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| Titel |
A seasonal agricultural drought forecast system for food-insecure regions of East Africa |
| VerfasserIn |
S. Shukla, A. McNally, G. Husak, C. Funk |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 18, no. 10 ; Nr. 18, no. 10 (2014-10-02), S.3907-3921 |
| Datensatznummer |
250120487
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| Publikation (Nr.) |
 copernicus.org/hess-18-3907-2014.pdf |
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| Zusammenfassung |
The increasing food and water demands of East Africa's growing population
are stressing the region's inconsistent water resources and rain-fed
agriculture. More accurate seasonal agricultural drought forecasts for this
region can inform better water and agropastoral management decisions,
support optimal allocation of the region's water resources, and mitigate
socioeconomic losses incurred by droughts and floods. Here we describe the
development and implementation of a seasonal agricultural drought forecast
system for East Africa (EA) that provides decision support for the Famine
Early Warning Systems Network's (FEWS NET) science team. We evaluate this
forecast system for a region of equatorial EA (2° S–8° N,
36–46° E) for the March-April-May (MAM) growing season. This
domain encompasses one of the most food-insecure, climatically variable, and
socioeconomically vulnerable regions in EA, and potentially the world; this
region has experienced famine as recently as 2011.
To produce an "agricultural outlook", our forecast system simulates soil
moisture (SM) scenarios using the Variable Infiltration Capacity (VIC)
hydrologic model forced with climate scenarios describing the upcoming
season. First, we forced the VIC model with high-quality atmospheric
observations to produce baseline soil moisture (SM) estimates (here after
referred as SM a posteriori estimates). These compared favorably
(correlation = 0.75) with the water requirement satisfaction index (WRSI), an index
that the FEWS NET uses to estimate crop yields. Next, we evaluated the SM
forecasts generated by this system on 5 March and 5 April of
each year between 1993 and 2012 by comparing them with the corresponding SM a
posteriori estimates. We found that initializing SM forecasts with
start-of-season (SOS) (5 March) SM conditions resulted in useful SM
forecast skill (> 0.5 correlation) at 1-month and, in some cases,
3-month lead times. Similarly, when the forecast was initialized with
midseason (i.e., 5 April) SM conditions, the skill of forecasting SM
estimates until the end-of-season improved (correlation > 0.5
over several grid cells). We also found these SM forecasts to be more
skillful than the ones generated using the Ensemble Streamflow Prediction
(ESP) method, which derives its hydrologic forecast skill solely from the
knowledge of the initial hydrologic conditions. Finally, we show that, in
terms of forecasting spatial patterns of SM anomalies, the skill of this
agricultural drought forecast system is generally greater (> 0.8
correlation) during drought years (when standardized anomaly of MAM
precipitation is below 0). This indicates that this system might be
particularity useful for identifying drought events in this region and can
support decision-making for mitigation or humanitarian assistance. |
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