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zQAIEcD58WQG{a6b(^LZqrZqg%8mR!rsG6l}iK&t?sf|x+V^9MPMruP*ngC%G#;FZ4 Ps>>ME(kabNpb!7qYp|#v literal 0 HcmV?d00001 From ac6007073d881d54a6ef2e79f5f8cc9c8699acec Mon Sep 17 00:00:00 2001 From: Jerika Christman <98853455+jerikac@users.noreply.github.com> Date: Fri, 13 Oct 2023 14:21:17 -0500 Subject: [PATCH 2/8] FLDAS Dataset Overview Page --- datasets/FLDAS-soilmoisture-anomalies.data.md | 92 +++++++++++++++++++ 1 file changed, 92 insertions(+) create mode 100644 datasets/FLDAS-soilmoisture-anomalies.data.md diff --git a/datasets/FLDAS-soilmoisture-anomalies.data.md b/datasets/FLDAS-soilmoisture-anomalies.data.md new file mode 100644 index 000000000..1d798f09b --- /dev/null +++ b/datasets/FLDAS-soilmoisture-anomalies.data.md @@ -0,0 +1,92 @@ +--- +id: fldas-soil-moisture-anomalies +name: 'FLDAS Surface Soil Moisture Anomalies' +description: "A 10 km global data product with 40 years of monthly soil moisture anomalies for food and water security monitoring from the Famine Early Warning System Network (FEWS NET) Land Data Assimilation System" +media: + src: ::file ./FLDAS_Dataset_Cover.jpg + alt: Landscape in Gondar, Ethiopia + author: + name: Amy McNally +taxonomy: + - name: Topics + values: + - Agriculture + - name: Source + values: + - NASA GES DISC +layers: + - id: SoilMoi00_10cm_tavg + stacCol: fldas-soil-moisture-anomalies + name: FLDAS Surface Soil Moisture Anomalies + type: raster + description: 'Surface soil moisture 0-10cm anomaly' + zoomExtent: + - 1 + - 14 + sourceParams: + colormap_name: rdbu + rescale: -0.3, 0.3 + resampling: bilinear + bidx: 1 + nodata: -9999 + compare: + datasetId: fldas-soil-moisture-anomalies + layerId: SoilMoi00_10cm_tavg + mapLabel: | + ::js ({ dateFns, datetime, compareDatetime }) => { + return `${dateFns.format(datetime, 'DD LLL yyyy')}`; + } + legend: + type: gradient + min: "-0.3" + max: "0.3" + stops: + - "#67001f" + - "#d6604d" + - "#fddbc7" + - "#d1e5f0" + - "#4393c3" + - "#053061" +--- + + + +FLDAS is the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System. The goal of FLDAS is to use observational and forecast datasets and advanced modeling methods to generate high quality fields of land surface states and fluxes used for FEWS NET decision support. The FLDAS systems are custom instances of the NASA Land Information System (LIS) that have been adapted to work with the domains, data streams, and monitoring and forecast requirements associated with food security assessment in data-sparse, developing countries. Surface soil moisture anomalies are an indicator of wet and dry extremes that have the potential to impact agricultural and food security outcomes. + +**Temporal Extent:** January 1982 to June 2023 +**Temporal Resolution:** Monthly +**Spatial Extent:** Quasi-Global ( -180.0,-60.0,180.0,90.0) +**Spatial Resolution:** 10 km x 10 km +**Data Units:** Fraction Soil moisture anomaly (mm3/mm3) difference from 1982-2016 monthly mean +**Data Type:** Research +**Data Latency:** Monthly + + +**Scientific Details:** +The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) contains a series of land surface parameters simulated from the Noah 3.6.1 model. The data are in 0.10 degree resolution and range from January 1982 to present. The temporal resolution is monthly and the spatial coverage is global (60S, 180W, 90N, 180E). The simulation was forced by a combination of the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) data and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) daily rainfall data that has been temporally downscaled using the NASA Land Data Toolkit. The simulation was initialized on January 1, 1982 using soil moisture and other state fields from a FLDAS/Noah model climatology for that day of the year. Soil moisture anomalies are computed based on monthly averages from 1982-2016. + + + + + + + +## Source