diff --git a/.veda/ui b/.veda/ui index 0db1fead6..6bcad3a6c 160000 --- a/.veda/ui +++ b/.veda/ui @@ -1 +1 @@ -Subproject commit 0db1fead693feaca57f33c1a1200866dc9bcf8fd +Subproject commit 6bcad3a6c6116e18afe3badd046e112075d431dd diff --git a/datasets/CMIP-winter-median-pr.data.mdx b/datasets/CMIP-winter-median-pr.data.mdx new file mode 100644 index 000000000..2a0be4d7d --- /dev/null +++ b/datasets/CMIP-winter-median-pr.data.mdx @@ -0,0 +1,90 @@ +--- +id: CMIP-winter-median-pr +name: 'Projections of changes to winter precipitation ' +description: "CMIP6 projections of changes to winter cumulative precipitation" +media: + src: ::file ./CMIP-winter-median.jpeg + alt: Photo of Nisqually glacier + author: + name: Justin Pflug + url: +thematics: + - eis +layers: + - id: CMIP245-winter-median-pr + stacCol: CMIP245-winter-median-pr + name: 'Percent-change to winter cumulative precipitation, SSP2-4.5' + type: raster + description: 'Percent difference in projected winter (January, February, March) cumulative precipitation, relative to a historical timeframe between 1995 and 2014. Outputs represent the median of 23 member ensembles from CMIP6 (SSP 2-4.5) with downscaling performed by NASA Earth Exchange' + sourceParams: + resampling: bilinear + bidx: 1 + nodata: nan + colormap_name: rdbu + rescale: + - -60 + - 60 + compare: + datasetId: CMIP-winter-median-pr + layerId: CMIP245-winter-median-pr + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + return `${dateFns.format(datetime, 'DD LLL yyyy')}`; + } + legend: + type: gradient + label: Precipitation percent-difference + min: "-60%" + max: "+60%" + stops: + - "#CA171C" + - "#DE6158" + - "#F2B089" + - "#F5D5C3" + - "#F8F8F8" + - "#CDE2EF" + - "#A0CBE4" + - "#5EA4D1" + - "#207BBD" + - id: CMIP585-winter-median-pr + stacCol: CMIP585-winter-median-pr + name: 'Percent-change to winter cumulative precipitation, SSP5-8.5' + type: raster + description: 'Percent difference in projected winter (January, February, March) cumulative precipitation, relative to a historical timeframe between 1995 and 2014. Outputs represent the median of 23 member ensembles from CMIP6 (SSP 5-8.5) with downscaling performed by NASA Earth Exchange' + sourceParams: + resampling: bilinear + bidx: 1 + nodata: nan + colormap_name: rdbu + rescale: + - -60 + - 60 + compare: + datasetId: CMIP-winter-median-pr + layerId: CMIP585-winter-median-pr + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + return `${dateFns.format(datetime, 'DD LLL yyyy')}`; + } + legend: + type: gradient + label: Precipitation percent-difference + min: "-60%" + max: "+60%" + stops: + - "#CA171C" + - "#DE6158" + - "#F2B089" + - "#F5D5C3" + - "#F8F8F8" + - "#CDE2EF" + - "#A0CBE4" + - "#5EA4D1" + - "#207BBD" +--- + + +## Overview +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](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6). + + diff --git a/datasets/CMIP-winter-median-ta.data.mdx b/datasets/CMIP-winter-median-ta.data.mdx new file mode 100644 index 000000000..3773fc9f8 --- /dev/null +++ b/datasets/CMIP-winter-median-ta.data.mdx @@ -0,0 +1,88 @@ +--- +id: CMIP-winter-median-ta +name: 'Projections of changes to winter temperature' +description: "CMIP6 projections of changes to winter average air temperature" +media: + src: ::file ./CMIP-winter-median.jpeg + alt: Photo of Nisqually glacier + author: + name: Justin Pflug + url: +thematics: + - eis +layers: + - id: CMIP245-winter-median-ta + stacCol: CMIP245-winter-median-ta + name: 'SSP2-4.5, Change to winter average air temperature' + type: raster + description: 'Difference in projected winter (January, February, March) average air temperature, relative to a historical timeframe between 1995 and 2014. Outputs represent the median of 23 member ensembles from CMIP6 (SSP 2-4.5) with downscaling performed by NASA Earth Exchange' + sourceParams: + resampling: bilinear + bidx: 1 + colormap_name: rdbu_r + rescale: + - -5.5 + - 5.5 + compare: + datasetId: CMIP-winter-median-ta + layerId: CMIP245-winter-median-ta + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + return `${dateFns.format(datetime, 'DD LLL yyyy')}`; + } + legend: + type: gradient + label: Air temperature difference [C] + min: "-5.5" + max: "+5.5" + stops: + - "#207BBD" + - "#5EA4D1" + - "#A0CBE4" + - "#CDE2EF" + - "#F8F8F8" + - "#F5D5C3" + - "#F2B089" + - "#DE6158" + - "#CA171C" + - id: CMIP585-winter-median-ta + stacCol: CMIP585-winter-median-ta + name: 'SSP5-8.5, Change to winter average air temperature' + type: raster + description: 'Difference in projected winter (January, February, March) average air temperature, relative to a historical timeframe between 1995 and 2014. Outputs represent the median of 23 member ensembles from CMIP6 (SSP 5-8.5) with downscaling performed by NASA Earth Exchange' + sourceParams: + resampling: bilinear + bidx: 1 + colormap_name: rdbu_r + rescale: + - -5.5 + - 5.5 + compare: + datasetId: CMIP-winter-median-ta + layerId: CMIP585-winter-median-ta + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + return `${dateFns.format(datetime, 'DD LLL yyyy')}`; + } + legend: + type: gradient + label: Air temperature difference [C] + min: "-5.5" + max: "+5.5" + stops: + - "#207BBD" + - "#5EA4D1" + - "#A0CBE4" + - "#CDE2EF" + - "#F8F8F8" + - "#F5D5C3" + - "#F2B089" + - "#DE6158" + - "#CA171C" +--- + + +## Overview +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/CMIP-winter-median.jpeg b/datasets/CMIP-winter-median.jpeg new file mode 100644 index 000000000..225db9e0e Binary files /dev/null and b/datasets/CMIP-winter-median.jpeg differ diff --git a/datasets/co2.data.mdx b/datasets/co2.data.mdx index eaacc8445..4cd523023 100644 --- a/datasets/co2.data.mdx +++ b/datasets/co2.data.mdx @@ -26,6 +26,13 @@ layers: rescale: - 0.000408 - 0.000419 + compare: + datasetId: co2 + layerId: co2-mean + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`; + } legend: type: gradient min: "< 408 ppm" diff --git a/datasets/global-reanalysis-da.data.mdx b/datasets/global-reanalysis-da.data.mdx new file mode 100644 index 000000000..e25ebd8d9 --- /dev/null +++ b/datasets/global-reanalysis-da.data.mdx @@ -0,0 +1,382 @@ +--- +id: global-reanalysis-da +name: 'A Global Reanalysis for Water, Energy, and Carbon Cycle Variables' +description: "A high-resolution (10 km) global data product that integrates NASA’s state-of-the-art model with satellite observations" +media: + src: ::file ./global_tws_blackbg_v2.png + alt: One day of terrestrial water storage from LIS outputs. + author: + name: NASA LIS + url: +pubDate: 2023-03-01 +thematics: + - eis +layers: + - id: lis-global-da-evap + stacCol: lis-global-da-evap + name: 'Evapotranspiration' + type: raster + description: 'Gridded total evapotranspiration (in kg m-2 s-1) from 10km global LIS with assimilation' + zoomExtent: + - 0 + - 11 + sourceParams: + resampling: bilinear + bidx: 1 + colormap_name: viridis + rescale: + - 0 + - 0.0001 + compare: + datasetId: global-reanalysis-da + layerId: lis-global-da-evap + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + return `${dateFns.format(datetime, 'DD LLL yyyy')}`; + } + legend: + type: gradient + label: Evapotranspiration [kg m-2 s-1] + min: "0" + max: "0.0001 kg m-2 s-1" + stops: + - '#440154' + - '#3b528b' + - '#21918c' + - '#5ec962' + - '#fde725' + + - id: lis-global-da-gpp + stacCol: lis-global-da-gpp + name: 'Gross Primary Productivity' + type: raster + description: 'Gridded gross primary productivity (in g m-2 s-1) from 10km global LIS with assimilation' + zoomExtent: + - 0 + - 11 + sourceParams: + resampling: bilinear + bidx: 1 + colormap_name: viridis + rescale: + - 0 + - 0.0001 + compare: + datasetId: global-reanalysis-da + layerId: lis-global-da-gpp + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + return `${dateFns.format(datetime, 'DD LLL yyyy')}`; + } + legend: + type: gradient + label: Gross primary productivity [g m-2 s-1] + min: "0" + max: "0.0001 g m-2 s-1" + stops: + - '#440154' + - '#3b528b' + - '#21918c' + - '#5ec962' + - '#fde725' + + - id: lis-global-da-gws + stacCol: lis-global-da-gws + name: 'Groundwater Storage' + type: raster + description: 'Gridded groundwater storage (in mm) from 10km global LIS with assimilation' + zoomExtent: + - 0 + - 11 + sourceParams: + resampling: bilinear + bidx: 1 + colormap_name: