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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 data 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.

The knowledge about potential flood damage is a key issue for disaster risk reduction. However, the scarcity of empirical data has limited flood damage modeling in several regions. As a result, studies in data-scarce regions have mostly been restricted to either building exposure assessment or identification of vulnerability indicators without a further linkage to probable damage. As expert-based approaches do not require empirical damage data, they have a high potential for flood damage modeling in data-scarce regions. In this study, we carried out a comparative assessment between an expert-based and a data-driven approach. The expert-based approach systematically combines the vulnerability indicator method and synthetic what-if analysis based on the knowledge of regional experts. The data-driven approach integrates empirical flood damage data in the analysis applying a multivariate random forest model. Flood damage data, collected through interviews after two flood events in 2017 and 2019 at separate locations in Nigeria, were used to evaluate the performance of both methods based on developed damage grades. Results from both methods showed i) a predictive accuracy of 30% and 38% for the expert-based and data-driven approaches respectively, ii) that distance to channel, wall material, building condition, and building quality are significant regional damage drivers, and iii) comparable model performance can be achieved even with a reduced number of variables. Furthermore, the study demonstrated how experts are likely to underestimate damage at low water depths and how a difference in conformity to building standards can add to challenges in flood damage prediction.

We develop a technique for reconstructing floods in small-scale data scarce regions using field interview data and hydro-dynamic modelling. The field interview data consist of flood depths and duration data collected from 300 buildings from a flood event in 2017 in Suleja/Tafa area, Nigeria. The flood event resulted from an overflow of water from five river reaches. The hydrodynamic model utilized, called CAESER LisFLOOD, is an integration of a landscape evolution model (CAESER) and a hydraulic model (LisFLOOD-FP). We employ three steps to reconstruct the 2017 Suleja/Tafa flood event. Firstly, we use a linearly increasing hydrograph to; (a) calibrate Manning’s coefficient and (b) determine optimal peak discharge on each reach. This was carried out by minimizing the Root Mean Square Error (RMSE) between the distributed observed flood depths and the simulated flood depths. Secondly, we use synthetic hydrographs with durations between 6, 12, 18, 20, 24 hours, having peak discharge (extracted from the previous step), to simulate flows on all upstream reaches. Using collected flood duration data, we minimized RMSE between distributed observed flood duration and simulated flood duration to determine optimal flow durations on each upstream reach. In the last step, utilizing peak discharge and flow duration for all upstream reaches, we carried out multiple spatial and temporal iterations to match downstream peak discharge. Thereafter, we use determined upstream hydrographs with their relative catchment response timing to simulate the entire river network. Minimum RMSE computed for the entire river network was between ±15 cm of many current studies that use distributed observed data to calibrate flood models. The method developed in this study is useful for simulating floods in regions where data such as high resolution DEMs, river bathymetry and river discharge are limited. In addition, the study extends current knowledge, on utilizing distributed flood data to determine peak discharge, from a single to multiple river networks.

Although the vulnerability indicator method has been applied to several data-scarce regions, a missing linkage with damage grades had hindered its application for loss evaluation to complement disaster risk reduction efforts. To address this gap, we present a review of physical vulnerability indicators and flood damage models to gain insights on best practice. Thereafter, we present a conceptual framework for linking the vulnerability indicators and damage grades using three phases (i) developing a vulnerability index, (ii) identifying regional damage grades, and (iii) linking vulnerability index classes with damage grades. The vulnerability index comprehensively integrates elements of the hazard using a Building Impact Index (BII) on one hand, and exposure, susceptibility and local protection elements using a Building Resistance Index (BRI) on the other hand. For the damage grades, local expert assessments are used for identifying and categorizing frequently observed regional damage patterns. Finally, by means of synthetic what-if analysis, experts are asked to estimate damage grades for each interval of the BII and class of BRI to develop a vulnerability curve. The proposed conceptual framework can be used for damage prediction in data-scarce regions to support loss assessment and to provide guidance for disaster risk reduction.