Fig. 1. Global mapping by the website Carbon Brief (2022), including 126 “rain and flooding” attribution studies, published until May 2022. Coloured markers indicate where a significant influence of human alteration of climate is found (red), not found (blue), and where evidence is inconclusive (grey).

The interest for attributing an extreme event commonly originates in the severity of the societal impacts (Stone et al., 2021), rather than in its remarkable meteorological features. For instance, there is frequent discussion of a potential role of extreme event attribution in the Loss and Damage Mechanism of the United Nations Framework Convention on Climate Change (Olsson et al., 2022; Parker et al., 2015). Extreme event attribution is typically based on analysis of meteorological variables. During an initial step, called ‘event definition’ (Philip et al., 2020), researchers define the intensity and domain of the meteorological event that contributed to the disaster (van der Wiel et al., 2017). For example, for the pluvial floods of July 28^th 2014 in the Netherlands, the event was defined as equal or surpassing 132 mm of daily precipitation over the whole country (Eden et al., 2018). However, in the case of river floods, which we consider here, it is not the extreme precipitation that directly causes the socioeconomic impacts. Impacts scale rather with the magnitude of the flood hazard, commonly defined as the “temporary covering by water of land not normally covered by water” (Barredo, 2007). While extreme precipitation does not necessarily lead to flooding, flooding can be generated by moderate precipitation, when other factors are at play (Berghuijs et al., 2019; van der Wiel et al., 2020). Thus, the intensity of precipitation is only a proxy for the magnitude of flood impacts. The validity of this proxy depends on the local context and specific event, but is generally not examined. To bring event attribution closer to the impacts of flooding, two solutions have been attempted: 1) using hydrological models to convert precipitation into discharge (e.g., Schaller et al., 2016; see Section 1.2); 2) directly relating the magnitude of the economic impacts to that of the precipitation event, thus bypassing the complications of solution 1 (Frame et al., 2020). Both approaches are infrequent.

Non-climatological changes can also affect river flood hazard, i.e. by regional and local hydrological changes (Boulange et al., 2021; Munoz et al., 2018; Sebastian et al., 2019; Syvitski & Brakenridge, 2013). The effects of these changes can in some cases oppose those of climate change. For example, dam construction can lower flood occurrence on a given location, even when climate change may have increased it; landscape change from forest to urban can increase flood occurrence at the site and downstream, even when climate change may have lowered it. Furthermore, hydrological change can alter sediment fluxes, in turn leading to geomorphic responses, such as in-channel sedimentation and channel enlargement, that affect the flow capacity of river channels and thus its tendency to flood (Hoffmann et al., 2010). If extreme event attribution included representations of these processes and changes, it would better isolate the influence of climate change on flood occurrence, yielding a more accurate climate attribution. Accordingly, it would also inform on the influence of other key drivers of floods. This addresses the mechanisms behind changes in flood hazards, one of the most pressing questions in hydrology and flood risk management (Blöschl et al., 2019). In the wake of a flood disaster, when the urgency of the problem is clearest to citizens and decision-makers, a multi-driver attribution of the event could offer a strong scientific basis for flood risk management. In fact, while stakeholders see general merit in attribution studies (James et al., 2019; Sippel et al., 2015), they seem doubtful about their present usefulness for the practice of climate adaptation and disaster management (Osaka & Bellamy, 2020).

The science of extreme event attribution has quickly advanced, and methods are becoming standardised (Philip et al., 2020; van Oldenborgh et al., 2021). But an explicit attribution of floods to their multiple drivers is still unattempted. In this Perspective: we summarise recent efforts by the scientific community; we examine the challenges to flood event attribution, differentiating between near-natural cases and cases where substantial hydrological change has occurred; and propose separate solutions for either case.

A handful of probabilistic extreme event attribution studies include hydrology in their analysis. Pall et al. (2011) propose runoff as a better proxy for flooding than precipitation, and use a simple precipitation-runoff model, fed with daily precipitation input from global climate model simulations, to produce daily runoff series. They do not compare their results to results that would have been obtained from precipitation alone. They calculate, however, the changes in precipitation that would follow thermodynamically from the warming of air masses, concluding that the fraction of attributable risk is similar across the two approaches. Kay et al. (2011) use the same meteorological input as Pall and colleagues, and expand on that study in two respects. They realise 1-year-long continuous simulations of precipitation-runoff with the semi-distributed model CLASSIC. They thus capture the effects of antecedent conditions in the basin and of spatio-temporal variations in precipitation. Further, they include snow-related processes resulting from temperature change, showing that these have a notable effect on runoff peaks. Schaller et al. (2016) also perform continuous simulations, for four years, and include snow processes, with the same CLASSIC model, this time coupled with the hydrodynamic model JFlow+. They highlight the importance of multi-year antecedent conditions. Moreover, they employ an empirical relationship to infer flooded property from peaks in discharge, assuming absence of flood defences. Kay et al. (2018) apply a similar approach to the larger domain of Great Britain, and also highlight the importance of including flood modelling and snow processes. Philip et al. (2019) aim to systematically assess the difference between attribution either based on precipitation only and with inclusion of modelling of river discharge. For the first time, they use multiple hydrological models: PCR-GLOBWB, SWAT, LISFLOOD and RFM. They find that results of discharge attribution differ from those based on precipitation. Sebastian et al. (2019) use hydrological model Vflo® to simulate discharge in the city of Houston resulting from extreme precipitation associated with Hurricane Harvey. Notably, they separate the effects of urban development and climate change in changing peak discharge, finding that urban development had a larger effect. No event attribution study, to our knowledge, has explicitly modelled flood hazard.