IN041-12
NASA’s seasonal hydrologic forecasting system for food insecurity warning in Africa
Wednesday, 16 December 2020: 04:33
Virtual
Abheera Hazra1,2, Amy McNally3, Kimberly Slinski1, Kristi R Arsenault1,4, Shraddhanand Shukla5, Augusto Getirana1, Christa Peters-Lidard1, Sujay V Kumar1 and Randal D Koster1, (1)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (2)Earth System Science Interdisciplinary Center, HSL, College PARK, MD, United States, (3)US Agency for International Development, Falls Church, United States, (4)SAIC, Greenbelt, MD, United States, (5)University of California Santa Barbara, Climate Hazards Group, Santa Barbara, CA, United States
Abstract:
Extreme hydrologic conditions such as droughts and floods contribute or lead to 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 the multi-model, remote sensing-based hydrological forecasting and analysis system, referred to as NHyFAS (NASA’s Hydrological Forecasting and Analysis System), was developed at NASA’s Goddard Space Flight Center 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 over continental Africa and the Middle East, using the North American Multi-Model Ensemble (NMME) and NASA’s Goddard Earth Observing System Model (GEOS) Seasonal to Sub-seasonal (S2S) forecasts and NASA’s Land Information System (LIS).
The work presented here describes the validation of the hydrological forecasting system, over southern Africa, and includes multiple climate model-based forecasts. The NMME suite currently provides near real-time monthly forecasts from global climate models such as the Climate Forecast System, version 2 (CFSv2); Geophysical Fluid Dynamics Laboratory’s (GFDL) forecast-oriented climate model version 2.5, Canadian Coupled Models (CanCM4i and GNEMO); the NCAR Climate System Model, version 4 (CCSM4); and GEOS S2S. Previous independent studies have shown that the forecast skill of the NMME ensemble mean, for both precipitation (P) and temperature (T), is equal to or higher than the forecast skill of any single model. It is thus hypothesized that multi-model climate forecasts will lead to improved skill in the hydrologic forecasts, such as root-zone soil-moisture and streamflow, generated by NHyFAS. Here, we provide a case study of the 2019/2020 drought in southern Africa countries and present the skill and probability evaluation of this system in relation to single model based forecasts, using independent, remotely sensed and in situ data as references. This study highlights the contribution of the NMME-based NHyFAS system to providing early warning of drought events in food-insecure regions.
