescription: | - ::markdown - A team at Harvard University along with EPA and other coauthors developed a gridded inventory of U.S. anthropogenic methane emissions with 0.1° x 0.1° spatial resolution, monthly temporal resolution, and detailed scale-dependent error characterization. The inventory is designed to be consistent with the 2016 U.S. [EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks](https://www.epa.gov/ghgemissions/us-greenhouse-gas-inventory-report-1990-2014) estimates for the year 2012, which presents national totals for different source types. The gridded inventory was developed using a wide range of databases at the state, county, local, and point source level to allocate the spatial and temporal distribution of emissions for individual source types. -layers: - - id: epa-annual-emissions_6b_wastewater_treatment_domestic - stacCol: EPA-annual-emissions_6B_Wastewater_Treatment_Domestic - name: Domestic Wastewater Treatment - type: raster - description: Emissions from sector 6B from wastewater treatment of domestic sewage. - zoomExtent: - - 0 - - 20 - sourceParams: - colormap_name: rainbow - rescale: - - 0 - - 91901814374 - nodata: 0 - legend: - type: gradient - min: 0 - max: 9190181437440 - stops: - - '#60007d' - - '#30137d' - - '#1960ae' - - '#7ac300' - - '#f2ce00' - - '#ef6a01' - - '#cc0019' - info: - source: EPA - spatialExtent: United States - temporalResolution: Annual - unit: Mg 1/a km² - - id: epa-annual-emissions_6b_wastewater_treatment_industrial - stacCol: EPA-annual-emissions_6B_Wastewater_Treatment_Industrial - name: Industrial Wastewater Treatment - type: raster - description: >- - Emissions from sector 6B from wastewater treatment of industrial and - commercial sources. - zoomExtent: - - 0 - - 20 - sourceParams: - colormap_name: rainbow - rescale: - - 0 - - 1283751631912 - nodata: 0 - legend: - type: gradient - min: 0 - max: 128375163191296 - stops: - - '#60007d' - - '#30137d' - - '#1960ae' - - '#7ac300' - - '#f2ce00' - - '#ef6a01' - - '#cc0019' - info: - source: EPA - spatialExtent: United States - temporalResolution: Annual - unit: Mg 1/a km² - - id: epa-annual-emissions_6a_landfills_industrial - stacCol: EPA-annual-emissions_6A_Landfills_Industrial - name: Industrial Landfills - type: raster - description: >- - Emissions from sector 6A from non-municipal solid waste landfills used to - to dispose of industrial solid waste. - zoomExtent: - - 0 - - 20 - sourceParams: - colormap_name: rainbow - rescale: - - 0 - - 249776633282 - nodata: 0 - legend: - type: gradient - min: 0 - max: 24977663328256 - stops: - - '#60007d' - - '#30137d' - - '#1960ae' - - '#7ac300' - - '#f2ce00' - - '#ef6a01' - - '#cc0019' - info: - source: EPA - spatialExtent: United States - temporalResolution: Annual - unit: Mg 1/a km² - - id: epa-annual-emissions_6a_landfills_municipal - stacCol: EPA-annual-emissions_6A_Landfills_Municipal - name: Municipal Landfills - type: raster - description: >- - Emissions from sector 6A from municipal solid waste landfills receiving - household waste. - zoomExtent: - - 0 - - 20 - sourceParams: - colormap_name: rainbow - rescale: - - 0 - - 675446396026 - nodata: 0 - legend: - type: gradient - min: 0 - max: 675446396026 - stops: - - '#60007d' - - '#30137d' - - '#1960ae' - - '#7ac300' - - '#f2ce00' - - '#ef6a01' - - '#cc0019' - info: - source: EPA - spatialExtent: United States - temporalResolution: Annual - unit: Mg 1/a km² - - id: epa-annual-emissions_6d_composting - stacCol: EPA-annual-emissions_6D_Composting - name: Composting - type: raster - description: Emissions from sector 6D from composting. - zoomExtent: - - 0 - - 20 - sourceParams: - colormap_name: rainbow - rescale: - - 0 - - 7718224527 - nodata: 0 - legend: - type: gradient - min: 0 - max: 771822452736 - stops: - - '#60007d' - - '#30137d' - - '#1960ae' - - '#7ac300' - - '#f2ce00' - - '#ef6a01' - - '#cc0019' - info: - source: EPA - spatialExtent: United States - temporalResolution: Annual - unit: Mg 1/a km² ---- - - - - ## Gridded 2012 Methane Emissions - - A team at Harvard University along with EPA and other coauthors developed a gridded inventory of U.S. anthropogenic methane emissions with 0.1° x 0.1° spatial resolution, monthly temporal resolution, and detailed scale-dependent error characterization. The inventory is designed to be consistent with the 2016 U.S. [EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks](https://www.epa.gov/ghgemissions/us-greenhouse-gas-inventory-report-1990-2014) estimates for the year 2012, which presents national totals for different source types. The gridded inventory was developed using a wide range of databases at the state, county, local, and point source level to allocate the spatial and temporal distribution of emissions for individual source types. - - This data can be used by researchers to better compare the national-level inventory with measurement results that may be at other scales. Users of this gridded inventory are asked to cite the original reference (Maasakkers et al., 2016) in their publications. Error estimates are given in that reference. - - Paper: [Maasakkers et. al. 2016, A Gridded National Inventory of U.S. Methane Emissions](https://www.epa.gov/ghgemissions/gridded-2012-methane-emissions#paper) - - - diff --git a/datasets/fire.data.mdx b/datasets/fire.data.mdx index 2f2a3e7d8..aa4ebdefb 100644 --- a/datasets/fire.data.mdx +++ b/datasets/fire.data.mdx @@ -15,7 +15,10 @@ media: taxonomy: - name: Topics values: - - EIS + - Wildfire + - name: Source + values: + - NASA EIS infoDescription: | ::markdown Fire perimeter data is generated by the FEDs algorithm. The FEDs algorithm tracks fire movement and severity by ingesting observations from the VIIRS thermal sensors on the Suomi NPP and NOAA-20 satellites. This algorithm uses raw VIIRS observations to generate a polygon of the fire, locations of the active fire line, and estimates of fire mean Fire Radiative Power (FRP). The VIIRS sensors overpass at ~1:30 AM and PM local time, and provide estimates of fire evolution ~ every 12 hours. The data produced by this algorithm describe where fires are in space and how fires evolve through time. This CONUS-wide implementation of the FEDs algorithm is based on [Chen et al 2020’s algorithm for California.](https://www.nature.com/articles/s41597-022-01343-0) diff --git a/datasets/frp-max-thomasfire.data.mdx b/datasets/frp-max-thomasfire.data.mdx index 0070893d6..b8373361c 100644 --- a/datasets/frp-max-thomasfire.data.mdx +++ b/datasets/frp-max-thomasfire.data.mdx @@ -11,7 +11,10 @@ media: taxonomy: - name: Topics values: - - EIS + - Wildfire + - name: Source + values: + - NASA EIS infoDescription: | ::markdown Maximum Fire Radiative Power recorded by the Suomi NPP VIIRS sensor per 12hr fire line segment for the Thomas Fire of 2017 diff --git a/datasets/global-reanalysis-da.data.mdx b/datasets/global-reanalysis-da.data.mdx index 6600f0974..e900d122c 100644 --- a/datasets/global-reanalysis-da.data.mdx +++ b/datasets/global-reanalysis-da.data.mdx @@ -10,9 +10,16 @@ media: url: pubDate: 2023-03-01 taxonomy: - - name: Topics - values: - - EIS +- name: Topics + values: + - Water Resources + - Water Model + - Precipitation + - Groundwater + - Hydrology +- name: Source + values: + - NASA EIS infoDescription: | ::markdown The reanalysis product is created using the [NASA Land Information System](https://lis.gsfc.nasa.gov/) modeling framework to merge land surface model simulations with observations from satellites through data assimilation. The team uses the Noah-MP land surface model and assimilates soil moisture from the European Space Agency’s Climate Change Initiative Program (ESA CCI), leaf area index from the Moderate Resolution Imaging Spectroradiometer (MODIS), and terrestrial water storage anomalies from the Gravity Recovery and Climate Experiment and the follow-on missions (GRACE/GRACE-FO). diff --git a/datasets/hls-events.ej.data.mdx b/datasets/hls-events.ej.data.mdx index fdd6da615..3ccb6ead3 100644 --- a/datasets/hls-events.ej.data.mdx +++ b/datasets/hls-events.ej.data.mdx @@ -19,6 +19,10 @@ taxonomy: - name: Topics values: - Environmental Justice + - Disasters + - name: Source + values: + - HLS infoDescription: | ::markdown Input data from Landsat 8/9 and Sentinel-2A/B is reprojected and Sentinel-2 data adjusted so that the output data products, HLSL30 (Landsat-derived) and HLSS30 (Sentinel-2-derived) can be used interchangeably. The harmonization of the Optical Land Imager (OLI) on Landsat 8/9 and Multispectral Imager (MSI) on Sentinel-2A/B increases the time series density of plot-scale observations such that data is available every 2-4 days over a given location. diff --git a/datasets/hls-ndvi-ian.data.mdx b/datasets/hls-ndvi-ian.data.mdx index be50b3cb7..03bc7efc6 100644 --- a/datasets/hls-ndvi-ian.data.mdx +++ b/datasets/hls-ndvi-ian.data.mdx @@ -11,8 +11,11 @@ media: taxonomy: - name: Topics values: - - Hurricane - - UAH + - Land Cover + - Disasters + - name: Source + values: + - HLS layers: - id: hls-ndvi stacCol: hls-ndvi @@ -105,4 +108,4 @@ This work has been supported by the USGS-NASA Landsat Science Team (LST) Program [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0) - \ No newline at end of file + diff --git a/datasets/is2sitmogr4.data.mdx b/datasets/is2sitmogr4.data.mdx index 6150da084..4664c188f 100644 --- a/datasets/is2sitmogr4.data.mdx +++ b/datasets/is2sitmogr4.data.mdx @@ -9,9 +9,9 @@ media: name: Matt Broch url: https://unsplash.com/photos/bwD3GLrV4pY taxonomy: - - name: Topics + - name: Source values: - - EIS + - NASA EIS infoDescription: | ::markdown This data set reports monthly, gridded winter sea ice thickness across the Arctic Ocean. Sea ice thickness is estimated using ATLAS/ICESat-2 L3A Sea Ice Freeboard (ATL10), Version 5 data and NASA Eulerian Snow On Sea Ice Model (NESOSIM) snow loading. diff --git a/datasets/lahaina-fire.data.mdx b/datasets/lahaina-fire.data.mdx index b12527eb9..da1945f3b 100644 --- a/datasets/lahaina-fire.data.mdx +++ b/datasets/lahaina-fire.data.mdx @@ -11,10 +11,14 @@ media: taxonomy: - name: Topics values: - - EIS + - Disasters + - Burn Severity + - Wildfire - name: Source values: - - UAH + - HLS + - Landsat + infoDescription: | ::markdown On August 8th, 2023, a devastating wildfire rapidly spread through the city of Lahaina, Hawai’i, which is located on the island of Maui and home to over 13,000 residents. This destructive wildfire was initially ignited by a downed powerline on Lahainaluna Road and was later fueled by intense wind gusts that persisted throughout the day. The National Weather Service recorded wind gusts as high as 67 mph in the area, contributing to the rapid spread of the wildfire across much of Lahaina during the afternoon hours of August 8th. diff --git a/datasets/lis-etsuppression.data.mdx b/datasets/lis-etsuppression.data.mdx index 9b252466c..e169e202b 100644 --- a/datasets/lis-etsuppression.data.mdx +++ b/datasets/lis-etsuppression.data.mdx @@ -11,7 +11,12 @@ media: taxonomy: - name: Topics values: - - EIS + - Evapotranspiration + - Land Surface Model + - Wildfire + - name: Source + values: + - NASA EIS infoDescription: | ::markdown Change in ET for 2020 fires using model outputs from Land Information System (LIS) framework that synthesizes multiple remote sensing observations within the Noah-MP land surface model. Change is calculated as the difference of ET in the immediate post-fire water year from that in the immediate pre-fire water year. The difference is normalized by pre-fire ET and negative values denote vegetation disturbance induced by fire or by a climatological anomaly resulting in the decline in ET. diff --git a/datasets/lis-tvegsuppression.data.mdx b/datasets/lis-tvegsuppression.data.mdx index 61d747ade..6e3e99eec 100644 --- a/datasets/lis-tvegsuppression.data.mdx +++ b/datasets/lis-tvegsuppression.data.mdx @@ -11,7 +11,13 @@ media: taxonomy: - name: Topics values: - - EIS + - Evapotranspiration + - Land Surface Model + - Wildfire + - Hydrology + - name: Source + values: + - NASA EIS infoDescription: | ::markdown Change in vegetation transpiration for 2020 fires using model outputs from Land Information System (LIS) framework that synthesizes multiple remote sensing observations within the Noah-MP land surface model. Change is calculated as the difference of transpiration in the immediate post-fire water year from that in the immediate pre-fire water year. The difference is normalized by pre-fire transpiration and negative values denote vegetation disturbance induced by fire or by a climatological anomaly resulting in the decline in transpiration. diff --git a/datasets/lis.da.trend.data.mdx b/datasets/lis.da.trend.data.mdx index ac951bb4c..d593af8c6 100644 --- a/datasets/lis.da.trend.data.mdx +++ b/datasets/lis.da.trend.data.mdx @@ -11,7 +11,15 @@ media: taxonomy: - name: Topics values: - - EIS + - Water Resources + - Water Model + - Precipitation + - Groundwater + - Hydrology + - Water Storage Trends + - name: Source + values: + - NASA EIS infoDescription: | ::markdown Realistic estimates of water and energy cycle variables are necessary for accurate understanding of earth system processes. We develop a 10 km global reanalysis product of water, energy, and carbon fluxes by assimilating satellite observed surface soil moisture, leaf area index, and terrestrial water storage anomalies into a land surface model within NASA Land Information System Framework. We applied a seasonal and trend decomposition algorithm to get the trend estimates for terrestrial water storage and gross primary production. The method can better help to deal with [nonstationarities](https://github.com/Earth-Information-System/sea-level-and-coastal-risk/blob/main/AMS_2023_Wanshu_Nie_for_VEDA_Discovery.pdf) and seasonal shifts and provide a more robust estimate of trends. diff --git a/datasets/mo_npp_vgpm.data.mdx b/datasets/mo_npp_vgpm.data.mdx index 74f2cc720..166d22886 100644 --- a/datasets/mo_npp_vgpm.data.mdx +++ b/datasets/mo_npp_vgpm.data.mdx @@ -19,6 +19,10 @@ taxonomy: - name: Topics values: - Water Quality + - name: Source + values: + - Oregon State University + infoDescription: | ::markdown Find information at the [Ocean Productivity website](https://sites.science.oregonstate.edu/ocean.productivity/index.php) diff --git a/datasets/modis-aerosol-dataset.data.mdx b/datasets/modis-aerosol-dataset.data.mdx index 366aaa002..b48d6f739 100644 --- a/datasets/modis-aerosol-dataset.data.mdx +++ b/datasets/modis-aerosol-dataset.data.mdx @@ -1,6 +1,6 @@ --- id: houston-aod -name: "MODIS MCD19A2 Product" +name: "MODIS Aerosol Optical Depth" description: "Using MODIS MCD19A2 to Analyze impacts of Aerosols in Urban Areas" media: src: ::file ./smog-city.png @@ -12,6 +12,9 @@ taxonomy: - name: Topics values: - Air Quality + - name: Source + values: + - MODIS infoDescription: | ::markdown The MCD19A2 product represents a dataset that offers insights into aerosol optical thickness over land surfaces, grounded in the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. Originating from both the Terra and Aqua MODIS satellites, this dataset is remarkable for its fusion of information from multiple satellite platforms. Generated daily, the data has a high spatial resolution of 1 km per pixel, allowing detailed observiations. diff --git a/datasets/mtbs-burn-severity.data.mdx b/datasets/mtbs-burn-severity.data.mdx index 69dd05639..29c179eb5 100644 --- a/datasets/mtbs-burn-severity.data.mdx +++ b/datasets/mtbs-burn-severity.data.mdx @@ -11,7 +11,11 @@ media: taxonomy: - name: Topics values: - - EIS + - Wildfire + - Burn Severity + - name: Source + values: + - NASA EIS infoDescription: | ::markdown MTBS is an interagency program whose goal is to consistently map the burn severity and extent of large fires across all lands of the United States from 1984 to present. This includes all fires 1000 acres or greater in the western United States and 500 acres or greater in the eastern Unites States. The extent of coverage includes the continental U.S., Alaska, Hawaii and Puerto Rico. diff --git a/datasets/nighttime-lights.data.mdx b/datasets/nighttime-lights.data.mdx index 24b9aaebf..b4514d20e 100644 --- a/datasets/nighttime-lights.data.mdx +++ b/datasets/nighttime-lights.data.mdx @@ -11,7 +11,11 @@ media: taxonomy: - name: Topics values: - - Covid 19 + - COVID 19 + - name: Source + values: + - Black Marble + infoDescription: | ::markdown Nightlights data are collected by the [Visible Infrared Radiometer Suite (VIIRS) Day/Night Band (DNB)](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/viirs/) on the Suomi-National Polar-Orbiting Partnership (Suomi-NPP) platform, a joint National Oceanic and Atmospheric Administration (NOAA) and NASA satellite. The images are produced by [NASA’s Black Marble](https://blackmarble.gsfc.nasa.gov/) products suite. All data are calibrated daily, corrected, and validated with ground measurements for science-ready analysis. diff --git a/datasets/nighttime-lights.ej.data.mdx b/datasets/nighttime-lights.ej.data.mdx index df0e19464..850fce354 100644 --- a/datasets/nighttime-lights.ej.data.mdx +++ b/datasets/nighttime-lights.ej.data.mdx @@ -12,6 +12,10 @@ taxonomy: - name: Topics values: - Environmental Justice + - name: Source + values: + - Black Marble + infoDescription: | ::markdown Nightlights data are collected by the [Visible Infrared Radiometer Suite (VIIRS) Day/Night Band (DNB)](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/viirs/) on the Suomi-National Polar-Orbiting Partnership (Suomi-NPP) platform, a joint National Oceanic and Atmospheric Administration (NOAA) and NASA satellite. The images are produced by [NASA’s Black Marble](https://blackmarble.gsfc.nasa.gov/) products suite. All data are calibrated daily, corrected, and validated with ground measurements for science-ready analysis. diff --git a/datasets/nlcd-urbanization.data.mdx b/datasets/nlcd-urbanization.data.mdx index 7709431e8..1eb842435 100644 --- a/datasets/nlcd-urbanization.data.mdx +++ b/datasets/nlcd-urbanization.data.mdx @@ -11,7 +11,7 @@ media: taxonomy: - name: Topics values: - - EIS + - Land Cover infoDescription: | ::markdown The National Land Cover Database (NLCD) stands as a paramount dataset offering an in-depth overview of the land cover characteristics in the United States. Spearheaded by the Earth Resources Observation and Science (EROS) Center, this database is renewed every two to three years to provide updated and accurate data for the nation. diff --git a/datasets/no2.data.mdx b/datasets/no2.data.mdx index a77167464..a4506e8be 100644 --- a/datasets/no2.data.mdx +++ b/datasets/no2.data.mdx @@ -20,7 +20,7 @@ taxonomy: - name: Topics values: - Air Quality - - Covid 19 + - COVID 19 infoDescription: | ::markdown OMI, which launched in 2004, preceded TROPOMI, which launched in 2017. While TROPOMI provides higher resolution information, the longer OMI data record provides context for the TROPOMI observations. diff --git a/datasets/ps_blue_tarp_detections.ej.data.mdx b/datasets/ps_blue_tarp_detections.ej.data.mdx index 5e78ae97a..c06e8cad1 100644 --- a/datasets/ps_blue_tarp_detections.ej.data.mdx +++ b/datasets/ps_blue_tarp_detections.ej.data.mdx @@ -12,6 +12,10 @@ taxonomy: - name: Topics values: - Environmental Justice + - Disasters + - name: Source + values: + - Planet infoDescription: | ::markdown Planetscope provides 3-band RGB imagery at 3-meter ground resolution which diff --git a/datasets/snow-projections-diff.data.mdx b/datasets/snow-projections-diff.data.mdx index c95d895e7..046e38d8e 100644 --- a/datasets/snow-projections-diff.data.mdx +++ b/datasets/snow-projections-diff.data.mdx @@ -11,7 +11,14 @@ media: taxonomy: - name: Topics values: - - EIS + - Snow + - Snow Water Equivalent + - Runoff + - Water Resources + - name: Source + values: + - NASA EIS + - CMIP6 infoDescription: | ::markdown Snow water equivalent (SWE) is defined as the amount of water in the snow. Here, we present the projected percent-change to projected snow in future periods, relative to the historical period (1995 - 2014). diff --git a/datasets/snow-projections-median.data.mdx b/datasets/snow-projections-median.data.mdx index 43c923e91..9f6bec08d 100644 --- a/datasets/snow-projections-median.data.mdx +++ b/datasets/snow-projections-median.data.mdx @@ -11,7 +11,14 @@ media: taxonomy: - name: Topics values: - - EIS + - Snow + - Snow Water Equivalent + - Runoff + - Water Resources + - name: Source + values: + - NASA EIS + - CMIP6 infoDescription: | ::markdown Snow water equivalent (SWE) is defined as the amount of water in the snow. It is expressed as a height (in millimeters), representative of the height of water that would exist if snow was only in a liquid state. diff --git a/datasets/so2.data.mdx b/datasets/so2.data.mdx index f1b82c179..96c034577 100644 --- a/datasets/so2.data.mdx +++ b/datasets/so2.data.mdx @@ -12,6 +12,11 @@ taxonomy: - name: Topics values: - Air Quality + - name: Source + values: + - OMI + - Aura + infoDescription: | ::markdown The OMI Sulfur Dioxide (SO2) Total Column layer indicates the column density of sulfur dioxide and is measured in Dobson Units (DU). Sulfur Dioxide and Aerosol Index products are used to monitor volcanic clouds and detect pre-eruptive volcanic degassing globally. This information is used by the Volcanic Ash Advisory Centers in advisories to airlines for operational decision diff --git a/datasets/sport-lis.data.mdx b/datasets/sport-lis.data.mdx index 1e123c041..3c6a4208f 100644 --- a/datasets/sport-lis.data.mdx +++ b/datasets/sport-lis.data.mdx @@ -9,9 +9,9 @@ media: name: Clay Banks url: https://unsplash.com/photos/EdscD_R28bM taxonomy: - - name: Topics + - name: Source values: - - EIS + - NASA EIS infoDescription: | ::markdown The NASA Land Information System (LIS) is a high-performance land surface modeling and data assimilation system used to characterize land surface states and fluxes by integrating satellite-derived datasets, ground-based observations, and model re-analyses. The NASA SPoRT Center at MSFC developed a real-time configuration of the LIS (“SPoRT-LIS”), which is designed for use in experimental operations by domestic and international users. SPoRT-LIS is an observations-driven, historical and real-time modeling setup that runs the Noah land surface model over a full CONUS domain. It provides soil moisture estimates at approximately 3-km horizontal grid spacing over a 2-meter-deep soil column and has been validated for regional applications. diff --git a/datasets/twsanomaly.data.mdx b/datasets/twsanomaly.data.mdx index 51bfc86f9..1f7b0904d 100644 --- a/datasets/twsanomaly.data.mdx +++ b/datasets/twsanomaly.data.mdx @@ -9,9 +9,9 @@ media: name: NASA LIS url: taxonomy: - - name: Topics + - name: Source values: - - EIS + - NASA EIS infoDescription: | ::markdown Terrestrial water storage (TWS) is defined as the summation of all water on the land surface and in the subsurface. It includes surface soil moisture, root zone soil moisture, groundwater, snow, ice, water stored in the vegetation, river and lake water. diff --git a/datasets/twsnonstationarity.data.mdx b/datasets/twsnonstationarity.data.mdx index d9286260e..4beaf0abd 100644 --- a/datasets/twsnonstationarity.data.mdx +++ b/datasets/twsnonstationarity.data.mdx @@ -11,7 +11,15 @@ media: taxonomy: - name: Topics values: - - EIS + - Water Resources + - Water Model + - Precipitation + - Groundwater + - Hydrology + - name: Source + values: + - NASA EIS + infoDescription: | ::markdown Terrestrial water storage (TWS) is defined as the summation of all water on the land surface and in the subsurface. It includes surface soil moisture, root zone soil moisture, groundwater, snow, ice, water stored in the vegetation, river and lake water. diff --git a/datasets/twstrend.data.mdx b/datasets/twstrend.data.mdx index 1f91d2eb8..ab0e6e160 100644 --- a/datasets/twstrend.data.mdx +++ b/datasets/twstrend.data.mdx @@ -1,6 +1,6 @@ --- id: tws-trend -name: 'Terrestrial Water Storage Trend' +name: 'Terrestrial Water Storage Anomaly Trend' description: "Trend in TWS anomalies modeled using data assimilation within Land Information System framework" media: src: ::file ./twsanomaly-globe.png @@ -9,16 +9,16 @@ media: name: NASA LIS url: taxonomy: - - name: Topics + - name: Source values: - - EIS + - NASA EIS infoDescription: | ::markdown Terrestrial water storage (TWS) is defined as the summation of all water on the land surface and in the subsurface. It includes surface soil moisture, root zone soil moisture, groundwater, snow, ice, water stored in the vegetation, river and lake water. layers: - id: lis-tws-trend stacCol: lis-tws-trend - name: 'TWS Trend' + name: 'TWS Anomaly Trend' type: raster description: 'Trends in TWS anomalies from LIS outputs' zoomExtent: diff --git a/datasets/urban-heating.data.mdx b/datasets/urban-heating.data.mdx index d773d73c4..13796d724 100644 --- a/datasets/urban-heating.data.mdx +++ b/datasets/urban-heating.data.mdx @@ -1,16 +1,25 @@ --- id: urban-heating name: Urban Heating -description: Urban Heating +description: Collection of urban heat datasets featured in the "Implications for Heat Stress" Data Story media: src: ::file ./urban-heat.jpg alt: Sunset over Tokyo author: name: Arto Marttinen url: https://unsplash.com/photos/6xh7H5tWj9c +taxonomy: + - name: Topics + values: + - Environmental Justice + - Land Cover + - Temperature + - name: Source + values: + - MODIS infoDescription: | ::markdown - Terra MODIS has been instrumental in capturing LST data. This platform, orbiting Earth, scans our planet in multiple spectral bands, allowing for a detailed analysis of LST values. The data periods 2000-20009 and 2010-2019 form this satellite have been particularly enlightening, revealing distinct shifts in Houston’s urban heat profile. + Terra MODIS has been instrumental in capturing LST data. This platform, orbiting Earth, scans our planet in multiple spectral bands, allowing for a detailed analysis of LST values. The data periods 2000-2009 and 2010-2019 form this satellite have been particularly enlightening, revealing distinct shifts in Houston’s urban heat profile. layers: - sourceParams: resampling: bilinear @@ -239,9 +248,64 @@ layers: --- - ## Introduction - - Urban heat islands (UHIs) are no longer mere academic concepts; they’re palpable urban challenges. In cities such as Houston in this case, understanding the dynamics of land surface temperature (LST) is not just about decoding satellite data, but comprehending its implications for urban planning, health, and socioeconomic dynamics. Leveraging data from NASA’s Terra MODIS (moderate Resolution Imaging Spectroradiometer) platform, we delve into Houston’s changing LST landscape over two decades, offering a technical perspective on this urban phenomenon. LST is the temperature of the earth’s surface derived from NASA’s Terra MODIS, encompassing both natural terrains and man-made infrastructures. Unlike ambient air temperature, which gauges the immediate atmospheric conditions we feel, LST provides a granular temperature profile of surfaces from park greens to asphalt roads. + ## Dataset Details + ##### Land Surface Temperature + - **Temporal Extent:** 2000-2019 + - **Temporal Resolution:** Decadal + - **Spatial Extent:** Houston, Texas + - **Spatial Resolution:** 1 km + - **Data Units:** Kelvin (K) + - **Data Type:** Research + - **Data Latency:** N/A + +
