Abstract. The use of different methods for physical flood vulnerability assessment has evolved over time, from traditional single-parameter stage–damage curves to multi-parameter approaches such as multivariate or indicator-based models. However, despite the extensive implementation of these models in flood risk assessment globally, a considerable gap remains in their applicability to data-scarce regions. Considering that these regions are mostly areas with a limited capacity to cope with disasters, there is an essential need for assessing the physical vulnerability of the built environment and contributing to an improvement of flood risk reduction. To close this gap, we propose linking approaches with reduced data requirements, such as vulnerability indicators (integrating major damage drivers) and damage grades (integrating frequently observed damage patterns). First, we present a review of current studies of physical vulnerability indicators and flood damage models comprised of stage–damage curves and the multivariate methods that have been applied to predict damage grades. Second, we propose a new conceptual framework for assessing the physical vulnerability of buildings exposed to flood hazards that has been specifically tailored for use in data-scarce regions. This framework is operationalized in three steps: (i) developing a vulnerability index, (ii) identifying regional damage grades, and (iii) linking resulting index classes with damage patterns, utilizing a synthetic “what-if” analysis. The new framework is a first step for enhancing flood damage prediction to support risk reduction in data-scarce regions. It addresses selected gaps in the literature by extending the application of the vulnerability index for damage grade prediction through the use of a synthetic multi-parameter approach. The framework can be adapted to different data-scarce regions and allows for integrating possible modifications to damage drivers and damage grades.

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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 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.

10Although 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 15 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 20 for damage prediction in data-scarce regions to support loss assessment and to provide guidance for disaster risk reduction.

<p>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.</p>