13 Search Results

Population growth, land use change, climate change, and natural resource extraction are driving the salinization of freshwater resources worldwide. Reversing these trends will require data-centric approaches that identify salt sources, environmental drivers, and ecosystem responses. In this study, we applied principal component analysis and hierarchical clustering to identify ion covariance patterns, or “ion clusters,” in Broad Run, an urban stream in the Mid-Atlantic United States. These clusters correspond to distinct hydrologic regimes and reveal specific salinization risks: (1) phosphorus pollution mobilized during summer storms (Cluster 1); (2) elevated concentrations of sulfate and bicarbonate during baseflow (Cluster 2), likely reflecting groundwatermore » discharge; and (3) elevated specific conductance and sodium, chloride, and potassium ion concentrations during snowmelt and rain-on-snow events (Cluster 3), driven by deicer and anti-icer wash-off. These ion fingerprints offer a transferable framework for diagnosing salt sources, assessing ecological risk, and identifying management targets. Our findings underscore the need for next-generation stormwater infrastructure and smart growth policies to protect aquatic life in rapidly urbanizing watersheds.« less

Hydropower is crucial for electric‐grid stability in the context of variable renewables but faces threats from changing hydrology. Here, we summarize the state of the science at the intersection of hydropower operations and planning, hydrologic science, and climate. We focus on the United States, outlining research, development, and training needs. Key knowledge gaps include the risk that intensification of compound extreme events poses to future generation, as well as uncertainties surrounding greenhouse gas emissions from hydropower reservoirs with relevance to hydropower's role in energy decarbonization. Quantifying such impacts and reducing uncertainty are critical where possible, but remaining irreducible or deepmore » uncertainty will require new approaches. Future monitoring and modeling methods must provide a better understanding of the complexity inherent in large watersheds that is critical to managing both hydropower and watersheds in the context of hydrologic change. Yet, research and development will have little impact if they do not inform practice. Standardization and consolidation of platforms are essential for data, modeling, and tool translation to local scales and small operators. An enhanced industry‐academia dialog is pivotal for fostering a robust pipeline of hydropower professionals. Collaboration among researchers, policymakers, authorities, and industry stakeholders emerges as a recurring theme, highlighting the imperative for collective efforts.« less

Although groundwater discharge is a critical stream temperature control process, it is not explicitly represented in many stream temperature models, an omission that may reduce predictive accuracy, hinder management of aquatic habitat, and decrease user confidence. We assessed the performance of a previously-described process-guided deep learning model of stream temperature in the Delaware River Basin (USA). We found lower accuracy (root mean square error [RMSE] of 1.71 versus 1.35°C) and stronger seasonal bias (absolute mean monthly bias of 1.06 vs. 0.68°C) for reaches primarily influenced by deep groundwater as compared to atmospheric conditions. We then tested four approaches for improvingmore » groundwater process representation: (a) a custom loss function leveraging the unique patterns of air and water temperature coupling characteristic of different temperature drivers, (b) inclusion of additional groundwater-relevant catchment attributes, (c) incorporation of additional process model outputs, and (d) a composite model. The custom loss function and the additional attributes significantly improved the predictive accuracy in groundwater-dominated reaches (RMSE of 1.37 and 1.26°C) and reduced the seasonal bias (absolute mean monthly bias of 0.44 and 0.48°C), but neither approach could identify holdout groundwater reaches. Variable importance analysis indicates the custom loss function nudges the model to use the existing inputs more efficiently, whereas with the added features the model relies on a broader suite of inputs. This analysis is a substantial step toward more accurately representing groundwater discharge processes in stream temperature models and will improve predictive accuracy and inform habitat management.« less

Reshaping the scale of planning for hydropower development, from reaches to basin-scales, has been recommended as a more effective way to ameliorate the environmental impacts of hydropower. One approach is identifying mutually exclusive areas where development is precluded for conservation purposes and areas of low conservation value that present fewer barriers to development. This strategy, however, is less adoptable in developed countries where hydropower is already widespread and large-scale construction of new dams is unlikely. To broaden the adoption of basin-scale planning, alternative approaches and planning tools are needed for identifying mutually beneficial opportunities for simultaneous increases in hydropower capacitymore » while improving environmental conditions. In this study, we present the Basin Scale Opportunity Assessment as a methodology to improve environmental conditions through either direct (on-site) or indirect (off-site) mitigation. We assess whether direct or indirect mitigation activities lead to optimal results in terms of added hydropower, environmental improvement, and monetary cost at a basin scale. We present two case studies for the Connecticut River and Roanoke River Basins, USA. Significant opportunities for expanding hydropower generating capacity are numerous in both basins. Results suggest that total hydropower capacity could be increased 4 to 7 % in the Roanoke and Connecticut Basins, respectively, without new dam construction and with net improvements in environmental conditions. We found that environmentally and economically optimal win-win strategies for increasing hydropower capacity and improving environmental conditions included improving environmental conditions in rivers downstream of existing dams. Off-site mitigation opportunities, such as dam removal and wetland mitigation, were identified as optimum solutions for achieving net environmental improvements only when they were associated with new hydropower construction. Our results demonstrate that opportunities to increase hydropower capacity and improve environmental conditions are expanded by viewing cumulative benefits at basin scales; however, increasing regulatory flexibility may be required to realize these opportunities.« less

