A method to reconstruct flood scenarios using field interviews and hydrodynamic modelling: application to the 2017 Suleja and Tafa, Nigeria flood

Malgwi, Mark Bawa; Ramirez, Jorge Alberto; Zischg, Andreas; Zimmermann, Markus; Schürmann, Stefan; Keiler, Margreth (2021). A method to reconstruct flood scenarios using field interviews and hydrodynamic modelling: application to the 2017 Suleja and Tafa, Nigeria flood. Natural Hazards, 108(2), pp. 1781-1805. Springer 10.1007/s11069-021-04756-z

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Abstract
The scarcity of model input and calibration data has limited efforts in reconstructing scenarios
of past floods in many regions globally. Recently, the number of studies that use
distributed post-flood observation data collected throughout flood-affected communities
(e.g. face-to-face interviews) are increasing. However, a systematic method that applies
such data for hydrodynamic modelling of past floods in locations without hydrological is
lacking. In this study, we developed a method for reconstructing plausible scenarios of past
flood events in data-scarce regions by applying flood observation data collected through
field interviews to a hydrodynamic model (CAESAR-Lisflood). We tested the method
using 300 spatially distributed flood depths and duration data collected using questionnaires
on five river reaches after the 2017 flood event in Suleja and Tafa region, Nigeria.
A stepwise process that aims to minimize the error between modelled and observed flood
depth and duration at the locations of interviewed households was implemented. Results
from the reconstructed flood depth scenario produced an error of ± 0.61 m for all observed
and modelled locations and lie in the range of error produced by studies using comparable
hydrodynamic models. The study demonstrates the potential of utilizing interview data
for hydrodynamic modelling applications in data-scarce regions to support regional flood
risk assessment. Furthermore, the method can provide flow depths and durations at houses
without observations, which is useful input data for physical vulnerability assessment to
complement disaster risk reduction efforts.

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