NASA's Seasonal Hydrological Forecast System for Improved Food Insecurity Early Warning in Africa

Hydrologic extremes such as droughts and floods contribute or lead to water and food insecurity, especially in vulnerable regions of Africa. Monitoring and forecasting such hydrological extremes thus provides an opportunity for early warning of food insecurity. With this in mind, an effort led at NASA’s Goddard Space Flight Center has developed a multi-model, remote sensing-based hydrological forecasting system, referred to as NHyFAS (NASA’s Hydrological Forecasting and Analysis System), to support food insecurity early warning efforts of the U.S. Agency for International Development’s (USAID) Famine Early Warning System Network (FEWS NET). For the past year, NHyFAS has been generating near real-time operational hydrological forecasts and analysis for continental Africa and the Middle East, using NASA’s Goddard Earth Observing System Model (GEOS) Seasonal to Sub-seasonal (S2S) forecasts and NASA’s Land Information System (LIS). These seasonal forecast model members are bias-corrected and downscaled to drive off-line land surface models (LSMs), such as the Noah Multi-parameterization (Noah-MP) and NASA’s Catchment LSMs, to produce a variety of hydrological products, including agricultural and hydrological drought indicators.

The work presented here demonstrates where skill may increase in relation to the forecast initial conditions due to assimilating different remotely sensed products, such as soil moisture from the Soil Moisture Active-Passive (SMAP) and terrestrial water storage (TWS) from the Gravity Recovery and Climate Experiment (GRACE) satellite missions. We provide a comprehensive skill evaluation of this system using independent remotely sensed and in situ data as reference. The evaluation focuses, in particular, on the performance of this system in early warning of past drought and flood events that have led to major food insecurity in the region. In addition, we describe ongoing efforts to expand and advance the hydrological forecasting system by including other North American Multi-Model Ensemble (NMME) climate model-based forecasts, in addition to GEOS, to improve the overall skill by having an increased model ensemble size.