viridis + rescale: + - 4500 + - 5000 + compare: + datasetId: global-reanalysis-da + layerId: lis-global-da-gws + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + return `${dateFns.format(datetime, 'DD LLL yyyy')}`; + } + legend: + type: gradient + label: Groundwater storage [mm] + min: "4500" + max: "5000 mm" + stops: + - '#440154' + - '#3b528b' + - '#21918c' + - '#5ec962' + - '#fde725' + + - id: lis-global-da-swe + stacCol: lis-global-da-swe + name: 'Snow Water Equivalent' + type: raster + description: 'Gridded snow water equivalent (in kg m-2) from 10km global LIS with assimilation' + zoomExtent: + - 0 + - 11 + sourceParams: + resampling: bilinear + bidx: 1 + colormap_name: blues + rescale: + - 0 + - 500 + compare: + datasetId: global-reanalysis-da + layerId: lis-global-da-swe + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + return `${dateFns.format(datetime, 'DD LLL yyyy')}`; + } + legend: + type: gradient + label: Snow Water Equivalent [mm] + min: "0" + max: "500 mm" + stops: + - "#F7FBFF" + - "#D0E1F2" + - "#94C4DF" + - "#4A98C9" + - "#2164AB" + - "#0E316B" + + - id: lis-global-da-streamflow + stacCol: lis-global-da-streamflow + name: 'Streamflow' + type: raster + description: 'Routed streamflow (in m3 s-1) from 10km global LIS+HyMAP with assimilation' + zoomExtent: + - 0 + - 11 + sourceParams: + resampling: bilinear + bidx: 1 + colormap_name: viridis + rescale: + - 0 + - 2500 + compare: + datasetId: global-reanalysis-da + layerId: lis-global-da-streamflow + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + return `${dateFns.format(datetime, 'DD LLL yyyy')}`; + } + legend: + type: gradient + label: Streamflow [m3 s-1] + min: "0" + max: "2500 m3 s-1" + stops: + - '#440154' + - '#3b528b' + - '#21918c' + - '#5ec962' + - '#fde725' + + - id: lis-global-da-qs + stacCol: lis-global-da-qs + name: 'Surface runoff' + type: raster + description: 'Gridded surface runoff (in kg m-2 s-1) from 10km global LIS with assimilation' + zoomExtent: + - 0 + - 11 + sourceParams: + resampling: bilinear + bidx: 1 + colormap_name: viridis + rescale: + - 0 + - 0.0001 + compare: + datasetId: global-reanalysis-da + layerId: lis-global-da-qs + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + return `${dateFns.format(datetime, 'DD LLL yyyy')}`; + } + legend: + type: gradient + label: Surface runoff [kg m-2 s-1] + min: "0" + max: "0.00001 kg m-2 s-1" + stops: + - '#440154' + - '#3b528b' + - '#21918c' + - '#5ec962' + - '#fde725' + + - id: lis-global-da-qsb + stacCol: lis-global-da-qsb + name: 'Subsurface runoff' + type: raster + description: 'Gridded subsurface runoff (in kg m-2 s-1) from 10km global LIS with assimilation' + zoomExtent: + - 0 + - 11 + sourceParams: + resampling: bilinear + bidx: 1 + colormap_name: viridis + rescale: + - 0 + - 0.0001 + compare: + datasetId: global-reanalysis-da + layerId: lis-global-da-qsb + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + return `${dateFns.format(datetime, 'DD LLL yyyy')}`; + } + legend: + type: gradient + label: Subsurface runoff [kg m-2 s-1] + min: "0" + max: "0.0001 kg m-2 s-1" + stops: + - '#440154' + - '#3b528b' + - '#21918c' + - '#5ec962' + - '#fde725' + + - id: lis-global-da-tws + stacCol: lis-global-da-tws + name: 'Terrestrial Water Storage' + type: raster + description: 'Gridded terrestrial water storage (in mm) from 10km global LIS with assimilation' + zoomExtent: + - 0 + - 11 + sourceParams: + resampling: bilinear + bidx: 1 + colormap_name: viridis + rescale: + - 5000 + - 5800 + compare: + datasetId: global-reanalysis-da + layerId: lis-global-da-tws + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + return `${dateFns.format(datetime, 'DD LLL yyyy')}`; + } + legend: + type: gradient + label: Terrestrial Water Storage [mm] + min: "5000" + max: "5800 mm" + stops: + - '#440154' + - '#3b528b' + - '#21918c' + - '#5ec962' + - '#fde725' + + - id: lis-global-da-totalprecip + stacCol: lis-global-da-totalprecip + name: 'Total Precipitation' + type: raster + description: 'Gridded total precipitation (in kg m-2 s-1) from 10km global LIS with assimilation' + zoomExtent: + - 0 + - 11 + sourceParams: + resampling: bilinear + bidx: 1 + colormap_name: blues + rescale: + - 0 + - 0.0001 + compare: + datasetId: global-reanalysis-da + layerId: lis-global-da-totalprecip + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + return `${dateFns.format(datetime, 'DD LLL yyyy')}`; + } + legend: + type: gradient + label: Total precipitation [kg m-2 s-1] + min: "0" + max: "0.00001 kg m-2 s-1" + stops: + - "#F7FBFF" + - "#D0E1F2" + - "#94C4DF" + - "#4A98C9" + - "#2164AB" + - "#0E316B" + +--- + + +## Introduction +🚧 This page presents work in progress and not a peer-reviewed data product! 🚧 + +Realistic estimates of water, energy, and carbon cycle variables are necessary for accurate understanding of earth system processes. Land surface models simulate processes at the Earth’s surface and can provide spatiotemporal estimates of a whole suite of variables like precipitation, soil moisture, and evapotranspiration. However, models often have biases that cause high uncertainties in important water budget variables. Therefore, tools like data assimilation, where observations are used to constrain model simulations, are often used. + +This dataset is a new global reanalysis that includes variables – such as terrestrial water storage, snow water equivalent, and gross primary productivity – to help quantify the water and energy budget. At the present time, the model reanalysis output is available at 10 km spatial resolution and a daily temporal resolution from January 1, 2003 through December 31, 2021. Details on the model setup are provided below, as well as links to how the reanalysis output can be used to address key science questions. + +Authors: Melissa Wrzesien, Wanshu Nie, Sujay Kumar, Kim Locke + + + + + + +## Modeling Setup +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). + +The output variables available on VEDA include evapotranspiration (ET), gross primary productivity (GPP), groundwater storage (GWS), snow water equivalent (SWE), streamflow, surface runoff, subsurface runoff, terrestrial water storage (TWS), and total precipitation. See the [VEDA Analysis tool](https://www.earthdata.nasa.gov/dashboard/eis/analysis) to make interactive plots of the variables over a user-specified domain and time period. + + + + + + +## Explore the Data +The global reanalysis is a large dataset with nearly two decades of daily output. Here we show a comparison of two dates for a single variable. We encourage users to [Explore the Data](https://www.earthdata.nasa.gov/dashboard/eis/datasets/global-reanalysis-da/explore) to look at different dates and to compare variables. + +To see an example of how trends calculated from the global reanalysis model output can be used to understand changes in TWS, GPP, and ET, [click here](https://www.earthdata.nasa.gov/dashboard/eis/discoveries/tws-trends). + +
+ + + Terrestrial water storage over North America compared on June 1, 2005 (left) and June 1, 2020 (right), captured by LIS assimilation. + +
