Abstract
AbstractWith increasing concerns on the impacts of climate change, there is wide interest in understanding whether hydrometric and environmental series display any sort of trend. Many studies however, focus on the analysis of highly variable individual series at each measuring location. We propose a novel and straightforward approach to trend detection, modelling the test statistic for trend at each location via an areal model in which the information across measuring locations is pooled together. We exemplify the method with a detailed study of change in high flows in Great Britain. Using areal models, we detect a statistically relevant signal for a positive trend across Great Britain in the recent decades. This evidence is also found when different temporal subsets of the records are analysed. Further, the model identifies areas where the increase has been higher or lower than average, thus providing a way to prioritise intervention.
Highlights
River flooding is a major natural hazard which threatens the well-being of communities and can have extremely high costs: the global annual average loss from river flooding is estimated to be USD 104 billion (United Nations Office for Disaster Risk Reduction (UNISDR), 2015) and in the United Kingdom (UK) alone the expected annual flood damages is GBP 560 million (Sayers et al, 2015)
Most statistical approaches used for trend detection would need very long records to perform optimally (Svensson et al, 2006), and such long records are sparse in Britain
Time here is used as a proxy for anthropogenic climate change, and the test statistic Ti is a standardised summary of the evidence in favour of a time trend, so of a change, at each station i
Summary
River flooding is a major natural hazard which threatens the well-being of communities and can have extremely high costs: the global annual average loss from river flooding is estimated to be USD 104 billion (United Nations Office for Disaster Risk Reduction (UNISDR), 2015) and in the United Kingdom (UK) alone the expected annual flood damages is GBP 560 million (Sayers et al, 2015). There is a widespread interest in understanding how climate change impacts fluvial flood risk (IPCC, 2012) so that appropriate management strategies can be put in place. This interest has resulted in a number of studies investigating projected and observed changes in peak flow magnitude (and/or frequency) at the global (Hirabayashi et al, 2013; Do et al, 2017), continental (Alfieri et al, 2015; Mediero et al, 2015) and national or regional scale (Giuntoli et al, 2015; Slater & Villarini, 2016; Kay et al, 2014a; Prosdocimi et al, 2014). Text S6 presents the results obtained when using the test statistics derived using robust regression
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