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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Factors influencing water quality in surface water and alluvial groundwaters downgradient of a reclaimed surface coal mine in the Powder River Basin of southeastern Montana, USA

Abstract Coal mining and reclamation can have a profound influence on hydrogeologic systems, with clear consequences for groundwater quality, yet their long-term influence on downgradient water quality over time following reclamation is less well documented. Geochemical trends were evaluated in water quality downgradient of a fully reclaimed landscape at the former Big Sky Mine in the Rosebud Creek watershed (southeastern Montana, USA), over a 3-year period (2020–2022), including bond release in 2022. Within 6 km downgradient from the reclaimed area, sulfate concentrations decreased from approximately 3500 to 1800 mg l −1 within the Miller Coulee alluvial aquifer. Major ions, δ 34 S SO4 values, and residence time tracers suggest that the observed decreases in sulfate concentration result from a combination of dilution by mixed-age inflows and incomplete transit of the high salinity plume from the mine boundary. Both bedrock and alluvial aquifers of the Rosebud Creek corridor contained contributions of millennia-old regional groundwater, which may serve to mitigate mine-derived high salinity waters. Rosebud Creek, which traverses the outflow zone of Miller Coulee in the study area, exhibited high sulfate concentrations during low flows and consistent downgradient increases in sulfate concentration. The possibility of plume dynamics in Miller Coulee suggests that the greatest water quality impacts may not yet have reached Rosebud Creek.

Keeshin, Skye I. (ORCID:0009000986449872)↗
DOE OSTI2022

Pollution in the Press: Employing Text Analytics to Understand Regional Water Quality Narratives

Drinking water has and will continue to be at the foundation of our nation’s well-being and there is a growing interest in United States (US) drinking water quality. Nearly 30% of the United States population obtained their water from community water systems that did not meet federal regulations in 2019. Given the heavy interactions between society and drinking water quality, this study integrates social constructionism, environmental injustice, and sociohydrological systems to evaluate local awareness of drinking water quality issues. By employing text analytics, we explore potential drivers of regional water quality narratives within 25 local news sources across the United States. Specifically, we assess the relationship between printed local newspapers and water quality violations in communities as well as the influence of social, political, and economic factors on the coverage of drinking water quality issues. Results suggest that the volume and/or frequency of local drinking water violations is not directly reflected in local news coverage. Additionally, news coverage varied across sociodemographic features, with a negative relationship between Hispanic populations and news coverage of Lead and Copper Rule, and a positive relationship among non-Hispanic white populations. These findings extend current understanding of variations in local narratives to consider nuances of water quality issues and indicate opportunities for increasing equity in environmental risk communication.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality?

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 for 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.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A GPU-based 2D shallow water quality model

In this paper, a 2D shallow water flow solver integrated with a water quality model is presented. The interaction between the main water quality constituents included is based on the Water Quality Analysis Simulation Program. Efficiency is achieved by computing with a combination of a Central Processing Unit (CPU) and a Graphics Processing Unit (GPU) device. This technique is intended to provide robust and accurate simulations with high computation speedups with respect to a single-core CPU in real events. The proposed numerical model is evaluated in cases that include the transport and reaction of water quality components over irregular bed topography and dry–wet fronts, verifying that the numerical solution in these situations conserves the required properties (C-property and positivity). The model can operate in any steady or unsteady form allowing an efficient assessment of the environmental impact of water flows. The field data from an unsteady river reach test case are used to show that the model is capable of predicting the measured temporal distribution of dissolved oxygen and water temperature, proving the robustness and computational efficiency of the model, even in the presence of noisy signals such as wind speed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Real-time and Autonomous Water Quality Monitoring System Based on Remotely Operated Vehicle

Existing water quality monitoring systems near hydropower facilities are limited by the lack of mobility of the sensors’ carrier platform. Most systems use a buoy, a mounting fixture attached to a solid structure, or a human worker, which significantly limits the selection of the sampling sites and poses safety risks during data collection and equipment maintenance. To improve on this technology, we developed an autonomous water quality monitoring system that can operate in dangerous water environments near hydropower facilities for water sampling at multiple locations. The goal is to enable safe, timely, and comprehensive water-quality data collection; maximize power generation revenue with improved operational control; and reduce Federal Energy Regulatory Commission and state water quality monitoring costs for compliance. The system incorporates a remotely operated vehicle as the mobile monitoring platform, a dissolved oxygen sensor for monitoring water quality, a tether management system for automatically winding the tether, a solar mobile docking platform for suppling power to the ROV, and a web-based graphical user interface for data post-processing and visualization. In addition, preliminary field research are presented to demonstrate the system capabilities.