+ + + + +### Explore the modeling framework: +* [Land Information System](https://lis.gsfc.nasa.gov/) +### Explore the remote sensing datasets: + * [GRACE-FO](https://gracefo.jpl.nasa.gov/data/grace-fo-data/) + * [ESA CCI](https://esa-soilmoisture-cci.org/) + * [MODIS](https://modis.gsfc.nasa.gov/) + + diff --git a/datasets/global_tws_blackbg_v2.png b/datasets/global_tws_blackbg_v2.png new file mode 100644 index 000000000..fa922d839 Binary files /dev/null and b/datasets/global_tws_blackbg_v2.png differ diff --git a/datasets/mo_npp_vgpm.data.mdx b/datasets/mo_npp_vgpm.data.mdx index e3ddd584a..32f6d9451 100644 --- a/datasets/mo_npp_vgpm.data.mdx +++ b/datasets/mo_npp_vgpm.data.mdx @@ -2,6 +2,13 @@ id: npp name: "Ocean Net Primary Production" description: "Ocean Net Primary Production (NPP)" +usage: + - url: 'https://github.com/NASA-IMPACT/veda-docs/blob/main/example-notebooks/ocean-npp-timeseries-analysis.ipynb' + label: View example notebook + title: 'Static view in VEDA documentation' + - url: "https://nasa-veda.2i2c.cloud/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FNASA-IMPACT%2Fveda-docs&branch=main&urlpath=lab%2Ftree%2Fveda-docs%2Fexample-notebooks%2Focean-npp-timeseries-analysis.ipynb" + label: Run example notebook + title: 'Interactive session in VEDA 2i2c JupyterHub (requires account)' media: src: ::file ./ocean-production--dataset-cover.jpg alt: Rocky ocean shore diff --git a/datasets/nceo_africa_2017.data.mdx b/datasets/nceo_africa_2017.data.mdx index 6812c7fba..1a2352111 100644 --- a/datasets/nceo_africa_2017.data.mdx +++ b/datasets/nceo_africa_2017.data.mdx @@ -1,7 +1,14 @@ --- id: nceo_africa_2017 name: "National Centre for Earth Observation (NCEO) Biomass" -description: The NCEO Africa Aboveground Woody Biomass (AGB) map for the year 2017 at 100 m spatial resolution +description: The NCEO Africa Aboveground Woody Biomass (AGB) map for the year 2017 at 100 m spatial resolution +usage: + - url: "https://github.com/NASA-IMPACT/veda-docs/blob/main/example-notebooks/nceo-biomass-statistics.ipynb" + label: View example notebook + title: 'Static view in VEDA documentation' + - url: "https://nasa-veda.2i2c.cloud/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FNASA-IMPACT%2Fveda-docs&branch=main&urlpath=lab%2Ftree%2Fveda-docs%2Fexample-notebooks%2Fnceo-biomass-statistics.ipynb" + label: Run example notebook + title: 'Interactive session in VEDA 2i2c JupyterHub (requires account)' media: src: ::file ./nceo-africa--dataset-cover.jpg alt: Green trees seen from above diff --git a/datasets/nighttime-lights.data.mdx b/datasets/nighttime-lights.data.mdx index f36660ad9..47a1578d7 100644 --- a/datasets/nighttime-lights.data.mdx +++ b/datasets/nighttime-lights.data.mdx @@ -25,6 +25,13 @@ layers: rescale: - 0 - 255 + compare: + datasetId: nighttime-lights + layerId: nightlights-hd-monthly + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`; + } legend: type: gradient min: Less diff --git a/datasets/snow-projections-diff.data.mdx b/datasets/snow-projections-diff.data.mdx index 5a133005a..8f513c332 100644 --- a/datasets/snow-projections-diff.data.mdx +++ b/datasets/snow-projections-diff.data.mdx @@ -22,8 +22,8 @@ layers: nodata: nan colormap_name: rdbu rescale: - - -1 - - 1 + - -100 + - 100 compare: datasetId: snow-projections-diff layerId: snow-projections-diff-scenario-245 @@ -33,9 +33,9 @@ layers: } legend: type: gradient - label: Snow Water Equivalent change [fractional] - min: "-1" - max: "1" + label: Snow Water Equivalent change + min: "-100%" + max: "+100%" stops: - "#670220" - "#D65F4D" @@ -54,8 +54,8 @@ layers: nodata: nan colormap_name: rdbu rescale: - - -1 - - 1 + - -100 + - 100 compare: datasetId: snow-projections-diff layerId: snow-projections-diff-scenario-585 @@ -65,9 +65,9 @@ layers: } legend: type: gradient - label: Snow Water Equivalent change [fractional] - min: "-1" - max: "1" + label: Snow Water Equivalent change + min: "-100%" + max: "+100%" stops: - "#670220" - "#D65F4D" @@ -79,7 +79,7 @@ layers: ## Overview -Snow water equivalent (SWE) is defined as the amount of water in the snow. Here, we present the projected fractional change to snow in future periods, relative to the historical period (1995 - 2014). Fractional changes of -1 represent total snow loss. +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/soil-moisture-histogram.png b/datasets/soil-moisture-histogram.png new file mode 100644 index 000000000..7d6126fde Binary files /dev/null and b/datasets/soil-moisture-histogram.png differ diff --git a/datasets/soil-moisture-main.jpg b/datasets/soil-moisture-main.jpg new file mode 100644 index 000000000..b373af6c3 Binary files /dev/null and b/datasets/soil-moisture-main.jpg differ diff --git a/datasets/soil-moisture-percentile.png b/datasets/soil-moisture-percentile.png new file mode 100644 index 000000000..a5c93702b Binary files /dev/null and b/datasets/soil-moisture-percentile.png differ diff --git a/datasets/sport-lis.data.mdx b/datasets/sport-lis.data.mdx new file mode 100644 index 000000000..409e72edf --- /dev/null +++ b/datasets/sport-lis.data.mdx @@ -0,0 +1,101 @@ +--- +featuredOn: [] +id: soil-moisture +name: SPoRT Land Information System +description: SPoRT’s real-time instance of the Land Information System provides low-latency soil moisture analyses that help diagnose rapid drought onset and elevated wildfire threat. +media: + src: ::file ./soil-moisture-main.jpg + alt: Dry Clay Wall with Cracks + author: + name: Clay Banks + url: https://unsplash.com/photos/EdscD_R28bM +thematics: + - eis +layers: + - sourceParams: + resampling: bilinear + bidx: 1 + colormap_name: rdylbu + rescale: + - 0 + - 100 + nodata: 9999.0 + name: 0-100 cm Soil Moisture Percentile of normal + legend: + type: gradient + min: "0" + max: "100" + stops: + - "#a50026" + - "#f46d43" + - "#fee090" + - "#e0f3f8" + - "#74add1" + - "#313695" + type: raster + id: sport-lis-vsm0_100cm-percentile + description: "The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed a Real-Time Land Information System (LIS). The Volumetric Soil Moisture product represents the actual moisture in a soil column from 0-100cm in depth." + stacCol: sport-lis-vsm0_100cm-percentile + zoomExtent: + - 2 + - 16 + compare: + datasetId: soil-moisture + layerId: sport-lis-vsm0_100cm-percentile + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + return `${dateFns.format(datetime, 'MMM yyyy')} VS ${dateFns.format(compareDatetime, 'MMM yyyy')}`; + } +--- + + + + 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. + + SPoRT-LIS consists of a 33-year soil moisture climatology spanning from 1981 to 2013, which is extended to the present time and forced by atmospheric analyses from the operational North American Land Data Assimilation System-Phase 2. The 33-year soil moisture climatology also provides the database for real-time soil moisture percentiles evaluated for all U.S. counties and at each modeled grid point. The present-day soil moisture analyses are compared to daily historical distributions to determine the soil wet/dry anomalies for the specific time of year. Soil moisture percentile maps are constructed for the model integrated layers, and these data are frequently referenced by scientists and operational agencies contributing to the weekly U.S. Drought Monitor product. + + The SPoRT-LIS bridges the 4-day latency gap in NLDAS-2 atmospheric forcing input by incorporating forcing from the National Centers for Environmental Prediction Global Data Assimilation System in combination with hourly Multi-Radar Multi-Sensor precipitation estimates. The real-time component of the SPoRT-LIS updates every 6 hours, and also includes a forecast component that produces soil moisture percentiles out to 2 weeks. Another unique feature of SPoRT-LIS is the incorporation of daily, real-time satellite retrievals of Green Vegetation Fraction since 2012, which results in more representative evapotranspiration and ultimately soil moisture estimates than using a fixed seasonal depiction of vegetation in the model. + + + + + + + ## Interpreting the Data + + A variety of output variables are used to depict soil moisture in the SPoRT-LIS analyses. Volumetric soil moisture shows the fraction of volume of water occupying the total soil volume and typically ranges from ~0.03 to 0.45, depending on the defined sand-silt-clay soil classification defined at a given grid point. The volumetric soil moisture is often spatially correlated to the pattern of soil classification; therefore, the relative soil moisture (or percent saturation) can be a preferred method to analyze the soil moisture based on the water-holding capacity of the soils. The relative soil moisture (RSM) is defined as the ratio of the modeled volumetric soil moisture (θ) between the wilting and saturation reference values for a given soil classification: + + *RSM=((θ - θwilt)/(θsat - θwilt )) x 100%. ... (1)* + + The RSM essentially normalizes the volumetric soil moisture based on the specified characteristics of each unique soil texture classification (sand-silt-clay composition). + +Meanwhile, the soil moisture percentiles depict where the current analysis value lies within the histogram distribution of historical soil moisture values. An example of the 0-2 meter soil moisture percentile over Napa County, California shows how the wet 2022-23 winter over central California has contributed to historically high soil moisture values, averaged over all SPoRT-LIS points lying within Napa County, yielding