Flood Monitoring through Data Assimilation of Space Observations-Nigeria Experience
Igbokwe, F.C.; Okpara, J.N.
Nigerian Meteorological Agency

Abstract In recent time, the knowledge of climate has grown wide and has reached a mature stage, where concept of a changing climate has become a reality and of great concern. It has also become clear, that the effects of climate change will be primarily felt through variability, especially from extremes such as floods, droughts, storms and heat-waves. No one can control the weather, but accurate observations and predictions of such impending hydrometeorological events with a higher level of accuracy and lead time can radically improve people’s chances of living in relative safety, and protecting precious natural resources effectively. This is why this study attempts to examine the predictions (outputs) obtained through data analysis and assimilation using an in-housed developed empirical model, with the aim of ascertaining its suitability and reliability in flood monitoring in the face of the changing climate. The scheme is based on equivalent potential temperature (өe) that is capable of addressing the year-to-year variability in the atmospheric conditions and, hence in precipitation. Also, it is implemented using readily available historical surface data. Results show that the model has the ability of not only predicting the dates of onset of rainy season (OR) 5-9 weeks ahead (with an error margin or +5 days), the cessation dates (CR) several months ahead( with an error margin of +5 days), before the actual date of onset, the length of rainy season (LRS) before planting / sowing of crops (with an error margin of + 10 days), annual (seasonal) rainfall before the actual rainy season onset(with error margin of + 20 days), but also able to detect when rainfall will be surplus( that may cause flooding) or deficit (that may ensue in drought), as well as monthly rainfall for the first three months of the rainy season 30 days in advance. Feedbacks from the users of the products confirm that the model has been doing well. Also the versatility and capabilities of the model gives the user or user community, the full compliment of very useful information required not only for early warning system, but for water and food security, enhanced and sustainable agriculture, as well as effective and reliable water resources management.


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