The NASA hydrological forecast system for food and water security applications

Kristi R. Arsenault; Shraddhanand Shukla; Abheera Hazra; Augusto Getirana; Amy McNally; Sujay V. Kumar; Randal D. Koster; Christa D. Peters-Lidard; Benjamin F. Zaitchik; Hamada Badr; Hahn Chul Jung; Bala Narapusetty; Mahdi Navari; Shugong Wang; David M. Mocko; Chris Funk; Laura Harrison; Gregory J. Husak; Alkhalil Adoum; Gideon Galu; Tamuka Magadzire; Jeanne Roningen; Michael Shaw; John Eylander; Karim Bergaoui; Rachael A. McDonnell; James P. Verdin

doi:10.1175/BAMS-D-18-0264.1

The NASA hydrological forecast system for food and water security applications

Kristi R. Arsenault, Shraddhanand Shukla, Abheera Hazra, Augusto Getirana, Amy McNally, Sujay V. Kumar, Randal D. Koster, Christa D. Peters-Lidard, Benjamin F. Zaitchik, Hamada Badr, Hahn Chul Jung, Bala Narapusetty, Mahdi Navari, Shugong Wang, David M. Mocko, Chris Funk, Laura Harrison, Gregory J. Husak, Alkhalil Adoum, Gideon GaluTamuka Magadzire, Jeanne Roningen, Michael Shaw, John Eylander, Karim Bergaoui, Rachael A. McDonnell, James P. Verdin

Department of Earth System Sciences

Research output: Contribution to journal › Article › peer-review

Abstract

Many regions in Africa and the Middle East are vulnerable to drought and to water and food insecurity, motivating agency efforts such as the U.S. Agency for International Development's (USAID) Famine Early Warning Systems Network (FEWS NET) to provide early warning of drought events in the region. Each year these warnings guide life-saving assistance that reaches millions of people. A new NASA multimodel, remote sensing-based hydrological forecasting and analysis system, NHyFAS, has been developed to support such efforts by improving the FEWS NET's current early warning capabilities. NHyFAS derives its skill from two sources: (i) accurate initial conditions, as produced by an offline land modeling system through the application and/or assimilation of various satellite data (precipitation, soil moisture, and terrestrial water storage), and (ii) meteorological forcing data during the forecast period as produced by a state-of-the-art ocean-land-atmosphere forecast system. The land modeling framework used is the Land Information System (LIS), which employs a suite of land surface models, allowing multimodel ensembles and multiple data assimilation strategies to better estimate land surface conditions. An evaluation of NHyFAS shows that its 1-5-month hindcasts successfully capture known historic drought events, and it has improved skill over benchmark-type hindcasts. The system also benefits from strong collaboration with end-user partners in Africa and the Middle East, who provide insights on strategies to formulate and communicate early warning indicators to water and food security communities. The additional lead time provided by this system will increase the speed, accuracy, and efficacy of humanitarian disaster relief, helping to save lives and livelihoods.

Original language	English
Pages (from-to)	E1007-E1025
Journal	Bulletin of the American Meteorological Society
Volume	101
Issue number	7
DOIs	https://doi.org/10.1175/BAMS-D-18-0264.1
Publication status	Published - 2020 Jul

Bibliographical note

Funding Information:
This work was supported by the NASA Earth Science Applications: Water Resources program, award 13-WATER13-0010 (PI: Peters-Lidard), and the FEWS NET's NASA PAPA Water Availability Monitoring Activity. We also acknowledge support to A. McNally from the NASA Harvest Consortium (NASA Applied Sciences Grant No. 80NSSC17K0625). Computing resources have been provided by NASA's Center for Climate Simulation (NCCS). We would like to acknowledge the many partners that were part of this effort, including from FEWS NET, USAID, ICBA, IWMI, USACE, and USGS, and others who helped provide valuable feedback to this paper, including Dr. Grey Nearing and three anonymous reviewers. We want to especially thank NASA's Global Modeling and Assimilation Office (GMAO) for their ongoing support of the MERRA-2 and GEOS datasets, Pete Peterson and the UCSB/CHC team for the CHIRPS dataset, the LIS team, and the multitude of teams supporting the land model development.

