Seasonal and annual partitioning of water within river floodplains has important implications for ecohydrologic links between the water cycle and tree growth. Climatic and hydrologic shifts alter water distribution between floodplain storage reservoirs (e.g., vadose, phreatic), affecting water availability to tree roots. Water partitioning is also dependent on the physical conditions that control tree rooting depth (e.g., gravel layers that impede root growth), the sources of contributing water, the rate of water drainage, and water residence times within particular storage reservoirs. We employ instrumental climate records alongside oxygen isotopes within tree rings and regional source waters, as well as topographic data and soil depth measurements, to infer the water sources used over several decades by two co-occurring tree species within a riparian floodplain along the Rhône River in France. We find that water partitioning to riparian trees is influenced by annual (wet versus dry years) and seasonal (spring snowmelt versus spring rainfall) fluctuations in climate. This influence depends strongly on local (tree level) conditions including floodplain surface elevation and subsurface gravel layer elevation. The latter represents the upper limit of the phreatic zone and therefore controls access to shallow groundwater. The difference between them, the thickness of the vadose zone, controls total soil moisture retention capacity. These factors thus modulate the climatic influence on tree ring isotopes. Additionally, we identified growth signatures and tree ring isotope changes associated with recent restoration of minimum streamflows in the Rhône, which made new phreatic water sources available to some trees in otherwise dry years.Key PointsWater shifts due to climatic fluctuations between floodplain storage reservoirsAnthropogenic changes to hydrology directly impact water available to treesEcohydrologic approaches to integration of hydrology afford new possibilities

Soil‐moisture patterns in floodplains are highly dynamic, owing to the complex relationships between soil properties, climatic conditions at the surface, and the position of the water table. Given this complexity, along with climate change scenarios in many regions, there is a need for a model to investigate the implications of different conditions on water availability to riparian vegetation. We present a model, HaughFlow, which is able to predict coupled water movement in the vadose and phreatic zones of hydraulically connected floodplains. Model output was calibrated and evaluated at six sites in Australia to identify key patterns in subsurface hydrology. This study identifies the importance of the capillary fringe in vadose zone hydrology due to its water storage capacity and creation of conductive pathways. Following peaks in water table elevation, water can be stored in the capillary fringe for up to months (depending on the soil properties). This water can provide a critical resource for vegetation that is unable to access the water table. When water table peaks coincide with heavy rainfall events, the capillary fringe can support saturation of the entire soil profile. HaughFlow is used to investigate the water availability to riparian vegetation, producing daily output of water content in the soil over decadal time periods within different depth ranges. These outputs can be summarized to support scientific investigations of plant‐water relations, as well as in management applications.

“Waiting on weather” is a costly restraint on offshore vessel operability. Vessel operating windows are determined based on the relationships between the weather and vessel movement, and uncertainties in these predictions may result in vessel operations being ceased prematurely. To improve the efficiency of offshore operations, existing assumptions and calculations based on conventional response amplitude operators (RAOs) should be challenged and improved. A machine learning approach is presented as a means of enriching these conventional RAOs with data. The machine learning model uses sea state forecasts to predict vessel response spectra. The model is cleverly formulated to use any existing RAO as a fallback solution in the absence of sufficient data. When applied to a comprehensive real-world scenario, the model predominantly outperforms the “best” available existing RAO. The results can be used not only to improve wave-vessel response predictions, but also to improve our understanding of existing RAOs and their shortcomings. Ultimately, the work can contribute to reducing overconservatism in weather-based restrictions on offshore vessel operability.