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How will the increased frequency of coastal inundation events induced by sea level rise impact residential insurance premiums, and when would insurance contracts be withdrawn? We model the contribution of localised sea level rise to the increased frequency of coastal inundation events. Examining four Aotearoa New Zealand cities, we combine historical tide-gauge extremes with geo-located property data to estimate the annual expected loss from this hazard, for each property, to establish when insurance retreat is likely to occur. We find that as sea level rise changes the frequency of inundation events, 99% of properties currently within 1% AEP coastal inundation zones can expect at least partial insurance retreat within a decade (associated with less than 10cm of sea level rise). Our modelling predicts that full insurance retreat is likely within 20 – 25 years, with timing dependent on the tidal range in each location, and, more intuitively, on the property's elevation and distance from the coast.

This report presents the application of the TopNet model suite as one of the ‘simple’ models considered in the Smart Models for Aquifer Management (SAM) research programme. Led by GNS Science, the SAM programme aimed to investigate the implications of model simplification for uncertainty and water resource management decision-making. Comparison of different models in terms of prediction uncertainty relevant to key decision variables, both pre (‘priori’) and post (‘posteriori’) calibration, has been carried out. Models investigated range from a simple conceptual model (e.g. V-Notch); through to a more complex semi-distributed model (the TopNet model suite); a complex distributed model (LUCI); and a highly complex model (MODFLOW). It should be noted that the aim of the programme was to compare the impact of model uncertainties on the decision-making process rather than an inter-comparison of the performance of the models per se. The TopNet model suite (i.e. TopNet-0 and TopNet-GW models) has been developed over the past 10 years by NIWA through MBIE funded programmes such as Waterscape (MBIE COX1006 2010–2016). The TopNet-0 hydrological model is routinely used for hydrological modelling applications in New Zealand. It is a spatially distributed, time-stepping model of water balance simulating water storage in the snowpack, plant canopy, rooting zone, shallow subsurface, lakes and rivers. The TopNet-GW hydrological model couples TopNet-0 to a conceptual groundwater module which replicates the surface water sub-catchment spatial layout as a groundwater sub-catchment network. In TopNet-GW, the connection between groundwater sub-catchments and surface water is based on observed or predicted locations of surface–groundwater interaction, rather than on related physically-based parameters. The model also currently only represents annually averaged (or static) conditions. In addition, such representation may be influenced by the impact of existing groundwater abstraction patterns rather than natural groundwater variability. As part of the SAM programme, the TopNet model suite was applied to three case study catchments (i) the Ruamāhanga Valley (Greater Wellington Region), (ii) Mid-Mataura (middle Mataura and Waimea catchments (Southland Region)) and (iii) a synthetic model developed to represent an idealised generic groundwater and surface water catchment with characteristics typical of many natural catchments in New Zealand. Water management decisions were represented by allocation scenarios for two of the case studies (i and ii). To identify the impacts on the water resource two hydrological metrics were evaluated: number of days below a river flow threshold, and number of consecutive days below the same flow threshold. These metrics were applied to both the natural flow time series and the flow series resulting from application of surface water or groundwater allocation scenarios. To assess the impact of model complexity and model status on key environmental decision variables (i.e. discharge in rivers for this report), a common process was developed across the modelling team for the different SAM hydrological/hydrogeological models considered. Prediction uncertainty was estimated for an uncalibrated model (i.e. a model parameterised using existing a-priori knowledge) and a calibrated model (i.e. a model parametrised following calibration based on an agreed objective function related to the hydrological metrics- refer to as posteriori). For this project a team of four modellers was used by NIWA to complete the TopNet model suite across the three case studies. As a result, model calibration methodology varied across the case studies, which reflects both the modeller experience and data availability. (auth)

The axon myelin sheath is prone to injury associated with N-methyl-D-aspartate (NMDA)- type glutamate receptor activation but the source of glutamate in this context is unknown. Myelin damage results in permanent action potential loss and severe functional deficit in the white matter of the CNS, for example in ischemic stroke. Here, we show that in rats and mice, ischemic conditions trigger activation of myelinic NMDA receptors incorporating GluN2C/D subunits following release of axonal vesicular glutamate into the peri-axonal space under the myelin sheath. Glial sources of glutamate such as reverse transport did not contribute significantly to this phenomenon. We demonstrate selective myelin uptake and retention of a GluN2C/D NMDA receptor negative allosteric modulator that shields myelin from ischemic injury. The findings potentially support a rational approach toward a low-impact prophylactic therapy to protect patients at risk of stroke and other forms of excitotoxic injury., peer-reviewed

L-lactate is a metabolite that is oxidized preferentially to glucose under conditions of high metabolic stress. The discovery and localization of the lactate receptor HCA1 in various brain regions suggests that lactate is additionally an important signaling molecule in the brain. Lactate is neuroprotective in various ischemia paradigms, reduces axonal injury in vitro and is avidly utilized by oligodendrocytes (OLs). The protective potential of L-lactate to reduce white matter (WM) injury in a mouse stroke model was investigated., N/A

Nitric oxide (NO) is a free radical that can act both as a signalling molecule and a neurotoxin. Previous literature suggest that it is involved in multiple brain pathologies. The main aim of this study was to investigate the role of NO in conduction block and structural axonal injury during and following glucose deprivation (GD)., N/A

New Zealand faces the challenge of using our land in ways that are not only resilient to future pressures and sustain our rural communities but also enhance our natural environment. For the public and private sectors to make robust land-use decisions under uncertainty, high-quality modelling tools and data are essential. The drivers of land-use decisions are complex and models provide a structured methodology for investigating these. While New Zealand is fortunate to have a range of different modelling tools, these have historically been used in a sporadic and ad hoc way, and underlying datasets are deficient in some areas. As the foundation for more strategic development of New Zealand’s modelling capability, this paper profiles the main land-sector and farm- and production-related models and datasets currently applied in New Zealand. It also explores priority policy areas where modelling is needed, such as achieving emission reduction targets; managing freshwater, biodiversity and soil quality; and understanding the distributional impacts of policy options as well as climate change. New Zealand’s modelling capability could be strengthened by collecting and sharing land-use data more effectively; building understanding of underlying relationships informed by primary research; creating more collaborative and transparent processes for applying common datasets, scenarios and assumptions, and conducting peer review; and conducting more integrated modelling across environmental issues. These improvements will require strategic policies and processes for refining model development, providing increased, predictable and sustained funding for modelling activity and underlying data collection and primary research, and strengthening networks across modellers inside and outside of government.