a soil moisture percentile value of 92.9 for 23 March 2023: + + SPoRT LIS Soil Moisture Histogram for 23rd march 2023 + + The national soil moisture percentile map on 23 March 2023 is shown below: + + SPoRT-LIS 0-200 cm Soil Moisture percentile valid 24 March 2023 + + + + + + + ## Additional Resources + + - [Real-time SPoRT-LIS viewer](https://weather.ndc.nasa.gov/sport/viewer/?dataset=lis_conus&product=rsoim0-100) + - [Access to real-time rolling archive of digital data and in various + formats](https://geo.nsstc.nasa.gov/SPoRT/modeling/lis/conus3km/) + - [Daily animations of experimental 2-week forecasts of SPoRT-LIS soil moisture + percentiles](https://geo.nsstc.nasa.gov/SPoRT/modeling/lis/conus3km/forecasts/) + - [NASA Land Information System project page](https://lis.gsfc.nasa.gov/) + + + diff --git a/discoveries/aggregated_climateDeltas.jpg b/discoveries/aggregated_climateDeltas.jpg new file mode 100644 index 000000000..689bdf78f Binary files /dev/null and b/discoveries/aggregated_climateDeltas.jpg differ diff --git a/discoveries/changes-fires.png b/discoveries/changes-fires.png new file mode 100644 index 000000000..48ef2b23a Binary files /dev/null and b/discoveries/changes-fires.png differ diff --git a/discoveries/changes-heat.png b/discoveries/changes-heat.png new file mode 100644 index 000000000..ebaaf3a19 Binary files /dev/null and b/discoveries/changes-heat.png differ diff --git a/discoveries/changes-parking-lot.png b/discoveries/changes-parking-lot.png new file mode 100644 index 000000000..a430fee07 Binary files /dev/null and b/discoveries/changes-parking-lot.png differ diff --git a/discoveries/changes-ports.png b/discoveries/changes-ports.png new file mode 100644 index 000000000..0ab42bf8c Binary files /dev/null and b/discoveries/changes-ports.png differ diff --git a/discoveries/changes-surf-temp-2018-04.png b/discoveries/changes-surf-temp-2018-04.png new file mode 100644 index 000000000..2ed256551 Binary files /dev/null and b/discoveries/changes-surf-temp-2018-04.png differ diff --git a/discoveries/changes-surf-temp-2020-04.png b/discoveries/changes-surf-temp-2020-04.png new file mode 100644 index 000000000..d65aac2d1 Binary files /dev/null and b/discoveries/changes-surf-temp-2020-04.png differ diff --git a/discoveries/changing-landscapes--cover.jpg b/discoveries/changing-landscapes--cover.jpg new file mode 100644 index 000000000..4da163a3e Binary files /dev/null and b/discoveries/changing-landscapes--cover.jpg differ diff --git a/discoveries/changing-landscapes.discoveries.mdx b/discoveries/changing-landscapes.discoveries.mdx new file mode 100644 index 000000000..bec005740 --- /dev/null +++ b/discoveries/changing-landscapes.discoveries.mdx @@ -0,0 +1,170 @@ +--- +id: 'changing-landscapes' +name: Changing landscapes +description: "COVID-19 related restrictions impacted the way people travelled, and as a result the ways in which we interact with the human-made and natural environments have changed" +media: + src: ::file ./changing-landscapes--cover.jpg + alt: Aerial view of highway intersections + author: + name: Jared Murray + url: https://unsplash.com/photos/NSuufgf-BME +pubDate: 2020-12-01 +thematics: + - covid-19 +--- + + + + Throughout the COVID-19 pandemic, governments have implemented, eased, and re-implemented restrictions limiting mobility and international travel to help slow the spread of the novel coronavirus. As a result, people have largely stayed home, and the ways in which we interact with the human-made and natural environments have changed. These changes have reverberated throughout Earth's systems and are observed in different ways by NASA satellites. + + + + + + ## A dimmer world during the pandemic + + When China implemented restrictions on businesses in the early months of 2020, NASA researchers documented changes in nighttime lights throughout the region. Images of Earth at night provide an extraordinary view of how human activity changes over time. + + During the COVID-19 pandemic, scientists are using satellite observations to track variations in nighttime lights, which show changes in transportation, energy use, and migration as human response to the pandemic evolves. + + From January through February 2020, nighttime lights in the central commercial district of Wuhan dimmed as people stayed home instead of shopping or socializing. Even highways, represented by the bright lines in the satellite imagery, darkened with less activity during shutdown periods. + + + + +
+ + + The High Definition Nightlights dataset is processed to eliminate light sources, including moonlight reflectance and other interferences. Darker colors indicate fewer night lights and less activity. Lighter colors indicate more night lights and more activity. + +
+ + + +
+ Satellite view of Los Angeles with blue patches indicating reduced car activity + + Slowdown Proxy Maps show areas with the greatest reduction in car activity shaded in blue. Darker blues indicate areas of greater change. + +
+ + ## Changes in traffic and parking lot patterns + + At different stages of the pandemic, nonessential businesses like shopping malls closed temporarily, while essential businesses like grocery stores were allowed to remain open. The effect of nonessential business closures on surface transportation around the world was so significant that it could be seen from space. + + For example, the imagery shown here provides a stark picture of empty parking lots near deserted commercial districts in and around Los Angeles. Blue areas represent places where slowdowns were most severe. + + Scientists obtained these data by combining remote sensing technology known as synthetic aperture radar, or SAR, with high-resolution imagery from Planet Labs. By comparing SAR images of the same areas before and after pandemic-related lockdowns, decreases in car activity in Los Angeles near airports, sports stadiums, and shopping malls were visible from space. + + + + + + ## Tracking changes in ports + + It wasn't just ground transportation in Los Angeles that was affected by COVID-related shutdowns - its port also showed less activity. During the pandemic, supply chains around the world dependent on cargo shipping saw interruptions as many ports closed, shipments canceled, and in some locations, altered routes prevented the efficient movement of cargo. + + According to the Port of Los Angeles, its port saw a 19% reduction in shipping cargo volume during the early months of the pandemic, compared to the same time period in 2019. The image here shows a reduction in the number of ships at the port, which could potentially also affect the area's overall water quality. + +
+ Satellite of Long Beach harbour with ships outlined by Machine Learning detection + + Ships detected in PlanetScope imagery are shown in orange (March 11th, 2020). + +
+ + + +
+ Satellite of Oakland Airport with blue patches indicating reduced car activity + + Slowdown Proxy Maps show areas with the greatest reduction in car activity shaded in blue. Darker blues indicate areas of greater change. + +
+ + ## Changes in urban heat + + Sudden changes in surface transportation may also be changing how cities trap and emit heat. In March, surface traffic in the San Francisco Bay Area dropped by 70%. Scientists found that the reduction in traffic corresponded to a 30% decrease in fine particulate and ozone pollution when compared to previous years. + + + + + + Satellite and thermal data also showed how the decreases in air pollution and the prevalence of empty parking lots during the pandemic changed how much solar radiation is absorbed and reflected from ground surfaces. + + Cleaner air meant that heat re-emitted during the day from dark asphalt and cement surfaces did not stay trapped near the ground as long. Instead, measurements from the joint NASA-U.S. Geological Survey [Landsat satellite](https://landsat.gsfc.nasa.gov/) and NASA's [ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS)](https://ecostress.jpl.nasa.gov/) instrument showed heat dissipated quickly, cooling the urban environment. + + As a result, scientists found that large parking lots, highway corridors, and commercial rooftops were on average 10-15°F cooler from March to May 2020, compared to previous years. + + + + +