Funding Information:
Acknowledgments. This work was supported by the NASA Earth Science Applications: Water Resources program, award 13-WATER13-0010 (PI: Peters-Lidard), and the FEWS NET’s NASA PAPA Water Availability Monitoring Activity. We also acknowledge support to A. McNally from the NASA Harvest Consortium (NASA Applied Sciences Grant No. 80NSSC17K0625). Computing resources have been provided by NASA’s Center for Climate Simulation (NCCS). We would like to acknowledge the many partners that were part of this effort, including from FEWS NET, USAID, ICBA, IWMI, USACE, and USGS, and others who helped provide valuable feedback to this paper, including Dr. Grey Nearing and three anonymous reviewers. We want to especially thank NASA’s Global Modeling and Assimilation Office (GMAO) for their ongoing support of the MERRA-2 and GEOS datasets, Pete Peterson and the UCSB/CHC team for the CHIRPS dataset, the LIS team, and the multitude of teams supporting the land model development.

Publisher Copyright:
© 2020 American Meteorological Society.

All Science Journal Classification (ASJC) codes

Atmospheric Science

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1175/BAMS-D-18-0264.1

Cite this

Arsenault, K. R., Shukla, S., Hazra, A., Getirana, A., McNally, A., Kumar, S. V., Koster, R. D., Peters-Lidard, C. D., Zaitchik, B. F., Badr, H., Jung, H. C., Narapusetty, B., Navari, M., Wang, S., Mocko, D. M., Funk, C., Harrison, L., Husak, G. J., Adoum, A., ... Verdin, J. P. (2020). The NASA hydrological forecast system for food and water security applications. Bulletin of the American Meteorological Society, 101(7), E1007-E1025. https://doi.org/10.1175/BAMS-D-18-0264.1

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title = "The NASA hydrological forecast system for food and water security applications",

abstract = "Many regions in Africa and the Middle East are vulnerable to drought and to water and food insecurity, motivating agency efforts such as the U.S. Agency for International Development's (USAID) Famine Early Warning Systems Network (FEWS NET) to provide early warning of drought events in the region. Each year these warnings guide life-saving assistance that reaches millions of people. A new NASA multimodel, remote sensing-based hydrological forecasting and analysis system, NHyFAS, has been developed to support such efforts by improving the FEWS NET's current early warning capabilities. NHyFAS derives its skill from two sources: (i) accurate initial conditions, as produced by an offline land modeling system through the application and/or assimilation of various satellite data (precipitation, soil moisture, and terrestrial water storage), and (ii) meteorological forcing data during the forecast period as produced by a state-of-the-art ocean-land-atmosphere forecast system. The land modeling framework used is the Land Information System (LIS), which employs a suite of land surface models, allowing multimodel ensembles and multiple data assimilation strategies to better estimate land surface conditions. An evaluation of NHyFAS shows that its 1-5-month hindcasts successfully capture known historic drought events, and it has improved skill over benchmark-type hindcasts. The system also benefits from strong collaboration with end-user partners in Africa and the Middle East, who provide insights on strategies to formulate and communicate early warning indicators to water and food security communities. The additional lead time provided by this system will increase the speed, accuracy, and efficacy of humanitarian disaster relief, helping to save lives and livelihoods.",

author = "Arsenault, {Kristi R.} and Shraddhanand Shukla and Abheera Hazra and Augusto Getirana and Amy McNally and Kumar, {Sujay V.} and Koster, {Randal D.} and Peters-Lidard, {Christa D.} and Zaitchik, {Benjamin F.} and Hamada Badr and Jung, {Hahn Chul} and Bala Narapusetty and Mahdi Navari and Shugong Wang and Mocko, {David M.} and Chris Funk and Laura Harrison and Husak, {Gregory J.} and Alkhalil Adoum and Gideon Galu and Tamuka Magadzire and Jeanne Roningen and Michael Shaw and John Eylander and Karim Bergaoui and McDonnell, {Rachael A.} and Verdin, {James P.}",