Data Product Citation +Amy McNally NASA/GSFC/HSL (2018), FLDAS Noah Land Surface Model L4 Global Monthly Anomaly 0.1 x 0.1 degree (MERRA-2 and CHIRPS), Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], 10.5067/GNKZZBAYDF4W + +## Dataset Accuracy +This dataset uses CHIRPS precipitation inputs and MERRA-2 reanalysis. While regional, relative, comparisons to remotely sensed estimates and other model products are favorable, users should verify that the data accuracy meets the requirements of their specific application, and interpret results accordingly. + +## Key Publications +McNally, A., Arsenault, K., Kumar, S. et al. A land data assimilation system for sub-Saharan Africa food and water security applications. Sci Data 4, 170012 (2017). https://doi.org/10.1038/sdata.2017.12 + ## Acknowledgment +We gratefully acknowledge the financial support from the NASA Earth Science Applications: Water Resources program award 13-WATER13-0010, and USAID FEWS NET and NASA Participating Agency Program Agreement and NASA Harvest. Computing was supported by the resources at the NASA Center for Climate Simulation (NCCS). Distribution of data from the Goddard Earth Sciences Data and Information Services Center (GES DISC) is funded by NASA's Science Mission Directorate (SMD). + +## License +[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode) (CC BY 4.0). + + + + + + \ No newline at end of file From 6535cf30a4e61d1f11ca3a8669568761cb6797c7 Mon Sep 17 00:00:00 2001 From: Jerika Christman <98853455+jerikac@users.noreply.github.com> Date: Fri, 13 Oct 2023 14:22:07 -0500 Subject: [PATCH 3/8] Update and rename FLDAS-soilmoisture-anomalies.data.md to FLDAS-soilmoisture-anomalies.data.mdx --- ...-anomalies.data.md => FLDAS-soilmoisture-anomalies.data.mdx} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename datasets/{FLDAS-soilmoisture-anomalies.data.md => FLDAS-soilmoisture-anomalies.data.mdx} (97%) diff --git a/datasets/FLDAS-soilmoisture-anomalies.data.md b/datasets/FLDAS-soilmoisture-anomalies.data.mdx similarity index 97% rename from datasets/FLDAS-soilmoisture-anomalies.data.md rename to datasets/FLDAS-soilmoisture-anomalies.data.mdx index 1d798f09b..2cd3daf34 100644 --- a/datasets/FLDAS-soilmoisture-anomalies.data.md +++ b/datasets/FLDAS-soilmoisture-anomalies.data.mdx @@ -89,4 +89,4 @@ We gratefully acknowledge the financial support from the NASA Earth Science Appl - \ No newline at end of file + From ad8938bce1c7951f6c2220a0582ffa81e026921d Mon Sep 17 00:00:00 2001 From: Jerika Christman <98853455+jerikac@users.noreply.github.com> Date: Fri, 13 Oct 2023 14:35:50 -0500 Subject: [PATCH 4/8] Update FLDAS-soilmoisture-anomalies.data.mdx --- datasets/FLDAS-soilmoisture-anomalies.data.mdx | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/datasets/FLDAS-soilmoisture-anomalies.data.mdx b/datasets/FLDAS-soilmoisture-anomalies.data.mdx index 2cd3daf34..750383ffa 100644 --- a/datasets/FLDAS-soilmoisture-anomalies.data.mdx +++ b/datasets/FLDAS-soilmoisture-anomalies.data.mdx @@ -37,6 +37,8 @@ layers: return `${dateFns.format(datetime, 'DD LLL yyyy')}`; } legend: +unit: + label: kg mm3/mm3 type: gradient min: "-0.3" max: "0.3" @@ -53,7 +55,7 @@ layers: FLDAS is the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System. The goal of FLDAS is to use observational and forecast datasets and advanced modeling methods to generate high quality fields of land surface states and fluxes used for FEWS NET decision support. The FLDAS systems are custom instances of the NASA Land Information System (LIS) that have been adapted to work with the domains, data streams, and monitoring and forecast requirements associated with food security assessment in data-sparse, developing countries. Surface soil moisture anomalies are an indicator of wet and dry extremes that have the potential to impact agricultural and food security