diff --git a/datasets/global-reanalysis-da.data.mdx b/datasets/global-reanalysis-da.data.mdx
index 3a4182551..97cab95d5 100644
--- a/datasets/global-reanalysis-da.data.mdx
+++ b/datasets/global-reanalysis-da.data.mdx
@@ -368,7 +368,7 @@ The output variables available on VEDA include evapotranspiration (ET), gross pr
## 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> to look at different dates and to compare variables.
+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 to look at different dates and to compare variables.
An example of how trends calculated from the global reanalysis model output can be used to understand changes in TWS, GPP, and ET, can be seen in the corresponding data story.
diff --git a/datasets/lis.da.trend.data.mdx b/datasets/lis.da.trend.data.mdx
index 0f39bdd58..633d242c6 100644
--- a/datasets/lis.da.trend.data.mdx
+++ b/datasets/lis.da.trend.data.mdx
@@ -90,7 +90,7 @@ layers:
* [EIS Freshwater](https://freshwater.eis.smce.nasa.gov/)
* [Land Information System](https://lis.gsfc.nasa.gov/)
- * [Global Reanalysis Dataset page](https://www.earthdata.nasa.gov/dashboard/eis/datasets)
+ * [Global Reanalysis Dataset page](https://www.earthdata.nasa.gov/dashboard/data-catalog?taxonomy=%7B%22Topics%22%3A%22eis%22%7D)
diff --git a/datasets/no2.data.mdx b/datasets/no2.data.mdx
index 71a5a4d84..732fb4b4e 100644
--- a/datasets/no2.data.mdx
+++ b/datasets/no2.data.mdx
@@ -24,9 +24,9 @@ taxonomy:
layers:
- id: no2-monthly
stacCol: no2-monthly
- name: No2
+ name: Nitrogen Dioxide (monthly)
type: raster
- description: 'Global nitrogen dioxide data organized into monthly metrics'
+ description: 'Global nitrogen dioxide (NO₂) data organized into monthly metrics'
zoomExtent:
- 0
- 20
@@ -57,9 +57,9 @@ layers:
- "#DD7059"
- id: no2-monthly-diff
stacCol: no2-monthly-diff
- name: No2 (Diff)
+ name: Nitrogen Dioxide (monthly difference)
type: raster
- description: 'Global nitrogen dioxide data which displays the difference from the same time 1 year ago'
+ description: 'Global nitrogen dioxide (NO₂) data which displays the difference from the same time 1 year ago'
zoomExtent:
- 0
- 20
@@ -68,8 +68,8 @@ layers:
bidx: 1
colormap_name: rdbu_r
rescale:
- - -8e15
- - 8e15
+ - -3e15
+ - 3e15
compare:
datasetId: no2
layerId: no2-monthly-diff
@@ -89,9 +89,9 @@ layers:
- "#DD7059"
- id: OMI_trno2-COG
stacCol: OMI_trno2-COG
- name: OMI_trno2 Annual
+ name: Nitrogen Dioxide Total and Tropospheric Column (NASA OMI/Aura)
type: raster
- description: "NASA OMI/Aura Nitrogen Dioxide (NO2) Total and Tropospheric Column"
+ description: "NASA OMI/Aura Nitrogen Dioxide (NO₂) Total and Tropospheric Column"
zoomExtent:
- 0
- 16
diff --git a/package.json b/package.json
index 2c0345e11..15067247d 100644
--- a/package.json
+++ b/package.json
@@ -1,7 +1,7 @@
{
"name": "veda-config",
"description": "Configuration for Veda",
- "version": "0.15.0",
+ "version": "0.15.1",
"source": "./.veda/ui/app/index.html",
"license": "Apache-2.0",
"scripts": {
diff --git a/stories/projected-changes-WUS-snow.stories.mdx b/stories/projected-changes-WUS-snow.stories.mdx
index 38452ba40..88e78a1c6 100644
--- a/stories/projected-changes-WUS-snow.stories.mdx
+++ b/stories/projected-changes-WUS-snow.stories.mdx
@@ -21,7 +21,7 @@ taxonomy:
## 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.
+ 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/data-catalog)). 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.
@@ -139,6 +139,6 @@ taxonomy:
- 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.
+ 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/data-catalog/snow-projections-median) and [projected percent-changes to snow water equivalent](https://www.earthdata.nasa.gov/dashboard/data-catalog/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/stories/recent-fires.stories.mdx b/stories/recent-fires.stories.mdx
deleted file mode 100644
index 397a13d17..000000000
--- a/stories/recent-fires.stories.mdx
+++ /dev/null
@@ -1,45 +0,0 @@
----
-id: "recent-fires"
-name: Recent fires
-description: "Showing large fires that were burning when the fire atlas was last updated"
-featured: true
-media:
- src: ::file ./recent-fires.webp
- alt: A vehicle exiting a burning forest
- author:
- name: Marcus Kauffman
- url: https://unsplash.com/photos/-iretlQZEU4
-pubDate: 2023-08-10
-taxonomy:
- - name: Topics
- values:
- - EIS
----
-
-
-
- Showing large fires that were burning when the fire atlas was last updated.