Salalila, Aljon L.↗
DOE OSTI2021

Assessing the Impacts of Recent Crop Expansion on Water Quality in the Missouri River Basin Using the Soil and Water Assessment Tool

The Missouri River Basin (MORB) has experienced a resurgence of grassland conversion to crop production, which raised concerns on water quality. We applied the Soil and Water Assessment Tool (SWAT) to address how this conversion would impact water quality. We designed three crop production scenarios representing conversion of grassland to: (a) continuous corn; (b) corn/soybean rotation; and (c) corn/wheat rotation to assess the impact. The SWAT model results showed: (a) the lower MORB produced high total nitrogen (TN) and total phosphorus (TP) load before conversion (baseline) due mainly to high precipitation and high agricultural activity; (b) the greatest percentage increases of TN and TP occurred in the North and South Dakotas, coinciding with the highest amount of grassland conversion to cropland; and (c) grassland conversion to continuous corn resulted in the greatest increase in TN and TP loads, followed by conversion to corn/soybean and then conversion to corn/wheat. Although the greatest percentage increases of TN and TP occurred in the North and South Dakotas, these areas still contributed relatively low TN and TP to total basin loads after conversion. However, watersheds, predominantly in the lower MORB continued to be “hotspots” that contributed the greatest amounts of TN and TP to the total basin loads—driven by a combination of grassland conversion, high precipitation, and loading from pre-existing cropland. At the watershed outlet, the TN and TP loads were increased by 6.4% (13,800 t/yr) and 8.7% (3,400 t/yr), respectively, during the 2008–2016 period for the conversion to continuous corn scenario.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Potential of water quality wetlands to mitigate habitat losses from agricultural drainage modernization

Given widespread biodiversity declines, a growing global human population, and demands to improve water quality, there is an immediate need to explore land management solutions that support multiple ecosystem services. Agricultural water quality wetlands designed to provide both water quality benefits and wetland and grassland habitat are an emerging restoration solution that may reverse habitat declines in intensive agricultural areas. Installation of water quality wetlands in the Upper Midwest, USA, when considered alongside the repair and modification of aging agricultural tile drainage infrastructure, is a likely scenario that may mitigate nutrient pollution exported from agricultural systems and improve crop yields. The capacity of water quality wetlands to provide habitat within the wetland pool and the surrounding grassland is not well-studied, particularly with respect to potential habitat changes resulting from drainage infrastructure upgrades. For the current study, we produced spatially explicit models of 37 catchments distributed throughout an important region for agriculture and biodiversity, the Des Moines Lobe of Iowa. Four scenarios were considered - with and without improved drainage and with and without water quality wetlands - to estimate the net potential habitat implications of these scenarios for amphibians, grassland birds, and wild bees. Model results indicate that drainage modification alone will likely result in moderate direct losses of suitable amphibian habitat and large declines in overall habitat quality. However, inclusion of water quality wetlands at the catchment scale may mitigate these amphibian habitat losses while also increasing grassland bird and pollinator habitat. In conclusion, the impacts of water quality wetlands and drainage modernization on waterfowl in the region require additional study.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The potential of carbon markets to accelerate green infrastructure based water quality trading

Green infrastructure solutions can improve in-stream water quality in lieu of building electricity-consuming gray infrastructure. Permitted under the United States Clean Water Act, these programs allow regulated utilities to trade point-source water quality obligations with non-point source mitigation efforts in the watershed. Carbon financing can provide an incentive for water quality trading. Here we combine data on impaired waters, treatment technologies, and life cycle greenhouse gas emissions in the Contiguous United States, and compare traditional treatment technologies to alternative green infrastructure. We find green infrastructure could save $\$15.6$ billion dollars, 21.2 terawatt-hours of electricity, and 29.8 million tonnes of carbon dioxide equivalent emissions per year while sequestering over 4.2 million tonnes CO2e per year over a 40 year time horizon. Green infrastructure solutions may have the potential to generate $\$679$ million annually in carbon credit revenue (at $\$20$ per credit), which represents a unique opportunity to help accelerate water quality trading.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Modeling Water Quality in Watersheds: From Here to the Next Generation