outcomes. -**Temporal Extent:** January 1982 to June 2023 +**Temporal Extent:** January 1982 to June 2023 **Temporal Resolution:** Monthly **Spatial Extent:** Quasi-Global ( -180.0,-60.0,180.0,90.0) **Spatial Resolution:** 10 km x 10 km @@ -87,6 +89,3 @@ We gratefully acknowledge the financial support from the NASA Earth Science Appl - - - From d82486149f488560927cabedf6aafc781e93f059 Mon Sep 17 00:00:00 2001 From: Jerika Christman <98853455+jerikac@users.noreply.github.com> Date: Fri, 13 Oct 2023 14:47:47 -0500 Subject: [PATCH 5/8] Update FLDAS-soilmoisture-anomalies.data.mdx small changes --- .../FLDAS-soilmoisture-anomalies.data.mdx | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/datasets/FLDAS-soilmoisture-anomalies.data.mdx b/datasets/FLDAS-soilmoisture-anomalies.data.mdx index 750383ffa..2fda60fae 100644 --- a/datasets/FLDAS-soilmoisture-anomalies.data.mdx +++ b/datasets/FLDAS-soilmoisture-anomalies.data.mdx @@ -4,7 +4,7 @@ name: 'FLDAS Surface Soil Moisture Anomalies' description: "A 10 km global data product with 40 years of monthly soil moisture anomalies for food and water security monitoring from the Famine Early Warning System Network (FEWS NET) Land Data Assimilation System" media: src: ::file ./FLDAS_Dataset_Cover.jpg - alt: Landscape in Gondar, Ethiopia + alt: Landscape in Gondar, Ethiopia author: name: Amy McNally taxonomy: @@ -37,7 +37,7 @@ layers: return `${dateFns.format(datetime, 'DD LLL yyyy')}`; } legend: -unit: + unit: label: kg mm3/mm3 type: gradient min: "-0.3" @@ -55,7 +55,7 @@ unit: FLDAS is the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System. The goal of FLDAS is to use observational and forecast datasets and advanced modeling methods to generate high quality fields of land surface states and fluxes used for FEWS NET decision support. The FLDAS systems are custom instances of the NASA Land Information System (LIS) that have been adapted to work with the domains, data streams, and monitoring and forecast requirements associated with food security assessment in data-sparse, developing countries. Surface soil moisture anomalies are an indicator of wet and dry extremes that have the potential to impact agricultural and food security outcomes. -**Temporal Extent:** January 1982 to June 2023 +**Temporal Extent:** January 1982 - June 2023 **Temporal Resolution:** Monthly **Spatial Extent:** Quasi-Global ( -180.0,-60.0,180.0,90.0) **Spatial Resolution:** 10 km x 10 km @@ -67,12 +67,10 @@ FLDAS is the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilat **Scientific Details:** The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) contains a series of land surface parameters simulated from the Noah 3.6.1 model. The data are in 0.10 degree resolution and range from January 1982 to present. The temporal resolution is monthly and the spatial coverage is global (60S, 180W, 90N, 180E). The simulation was forced by a combination of the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) data and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) daily rainfall data that has been temporally downscaled using the NASA Land Data Toolkit. The simulation was initialized on January 1, 1982 using soil moisture and other state fields from a FLDAS/Noah model climatology for that day of the year. Soil moisture anomalies are computed based on monthly averages from 1982-2016. - - + - - + ## Source Data Product Citation Amy McNally NASA/GSFC/HSL (2018), FLDAS Noah Land Surface Model L4 Global Monthly Anomaly 0.1 x 0.1 degree (MERRA-2 and CHIRPS), Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], 10.5067/GNKZZBAYDF4W @@ -80,12 +78,13 @@ Amy McNally NASA/GSFC/HSL (2018), FLDAS Noah Land Surface Model L4 Global Monthl This dataset uses CHIRPS precipitation inputs and MERRA-2 reanalysis. While regional, relative, comparisons to remotely sensed estimates and other model products are