3. Possible solutions for flood event
attribution
The challenge of non-linear relations between extreme precipitation and
flooding applies to all flood cases, and thus also to cases of
near-natural hydrology, where hydrological changes during the time
interval of interest can be neglected (Fig. 2). We argue that for these
simpler cases an approach that can be explored is the expansion of the
existing framework of probabilistic extreme event attribution.
On the other hand, cases where relevant hydrological changes have
occurred, i.e., land-cover changes and human hydrological interventions,
present the additional challenge that the effect of these flood drivers
overlaps with the effect of climate change. For these more complex
cases, we argue that it is necessary to establish a multi-driver
framework for conditional event attribution.
3.1 Possible solution for near-natural
cases
The methods of probabilistic event attribution can be expanded to
include representation of the relevant hydrological and hydrodynamic
processes, by explicitly using flood data and flood modelling. This
allows overcoming the problem posed by the non-linearity between
precipitation and floods, in two main respects. First, explicit flood
attribution overcomes the errors associated with the initial step of
event definition. When using precipitation as a proxy for floods, the
precipitation event needs to be defined in terms of intensity and extent
in space and time, such that it most closely captures the generation of
the flood. This involves arbitrary choices (van Oldenborgh et al.,
2021). For example, after consultation with local experts and
consideration of impacts, it can be defensible to define the triggering
event as the cumulative precipitation over either 2 or 5 days, and over
an area of either 500 or 2000 km2. However, the
results of the attribution may strongly diverge in either case (Angélil
et al., 2018), requiring sensitivity tests (Luu et al., 2021). If,
instead, the definition is an observed metric of flood hazard, this
problem is largely negated. Second, explicit flood attribution addresses
the issue of antecedent conditions. Multi-year hydrological simulations,
followed by flood simulations, can adequately reproduce the state of,
e.g., soil moisture and snow pack.
In the following, we explore possible solutions for: the definition of
an appropriate flood metric; the availability of suitable flood data;
and the flood modelling.
3.1.1 Flood metric
Adding explicit consideration of floods to attribution means that all
analysis is based on a metric of flood hazard, instead of precipitation.
As established in event attribution science, the event definition should
be closely informed by the socio-economic impacts of the event. For any
type of exposed element, the impact is primarily determined by the depth
of the flood waters. Other relevant quantities are the flow velocity,
flood duration, and any pollution or sediment carried by the water
(Vogel et al., 2018). The total impact of the event is determined by the
sum of the impacts at each point over the flooded area. Therefore, a
flood metric that reasonably relates to the total impact is the total
flood area. However, since exposure widely varies over the territory, to
better approach impacts, a finer analysis should take into account the
location of population and of valuable and critical assets.
3.1.2 Flood data
Probabilistic extreme event attribution requires the following
categories of data: 1) observed magnitude of the event, for the event
definition; 2) time-series of observations, for trend detection; 3)
model-based time-series, representing both factual and counterfactual
climates, for the actual attribution (Philip et al., 2020).
For the magnitude of the flood event, it is ideal if collaboration with
the local relevant institutions is established; the change is then
higher that data from remote sensing, gauges or field surveys can be
accessed. Sometimes, local measurements are compromised by the flood
(Kreienkamp et al., 2021). In the absence of local data, recent products
at continental or global scale are potentially suitable replacements:
- Global Drought and Flood Catalogue (He et al., 2020). Includes data on
severity, inundation area, inundation fraction and flood duration for
events during 1950–2016. It is obtained by merging in situ and remote
sensing datasets with land surface and hydrodynamic modelling.
Available at
https://registry.opendata.aws/global-drought-flood-catalogue/.
- Global Flood Database (Tellman et al., 2021). Includes satellite maps
of 913 floods from 2000 to 2018, documented by the Dartmouth Flood
Observatory, at 250 m resolution.
https://global-flood-database.cloudtostreet.ai/.
- WorldFloods database (Mateo-Garcia et al., 2021). 422 flood maps,
satellite-based and validated, for 119 events between 2015 and 2019,
assembled from disaster response organisations.
https://www.nature.com/articles/s41598-021-86650-z#data-availability.
