5 research outputs found
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
Urban textures and flood hazard impacts from 2008 to 2018 in Nairobi, Kenya
No full text&lt;p&gt;This research develops a methodology to examine the change over time of urban textures for Nairobi in relation to flood hazard impact on infrastructure. We use three Landsat 7 (30 m resolution) images of Nairobi (2008, 2013, 2018). &amp;#8216;Urban textures&amp;#8217; are the spatial distribution, shape and relative arrangement of urban elements such as green spaces, trees, roads and height of buildings and their geometry in a given urban city. Here, revising Stewart and Oke&amp;#8217;s classifications for built-up areas and land cover types, we classify each of the three Landsat images into 14 urban textures using maximum likelihood under supervised classification. The building structure types were then examined using local knowledge, YouTube videos, Google Street View and ground truthing. We find that from 2008 to 2018 the urban textures with the largest total increases in area were compact mid-rise by 49.9km2 (6.9%) and compact high-rise by 11.3 km2 (1.5%). In contrast, the compact low-rise residential urban texture decreased greatly (29.2 km2). This suggests that for non-industrial land uses, Nairobi has grown upward. Accuracy assessments for the 2008 [2018] map were 83.6% [87.9%] with 95% confidence interval of 75.4&amp;#8211;90.0% [80.6&amp;#8211;93.2%] and kappa statistic 0.777 [0.834]. We then examine the spatial temporal change of intensive (high severity &amp;#8211; low frequency) and extensive (low severity &amp;#8211; high frequency) flood hazard events in terms of pattern, trend and impact in relation to rainfall, elevation, and urban textures. We find that urban textures for 2018 have reduced area coverage of the urban texture lightweight low-rise, having partly changed to compact midrise. The impact of change in land use through the development of urban areas greatly affects flooding and impacts in terms of severity. Flooding is more prevalent close to the major rivers in Nairobi, some of which occur in the non-informal settlements. Flood water flows from the higher areas of Ngong and Kikuyu towards the town centre, Nairobi west into industrial area going towards east lands. Rivers in Nairobi regularly overflow their banks and inundate low-lying areas like T-Mall, Nairobi west, industrial area and Mathare valley. These are the flood hotspots of Nairobi that also have high severity of fatalities and impact on infrastructure. We believe that our methodology of examining urban textures over time, using remote sensing images, combined with flood hazard impact information, will help scientists and hazard managers better understand, and prepare for, the interlinked nature of urban change with the flood hazard.&lt;/p&gt;</jats:p
Use of blended evidence sources to build a history of flooding impact and an impact severity scale: A case study of Nairobi, Kenya
No full text&lt;p&gt;Many urban areas in the Global South are often data-poor and lacking in longer-term records of the occurrence and impact of natural hazards. Here we explore a methodology for using blended evidence sources to build a history of flooding impacts, along with a hazard severity scale, in Nairobi, Kenya, from 1978 to 2018. The evidence we use to build our Nairobi Flood Impact Database includes existing digital flood databases, newspapers, radio/TV broadcasts, government and NGO reports, peer-review journal articles, insurance company and emergency service records, online website reports, blogs, Google Analytic records and 330 photos/video from social media sources. For each record, we systematically extract from the source material, available information on the flood&amp;#8217;s location, timing, and impact, with impact broken up into human (7 subcategories, e.g., fatalities), infrastructure (18 subcategories, e.g., building damage) and environment (6 subcategories, e.g., trees fallen). The resultant Flood Impact database has 1495 entries, which when entries that refer to the same flood event are grouped, result in a total of 354 flood events for 1978 to 2018 (41 years) in Nairobi, a much more extensive record than available previously. The flood database has the largest number of records for 2011 to 2018, given the increased use of social media and newspapers to report flood event impacts in recent years. We also see a peak in the years 1996 to 2000 (when there was a particularly heavy amount of rain due to El Ni&amp;#241;o) and then again 2016 to 2018. We then develop a five-point Likert scale for evaluating the adequacy of evidence types for recording location, timing and impact of floods. Finally, using a combination of existing impact-related natural hazard scales from the literature and our database, we build a five-part flood severity index combining the different types of impact, ranging from minor to catastrophic floods. Each of the impact types (31 subcategories) from our impact database is given a weighting from which inform this five-point severity index and map this severity index onto selected flood events from our Nairobi Flood Impact Database. Our database was then examined for temporal and spatial clustering in Nairobi and compared to different types of urban built up areas within Nairobi. This research provides a methodology and extensive blended database of the impact of floods over a 41-year period in a data poor area, providing a resource for better understanding the past history of spatial temporal hazard patterns, which can then be further expanded as to the causes, impact and how the flood events were dealt with in terms of recovery, lessons learnt and ways of mitigation and resilience building.&lt;/p&gt;</jats: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
