A strategy-based framework for assessing the flood resilience of cities – the Hamburg case study

Climate change and continuous urbanization contribute to an increased urban vulnerability towards flooding. Only relying on traditional flood control measures is recognized as inadequate, since the damage can be catastrophic if flood controls fail. The idea of a flood-resilient city – one which can withstand or adapt to a flood event without being harmed in its functionality – seems promising. But what does resilience actually mean when it is applied to urban environments exposed to flood risk, and how can resilience be achieved? This paper presents a heuristic framework for assessing the flood resilience of cities, for scientists and policy-makers alike. It enriches the current literature on flood resilience by clarifying the meaning of its three key characteristics – robustness, adaptability and transformability – and identifying important components to implement resilience strategies. The resilience discussion moves a step forward, from predominantly defining resilience to generating insight into “doing” resilience in practice. The framework is illustrated with two case studies from Hamburg, showing that resilience, and particularly the underlying notions of adaptability and transformability, first and foremost require further capacity-building among public as well as private stakeholders. The case studies suggest that flood resilience is currently not enough motivation to move from traditional to more resilient flood protection schemes in practice; rather, it needs to be integrated into a bigger urban agenda

MODIS__Arctic__MeltPondFraction__UHAM-CliSAP-ICDC__v01__12.5km__8day

Reflectances measured in the visible frequency range at three channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Earth Observation Satellite (EOS) TERRA were used to derive the melt pond fraction on Arctic sea ice using an artificial neural network. This analysis was done on reflectances gridded onto a polar-stereographic grid tangent to the Earths' surface at 70 deg N with 500 m grid resolution. The reflectances used originate from the 8-day composite reflectances provided via https://wist.echo.nasa.gov/api/ as product: "MODIS surface Reflectance 8-Day L3 Global 500m SIN Grid V005". After gridding and flagging for clouds and other disturbances the artificial neural network was applied, providing fractions of three surface classes: 1) melt ponds, 2) sea ice and snow, and 3) open water at 500 m grid resolution. This data has been interpolated onto a similar polar-stereographic grid but with 12.5 km grid resolution. The data set offered here comprises several data layers: the melt pond fraction, its standard deviation, the open water fraction, and the number of individual valid grid cells with 500 m grid resolution included in each 12.5 km grid cell. In addition, in three separate data layers melt pond fraction, its standard deviation, and the open water fraction are given only for those grid cells (with 12.5 km grid resolution) where more than 90 % of the native 500 m grid resolution data indicate clear sky conditions. Grid cells with an open water fraction larger than 85 % have been generally flagged as invalid. The data set is updated annually