Conclusion

The main steps of the work flow in the developed methodology are illustrated in Fig.

7. 1 to quantify the hydrological impacts by climate change on the flood probability in SUCAs and the subsequent simulation of adaptation measure scenarios with SUDS. Features to optimize the work flow and the application of the implemented new SUDS simulation tool are indicated additionally.

In the pre-processing, criteria for selecting climate model and scenario data series have been defined, which could assist further projects to create a basis for comparability. The file formats provided by a variety of climate models differ significantly. Therefore, tools are provided to transfer climate model data files (e.g.

NetCDF, ASCII, IEG files) into usable formats for impact studies, but the handling of the tools as well as the required further data processing is left to the climate model data user. For the transformation of the often used NetCDF format, the procedure is described in the developed methodology and a Java tool has been created at the Institute of River and Coastal Engineering in Hamburg for further applications. The computation of additional variables of climate model data series has been depicted in this thesis with the calculation of potential evaporation data series on the basis of available data series of the climate models REMO and CLM. Calculating such additional data series depends on the required input data series for the applied hydrological model.

Fig. 7. 1 Illustration of the work flow to quantify climate change impacts on the flood probability in SUCAs and simulating adaptation measure scenarios (SUDS-Scenarios); with notes for optimisation and outlook.

It has been defined as an open question, if the spatial resolution of the currently available climate model data is adequate for the complex flood probability studies in SUCAs. It has been illustrated in the application scenario studies, that the spatial resolution of about 11km x 6.5km provided by the climate model REMO in the datastream D3 interpolated on a regular grid, is appropriate for the scenario studies in the Krückau catchment, which is characterised by rural areas. The displayed spatial resolution is finer as provided by the observed climate data series of gauging stations (≈30km x 30km), used for the calibration of the hydrological model. But the applicability can not be generalised for other study areas. In mountainous or dense urban catchments a finer spatial resolution could be required and has to be analysed further on.

The restricted temporal resolution of data series provided by climate models has been analysed in comparative studies. The results of flood hydrographs with hourly and 15-minute simulation time steps have been compared for a rural, an urban and a discharge node in the urban area of Elmshorn. The differences between these two simulations are significant for the urban sub-catchments and therefore smaller timesteps than hourly data series are required for flood probability simulations in SUCAs.

Climate data series describe the statistical sums and averages of weather phenomena.

Therefore, climate data series neither of the past nor of the future can be analysed according to a specific event or short term trend. Therefore, strategies have been outlined for the processing of the data series, to analyse the overall changes of the climate variables and derived impacts in a whole climate period. The most important results for flood probability analyses are gained by statistical evaluations, which are less often applied in climate change studies up to now and only some related studies can be referred here. In this thesis, for each of the five scenarios (0, C20, A1B, B1 and A2) and seasonal differentiation (summer, winter and yearly) statistical evaluations have been computed. In this way, fifteen statistical evaluations have been worked out with the results of overall 750 short term flood peak simulations after respective long term simulations. In the developed methodology, a calculation loop for statistical evaluations has been introduced. This loop starts with the trend adjustment of data series over the climate period for a reference year and the results are used for the calculation of probability distribution curves. If outliers are detected in the distribution curves, which are not appropriately represented and distort the statistical evaluation results, these outliers have to be handled separately and the loop has to be repeated with the trend adjustment till the outlier test is negative.

Additionally, the representativeness of the probability distribution curve is controlled with goodness-of-fit tests as the last step.

With the results of the statistical evaluations, another open question has been discussed, namely if the IPCC scenario representing the largest increase in CO2

emissions and which projects the highest temperature changes, also leads to a larger increase of the probability of extreme rainfall events as well as flood events. In the studies of this thesis, the largest changes in flood probability compared to the reference year 2000, are displayed by the A1B scenario, which is by definition a medium emissions scenario (see 3.2). Therewith, shifts in the probability of design events in the Krückau catchment area has been computed; e.g. an actual summer flood peak of 11m³/s with a return period of once in 100years (HQ100) at the node Langelohe in Elmshorn changes due to the projected effects in the A1B scenario to a flood event with a return period of once in 20years (HQ_20,C). Extreme summer rainfall events with a current probability of occurrence of once in 100years (T=100a) and an intensity of 24.6mm/h, are calculated to be shifted to an event with a return period of once in 30years (T = 30a). The highest emissions scenario analysed in the application studies, is the A2 scenario which displayed lower changes in flood probability and extreme rainfall for the climate period from 2040 to 2070. These outcomes of the statistical evaluations have been discussed in chapter 6 with results of the INKLIM 2012 II Plus project, where the largest changes in flood probability have been defined as well for the A1B scenario with comparable results.