+ + + Comparison of decadally-averaged daytime land surface temperature (LST) between 2000-2009 and 2010-2019 showing urban heating in the Houston Metropolitan Area. + +
+ + + +
+ + + Comparison of decadally-averaged Normalized Difference Vegetation Index (NDVI) between 2000-2009 and 2010-2019 showing green space reduction in the Houston Metropolitan Area. + +
+ + ##### Normalized Difference Vegetation Index + - **Temporal Extent:** 2000-2019 + - **Temporal Resolution:** Decadal + - **Spatial Extent:** Houston, Texas + - **Spatial Resolution:** 250 m + - **Data Units:** Unitless + - **Data Type:** Research + - **Data Latency:** N/A + + + + + + ## Overview + + Urban heat islands (UHIs) are no longer merely academic concepts; they’re palpable urban challenges. In rapdily urbanizing cities such as Houston, understanding the dynamics of land surface temperature (LST) is not just about decoding satellite data, but comprehending its implications for urban planning, health, and socioeconomic dynamics. Leveraging data from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS)) platform, we delve into Houston’s changing landscape over two decades by examining multi-decadal changes both LST and the Normalized Difference Vegetation Index (NDVI), offering a technical perspective on this urban phenomenon. LST is the temperature of the earth’s surface derived from the Terra satellite that houses the MODIS instrumentation, encompassing both natural terrains and man-made infrastructures. Unlike ambient air temperature, which gauges the immediate atmospheric conditions we feel, LST provides a granular temperature profile of surfaces from park greens to asphalt roads. Exmaining this in conjunction with NDVI gives an idea of the changing access to green space in sprawling urban spaces such as Houston. @@ -249,24 +313,54 @@ layers: ## Data Acquisition - Terra MODIS has been instrumental in capturing LST data. This platform, orbiting Earth, scans our planet in multiple spectral bands, allowing for a detailed analysis of LST values. The data periods 2000-20009 and 2010-2019 form this satellite have been particularly enlightening, revealing distinct shifts in Houston’s urban heat profile. + Terra has been instrumental in capturing this data. This platform, orbiting Earth, scans our planet in multiple spectral bands, allowing for a detailed analysis of LST and NDVI. The decadal periods of 2000-20009 and 2010-2019 were examined specifically to study the growth of Houston's UHI. Comparative analysis of LST data from the two decades indicate a tangible uptick in surface temperatures, especially in Houston’s southwestern regions. Urban expansion is likely culprit, with infrastructural growth leading to increased heat absorption and radiation. This phenomenon, known as the urban heat island effect, can intensify local temperatures, leading to a cascade of socio-environmental effects. + + Users can access MODIS data for anywhere across the globe [here](https://modis.gsfc.nasa.gov/data/), or click 'Explore Data' at the top of this page for a quick examination of the specific data used in this study. - ## Conclusion + ## Importance of Heat Stress Datasets - Houston’s LST data, meticulously captured by Terra MODIS, serves as a crucial pointer for urban planners, environmentalists, and policymakers. By understanding the nexus of urban heat, infrastructure, and socio-economics, we can shape urban features that are not only sustainable but also equitable. As Houston continues its urban journey, armed with this data, it has the potential to redefine urban resilience in the face of escalating heat challenges. + MODIS LST and NDVI data serves as a crucial pointer for urban planners, environmentalists, and policymakers. By understanding the nexus of urban heat, infrastructure, and socioeconomics, we can shape urban features that are not only sustainable but also equitable. As Houston continues to rapidly urbanize, it has the potential to redefine urban resilience in the face of escalating heat challenges. + + + + + + ## Disclaimer + All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly. - -## Conclusion + + ## Key Publications + + Didan, K., Munoz, A.B., Solano, R., and Huete, A. (2015). MODIS vegetatin index user's guide (MOD13 series). University of Arizona Veg. Index Phenol. Lab + + Wan, Z. (2014). New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product. *Remote Sensing of the Environment, 140*, 36-45. https://doi.org/10.1016/j.rse.2013.08.027 + + **The citation below is a peer-reviewed study that stemmed from this research, written by the authors of the associated data story:** + + Blackford, A., Cowan, T., Nair, U., Phillips, C., Kaulfus, A., and Freitag, B. (2024). Synergy of urban heat, pollution, and social vulnerability in one of America's most rapidly growing cities: Houston, we have a problem. *GeoHealth, 8*, e2024GH001079. https://doi.org/10.1029/2024GH001079 + + + + + + ## Data Stories Using This Dataset + **[Implications for Heat Stress](https://www.earthdata.nasa.gov/dashboard/stories/urban-heating)** + + -Houston’s LST data, meticulously captured by Terra MODIS, serves as a crucial pointer for urban planners, environmentalists, and policymakers. By understanding the nexus of urban heat, infrastructure, and socio-economics, we can shape urban features that are not only sustainable but also equitable. As Houston continues its urban journey, armed with this data, it has the potential to redefine urban resilience in the face of escalating heat challenges. - + + + ## License + [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0) + + diff --git a/stories/camp-fire-burn-scar.stories.mdx b/stories/camp-fire-burn-scar.stories.mdx index 90045e2a9..c73e8d82e 100644 --- a/stories/camp-fire-burn-scar.stories.mdx +++ b/stories/camp-fire-burn-scar.stories.mdx @@ -13,6 +13,10 @@ taxonomy: - name: Topics values: - Wildfire + - Natural Disasters + - name: Source + values: + - Community Contributed --- @@ -21,6 +25,8 @@ taxonomy: Authors: Andrew Blackford1, Trent Cowan1, Udaysankar Nair1\ 1 The University of Alabama in Huntsville + 🚧 This Data Story presents work that has grown into a Master's thesis, and a peer-reviewed article is currently being prepared. 🚧 + @@ -28,7 +34,7 @@ taxonomy: ## Introduction: The 2018 Camp Fire - 🚧 This Discovery presents work in progress and not peer-reviewed results! 🚧 + Wildfires burn thousands of acres of land every year, resulting in drastic changes in land use and land cover (LULC). The burn scars left behind by these wildfires have the potential to alter local weather, climate, and hydrology. A typical example of the drastic change in LULC is the burn scar that resulted from the November 2018 Camp Fire that devastated Paradise, California. The Camp Fire occurred from November 8 to 25, 2018, burning over 153,000 acres and causing $16.65 billion (2018 USD) in damages. The fire was initiated by a faulty transmission line maintained by Pacific Gas and Electric (PG&E), and resulted in 85 fatalities and 17 injuries. The Camp Fire was the most expensive natural disaster in the world in 2018 and remains the seventh deadliest wildfire in U.S. history as of October 2023. Among several communities impacted by the fire, the city of Paradise was the most severely impacted, with 95% of the city burned and 18,804 of the city’s buildings destroyed. @@ -332,6 +338,13 @@ taxonomy: + + +## How to Cite This Work +Blackford, Andrew C., "The impact of the 2018 camp fire on land-atmosphere interactions" (2024). Theses. 658. https://louis.uah.edu/uah-theses/658 + + + ## Additional Resources diff --git a/stories/coastal-flooding-and-slr.stories.mdx b/stories/coastal-flooding-and-slr.stories.mdx index 9d745982d..b0f01f483 100644 --- a/stories/coastal-flooding-and-slr.stories.mdx +++ b/stories/coastal-flooding-and-slr.stories.mdx @@ -13,7 +13,15 @@ pubDate: 2022-11-15 taxonomy: - name: Topics values: - - EIS + - Coastal Risk + - Sea Level Rise + - Water Cycle + - Groundwater + - Glaciers + - Land Use + - name: Source + values: + - NASA EIS --- diff --git a/stories/crystal-lake-mercury.stories.mdx b/stories/crystal-lake-mercury.stories.mdx index 8e86bc9ce..0a0b07dde 100644 --- a/stories/crystal-lake-mercury.stories.mdx +++ b/stories/crystal-lake-mercury.stories.mdx @@ -14,6 +14,9 @@ taxonomy: values: - Wildfire - Water Quality + - name: Source + values: + - Community Contributed --- @@ -21,7 +24,15 @@ taxonomy: Authors: Trent Cowan[1], Andrew Blackford[1], Udaysankar Nair[1]\ [1] University of Alabama in Huntsville(UAH) + 🚧 This Data Story presents work in progress and not peer-reviewed results! 🚧 + + + + + + ## Introduction + Over the last five decades, the land area burned by forest fires in the western United States has increased tenfold. This trend is expected to continue and even accelerate under the influence of climate change. Given this scenario, the societal impacts of wildfires are of interest. While the impact of wildfires on the loss of human life, property, and air pollution is obvious, less understood are secondary environmental impacts that affect regions remote from the location of the fires. Recent studies show that smoke from wildfires is expected to become the major air pollutant in the United States. However, the potential impact of wildfires on water quality, especially mercury contamination, is not well studied.