- European Flood Database (Hall et al., 2015). Includes discharge
time-series from >7000 European stations, and coordinates
and dates of >170,000 floods during 1960-2010.
https://www.eea.europa.eu/data-and-maps/data/external/european-floods-database.
- HANZE dataset (Paprotny & Mengel, 2023). Includes dates, locations,
area inundated, number of persons killed and affected, and losses for
more than 1500 European floods during 1870-2020.
https://www.nature.com/articles/s41597-023-02282-0.
- Flood Phenomena dataset of European floods (EEA, 2018). Includes flood
area, impacts and other flood characteristics for more than 11,000
floods during 1980-2015.
https://www.eea.europa.eu/data-and-maps/data/european-past-floods.
For future events, ongoing developments in remote sensing are promising.
New flood observation data are becoming available, e.g., in the
Fractional Water data from NASA’s Soil Moisture Active Passive satellite
(Du et al., 2021). New algorithms may soon reconstruct near-real time
observations of flood area, integrating imaging from satellite and
aerial sensors with elevation maps (Muñoz et al., 2021).
Time-series of local observations should have sufficient length for
trend detection, ideally covering the whole period of climate change,
i.e., the last 150 years, or as a minimum the last 4-5 decades, to
capture most of climate change. The key issue is that complete, uniform
time-series of floods are very rare. For a few locations, the data
listed in the previous section may contain a short time-series of
historical floods. Failing that, the next best option is to resort to a
flood proxy better than precipitation: river discharge. If that is also
not available, the best approach is to use modelling reconstructions
that are based on observations or on climate reanalysis. The key
datasets are:
- Global Runoff Data Centre (GRDC). Comprises observations for 9900
stations globally of daily or monthly discharge. Length varies, up to
200 years, and reaching until near-present.
https://www.bafg.de/GRDC/EN/01_GRDC/grdc_node.html.
- Global Flood Monitoring System (GFMS; Wu et al., 2019). Contains
3-hourly quasi-global (50°S - 50°N) modelled precipitation, runoff,
discharge and flood depth, from 2001 to the present with real-time
update; the simulation resolution is 1/8°, and results are downscaled
to 1 km. It takes precipitation input from several products to run the
VIC hydrological model coupled with the DRTR flood model.
http://flood.umd.edu/.
- Global Flood Awareness System (GloFAS; Harrigan et al., 2020).
Contains daily discharge at 0.1° resolution, from 1979 to the present
with real-time update. It takes runoff from hydrological model HTESSEL
(part of ERA5 climate reanalysis) to run the hydrodynamic model
LISFLOOD. https://www.globalfloods.eu/.
- Global Reach-scale A priori Discharge Estimates (Lin et al., 2019) and
Global Reach-Level Flood Reanalysis (Yang et al., 2021). Contains
model-based daily discharge for ~2.94 million river
reaches, for 1980-2019. It takes meteorological input from MSWEP v2.1
and other datasets, to run the hydrological model VIC at 0.25° and the
river-routing model RAPID.
https://www.reachhydro.org/home/records/grades.
3.1.3 Flood modelling
Flood modelling should be used to produce time-series of flood events,
based both on observed and on modelled boundary conditions. In
probabilistic attribution, the former are needed in the step of trend
detection, and the latter are needed in the model-based attribution step
that compares floods in factual and counterfactual climates.
To produce an observation-based flood time-series, the modelling chain
needs to include both a hydrological and a hydrodynamic modelling step.
The hydrological model uses meteorological observations on precipitation
and temperature, typically at daily time-step; solve key processes like
evapotranspiration, infiltration, exchanges between storage in snow,
soil and groundwater; and produce time-series of discharge or runoff at
a suitable resolution. In turn, the discharge or runoff series are used
in the hydrodynamic model, which solves processes related to the surface
flow of water more accurately than the hydrological model, and yields
peak discharge and flood metrics.