note = "Funding Information: This work was supported by the NASA Earth Science Applications: Water Resources program, award 13-WATER13-0010 (PI: Peters-Lidard), and the FEWS NET's NASA PAPA Water Availability Monitoring Activity. We also acknowledge support to A. McNally from the NASA Harvest Consortium (NASA Applied Sciences Grant No. 80NSSC17K0625). Computing resources have been provided by NASA's Center for Climate Simulation (NCCS). We would like to acknowledge the many partners that were part of this effort, including from FEWS NET, USAID, ICBA, IWMI, USACE, and USGS, and others who helped provide valuable feedback to this paper, including Dr. Grey Nearing and three anonymous reviewers. We want to especially thank NASA's Global Modeling and Assimilation Office (GMAO) for their ongoing support of the MERRA-2 and GEOS datasets, Pete Peterson and the UCSB/CHC team for the CHIRPS dataset, the LIS team, and the multitude of teams supporting the land model development. Funding Information: Acknowledgments. This work was supported by the NASA Earth Science Applications: Water Resources program, award 13-WATER13-0010 (PI: Peters-Lidard), and the FEWS NET{\textquoteright}s NASA PAPA Water Availability Monitoring Activity. We also acknowledge support to A. McNally from the NASA Harvest Consortium (NASA Applied Sciences Grant No. 80NSSC17K0625). Computing resources have been provided by NASA{\textquoteright}s Center for Climate Simulation (NCCS). We would like to acknowledge the many partners that were part of this effort, including from FEWS NET, USAID, ICBA, IWMI, USACE, and USGS, and others who helped provide valuable feedback to this paper, including Dr. Grey Nearing and three anonymous reviewers. We want to especially thank NASA{\textquoteright}s Global Modeling and Assimilation Office (GMAO) for their ongoing support of the MERRA-2 and GEOS datasets, Pete Peterson and the UCSB/CHC team for the CHIRPS dataset, the LIS team, and the multitude of teams supporting the land model development. Publisher Copyright: {\textcopyright} 2020 American Meteorological Society.",

year = "2020",

month = jul,

doi = "10.1175/BAMS-D-18-0264.1",

language = "English",

volume = "101",

pages = "E1007--E1025",

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Arsenault, KR, Shukla, S, Hazra, A, Getirana, A, McNally, A, Kumar, SV, Koster, RD, Peters-Lidard, CD, Zaitchik, BF, Badr, H, Jung, HC, Narapusetty, B, Navari, M, Wang, S, Mocko, DM, Funk, C, Harrison, L, Husak, GJ, Adoum, A, Galu, G, Magadzire, T, Roningen, J, Shaw, M, Eylander, J, Bergaoui, K, McDonnell, RA & Verdin, JP 2020, 'The NASA hydrological forecast system for food and water security applications', Bulletin of the American Meteorological Society, vol. 101, no. 7, pp. E1007-E1025. https://doi.org/10.1175/BAMS-D-18-0264.1

TY - JOUR

T1 - The NASA hydrological forecast system for food and water security applications

AU - Arsenault, Kristi R.

AU - Shukla, Shraddhanand

AU - Hazra, Abheera

AU - Getirana, Augusto

AU - McNally, Amy

AU - Kumar, Sujay V.

AU - Koster, Randal D.

AU - Peters-Lidard, Christa D.

AU - Zaitchik, Benjamin F.

AU - Badr, Hamada

AU - Jung, Hahn Chul

AU - Narapusetty, Bala

AU - Navari, Mahdi

AU - Wang, Shugong

AU - Mocko, David M.

AU - Funk, Chris

AU - Harrison, Laura

AU - Husak, Gregory J.

AU - Adoum, Alkhalil

AU - Galu, Gideon

AU - Magadzire, Tamuka

AU - Roningen, Jeanne

AU - Shaw, Michael

AU - Eylander, John

AU - Bergaoui, Karim

AU - McDonnell, Rachael A.

AU - Verdin, James P.