Furthermore, it has been stated as questionable, if interdependencies between the average changes in climate scenarios and the changes in extreme events can be detected. It has been found out that the correlation between average climate changes published in research studies can not be taken as a basis to provide a statement about the changes of extreme rainfall or flood probabilities in a local study area. Statistical evaluations or the number of occurrence of events above a defined threshold value have to be quantified for this purpose. Additionally, it can not be stated that extreme flood events change in a corresponding way like extreme rainfall events in climate change scenarios with specific return periods as discussed in this thesis, where only a corresponding tendency of increase could be identified. Overall, larger increases of flood and extreme rainfall events in the summer periods are calculated than in the winter periods. In the scenarios A2 and B1 even a decrease of extreme events is computed for the winter periods, although the average winter precipitation is calculated to be increased significantly.

The differences in the results of using computed control scenario data series in comparison to the results using observed data series are significant. Therefore in the outlined post-processing methodology, two approaches are derived, namely the

‘percentage change approach’ and the ‘absolute change approach’, to handle the differences and to calculate the magnitude of climate change impacts. For the applicability of the climate change scenario study results in practice, climate change

factors (CCFs) are calculated. These factors are applied to obtain the respective design rainfall or flood peak event with a specific return period under climate change conditions for further locations in the study areas. The developed Averaging Ensemble CCF method has been proved to be more applicable, compared to another approach (namely the ‘Delta Runoff Rate CCF’) discussed in (6.1.3).

The results of the post-processing are further used in post-impact studies, like done in this thesis for the assessment of the effectiveness of flood risk mitigation measures in SUCAs. In this context, Sustainable Drainage Systems (SUDSs) have been identified as appropriate flexible and no-regret strategies, which can be adapted to uncertain future impacts derived by climate change and urban developments. The focus has been set on green roofs combined with swales, swale-filter-drain systems and unsealing. For this purpose, a new software tool has been implemented to simulate the effectiveness of green roofs (chapter 4). It is based on a catchment level approach and enables the simulation of the complex hydrological vertical (e.g.

infiltration, percolation, evaporation) as well as horizontal (e.g. flow trough drainage layer) and the storage processes of water in each layer of the SUDS element. In this way, a detailed and comprehensive simulation of SUDSs is facilitated. The functions and calculations in the developed sub-routines, written in the FORTRAN programming language, have been described with Nassi-Shneiderman diagrams to provide a detailed documentation for further studies and software updates. The software tool has been implemented in the development of the Decision Support Tool Kalypso Planer Client, which was supported by the Agency for Roads, Bridges and Waters in Hamburg (LSBG). Additionally, an add-on tool had to be worked out, to assist the simulation of SUDS with a separate Kalypso Hydrology model. The simulations are restricted to be done with ASCII files and the executable of the core up to now. Testings of the developed SUDS simulation software tool for green roofs have been performed in combination with testing the software tools of swales and swale-filter-drain systems and if required, these tools have been revised in the scope of this thesis. The results of the SUDS simulations display acceptable differences in the water balance calculations of 0.1% to 0.01%. Additionally, a discussion of the effectiveness of SUDS compared to the results of a natural state scenario has been done. Therewith, it can be stated that not just the simulation of the hydrological processes of each SUDS element is adequately enabled, but that the assessment of the effectiveness of SUDS on a catchment level is appropriately and successful facilitated.

In the SUDS application scenarios (5.6) it has been found out that SUDS display larger effectiveness for flood probability mitigation as closer as the measures are located to the urban areas of interest. In the sub-catchments with the hot spots of the “Badewanne” and the Steindammwiesen Park in Elmshorn, the projected flood peak probabilities are even reduced below the current reference situation (Scenario

0). But this high effectiveness of SUDS can only be reached in dense urban areas and for events with higher probabilities of occurrence. By rainfall and flood events with lower return periods of e.g. once in 30years or 50years, the effectiveness of the SUDS technique is reduced due to the generation of overflow from the limited storage capacity of SUDS.

Finally, the implemented combination of SUDS comprising green roofs, swales, swale-filter-drain systems and unsealing of surfaces, illustrated high potential to mitigate and even compensate climate change impacts on the flood probability in the Krückau catchment.