# Reconstructing the 26 June 1917 Samoa Tsunami Disaster
## 3 Methods and Data
### Tsunami Exposure and Damage Analysis
#### 3.2.3 Tsunami Inundation Exposure and Damage Model  
A deterministic model is applied to quantify the present-day building and population exposure as well as damage from tsunami inundation. To this end, we use RiskScape, an open-source software that provides a multi-hazard risk modelling framework for deterministic analysis of tsunami impacts [36]. Here, a deterministic model 'pipeline' is developed to analyze the exposure and damage based on the tsunami model, exposure inventory, and fragility model components described in Sections 3.2.1 and 3.2.2. These components formed the 'input data' for the model pipeline used, which sequences a series of steps and step-functions to sample and analyze deterministic tsunami impacts (Figure 3). The 1917 event input hazard data layer represented at the adaptive grid resolutions 10 m, 20 m, and 40 m were segmented using a geoprocessing step-function (i.e., 'cut by segment') to extract all tsunami inundation grid cells within the exposure data layer (building outlines). Extracted grid cells were then spatially sampled (i.e., 'all intersections') to determine the maximum inundation flow depth (\(MaxD\)) at each building location. The consequence analysis applies \(MaxD\) to determine: (1) building and population exposure to tsunami inundation; and (2) building damage state. Individual building exposure (\(Bld_{exp}\)) to inundation is quantified using a simple binary function:  
\[Bld_{exp}=\left\{ \begin{array}{ll}1,\ MaxD<0\ m\\ 0,\ MaxD\geq 0\ m\end{array} \right. \tag{4}\] \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Damage State (DS)**} & \multicolumn{6}{c}{**Construction Frame**} \\ \cline{2-9}  & \multicolumn{2}{c}{**Timber**} & \multicolumn{2}{c}{**Masonry**} & \multicolumn{2}{c}{**Reinforced Concrete**} & \multicolumn{1}{c}{**Damage Description**} \\ \cline{2-9}  & \(\mu\) & \(\sigma\) & \(\mu\) & \(\sigma\) & \(\mu\) & \(\sigma\) & \\ \hline DS0 & None & & & - & & None \\ DS1 & Light & - & 0.29 & 0.46 & - & & Non-structural damage only \\ DS2 & Minor & - & 0.46 & 0.4 & - & & Significant non-structural and \\ DS3 & Moderate & 1.15 & 0.38 & 1.28 & 0.35 & 1.38 & 0.56 & Significant structural and \\ DS4 & Severe & 1.26 & 0.4 & 1.86 & 0.41 & 3.45 & 0.54 &
\begin{tabular}{c} Irreparable structural damage, \\ will require demolition \\ Complete structural collapse \\ \end{tabular} \\ DS5 & Collapse & 1.62 & 0.28 & 2.49 & 0.4 & 7.3 & 0.94 & Complete structural collapse \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Building fragility model parameters applied in this study. Figure 3: A schematic representation of the RiskScape model pipeline steps and functions applied in this study. When inundation is present or not at a building location, the corresponding binary value is assigned to the building in the 'event impact table' (EIT). Where inundation is not present (i.e., '0'), no damage (DS0) is assumed. Where inundation is present (i.e., '1'), conditional probability (i.e., 0-1) of damage states DS1 to DS5 based on fragility curves from [9] is calculated in response to the independent variable _MaxD_. Fragility curves scripted in Python using nested statements apply a lognormal function for each curve based on the dependant variables shown in Table 2 for the corresponding building construction frame in Table 1. The conditional probability determined from each fragility curve is then reported in the EIT for each building exposed to tsunami inundation. The resulting EIT contains tsunami exposure and damage information for model output reporting. In this study, the EIT includes attributes, hazard intensity (i.e., _MaxD_), exposure (i.e., _Bldexp_), and damage state information for each building object. The 'output data' pipeline step-function 'group results by attribute' is applied here to report descriptive statistics of model results.