favorable, users should verify that the data accuracy meets the requirements of their specific application, and interpret results accordingly. ## Key Publications -McNally, A., Arsenault, K., Kumar, S. et al. A land data assimilation system for sub-Saharan Africa food and water security applications. Sci Data 4, 170012 (2017). https://doi.org/10.1038/sdata.2017.12 - ## Acknowledgment +McNally, A., Arsenault, K., Kumar, S. et al. A land data assimilation system for sub-Saharan Africa food and water security applications. Sci Data 4, 170012 (2017). [https://doi.org/10.1038/sdata.2017.12] (https://doi.org/10.1038/sdata.2017.12) + +## Acknowledgment We gratefully acknowledge the financial support from the NASA Earth Science Applications: Water Resources program award 13-WATER13-0010, and USAID FEWS NET and NASA Participating Agency Program Agreement and NASA Harvest. Computing was supported by the resources at the NASA Center for Climate Simulation (NCCS). Distribution of data from the Goddard Earth Sciences Data and Information Services Center (GES DISC) is funded by NASA's Science Mission Directorate (SMD). ## License [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode) (CC BY 4.0). - + From 7e43b1ddfc703430f2ff7d2ad27a8f4986c64ab6 Mon Sep 17 00:00:00 2001 From: Jerika Christman <98853455+jerikac@users.noreply.github.com> Date: Fri, 13 Oct 2023 14:56:58 -0500 Subject: [PATCH 6/8] Update FLDAS-soilmoisture-anomalies.data.mdx updated scientific details --- datasets/FLDAS-soilmoisture-anomalies.data.mdx | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/datasets/FLDAS-soilmoisture-anomalies.data.mdx b/datasets/FLDAS-soilmoisture-anomalies.data.mdx index 2fda60fae..73359a4cd 100644 --- a/datasets/FLDAS-soilmoisture-anomalies.data.mdx +++ b/datasets/FLDAS-soilmoisture-anomalies.data.mdx @@ -55,13 +55,13 @@ layers: FLDAS is the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System. The goal of FLDAS is to use observational and forecast datasets and advanced modeling methods to generate high quality fields of land surface states and fluxes used for FEWS NET decision support. The FLDAS systems are custom instances of the NASA Land Information System (LIS) that have been adapted to work with the domains, data streams, and monitoring and forecast requirements associated with food security assessment in data-sparse, developing countries. Surface soil moisture anomalies are an indicator of wet and dry extremes that have the potential to impact agricultural and food security outcomes. -**Temporal Extent:** January 1982 - June 2023 -**Temporal Resolution:** Monthly -**Spatial Extent:** Quasi-Global ( -180.0,-60.0,180.0,90.0) -**Spatial Resolution:** 10 km x 10 km -**Data Units:** Fraction Soil moisture anomaly (mm3/mm3) difference from 1982-2016 monthly mean -**Data Type:** Research -**Data Latency:** Monthly +- **Temporal Extent:** January 1982 - June 2023 +- **Temporal Resolution:** Monthly +- **Spatial Extent:** Quasi-Global ( -180.0,-60.0,180.0,90.0) +- **Spatial Resolution:** 10 km x 10 km +- **Data Units:** Fraction Soil moisture anomaly (mm3/mm3) difference from 1982-2016 monthly mean +- **Data Type:** Research +- **Data Latency:** Monthly **Scientific Details:** @@ -72,7 +72,7 @@ The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation Syste ## Source Data Product Citation -Amy McNally NASA/GSFC/HSL (2018), FLDAS Noah Land Surface Model L4 Global Monthly Anomaly 0.1 x 0.1 degree (MERRA-2 and CHIRPS), Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], 10.5067/GNKZZBAYDF4W +Amy McNally, NASA/GSFC/HSL (2018), FLDAS Noah Land Surface Model L4 Global Monthly Anomaly 0.1 x 0.1 degree (MERRA-2 and CHIRPS), Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], [https://doi.org/10.5067/GNKZZBAYDF4W] (10.5067/GNKZZBAYDF4W) ## Dataset Accuracy This dataset uses CHIRPS precipitation inputs and MERRA-2 