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.
|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
|Conference||International Workshop on Statistical Modelling 2018|
|Abbreviated title||IWSM 2018|
|Country||UK United Kingdom|
|Period||15/07/18 → 20/07/18|
Brady, A., Faraway, J., & Prosdocimi, I. (2018). Attribution of large-scale drivers of peak river flows in Ireland. 54-58. Paper presented at International Workshop on Statistical Modelling 2018, Bristol, UK United Kingdom.