No abstract

Abstract:Over the past few decades, the concept of resilience has emerged as an important consideration in the planning and management of water infrastructure systems. Accordingly, various resilience measures have been developed for the quantitative evaluation and decision-making of systems. There are, however, numerous considerations and no clear choice of which measure, if any, provides the most appropriate representation of resilience for a given application. This study provides a critical review of quantitative approaches to measure the resilience of water infrastructure systems, with a focus on water resources and distribution systems. A compilation of 11 criteria evaluating 21 selected resilience measures addressing major features of resilience is developed using the Axiomatic Design process. Existing gaps of resilience measures are identified based on the review criteria. The results show that resilience measures have generally paid less attention to cascading damage to interrelated systems, rapid identification of failure, physical damage of system components, and time variation of resilience. Concluding the paper, improvements to resilience measures are recommended. The findings contribute to our understanding of gaps and provide information to help further improve resilience measures of water infrastructure systems.

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This study presents a new Monte Carlo-based flood inundation modelling framework for estimating probability weighted flood risk using a computationally efficient graphics processing unit (GPU) two dimensional (2D) hydraulic model. The 2D flood model is programmed in the GPU framework providing a unique ability to run numerous simulations in a short period of time, permitting the integration of 2D hydraulic modelling into Monte Carlo analysis. The framework operates by performing many 2D flood simulations of randomly sampled input parameters to develop a spatially varied flood hazard map. The probabilistic framework is demonstrated using a 1% annual probability flood event and simulating 1000 different flood simulations by randomly selected peak flows of the Swannanoa River in Buncombe County, USA. The results, in general, display benefits of probabilistic flood risk approach compared with a single simulation approach. The latter approach underestimated 28% of flood risk relative to the former. As the number of simulations increased from 1 to 1000, areas identified as low danger and judgment zone increased by 87.4% and 36.8% respectively, whereas the high danger zone increased by 9.3%. In conclusion, the new Monte Carlo flood risk modelling framework has the ability to provide improved accuracy of flood risk and greater insight into the spatial distribution of flood risk. Predefined flow directionThe 1D models require prior knowledge of flow directions (e.g. in HEC-RAS, flow direction is represented by the main channel and banks). This is not appropriate in an urban J Flood Risk Management 5 (2012) 37-48

This research compares the relative contributions of potential contaminants discharged in dry weather flow (DWF) and wet weather flow (WWF) from the highly urbanized Ballona Creek watershed (BCW) in southern California using empirical and deterministic models. These models were used to compare the loading of the following pollutants: total suspended solids, biochemical oxygen demand, total nitrogen, total inorganic nitrogen, total Kjeldahl nitrogen, total phosphorus, copper, lead, arsenic, nickel, cadmium, and chromium. The results indicate DWF contributes approximately 10-30% of the total annual flow discharged from Ballona Creek. The annual DWF volume was fairly consistent; the variation in DWF percentage contribution was dependent on the highly variable volume of WWF. The relative contribution to the annual pollutant load varied considerably between each pollutant. In general, the DWF load was found to be significant, especially in years with lower precipitation totals. The results from this investigation have identified the relative relationship between DWF and WWF loads in the BCW and will aid in the decision-making process during the development of an integrated DWF-WWF management plan and allocation of water pollution control funds between DWF and WWF management.