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Acute and chronic water scarcity impacts four billion people, a number likely to climb with population growth and increasing demand for food and energy production. Chronic water insecurity and long-term trends are well studied at the global and regional level; however, there have not been adequate systems in place for routinely monitoring acute water scarcity. To address this gap, we developed a monthly monitoring system that computes annual water availability per capita based on hydrologic data from the Famine Early Warning System Network (FEWS NET) Land Data Assimilation System (FLDAS) and gridded population data from WorldPop. The monitoring system yields maps of acute water scarcity using monthly Falkenmark classifications and departures from the long-term mean classification. These maps are designed to serve FEWS NET monitoring objectives; however, the underlying data are publicly available and can support research on the roles of population and hydrologic change on water scarcity at sub-annual and sub-national scales.
Amy McNally; Kristine Verdin; Laura Harrison; Augusto Getirana; Jossy Jacob; Shraddhanand Shukla; Kristi Arsenault; Christa Peters-Lidard; James P. Verdin. Acute Water-Scarcity Monitoring for Africa. Water 2019, 11, 1968 .
AMA StyleAmy McNally, Kristine Verdin, Laura Harrison, Augusto Getirana, Jossy Jacob, Shraddhanand Shukla, Kristi Arsenault, Christa Peters-Lidard, James P. Verdin. Acute Water-Scarcity Monitoring for Africa. Water. 2019; 11 (10):1968.
Chicago/Turabian StyleAmy McNally; Kristine Verdin; Laura Harrison; Augusto Getirana; Jossy Jacob; Shraddhanand Shukla; Kristi Arsenault; Christa Peters-Lidard; James P. Verdin. 2019. "Acute Water-Scarcity Monitoring for Africa." Water 11, no. 10: 1968.
An evapotranspiration (ET) ensemble composed of 36 land surface model (LSM) experiments and four diagnostic datasets (GLEAM, ALEXI, MOD16, and FLUXNET) is used to investigate uncertainties in ET estimate over five climate regions in West Africa. Diagnostic ET datasets show lower uncertainty estimates and smaller seasonal variations than the LSM-based ET values, particularly in the humid climate regions. Overall, the impact of the choice of LSMs and meteorological forcing datasets on the modeled ET rates increases from north to south. The LSM formulations and parameters have the largest impact on ET in humid regions, contributing to 90% of the ET uncertainty estimates. Precipitation contributes to the ET uncertainty primarily in arid regions. The LSM-based ET estimates are sensitive to the uncertainty of net radiation in arid region and precipitation in humid region. This study serves as support for better determining water availability for agriculture and livelihoods in Africa with earth observations and land surface models.
Hahn Chul Jung; Augusto Getirana; Kristi R. Arsenault; Thomas R.H. Holmes; Amy McNally. Uncertainties in Evapotranspiration Estimates over West Africa. Remote Sensing 2019, 11, 892 .
AMA StyleHahn Chul Jung, Augusto Getirana, Kristi R. Arsenault, Thomas R.H. Holmes, Amy McNally. Uncertainties in Evapotranspiration Estimates over West Africa. Remote Sensing. 2019; 11 (8):892.
Chicago/Turabian StyleHahn Chul Jung; Augusto Getirana; Kristi R. Arsenault; Thomas R.H. Holmes; Amy McNally. 2019. "Uncertainties in Evapotranspiration Estimates over West Africa." Remote Sensing 11, no. 8: 892.
In a globalizing and rapidly-developing world, reliable, sustainable access to water and food are inextricably linked to each other and basic human rights. Achieving security and sustainability in both requires recognition of these linkages, as well as continued innovations in both science and policy. We present case studies of how Earth observations are being used in applications at the nexus of water and food security: crop monitoring in support of G20 global market assessments, water stress early warning for USAID, soil moisture monitoring for USDA's Foreign Agricultural Service, and identifying food security vulnerabilities for climate change assessments for the UN and the UK international development agency. These case studies demonstrate that Earth observations are essential for providing the data and scalability to monitor relevant indicators across space and time, as well as understanding agriculture, the hydrological cycle, and the water-food nexus. The described projects follow the guidelines for co-developing useable knowledge for sustainable development policy. We show how working closely with stakeholders is essential for transforming NASA Earth observations into accurate, timely, and relevant information for water-food nexus decision support. We conclude with recommendations for continued efforts in using Earth observations for addressing the water-food nexus and the need to incorporate the role of energy for improved food and water security assessments.
Amy McNally; Sean McCartney; Alex C. Ruane; Iliana E. Mladenova; Alyssa K. Whitcraft; Inbal Becker-Reshef; John D. Bolten; Christa D. Peters-Lidard; Cynthia Rosenzweig; Stephanie Schollaert Uz. Hydrologic and Agricultural Earth Observations and Modeling for the Water-Food Nexus. Frontiers in Environmental Science 2019, 7, 1 .
AMA StyleAmy McNally, Sean McCartney, Alex C. Ruane, Iliana E. Mladenova, Alyssa K. Whitcraft, Inbal Becker-Reshef, John D. Bolten, Christa D. Peters-Lidard, Cynthia Rosenzweig, Stephanie Schollaert Uz. Hydrologic and Agricultural Earth Observations and Modeling for the Water-Food Nexus. Frontiers in Environmental Science. 2019; 7 ():1.
