Abstract. In August 2017 Bangladesh faced one of its worst river flooding events in
recent history. This paper presents, for the first time, an attribution of this
precipitation-induced flooding to anthropogenic climate change from a
combined meteorological and hydrological perspective. Experiments were
conducted with three observational datasets and two climate models to
estimate changes in the extreme 10-day precipitation event frequency over the
Brahmaputra basin up to the present and, additionally, an outlook to 2 ∘C warming since pre-industrial times.
The precipitation fields were then used as meteorological input for four
different hydrological models to estimate the corresponding changes in river
discharge, allowing for comparison between approaches and for the robustness
of the attribution results to be assessed. In all three observational precipitation datasets the climate change trends
for extreme precipitation similar to that observed in August 2017 are not
significant, however in two out of three series, the sign of this
insignificant trend is positive. One climate model ensemble shows a
significant positive influence of anthropogenic climate change, whereas the
other large ensemble model simulates a cancellation between the increase due
to greenhouse gases (GHGs) and a decrease due to sulfate aerosols. Considering
discharge rather than precipitation, the hydrological models show that
attribution of the change in discharge towards higher values is somewhat less
uncertain than in precipitation, but the 95 % confidence intervals still
encompass no change in risk. Extending the analysis to the future, all models
project an increase in probability of extreme events at 2 ∘C global
heating since pre-industrial times, becoming more than 1.7 times more likely
for high 10-day precipitation and being more likely by a factor of about 1.5 for
discharge. Our best estimate on the trend in flooding events similar to the
Brahmaputra event of August 2017 is derived by synthesizing the observational
and model results: we find the change in risk to be greater than 1 and of
a similar order of magnitude (between 1 and 2) for both the meteorological and
hydrological approach. This study shows that, for precipitation-induced
flooding events, investigating changes in precipitation is useful, either as
an alternative when hydrological models are not available or as an
additional measure to confirm qualitative conclusions. Besides this, it highlights
the importance of using multiple models in attribution studies, particularly
where the climate change signal is not strong relative to natural variability
or is confounded by other factors such as aerosols.
Drivers behind the summer 2010 wave train leading to Russian heatwave and Pakistan flooding
