This paper presents the architecture and features of RiskScape software. RiskScape is an open-source software with a flexible modelling engine for multi-hazard risk analysis. The RiskScape engine implements modeller-defined risk quantification workflows as ‘model pipelines’. Model pipeline steps and functions analyse hazard, exposure, and vulnerability data across different spatio-temporal domains using geoprocessing and spatial sampling operations. The RiskScape engine supports deterministic and probabilistic risk quantification, with several probabilistic-based modes described in this paper. RiskScape advances modelling software for multi-hazard risk analysis through several implementation features. The RiskScape engine operates model pipelines independent of system prescribed model input data classifications or standards. Multiple hazard types, metric intensities, and temporal occurrence information is geometry processed and sampled to create coverage data of simultaneous or sequenced multi-hazard events at object-exposure locations. Escalating multi-hazard event impacts are then determined for object-exposures using scripted conditional or nested statements that apply vulnerability functions in a logical sequence of temporal hazard and impact occurrence. These model features, supported by open geospatial consortium standard geospatial data files and operations, expedite RiskScape for modelling multi-hazard risk at any geographical location or scale.

Coastal flooding from extreme sea levels will increase in frequency and magnitude as global climate change forces sea-level rise (SLR). Extreme sea-level events, rare in the recent past (i.e., once per century), are projected to occur at least once per year by 2050 along many of the world’s coastlines. Information showing where and how built-environment exposure increases with SLR, enables timely adaptation before damaging thresholds are reached. This study presents a first national-scale assessment of New Zealand’s built-environment exposure to future coastal flooding. We use an analytical risk model framework, “RiskScape”, to enumerate land, buildings and infrastructure exposed to a present and future 100-year extreme sea-level flood event (ESL₁₀₀). We used high-resolution topographic data to assess incremental exposure to 0.1 m SLR increases. This approach detects variable rates in the potential magnitude and timing of future flood exposure in response to SLR over decadal scales. National built-land and asset exposure to ESL₁₀₀ flooding doubles with less than 1 m SLR, indicating low-lying areas are likely to experience rapid exposure increases from modest increases in SLR expected within the next few decades. This highlights an urgent need for national and regional actions to anticipate and adaptively plan to reduce future socio-economic impacts arising from flood exposure to extreme sea-levels and SLR.

Flood damage assessments provide critical insights on processes controlling building damage and loss. Here, we present a novel damage assessment approach to develop an empirical residential building damage database from five flood events in New Zealand. Object‐level damage data was collected for flood hazard and building characteristics, along with relative building component and sub‐components damage ratios. A Random Forest Model and Spearman's Rank correlation test were applied to analyse damage data variable importance and monotonic relationships. Model and test results reveal flood inundation depth above first finished floor level is highly important and strongly correlated with total building damage ratios while flow velocity is important for structure component damage. Internal finishes components contribute highly to total building damage ratios as higher value sub‐component materials are susceptible to direct damage from water contact and indirect damage during repair. The empirical damage data has several implications for damage model development due to the limited heterogeneity of flood hazard intensities and building attributes observed. Extending empirical damage data with synthetic damage data in future would support development of more representative object‐specific damage models to evaluate direct tangible damages for local contexts.