N1 - Funding Information: This work was supported by the NASA Earth Science Applications: Water Resources program, award 13-WATER13-0010 (PI: Peters-Lidard), and the FEWS NET's NASA PAPA Water Availability Monitoring Activity. We also acknowledge support to A. McNally from the NASA Harvest Consortium (NASA Applied Sciences Grant No. 80NSSC17K0625). Computing resources have been provided by NASA's Center for Climate Simulation (NCCS). We would like to acknowledge the many partners that were part of this effort, including from FEWS NET, USAID, ICBA, IWMI, USACE, and USGS, and others who helped provide valuable feedback to this paper, including Dr. Grey Nearing and three anonymous reviewers. We want to especially thank NASA's Global Modeling and Assimilation Office (GMAO) for their ongoing support of the MERRA-2 and GEOS datasets, Pete Peterson and the UCSB/CHC team for the CHIRPS dataset, the LIS team, and the multitude of teams supporting the land model development. Funding Information: Acknowledgments. This work was supported by the NASA Earth Science Applications: Water Resources program, award 13-WATER13-0010 (PI: Peters-Lidard), and the FEWS NET’s NASA PAPA Water Availability Monitoring Activity. We also acknowledge support to A. McNally from the NASA Harvest Consortium (NASA Applied Sciences Grant No. 80NSSC17K0625). Computing resources have been provided by NASA’s Center for Climate Simulation (NCCS). We would like to acknowledge the many partners that were part of this effort, including from FEWS NET, USAID, ICBA, IWMI, USACE, and USGS, and others who helped provide valuable feedback to this paper, including Dr. Grey Nearing and three anonymous reviewers. We want to especially thank NASA’s Global Modeling and Assimilation Office (GMAO) for their ongoing support of the MERRA-2 and GEOS datasets, Pete Peterson and the UCSB/CHC team for the CHIRPS dataset, the LIS team, and the multitude of teams supporting the land model development. Publisher Copyright: © 2020 American Meteorological Society.

PY - 2020/7

Y1 - 2020/7

N2 - Many regions in Africa and the Middle East are vulnerable to drought and to water and food insecurity, motivating agency efforts such as the U.S. Agency for International Development's (USAID) Famine Early Warning Systems Network (FEWS NET) to provide early warning of drought events in the region. Each year these warnings guide life-saving assistance that reaches millions of people. A new NASA multimodel, remote sensing-based hydrological forecasting and analysis system, NHyFAS, has been developed to support such efforts by improving the FEWS NET's current early warning capabilities. NHyFAS derives its skill from two sources: (i) accurate initial conditions, as produced by an offline land modeling system through the application and/or assimilation of various satellite data (precipitation, soil moisture, and terrestrial water storage), and (ii) meteorological forcing data during the forecast period as produced by a state-of-the-art ocean-land-atmosphere forecast system. The land modeling framework used is the Land Information System (LIS), which employs a suite of land surface models, allowing multimodel ensembles and multiple data assimilation strategies to better estimate land surface conditions. An evaluation of NHyFAS shows that its 1-5-month hindcasts successfully capture known historic drought events, and it has improved skill over benchmark-type hindcasts. The system also benefits from strong collaboration with end-user partners in Africa and the Middle East, who provide insights on strategies to formulate and communicate early warning indicators to water and food security communities. The additional lead time provided by this system will increase the speed, accuracy, and efficacy of humanitarian disaster relief, helping to save lives and livelihoods.

AB - Many regions in Africa and the Middle East are vulnerable to drought and to water and food insecurity, motivating agency efforts such as the U.S. Agency for International Development's (USAID) Famine Early Warning Systems Network (FEWS NET) to provide early warning of drought events in the region. Each year these warnings guide life-saving assistance that reaches millions of people. A new NASA multimodel, remote sensing-based hydrological forecasting and analysis system, NHyFAS, has been developed to support such efforts by improving the FEWS NET's current early warning capabilities. NHyFAS derives its skill from two sources: (i) accurate initial conditions, as produced by an offline land modeling system through the application and/or assimilation of various satellite data (precipitation, soil moisture, and terrestrial water storage), and (ii) meteorological forcing data during the forecast period as produced by a state-of-the-art ocean-land-atmosphere forecast system. The land modeling framework used is the Land Information System (LIS), which employs a suite of land surface models, allowing multimodel ensembles and multiple data assimilation strategies to better estimate land surface conditions. An evaluation of NHyFAS shows that its 1-5-month hindcasts successfully capture known historic drought events, and it has improved skill over benchmark-type hindcasts. The system also benefits from strong collaboration with end-user partners in Africa and the Middle East, who provide insights on strategies to formulate and communicate early warning indicators to water and food security communities. The additional lead time provided by this system will increase the speed, accuracy, and efficacy of humanitarian disaster relief, helping to save lives and livelihoods.

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The NASA hydrological forecast system for food and water security applications

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

UN SDGs

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