Department of Environment, Land, Water and Planning

Abstract Zonal Wave‐3 (ZW3) is a key zonally asymmetric component of the large‐scale extratropical atmospheric circulation, facilitating mass and energy exchanges between middle and high latitudes in the Southern Hemisphere. Despite the crucial role of sea‐ice thickness (SIT) in modulating sea‐ice volume and climate feedbacks, its response to ZW3 variability remains largely unexplored compared to the more widely studied sea‐ice concentration. Our research identifies variability in the ZW3 pattern and assesses its influence on Antarctic SIT and concentration using a combination of reanalysis and data from a coupled climate model. We project a doubling in the mean frequency of extreme ZW3 events under a high emission scenario, which is likely to contribute to a decline in SIT, primarily through dynamic mechanisms. These extreme events drive mechanical displacement of sea‐ice more than direct thermodynamic melting. Such displacements may expose the underlying ocean, potentially enhancing basal melting through ocean thermal influences.

In this paper, the behaviour of piled raft foundation systems in expansive soils is analysed and discussed. The ground movement under the raft and along the pile shaft is analysed in order to address its influence on the overall foundation behaviour. In order to undertake the analysis, the raft is assumed as a plate, resting on soil and on the piles. A computer program PRAES (Piled Raft Analysis in Expansive Soils) is developed to carry out the necessary analytical steps. A “non linear soil-structure interactive1’ approach is adopted to undertake the analysis of stresses. The characteristics of the foundation behaviour are analysed and studied for various ground movements, caused by the swelling or the shrinking of the supporting soil. The results are compared with the “flat ground” condition. The implication of these effects on the overall foundation performance is discussed. Attention is also drawn towards the potential need for the further investigation to enhance the understanding of pile-raft-soil interaction under other conditions. Dans l’étude, le comportement des régimes des fondation sur pilotis avec chevrons dans les sols de nature expansive sont analysés et discutés. Le mouvement terrien sous les chevrons et le long des pilotis avec chevrons est analysé pour adresser son influence sur le comportement de l’ensemble des fondations. Pour pourvoir conduire cette analyse, le chevron est considéré comme une plaque reposant sur sol et pilotis. Un programme d’ordinateur PRAES (Analyses des pilotis avec chevrons dans les sols de nature expansive) est en voie de développement pour entreprendre les mesures analytiques nécessaires. Une approche “interactive sol — structure non linéaire est adoptée pour conduire l’analyse des forces. Les caractéristiques du comportement des fondations son analysées et étudiées pour nombreux mouvements terriens causés par l’expansion ou les contractions des sols d’appui. Les résultats sont comparés aves les conditions du terrain plat. L’implication de ces effets sur l’ensemble de la performance des fondations y est discutée. Nours attirons l’attention sur les besoins potentiels de futures recherches pour enrichir la compréhension sur les interactions des pilotis avec chevrons (plaques) et sol sous d’autres conditions.

<strong class="journal-contentHeaderColor">Abstract.</strong> Ocean acidification monitoring and carbon accounting require accurate estimates of marine carbonate system variables, particularly in dynamic coastal regions where observations remain sparse. This study presents an approach to improving carbonate system state estimates in the California Current System through the assimilation of underwater glider observations with both dynamical and statistical models. We implement a 4D-Var data assimilation system that jointly assimilates physical variables, chlorophyll, and glider-based pH and alkalinity data into a regional coupled physical-biogeochemical model. Our results demonstrate that joint assimilation of carbonate system variables successfully improves pH and alkalinity estimates while maintaining the quality of physical and chlorophyll estimates. Cross-validation experiments reveal that pH data assimilation typically improves estimates near the observation network, although downstream advection of increments can occasionally degrade results. We also show that hybrid estimates that combine the output of the dynamical, physical ocean model with a statistical model produce accurate carbonate system estimates without requiring a biogeochemical model. This finding suggests that physical ocean models and data assimilation systems can obtain reasonable carbonate system estimates by combining statistical methods with model estimates of temperature and salinity.

Abstract. The stable isotopic composition of precipitation (δ2HP, δ18OP; “water isotopes”) is a powerful tool for tracking water through the atmosphere, as well as fingerprinting land-surface water masses and identifying water cycle biases in isotope-enabled climate models. Water isotopes also underpin our understanding of multi-decadal to multi-centennial water cycle variability via their retrieval from palaeoclimate archives. Water isotopes thereby increase our understanding of past and present – and hence future – water cycle variability. Understanding the drivers of spatial and temporal water isotope variability is a critical first step in applying these tracers for a better understanding of the water cycle. However, water isotope observations are sparse in both space and time. Here we develop and apply a machine learning (random forest) approach to predict spatially continuous monthly δ2HP and δ18OP across the Australian continent at 0.25° resolution from 1962–2023. We train the random forest models on monthly δ2HP (n=5199) and δ18OP (n=5217) observations from 60 sites across Australia. We also predict the deuterium excess of precipitation (dxsP, defined as δ2HP-8×δ18OP). Out-of-sample δ2HP and δ18OP prediction skill is high both geographically and temporally. Skill is slightly lower for the secondary parameter dxsP, likely reflecting the larger reliance of spatio-temporal dxsP variability on moisture source conditions. The random forest models accurately capture both the seasonal cycle of precipitation isotopic variability and long-term annual-mean precipitation isotopic variability across the continent, and outperform estimates from an isotope-enabled atmosphere general circulation model over an equivalent time period. We show that spatio-temporal variability in precipitation amount, precipitation intensity, and surface temperature are particularly important for monthly δ2HP and δ18OP variations across the continent, with local surface pressure also important for dxsP. Drivers of site-level δ2HP, δ18OP, and dxsP are more varied. Overall, the new random forest modelled dataset reveals clear spatial and temporal variability in δ2HP, δ18OP, and dxsP across the Australian continent over the past decades – providing a robust foundation for hydrology, ecology, and palaeoclimate research, as well as an accessible framework for predicting water isotope values in other locations.

