Abstract : For many areas in the world, there is a need for future projections of flood risk in order to improve the possible
mitigation actions. However, such an exercise is often made difficult in data-sparse regions, where the limited
access to hydrometric data does not allow calibrating hydrological models in a robust way under non-stationary
conditions. In this study we present an approach to estimate the possible changes in flood risks, which incorporates flood generating processes into statistical models for extreme values. This approach is illustrated for a West
African catchment, the Mono River (12900km2
), with discharge, precipitation and temperature data are available
between 1988 and 2010 in a few stations and where the dominant flood generating process is soil saturation. A soil
moisture accounting model, calibrated against a merged surface soil moisture microwave satellite dataset, is used
to estimate the annual maximum soil saturation level that is then related to the location parameter of a Generalized
Extreme Value model of annual maximum discharge. With such a model, it is possible to estimate the changes in
flood quantiles from the changes in the annual maximum soil saturation level. An ensemble of regional climate
models from the ENSEMBLES-AMMA project are then considered to estimate the potential future changes in
soil saturation and subsequently the changes in flood risks for the period 2028-2050. A sensitivity analysis of the
non-stationary flood quantiles has shown that with the projected changes on precipitation (-2%) and temperature
(+1.22◦
) under the scenario A1B, the projected flood quantiles would stay in the range of the observed variability
during 1988-2010. The proposed approach, relying on low data requirements and few assumptions, could be useful
to estimate the projected changes in flood risks for other data-sparse catchments.
