Climate Change Flood Impact Estimation on a National Scale

A van Vugt, E Butcher, C Cameron (Pattle Delamore Partners Ltd) & C Read (Z Energy Ltd)

As part of the Government’s mandatory reporting on climate risks, large entities must prepare climate statements, that comply with Aotearoa New Zealand Climate Standards. In order to achieve this reporting, entities rely on a variety of datasets and modelling, based on different climate scenarios, to inform high level nationwide climate risk assessments. However, data gaps still exist for some key parameters in Representative Concentration Pathway (RCP) scenarios. Two tools that can be used to understand and disclose climate-related risks are CoreLogic flood data, which provides detailed information about flood risk at the property level for a range of Annual Exceedance Probabilities (AEP), and NZSeaRise projections, which estimate sea level rise. Data from these sources, which include estimates of sea level rise, as well as tidal, pluvial and fluvial flooding, enables companies to assess the potential impact of these hazards on their assets. Data from NIWA’s regional climate model can also be used to inform projected change in precipitation across the country together with changes in wind severity, or extended drought risk. By analysing this data, companies can evaluate the effectiveness of their risk management strategies and identify opportunities for adaptation.

This paper focusses on how climate impacts can initially be addressed using existing tools, such as CoreLogic flood data, NZ SeaRise Programme national projections, and NIWA’s regional climate projections derived from General Circulation Models (GCMs), which are available at nationwide scale regarding flood risk, and temperature/wind/precipitation/drought. Limitations in existing datasets and future forecasted improvements are also discussed. A case studywas undertaken to demonstrate the use of these datasets using a geospatial platform to visually inform risk assessments for Z Energy Limited (Z) nationwide and to determine expected climate variation at each asset location. Results identified Z assets which may be exposed to sea level rise, pluvial/fluvial flood, increased precipitation intensity, drought or other risks. Estimated changes in risk exposure for each site were extracted as point sources for each asset location and reported using an online geospatial tool. This information has allowed Z to further investigate asset vulnerability at ‘higher’ risk asset locations for the identified increased climate impact risks.

By leveraging these resources and other climate related models as they become available, large entities can better understand and manage their climate-related risks and provide transparent and consistent information to stakeholders about their efforts to address these risks. This along with utilising geospatial tools has enhanced communication of identified risks and improved understanding of these climate impacts.