Comment on essd-2021-420

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.