diff --git a/stories/darnah-flood.stories.mdx b/stories/darnah-flood.stories.mdx index 0a4f0714f..1d5272e50 100644 --- a/stories/darnah-flood.stories.mdx +++ b/stories/darnah-flood.stories.mdx @@ -12,7 +12,11 @@ pubDate: 2024-07-22 taxonomy: - name: Topics values: - - Flood + - Floods + - Natural Disasters + - name: Source + values: + - Community Contributed --- @@ -20,6 +24,8 @@ taxonomy: Authors: Andrew Blackford1, Trent Cowan1, Udaysankar Nair1\ 1 The University of Alabama in Huntsville + + 🚧 This Data Story presents work in progress and not peer-reviewed results! 🚧 @@ -40,9 +46,7 @@ taxonomy:
- ## Overview - 🚧 This Data Story presents work in progress and not peer-reviewed results! 🚧 - + ## Overview On Monday, September 11, 2023, the city of Darnah, Libya experienced the [deadliest flood disaster of the 21st century](https://www.google.com/url?q=https://www.aa.com.tr/en/environment/floods-in-libya-s-derna-worst-disaster-in-21st-century/2992617&sa=D&source=docs&ust=1709231595507737&usg=AOvVaw3MuRygRSSxtExzI_shVddG), and Africa’s deadliest flood ever recorded. A storm in the Mediterranean Sea dubbed ‘Medicane Daniel’ moved over northeastern Libya on the evening of the 10th, dumping prolific rain over the desert the morning of the 11th. A record 16” of rainfall was measured in 24 hours at the city of Al-Bayda, Libya (just west of Derna) from ‘Medicane’ Daniel. Two dams upstream of Darnah collapsed during the heavy rains leading to approximately [25% of the city being destroyed](https://www.google.com/url?q=https://www.reuters.com/world/africa/more-than-1000-bodies-recovered-libyan-city-after-floods-minister-2023-09-12/&sa=D&source=docs&ust=1709231595509452&usg=AOvVaw083l0kMybsbbwT18u4SVTm). The first dam broke around 3:00 AM local time on September 11th, and the second followed suit shortly thereafter, which exacerbated the death toll greatly. The International Committee of the Red Cross (ICRC) reported that proceeding the dam bursts, a wave as high as 23 feet (7 meters) rushed towards the city. With a population of 120,000, the major city of Darnah saw massive destruction, with entire districts of the city being washed away.Nearly 1,000 buildings are estimated to have been completely destroyed as well as 5 major bridges that connect the west and east sides of the city. The United Nations Office for the Coordination of Humanitarian Affairs initially reported a death toll currently sits at 11,300 with another 10,100 reported missing. This estimate was later revised to [3,958 fatalities](https://www.aljazeera.com/news/2023/9/18/libya-floods-conflicting-death-tolls-greek-aid-workers-die-in-crash) on September 18. diff --git a/stories/fire-life-cycle.stories.mdx b/stories/fire-life-cycle.stories.mdx index 1b42281f2..1ee21fa05 100644 --- a/stories/fire-life-cycle.stories.mdx +++ b/stories/fire-life-cycle.stories.mdx @@ -14,7 +14,15 @@ pubDate: 2023-01-25 taxonomy: - name: Topics values: - - EIS + - Wildfire + - Fire Severity + - Fire Risk + - Fire Impacts + - Hydrology + - Debris Flow + - name: Source + values: + - NASA EIS --- diff --git a/stories/hog-bar-chart.png b/stories/hog-bar-chart.png new file mode 100644 index 000000000..fd9db37c8 Binary files /dev/null and b/stories/hog-bar-chart.png differ diff --git a/stories/hog-farm.jpg b/stories/hog-farm.jpg new file mode 100644 index 000000000..e11e2cf46 Binary files /dev/null and b/stories/hog-farm.jpg differ diff --git a/stories/houston-aod.stories.mdx b/stories/houston-aod.stories.mdx index dd09ab70b..497cbc4bc 100644 --- a/stories/houston-aod.stories.mdx +++ b/stories/houston-aod.stories.mdx @@ -14,8 +14,20 @@ taxonomy: values: - Environmental Justice - Air Quality + - Urban + - name: Source + values: + - Community Contributed --- + + + + ###### This story is part of a study conducted on both heat and pollution stress in the Houston Metropolitan Area. The data story that higlights heat stress can be found here + + + + @@ -23,8 +35,6 @@ taxonomy: [1] University of Alabama in Huntsville(UAH) - **Disclaimer**: Correlation of satellite-derived AOD and surface air quality varies by location meaning the proposed correlations in this data story require further investigation to be deemed science-quality - ### Introduction The Houston metropolitan area is the fourth most populous city in the United States and top-10 in terms of spatial extent. It’s well known that the impact cities can have on the surrounding environment correlates with the architectural characteristics and spatial extent of the city. Houston’s notably large spatial extent and high population pave the way for high levels of human-produced emissions within its transportation infrastructure. These emissions and its proximity to the Gulf of Mexico, make Houston especially susceptible to environmental changes related to urban development. @@ -46,6 +56,8 @@ taxonomy: dateTime='2000-01-01' compareDateTime='2010-01-01' compareLabel='2000-2009 mean / 2010-2019 mean' + center={[-95.35, 29.8]} + zoom={8.75} /> Figure 2: Aerosol Optical Depth Compared Decadally from 2000-2009 & 2010-2019. The map shown shows the change in AOD over the last 20 years over the Houston metropolitan area. @@ -73,6 +85,8 @@ taxonomy: dateTime='2000-01-01' compareDateTime='2001-01-01' compareLabel='AOD Difference vs Houston Urbanization' + center={[-95.35, 29.8]} + zoom={8.75} /> Figure 3: Aerosol Optical Depth Compared Decadally from 2000-2009 & 2010-2019 vs the Urbanization of the Houston metro from 2000-2019. The left portion of the map shows the subtracted difference in AOD over the last 20 years over the Houston metropolitan area. @@ -90,7 +104,7 @@ taxonomy: layerId='houston-aod-diff' datetime='2000-01-01' > - ## AOD Timeseries for Galveston Bay + ## AOD Time series for Galveston Bay The monthly and spatially averaged time series of AOD for Galveston Bay (just east of downtown Houston) show a statistically significant (p < 0.005) decreasing trend in AOD from 2000 to 2019. Note the measure of statistical significance of p < 0.005 indicates that there is very high confidence the decreasing trend in AOD is real and not due to chance. This decreasing trend in AOD could potentially be attributed to air quality regulations implemented as a part of the Clean Air Act between 2010 and 2019. - ## AOD Timeseries for Western Houston + ## AOD Time series for Western Houston Unlike the Galveston Bay area, the monthly and spatially averaged time series of AOD for the Western Suburbs of Houston, TX shows a trend where there is a small, statistically insignificant increase over the time period of 2000 to 2019. Note that the spatial pattern of difference in decadal mean AOD between the second and first decade of this century shows a localized increase that correlates to urbanization. The small increase in AOD concentrations could be potentially explained by vehicular and other urban sources of pollution counteracting the effects of the Clean Air Act. + + + ## How to Cite This Work + Blackford, A., Cowan, T., Nair, U., Phillips, C., Kaulfus, A., and Freitag, B. (2024). Synergy of urban heat, pollution, and social vulnerability in one of America's most rapidly growing cities: Houston, we have a problem. *GeoHealth, 8*, e2024GH001079. https://doi.org/10.1029/2024GH001079 + + + ### Data Access diff --git a/stories/hurricane-ian.stories.mdx b/stories/hurricane-ian.stories.mdx index 3b0962ca1..119199012 100644 --- a/stories/hurricane-ian.stories.mdx +++ b/stories/hurricane-ian.stories.mdx @@ -12,18 +12,26 @@ pubDate: 2024-07-22 taxonomy: - name: Topics values: - - UAH - - Hurricanes + - Natural Disasters + - Tropical + - name: Source + values: + - Community Contributed --- - - ## Introduction Trent Cowan1, Andrew Blackford1, Udaysankar Nair1, and Ashley Riddle1 1: University of Alabama in Huntsville - Disclaimer: This research is ongoing and is not yet peer-reviewed. + 🚧 This Data Story presents work in progress and not peer-reviewed results! 