Flood map interpolation from design storms under global warming using a national hydrodynamic model database in Aotearoa New Zealand

Severe storms are among Aotearoa New Zealand’s costliest natural hazards, with escalating impacts under climate change due to both direct flood damage and cascading disruptions to infrastructure and supply chains. As part of a national flood risk assessment framework, we present a methodology for the rapid estimation of flood hazard maps corresponding to storm-driven rainfall events under current and future climate conditions.

The approach leverages NIWA’s national database of hydrodynamic flood model outputs (280+ domains, 64–8 m adaptive resolution) which simulate design storms with annual recurrence intervals (ARIs) from 10 to 1000 years under current conditions. Multiple regression techniques are evaluated for interpolating flood maps at unmodelled ARIs. Polynomial regression consistently yields better fits and is used to generate a library of gridded regression coefficients that enables rapid flood extent estimation across domains.

To account for global warming (ΔT), current rainfall intensities are converted to temperature-adjusted ARI maps using both an iterative solver and a log-cubic regression, with comparable accuracy. Considering the spatial variability of the mapped ARI in some domains, relevant aggregation methods are discussed to compute a single representative ARI_DT estimates for each modelled domain. For ΔT=+1°C and +3°C, the available flood depth and extents show appreciable agreement with hydrodynamic output modelled for [ARIΔT, ΔT]-based design storms in domains with high ARI_DT dispersion.

Rather than bypassing full hydrodynamic modelling, these combined approaches allow computational resources to be focused on simulating the most severe design storm conditions (ARI>200y). This offers a scalable framework to prioritize the enhancement of the national flood hazard mapping database under evolving climate conditions. The database will later be extended to incorporate sea-level rise, enabling a matrix of future flood risk scenarios.