Risk analysis of climate change impacts on the quantitative drinking water supply
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Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Infrastructure and environmental engineering (MPIEE), MSc
Publicerad
2023
Författare
Starcevic, Ivan
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
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.
Beskrivning
Ämne/nyckelord
water demand , climate change , machine learning , Gaussian process regression , climate projections