To produce the entirely model-based flood time-series representing
factual and counterfactual climate, the modelling chain is the same, but
will take precipitation and temperature from climate models. These could
come from the simulations coordinated by the Coupled Model
Intercomparison Project Phase 6 (CMIP6). CMIP6 includes simulations
called ‘piControl’, i.e., ‘pre-industrial control’, reflecting the
counterfactual climate of AD 1850 (Eyring et al., 2016), unaffected by
anthropogenic greenhouse gas emissions, using constant forcing along the
whole duration, of at least 100 years. The factual climate is addressed
by the ‘historical’ simulations of CMIP6. These simulations are
transient, meaning that increasing levels of greenhouse gas are applied
to reflect the history of anthropogenic emissions during 1850-2014. To
extend the series until the present, results of the ‘scenarioMIP’
experiments of CMIP6 can be used. CMIP6 results are available at
https://esgf-node.llnl.gov/projects/cmip6/. Such results have been
used to globally simulate high-resolution floods for the pre-industrial
(counterfactual) climate (Scussolini et al., 2020), and both
pre-industrial and modern (factual) climates (project ISIMIP2b; Lange et
al., 2020).
Additionally, to force flood models at higher resolution, results from
regional climate models can be used. A group of dynamical downscaling
experiments using different regional climate models is coordinated by
CORDEX (Diez-Sierra et al., 2022; https://cordex.org/data-access/).
These experiments cover 14 continental domains, at resolution between 12
and 50 km. Simulations start at 1950, and thus do not include the
pre-industrial, fully counterfactual climate. Also, they are presently
based on CMIP5 global climate models, but results based on CMIP6 will
become available in the near future.
However, it is necessary to consider whether the meteorological input
from global or regional climate models has adequate resolution and
skill, especially with respect to convective precipitation events. These
fine-scale events are not adequately represented by parameterisation
schemes in those models (Coppola et al., 2020). This can be overcome by
the recent emergence of convection-permitting climate models, with
resolution finer than 4 km (Luu et al., 2022; Manola et al., 2018). Such
input into the flood modelling can improve skills in reproducing flood
properties. However, this comes at massive computing costs; hence cannot
be performed over a large area or for a large ensemble and their
application is rare (Pichelli et al., 2021).
Hydrological simulations for attribution should be carried out
continuously for multiple years, as first evidenced by Schaller et al.
(2016). This is the only way to capture antecedent conditions, as it
allows the hydrological model to adjust to long term effects on, e.g.,
storage of snow, lakes and groundwater (Ajami et al., 2014). Thus,
results will also include floods generated by moderate precipitation,
for example, when it coincides with snow-melt, or when it occurs after
prolonged cold/wet conditions that have saturated the soil in the basin.
Ideally, simulations should be run continuously over the whole interval
covered by the multi-decadal climate time-series available. If that is
computationally too expensive, and if long-term changes in water storage
are negligible, a reasonable choice is to apply a threshold to extract
extreme events from the precipitation time-series, and to simulate
hydrology and floods during multi-year intervals culminating with these
events.
The step of model evaluation should include, besides precipitation and
temperature, comparison of modelled floods and discharge with the
respective observations, both for the event and for historical
time-series. For this, the datasets mentioned above can be used.
3.2 Possible solution for complex
cases
When relevant hydrological change has occurred, changes in flood
occurrence have more than one driver. This is not contemplated in the
probabilistic attribution framework, where the question is a variant of
“has anthropogenic climate change increased the frequency of events
like this one?” (NASEM, 2016).
3.2.1 A multi-driver framework for conditional event
attribution
A multi-driver framework should be formulated to address driver-specific
questions that condition the attribution on the states of the other
drivers. By systematically including and excluding, in the hydrological
and hydrodynamic modelling steps, the effect of land-cover change and of
human hydrological interventions, explicit flood attribution can
disentangle the relevance of each driver, including climate change. Such
framework takes inspiration from and goes beyond emerging literature on
storyline attribution for flood management (de Bruijn et al., 2016;
Sillmann et al., 2021).
The core steps should be: event definition; definition of hydrological
drivers; evaluation of the modelling chain; conditional model-based
attribution. Whereas in probabilistic attribution the step of trend
detection is essential to determine if attribution even makes sense, in
a multi-driver framework this is not pertinent, as any driver-specific
trends are confounded by the effects of the other drivers. The event
definition and the evaluation of the modelling chain present the same
challenges as in the near-natural cases discussed above. We here discuss
available data about hydrological changes, to inform the definition of
the relevant hydrological drivers, and how to represent hydrological
changes in models, to enable the conditional model-based attribution.