Flood trends along the Rhine: the role of river training

Several previous studies have detected positive trends in flood flows in German rivers, among others, at Rhine gauges over the past six decades. The presence and detectability of the climate change signal in flood records has been controversially discussed, particularly against the background of massive river training measures in the Rhine. In the past the Rhine catchment has been heavily trained, including the construction of the Rhine weir cascade, flood protection dikes and detention basins. The present study investigates the role of river training on changes in annual maximum daily flows at Rhine gauges starting from Maxau down to Lobith. In particular, the effect of the Rhine weir cascade and of a series of detention basins was investigated. By homogenising the original flood flow records in the period from 1952 till 2009, the annual maximum series were computed that would have been recorded had river training measures not been in place. Using multiple trend analysis, relative changes in the homogenised time series were found to be from a few percentage points to more than 10 percentage points smaller compared to the original records. This effect is attributable to the river training measures, and primarily to the construction of the Rhine weir cascade. The increase in Rhine flood discharges during this period was partly caused by an unfavourable superposition of the Rhine and Neckar flood waves. This superposition resulted from an acceleration of the Rhine waves due to the construction of the weir cascade and associated channelisation and dike heightening. However, at the same time, tributary flows across the entire Upper and Lower Rhine, which enhance annual maximum Rhine peaks, showed strong positive trends. This suggests the dominance of another driver or drivers which acted alongside river training

Event generation for probabilistic flood risk modelling: Multi-site peak flow dependence model vs. weather-generator-based approach

Flood risk assessment is an important prerequisite for risk management decisions. To estimate the risk, i.e. the probability of damage, flood damage needs to be either systematically recorded over a long period or modelled for a series of synthetically generated flood events. Since damage records are typically rare, time series of plausible, spatially coherent event precipitation or peak discharges need to be generated to drive the chain of process models. In the present study, synthetic flood events are generated by two different approaches to modelling flood risk in a meso-scale alpine study area (Vorarlberg, Austria). The first approach is based on the semi-conditional multi-variate dependence model applied to discharge series. The second approach relies on the continuous hydrological modelling of synthetic meteorological fields generated by a multi-site weather generator and using an hourly disaggregation scheme. The results of the two approaches are compared in terms of simulated spatial patterns of peak discharges and overall flood risk estimates. It could be demonstrated that both methods are valid approaches for risk assessment with specific advantages and disadvantages. Both methods are superior to the traditional assumption of a uniform return period, where risk is computed by assuming a homogeneous return period (e.g. 100-year flood) across the entire study area. © Author(s) 2020

Quasi 2D hydrodynamic modelling of the flooded hinterland due to dyke breaching on the Elbe River

In flood modeling, many 1D and 2D combination and 2D models are used to simulate diversion of water from rivers through dyke breaches into the hinterland for extreme flood events. However, these models are too demanding in data requirements and computational resources which is an important consideration when uncertainty analysis using Monte Carlo techniques is used to complement the modeling exercise. The goal of this paper is to show the development of a quasi-2D modeling approach, which still calculates the dynamic wave in 1D but the discretisation of the computational units are in 2D, allowing a better spatial representation of the flow in the hinterland due to dyke breaching without a large additional expenditure on data pre-processing and computational time. A 2D representation of the flow and velocity fields is required to model sediment and micro-pollutant transport. The model DYNHYD (1D hydrodynamics) from the WASP5 modeling package was used as a basis for the simulations. The model was extended to incorporate the quasi-2D approach and a Monte-Carlo Analysis was used to conduct a flood sensitivity analysis to determine the sensitivity of parameters and boundary conditions to the resulting water flow. An extreme flood event on the Elbe River, Germany, with a possible dyke breach area was used as a test case. The results show a good similarity with those obtained from another 1D/2D modeling study

Corrigendum: Projections for headwater catchments of the Tarim River reveal glacier retreat and decreasing surface water availability but uncertainties are large (2016 Environ. Res. Lett. 11 054024)

This is a correction for 2016 Environ. Res. Lett. 11 05402

Estimating parameter values of a socio-hydrological flood model

Socio-hydrological modelling studies that have been published so far show that dynamic coupled human-flood models are a promising tool to represent the phenomena and the feedbacks in human-flood systems. So far these models are mostly generic and have not been developed and calibrated to represent specific case studies. We believe that applying and calibrating these type of models to real world case studies can help us to further develop our understanding about the phenomena that occur in these systems. In this paper we propose a method to estimate the parameter values of a socio-hydrological model and we test it by applying it to an artificial case study. We postulate a model that describes the feedbacks between floods, awareness and preparedness. After simulating hypothetical time series with a given combination of parameters, we sample few data points for our variables and try to estimate the parameters given these data points using Bayesian Inference. The results show that, if we are able to collect data for our case study, we would, in theory, be able to estimate the parameter values for our socio-hydrological flood model

