Abstract
Several large flooding events in recent years have led to increased concerns that climate change may be affecting the risk of flooding. At-site tests assessing whether change can be detected in observed data are not very powerful and cannot fully differentiate between possible confounders. It is also difficult to detect fully climate-driven trends, and separate these from other anthropogenic impacts such as urbanisation. We propose a change in focus from detection only towards both detecting and attributing trends in peak river flows to large-scale climate drivers such as the North Atlantic Oscillation index. We focus on a set of near-natural “benchmark” catchments in Ireland in order to detect those non-human driven trends. In order to enhance our ability to detect a signal, we model all stations together in a Bayesian framework which is implemented through Stan.
| Original language | English |
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| Pages | 54-58 |
| Number of pages | 5 |
| Publication status | Published - 16 Jul 2018 |
| Event | International Workshop on Statistical Modelling 2018 - University of Bristol, Bristol, UK United Kingdom Duration: 15 Jul 2018 → 20 Jul 2018 https://people.maths.bris.ac.uk/~sw15190/IWSM2018/ |
Conference
| Conference | International Workshop on Statistical Modelling 2018 |
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| Abbreviated title | IWSM 2018 |
| Country/Territory | UK United Kingdom |
| City | Bristol |
| Period | 15/07/18 → 20/07/18 |
| Internet address |