Large-scale assessment of flood risk and the effects of mitigation measures along the Elbe River

The downstream effects of flood risk mitigation measures and the necessity to develop flood risk management strategies that are effective on a basin scale call for a flood risk assessment methodology that can be applied at the scale of a large river. We present an example of a rapid flood risk assessment methodology for the Elbe River. A 1D hydraulic routing model is extended by including the effect of planned (regulated and unregulated) and unintended retention (dike breaches) on the peak water levels. We further add an inundation model for dike breaches due to dike overtopping and a macroscale economic approach to assess the flood damage. The flexible approach to model the effects of measures by means of volume storage functions allows for rapid assessment of combinations of retention measures of various proposed dimensions and at multiple locations. The method allows for the comparison of the flood risk at the scale of the main river trajectory, which has not been possible for the Elbe River to date. The model is applied to a series of exemplary flood risk mitigation measures to show the downstream effects and the additive effects of combinations of measures on the flood risk along the river. We further demonstrate the increase in the downstream flood risk resulting from unilateral decisions to increase the dike height at upstream locations. As expected, the results underline the potential effectiveness of increased retention along the river. The effects of controlled retention at the most upstream possible location and largest possible extent generate the most pronounced reduction of average annual damage. As expected, the effect of uncontrolled retention with dike relocations is significantly lower.     

Adapting to climate change: a comparison of two strategies for dike heightening

In the Netherlands the current dike design policy is to design flood defence structures corresponding to an agreed flooding probability with an extra safety board of at least 0.5 m. For river dikes a return period of 1,250 years is used to determine the design water levels. A problem with this strategy is that it builds on assumptions with regard to the intrinsically uncertain probability distributions for the peak discharges. The uncertainty is considerable and due to (1) the measuring records that are limited to about 100 years and (2) the changing natural variability as a result of climate change. Although the probability distributions are regularly updated based on new discharge data the nature of the statistics is such that a change in the natural variability of the peak discharge affects the probability distribution only long after the actual change has happened. Here we compare the performance of the probabilistic dike design strategy with the older strategy, referred to as the ‘self-learning dike’. The basic principle of the latter strategy is that the dike height is kept at a level equal to the highest recorded water level plus a certain safety margin. The two flood prevention strategies are compared on the basis of the flooding safety over a 100-year period. The Rhine gauge station at Lobith serves as case study. The results indicate that the self-learning dike performs better than the probabilistic design in terms of safety and costs, both under current and climate change conditions.     

Projections of Extreme Rainfall in Hong Kong in the 21st Century

The possible changes in the frequency of extreme rainfall events in Hong Kong in the 21st century wereinvestigated by statistically downscaling 30 sets of the daily global climate model projections (involvinga combination of 12 models and 3 greenhouse gas emission scenarios,namely,A2,A1B,and B1) of theFourth Assessment Report of the Intergovernmental Panel on Climate Change.To cater for the intermittentand skewed character of the daily rainfall,multiple stepwise logistic regression and multiple stepwise linearregression were employed to develop the downscaling models for predicting rainfall occurrence and rainfallamount,respectively.Verification of the simulation of the 1971-2000 climate reveals that the models ingeneral have an acceptable skill in reproducing past statistics of extreme rainfall events in Hong Kong.Theprojection results suggest that,in the 21st century,the annual number of rain days in Hong Kong is expectedto decrease while the daily rainfall intensity will increase,concurrent with the expected increase in annualrainfall.Based on the multi-model scenario ensemble mean,the annual number of rain day is expected todrop from 104 days in 1980-1999 to about 77 days in 2090-2099.For extreme rainfall events,about 90% ofthe model-scenario combinations indicate an increase in the annual number of days with daily rainfall 100mm (R100) towards the end of the 21st century.The mean number of R100 is expected to increase from 3.5days in 1980-1999 to about 5.3 days in 2090-2099.The projected changes in other extreme rainfall indicesalso suggest that the rainfall in Hong Kong in the 21st century may also become more extreme with moreuneven distributions of wet and dry periods.While most of the model-emission scenarios in general projectconsistent trends in the change of rainfall extremes in the 21st century,there is a large divergence in theprojections among different model/emission scenarios.This reflects that there are still large uncertainties inmodel simulations of future extreme rainfall events.