Flooding is the costliest disaster globally with $82 billion in losses
constituting a third of damages from natural disasters. Unlike
slow onset hazards like droughts, flooding is also complicated to
predict, simulate, and measure due to ever-changing variables
like terrain, soil moisture, land cover, climate, and local
infrastructure. With the recent rise in donor support and
attention (G7, COP26) to increase financial support in
anticipatory action programming, a major focus for flood
financing in low income economies has shifted towards (A)
forecast-based and (B) index insurance approaches to supporting
shock responsive safety nets triggered by best-in-class
information systems localized to a given national context.
In this panel we seek to understand and evaluate technical and
implementation trade-offs through real case studies in the Global
South, considering flood disaster risk financing schemes from a
from both public and private sector perspectives. Attention is
particularly focused on (1) uncertainty in flood forecasting skill
and approaches for broader validation before early action
programming, (2) new approaches to index-based parametric
financing based on emerging methodologies in earth
observation and deep learning, and (3) case studies where
multiple triggers were developed pre- and post- flood to enable
highly effective disaster risk financing and contingency planning
aimed at both bolstering prevention activities and resourcing for
the inevitable impacts that occur.
The objective is to highlight nascent lessons learned from new
approaches to building and evaluating accurate systems for
thresholding floods locally, to reliably support the protection of
vulnerable populations at risk.
Video recording not available.