+ + + Surface Temperature from Landsat-8, Collection 2 Level 2 data. + Darker blue: 15ºC + Red: 45ºC + +
+ + + + + ## Fewer prescribed burns during the pandemic + + Limits on work and travel during the pandemic also affected how humans interact with the natural environment. For example, satellites observed a reduction in managed forest fires, otherwise known as prescribed burns, on federal lands. + + Prescribed burns are an important way to reduce fuel loads and maintain biodiversity. In March 2020, the U.S. Forest Service and other federal agencies temporarily suspended all prescribed burns on federal lands in the Southeast United States. State agencies in Mississippi, South Carolina, and North Carolina also issued spring burning bans in response to the COVID-19 pandemic. + + The Forest Service's suspension aimed to prevent virus exposure to employees and to reduce smoke exposure to vulnerable communities, since COVID-19 is a respiratory illness. Using fire data from the [Visible Infrared Imaging Radiometer Suite (VIIRS)](https://www.jpss.noaa.gov/viirs.html) on the Suomi NPP satellite, researchers at NASA's Goddard Space Flight Center detected a 42% reduction in active fires in the Southeastern U.S. this spring compared to previous years. + + + + +
+ Multi map composition of the US dated from March 2020 to August 2020 showing change in active fires. + + This data shows the change in active fires compared to the baseline of 2012-2019. + Blue: -10% + Red: +10% + +
+ + + + + ## What have we learned and what opportunities are there for this research in the future? + + How we interact with the environment has a noticeable effect on the land, air, and water. Scientists will continue to monitor the social and environmental changes associated with the pandemic, which provides a unique opportunity to characterize and study the effect we have on our planet. + + \ No newline at end of file diff --git a/discoveries/covid-climate--cover.jpg b/discoveries/covid-climate--cover.jpg new file mode 100644 index 000000000..7e966186f Binary files /dev/null and b/discoveries/covid-climate--cover.jpg differ diff --git a/discoveries/covid-climate-co2-natural-cycle.webp b/discoveries/covid-climate-co2-natural-cycle.webp new file mode 100644 index 000000000..e9d1c0418 Binary files /dev/null and b/discoveries/covid-climate-co2-natural-cycle.webp differ diff --git a/discoveries/covid-climate-urban-areas.webp b/discoveries/covid-climate-urban-areas.webp new file mode 100644 index 000000000..17f1d4460 Binary files /dev/null and b/discoveries/covid-climate-urban-areas.webp differ diff --git a/discoveries/covid-climate.discoveries.mdx b/discoveries/covid-climate.discoveries.mdx new file mode 100644 index 000000000..a0f73148c --- /dev/null +++ b/discoveries/covid-climate.discoveries.mdx @@ -0,0 +1,280 @@ +--- +id: "climate-and-covid" +name: "Climate Change and COVID-19" +description: "The pandemic has provided an unprecedented opportunity for scientists to study how human activities impact the global carbon cycle and better understand the relative roles of natural carbon cycle variations and humans in driving climate change." +media: + src: ::file ./covid-climate--cover.jpg + alt: Clouds over a mountain + author: + name: Filip Zrnzević + url: https://unsplash.com/photos/QsWG0kjPQRY +pubDate: 2020-12-01 +thematics: + - covid-19 +--- + + + + Throughout the COVID-19 pandemic, communities have seen significant + reductions in automobile traffic, energy consumption, and other related + activities. The sudden change in our everyday lives has led to substantial, + yet temporary, reductions in fossil fuel use across the globe. These changes + had an immediate and easily observed impact on pollutants associated with + fossil fuel combustion, such as nitrogen dioxide (NO2), shown + here. The combustion of fossil fuels also produces greenhouse gases, such as + carbon dioxide (CO2) and methane (CH4), which play a + major role in regulating Earth's climate. Given the observed associated + declines in nitrogen dioxide, scientists + wondered whether corresponding decreases in carbon dioxide could be observed + from satellites and what impact, if any, such decreases could have on future + climate projections. These temporary changes are giving scientists an + unprecedented opportunity to study how changes in human activities impact + the global carbon cycle and to better understand the relative roles natural + carbon cycle variations and humans play in driving climate change. + + + + +
+ + + This layer shows changes in nitrogen dioxide (NO₂) levels in µmol/m₂. + Redder colors indicate increases in NO₂. Bluer colors indicate lower + levels of NO₂. + +
+ + + +
+ + + This layer shows changes in carbon dioxide (CO₂) levels during coronavirus lockdowns versus previous years. Redder colors indicate increases in CO₂. Bluer colors indicate lower levels of CO₂. + +
+ + ## CO₂ Emissions During Lockdowns + + At the peak of initial pandemic-related shutdowns, fossil fuel use in urban areas declined significantly compared to previous years. This was largely due to local directives that required most people to stay at home. As a result of these measures, there were fewer cars on the road, planes in the sky, and ships at sea, leading to far less fossil fuel emissions. Estimates of global fossil fuel consumption from the Global Carbon Project confirmed these reductions. + + However, while NASA scientists could easily detect decreases in other fossil fuel combustion byproducts from space, such as nitrogen dioxide, corresponding reductions in carbon dioxide were not as readily apparent. It took careful analysis to produce the map shown here, depicting global carbon dioxide changes in March 2020, at the height of the initial onset of the pandemic in the United States. + + + + + + + ## The Difference Between CO₂ and NO₂ + + Unlike nitrogen dioxide, which dissipates in the atmosphere a few hours after emission, carbon dioxide can last for centuries. Because of this, nitrogen dioxide emissions are typically more localized, while winds can carry carbon dioxide emissions far from their original source. This creates a smooth, global distribution pattern for carbon dioxide, such as the one seen here, as new emissions mix with the surrounding air. As a result, atmospheric carbon dioxide concentrations are relatively steady over the entire planet. + + This makes it very difficult to discern the impact of temporary changes in carbon dioxide emissions during coronavirus-related shutdowns, as scientists expected that any observed fluctuations would be no larger than 0.1% below normal levels. Teasing out the impacts of such reductions for specific locations during the pandemic has required NASA and its partners in the Japan Aerospace Exploration Agency (JAXA) to use advanced modeling and develop new analysis techniques. + + +
+ + + + Top: redder colors indicate higher nitrogen dioxide (NO₂) levels associated and more activity. Bluer colors indicate lower levels of NO₂ and less activity. Bottom: redder colors indicate more CO₂. Bluer colors indicate less CO₂. + +
+ + + + + ## CO₂ in the Natural Carbon Cycle + + Further complicating the ability to discern pandemic-related changes in carbon dioxide concentrations is the fact that initial lockdowns corresponded with the beginning of spring in the Northern Hemisphere - when trees and other plants began to rapidly absorb carbon dioxide from the atmosphere. Trees, grasslands, and other land ecosystems absorb billions of tons of carbon dioxide as they bloom in the spring and then release most of that carbon dioxide back into the atmosphere as they decay in the fall. The ocean also exchanges billions of tons of carbon dioxide with the atmosphere each year. Typically, these components of the natural carbon cycle are reasonably well balanced when averaged over the year: absorbing about as much carbon dioxide as the emit, along with about half of the carbon dioxide produced by human activities. However, seasonal swings in the uptake and release of carbon dioxide by plants on land, and temperature and rainfall changes associated with naturally occurring climatological phenomena such as the El Niño Southern Oscillation, can mask any smaller changes related to the COVID-19 pandemic. + + + + + +
