Data pertaining to 'Dunedin groundwater monitoring, spatial observations and forecast conditions under sea level rise'

Dunedin City in the South Island of New Zealand has many assets and critical infrastructure sitting on a low-lying coastal plain that is underlain by a largely unseen and relatively poorly understood hazard. Shallow groundwater in this area limits the unsaturated ground available to store rain and runoff, promotes flooding and creates opportunities for infiltration into stormwater and wastewater networks. Groundwater levels are expected to rise as sea level rises, causing greater frequency of flooding and/or direct inundation once it nears the ground surface.  This zipped archive contains ArcGIS 10.8 geodatabases and spatial analysis of data gathered from a shallow groundwater monitoring network between 6/3/2019 and 1/5/2023. A series of statistical surfaces represent the present-day (2023) water table elevation and depth to groundwater, the response to rainfall recharge and tidal forcing, the available subsurface storage of rain infiltration.  Simple geometric models have also been developed using the present shape and position of the water table, combined with tidal fluctuations, to forecast the future state of groundwater levels at 10 cm increments of sea level rise (up to 1 m). The geometric models are strongly empirical, with many implicit assumptions and caveats – particularly, that they do not account for groundwater flow and possible changes in water-budget mass balance. Although many variables and controlling processes are simplified into a single parameter, the projected groundwater levels highlight how local variations in the water table shape and slope interact locally with the ground elevation or infrastructure networks. They are best considered as a worst-case analysis of groundwater-related contribution to hazard and how this will evolve over time. Data are licensed under Creative Commons Attribution 4.0 (CC-BY-4.0) license without warranty. Further description of these data, and implications from the analysis, can be found in the GNS Science metadata catalogue https://data.gns.cri.nz/metadata/srv/eng/catalog.search#/metadata/06a86338-a0fa-436f-9c80-2d8da1dcd64f oe Cox et al. (2023) GNS Science Report 2023/43 doi:10.21420/5799-N894.