Quantifying changes to historic fish habitat extent on north coast NSW floodplains,Australia

Global degradation of coastal ecosystems is influencing the provision of ecosystem services, including fisheries maintenance services. Degradation of the Australian coastal zone and its resources following European occupation has been recognised for some time. This includes the loss of ecologically important coastal wetlands, which have strong trophic and habitat links to fisheries. In NSW, structural flood mitigation works are a principle driver of the decline of coastal wetlands; however, little action has been taken to quantify the extent of decline due to limited information of the pre-European settlement extent of coastal wetlands. We use spatial data sets in GIS to quantify prime fish habitat and calculate the loss of fish habitat for the large coastal floodplains of northern NSW, which are significant contributors to the commercial and recreational fisheries of NSW. The technique is validated by comparison with early maps of wetland distribution. We identified pre-European distribution of available fish habitat of approximately 477,000 ha, of which 87,000 ha was identified as prime fish habitat. Approximately 62,000 ha of prime fish habitat was impacted by drainage of the coastal floodplains in association with flood mitigation works which intensified in the mid-1950s and were largely completed by 1971, equating to a loss of approximately 72 % of prime fish habitat. The declining value of the ecosystem services provided by prime fish habitat following drainage is likely to be substantial. Some actions have taken place to restore the functions of this habitat although significant opportunities remain to reverse this decline through management actions that restore natural drainage and reinstate tidal exchange. These actions become even more important as pressures on coastal wetlands increase with climate change and associated sea-level rise.     

Assessment of a regulatory model's performance relative to large spatial heterogeneity in observed ozone in Houston,Texas

In Houston, some of the highest measured 8-hr ozone (O₃) peaks are characterized by sudden increases in observed concentrations of at least 40 ppb in 1 hr, or 60 ppb in 2 hr. Measurements show that these large hourly changes appear at only a few monitors and span a narrow geographic area, suggesting a spatially heterogeneous field of O₃ concentrations. This study assessed whether a regulatory air quality model (AQM) can simulate this observed behavior. The AQM did not reproduce the magnitude or location of some of the highest observed hourly O₃ changes, and it also failed to capture the limited spatial extent. On days with measured large hourly changes in O₃ concentrations, the AQM predicted high O₃ over large regions of Houston, resulting in overpredictions at several monitors. This analysis shows that the model can make high O₃, but on these days the predicted spatial field suggests that the model had a different cause. Some observed large hourly changes in O₃ concentrations have been linked to random releases of industrial volatile organic compounds (VOCs). In the AQM emission inventory, there are several emission events when an industrial point source increases VOC emissions in excess of 10,000 mol/hr. One instance increased predicted downwind O₃ concentrations up to 25 ppb. These results show that the modeling system is responsive to a large VOC release, but the timing and location of the release, and meteorological conditions, are critical requirements. Attainment of the O₃ standard requires the use of observational data and AQM predictions. If the large observed hourly changes are indicative of a separate cause of high O₃, then the model may not include that cause, which might result in regulators enacting control strategies that could be ineffective.

Implications To show the attainment of the O₃ standard, the U.S. Environmental Protection Agency (EPA) requires the use of observations and model predictions under the assumption that simulations are capable of reproducing observed phenomena. The regulatory model is unable to reproduce observed behavior measured in the observational database. If the large observed hourly changes were indicative of a separate cause of high O₃, then the model would not include that cause. Inaccurate model predictions may prompt air quality regulators to enact control strategies that are effective in the modeling system, but prove ineffective in the real world.