Established closed-form analytical solutions for using heat as a tracer of vertical groundwater fluxes typically rely on assumptions of steady hydraulic conditions. We introduce a novel analytical approach and associated tool, PyTFLUX, to account for transient changes in vertical groundwater fluxes. The analytical solution uses a Fourier series to represent diurnal surface temperature variability and a differential method to represent vertical flux changes. Optimization techniques are employed to achieve faster convergence and prevent the estimation of unreasonable vertical fluxes. The PyTFLUX script, presented in a Python Jupyter notebook, enables the easy adoption of the new analytical framework. To test the new approach, illustrative transient vertical flux time series were developed for three time-varying groundwater flux scenarios: a step-change, a single sine-wave, and a mixed sine-wave. These profiles were analyzed to infer vertical groundwater flux time series using PyTFLUX and previously published methods implemented in VFLUX2. Results show that PyTFLUX can reproduce temporal variability in groundwater fluxes not typically captured by existing methods. Finally, previously published high-resolution sediment temperature data from the Quashnet River in Massachusetts, USA, were analyzed to demonstrate the efficacy of PyTFLUX in analyzing complex field data. The analysis of field data yielded a vertical flux time series with mean values that agreed with fluxes yielded from other approaches, but the new approach also revealed pronounced temporal flux variability that was obscured by other methods.

With global warming, the hydrological cycle is intensifying with more frequent and severe droughts and floods, placing water resources and their dependent communities under increasing stress. Guidance and insights into the projection of future water conditions are, therefore, increasingly needed to inform climate change adaptation. Hydrological projections can provide such insights when suitably designed for user needs, produced from the best available climate knowledge, and leverage appropriate hydrological models. However, producing such hydrological projections is a complex process that requires skills and knowledge spanning from the often-siloed disciplines of climate, hydrology, communication, and decision-making. Accordingly, this paper bridges these silos, by providing detailed guidance on the important steps and best practices to develop hydrological projections that can effectively support decision-making. Using an extensive literature review as well as our practical experience as climate scientists, hydrologists, numerical modelers, uncertainty experts and decision-makers, here we provide: (i) an overview of climate change hydrological impacts as background knowledge; (ii) a step-by-step guide to produce hydrological projections under climate change that are targeted to water practitioners and decision-making applications, (iii) a summary of important considerations related to hydrological projection uncertainty; and (iv) insights to use hydrological projections and their associated uncertainty for impactful communication and decision-making. By providing this guide for water practitioners, our paper addresses a critical interdisciplinary knowledge gap and supports enhanced decision-making and resilience to climate change threats.

Cryogenic vacuum distillation (CVD) is a widely used technique for extracting plant water from stems for isotopic analysis, but concerns about potential isotopic biases have emerged. Here, we leverage the Cavitron centrifugation technique to extract xylem water and compare its isotopic signature to that of CVD-extracted stem water as well as source water. Conducted under field conditions in tropical northern Australia, our study spans seven tree species naturally experiencing a range of water stress levels. Our findings reveal a significant deuterium bias in CVD-extracted bulk stem water when compared to xylem water (median bias \($-14.9\textperthousand$\)), whereas xylem water closely aligned with source water (median offset \($-1.9\textperthousand$\)). We find substantial variations in deuterium bias among the seven tree species (bias ranging from -19.3 to \($-9.1\textperthousand$\)), but intriguingly, CVD-induced biases were unrelated to environmental factors such as relative stem water content and pre-dawn leaf water potential. These results imply that inter-specific differences may be driven by anatomical traits rather than tree hydraulic functioning. Additionally, our data highlight the potential to use a site-specific deuterium offset, based on the isotopic signature of local source water, for correcting CVD-induced biases.