Fig 4
Fast quantitative climate change impact assement for Hyderabad/India with regard to the expected number of slum dwellers severely affected by pluvial flooding under climate change. a) Driver: once in two year percentile of expected daily precipation under different global emission scenarios (B1, A2, for details see text). Flow-accumulation based identification of areas severely affected by the resulting pluvial flooding (Kit et al., 2011). b) Remote sensing based identification of slum areas (Kit et al., 2012). c) Ward-wise evaluation of the number of slum dwellers additionally severly affected under future pluvial flooding (for details see text) under the A2 scenario and the assumption of exponential population growth within the city.
Source publication
Bridging the global‐regional divide inclimate impact research for urban areas means to establish a comprehensive picture which covers all urban agglomeration of the world. This is different from the case of, e.g., hydrological impact modeling where coarse‐scaled (spatial and functional) global models and detailed regional studies have to be brought...
Contexts in source publication
Context 1
... choose the impact path of pluvial flooding of slum settlements for which we introduced the global filtering in section 3. The identified urban agglomerations are affected by this process but the quantitative impact has still to be deter- mined. In Figure 4 we show all steps to be performed for obtaining the quantitative impact and its uncertainty for the example of Hyderabad/India. Fig. 4a shows urban locations which are severely flooded under different projections of the "once in two year percentile" of expected daily precipation depending on different global emission scenarios (B1, A2). ...
Context 2
... of slum settlements for which we introduced the global filtering in section 3. The identified urban agglomerations are affected by this process but the quantitative impact has still to be deter- mined. In Figure 4 we show all steps to be performed for obtaining the quantitative impact and its uncertainty for the example of Hyderabad/India. Fig. 4a shows urban locations which are severely flooded under different projections of the "once in two year percentile" of expected daily precipation depending on different global emission scenarios (B1, A2). For the present Hyderabad climate this percentile amounts to 80mm/day and was chosen due to historical evidence of severe, city-wide ...
Context 3
... Hyderabad climate this percentile amounts to 80mm/day and was chosen due to historical evidence of severe, city-wide im- pacts. If possible, for other cities affected by this impact path this threshold has to be empirically ver- fified. The range of the projections of the considered climate variable is denoted by the hatched rec- tangles in Fig. 4a, top. Half of the considered global climate models (AOGCMs from the IPCC AR4 model ensemble) project values within this range after they were statistically downscaled to the Hy- derabad region ( Lüdeke et al., 2012). To identify which additional areas will be affected by severe flooding in the future a flow-accumulation analysis was ...
Context 4
... Here we use the relation of the urban texture (measured by lacunarity) with the probability of slum occurrence because slum areas show a typical settlement structure (Kit et al., 2012). Applied to different QuickBird time slices it allows to identify spatially explicit trends in slum development during 2003 to 2010(Kit et al., 2013) as shown in Fig. 4b. This current trend (roughly: reduced slum population in the central part of the city, mostly due to slum upgrade and newly occurring slum areas at the fringe of the inner city) was used together with 6 projections of the total population to produce plausible scenarios of future slum development up to ...
