This study characterizes changes to the ocean structure and ocean thermal energy budget produced by tropical cyclones (TCs) over the North West Shelf of Australia using a 20-year composite of cyclone data (1996-2016) created from Bluelink ReANalysis data (BRAN). Part 1 focuses on the general effects of the passage of a TC over the region and the effects of the location in respect to the shelf edge. Cold temperature anomalies develop at the ocean surface beneath the TC core in response to strong mixing and deep upwelling driven by surface divergence and Ekman pumping. In the TC outer circulation, surface cold anomalies also develop. However, warm temperature anomalies appear in the subsurface below the mixed layer due to wind-driven mixing across the mixed layer. In the month following the passage of the TC, the surface cold temperature anomalies recover relatively quickly due to air-sea fluxes, while the subsurface warm anomalies take longer to recover. Over the shelf, stronger cold anomalies develop at the surface, and the TC direction of motion can further impact the development of currents. TCs that move perpendicular to the shelf edge drive stronger vertical currents in a wide region around the track and stronger SST anomalies and TCs that move parallel to the coast drive colder temperature anomalies at depth and stronger upward currents beneath the track. The presence of the shelf can affect the development of downwelling currents along the coast for TCs that are further offshore but move parallel to the coast.

The seasonality of Arctic sea ice cover significantly influences heat, salt, buoyancy fluxes, ocean-ice stresses, and the potential and kinetic energy stored in the ocean mixed layer. This study examines the seasonal variability of oceanic scales and cross-scale flux of kinetic energy in the seasonally ice-covered Arctic, using a high-resolution, idealized coupled ocean-sea ice model. Our simulations demonstrate pronounced seasonality in the scales of oceanic motion within the mixed layer, governed by distinct mechanisms during summer and winter. In summer, an inverse energy cascade sustains mesoscale dynamics and enhances kinetic energy. In winter, ice-induced dissipation suppresses kinetic energy and mesoscale, allowing only the persistence of submesoscale processes. These results underscore the critical role of sea ice in modulating the seasonal dynamics of oceanic motion and their dominant scales, a behavior markedly different from that in the open ocean. Thus, understanding these coupled processes is essential for improving predictions of the ocean's energy evolution as the Arctic transitions toward a summer ice-free regime.

Groundwater flow paths in mountainous regions dictate how water is transferred from the surface, through the weathering zone, and into the bedrock aquifer system. Previous studies have established the critical role of bedrock aquifers in sustaining mountain river flow through the gradual release of water. However, the specific pathways that groundwater takes in the weathering zone to reach deeper aquifers remain poorly understood. In this study, we investigate the compartmentalization and transfer of water from surface rainfall input to a deep groundwater reservoir within a steep Himalayan catchment in central Nepal covering a pre-, during and post-monsoon season. We employed a comprehensive approach, integrating inverted electrical resistivity changes in the upper few tens of meters below the surface with geochemical and hydrological measurements, including infiltration rates. This has helped us to determine groundwater distribution and dynamics. We found a laterally extensive weathering zone with predominantly very high surface infiltration capacity. In steep mountain topography, this permeable mantle plays a key role in regulating catchment hydrology by efficiently absorbing rainfall and transferring surface water into groundwater from the onset of monsoon. In the study catchment, groundwater flows both vertically downward into the mountain bedrock interior, and parallel to the subsurface within the weathering zone and colluvial hollows. Our work enhances understanding of the mechanisms that recharge the mountain groundwater reservoir, govern spring location and activity, and may support water resource management in regions vulnerable to climate variability and change.

We present a hybrid surf-zone model that combines numerical simulations and statistical/machine learning techniques, enabling accurate calculations of nearshore wave and hydrodynamic parameters with high computational efficiency. The approach involves defining representative forcing conditions, carrying out numerical model (XBeach) simulations for these cases, and using the results to train machine learning models capable of predicting selected model output variables. Data decomposition via Empirical Orthogonal Function analysis further simplifies the process, reducing the output data dimensionality, with minimal accuracy loss (with exception of certain wetting-drying processes). Three machine learning approaches of increasing complexity are compared: a multi-variate linear regression (LR), a Radial Basis Functions (RBF) interpolator and a Deep Neural Network (DNN). The LR model fails to account for the complex non-linearities in coastal wave dynamics, which warrants the use of more complex machine learning techniques. Both the RBF interpolator and the DNN models demonstrate high levels of accuracy in the prediction of standard wave parameters, including short and long (infragravity) wave heights, mean wavelength, fraction of breaking waves, and depth-averaged currents. The proposed surrogate model thus offers an efficient alternative to computationally expensive numerical model simulations, enabling rapid and reliable calculations of climatologies of nearshore hydrodynamic conditions and modelling of specific event scenarios. We provide a comprehensive description of the implementation details and assess the surrogate model’s performance in representing various wave and hydrodynamic parameters. We discuss potential use cases and limitations, noting that this hybrid modelling technique can be adapted for use with other numerical models in various settings.