- Only includes fires with an area greater than 2.
-
- You can learn more about how to access [this data](https://firenrt.delta-backend.com/collections/public.eis_fire_snapshot_perimeter_nrt) directly in this [VEDA documentation page](https://nasa-impact.github.io/veda-docs/notebooks/tutorials/mapping-fires.html).
-
-
-
-
-
-
-
-
-
-
-
-
- ⚠️ This visualization was created using a [jupyterlite-pyodide-kernel](https://github.com/jupyterlite/pyodide-kernel) and
- [voici](https://voici.readthedocs.io/en/latest/). It is rendered client-side (in your browser!) via the magic of [Wasm](https://webassembly.org/).
- This is an exciting and rapidly evolving space which means that this
- visualization is highly experimental and likely to break. ⚠️
-
-
diff --git a/stories/recent-fires.webp b/stories/recent-fires.webp
deleted file mode 100644
index f078df260..000000000
Binary files a/stories/recent-fires.webp and /dev/null differ
diff --git a/stories/tws-trends.stories.mdx b/stories/tws-trends.stories.mdx
index 311f81344..ce0a89379 100644
--- a/stories/tws-trends.stories.mdx
+++ b/stories/tws-trends.stories.mdx
@@ -22,7 +22,7 @@ taxonomy:
Freshwater is what makes Earth habitable, sustaining ecosystems and human civilization. The global water cycle supplies water and regulates weather patterns. The cycling of water links the changes on land with the ocean and atmosphere. Understanding the variability and availability of freshwater is challenging because of multiple earth processes that continually interact with each other, including those that govern precipitation, ground soil moisture retention, snow accumulation and melt, evapotranspiration and vegetation dynamics. Such processes become even more complex under human water resources management.
- The EIS team integrates the Noah-MP land surface model within [NASA’s LIS framework](https://lis.gsfc.nasa.gov/) and Earth observations by assimilating soil moisture from the Climate Change Initiative Program released by European Space Agency ([ESA CCI](https://esa-soilmoisture-cci.org/)), leaf area index from Moderate Resolution Imaging Spectroradiometer ([MODIS](https://lpdaac.usgs.gov/products/mcd15a2hv006/)), and terrestrial water storage anomalies from Gravity Recovery and Climate Experiment and the follow-on satellites ([GRACE/GRACE-FO](https://earth.gsfc.nasa.gov/geo/data/grace-mascons)). Using this data assimilation approach, the team provides a daily global water cycle reanalysis product for 2003-2021 at a 10 km spatial resolution. This allows us to better quantify surface variables and groundwater, human management influence, and hydrological extremes. These resulting reanalysis datasets are publicly available and interactable via this NASA VEDA platform, including key water, energy, and carbon fluxes such as terrestrial water storage (TWS) and gross primary production (GPP). For more information, please visit the corresponding [VEDA dataset page](https://www.earthdata.nasa.gov/dashboard/eis/datasets).
+ The EIS team integrates the Noah-MP land surface model within [NASA’s LIS framework](https://lis.gsfc.nasa.gov/) and Earth observations by assimilating soil moisture from the Climate Change Initiative Program released by European Space Agency ([ESA CCI](https://esa-soilmoisture-cci.org/)), leaf area index from Moderate Resolution Imaging Spectroradiometer ([MODIS](https://lpdaac.usgs.gov/products/mcd15a2hv006/)), and terrestrial water storage anomalies from Gravity Recovery and Climate Experiment and the follow-on satellites ([GRACE/GRACE-FO](https://earth.gsfc.nasa.gov/geo/data/grace-mascons)). Using this data assimilation approach, the team provides a daily global water cycle reanalysis product for 2003-2021 at a 10 km spatial resolution. This allows us to better quantify surface variables and groundwater, human management influence, and hydrological extremes. These resulting reanalysis datasets are publicly available and interactable via this NASA VEDA platform, including key water, energy, and carbon fluxes such as terrestrial water storage (TWS) and gross primary production (GPP). For more information, please visit the corresponding [VEDA dataset page](https://www.earthdata.nasa.gov/dashboard/data-catalog?taxonomy=%7B%22Topics%22%3A%22eis%22%7D).
Join the discussion and provide comments on this Discovery at https://github.com/orgs/Earth-Information-System/discussions.
@@ -77,7 +77,7 @@ taxonomy:
## Comparing the trends in water and carbon cycles
- 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. These trend data sets are also provided in the [VEDA dataset page](https://www.earthdata.nasa.gov/dashboard/eis/datasets).
+ 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. These trend data sets are also provided in the [VEDA dataset page](https://www.earthdata.nasa.gov/dashboard/data-catalog?taxonomy=%7B%22Topics%22%3A%22eis%22%7D).
⚠️ Our results of the GPP trends for some areas are contradictory to the greening trends reported by [Chen et al. 2019](https://doi.org/10.1038/s41893-019-0220-7), which may stem from uncertainties and discrepancies of data sources and the limitation of the model physics. This requires a more in-depth assessment. ⚠️