🚧 + + + + + + + ## Introduction Atlantic hurricane season stretches from June 1 to November 30, and generally reaches a peak in August and September. On September 28, 2022, Hurricane Ian made landfall in southwest Florida as a Category 4 hurricane (based on the Saffir-Simpson Wind Scale), with estimated wind speeds of 150 MPH. The toll was high, leaving 158 people dead and producing $110 billion in damage in Florida alone. The hurricane also left a devastating impact on the landscape in portions of southwest Florida near Fort Myers. A storm surge reached unprecedented levels of 12 to 18 feet around the regions of Cape Coral and Fort Myers. A storm surge is the sudden rise in ocean water above normal tide levels during a storm such as a tropical cyclone or other strong low pressure system as wind from the storm pushes water ashore. An analysis in Figure 1 from the U.S. National Hurricane Center’s review of Hurricane Ian shows the storm surge’s heavy impact on the southwest Florida coast. The storm surge is attributed as the primary cause of fatalities along the coast of southwestern Florida. diff --git a/stories/hurricane-maria-and-ida.stories.mdx b/stories/hurricane-maria-and-ida.stories.mdx index 03bdf4028..ca5c242c1 100644 --- a/stories/hurricane-maria-and-ida.stories.mdx +++ b/stories/hurricane-maria-and-ida.stories.mdx @@ -6,10 +6,16 @@ featured: true media: src: ::file ./ej--discovery-cover.jpeg alt: Nighttime view of New Orleans +pubDate: 2022-09-08 taxonomy: - name: Topics values: - Environmental Justice + - Natural Disasters + - Tropical + - name: Source + values: + - Community Contributed --- + + + + + Authors: Trent Cowan[1], Andrew Blackford[1], Udaysankar Nair[1] + + [1] University of Alabama in Huntsville (UAH) + + 🚧 This Data Story presents work in progress and not peer-reviewed results! 🚧 + + ### Introduction + Pork producers in eastern North Carolina call the stench of pig waste [“the smell of money”](https://www.vox.com/future-perfect/23003487/north-carolina-hog-pork-bacon-farms-environmental-racism-black-residents-pollution-meat-industry) but to the local residents, it’s the scent of environmental inequalities. The proliferation of hog farms in this area has raised significant environmental justice concerns, particularly within low socioeconomic status communities. For example, black North Carolinians are 150% more likely than white residents to live within three miles of a hog farm. Thus, minority populations face disproportionate exposure to the harmful effects of Concentrated Animal Farming Operations (CAFOs). + + These operations impose an environmental burden encompassing a wide range of issues. Hog farm production waste is typically collected in lagoons or vats, where it is often transferred to nearby fields to be sprayed as fertilizer or converted to biogas. The waste releases methane and ammonia into the atmosphere. When sprayed as a fertilizer, the wind catches the waste and spreads it over the neighboring area, blanketing it in a layer of feces, urine, pus, and blood. The pervasive odor from these lagoons makes basic outdoor activities and chores challenging for communities in these regions. Not only do emissions from waste cause foul odors, they can also be responsible for short-term and long-term health risks. For example, a 2021 [report](https://www.washingtonpost.com/climate-environment/2021/05/10/farm-pollution-deaths/) from Duplin County, NC found that about 89 premature deaths per year can be attributed to hog farm emissions. Disasters such as Hurricane Florence in 2018 have exacerbated the environmental impacts of CAFOs by causing the waste lagoons to overflow and leak into the local water supply. + + In this story, we utilize the gridded methane emissions data compiled by the Environmental Protection Agency (EPA) and the Social Vulnerability Index (SVI) developed by the Center for Disease Control to examine how the socially vulnerable population residing in the Wayne, Duplin, Sampson, and Bladen counties of North Carolina are affected by CAFOs. The United States Department of Agriculture (USDA) hog farm inventory data show that a substantial portion of CAFOs in the state of North Carolina are located in these four counties (Figure 1). Time series of hog farm inventory show (Figure 2) a sharp increase during the 1990s driven by technological advancements including waste management and economic incentives that boosted efficiency and profitability. However, the industry’s growth stabilized in early 2000 because of the adoption of a moratorium on new hog farms in 1997 and stricter regulations by the state of North Carolina prompted by mounting environmental and community concerns. + + + + + +
+ + + + Figure 1: Time lapse of the spatial distribution of hog farm inventory in North Carolina with the counties of interest outlined in red. + +
+ + + +
+ + + Figure 2: USDA hog farm inventory for the counties of Bladen, Duplin, Sampson, and Wayne in North Carolina time series from 1974 through 2022. + +
+ + + +
+ + + Figure 3: Gridded methane emissions from manure management in 2020 compared to the Social Vulnerability Index in 2018. + +
+ + ### Methane vs SVI + + A spatial map produced using EPA’s the 2020 U.S. Gridded Anthropogenic Methane Emissions Inventory Agriculture - Manure Management dataset shows the presence of a local maximum over the four counties of Wayne, Duplin, Sampson, and Bladen (Figure 3). The spatial map of CDC SVI shows that the local maximum of methane emissions also coincides with high social vulnerability. Note that social vulnerability describes socioeconomic and demographic factors that affect the resilience or incapability of communities to adapt to external stresses (Flanagan et al., 2011; Mah et al., 2023 ). One of the ways to quantify social vulnerability is through the use of a social vulnerability index (SVI), which is an aggregation of socioeconomic and demographic factors. The Center for Disease Control’s (CDC) SVI dataset is one such quantification available for the US and is derived from socioeconomic status, household composition, disability, minority status, housing, and transportation attributes of the U.S. Census Bureau’s census tract data (Flanagan et al., 2011). + + + + + + + With the SVI dataset produced at the census-tract level the methane emissions from manure management are averaged and binned by SVI. SVI bins correspond to low, medium, high, and very high vulnerabilities defined as; 0 - 0.25, 0.25 - 0.50, 0.50 - 0.75, and 0.75-1 respectively (Figure 4). This analysis shows that populations that reside in census tracts with high and very high social vulnerability census tracts within the four counties of Wayne, Duplin, Sampson, and Bladen are subject to environments with manure-based methane emissions 4 to 5 times higher compared to those in low and medium social vulnerability census tracts. Note that the higher methane emissions from manure management also indicate other CAFO emissions (e.g. ammonia) are higher in these counties. + +
+ Normalized Methane Vs SVI + + Figure 4: Normalized methane emissions vs binned social vuilnerability index from low to very high. + +
+ + + + + The resulting air and water quality degradation can have both short and long-term health effects. Short-term health effects include nausea and vomiting, asthma, and headaches while serious long-term health risks include cancer, increased infant mortality, anemia, kidney disease, tuberculosis and septicemia. + + To tackle this and other environmental justices across the country, the White House introduced the [White House Climate and Economic Justice Screening Tool](https://screeningtool.geoplatform.gov/en/#3/33.47/-97.5) in 2022, which includes factors such as air pollution, health outcomes, and economic status to identify communities vulnerable to environmental and economic injustice. While the tool currently excludes race as a factor, data from the White House's Council on Environmental Quality has the potential to enhance decision making related to environmental justice by directing federal aid for climate, clean energy, and environmental improvements to underserved communities. + + + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Region Census Tracts % White % Non-White % Disadvantaged Tracts Total Population % Disadvantaged Population
Bladen County60.550.461.0033,4071.00
Duplin County110.510.490.7358,9670.76
Sampson County110.510.490.7363,3850.75
Wayne County260.500.500.62123,6030.59
North Carolina21950.620.380.4010,264,8760.37
+
+ + Table 1: Disadvantaged population data pulled from the [White House Climate and Economic Justice Screening Tool](https://screeningtool.geoplatform.gov/en/#3/33.47/-97.5) for all of North Carolina and each of the four counties of interest (yellow = high vulnerability, red = very high vulnerability). + +
