Thursday, 27 January 2011
Washington State Convention Center
Tana River basin, Kenya, located within Greater Horn of Africa sub-region, experiences bimodal rainfall pattern with the long rain season from March to May and the short rain season between October and December (OND). The year to year rainfall seasons over the sub-region are strongly influenced by the north-south migration of the Inter-Tropical Convergence Zone (ITCZ) with the rains exhibiting strong variability in space and time. The other major global and regional systems that are often associated with climate variability in the Greater Horn of Africa sub-region include the El Nino Southern Oscillation (ENSO) phenomenon, Sea Surface Temperature (SST) anomalies over the Indian and Atlantic Oceans, Tropical cyclone activities in the Indian Ocean including modes of variability such as the Indian Ocean Dipole (IOD). Over 50% of the total annual rainfall within the Tana River basin is received during the long rain season (and even more during the ENSO years) ensuring the water and energy sustainability of the region. Several studies highlight that although seasonal predictability is greater during the short rain season (due to peak activity of ENSO during OND), precipitation forecasts during the long rain season exhibit relatively lesser skill. This in turn reduces the monthly streamflow forecasting skill during the dominant runoff season (April to June, AMJ). This study will focus on understanding the combined influence of land, atmospheric and oceanic states for improving monthly streamflow prediction during AMJ. Relationships between ENSO, Indian Ocean Dipole (IOD), Outgoing Longwave Radiation (OLR) and initial land surface conditions on monthly streamflow during AMJ are investigated in detail to quantify the variability explained by each of the predictors.
In this study, we will use a combined approach of statistical and land surface modeling. We will implement a large scale Variable Infiltration Capacity (VIC) land surface model to determine initial soil moisture conditions prior to the long rain season to improve forecasting skill of state-of-the-art statistical models.
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