In this synthesis, we assess present research and anticipate future development needs in modeling water quality in watersheds. We first discuss areas of potential improvement in the representation of freshwater systems pertaining to water quality, including representation of environmental interfaces, in-stream water quality and process interactions, soil health and land management, and (peri-)urban areas. In addition, we provide insights into the contemporary challenges in the practices of watershed water quality modeling, including quality control of monitoring data, model parameterization and calibration, uncertainty management, scale mismatches and provisioning of modeling tools. Finally, we make three recommendations to provide a path forward for improving watershed water quality modeling science, infrastructure and practices. These include building stronger collaborations between experimentalists and modelers, bridging gaps between modelers and stakeholders, and cultivating and applying procedural knowledge to better govern and support water quality modeling processes within organizations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Monitoring water quality in the lower Kansas River using remote sensing

Abstract We demonstrate how to combine remote sensing data from satellite imagery (Sentinel‐2) with in situ water quality gauging (USGS Super Gages and the Gybe hyperspectral radiometer) to create spatially dense maps of water quality parameters (chlorophyll‐a concentration, turbidity, and nitrate plus nitrite concentration) along the lower Kansas River. The water quality maps are created using locally tuned models of the target water quality parameters, and this study describes the steps used to design, calibrate, and validate the empirical correlations. Water quality parameters such as chlorophyll‐a concentration are correlated with well‐studied absorption and scattering features in the visible spectrum (roughly 400–700 nm). Nutrients (such as nitrate plus nitrite concentration) lack strong absorption features in the visible spectrum, and in those cases we describe a novel surrogate data modeling approach that identifies overlapping water parcels between the in situ gauging and the remote sensing imagery. Measurements from the overlapping water parcels yield excellent correlations () for the target water quality parameters for limited windows of time (or limited sections of river reaches). Examples are provided illustrating how the water quality maps can be used to track river inputs from ungauged sources (such as creeks), or reveal the mixing patterns at the confluences.

Tufillaro, Nicholas↗
DOE OSTI2025

Postfire Biogeochemical Processes: Implications to Source Water Quality in Fire-Influenced Watersheds

Forested watersheds are instrumental in providing purified and reliable water to millions of people worldwide. The changing climate has increased the frequency and severity of global fire events. Forested watersheds and their ecosystem functions are greatly disrupted during fire activity. Postfire concerns in forested watersheds include unpredictable and potentially simultaneous alterations in source water quality and hydro-biogeochemical processes. Here, the degree of fire severity can complexly modify water quality through the production of fire-transformed constituents on the burned forest floor (i.e., nutrients, metal(loid)s, dissolved organic matter, and the formation of disinfection byproducts). Correspondingly, fire severity and postfire rainfall patterns can refine hydro-biogeochemical processes that influence the transport of the fire-transformed constituents (i.e., vegetation function, soil structure, hydrological pathways, and microbial communities). Postfire alterations to water quality and hydro-biogeochemical processes introduce further complexity with varying temporal influence, which ranges from months to decades. As postfire water quality and watershed response research progresses, it is essential to homogenize interdisciplinary expertise to bridge knowledge gaps between fields ranging from forest ecology, hydrology, microbiology, and geochemistry. A multidisciplinary approach in wildfire research will facilitate a comprehensive perception of the diverse water quality risks associated with fire activity and mitigate fire concerns on a global level.

Disinfection Byproducts↗
DOE OSTI2021

Comparison of Deterministic and Statistical Models for Water Quality Compliance Forecasting in the San Joaquin River Basin, California