<strong class="journal-contentHeaderColor">Abstract.</strong> Ocean acidification monitoring and carbon accounting require accurate estimates of marine carbonate system variables, particularly in dynamic coastal regions where observations remain sparse. This study presents an approach to improving carbonate system state estimates in the California Current System through the assimilation of underwater glider observations with both dynamical and statistical models. We implement a 4D-Var data assimilation system that jointly assimilates physical variables, chlorophyll, and glider-based pH and alkalinity data into a regional coupled physical-biogeochemical model. Our results demonstrate that joint assimilation of carbonate system variables successfully improves pH and alkalinity estimates while maintaining the quality of physical and chlorophyll estimates. Cross-validation experiments reveal that pH data assimilation typically improves estimates near the observation network, although downstream advection of increments can occasionally degrade results. We also show that hybrid estimates that combine the output of the dynamical, physical ocean model with a statistical model produce accurate carbonate system estimates without requiring a biogeochemical model. This finding suggests that physical ocean models and data assimilation systems can obtain reasonable carbonate system estimates by combining statistical methods with model estimates of temperature and salinity.

With growing concerns regarding microplastic pollution, there is an urgent need to improve understanding of their presence, distribution, and environmental impacts. This necessitates more coordinated and harmonised large-scale microplastic monitoring initiatives. However, such assessments are traditionally expensive, labour-intensive, and hindered by a lack of standardised sampling and analytical protocols, which impede rapid, yet accurate identification of microplastic sources and ecological risks. To improve environmental microplastic contamination estimates, this study proposes a rapid, cost-effective, and bulk-processing approach within a criteria-driven Tiered Microplastics Workflow (TMW). This approach enables the efficient quantification of microplastic contamination in estuarine surface waters, offering adaptable levels of analytical resolution, that is scalable for environmental monitoring. Key features of the TMW include:•Streamlined processing: sieving, digestion, density separation, vacuum degassing, size-classed filtration, Nile Red staining, and automated fluorescent particle counts via a Python script, enabling 24 samples to be processed in five days.•Rapid Count Method: Enabling microplastic identification in broadscale monitoring within a 20 % error margin. Script-based microplastic counts align with FTIR results (R² = 0.83).•Flexible resolution: Sample processing can be paused and switched to other analytical methods while maintaining data comparability ensuring data harmonisation.

Tidal freshwater systems are dynamic biogeochemical hotspots that play a crucial role in nutrient cycling and the attenuation of catchment derived material. Traditional biogeochemical analysis typically relies on estimation techniques such as interpolation, regression, and remote sensing based on limited data sets, which can result in significant uncertainties. This study applies a novel sampling approach to a tidal freshwater system located on the far northeast coast of New South Wales, Australia, to quantify nutrient fluxes within a “control volume” (CV) by combining high-resolution discrete time series and hydrodynamic measurements in combination with a traditional benthic flux methodology. The study found high NH 4 consumption and high NO x production reflective of nitrification within the aerobic water column. The total oxygen demand within the control volume was very close to the aggregate of the sediment oxygen demand and nitrification oxygen demand, highlighting the importance of sediment biogeochemical processes within the TFZ. Control volume NOx consumption and P release were orders of magnitude larger than those obtained from traditional sediment core incubations, suggesting that NOx and P dynamics in the Richmond River TFZ are driven by processes other than sediment dynamics and/or sediment incubations underestimated in situ fluxes. The novel control volume methodology offers a high-resolution sampling technique with fewer sampling artifacts and an enhanced understanding of biogeochemical trends over the tidal and diel cycles. By integrating biogeochemical measurements with hydrodynamic processes, this method effectively constrain nutrient cycling within a defined reach, providing detailed insights into nutrient processing during the measurement period. • Novel control volume methodology developed for the tidal freshwater zone (TFZ). • High-resolution flux time series over the tidal and diel cycle. • Method integrates all physical and biogeochemical processes. • Improves understanding of processes within the TFZ with fewer sampling artifacts. • The TFZ is a dynamic zone along the aquatic continuum for nitrogen and phosphorus cycling. A novel high-resolution control volume sampling methodology is presented to constrain nutrient cycling within the tidal freshwater zone of an Australian river.

ABSTRACTThe rise of AI has seen an explosion in the use of deep learning methods that automate the analysis of image and video data, saving ecologists vast amounts of time and resources. Ecological imagery poses unique challenges; however, with cryptic species struggling to be detected among poor visibility and diverse environments. We propose leveraging movement information to attempt to improve the predictions produced by a high‐performing object detection algorithm. Frame differencing, background subtraction, optical flow and multi‐object tracking are trialed on four diverse datasets containing over 35,000 annotated images sourced from terrestrial, marine and freshwater habitats. We find that leveraging movement information is useful for smaller sized studies and rarer species, however is not needed for well annotated studies (> 400 annotations per class). Out of the methods that utilise movement, we find that a simple ‘differencing’ of neighbouring frames generally performed the best, whilst attempting to track taxa to boost prediction scores performed poorly. Other studies in this area tend to focus only on 1–2 datasets and a single method that utilises movement information, making it difficult for ecologists to generalise results. Our study provides key lessons for ecologists to determine whether it is useful to incorporate methods that leverage movement information when attempting to automatically predict taxa. We offer straightforward code for practical implementation via our GitHub repository, BenMaslen/MCD, along with an evaluation benchmark dataset called ‘Tassie BRUV’ that can be accessed from the Dryad public repository https://doi.org/10.5061/dryad.sbcc2frf7.