Adaptable and comprehensive vulnerability assessments for water resources systems in a rapidly changing world.

Comprehensive and adaptive approaches to vulnerability assessment are crucial for guiding effective adaptation in global water resources systems. A common approach to quantify vulnerability is through indicators, which capture the 'spirit of vulnerability' while retaining practical ease-of-use benefits. However, a comprehensive meta-analysis of reveals two specific limitations of global indicator-based vulnerability assessments for water resources systems: 1) vulnerability is influenced by complex interactions among multi-domain factors, for which indicator quality and data vary; and 2) vulnerability is dynamic and evolves over time, an aspect overlooked in most approaches. In response to these identified challenges, we propose a new dynamic "build-your-own" approach to vulnerability assessment. Our approach focuses on correcting for the identified gaps and biases in indicators and data to improve assessment comprehensiveness. This approach also incorporates guidance around adapting assessments over time to better reflect vulnerability under changing conditions. The open-source nature of our approach and underlying data can facilitate the development and customization of indicator-based vulnerability assessments for diverse applications, supporting practical and relevant planning for more resilient water resources systems.

From Hydrometeorology to River Water Quality: Can a Deep Learning Model Predict Dissolved Oxygen at the Continental Scale?

Dissolved oxygen (DO) reflects river metabolic pulses and is an essential water quality measure. Our capabilities of forecasting DO however remain elusive. Water quality data, specifically DO data here, often have large gaps and sparse areal and temporal coverage. Earth surface and hydrometeorology data, on the other hand, have become largely available. Here we ask: can a Long Short-Term Memory (LSTM) model learn about river DO dynamics from sparse DO and intensive (daily) hydrometeorology data? We used CAMELS-chem, a new data set with DO concentrations from 236 minimally disturbed watersheds across the U.S. The model generally learns the theory of DO solubility and captures its decreasing trend with increasing water temperature. It exhibits the potential of predicting DO in "chemically ungauged basins", defined as basins without any measurements of DO and broadly water quality in general. The model however misses some DO peaks and troughs when in-stream biogeochemical processes become important. Surprisingly, the model does not perform better where more data are available. Instead, it performs better in basins with low variations of streamflow and DO, high runoff-ratio (>0.45), and winter precipitation peaks. Results here suggest that more data collections at DO peaks and troughs and in sparsely monitored areas are essential to overcome the issue of data scarcity, an outstanding challenge in the water quality community.

Humidity determines snowpack ablation under a warming climate.

Climate change is altering historical patterns of snow accumulation and melt, threatening societal frameworks for water supply. However, decreases in spring snow water equivalent (SWE) and changes in snowmelt are not ubiquitous despite widespread warming in the western United States, highlighting the importance of latent and radiant energy fluxes in snow ablation. Here we demonstrate how atmospheric humidity and solar radiation interact with warming temperature to control snowpack ablation at 462 sites spanning a gradient in mean winter temperature from -8.9 to +2.9 °C. The most widespread response to warming was an increase in episodic, midwinter ablation events. Under humid conditions these ablation events were dominated by melt, averaging 21% (202 mm/year) of SWE. Winter ablation under dry atmospheric conditions at similar temperatures was smaller, averaging 12% (58 mm/year) of SWE and likely dominated by sublimation fluxes. These contrasting patterns result from the critical role that atmospheric humidity plays in local energy balance, with latent and longwave radiant fluxes cooling the snowpack under dry conditions and warming it under humid conditions. Similarly, spring melt rates were faster under humid conditions, yet the second most common trend was a reduction in spring melt rates associated with earlier initiation when solar radiation inputs are smaller. Our analyses demonstrate that regional differences in atmospheric humidity are a major cause of the spatial variability in snowpack response to warming. Better constraints on humidity will be critical to predicting both the amount and timing of surface water supplies under climate change.

Hydrogeomorphology explains acidification-driven variation in aquatic biological communities in the Neversink Basin, USA.

Describing the distribution of aquatic habitats and the health of biological communities can be costly and time-consuming; therefore, simple, inexpensive methods to scale observations of aquatic biota to watersheds that lack data would be useful. In this study, we explored the potential of a simple "hydrogeomorphic" model to predict the effects of acid deposition on macroinvertebrate, fish, and diatom communities in 28 sub-watersheds of the 176-km2 Neversink River basin in the Catskill Mountains of New York State. The empirical model was originally developed to predict stream-water acid neutralizing capacity (ANC) using the watershed slope and drainage density. Because ANC is known to be strongly related to aquatic biological communities in the Neversink, we speculated that the model might correlate well with biotic indicators of ANC response. The hydrogeomorphic model was strongly correlated to several measures of macroinvertebrate and fish community richness and density, but less strongly correlated to diatom acid tolerance. The model was also strongly correlated to biological communities in 18 sub-watersheds independent of the model development, with the linear correlation capturing the strongly acidic nature of small upland watersheds (< 1 km2). Overall, we demonstrated the applicability of geospatial data sets and a simple hydrogeomorphic model for estimating aquatic biological communities in areas with stream-water acidification, allowing estimates where no direct field observations are available. Similar modeling approaches have the potential to complement or refine expensive and time-consuming measurements of aquatic biota populations and to aid in regional assessments of aquatic health.

Variation in root density along stream banks.

While it is recognized that vegetation plays a significant role in stream bank stabilization, the effects are not fully quantified. The study goal was to determine the type and density of vegetation that provides the greatest protection against stream bank erosion by determining the density of roots in stream banks. To quantify the density of roots along alluvial stream banks, 25 field sites in the Appalachian Mountains were sampled. The riparian buffers varied from short turfgrass to mature riparian forests, representing a range of vegetation types. Root length density (RLD) with depth and aboveground vegetation density were measured. The sites were divided into forested and herbaceous groups and differences in root density were evaluated. At the herbaceous sites, very fine roots (diameter < 0.5 mm) were most common and more than 75% of all roots were concentrated in the upper 30 cm of the stream bank. Under forested vegetation, fine roots (0.5 mm < diameter < 2.0 mm) were more common throughout the bank profile, with 55% of all roots in the top 30 cm. In the top 30 cm of the bank, herbaceous sites had significantly greater overall RLD than forested sites (alpha = 0.01). While there were no significant differences in total RLD below 30 cm, forested sites had significantly greater concentrations of fine roots, as compared with herbaceous sites (alpha = 0.01). As research has shown that erosion resistance has a direct relationship with fine root density, forested vegetation may provide better protection against stream bank erosion.