+ This layer shows the average background concentration of carbon dioxide (CO₂) in our atmosphere for 2020. Redder colors indicate more CO₂. Bluer colors indicate less CO₂. + + This layer shows the average background concentration of carbon dioxide + (CO₂) in our atmosphere for 2020. Redder colors indicate more CO₂. Bluer + colors indicate less CO₂. + +
+ + + +
+ + + This layer shows the average background concentration of carbon dioxide (CO₂) in our atmosphere for 2020. Redder colors indicate more CO₂. Bluer colors indicate less CO₂. + +
+ + ## OCO-2 and GOSAT Provide Two Complementary Ways to Track Changes in CO₂ + + Two Earth-observing satellites - NASA's [Orbiting Carbon Observatory-2 (OCO-2)](https://ocov2.jpl.nasa.gov/) and Japan's [Greenhouse gases Observing SATellite (GOSAT)](https://www.eorc.jaxa.jp/GOSAT/index.html) - have tracked changes in atmospheric carbon dioxide emissions resulting from the COVID-19 pandemic. + + Measurements collected in 2020 were compared with results from previous years to glean insights into any small variations. Each of these satellites plays a unique role in studying carbon dioxide. OCO-2, whose data are shown here, collects measurements at relatively high spatial resolution and monitors regional-scale changes in carbon dioxide. GOSAT collects measurements at isolated points and can be targeted to track changes in carbon dioxide and methane emissions in large urban areas, such as Beijing, Tokyo, and Los Angeles. While both satellites provide a large number of measurements over the globe each day, thick clouds and airborne particles such as dust, smoke or smog reduce the number of reliable estimates they produce, so their spatial sampling is relatively sparse. + + + + + + + ## Looking for a Needle in a Haystack + + Searching for small changes in regional atmospheric carbon dioxide emissions against the backdrop of existing carbon dioxide levels is like looking for a needle in a haystack. To do this, scientists incorporated OCO-2 data from November 2019 through September 2020 into a NASA computer model that simulates how Earth's atmosphere moves. The result was a gap-free global map that compensated for OCO-2's relatively sparse carbon dioxide measurements. Scientists then compared the modeled projection with an averaged set of OCO-2 data collected over the same months from 2015 through 2019. This method helped distinguish between changes in carbon dioxide due to emissions and those associated with year-to-year variations in patterns of how winds move around the globe, which can make it hard to spot the smaller reductions in carbon dioxide that scientists expected to observe as a result of the pandemic. What they saw were small reductions of 0.5 parts per million (ppm) (about 0.125%) in carbon dioxide over China, Europe, and the United States, at times during the months corresponding to the largest reported emissions reductions in those regions. + + +
+ + + + Mean: redder colors indicate more CO₂. Bluer colors indicate less CO₂. Difference: redder colors indicate increases in CO₂ in relation to the baseline. Bluer colors indicate lower levels of CO₂. + +
+ + + + + + ## Not all Changes Observed in CO₂ Were Related to the Pandemic + + Scientists + were also able to infer regional-scale differences in carbon dioxide that + were completely unrelated to the pandemic using these new techniques. + Increased carbon dioxide observed over Australia early in the year was + likely due to intense wildfires in late 2019. In addition, a small, 1ppm + decline in carbon dioxide concentrations across Central India and Southern + and Eastern Africa in January, shown here, was likely due to the additional + absorption of carbon dioxide by land ecosystems and the Indian Ocean Dipole, + an El Niño-like disturbance across the Indian Ocean basin. + + + + +
+ + + This layer shows changes in carbon dioxide (CO₂) levels during coronavirus + lockdowns versus previous years. Redder colors indicate increases in CO₂. + Bluer colors indicate lower levels of CO₂. + +
+ + + +
+ Redder colors indicate increased CO2 enhancements. Bluer colors indicate decreased CO2 enhancements. + + Redder colors indicate increased CO2 enhancements. Greener colors indicate + decreased CO2 enhancements. + +
+ + ## CO₂ Changes Over Large Urban Areas + + The most severe changes in economic + activity associated with the pandemic - and, subsequently, the largest + reductions in fossil fuel emissions - occurred in large urban areas. + Tracking these changes from space is challenging. Spacecraft don't fly + directly over cities very often and they frequently are covered by clouds + and passing weather systems that can easily transport carbon dioxide in and + out. GOSAT's agile pointing system can look at cities and distinguish + between changes in carbon dioxide that occur near Earth's surface, which are + more likely to originate from a city, and those at higher altitudes, which + are more likely to have been transported from somewhere else. Scientists + used this capability to devise a new technique to tease out changes in + carbon dioxide emissions due to the pandemic based on their altitude. Using + this process, scientists found a lower average concentration of carbon + dioxide over Beijing, Tokyo, New York, Mumbai, Delhi, and Dhaka during the + pandemic months studied in 2020, compared to previous years. + + + + + + ## What We've Learned So Far, and Why it Matters + + Scientists met the challenge of detecting and tracking the small, temporary changes in atmospheric carbon dioxide caused by the COVID-19 pandemic using data from OCO-2 and GOSAT. While the rate at which carbon dioxide was added to our atmosphere was temporarily decreased, an overall reduction in the global concentration of atmospheric carbon dioxide is not expected. + + This research provided a real-world test for these first-generation greenhouse-gas monitoring satellites. It showed that even small, year-to-year reductions in carbon dioxide emissions can be tracked using space-based sensors. This capability will be increasingly important to inform and test carbon management policies. Another key takeaway from this research is the need to improve our understanding of the contributions of the natural carbon cycle. By improving our ability to track and diagnose its changes, scientists will be able to more accurately attribute and interpret changes in carbon dioxide levels caused by human activities. + + A key takeaway of the research is the need to improve our understanding of the contributions of the natural carbon cycle. By improving our ability to track and diagnose its changes, scientists will be able to more accurately attribute and interpret changes in carbon dioxide due to human activities. For example, we would have to reduce carbon dioxide emissions by as much as we have done this year for every year in the next couple of decades to meet the ambitious goals of the Paris agreement and limit temperature increases to less than 2 degrees C. This test shows we are learning how to do that. + + The research is also providing new insights into the sensors and analysis tools needed to track changes in carbon dioxide from space. For example, it reinforced the value of combining different types of measurements to detect small changes in carbon dioxide emission sources. Results of these COVID-related studies will benefit development of several next-generation satellites launching in the next few years, including Japan's GOSAT-GW satellite, the Copernicus CO2M constellation, and NASA's [GeoCarb](//www.nasa.gov/feature/jpl/geocarb-a-new-view-of-carbon-over-the-americas) + + + + + +
+ + + This layer shows changes in carbon dioxide (CO₂) levels during coronavirus lockdowns versus previous years. Redder colors indicate increases in CO₂. Bluer colors indicate lower levels of CO₂. + +
