From bfd6bb5a8f41b96c742f7396b9e64b79932b90db Mon Sep 17 00:00:00 2001 From: Wanshu Nie Date: Wed, 12 Apr 2023 10:25:38 -0400 Subject: [PATCH] update link and remove old trend dataset --- datasets/twstrend.data.mdx | 109 ------------------------- discoveries/tws-trends.discoveries.mdx | 4 +- 2 files changed, 2 insertions(+), 111 deletions(-) delete mode 100644 datasets/twstrend.data.mdx diff --git a/datasets/twstrend.data.mdx b/datasets/twstrend.data.mdx deleted file mode 100644 index 7e831336e..000000000 --- a/datasets/twstrend.data.mdx +++ /dev/null @@ -1,109 +0,0 @@ ---- -id: tws-trend -name: 'Terrestrial Water Storage Trend' -description: "Trend in TWS anomalies modeled using data assimilation within Land Information System framework" -media: - src: ::file ./twsanomaly-globe.png - alt: TWS trend of anomalies from LIS outputs. - author: - name: NASA LIS - url: -thematics: - - eis -layers: - - id: lis-tws-trend - stacCol: lis-tws-trend - name: 'TWS Trend' - type: raster - description: 'Trends in TWS anomalies from LIS outputs' - zoomExtent: - - 0 - - 11 - sourceParams: - bidx: 1 - colormap_name: rdylbu - rescale: - - -1 - - 1 - nodata: -9999 - legend: - type: gradient - label: Trend in TWS Anomaly - min: "-1" - max: "1" - stops: - - "#a50026" - - "#f46d43" - - "#fee090" - - "#e0f3f8" - - "#74add1" - - "#313695" ---- - - -## Overview -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. - -
- - - Depleting TWS over CA captured by negative TWS anomaly trends - -
- - - - -## Modeling TWS -TWS is modeled using Noah-MP land surface model (LSM) within LIS framework by assimilating NASA earth observations of soil moisture from Soil Moisture Active Passive (SMAP), leaf area index from MODIS sensor, and TWS from GRACE/GRACE-FO. The modeled TWS is produced over global domain at a resolution of 10 km. - - - - -
- - - Trend in TWS anomalies over India showing large groundwater extraction leading to depleting TWS - -
- -## Interpreting the data -The TWS anomalies are calculated as differences of raw TWS with the climatology obtained over 2001-2021. Negative anomalies denote lower than normal TWS and vice-versa. The trend over time is thus deseasonalized and reflective of changes due to human impacts. - - - - - - -## Additional resources -* [EIS Freshwater](https://freshwater.eis.smce.nasa.gov/) - -* [Land Information System](https://lis.gsfc.nasa.gov/) - -### Explore the Missions -* [GRACE-FO](https://gracefo.jpl.nasa.gov/data/grace-fo-data/) - -* [SMAP](https://smap.jpl.nasa.gov/) - -* [MODIS](https://modis.gsfc.nasa.gov/) - - - diff --git a/discoveries/tws-trends.discoveries.mdx b/discoveries/tws-trends.discoveries.mdx index f7d800ef8..281a3fbcc 100644 --- a/discoveries/tws-trends.discoveries.mdx +++ b/discoveries/tws-trends.discoveries.mdx @@ -21,7 +21,7 @@ thematics: 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/eis/datasets/global-reanalysis-da). Join the discussion and provide comments on this Discovery at https://github.com/orgs/Earth-Information-System/discussions. @@ -76,7 +76,7 @@ thematics: ## 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/eis/datasets/lis-global-da-trends). ⚠️ 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. ⚠️