+ + + + + For example, data from the screening tool provides additional valuable information on the disproportionate impacts of hog farms for Bladen, Duplin, Sampson, and Wayne counties when compared to the rest of North Carolina (Table 1). More than 70% of the population in these counties are classified as disadvantaged, impacting nearly 200,000 people in counties where hog farming is prevalent. The population in these counties is nearly evenly distributed between white and non-white races. Compared to the entire state of North Carolina, 38% of the population is non-white and only 37% is classified as disadvantaged. Hog farms within Bladen, Duplin, Sampson, and Wayne counties significantly increase the percentage of the population classified as disadvantaged when compared to the overall population in North Carolina. + + For environmental justice related decision making, development of comprehensive tools that address social vulnerability (intersecting factors including age, race, access to transportation, domicile and economics) and health are critical to ensure equitable access of resources and opportunities. + + + + + + ### Additional Resources + + * [Economic Impact of Hog Farms](https://ncpork.org/economic-impact/) + * [Race and Environmental Justice in North Carolina](https://psmag.com/social-justice/environmental-racism-in-north-carolina) + * [CDC/ATSDR SVI Data and Documentation Download | Place and Health | ATSDR](https://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.html) + * [General Assembly of North Carolina](https://www.ncleg.net/enactedlegislation/sessionlaws/html/1997-1998/sl1997-458.html) + + + diff --git a/stories/nldas3.stories.mdx b/stories/nldas3.stories.mdx index ccc765ed0..25c1042a9 100644 --- a/stories/nldas3.stories.mdx +++ b/stories/nldas3.stories.mdx @@ -9,7 +9,7 @@ media: author: name: LDAS-NASA url: https://eoimages.gsfc.nasa.gov/images/imagerecords/151000/151897/mississippi_oli_2023259_lrg.jpg -pubDate: 2024-07-25 +pubDate: 2024-10-22 taxonomy: - name: Topics values: @@ -23,8 +23,8 @@ taxonomy: --- - **Authors**: David Mocko, Fadji Maina, Kim Locke, Sujay Kumar, Kristen Whitney, Rishi Anand, Siddharth Chaudhary, Chris Hain - + **Authors**: NLDAS-3 developers and science teams at the NASA Goddard Hydrological Sciences Laboratory and Marshall Space Flight Center's Earth Science Branch (including David Mocko, Fadji Maina, Kim Locke, Sujay Kumar, Kristen Whitney, Rishi Anand, Siddharth Chaudhary, Chris Hain, Shahryar Ahmad, Rajat Bindlish, Nishan Biswas, Jim Geiger, Augusto Getirana, Thomas Holmes, Timothy Lahmers, Pang-Wei Liu, Wanshu Nie, Melissa, Wrzesien, John Bolten, Andrew White, Vikalp Mishra, Robert Junod, Ryan Wade, Mitchell Dodson, Jonathan Case, Justin Pflug, and Olya Skulovich) + ## The North American Land Data Assimilation System (NLDAS) NLDAS is a widely used land modeling environment that generates estimates of land surface fluxes and states such as soil moisture, snow, and streamflow. These estimates are critical for drought and flood monitoring, water availability and water resource management, climate assessments, and other uses. For instance, the University of Nebraska-Lincoln’s [National Drought Mitigation Center](https://drought.unl.edu/) (NDMC) relies on NLDAS data for their drought assessments published weekly in the [U.S. Drought Monitor](https://www.drought.gov/data-maps-tools/us-drought-monitor). Applications like [OpenET](https://etdata.org/) and [QuickDRI](https://quickdri.unl.edu/) also rely on the quality of NLDAS meteorological forcing and model outputs. The phase 2 of NLDAS (NLDAS-2) is currently operational at NOAA, with long-term archives of data back to Jan 1979 available from NASA.
@@ -250,7 +250,7 @@ taxonomy: GRACE/GRACE-FO - Radar Altimetry Water Level + Surface Water Elevation SWOT @@ -321,7 +321,7 @@ taxonomy: - [NASA NLDAS-3 Website](https://ldas.gsfc.nasa.gov/nldas/v3) - [NASA NLDAS-3 GitHub Discussions Page](https://github.com/Earth-Information-System/NLDAS-3/discussions) ## Acknowledgements - Thanks to the NLDAS-3 developers and science teams at the NASA Goddard Hydrological Sciences Laboratory and Marshall Space Flight Center SPoRT and all the NLDAS stakeholders who have participated in workshops, provided valuable feedback, and developed downstream applications for assessing water availability. + Thanks to all the NLDAS stakeholders who have participated in workshops, provided valuable feedback, and developed downstream applications for assessing water availability. diff --git a/stories/projected-changes-WUS-snow.stories.mdx b/stories/projected-changes-WUS-snow.stories.mdx index 88e78a1c6..9f2266ffb 100644 --- a/stories/projected-changes-WUS-snow.stories.mdx +++ b/stories/projected-changes-WUS-snow.stories.mdx @@ -14,7 +14,14 @@ pubDate: 2023-02-01 taxonomy: - name: Topics values: - - EIS + - Water Resources + - Snow + - Precipitation + - Climate Projections + - Habitat + - name: Source + values: + - NASA EIS --- diff --git a/stories/svi-binned-methane-hogs.csv b/stories/svi-binned-methane-hogs.csv new file mode 100644 index 000000000..2477dce45 --- /dev/null +++ b/stories/svi-binned-methane-hogs.csv @@ -0,0 +1,5 @@ +SVI,source,Value +0-0.25,Normalized Methane,33926345679 +0.25-0.50,Normalized Methane,161420223634 +0.50-0.75,Normalized Methane,978061261288 +0.75-1.00,Normalized Methane,775675008341 \ No newline at end of file diff --git a/stories/tws-trends.stories.mdx b/stories/tws-trends.stories.mdx index ce0a89379..afcb31845 100644 --- a/stories/tws-trends.stories.mdx +++ b/stories/tws-trends.stories.mdx @@ -13,7 +13,15 @@ pubDate: 2023-03-01 taxonomy: - name: Topics values: - - EIS + - Water Resources + - Hydrology + - Drought + - Precipitation + - Soil Moisture + - Evapotranspiration + - name: Source + values: + - NASA EIS --- diff --git a/stories/urban-heating.stories.mdx b/stories/urban-heating.stories.mdx index daf40daff..74582b79b 100644 --- a/stories/urban-heating.stories.mdx +++ b/stories/urban-heating.stories.mdx @@ -13,8 +13,21 @@ taxonomy: - name: Topics values: - Environmental Justice + - Urban + - Heat + - name: Source + values: + - Community Contributed --- + + + + ###### This story is part of a study conducted on both heat and pollution stress in the Houston Metropolitan Area. The data story that higlights pollution stress can be found here + + + +
Authors: Andrew Blackford[1], Trent Cowan[1], Udaysankar Nair[1]\ - [1] University of Alabama in Huntsville(UAH) + [1] The University of Alabama in Huntsville ## Implications for Heat Stress - 🚧 This Discovery presents work in progress and not peer-reviewed results! 🚧 - Heat stress includes a series of conditions where the body undergoes stress due to overheating– typically from exposure to hot weather. It is a natural hazard that causes a large number of fatalities globally. Those at a greatest risk of heat stress include children, the elderly, and people with medical conditions, however, even young and healthy individuals can experience heat stress when exposed to intense heat or when conducting strenuous activities in hotter conditions. In the coming decades, climate change may cause more frequent heat waves, which could exacerbate heat-induced health issues. Future urban growth could also strengthen the urban heat island effect–in which infrastructure absorbs and re-emit the sun's heat at rates higher than natural landscapes–causing more severe heat events in urban populations. In urban areas a variety of individual, social, and geographic factors determine an individual's heat risk (Reckien et al. 2018). Social structures and segregation in urban areas can increase the risk of unequal heat exposure. This discovery will be using NASA Earth observations to explore heat stress inequalities throughout areas of urban growth in Houston, Texas over the past 20 years. @@ -45,7 +56,7 @@ taxonomy: - ## Environmental inequality in Houston, TX + ## Environmental Inequality in Houston, TX Houston is the site of some of the earliest studies of environmental inequality. Robert Bullard, known as the “father of environmental justice,” proved that waste facilities were disproportionately sited in poor and minority neighborhoods as far back as the late 1970s (Bullard 1983). Today, environmental justice research is still important in Houston, which is among the most racially segregated cities in the United States (Logan et al. 2013). In 2012, Houston was ranked the most economically segregated metropolitan area in the entire country (Fry and Taylor 2012). According to this study, 24% of high-income households live in high-income neighborhoods and 37% of low-income households reside in low-income neighborhoods (Fry and Taylor 2012). @@ -58,13 +69,15 @@ taxonomy: layerId='houston-landcover' dateTime='2001-01-01' compareDateTime='2019-01-01' + center={[-95.35, 29.8]} + zoom={8.75} /> - Figure 1. Land cover classification between 2001 and 2019 showing urban growth in the Houston metro area. For a full list of land cover classes, see the class legend and descriptions [here](https://www.mrlc.gov/data/legends/national-land-cover-database-class-legend-and-description). + Figure 1. NLCD land cover classifications for 2001 and 2019 show urban growth in the Houston metro area. For a full list of land cover classes, see the class legend and descriptions [here](https://www.mrlc.gov/data/legends/national-land-cover-database-class-legend-and-description).
