Post-Processing of Scenario Results

The magnitude of climate change impacts can not be derived by comparing directly the computed future climate scenario results with observed data series, but has to be calculated in correlation to the control scenario data results (3.5.1). With the computed magnitude of change, climate change factors (CCFs) can be calculated (3.5.2) and design events for post-impact studies can be defined (3.5.3).

3.5.1 The Magnitude of Climate Change Impacts

The change in extreme rainfall and flood peak events can be calculated with the difference between the climate change scenarios and the computed control scenario.

On the one hand the percentage change of the extreme rainfall [mm/D] or flood peak [m³/s] (ΔHT,C,[%]) per return period (T) and under climate change conditions (C) can be calculated, or the absolute value of change (ΔHT,C,abs) can be computed. In both approaches the magnitude of change has to be referred to the observed data series of the past (HT) to obtain the projected extreme event (HT,C) under climate change conditions.

The calculation of the percentage change of the extreme rainfall or flood peak per probability of occurrence (ΔHT,C,[%]) is depicted in eq.3. 8. The difference between the extreme precipitation height (‘HP’ in mm/D) or flood peak (‘HQ’ in m³/s) with a return period (T) computed in an IPCC climate change scenario (HT,IPCC-scenario) and the respective extreme event computed with the control scenario data of the past (HT,control-scenario) is calculated.

100

The calculation of the magnitude of the extreme event with a return period (T) (HT,C,[%]) is displayed in eq.3. 9. The percentage change of the extreme event per return period (ΔHT,C,[%]) is referred to the extreme event computed with observed data series (HT).

In the second approach, the magnitude of change (ΔHT,C,abs) is computed with the absolute difference between the extreme event with a return period (T) computed in an IPCC climate change scenario (HT,IPCC-scenario) and the corresponding extreme event computed in the control scenario of the past (HT,control-scenario).

scenario

The absolute value of change of the extreme event (ΔHT,C,abs) is added to the extreme event with a return period (T) computed with observed data series (HT) to calculate the projected magnitude of the extreme event (HT,C,abs) under climate change conditions.

3.5.2 Computation of Climate Change Factors (CCFs)

With the computation of climate change factors (CCFs) and statistical evaluations of observed data series (HT), it is possible to obtain the respective extreme rainfall or flood peak events (HT,C) for future climate scenarios (C) for further locations in the study area. In this thesis an approach has been developed to calculate the CCF with the average change of an ensemble of scenario study results. It is indicated here as

Averaging Ensemble CCF (fT,C), which has been derived on the basis of the following equation (eq.3. 12) indicated in Katzenberger (2004).

T

CCFs are restricted to be used in the project area where it has been computed. The factor can be calculated with both approaches displayed in 3.5.1. In the percentage change approach, the average over the number of scenario study results (n) of the differences of the events per return period (ΔHT,C,[%]; eq.3. 8) is calculated.

n

In the absolute change approach, the difference between the extreme event under climate change conditions HT,C,abs (eq.3. 11) and the observed statistical results (HT) is divided by the observed statistical results (HT). And the average is calculated of all scenario study results (n).

3.5.3 Design Events for Post-Impact Studies

For the planning of measures, design conditions have to be determined. Such design conditions are for example the ‘design wave’ for dike constructions or the maximal number of persons with an overall weight, who are allowed to step into an elevator.

Different approaches can be used to determine design conditions, e.g. regulations by law, maximal acceptable risk of damages and specific hazards.

It is recommended that at least for the climate scenario with the largest increase in frequency and magnitude of climate change impacts, design rainfall and flood events are created for further post-impact studies of adaptation measures. The changed design conditions are calculated with the magnitudes of change (3.5.1) and with the climate change factors (3.5.2).

Criteria for deriving design events

A design flood or rainfall event for further studies could be a calculated (“synthetic”) event or a representative event observed in the past. In this context, a seasonal differentiation of representative design events is recommended. For example, it could be distinguished that summer events are characterised by short term intense rainfall causing high flood peaks, but a lower overall discharge volume, and that winter

rainfall events are defined by a lower intensity, but longer durations, which derives lower discharge peaks, but come along with a larger overall discharge volume.

In this methodology an approach to select a representative observed event is outlined. It is suggested to prefer the largest rainfall or flood event in the data series, if it is defined as representative. Additional criteria are derived, when the scenario study flood event shall be used as well for the adjustment of the representative design rainfall event. In this case the duration of the observed rainfall event shall correspond to the duration of the design conditions. For the adjustment of the magnitude or intensity of the derived design event, matching coefficients have to be calculated.

Calculation of matching coefficients

Two main strategies can be pursued. In the first approach the matching coefficient is defined with respect to calculate a design rainfall event with a specific probability of occurrence. In the second approach, the matching coefficient is defined with regard to simulate design flood events with a specific probability of occurrence. In both approaches the matching coefficients are applied on the rainfall data series for the hydrological simulations of post-impact studies.

In the first approach, the matching coefficient is iteratively calculated for adjusting observed rainfall heights (HPD) with a specific duration (D) to the respective design rainfall intensity derived under climate change conditions (HPD,T,C).

In the second approach, the matching coefficients have to be obtained by iterative flow simulations with the hydrological model and adjusted rainfall intensities. For this purpose the overall catchment is divided into areas, which drain to specific nodes of interest, and for which the specific matching coefficient can be assigned to simulate design flood events.

3.6 Post-Impact Studies to Mitigate Climate Change Impacts on