5 research outputs found
Drivers and subseasonal predictability of heavy rainfall in equatorial East Africa and relationship with flood risk
Get PDFEquatorial East Africa (EEA) suffers from significant flood risks. These can be mitigated with pre-emptive action, however currently available early warnings are limited to a few days lead time. Extending warnings using subseasonal climate forecasts could open a window for more extensive preparedness activity. However before these forecasts can be used, the basis of their skill and relevance for flood risk must be established. Here we demonstrate that subseasonal forecasts are particularly skillful over EEA. Forecasts can skillfully anticipate weekly upper quintile rainfall within a season, at lead times of two weeks and beyond. We demonstrate the link between the Madden-Julian Oscillation (MJO) and extreme rainfall events in the region, and confirm that leading forecast models accurately represent the EEA teleconnection to the MJO. The relevance of weekly rainfall totals for fluvial flood risk in the region is investigated using a long record of streamflow from the Nzoia river in Western Kenya. Both heavy rainfall and high antecedent rainfall conditions are identified as key drivers of flood risk, with upper quintile weekly rainfall shown to skillfully discriminate flood events. We additionally evaluate GloFAS global flood forecasts for the Nzoia basin. Though these are able to anticipate some flooding events with several weeks lead time, analysis suggests action based on these would result in a false alarm more than 50% of the time. Overall, these results build on the scientific evidence base that supports the use of subseasonal forecasts in EEA, and activities to advance their use are discussed
Advancing operational flood forecasting, early warning and risk management with new emerging science: Gaps, opportunities and barriers in Kenya
Get PDFKenya and the wider East African region suffer from significant flood risk, as illustrated by major losses of lives, livelihoods and assets in the most recent years. This is likely to increase in future as exposure rises and rainfall intensifies under climate change. Accordingly, flood risk management is a priority action area in Kenya's national climate change adaptation planning. Here, we outline the opportunities and challenges to improve end-to-end flood early warning systems, considering the scientific, technical and institutional/governance dimensions. We demonstrate improvements in rainfall forecasts, river flow, inundation and baseline flood risk information. Notably, East Africa is a ‘sweetspot’ for rainfall predictability at sub-seasonal to seasonal timescales for extending forecast lead times beyond a few days and for ensemble flood forecasting. Further, we demonstrate coupled ensemble flow forecasting, new flood inundation simulation, vulnerability and exposure data to support Impact based Forecasting (IbF). We illustrate these advances in the case of fluvial and urban flooding and reflect on the potential for improved flood preparedness action. However, we note that, unlike for drought, there remains no national flood risk management framework in Kenya and there is need to enhance institutional capacities and arrangements to take full advantage of these scientific advances
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Characteristics, drivers, and predictability of flood events in the Tana River Basin, Kenya
No full textFlooding represents a significant challenge in Kenya, particularly in the Tana River Basin (TRB). Flood risks is expected to exacerbate due to rising exposure and vulnerability levels and also increase in the frequency of flood events due to climate change. There is therefore a need for understanding the nature, drivers and predictability of flood events in TRB. This PhD research fills this gap through scientific analysis using available hydrological, meteorological and Earth observation (EO) datasets so as to inform and support the development of existing and planned flood early warning systems (FldEWS) in the TRB.This was achieved through analysis of: (i) extreme rainfall and flood events, flood extent and inundation, (ii) drivers of flood events and (iii) flood predictability using the Global Flood Awareness System (Glo- FAS). Research findings show that extreme rainfall and peak flow are confined to the two main rainy seasons of April–June (‘long’ rains) and October–December (‘short’ rains). ‘Long’ rains are strongly correlated to flows in the preceding ‘short’ rains. Peak-to-peak analysis of extreme rainfall and peak flow suggests a lag of ∼3 days on average. Extreme flood events in December 2019 and May 2020 demonstrated the potential of mapping flood extent using Sentinel Mission Satellites. Flood events are driven by warm tropical Sea Surface Temperatures (SSTs) over the Ni˜no3.4 region over the Central East Pacific and over the Indian Ocean, as indicated by a statistically significant strong positive correlation (r = 0.79; p 50% to lead times of 7 days for small and medium flood thresholds (1 and 2-year return periods). The research findings strengthens the science of flood forecasting, preparedness, and response, which is key to addressing existing gaps in flood risk management by mandated agencies in both civil and humanitarian organisations in Kenya.</p
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Characteristics, drivers, and predictability of flood events in the Tana River Basin, Kenya
No full textStudy area: Tana River Basin in Kenya. Study Focus: Flood-related impacts and losses have been rising. Therefore, understanding flood characteristics, drivers, and predictability is critical for informed decisions in the ongoing flood early warning (FldEWS) projects. This study presents an in-depth analysis of hydro-meteorological, Sentinel Mission (SM), and ensemble hydrological model datasets. We examine flood characteristics using observed hydro-meteorological and SM datasets, followed by statistical analysis of climate drivers of flood events at inter-annual and sub-seasonal (S2S) time scales. Finally, reforecasts from Global Flood Awareness System (GloFAS) are assessed against observed river flows. New hydrological insights for the study region: There is a high inter-annual variability of flood events with flood peaks occurring in May and December. SM satellites have the ability to map flooded areas in near-real time. At inter-annual timescales, positive Indian Ocean Dipole (IOD) and warm El Niño Southern Oscillation (ENSO) drives short rains (October to December). At Sub-Seasonal (S2S) timescales, Madden Julian Oscillation (MJO; phases 2-4) is a notable driver of flood related extreme rainfall. GloFAS offers reliable forecasts depending on the flood magnitude, trigger probability, and 'anticipation window' and it meets the tolerable skill requirements for flood preparedness actions (FAR 50%) with up to a 20-day lead time for 1 and 2-year return periods. We subsequently discuss how our research findings can inform the development of FldEWS in Kenya, with an emphasis on improved co-production of flood forecast information with relevant stakeholders.</p
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Advancing operational flood forecasting, early warning and risk management with new emerging science: Gaps, opportunities and barriers in Kenya
No full textKenya and the wider East African region suffer from significant flood risk, as illustrated by major losses of lives, livelihoods and assets in the most recent years. This is likely to increase in future as exposure rises and rainfall intensifies under climate change. Accordingly, flood risk management is a priority action area in Kenya's national climate change adaptation planning. Here, we outline the opportunities and challenges to improve end-to-end flood early warning systems, considering the scientific, technical and institutional/governance dimensions. We demonstrate improvements in rainfall forecasts, river flow, inundation and baseline flood risk information. Notably, East Africa is a ‘sweetspot’ for rainfall predictability at sub-seasonal to seasonal timescales for extending forecast lead times beyond a few days and for ensemble flood forecasting. Further, we demonstrate coupled ensemble flow forecasting, new flood inundation simulation, vulnerability and exposure data to support Impact based Forecasting (IbF). We illustrate these advances in the case of fluvial and urban flooding and reflect on the potential for improved flood preparedness action. However, we note that, unlike for drought, there remains no national flood risk management framework in Kenya and there is need to enhance institutional capacities and arrangements to take full advantage of these scientific advances