Chicago/Turabian StyleAmy McNally; Sean McCartney; Alex C. Ruane; Iliana E. Mladenova; Alyssa K. Whitcraft; Inbal Becker-Reshef; John D. Bolten; Christa D. Peters-Lidard; Cynthia Rosenzweig; Stephanie Schollaert Uz. 2019. "Hydrologic and Agricultural Earth Observations and Modeling for the Water-Food Nexus." Frontiers in Environmental Science 7, no. : 1.
In operational analyses of the surface moisture imbalance that defines drought, the supply aspect has generally been well characterized by precipitation; however, the same count be said of the demand side—a function of evaporative demand (E0) and surface moisture availability. In drought monitoring, E0 is often poorly parameterized by a climatological mean, by non-physically based estimates, or is neglected entirely. One problem has been a paucity of driver data—on temperature, humidity, solar radiation, and wind speed—required to fully characterize E0. This deficient E0 modeling is particularly troublesome over data-sparse regions that are also home to drought-vulnerable populations, such as across much of Africa. There is thus urgent need for global E0 estimates for physically accurate drought analyses and food security assessments; further we need an improved understanding of how E0 and drought interact and to exploit these interactions in drought monitoring. In this presentation we explore ways to meet these needs. From MERRA-2—an accurate, fine-resolution land-surface/atmosphere reanalysis—we have developed a >38-year, daily, global Penman-Monteith reference ET dataset as a fully physical metric of E0. This dataset is valuable for examining hydroclimatic changes and extremes. A novel drought index based on this dataset—the Evaporative Demand Drought Index (EDDI)—represents drought’s demand perspective, and permits early warning and ongoing monitoring of agricultural flash drought and hydrologic drought. We highlight the findings of our examination of E0-drought interactions and using EDDI in Africa. Using reference ET as an E0 metric has permitted explicit attribution of the variability of E0 across Africa, and of E0 anomalies associated with canonical droughts in the Sahel region. This analysis determines where, when, and to what relative degree each of the individual drivers of E0 affects the demand side of drought. Using independent estimates of drought across space and time—CHIRPS precipitation and the Normalized Difference Vegetation Index for 1982-2015—we examine the differences between drought and non-drought periods, and between precipitation-forced droughts and droughts forced by a combination of precipitation and E0.
Mike Hobbins; Laura Harrison; Sari Blakeley; Candida Dewes; Greg Husak; Shraddhanand Shukla; Harikishan Jayanthi; Amy McNally; Daniel Sarmiento; James Verdin. Drought in Africa: Understanding and Exploiting the Demand Perspective Using a New Evaporative Demand Reanalysis. 2019, 1 .
AMA StyleMike Hobbins, Laura Harrison, Sari Blakeley, Candida Dewes, Greg Husak, Shraddhanand Shukla, Harikishan Jayanthi, Amy McNally, Daniel Sarmiento, James Verdin. Drought in Africa: Understanding and Exploiting the Demand Perspective Using a New Evaporative Demand Reanalysis. . 2019; ():1.
Chicago/Turabian StyleMike Hobbins; Laura Harrison; Sari Blakeley; Candida Dewes; Greg Husak; Shraddhanand Shukla; Harikishan Jayanthi; Amy McNally; Daniel Sarmiento; James Verdin. 2019. "Drought in Africa: Understanding and Exploiting the Demand Perspective Using a New Evaporative Demand Reanalysis." , no. : 1.
Seasonal agricultural drought monitoring systems, which rely on satellite remote sensing and land surface models (LSMs), are important for disaster risk reduction and famine early warning. These systems require the best available weather inputs, as well as a long-term historical record to contextualize current observations. This article introduces the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), a custom instance of the NASA Land Information System (LIS) framework. The FLDAS is routinely used to produce multi-model and multi-forcing estimates of hydro-climate states and fluxes over semi-arid, food insecure regions of Africa. These modeled data and derived products, like soil moisture percentiles and water availability, were designed and are currently used to complement FEWS NET’s operational remotely sensed rainfall, evapotranspiration, and vegetation observations. The 30+ years of monthly outputs from the FLDAS simulations are publicly available from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) and recommended for use in hydroclimate studies, early warning applications, and by agro-meteorological scientists in Eastern, Southern, and Western Africa.
Amy McNally; Kristi Arsenault; Sujay Kumar; Shraddhanand Shukla; Pete Peterson; Shugong Wang; Chris Funk; Christa D. Peters-Lidard; James P. Verdin. A land data assimilation system for sub-Saharan Africa food and water security applications. Scientific Data 2017, 4, 170012 .
AMA StyleAmy McNally, Kristi Arsenault, Sujay Kumar, Shraddhanand Shukla, Pete Peterson, Shugong Wang, Chris Funk, Christa D. Peters-Lidard, James P. Verdin. A land data assimilation system for sub-Saharan Africa food and water security applications. Scientific Data. 2017; 4 (1):170012.
