A review is made of current methods for assessing future changes in urban rainfall extremes and their effects on urban drainage systems, due to anthropogenic-induced climate change. The review concludes that in spite of significant advances there are still many limitations in our understanding of how to describe precipitation patterns in a changing climate in order to design and operate urban drainage infrastructure. Climate change may well be the driver that ensures that changes in urban drainage paradigms are identified and suitable solutions implemented. Design and optimization of urban drainage infrastructure considering climate change impacts and co-optimizing these with other objectives will become ever more important to keep our cities habitable into the future.

No abstract

In this study the potential increase of extreme precipitation in a future warmer European climate has been examined. Output from the regional climate model (RCM) HIRHAM4 covering Europe has been analysed for two periods, a control period 1961-1990 and a scenario 2071-2100, the latter following the IPCC scenario A2. The model has a resolution of about 12 km, which is unique compared with existing RCM studies that typically operate at 25-50 km scale, and make the results relevant to hydrological phenomena occurring at the spatial scale of the infrastructure designed to drain off rainfall in large urban areas. Extreme events with one- and 24-hour duration were extracted using the Partial Duration Series approach, a Generalized Pareto Distribution was fitted to the data and T-year events for return periods from 2 to 100 years were calculated for the control and scenario period in model cells across Europe. The analysis shows that there will be an increase of the intensity of extreme events generally in Europe; Scandinavia will experience the highest increase and southern Europe the lowest. A 20 year 1-hour precipitation event will for example become a 4 year event in Sweden and a 10 year event in Spain. Intensities for short durations and high return periods will increase the most, which implies that European urban drainage systems will be challenged in the future.

A regional partial duration series (PDS) model is applied for estimation of intensity duration frequency relationships of extreme rainfalls in Denmark. The model uses generalised least squares regression to relate the PDS parameters to gridded rainfall statistics from a dense network of rain gauges with daily measurements. The Poisson rate is positively correlated to the mean annual precipitation for all durations considered (1 min to 48 hours). The mean intensity can be assumed constant over Denmark for durations up to 1 hour. For durations larger than 1 hour, the mean intensity is significantly correlated to the mean extreme daily precipitation. A Generalised Pareto distribution with a regional constant shape parameter is adopted. Compared to previous regional studies in Denmark, a general increase in extreme rainfall intensity for durations up to 1 hour is found, whereas for larger durations both increases and decreases are seen. A subsample analysis is conducted to evaluate the impacts of non-stationarities in the rainfall data. The regional model includes the non-stationarities as an additional source of uncertainty, together with sampling uncertainty and uncertainty caused by spatial variability.

[1] Changes in the properties of extreme rainfall events have been observed worldwide. In relation to the discussion of ongoing climatic changes, it is of high importance to attribute these changes to known sources of climate variability. Focusing on spatial and temporal changes in the frequency of extreme rainfall events, a statistical model is tested for this purpose. The model is built on the theory of generalized linear models and uses Poisson regression solved by generalized estimation equations. Spatial and temporal explanatory variables can be included simultaneously, and their relative importance can be assessed. Additionally, the model allows for a spatial correlation between the measurements. Data from a Danish rain gauge network are used as a case study for model evaluation. Focusing on 10 min and 24 h rainfall extremes, it was found that regional variation in the mean annual precipitation could explain a significant part of the spatial variability. Still, this variable was found to be of minor influence in comparison to explanatory variables in the temporal domain. The identified significant temporal variables comprise the East Atlantic pattern, the average summer precipitation, and the average summer temperature. The two latter showed a high relative importance. The established link will be beneficial when predicting future occurrences of precipitation extremes.