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Many urban areas experience both fluvial and pluvial floods, because locations next to rivers are preferred settlement areas, and the predominantly sealed urban surface prevents infiltration and facilitates surface inundation. The latter problem is enhanced in cities with insufficient or non-existent sewer systems. While there are a number of approaches to analyse either fluvial or pluvial flood hazard, studies of combined fluvial and pluvial flood hazard are hardly available. Thus this study aims at the analysis of fluvial and pluvial flood hazard individually, but also at developing a method for the analysis of combined pluvial and fluvial flood hazard. This combined fluvial-pluvial flood hazard analysis is performed taking Can Tho city, the largest city in the Vietnamese part of the Mekong Delta, as example. In this tropical environment the annual monsoon triggered floods of the Mekong River can coincide with heavy local convective precipitation events causing both fluvial and pluvial flooding at the same time. Fluvial flood hazard was estimated with a copula based bivariate extreme value statistic for the gauge Kratie at the upper boundary of the Mekong Delta and a large-scale hydrodynamic model of the Mekong Delta. This provided the boundaries for 2-dimensional hydrodynamic inundation simulation for Can Tho city. Pluvial hazard was estimated by a peak-over-threshold frequency estimation based on local rain gauge data, and a stochastic rain storm generator. Inundation was simulated by a 2-dimensional hydrodynamic model implemented on a Graphical Processor Unit (GPU) for time-efficient flood propagation modelling. All hazards - fluvial, pluvial and combined - were accompanied by an uncertainty estimation considering the natural variability of the flood events. This resulted in probabilistic flood hazard maps showing the maximum inundation depths for a selected set of probabilities of occurrence, with maps showing the expectation (median) and the uncertainty by percentile maps. The results are critically discussed and ways for their usage in flood risk management are outlined. [ABSTRACT FROM AUTHOR]

Sediment dynamics play a major role for the agricultural and fishery productivity of the Mekong Delta. However, the understanding of sediment dynamics in the Mekong Delta, one of the most complex river deltas in the world, is very limited. This is a consequence of its large extent, the intricate system of rivers, channels and floodplains and the scarcity of observations. This study quantifies, for the first time, the suspended sediment transport and sediment-nutrient deposition in the whole Mekong Delta. To this end, a quasi-2-D hydrodynamic model is combined with a cohesive sediment transport model. The combined model is calibrated automatically using six objective functions to represent the different aspects of the hydraulic and sediment transport components. The model is calibrated for the extreme flood season in 2011 and shows good performance for the two validation years with very different flood characteristics. It is shown how sediment transport and sediment deposition vary from Kratie at the entrance of the Delta to the coast. The main factors influencing the spatial sediment dynamics are the setup of rivers, channels and dike-rings, the sluice gate operations, the magnitude of the floods and tidal influences. The superposition of these factors leads to high spatial variability of sediment transport, in particular in the Vietnamese floodplains. Depending on the flood magnitude, the annual sedimentation rate averaged over the Vietnamese floodplains varies from 0.3 to 2.1 kgm-2 yr-1, and the ring dike floodplains trap between 1 and 6% of the total sediment load at Kratie. This is equivalent to 29 x 10³-440 x 10³ t of nutrients (N, P, K, TOC) deposited in the Vietnamese floodplains. This large-scale quantification provides a basis for estimating the benefits of the annual Mekong floods for agriculture and fishery, and is important information for assessing the effects of deltaic subsidence and climate change related sea level rise. [ABSTRACT FROM AUTHOR]

Automatic and multi-objective calibration of hydrodynamic models is - compared to other disciplines like e.g. hydrology - still underdeveloped. This has mainly two reasons: the lack of appropriate data and the large computational demand in terms of CPU-time. Both aspects are aggravated in large-scale applications. However, there are recent developments that improve the situation on both the data and computing side. Remote sensing, especially radar-based techniques proved to provide highly valuable information on flood extents, and in case high precision DEMs are present, also on spatially distributed inundation depths. On the computing side the use of parallelization techniques brought significant performance gains. In the presented study we build on these developments by calibrating a large-scale 1-dimensional hydrodynamic model of the whole Mekong Delta downstream of Kratie in Cambodia: we combined in-situ data from a network of river gauging stations, i.e. data with high temporal but low spatial resolution, with a series of inundation maps derived from ENVISAT Advanced Synthetic Aperture Radar (ASAR) satellite images, i.e. data with low temporal but high spatial resolution, in an multi- objective automatic calibration process. It is shown that an automatic, multi-objective calibration of hydrodynamic models, even of such complexity and on a large scale and complex as a model for the Mekong Delta, is possible. Furthermore, the calibration process revealed model deficiencies in the model structure, i.e. the representation of the dike system in Vietnam, which would have been difficult to detect by a standard manual calibration procedure. [ABSTRACT FROM AUTHOR]

Calibration of hydrodynamic models is -- compared to other disciplines like e.g. hydrology -- still underdeveloped. This has mainly two reasons: the lack of appropriate data and the large computational demand in terms of CPU-time. Both aspects are aggravated in large-scale applications. However, there are recent developments that improve the situation on both the data and computing side. Remote sensing, especially radar-based techniques proved to provide highly valuable information on food extents, and in case high precision DEMs are present, also on spatially distributed inundation depths. On the computing side the use of parallelization techniques brought significant performance gains. In the presented study we build on these developments by calibrating a large-scale 1-D hydrodynamic model of the whole Mekong Delta downstream of Kratie in Cambodia: we combined in-situ data from a network of river gauging stations, i.e. data with high temporal but low spatial resolution, with a series of inundation maps derived from ENVISAT Advanced Synthetic Aperture Radar (ASAR) satellite images, i.e. data with low temporal but high spatial resolution, in an multi-objective automatic calibration process. It is shown that an automatic, multi-objective calibration of hydrodynamic models, even of such complexity and on a large scale and complex as a model for the Mekong Delta, is possible. Furthermore, the calibration process revealed model de?ciencies in the model structure, i.e. the representation of the dike system in Vietnam, which would have been di?cult to detect by a standard manual calibration procedure. [ABSTRACT FROM AUTHOR]