- ### Using satellite data to study urban heat + ### Using Satellite Data to Study Urban Heat Using satellite data and socioeconomic information, a complete picture of Houston’s urban growth can be examined to further understand the impacts of heat stress. To study how the city is changing over time, land cover data from the National Land Cover Database (NLCD) was used. This data classifies land cover types in areas as small as 30m x 30m every 3 years. Greenspace–a dedicated space for vegetation in an urban space, such as a park–also plays a big role in urban heat. To measure vegetation, such as trees and grass, the Normalized Difference Vegetation Index (NDVI) metric is used. On this scale, bare ground = 0 and dense vegetation = 1. This information comes from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on the Terra platform. @@ -78,7 +91,7 @@ taxonomy: - ### Utilizing Land cover maps + ### Utilizing Land Cover Maps Using land cover maps, the increase of urban and built-up land-cover was measured. It was found that the area of urban and built-up land cover increased by more than 20% in the last two decades. By comparing land cover maps from 2001 and 2019, a substantial increase in urban areas along the southwestern part of the city can be seen (Figure 1 - slide to compare years). A similar comparison can be done with average temperature. Figure 2 shows Houston’s average temperature from 2000-2009 compared to 2010-2019. The maps show higher average temperatures over similar regions in the southwestern part of the city. These higher temperatures carry into the night, too, although they are less intense. @@ -91,6 +104,8 @@ taxonomy: dateTime='2000-01-01' compareDateTime='2010-01-01' compareLabel='2000-2009 avg / 2010-2019 avg' + center={[-95.35, 29.8]} + zoom={8.75} /> Comparison of decadally-averaged daytime land surface temperature (LST) between 2000-2009 and 2010-2019 showing urban heating in the Houston metropolitan area. @@ -106,6 +121,8 @@ taxonomy: dateTime='2000-01-01' compareDateTime='2010-01-01' compareLabel='2000-2009 avg / 2010-2019 avg' + center={[-95.35, 29.8]} + zoom={8.75} /> Comparison of decadaly-averaged normalized differential vegetation index (NDVI) between 2000-2009 and 2010-2019 showing a reduction in healthy vegetation in the Houston metropolitan area. @@ -124,12 +141,12 @@ taxonomy: yAxisLabel='Kelvin' /> - Land surface Temperature (LST) trend in various socio-economic (Social Vulnerability Index) ranges in Harris county, Houston (2020) + Land surface Temperature (LST) trend in various socio-economic (Social Vulnerability Index 150% Poverty Threshhold) ranges in Harris County, Texas (2020)
- ### What it means for economically disadvantaged population - When these changes in heat and land cover were combined with Houston's socioeconomic data, it was found that economically disadvantaged populations are subject to greater heat stress. At the same time, these populations may also face decreased access to greenspaces. In 2020, areas in Harris county with the lowest poverty levels < 5% were an average of 2°F (1°C) cooler than areas with higher poverty levels [F(4, 1584) = 47.3, p < .001]. + ### What It Means For the Socioeconomically Disadvantaged Population + When these changes in heat and land cover were combined with Houston's socioeconomic data, it was found that socioeconomically disadvantaged populations are subject to greater heat stress. At the same time, these populations may also face decreased access to greenspaces. In 2020, areas in Harris county with the lowest poverty levels < 5% were an average of 2°F (1°C) cooler than areas with higher poverty levels [F(4, 1584) = 47.3, p < .001]. @@ -146,16 +163,24 @@ taxonomy: yKey='NDVI' /> - Normalized Difference Vegetation Index (NDVI) trend in various socio-economic (Social Vulnerability Index) ranges in Harris county, Houston (2020) + Normalized Difference Vegetation Index (NDVI) trend in various socio-economic (Social Vulnerability Index 150% Poverty Threshhold) ranges in Harris County, Texas (2020) + + + ## How to Cite This Work + Blackford, A., Cowan, T., Nair, U., Phillips, C., Kaulfus, A., and Freitag, B. (2024). Synergy of urban heat, pollution, and social vulnerability in one of America's most rapidly growing cities: Houston, we have a problem. *GeoHealth, 8*, e2024GH001079. https://doi.org/10.1029/2024GH001079 + + + - For more information about how to access and use NASA data to study Extreme Heat please visit our Data Pathfinders: https://www.earthdata.nasa.gov/learn/pathfinders/disasters/extreme-heat-data-pathfinder + ### Data Access + * [NASA Extreme Heat Data - Pathfinders](https://www.earthdata.nasa.gov/learn/pathfinders/disasters/extreme-heat-data-pathfinder) - For more information about additional datasets that can be used to study Extreme Heat and Environmental Justice please visit the new EJ Data Catalog: https://www.earthdata.nasa.gov/learn/environmental-justice-data-catalog + * [Earthdata EJ Data Catalog](https://www.earthdata.nasa.gov/learn/environmental-justice-data-catalog) diff --git a/stories/wq-models.stories.mdx b/stories/wq-models.stories.mdx index 9170862d4..2a8063e69 100644 --- a/stories/wq-models.stories.mdx +++ b/stories/wq-models.stories.mdx @@ -11,9 +11,12 @@ media: url: https://www.nasa.gov pubDate: 2023-03-01 taxonomy: + - name: Source + values: + - NASA EIS - name: Topics values: - - EIS + - Water Quality --- diff --git a/veda.config.js b/veda.config.js index a2a606660..00ea03f04 100644 --- a/veda.config.js +++ b/veda.config.js @@ -2,25 +2,29 @@ module.exports = { /** * Glob path for the datasets. */ - datasets: './datasets/*.data.mdx', + datasets: "./datasets/*.data.mdx", /** * Glob path for the stories. */ - stories: './stories/*.stories.mdx', + stories: "./stories/*.stories.mdx", // App component and content overrides. // See docs/CONFIGURATION.md for more information. pageOverrides: { // Content for the about page. // Type: Content override - aboutContent: './overrides/about.mdx' + aboutContent: "./overrides/about.mdx", }, strings: { stories: { - one: ' Data Story', - other: 'Data Stories' - } - } + one: " Data Story", + other: "Data Stories", + }, + }, + cookieConsentForm: { + title: "Cookie Consent", + copy: "We use cookies to enhance your browsing experience and to help us understand how our website is used. These cookies allow us to collect data on site usage and improve our services based on your interactions. To learn more about it, see our [Privacy Policy](https://www.nasa.gov/privacy/#cookies)", + }, };