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== Seasonal drought forecast system for food-insecure regions of East Africa == | == Seasonal drought forecast system for food-insecure regions of East Africa == | ||
| − | + | ===Authors=== | |
| − | Greg | + | *Shraddhanand Shukla (1,2) |
| − | + | *Amy McNally (1,4,5) | |
| + | *Balachandrudu Narapusetty (5) | ||
| + | *Greg Husak (1) | ||
| + | *Christopher Funk (1,3) | ||
| + | *Christa Peters-Lidard (5) | ||
| + | *Jim Verdin (3) | ||
| + | 1: Climate Hazards Group, Department of Geography, University of California, Santa Barbara | ||
| + | 2: University Corporation For Atmospheric Research, Boulder, CO | ||
| + | 3: EROS, U.S. Geological Survey | ||
| + | 4: Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA | ||
| + | 5: Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA | ||
| + | |||
| + | ===Summary=== | ||
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 agricultural management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts and floods. | 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 agricultural management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts and floods. | ||
In October-November-December growing season of 2013, US Agency for International Development’s (USAID) Famine Early Warning Systems Network (FEWS NET) science team developed and implemented a seasonal agricultural drought forecast system for this region to assist with agricultural drought assessment. Initially the agricultural drought outlook was based on the Variable Infiltration Capacity (VIC) model simulated soil moisture scenarios forced with climate scenarios for the upcoming season conditioned to NCEP’s Climate Forecast System (CFSv2) seasonal forecasts. However since then we have tested and implemented different approaches to improve this agricultural drought forecast system to better meet the decision making needs of FEWS NET’s Food Analysts in the region. | In October-November-December growing season of 2013, US Agency for International Development’s (USAID) Famine Early Warning Systems Network (FEWS NET) science team developed and implemented a seasonal agricultural drought forecast system for this region to assist with agricultural drought assessment. Initially the agricultural drought outlook was based on the Variable Infiltration Capacity (VIC) model simulated soil moisture scenarios forced with climate scenarios for the upcoming season conditioned to NCEP’s Climate Forecast System (CFSv2) seasonal forecasts. However since then we have tested and implemented different approaches to improve this agricultural drought forecast system to better meet the decision making needs of FEWS NET’s Food Analysts in the region. | ||
This presentation describes those approaches and their evaluation in providing skillful agricultural drought forecasts for the region. Mainly we describe a (1) hybrid approach that combines National Multi-Model Ensemble global seasonal forecasts with statistical methods to generate climate scenarios for the upcoming season, (2) multi-model approach to generate scenarios of available soil moisture by using a simple-water-balance model (i.e. Food and Agriculture Organization’s Water Requirement Satisfaction Index) as well as NASA’s FEWS NET Land Data Assimilation System (that consists of much more complex land surface models such as the VIC and Noah models) and (3) data assimilation approach that assimilates satellite observed soil moisture/total water storage into FLDAS models to improve the estimates of the initial hydrologic state. We also evaluate how each of those approaches contributes to the improvement in the skill of the agricultural drought forecast system. Finally we demonstrate the potential value of this system to the USAID, whose efforts provide timely and rigorous early warning and vulnerability information on emerging and evolving food security issues. | This presentation describes those approaches and their evaluation in providing skillful agricultural drought forecasts for the region. Mainly we describe a (1) hybrid approach that combines National Multi-Model Ensemble global seasonal forecasts with statistical methods to generate climate scenarios for the upcoming season, (2) multi-model approach to generate scenarios of available soil moisture by using a simple-water-balance model (i.e. Food and Agriculture Organization’s Water Requirement Satisfaction Index) as well as NASA’s FEWS NET Land Data Assimilation System (that consists of much more complex land surface models such as the VIC and Noah models) and (3) data assimilation approach that assimilates satellite observed soil moisture/total water storage into FLDAS models to improve the estimates of the initial hydrologic state. We also evaluate how each of those approaches contributes to the improvement in the skill of the agricultural drought forecast system. Finally we demonstrate the potential value of this system to the USAID, whose efforts provide timely and rigorous early warning and vulnerability information on emerging and evolving food security issues. | ||
Latest revision as of 12:24, 20 May 2015
Seasonal drought forecast system for food-insecure regions of East Africa
Authors
- Shraddhanand Shukla (1,2)
- Amy McNally (1,4,5)
- Balachandrudu Narapusetty (5)
- Greg Husak (1)
- Christopher Funk (1,3)
- Christa Peters-Lidard (5)
- Jim Verdin (3)
1: Climate Hazards Group, Department of Geography, University of California, Santa Barbara 2: University Corporation For Atmospheric Research, Boulder, CO 3: EROS, U.S. Geological Survey 4: Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA 5: Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Summary
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 agricultural management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts and floods. In October-November-December growing season of 2013, US Agency for International Development’s (USAID) Famine Early Warning Systems Network (FEWS NET) science team developed and implemented a seasonal agricultural drought forecast system for this region to assist with agricultural drought assessment. Initially the agricultural drought outlook was based on the Variable Infiltration Capacity (VIC) model simulated soil moisture scenarios forced with climate scenarios for the upcoming season conditioned to NCEP’s Climate Forecast System (CFSv2) seasonal forecasts. However since then we have tested and implemented different approaches to improve this agricultural drought forecast system to better meet the decision making needs of FEWS NET’s Food Analysts in the region. This presentation describes those approaches and their evaluation in providing skillful agricultural drought forecasts for the region. Mainly we describe a (1) hybrid approach that combines National Multi-Model Ensemble global seasonal forecasts with statistical methods to generate climate scenarios for the upcoming season, (2) multi-model approach to generate scenarios of available soil moisture by using a simple-water-balance model (i.e. Food and Agriculture Organization’s Water Requirement Satisfaction Index) as well as NASA’s FEWS NET Land Data Assimilation System (that consists of much more complex land surface models such as the VIC and Noah models) and (3) data assimilation approach that assimilates satellite observed soil moisture/total water storage into FLDAS models to improve the estimates of the initial hydrologic state. We also evaluate how each of those approaches contributes to the improvement in the skill of the agricultural drought forecast system. Finally we demonstrate the potential value of this system to the USAID, whose efforts provide timely and rigorous early warning and vulnerability information on emerging and evolving food security issues.