Model system development and uncertainty for the provisionary management of extreme floods in large river basins

International audienceA research project is introduced in which a modelling system is being developed to quantify risks of extreme flooding in large river basins. In the system, computer models and modules are coupled to simulate the functional chain: hydrology - hydraulics - polder diversion - dyke failure - flooding - damage estimate - risk assessment. In order to reduce uncertainty in flood frequency analyses, data sets are complimented with information from historical chronicles and artwork. Probable maximum precipitation and discharge are calculated to indicate upper bounds of meteorological and hydrological extremes. Uncertainty analysis is investigated for different degrees of model complexity and compared at different basin scales

The role of flood wave superposition in the severity of large floods

The severity of floods is shaped not only by eventand catchment-specific characteristics but also depends on the river network configuration. At the confluence of relevant tributaries with the main river, flood event characteristics may change depending on the magnitude and temporal match of flood waves. This superposition of flood waves may potentially increase the flood severity downstream in the main river. However, this aspect has not been analysed for a large set of river confluences to date. To fill this gap, the role of flood wave superposition in the flood severity at downstream gauges is investigated in four large river basins in Germany and Austria (the Elbe, the Danube, the Rhine and the Weser). A novel methodological approach to analyse flood wave superposition is presented and applied to mean daily discharge data from 37 triple points. A triple point consists of three gauges: one in the tributary as well as one upstream and downstream of the confluence with the main river respectively. At the triple points, differences and similarities in flood wave characteristics between the main river and the tributary are analysed in terms of the temporal match and the magnitudes of flood peaks. At many of the confluences analysed, the tributary peaks consistently arrive earlier than the main river peaks, although high variability in the time lag is generally detected. No large differences in temporal matching are detected for floods of different magnitudes. In the majority of cases, the largest floods at the downstream gauge do not occur due to perfect temporal match between the tributary and the main river. In terms of spatial variability, the impact of flood wave superposition is site-specific. Characteristic patterns of flood wave superposition are detected for flood peaks in the Danube River, where peak discharges largely increase due to inflow from alpine tributaries. Overall, we conclude that the superposition of flood waves is not the driving factor behind flood peak severity at the major confluences in Germany; however, a few confluences show the potential for strong flood magnifications if a temporal shift in flood waves was to occur

A multi‐scale framework for flood risk analysis at spatially distributed locations

This paper presents a multi‐scale framework for flood risk analysis from fluvial and coastal sources at broad (including national) scales. The framework combines an extreme value spatial model of fluvial and coastal flood hazards using the Heffernan and Tawn conditional dependence model, with a new Markov approach to representing the spatial variability of flood defences. The nested multi‐scale structure enables spatial and temporal dependence at a national scale to be combined with detailed local analysis of inundation and damage. By explicitly considering each stage of the process, potential uncertainties in the risk estimate are identified and can be communicated to end users to encourage informed decision making. The framework is demonstrated by application to an insurance portfolio of static caravan sites across the UK worth over £2bn. In the case study, the largest uncertainties are shown to derive from the spatial structure used in the statistical model and limited data on flood defences and receptor vulnerability

Large-scale stochastic flood hazard analysis applied to the Po River

Reliable hazard analysis is crucial in the flood risk management of river basins. For the floodplains of large, developed rivers, flood hazard analysis often needs to account for the complex hydrology of multiple tributaries and the potential failure of dikes. Estimating this hazard using deterministic methods ignores two major aspects of large-scale risk analysis: the spatial–temporal variability of extreme events caused by tributaries, and the uncertainty of dike breach development. Innovative stochastic methods are here developed to account for these uncertainties and are applied to the Po River in Italy. The effects of using these stochastic methods are compared against deterministic equivalents, and the methods are combined to demonstrate applications for an overall stochastic hazard analysis. The results show these uncertainties can impact extreme event water levels by more than 2 m at certain channel locations, and also affect inundation and breaching patterns. The combined hazard analysis allows for probability distributions of flood hazard and dike failure to be developed, which can be used to assess future flood risk management measures