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).
1.1 Problem statement and
objectives
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 28th 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.
1.2 State-of-the-art: Hydrological modelling in event
attribution
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.