Chicago/Turabian StyleAmy McNally; Kristi Arsenault; Sujay Kumar; Shraddhanand Shukla; Pete Peterson; Shugong Wang; Chris Funk; Christa D. Peters-Lidard; James P. Verdin. 2017. "A land data assimilation system for sub-Saharan Africa food and water security applications." Scientific Data 4, no. 1: 170012.
To assess growing season conditions where ground based observations are limited or unavailable, food security and agricultural drought monitoring analysts rely on publicly available remotely sensed rainfall and vegetation greenness. There are also remotely sensed soil moisture observations from missions like the European Space Agency (ESA), Soil Moisture and Ocean Salinity (SMOS) and NASA’s Soil Moisture Active Passive (SMAP); however, these time series are still too short to conduct studies that demonstrate the utility of these data for operational applications, or to provide historical context for extreme wet or dry events. To promote the use of remotely sensed soil moisture in agricultural drought and food security monitoring, we evaluate the quality of a 30+ year time series of merged active–passive microwave soil moisture from the ESA Climate Change Initiative (CCI-SM) over East Africa. Compared to the Normalized Difference Vegetation index (NDVI) and modeled soil moisture products, we find substantial spatial and temporal gaps in the early part of the CCI-SM record, with adequate data coverage beginning in 1992. From this point forward, growing season CCI-SM anomalies are well correlated (R > 0.5) with modeled soil moisture, and in some regions, NDVI. We use pixel-wise correlation analysis and qualitative comparisons of seasonal maps and time series to show that remotely sensed soil moisture can inform remote drought monitoring that has traditionally relied on rainfall and NDVI in moderately vegetated regions.
Amy McNally; Shraddhanand Shukla; Kristi R. Arsenault; Shugong Wang; Christa D. Peters-Lidard; James P. Verdin. Evaluating ESA CCI soil moisture in East Africa. International Journal of Applied Earth Observation and Geoinformation 2016, 48, 96 -109.
AMA StyleAmy McNally, Shraddhanand Shukla, Kristi R. Arsenault, Shugong Wang, Christa D. Peters-Lidard, James P. Verdin. Evaluating ESA CCI soil moisture in East Africa. International Journal of Applied Earth Observation and Geoinformation. 2016; 48 ():96-109.
Chicago/Turabian StyleAmy McNally; Shraddhanand Shukla; Kristi R. Arsenault; Shugong Wang; Christa D. Peters-Lidard; James P. Verdin. 2016. "Evaluating ESA CCI soil moisture in East Africa." International Journal of Applied Earth Observation and Geoinformation 48, no. : 96-109.
The Soil Moisture Active Passive (SMAP) mission will provide soil moisture data with unprecedented accuracy, resolution, and coverage, enabling models to better track agricultural drought and estimate yields. In turn, this information can be used to shape policy related to food and water from commodity markets to humanitarian relief efforts. New data alone, however, do not translate to improvements in drought and yield forecasts. New tools will be needed to transform SMAP data into agriculturally meaningful products. The objective of this study is to evaluate the possibility and efficiency of replacing the rainfall-derived soil moisture component of a crop water stress index with SMAP data. The approach is demonstrated with 0.1°-resolution, ~10-day microwave soil moisture from the European Space Agency and simulated soil moisture from the Famine Early Warning Systems Network Land Data Assimilation System. Over a West Africa domain, the approach is evaluated by comparing the different soil moisture estimates and their resulting Water Requirement Satisfaction Index values from 2000 to 2010. This study highlights how the ensemble of indices performs during wet versus dry years, over different land-cover types, and the correlation with national-level millet yields. The new approach is a feasible and useful way to quantitatively assess how satellite-derived rainfall and soil moisture track agricultural water deficits. Given the importance of soil moisture in many applications, ranging from agriculture to public health to fire, this study should inspire other modeling communities to reformulate existing tools to take advantage of SMAP data.
Amy McNally; Gregory J. Husak; Molly Brown; Mark Carroll; Chris Funk; Soni Yatheendradas; Kristi Arsenault; Christa Peters-Lidard; James P. Verdin. Calculating Crop Water Requirement Satisfaction in the West Africa Sahel with Remotely Sensed Soil Moisture. Journal of Hydrometeorology 2015, 16, 295 -305.
AMA StyleAmy McNally, Gregory J. Husak, Molly Brown, Mark Carroll, Chris Funk, Soni Yatheendradas, Kristi Arsenault, Christa Peters-Lidard, James P. Verdin. Calculating Crop Water Requirement Satisfaction in the West Africa Sahel with Remotely Sensed Soil Moisture. Journal of Hydrometeorology. 2015; 16 (1):295-305.
Chicago/Turabian StyleAmy McNally; Gregory J. Husak; Molly Brown; Mark Carroll; Chris Funk; Soni Yatheendradas; Kristi Arsenault; Christa Peters-Lidard; James P. Verdin. 2015. "Calculating Crop Water Requirement Satisfaction in the West Africa Sahel with Remotely Sensed Soil Moisture." Journal of Hydrometeorology 16, no. 1: 295-305.