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NASA's NMME-Based S2S Hydrologic Forecast System for Food Insecurity Early Warning in Southern AfricaIn situ hydrologic monitoring over regions most susceptible to food insecurity can be a challenge in current times due to various socio-economic and political issues in combination with environmental factors such as ongoing famine or drought. Hydrologic monitoring and initializing forecasts based on remotely sensed and analyzed data can contribute significantly to early warning in such regions. Routine hydrologic forecasts, as provided by NASA’s Hydrologic Forecasting and Analysis System (NHyFAS), are a recent addition to early warning systems. A custom instance of NHyFAS, termed FLDAS-Forecast, is used by FEWS NET’s Land Data Assimilation System (FLDAS). The FLDAS-Forecast’s dynamic forecasting component was originally set up with Goddard Earth Observing System (GEOS) forecast inputs and has been recently expanded with precipitation forecast forcing from the North American Multi-Model Ensemble (NMME). This paper describes the improvements in seasonal hydrologic forecasts produced with this updated system. Evaluations in this study focus on soil moisture across southern Africa’s growing season. Soil moisture forecasts are benchmarked and evaluated relative to climatology-based forecasts and historic runs, which are driven by observation-based meteorological forcing fields, and they are verified with remotely sensed observations of soil moisture and vegetation. Through multiple deterministic and probabilistic skill assessments, we show that using the larger ensemble of NMME precipitation inputs in the forecast system results in higher quality hydrologic forecasts than are allowed by climatology- or GEOS-only-based forecasts. Further, the near-real-time NMME-based rootzone soil moisture forecasts were able to correctly predict developing drought conditions over southern Africa through late 2019 and into early 2020.
Document ID
20230001886
Acquisition Source
Goddard Space Flight Center
Document Type
Accepted Manuscript (Version with final changes)
Authors
Abheera Hazra
(University of Maryland, College Park College Park, Maryland, United States)
Amy McNally
(Science Applications International Corporation (United States) McLean, Virginia, United States)
Kimberly Slinski
(University of Maryland, College Park College Park, Maryland, United States)
Kristi R. Arsenault
(Science Applications International Corporation (United States) McLean, Virginia, United States)
Shraddhanand Shukla
(University of California, Santa Barbara Santa Barbara, California, United States)
Augusto Getirana
(Science Applications International Corporation (United States) McLean, Virginia, United States)
Jossy P. Jacob
(Science Systems and Applications (United States) Lanham, Maryland, United States)
Daniel P. Sarmiento
(Science Applications International Corporation (United States) McLean, Virginia, United States)
Christa Peters-Lidard
(Goddard Space Flight Center Greenbelt, Maryland, United States)
Sujay V. Kumar
(Goddard Space Flight Center Greenbelt, Maryland, United States)
Randal D. Koster
(Goddard Space Flight Center Greenbelt, Maryland, United States)
Date Acquired
February 8, 2023
Publication Date
December 19, 2022
Publication Information
Publication: Journal of Hydrology
Publisher: Elsevier
Volume: 617
Issue: Part B
Issue Publication Date: February 1, 2023
ISSN: 0022-1694
Subject Category
Life Sciences (General)
Earth Resources and Remote Sensing
Funding Number(s)
WBS: 199008.02.04.10.ED92.22
CONTRACT_GRANT: 80NSSC23M0011
CONTRACT_GRANT: 80GSFC20C0044
CONTRACT_GRANT: 720BHAH00005
CONTRACT_GRANT: USAID 72DFFP19CA00001
Distribution Limits
Public
Copyright
Portions of document may include copyright protected material.
Technical Review
External Peer Committee
Keywords
Hydrologic forecasts
Hydrologic forecast of extremes
droughts
North American Multi Model Ensembles (NMME)
Land Information System (LIS)
NoahMP
Catchment Land Surface Model
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