Abstract
Abstract. 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.
Highlights
The 2011 famine in the Horn of Africa was one of the most severe humanitarian disasters of this century
First we evaluated the suitability of Variable Infiltration Capacity (VIC)-derived soil moisture (SM) for providing agricultural drought assessments across our domain (Fig. 1)
Our primary findings are as follows: 1. The VIC-model-derived SM values over the crop zones of the focus domain aligns well with end-of-season water requirement satisfaction index (WRSI), the Food and Agriculture Organization (FAO) indicator that is often used for providing crop yield assessments
Summary
The 2011 famine in the Horn of Africa was one of the most severe humanitarian disasters of this century. Due to the shortage of real-time-observed SM measurements, estimates computed using hydrologic models are among the best indicator of antecedent SM conditions and agricultural drought (Keyantash and Dracup, 2002) These same hydrologic models can be driven with climate forecasts for the upcoming season to provide SM forecasts. The intended domain of this system expands over the Greater Horn of Africa, we focus on the equatorial East Africa (EA) (i.e., southeastern Ethiopia, northern Kenya, and southern Ethiopia as captured in Fig. 1) as a testbed This region is predominantly a pastoral area with some crop zones. In the remainder of this manuscript we describe the approach and data used to implement the agricultural drought forecasts system, its evaluation, and future directions
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