Model selection for water quality forecasting depends on many factors including analyst expertise and cost, stakeholder involvement and expected performance. Water quality forecasting in arid river basins is especially challenging given the importance of protecting beneficial uses in these environments and the livelihood of agricultural communities. In the agriculture-dominated San Joaquin River Basin of California, real-time salinity management (RTSM) is a state-sanctioned program that helps to maximize allowable salt export while protecting existing basin beneficial uses of water supply. The RTSM strategy supplants the federal total maximum daily load (TMDL) approach that could impose fines associated with exceedances of monthly and annual salt load allocations of up to $1 million per year based on average year hydrology and salt load export limits. The essential components of the current program include the establishment of telemetered sensor networks, a web-based information system for sharing data, a basin-scale salt load assimilative capacity forecasting model and institutional entities tasked with performing weekly forecasts of river salt assimilative capacity and scheduling west-side drainage export of salt loads. Web-based information portals have been developed to share model input data and salt assimilative capacity forecasts together with increasing stakeholder awareness and involvement in water quality resource management activities in the river basin. Two modeling approaches have been developed simultaneously. The first relies on a statistical analysis of the relationship between flow and salt concentration at three compliance monitoring sites and the use of these regression relationships for forecasting. The second salt load forecasting approach is a customized application of the Watershed Analysis Risk Management Framework (WARMF), a watershed water quality simulation model that has been configured to estimate daily river salt assimilative capacity and to provide decision support for real-time salinity management at the watershed level. Analysis of the results from both model-based forecasting approaches over a period of five years shows that the regression-based forecasting model, run daily Monday to Friday each week, provided marginally better performance. However, the regression-based forecasting model assumes the same general relationship between flow and salinity which breaks down during extreme weather events such as droughts when water allocation cutbacks among stakeholders are not evenly distributed across the basin. A recent test case shows the utility of both models in dealing with an exceedance event at one compliance monitoring site recently introduced in 2020.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Sensor-Equipped Unmanned Surface Vehicle for High-Resolution Mapping of Water Quality in Low- to Mid-Order Streams

Longitudinal profiling of water quality via the deployment of sensors from watercraft has advanced the understanding of spatial patterns in large rivers and lakes; however, a similar approach in low- to mid-order streams is lacking. To fill this gap, we developed an unmanned surface vehicle (USV)-water quality measurement platform (the “AquaBOT”). The components of the AquaBOT included a nitrate sensor, multiparameter sonde (temperature, conductivity, turbidity, dissolved oxygen, chlorophyll), quantum sensor, and global positioning system (GPS) mounted to a small pontoon-style USV. The AquaBOT was tested in four streams and rivers in Iowa and Tennessee. All measured water quality parameters varied longitudinally, and greater ranges were generally observed along the low-order, agriculturally influenced streams in Iowa. Nitrate, in particular, was spatially heterogeneous. For example, during one run in early June, concentrations ranged from 10.5 to 12.5 mg N L –1 along a 2.3 km reach and hotspots were observed directly downstream of some tile drains. The spatial resolution of AquaBOT data collected in June was 10× higher than grab sampling data, and measurements were collected in less time and at a comparable cost. Here, the AquaBOT can complement existing measurement approaches and will lead to advancements in understanding the processes driving water quality along the stream-to-river continuum.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Quantifying co-benefits of water quality policies: An integrated assessment model of land and nitrogen management

Due to the nature of nitrogen cycling, policies designed to address water quality concerns have the potential to provide benefits beyond the targeted water quality improvements. For example, actions to protect water quality by reducing nitrate leaching from agriculture also reduce emissions of nitrous oxide, a potent greenhouse gas. These positive effects, which are incidental to the regulation's intended target, are termed “co-benefits.” To quantify the co-benefits associated with reduced nitrate leaching, we integrate an economic model of farmer decision making with a model of terrestrial nitrogen cycling for the watershed surrounding Lake Mendota, Wisconsin, USA. Our modeling approach provides a framework that links air and water pollutants in an agri-environmental system and offers a direction for future studies. Our model results highlight the finding that the co-benefits from nitrous oxide abatement are substantial, and their inclusion increases the benefit–cost ratio of water quality policies. Consideration of these co-benefits has the potential to reverse the conclusions of benefit–cost analysis in the assessment of current water quality policies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Recent Progress on Surface Water Quality Models Utilizing Machine Learning Techniques