The US Environmental Protection Agency (EPA) uses a water quality index (WQI) to estimate benefits of proposed Clean Water Act regulations. The WQI is relevant to human use value, such as recreation, but may not fully capture aspects of nonuse value, such as existence value. Here, we identify an index of biological integrity to supplement the WQI in a forthcoming national stated preference survey that seeks to capture existence value of streams and lakes more accurately within the conterminous United States (CONUS). We used literature and focus group research to evaluate aquatic indices regularly reported by the EPA’s National Aquaticmore » Resource Surveys. We chose an index that quantifies loss in biodiversity as the observed-to-expected (O/E) ratio of taxonomic composition because focus group participants easily understood its meaning and the environmental changes that would result in incremental improvements. However, available datasets of this index do not provide the spatial coverage to account for how conditions near survey respondents affect their willingness to pay for its improvement. Therefore, we modeled and interpolated the values of this index from sampled sites to 1.1 million stream segments and 297,071 lakes across the CONUS to provide the required coverage. The models explained 13 to 36% of the variation in O/E scores and demonstrate how modeling can provide data at the required density for benefits estimation. We close by discussing future work to improve performance of the models and to link biological condition with water quality and habitat models that will allow us to forecast changes resulting from regulatory options.« less

Abstract The global decline of water quality in rivers and streams has resulted in a pressing need to design new watershed management strategies. Water quality can be affected by multiple stressors including population growth, land use change, global warming, and extreme events, with repercussions on human and ecosystem health. A scientific understanding of factors affecting riverine water quality and predictions at local to regional scales, and at sub‐daily to decadal timescales are needed for optimal management of watersheds and river basins. Here, we discuss how machine learning (ML) can enable development of more accurate, computationally tractable, and scalable models formore » analysis and predictions of river water quality. We review relevant state‐of‐the art applications of ML for water quality models and discuss opportunities to improve the use of ML with emerging computational and mathematical methods for model selection, hyperparameter optimization, incorporating process knowledge into ML models, improving explainablity, uncertainty quantification, and model‐data integration. We then present considerations for using ML to address water quality problems given their scale and complexity, available data and computational resources, and stakeholder needs. When combined with decades of process understanding, interdisciplinary advances in knowledge‐guided ML, information theory, data integration, and analytics can help address fundamental science questions and enable decision‐relevant predictions of riverine water quality.« less

Temporal patterns in stream chemistry provide integrated signals describing the hydrological and ecological state of whole catchments. However, stream chemistry integrates multi-scale signals of processes occurring in both the catchment and stream. Deconvoluting these signals could identify mechanisms of solute transport and transformation and provide a basis for monitoring ecosystem change. Here, we applied trend analysis, wavelet decomposition, multivariate autoregressive state-space modeling, and analysis of concentration-discharge relationships to assess temporal patterns in high-frequency (15 min) stream chemistry from permafrost-influenced boreal catchments in Interior Alaska at diel, storm, and seasonal time scales. We compared catchments that varied in spatial extent ofmore » permafrost to identify characteristic biogeochemical signals. Catchments with higher spatial extents of permafrost were characterized by increasing nitrate concentration through the thaw season, an abrupt increase in nitrate and fluorescent dissolved organic matter (fDOM) and declining conductivity in late summer, and flushing of nitrate and fDOM during summer rainstorms. In contrast, these patterns were absent, of lower magnitude, or reversed in catchments with lower permafrost extent. Solute dynamics revealed a positive influence of permafrost on fDOM export and the role of shallow, seasonally dynamic flowpaths in delivering solutes from high-permafrost catchments to streams. Lower spatial extent of permafrost resulted in static delivery of nitrate and limited transport of fDOM to streams. Shifts in concentration-discharge relationships and seasonal trends in stream chemistry toward less temporally dynamic patterns might therefore indicate reorganized catchment hydrology and biogeochemistry due to permafrost thaw.« less

Here, travel time–based representations of transport, a highly successful strategy for modeling conservative tracer transport in stream corridors, are extended to accommodate multicomponent reactive transport. Specifically, convolution representations used to model exchange of solute with the hyporheic zone are shown to be equivalent to solving one–dimensional subgrid models in Lagrangian form coupled to the advection dispersion equation for the stream channel. Unlike the convolution–based representations of previous travel time–based stream transport models, the subgrid model generalizes to include multicomponent reactive transport with general nonlinear reactions. An example involving biomass growth, the establishment of redox zonation, and the resulting impact onmore » denitrification rates demonstrate reach–scale application of the new approach. Although simplified, those example simulations show some of the key phenomena associated with hyporheic–zone denitrification that are not represented with conventional first–order estimates.« less

Though their importance varies greatly among species and ecosystems, animals can be important in modulating ecosystem-level nutrient cycling. Nutrient cycling rates of individual animals represent valuable data for testing the predictions of important frameworks such as the Metabolic Theory of Ecology (MTE) and ecological stoichiometry (ES). They also represent an important set of functional traits that may reflect both environmental and phylogenetic influences. Over the past two decades, studies of animal-mediated nutrient cycling have increased dramatically, especially in aquatic ecosystems. Here we present a global compilation of aquatic animal nutrient excretion rates. The dataset includes 10,534 observations from freshwater andmore » marine animals of N and/or P excretion rates. Furthermore, these observations represent 491 species, including most aquatic phyla. Coverage varies greatly among phyla and other taxonomic levels. The dataset includes information on animal body size, ambient temperature, taxonomic affiliations, and animal body N:P. We used this data set to test predictions of MTE and ES, as described in Vanni and McIntyre (2016; Ecology DOI: 10.1002/ecy.1582).« less

Primary consumers play important roles in the cycling of nutrients in headwater streams, storing assimilated nutrients in growing tissue and recycling them through excretion. Though environmental conditions in most headwater streams and their surrounding terrestrial ecosystems vary considerably over the course of a year, relatively little is known about the effects of seasonality on consumer nutrient recycling these streams. Here, we measured nitrogen accumulated through growth and excreted by the grazing snail Elimia clavaeformis (Pleuroceridae) over the course of 12 months in Walker Branch, identifying close connections between in-stream nitrogen processing and seasonal changes in the surrounding forest.