A framework for global river flood risk assessments

There is an increasing need for strategic global assessments
of flood risks in current and future conditions. In
this paper, we propose a framework for global flood risk assessment
for river floods, which can be applied in current
conditions, as well as in future conditions due to climate and
socio-economic changes. The framework’s goal is to establish
flood hazard and impact estimates at a high enough resolution
to allow for their combination into a risk estimate,
which can be used for strategic global flood risk assessments.
The framework estimates hazard at a resolution of ∼ 1 km2
using global forcing datasets of the current (or in scenario
mode, future) climate, a global hydrological model, a global
flood-routing model, and more importantly, an inundation
downscaling routine. The second component of the framework
combines hazard with flood impact models at the same
resolution (e.g. damage, affected GDP, and affected population)
to establish indicators for flood risk (e.g. annual expected
damage, affected GDP, and affected population). The
framework has been applied using the global hydrological
model PCR-GLOBWB, which includes an optional global
flood routing model DynRout, combined with scenarios from
the Integrated Model to Assess the Global Environment (IMAGE).
We performed downscaling of the hazard probability
distributions to 1 km2
resolution with a new downscaling algorithm,
applied on Bangladesh as a first case study application
area. We demonstrate the risk assessment approach
in Bangladesh based on GDP per capita data, population,
and land use maps for 2010 and 2050. Validation of the
hazard estimates has been performed using the Dartmouth
Flood Observatory database. This was done by comparing
a high return period flood with the maximum observed extent,
as well as by comparing a time series of a single event
with Dartmouth imagery of the event. Validation of modelled
damage estimates was performed using observed damage estimates
from the EM-DAT database and World Bank sources.
We discuss and show sensitivities of the estimated risks with
regard to the use of different climate input sets, decisions
made in the downscaling algorithm, and different approaches
to establish impact models.