Surface waterbodies are heavily exposed to pollutants caused by natural disasters and human activities. Empowering sensor technologies in water quality monitoring, sufficient measurements have become available to develop machine learning (ML) models. Numerous ML models have quickly been adopted to predict water quality indicators in various surface waterbodies. This paper reviews 78 recent articles from 2022 to October 2024, categorizing water quality models utilizing ML into three groups: Point-to-Point (P2P), which estimates the current target value based on other measurements at the same time point; Sequence-to-Point (S2P), which utilizes previous time series data to predict the target value at one time point ahead; and Sequence-to-Sequence (S2S), which uses previous time series data to forecast sequential target values in the future. The ML models used in each group are classified and compared according to water quality indicators, data availability, and model performance. Widely used strategies for improving performance, including feature engineering, hyperparameter tuning, and transfer learning, are recognized and described to enhance model effectiveness. The interpretability limitations of ML applications are discussed. This review provides a perspective on emerging ML for surface water quality models.

machine learning (ML)↗
DOE OSTI2023

Systems and methods for modeling water quality

A system, method, device and computer-readable medium for creating an ensemble model of water quality. The ensemble model is generated by determining a set of optimal component models for spectral regions of a body of water, and combining the optimal models. The optimal models can be based on remote sensing data, including satellite imagery. A K-fold partition approach or a global approach can be used to determine the optimal component models, and the optimal component models can be combined through spectral space partition rules to generate an ensemble model of water quality. The ensemble model not only has improved water quality prediction ability, but also has strong spatial and temporal extensibility. The spatial and temporal extensibility of the ensemble model is fundamentally important and desirable for long-term and large-scale remote sensing monitoring and assessment of water quality.

Liu, Hongxing↗
DOE OSTI2020

Real-time monitoring and prediction of water quality parameters and algae concentrations using microbial potentiometric sensor signals and machine learning tools

We report the overarching hypothesis of this study was that temporal microbial potentiometric sensor (MPS) signal patterns could be used to predict changes in commonly monitored water quality parameters by using artificial intelligence/machine learning tools. To test this hypothesis, the study first examines a proof of concept by correlating between MPS's signals and high algae concentrations in an algal cultivation pond. Then, the study expanded upon these findings and examined if multiple water quality parameters could be predicted in real surface waters, like irrigation canals. Signals generated between the MPS sensors and other water quality sensors maintained by an Arizona utility company, including algae and chlorophyll, were collected in real time at time intervals of 30 min over a period of 9 months. Data from the MPS system and data collected by the utility company were used to train the ML/AI algorithms and compare the predicted with actual water quality parameters and algae concentrations. Based on the composite signal obtained from the MPS, the ML/AI was used to predict the canal surface water's turbidity, conductivity, chlorophyll, and blue-green algae (BGA), dissolved oxygen (DO), and pH, and predicted values were compared to the measured values. Initial testing in the algal cultivation pond revealed a strong linear correlation (R 2 = 0.87) between mixed liquor suspended solids (MLSS) and the MPSs' composite signals. The Normalized Root Mean Square Error (NRMSE) between the predicted values and measured values were <6.5%, except for the DO, which was 10.45%. The results demonstrate the usefulness of MPSs to predict key surface water quality parameters through a single composite signal, when the ML/AI tools are used conjunctively to disaggregate these signal components. The maintenance-free MPS offers a novel and cost-effective approach to monitor numerous water quality parameters at once with relatively high accuracy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Beaver dams overshadow climate extremes in controlling riparian hydrology and water quality

Abstract Hydrologic extremes dominate chemical exports from riparian zones and dictate water quality in major river systems. Yet, changes in land use and ecosystem services alongside growing climate variability are altering hydrologic extremes and their coupled impacts on riverine water quality. In the western U.S., warming temperatures and intensified aridification are increasingly paired with the expanding range of the American beaver—and their dams, which transform hydrologic and biogeochemical cycles in riparian systems. Here, we show that beaver dams overshadow climatic hydrologic extremes in their effects on water residence time and oxygen and nitrogen fluxes in the riparian subsurface. In a mountainous watershed in Colorado, U.S.A., we find that the increase in riparian hydraulic gradients imposed by a beaver dam is 10.7–13.3 times greater than seasonal hydrologic extremes. The massive hydraulic gradient increases hyporheic nitrate removal by 44.2% relative to seasonal extremes alone. A drier, hotter climate in the western U.S. will further expand the range of beavers and magnify their impacts on watershed hydrology and biogeochemistry, illustrating that ecosystem feedbacks to climate change will alter water quality in river systems.

54 ENVIRONMENTAL SCIENCES↗