+ + diff --git a/discoveries/habitat_spaghettiPlots.jpg b/discoveries/habitat_spaghettiPlots.jpg new file mode 100644 index 000000000..7c7e83a7e Binary files /dev/null and b/discoveries/habitat_spaghettiPlots.jpg differ diff --git a/discoveries/projected-changes-WUS-snow.discoveries.mdx b/discoveries/projected-changes-WUS-snow.discoveries.mdx new file mode 100644 index 000000000..befb298e9 --- /dev/null +++ b/discoveries/projected-changes-WUS-snow.discoveries.mdx @@ -0,0 +1,142 @@ +--- +featuredOn: + - eis +id: 'eis-snow-projections' +name: Future projections of Western US montane snowpack +description: "Combining NASA climate projections and NASA models to infer the future state of snow water resources in the Rocky Mountains and North Cascades Mountain ranges" +media: + src: ::file ./snow-projections-median.jpg + alt: Matterhorn glacier field + author: + name: Justin Pflug + url: +pubDate: 2023-02-01 +thematics: + - eis +--- + + + ## Introduction + 🚧 This Discovery presents work in progress and not peer-reviewed results! 🚧 + + Over half of the annual runoff in the Western United States originates from seasonal snowpack. However, seasonal snowpack is threatened by future changes to climate. The impacts of climate change on snowpack are particularly important in mountainous regions, which behave like “water towers”, storing water in the winter, and releasing water through snowmelt in the spring and summer. In this discovery, we combine [NASA-downscaled climate projections](https://www.nasa.gov/nex/gddp) and [NASA land surface modeling tools](https://lis.gsfc.nasa.gov/) to investigate how climate change could impact snow water resources (datasets available [here](https://www.earthdata.nasa.gov/dashboard/eis/datasets)). We look at five mountainous domains in the Western U.S., and infer how changes to snow water resources could change the availability of wildlife habitat. This research was performed in collaboration with the Cooperative Institute for Research in Environmental Sciences, with feedback from the US Fish and Wildlife Service. + + Join the discussion and provide comments on this Discovery at https://github.com/orgs/Earth-Information-System/discussions. + + Authors: Justin Pflug, Sujay Kumar, Ben Livneh, Melissa Wrzesien, Kim Locke + + + + + + ## Climate change projections + Climate projections came from the [Climate Model Intercomparison Project, Phase 6](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6). CMIP6 Projections of air temperature, precipitation, relative humidity, wind speed, and radiation were downscaled to finer spatial resolutions by the NASA Earth Exchange Global Daily Downscaled Projections ([NEX-GDDP](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6)). Projections show that most of the Western US average winter air temperature and precipitation are expected to increase by the end of the century (2076 - 2095), relative to the historical timeframe (1995 - 2016). + +
+ + + Median projected changes to end-of-century winter air temperature from a 23-member ensemble of CMIP6 models (SSP2-4.5). + +
+ + +
+ + + Median projected percent-changes to end-of-century total winter precipitation from a 23-member ensemble of CMIP6 models (SSP2-4.5). + +
+ + + + + Average climate change signals were aggregated across 20-year periods at monthly timesteps. This was repeated for 23 different CMIP6 climate models, using two representations of future climate: 1) a “middle of the road” projection with small progressions in environmental sustainability (Shared Socioeconomic Pathway, SSP 2-4.5), and 2) a worst-case-scenario fossil-fueled future (SSP 5-8.5). This chart presents the average monthly increases in temperautre and precipitation in the Washington state North Cascades mountain range. + + + + +
+ changes to thresholded habitat across elevation bands + + Monthly climate change signals in air temperature (left) and precipitation (right) for 20-year periods and two emissions scenarios (colors). Individual climate models are shown by transparent scatter points and ensemble-medians are shown by the bolded scatter points. + +
+ + + + + ## The Washington Cascades + Montane snow in the Pacific Northwest exhibits high sensitivies to climate change. As compared to US mountain ranges in the US interior, this region is at lower elevations and has warmer winter temperatures. This results in a larger portion of precipitation that transitions to rain and earlier snowmelt onset in future climates. By the end of the century, April 1st SWE is projected to largely disappear at elevations of less than 700m, with greater than 50% decreases at elevations less than 1200m. Only at elevations greater than 1400m, accounting for approximately 20% of the domain's total snow volume, is climate change projected to either increase or change snow by less than 10%. However, these increases are largely offset by earlier snowmelt onset and more rapid snowmelt throughout spring. + + + ## Northern Rocky Mountains + Although at higher elevations and colder temperatures than the Washington Cascades, average end-of-century snowpack is expected to reduce by 41%, relative to the historical period (1995 - 2014). The largest projected decreases occur in Northern Idaho and Northwest Montana. Some portions of Northern Montana near Glacier National Park could have temperatures that are sufficiently cold enough to sustain snowfall with future warming and increases in precipitation, resulting in small increases in April 1 snow volume in those regions. + + + ## Central and Southern Rocky Mountains + Although end-of-century winter temperatures in this region are projected to increase by approximately 3 degrees Celsius (emissions scenario SSP2-4.5), winter temperatures may be sufficiently cold enough to sustain snowfall at high elevations. This could result in high-elevation increases in Apri snowpack, particularly in the cold Wyoming Rockies. However, projections often disagree on whether high-elevation winter snow will increase or decrease, and high elevation increases are not large enough to offset lower-elevation decreases in snow volume, resulting in average winter snow losses of approximately 21%. + + + +
+ changes to thresholded habitat across elevation bands +
+ + The snow projections developed here can be used for multiple applications. For example, wolverine habitat assessments performed by the US Fish and Wildlife Service suggested that snowy regions were more likely to support denning if mid-spring (e.g., May 15) snow depth exceeded 0.5m. Using these projections, we could identify the percent-change to the amount of area that had snow depth exceeding the spring snow depth threshold for different elevation bands. The plots shown here show the changes across elevation bands for the four future periods (emissions scenario SSP2-4.5), for three domains, accounting for both the ensemble mean (line) and spread (shaded) across 23 different climate models. Note that although some of the colder domains like the Colorado Rockies and Montana Rockies had high-elevation increases to end-of-century projected snow, the prevelence of area exceeding this habitat threshold only decreased for all elevation bands in the last simulation period (2076 - 2095). + + + + + + The data presented in this discovery includes model results generated using [NASA-downscaled climate projections](https://www.nasa.gov/nex/gddp) and [NASA modeling tools](https://lis.gsfc.nasa.gov/). Additional model outputs can be accessed in the VEDA datasets pages, ensemble-median [snow projections](https://www.earthdata.nasa.gov/dashboard/eis/datasets/snow-projections-median) and [projected percent-changes to snow water equivalent](https://www.earthdata.nasa.gov/dashboard/eis/datasets/snow-projections-diff). The data presented is preliminary, and not yet peer-reviewed. Users are encouraged to contact the project authors for inquiries about this data. + + diff --git a/discoveries/snow-projections-median.jpg b/discoveries/snow-projections-median.jpg new file mode 100644 index 000000000..d7288459b Binary files /dev/null and b/discoveries/snow-projections-median.jpg differ diff --git a/discoveries/tws-trends.discoveries.mdx b/discoveries/tws-trends.discoveries.mdx index ca788f7fd..f7d800ef8 100644 --- a/discoveries/tws-trends.discoveries.mdx +++ b/discoveries/tws-trends.discoveries.mdx @@ -10,7 +10,7 @@ media: author: name: USGS/NASA url: https://eros.usgs.gov/media-gallery/earth-as-art/2/jordan -pubDate: 2020-12-01 +pubDate: 2023-03-01 thematics: - eis --- diff --git a/discoveries/wq-models.discoveries.mdx b/discoveries/wq-models.discoveries.mdx index db508df26..e8e0bdcaf 100644 --- a/discoveries/wq-models.discoveries.mdx +++ b/discoveries/wq-models.discoveries.mdx @@ -10,7 +10,7 @@ media: author: name: NASA url: https://www.nasa.gov -pubDate: 2023-03-14 +pubDate: 2023-03-01 thematics: - eis --- diff --git a/docs/CONTENT.md b/docs/CONTENT.md index 7c1d3d930..e31e0ebb2 100644 --- a/docs/CONTENT.md +++ b/docs/CONTENT.md @@ -3,6 +3,7 @@ - Check the [MDX_BLOCK.md](./MDX_BLOCKS.md) for information on the different blocks that can be used to compose the content layout. - Check [layer.md](./frontmatter/layer.md) for details on the different properties of a dataset layer. - Check [media.md](./frontmatter/media.md) for information on the images needed for the content's covers. +- Check [GH_CODESPACES.md](./GH_CODESPACES.md) for a video walktrhough of adding content to VEDA with github codespaces. ---- diff --git a/docs/GH_CODESPACES.md b/docs/GH_CODESPACES.md new file mode 100644 index 000000000..2d1ad451d --- /dev/null +++ b/docs/GH_CODESPACES.md @@ -0,0 +1,14 @@ +# Video Walkthrough +The following videos explain how to add content to VEDA using github codespaces. + +## Setting up github codespaces + +Codespaces will allow you to have a development environment in the cloud without the need to setup anything on your local machine. + +🎥 https://drive.google.com/file/d/1u2hkokW3ZDmrjYNkg10OgWU0-nNtHpJ6/view + +## Creating a discovery + +Walkthrough of how to use github codespaces to create a discovery. From creating the needed files to the Pull Request that will eventually get the content published. + +🎥 https://drive.google.com/file/d/1Jkbt2csXntPPe8G5TBGic9UYZsj2rgW3/view \ No newline at end of file diff --git a/docs/frontmatter/layer.md b/docs/frontmatter/layer.md index 6cd02758e..2fd07ef98 100644 --- a/docs/frontmatter/layer.md +++ b/docs/frontmatter/layer.md @@ -41,8 +41,8 @@ Name of the layer for display purposes. **type** `raster | vector` -The type of the layer will define how the data is displayed on the map. -- ⚠️ Only raster is currently supported. +The type of the layer will define how the data is displayed on the map. +⚠️ Vector datasets are should be in vector titles format with a source layer named `default`. It is currently not possible to customize the style of the dataset's features. **description** `string` diff --git a/package.json b/package.json index 4fd65d634..d9ca654a9 100644 --- a/package.json +++ b/package.json @@ -1,7 +1,7 @@ { "name": "veda-config", "description": "Configuration for Veda", - "version": "0.6.1", + "version": "0.7.0", "source": "./.veda/ui/app/index.html", "license": "Apache-2.0", "scripts": { @@ -18,6 +18,8 @@ "node": "16.x" }, "devDependencies": { + "@parcel/packager-raw-url": "2.7.0", + "@parcel/transformer-webmanifest": "2.7.0", "dotenv": "^10.0.0", "netlify-cms-proxy-server": "^1.3.24" }, diff --git a/yarn.lock b/yarn.lock index 6beb20181..c998a22d2 100644 --- a/yarn.lock +++ b/yarn.lock @@ -68,6 +68,251 @@ resolved "https://registry.yarnpkg.com/@kwsites/promise-deferred/-/promise-deferred-1.1.1.tgz#8ace5259254426ccef57f3175bc64ed7095ed919" integrity sha512-GaHYm+c0O9MjZRu0ongGBRbinu8gVAMd2UZjji6jVmqKtZluZnptXGWhz1E8j8D2HJ3f/yMxKAUC0b+57wncIw== 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type-is "~1.6.18" unpipe "1.0.0" +braces@^3.0.2: + version "3.0.2" + resolved "https://registry.yarnpkg.com/braces/-/braces-3.0.2.tgz#3454e1a462ee8d599e236df336cd9ea4f8afe107" + integrity sha512-b8um+L1RzM3WDSzvhm6gIz1yfTbBt6YTlcEKAvsmqCZZFw46z626lVj9j1yEPW33H5H+lBQpZMP1k8l+78Ha0A== + dependencies: + fill-range "^7.0.1" + bytes@3.1.2: version "3.1.2" resolved "https://registry.yarnpkg.com/bytes/-/bytes-3.1.2.tgz#8b0beeb98605adf1b128fa4386403c009e0221a5" @@ -131,6 +390,19 @@ call-bind@^1.0.0: function-bind "^1.1.1" get-intrinsic "^1.0.2" +chalk@^4.1.0: + version "4.1.2" + resolved "https://registry.yarnpkg.com/chalk/-/chalk-4.1.2.tgz#aac4e2b7734a740867aeb16bf02aad556a1e7a01" + integrity sha512-oKnbhFyRIXpUuez8iBMmyEa4nbj4IOQyuhc/wy9kY7/WVPcwIO9VA668Pu8RkO7+0G76SLROeyw9CpQ061i4mA== + dependencies: + ansi-styles "^4.1.0" + supports-color "^7.1.0" + +chrome-trace-event@^1.0.2: + version "1.0.3" + resolved 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"https://registry.yarnpkg.com/path-to-regexp/-/path-to-regexp-0.1.7.tgz#df604178005f522f15eb4490e7247a1bfaa67f8c" integrity sha512-5DFkuoqlv1uYQKxy8omFBeJPQcdoE07Kv2sferDCrAq1ohOU+MSDswDIbnx3YAM60qIOnYa53wBhXW0EbMonrQ== +picomatch@^2.3.1: + version "2.3.1" + resolved "https://registry.yarnpkg.com/picomatch/-/picomatch-2.3.1.tgz#3ba3833733646d9d3e4995946c1365a67fb07a42" + integrity sha512-JU3teHTNjmE2VCGFzuY8EXzCDVwEqB2a8fsIvwaStHhAWJEeVd1o1QD80CU6+ZdEXXSLbSsuLwJjkCBWqRQUVA== + proxy-addr@~2.0.7: version "2.0.7" resolved "https://registry.yarnpkg.com/proxy-addr/-/proxy-addr-2.0.7.tgz#f19fe69ceab311eeb94b42e70e8c2070f9ba1025" @@ -589,6 +994,11 @@ safe-stable-stringify@^2.3.1: resolved "https://registry.yarnpkg.com/safer-buffer/-/safer-buffer-2.1.2.tgz#44fa161b0187b9549dd84bb91802f9bd8385cd6a" integrity sha512-YZo3K82SD7Riyi0E1EQPojLz7kpepnSQI9IyPbHHg1XXXevb5dJI7tpyN2ADxGcQbHG7vcyRHk0cbwqcQriUtg== +semver@^5.7.1: + version "5.7.1" + resolved "https://registry.yarnpkg.com/semver/-/semver-5.7.1.tgz#a954f931aeba508d307bbf069eff0c01c96116f7" + integrity sha512-sauaDf/PZdVgrLTNYHRtpXa1iRiKcaebiKQ1BJdpQlWH2lCvexQdX55snPFyK7QzpudqbCI0qXFfOasHdyNDGQ== + send@0.18.0: version "0.18.0" resolved "https://registry.yarnpkg.com/send/-/send-0.18.0.tgz#670167cc654b05f5aa4a767f9113bb371bc706be" @@ -665,11 +1075,25 @@ string_decoder@^1.1.1: dependencies: safe-buffer "~5.2.0" +supports-color@^7.1.0: + version "7.2.0" + resolved "https://registry.yarnpkg.com/supports-color/-/supports-color-7.2.0.tgz#1b7dcdcb32b8138801b3e478ba6a51caa89648da" + integrity sha512-qpCAvRl9stuOHveKsn7HncJRvv501qIacKzQlO/+Lwxc9+0q2wLyv4Dfvt80/DPn2pqOBsJdDiogXGR9+OvwRw== + dependencies: + has-flag "^4.0.0" + text-hex@1.0.x: version "1.0.0" resolved "https://registry.yarnpkg.com/text-hex/-/text-hex-1.0.0.tgz#69dc9c1b17446ee79a92bf5b884bb4b9127506f5" integrity sha512-uuVGNWzgJ4yhRaNSiubPY7OjISw4sw4E5Uv0wbjp+OzcbmVU/rsT8ujgcXJhn9ypzsgr5vlzpPqP+MBBKcGvbg== +to-regex-range@^5.0.1: + version "5.0.1" + resolved "https://registry.yarnpkg.com/to-regex-range/-/to-regex-range-5.0.1.tgz#1648c44aae7c8d988a326018ed72f5b4dd0392e4" + integrity sha512-65P7iz6X5yEr1cwcgvQxbbIw7Uk3gOy5dIdtZ4rDveLqhrdJP+Li/Hx6tyK0NEb+2GCyneCMJiGqrADCSNk8sQ== + dependencies: + is-number "^7.0.0" + toidentifier@1.0.1: version "1.0.1" resolved "https://registry.yarnpkg.com/toidentifier/-/toidentifier-1.0.1.tgz#3be34321a88a820ed1bd80dfaa33e479fbb8dd35" @@ -703,6 +1127,11 @@ util-deprecate@^1.0.1: resolved "https://registry.yarnpkg.com/util-deprecate/-/util-deprecate-1.0.2.tgz#450d4dc9fa70de732762fbd2d4a28981419a0ccf" integrity sha512-EPD5q1uXyFxJpCrLnCc1nHnq3gOa6DZBocAIiI2TaSCA7VCJ1UJDMagCzIkXNsUYfD1daK//LTEQ8xiIbrHtcw== +utility-types@^3.10.0: + version "3.10.0" + resolved "https://registry.yarnpkg.com/utility-types/-/utility-types-3.10.0.tgz#ea4148f9a741015f05ed74fd615e1d20e6bed82b" + integrity sha512-O11mqxmi7wMKCo6HKFt5AhO4BwY3VV68YU07tgxfz8zJTIxr4BpsezN49Ffwy9j3ZpwwJp4fkRwjRzq3uWE6Rg== + utils-merge@1.0.1: version "1.0.1" resolved "https://registry.yarnpkg.com/utils-merge/-/utils-merge-1.0.1.tgz#9f95710f50a267947b2ccc124741c1028427e713" @@ -713,6 +1142,11 @@ vary@^1, vary@~1.1.2: resolved "https://registry.yarnpkg.com/vary/-/vary-1.1.2.tgz#2299f02c6ded30d4a5961b0b9f74524a18f634fc" integrity sha512-BNGbWLfd0eUPabhkXUVm0j8uuvREyTh5ovRa/dyow/BqAbZJyC+5fU+IzQOzmAKzYqYRAISoRhdQr3eIZ/PXqg== +weak-lru-cache@^1.2.2: + version "1.2.2" + resolved "https://registry.yarnpkg.com/weak-lru-cache/-/weak-lru-cache-1.2.2.tgz#fdbb6741f36bae9540d12f480ce8254060dccd19" + integrity sha512-DEAoo25RfSYMuTGc9vPJzZcZullwIqRDSI9LOy+fkCJPi6hykCnfKaXTuPBDuXAUcqHXyOgFtHNp/kB2FjYHbw== + what-the-diff@^0.6.0: version "0.6.0" resolved "https://registry.yarnpkg.com/what-the-diff/-/what-the-diff-0.6.0.tgz#445cc56a9d8ee9aea0ee1ed943f4957ae009291e" @@ -743,3 +1177,8 @@ winston@^3.3.3: stack-trace "0.0.x" triple-beam "^1.3.0" winston-transport "^4.5.0" + +xxhash-wasm@^0.4.2: + version "0.4.2" + resolved "https://registry.yarnpkg.com/xxhash-wasm/-/xxhash-wasm-0.4.2.tgz#752398c131a4dd407b5132ba62ad372029be6f79" + integrity sha512-/eyHVRJQCirEkSZ1agRSCwriMhwlyUcFkXD5TPVSLP+IPzjsqMVzZwdoczLp1SoQU0R3dxz1RpIK+4YNQbCVOA==