Brief communication: SWM - stochastic weather model for precipitation-related hazard assessments using ERA5-Land data

Abstract

Long-term multi-hazard and risk assessments are produced by combining many hazard-model simulations, each using a slightly different set of inputs to cover the uncertainty space. While most input parameters for these models are relatively well constrained, atmospheric parameters remain problematic unless working on very short timescales (hours to days). Precipitation is a key trigger for many natural hazards including floods, landslides, and lahars. This work presents a stochastic weather model that takes openly available ERA5-Land data and produces long-term, spatially varying precipitation data that mimic the statistical dimensions of real data. This allows precipitation to be robustly included in hazard-model simulations. A working example is provided using 1981–2020 ERA5-Land data for the Rangitāiki–Tarawera catchment, Te Moana-a-Toi / Bay of Plenty, New Zealand.