Resilience in relation to flood risk management (FRM) is not a new concept, yet parts of the FRM community are still struggling to apply it. The main challenge this study addresses is the question as to whether parts of the FRM community should still adopt, or rather “leap‐frog,” resilience. The main purpose is to evaluate whether resilience is a still on‐going trend or, already subsiding. Research suggests that resilience is an on‐going trend that connects research and policy and has gained international recognition as expressed by international guidelines and bodies promoting its research but also its operationalization. Academic literature in the area of FRM also shows a significant continuing development. Resilience enables to analyze dynamics and transformations of riverine areas, or coastal zones in connection to an integrated social‐environmental system approach with more emphasis and conceptual basis than previous concepts. Resilience is more than a short‐lived notion and it appears that FRM researchers cannot avoid addressing it. Resilience often is a convergence of ideas and mainstreaming of efforts, which in many venues is absolutely necessary and can help, for example, to decrease silo‐thinking. But as academics, we have a mandate to remain skeptical and remain on the look‐out for novel ideas, too.
This article is categorized under:
Engineering Water > Planning Water
ABSTRACT:Ice jams can sometimes occur in high latitude rivers during winter and the resulting water level rise may generate costly and dangerous flooding such as the recent ice jam flooding in the Nechako River in downtown Prince George in Canada. Thus, the forecast of water level and ice jam thickness is of great importance. This study compares three methods to simulate and forecast water level and ice jam thickness based on field observations of river ice jams in the Quyu Reach of the Yellow River in China. More specifically, simulation results generated by the traditional multi variant regressional method are compared to those of the back propagation neural network and the support vector machine methods. The forecast of ice jam thickness and water level under ice jammed condition have been conducted in two different approaches, 1) simulation of water level and ice jam thickness in the second half of the period of measurement using models developed based on data gained during the first half of the period of measurement, 2) simulation of water level and ice jam thickness at the downstream cross sections using models developed based on data gained at the upstream cross sections. For this reason, as the results of simulation and field observations indicated, the back propagation neural network method and the support vector machine method are superior in terms of accuracy to the multi-variant regressional method.
Flood resilience is increasingly discussed in academia and practice as a complement to existing flood risk management approaches (Fekete, Hartmann, & Jüpner 2020). It is seen as a promising concept to deal with increasingly severe consequences of climate change in general, and with increasing flood risk in particular. The debate on flood resilience is linked to the paradigm shift from flood protection to risk management, which started in Europe after the major river flood events in 1993 and 1995 along the river Rhine (Hartmann, 2012), and has developed over the past decadespushed by further major fluvial and pluvial flood events (
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