Risk analysis of climate change impacts on the quantitative drinking water supply
Examensarbete för masterexamen
Infrastructure and environmental engineering (MPIEE), MSc
As the construction, maintenance and expansion of water supply systems require considerable investments and their operational flexibility is only given to a certain degree, special attention should be paid to ensure that the system components are designed for a long service life so that the future water demand can be met. For this purpose, future water supply parameters, for instance, the daily peak demand and various other factors must be determined. In past decades, when determining supply parameters little to no consideration was given to the negative impacts of climate change on the supply situation. As a result, past heat summers, such as those in 2018 or 2022, have pushed water supplies to their limits in much of Germany. Therefore, in the context of this thesis, a risk analysis is conducted with the aim of determining the future water demand in Southern Germany. In order to achieve this, a surrogate model that is based on a machine learning approach and operates on the basis of Gaussian process regression is applied. The results generated in this process are used to set up an early warning system, which can be used by the water utility companies of the study area to determine their future water balance and to assess whether the water resources at their disposal will be sufficient to provide the necessary future water demand. Furthermore, the early warning system can be used to investigate the effect of planned countermeasures. In addition to the early warning system, a catalog of measures was compiled, which should serve as a guide in the successful adaptation of water supply systems to the negative effects of climate change.
water demand , climate change , machine learning , Gaussian process regression , climate projections