Closure of the soil water balance is fundamental to ecohydrology. But closing the soil water balance with hydrometric information offers no insight into the age distribution of water transiting the soil column via deep drainage or the combination of soil evaporation and transpiration. This is a major challenge in our discipline currently; tracing the water balance is the needed next step. Here we report results from a controlled tracer experiment aimed at both closing the soil water balance and tracing its individual components. This was carried out on a 2.5 m3 lysimeter planted with a willow tree. We applied 25 mm of isotopically enriched water on top of the lysimeter and tracked it for 43 days through the soil water, the bottom drainage, and the plant xylem. We then destructively sampled the system to quantify the remaining isotope mass. More than 900 water samples were collected for stable isotope analysis to trace the labeled irrigation. We then used these data to quantify when and where the labeled irrigation became the source of plant uptake or deep percolation. Evapotranspiration dominated the water balance outflow (88%). Tracing the transpiration flux showed further that transpiration was soil water that had fallen as precipitation 1–2 months prior. The tracer breakthrough in transpiration was complex and different from the breakthrough curves observed within the soil or in the bottom drainage. Given the lack of direct experimental data on travel time to transpiration, these results provide a first balance closure where all the relevant outflows are traced.

This commentary explores the challenges and opportunities associated with a possible transition of Water Resources Research to a publication model where all articles are freely available upon publication (“Gold” open access). It provides a review of the status of open access publishing models, a summary of community input, and a path forward for AGU leadership. The decision to convert to open access is framed by a mix of finances and values. On the one hand, the challenge is to define who pays, and how, and what we can do to improve the affordability of publishing. On the other hand, the challenge is to increase the extent to which science is open and accessible. The next steps for the community include an incisive analysis of the financial feasibility of different cost models, and weighing the financial burden for open access against the desire to further advance open science.

Projections of change in high-flow extremes with global warming vary widely among, and within, large midlatitude river basins. The spatial variability of these changes is attributable to multiple causes. One possible and little-studied cause of changes in high-flow extremes is a change in the synchrony of mainstem and tributary streamflow during high-flow extremes at the mainstem-tributary confluence. We examined reconstructed and simulated naturalized daily streamflow at confluences on the Columbia River in western North America, quantifying changes in synchrony in future streamflow projections and estimating the impact of these changes on high-flow extremes. In the Columbia River basin, projected flow regimes across colder tributaries initially diverge with warming as they respond to climate change at different rates, leading to a general decrease in synchrony, and lower high-flow extremes, relative to a scenario with no changes in synchrony. Where future warming is sufficiently large to cause most subbasins upstream from a confluence to transition toward a rain-dominated, warm regime, the decreasing trend in synchrony reverses itself. At one confluence with a major tributary (the Willamette River), where the mainstem and tributary flow regimes are initially very different, warming increases synchrony and, therefore, high-flow magnitudes. These results may be generalizable to the class of large rivers with large contributions to flood risk from the snow (i.e., cold) regime, but that also receive considerable discharge from tributaries that drain warmer basins.

Hydrometeorological flood generating processes (excess rain, short rain, long rain, snowmelt, and rain-on-snow) underpin our understanding of flood behavior. Knowledge about flood generating processes improves hydrological models, flood frequency analysis, estimation of climate change impact on floods, etc. Yet, not much is known about how climate and catchment attributes influence the spatial distribution of flood generating processes. This study aims to offer a comprehensive and structured approach to close this knowledge gap. We employ a large sample approach (671 catchments across the contiguous United States) and evaluate how catchment attributes and climate attributes influence the distribution of flood processes. We use two complementary approaches: A statistics-based approach which compares attribute frequency distributions of different flood processes; and a random forest model in combination with an interpretable machine learning approach (accumulated local effects [ALE]). The ALE method has not been used often in hydrology, and it overcomes a significant obstacle in many statistical methods, the confounding effect of correlated catchment attributes. As expected, we find climate attributes (fraction of snow, aridity, precipitation seasonality, and mean precipitation) to be most influential on flood process distribution. However, the influence of catchment attributes varies both with flood generating process and climate type. We also find flood processes can be predicted for ungauged catchments with relatively high accuracy (R2 between 0.45 and 0.9). The implication of these findings is flood processes should be considered for future climate change impact studies, as the effect of changes in climate on flood characteristics varies between flood processes.