reanalysis. While regional, relative, comparisons to remotely sensed estimates and other model products are favorable, users should verify that the data accuracy meets the requirements of their specific application, and interpret results accordingly. From 2afac4c1eeae626a6c77b1538b69e209a17e8b17 Mon Sep 17 00:00:00 2001 From: Jerika Christman <98853455+jerikac@users.noreply.github.com> Date: Fri, 13 Oct 2023 16:29:08 -0500 Subject: [PATCH 7/8] Update FLDAS-soilmoisture-anomalies.data.mdx URL updates --- datasets/FLDAS-soilmoisture-anomalies.data.mdx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/datasets/FLDAS-soilmoisture-anomalies.data.mdx b/datasets/FLDAS-soilmoisture-anomalies.data.mdx index 73359a4cd..90e162dbf 100644 --- a/datasets/FLDAS-soilmoisture-anomalies.data.mdx +++ b/datasets/FLDAS-soilmoisture-anomalies.data.mdx @@ -72,13 +72,13 @@ The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation Syste ## Source Data Product Citation -Amy McNally, NASA/GSFC/HSL (2018), FLDAS Noah Land Surface Model L4 Global Monthly Anomaly 0.1 x 0.1 degree (MERRA-2 and CHIRPS), Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], [https://doi.org/10.5067/GNKZZBAYDF4W] (10.5067/GNKZZBAYDF4W) +Amy McNally, NASA/GSFC/HSL (2018), FLDAS Noah Land Surface Model L4 Global Monthly Anomaly 0.1 x 0.1 degree (MERRA-2 and CHIRPS), Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], [10.5067/GNKZZBAYDF4W](https://doi.org/10.5067/GNKZZBAYDF4W) ## Dataset Accuracy This dataset uses CHIRPS precipitation inputs and MERRA-2 reanalysis. While regional, relative, comparisons to remotely sensed estimates and other model products are favorable, users should verify that the data accuracy meets the requirements of their specific application, and interpret results accordingly. ## Key Publications -McNally, A., Arsenault, K., Kumar, S. et al. A land data assimilation system for sub-Saharan Africa food and water security applications. Sci Data 4, 170012 (2017). [https://doi.org/10.1038/sdata.2017.12] (https://doi.org/10.1038/sdata.2017.12) +McNally, A., Arsenault, K., Kumar, S. et al. A land data assimilation system for sub-Saharan Africa food and water security applications. Sci Data 4, 170012 (2017). https://doi.org/10.1038/sdata.2017.12 ## Acknowledgment We gratefully acknowledge the financial support from the NASA Earth Science Applications: Water Resources program award 13-WATER13-0010, and USAID FEWS NET and NASA Participating Agency Program Agreement and NASA Harvest. Computing was supported by the resources at the NASA Center for Climate Simulation (NCCS). Distribution of data from the Goddard Earth Sciences Data and Information Services Center (GES DISC) is funded by NASA's Science Mission Directorate (SMD). From 55b680cb08b20fbb6a9e6d5d7092ef93c3245692 Mon Sep 17 00:00:00 2001 From: Hanbyul Jo Date: Mon, 30 Oct 2023 14:33:04 -0400 Subject: [PATCH 8/8] Edit zoom extent, lint --- .../FLDAS-soilmoisture-anomalies.data.mdx | 34 ++++++++++--------- 1 file changed, 18 insertions(+), 16 deletions(-) diff --git a/datasets/FLDAS-soilmoisture-anomalies.data.mdx b/datasets/FLDAS-soilmoisture-anomalies.data.mdx index 90e162dbf..870a4850f 100644 --- a/datasets/FLDAS-soilmoisture-anomalies.data.mdx +++ b/datasets/FLDAS-soilmoisture-anomalies.data.mdx @@ -1,6 +1,6 @@ --- id: fldas-soil-moisture-anomalies -name: 'FLDAS Surface Soil Moisture Anomalies' +name: "FLDAS Surface Soil Moisture Anomalies" description: "A 10 km global data product with 40 years of monthly soil moisture anomalies for food and water security monitoring from the Famine Early Warning System Network (FEWS NET) Land Data Assimilation System" media: src: ::file ./FLDAS_Dataset_Cover.jpg @@ -19,9 +19,9 @@ layers: stacCol: fldas-soil-moisture-anomalies name: FLDAS Surface Soil Moisture Anomalies type: raster - description: 'Surface soil moisture 0-10cm anomaly' + description: "Surface soil moisture 0-10cm anomaly" zoomExtent: - - 1 + - 0 - 14 sourceParams: colormap_name: rdbu @@ -43,27 +43,26 @@ layers: min: "-0.3" max: "0.3" stops: - - "#67001f" - - "#d6604d" - - "#fddbc7" - - "#d1e5f0" - - "#4393c3" - - "#053061" + - "#67001f" + - "#d6604d" + - "#fddbc7" + - "#d1e5f0" + - "#4393c3" + - "#053061" --- FLDAS is the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System. The goal of FLDAS is to use observational and forecast datasets and advanced modeling methods to generate high quality fields of land surface states and fluxes used for FEWS NET decision support. The FLDAS systems are custom instances of the NASA Land Information System (LIS) that have been adapted to work with the domains, data streams, and monitoring and forecast requirements associated with food security assessment in data-sparse, developing countries. Surface soil moisture anomalies are an indicator of wet and dry extremes that have the potential to impact agricultural and food security outcomes. -- **Temporal Extent:** January 1982 - June 2023 -- **Temporal Resolution:** Monthly +- **Temporal Extent:** January 1982 - June 2023 +- **Temporal Resolution:** Monthly - **Spatial Extent:** Quasi-Global ( -180.0,-60.0,180.0,90.0) - **Spatial Resolution:** 10 km x 10 km -- **Data Units:** Fraction Soil moisture anomaly (mm3/mm3) difference from 1982-2016 monthly mean -- **Data Type:** Research +- **Data Units:** Fraction Soil moisture anomaly (mm3/mm3) difference from 1982-2016 monthly mean +- **Data Type:** Research - **Data Latency:** Monthly - **Scientific Details:** The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) contains a series of land surface parameters simulated from the Noah 3.6.1 model. The data are in 0.10 degree resolution and range from January 1982 to present. The temporal resolution is monthly and the spatial coverage is global (60S, 180W, 90N, 180E). The simulation was forced by a combination of the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) data and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) daily rainfall data that has been temporally downscaled using the NASA Land Data Toolkit. The simulation was initialized on January 1, 1982 using soil moisture and other state fields from a FLDAS/Noah model climatology for that day of the year. Soil moisture anomalies are computed based on monthly averages from 1982-2016. @@ -78,13 +77,16 @@ Amy McNally, NASA/GSFC/HSL (2018), FLDAS Noah Land Surface Model L4 Global Month This dataset uses CHIRPS precipitation inputs and MERRA-2 reanalysis. While regional, relative, comparisons to remotely sensed estimates and other model products are favorable, users should verify that the data accuracy meets the requirements of their specific application, and interpret results accordingly. ## Key Publications -McNally, A., Arsenault, K., Kumar, S. et al. A land data assimilation system for sub-Saharan Africa food and water security applications. Sci Data 4, 170012 (2017). https://doi.org/10.1038/sdata.2017.12 + +McNally, A., Arsenault, K., Kumar, S. et al. A land data assimilation system for sub-Saharan Africa food and water security applications. Sci Data 4, 170012 (2017). https://doi.org/10.1038/sdata.2017.12 ## Acknowledgment + We gratefully acknowledge the financial support from the NASA Earth Science Applications: Water Resources program award 13-WATER13-0010, and USAID FEWS NET and NASA Participating Agency Program Agreement and NASA Harvest. Computing was supported by the resources at the NASA Center for Climate Simulation (NCCS). Distribution of data from the Goddard Earth Sciences Data and Information Services Center (GES DISC) is funded by NASA's Science Mission Directorate (SMD). ## License -[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode) (CC BY 4.0). + +[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode) (CC BY 4.0).