23 Dec 2021

23 Dec 2021

Review status: this preprint is currently under review for the journal ESSD.

A Hydrologic Monitoring Dataset for Food and Water Security Applications in Central Asia

Amy McNally1,2,3, Jossy Jacob1,4, Kristi Arsenault1,2, Kimberly Slinski1,5, Daniel Sarmiento1,2, Andrew Hoell6, Shahriar Pervez7, James Rowland8, Mike Budde8, Sujay Kumar1, Christa Peters-Lidard1, and James Verdin3 Amy McNally et al.
  • 1NASA Goddard Space Flight Center, Greenbelt, MD, 20771, United States
  • 2SAIC, Reston, VA, 20190, United States
  • 3United States Agency for International Development, Washington, DC, 20523, United States
  • 4SSAI, Inc., Lanham, MD, postal code, United States
  • 5University of Maryland Earth Systems Science Interdisciplinary Center, College Park, MD, 20740, United States
  • 6National Oceanic and Atmospheric Administration, Physical Science Laboratory, Boulder, CO, 80305, United States
  • 7Arctic Slope Regional Corporation (ASRC) Federal Data Solutions, Contractor to U.S. Geological Survey, Earth Resources Observation and Science Center (EROS), Sioux Falls, SD, 57198 United States
  • 8United States Geological Survey, EROS Center, Sioux Falls, South Dakota, 57198, United States

Abstract. From the Hindu Kush Mountains to the Registan desert, Afghanistan is a diverse landscape where droughts, floods, conflict, and economic market accessibility pose challenges for agricultural livelihoods and food security. The ability to remotely monitor environmental conditions is critical to support decision making for humanitarian assistance. The FEWS NET Land Data Assimilation System (FLDAS) global and Central Asia data streams described here combine meteorological reanalysis datasets and land surface models to generate routine estimates of snow-covered fraction, snow water equivalent, soil moisture, runoff and other variables representing the water and energy balance. This approach allows us to fill the gap created by the lack of in situ hydrologic data in the region. First, we describe the configuration of the FLDAS and the two resultant data streams: one, global, at ~1 month latency, provides monthly average outputs on a 10 km2 grid from 1982–present. The second data stream, Central Asia, at ~1 day latency, provides daily average outputs on a 1 km2 grid from 2001–present. We describe our verification of these data that are compared to other remotely sensed estimates as well as qualitative field reports. These data and value-added products (e.g., anomalies and interactive time series) are hosted by NASA and USGS data portals for public use. The global data stream with a longer record, is useful for exploring interannual variability, relationships with atmospheric-oceanic teleconnections (e.g., ENSO), trends over time, and monitoring drought. Meanwhile, the higher spatial resolution Central Asia data stream, with lower latency, is useful for simulating snow-hydrologic dynamics in complex topography for monitoring snowpack and flood risk.

Amy McNally et al.

Status: open (until 02 Mar 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2021-420', Anonymous Referee #1, 25 Jan 2022 reply

Amy McNally et al.

Data sets

FLDAS Noah Land Surface Model L4 Central Asia Daily 0.01 x 0.01 degree (FLDAS_NOAH001_G_CA_D) Jossy Jacob, Kimberly Slinski, Amy McNally

Amy McNally et al.


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Short summary
The FEWS NET Land Data Assimilation System (FLDAS) global and Central Asia data streams described here generate routine estimates of snow, soil moisture, runoff and other variables useful for tracking water availability. These data are hosted by NASA and USGS data portals for public use.