Wednesday, 13 January 2016: 11:00 AM
La Nouvelle A ( New Orleans Ernest N. Morial Convention Center)
The water infrastructures in East Africa are operated and managed at much lower efficiency than their optimum potential. Moreover, most of the natural hazards in East Africa are caused by the severity and variability of extreme climate events. Therefore, effective utilization and seamless integration of seasonal climate forecasts into decision support system will improve the efficiency of water infrastructures and reduce disaster risks of climate-related hazards. This study investigates the prediction skills and suitability of the North America Multi-Model Ensemble (NMME) seasonal forecasts for decision making in water infrastructure operations and disaster risk management in East Africa. The NMME seasonal retrospective forecasts for the East Africa Power Pool (EAPP) region are compared with the TRMM rainfall estimates and the CPC unified gauged rainfall data. The prediction skills of the NMME seasonal forecasts have significant spatial and temporal variability in the EAPP region. The root mean square errors, the mean and variability biases, and quartile distributions of the seasonal forecasts are relatively higher for wetter locations and months. Even though the prediction skills of the NMME seasonal forecasts vary among the participating models, the forecasts are not significantly depreciating with lead time in the study region. The physical factors and mechanisms that would potentially affect the seasonal prediction skills are also investigated. Four different error-correction and forecast-combination methods are evaluated using both remote sensing rainfall estimates and local rainfall observations. The NMME seasonal forecasts, along with robust and versatile error-correction and forecat-combination approaches, are found very valuable for operational planning of water infrastructures and early warning of climate-induced hazards and disaster risks.
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