Jennifer C. Murphy

Illinois contributes substantial nutrient loads to the Gulf of America, warranting watershed‐scale assessment. This study estimated nitrate‐nitrogen (nitrate‐N) and total phosphorus (TP) loads and yields for 49 Illinois 8‐digit hydrologic unit code (HUC8) watersheds draining to the Mississippi River Basin from 1997–2022, comparing recent (2018–2022) to baseline (1997–2011) conditions. Estimates included point and nonpoint source contributions, dissolved phosphorus, and water yields. During the recent period, nonpoint sources dominated nutrient export (82% nitrate‐N, 78% TP), though point sources drove high yields in the Chicago area. Spatially, nonpoint source nutrient hotspots persisted with nitrate‐N yields highest in east‐central and northern Illinois and TP yields higher in southern and western Illinois. Temporally, statewide nitrate‐N loads decreased 9%, while TP loads increased 27%. Nitrate‐N yields increased in 22 HUC8s and decreased in 20, while TP yields increased in 32 HUC8s and decreased in 9. For both nutrients, baseline yields were negatively correlated with yield changes, indicating high‐yielding watersheds tended toward larger decreases or smaller increases. Water yields increased 19% on average but were weakly correlated with nutrient yield changes ( r = 0.23 and 0.20 for nitrate‐N and TP). These results reveal spatially persistent yet temporally divergent nutrient export across Illinois, with contrasting nitrate‐N and TP trajectories for nonpoint sources.

Contamination by nutrients, major ions, and metals poses a major threat to global water sustainability. Understanding how these pollutants vary across time and space requires long-term monitoring and robust statistical approaches. Traditional methods, however, often struggle to account for streamflow variability, seasonality, and nonlinear responses. Introduced in 2010, the Weighted Regressions on Time, Discharge, and Season (WRTDS) method offers a flexible, data-driven framework that generates both observed and flow-normalized estimates of concentration and load. Over the past 15 years, WRTDS has become a state-of-the-art tool for water-quality science and management, with applications spanning a wide range of hydrologic, climatic, and policy contexts─including major watersheds across North America, Europe, Asia, Australia, and the Arctic. In this review of WRTDS, we document the method’s major advancements, examine its expanding geographic and thematic applications, and summarize its relevance to water-quality management programs and policies worldwide. We also discuss its performance relative to other regression and machine-learning approaches. Finally, we identify key priorities for future development to support the continued evolution of WRTDS as a trusted and practical tool for scientists and managers working to protect and sustain water resources.

Community concerns about water availability vary depending on local economic, regulatory, environmental, and ecological considerations. In water-rich basins, water quality is often the focus of community concerns. As such, understanding community priorities in the context of water quality is crucial for informing scientists working in water-rich basins. In this work, we compiled over 6,500 local news articles (public discourse) and 190 scientific abstracts (scientific discourse) related to water-quality issues in the water-rich Illinois River Basin (ILRB) published between 2018 and 2022. We applied a Structural Topic Model (STM) to identify key water-quality topics within both datasets and explore the variability of newspaper topics geographically across the basin. Prevalent topics in both the public (local news articles) and scientific (abstracts) discourses were agriculture, drinking water quality, PFAS (per- and polyfluoroalkyl substances), and river ecosystem/fish. Topics exclusive to public discourse included water infrastructure, community development, and public water supply, while the scientific discourse focused more heavily on a wider range of agricultural issues. Furthermore, the public discourse varied geographically across the basin. Some topics are correlated with land use or urban/rural divides within the basin, and the frequency of many topics clearly varied across state (political) boundaries. Understanding and quantifying public and scientific discourses related to water-quality are important for scientists and water managers working in the basin to improve communication of critical science to the public.

Occurrences of harmful algal blooms (HABs) in rivers challenge the belief that rivers are not susceptible to HABs because of their short residence times and fluctuating hydrology. Here we present a systematic literature review of predictive and forecasting models for HABs in flowing waters, including rivers, flowing in-stream reservoirs (e.g., run-of-river reservoirs and lock-and-dam systems) and tidal or estuarine systems with riverine processes. The review aimed to understand current and historical modeling approaches for predicting and forecasting river HABs, without restricting to specific taxa, such as cyanobacteria, or modeling endpoints. The review included 162 articles published over nearly 50 years, covering more than 80 rivers worldwide. Eutrophic, non-wadable rivers with in-stream obstruction were commonly modeled, though diverse environmental characteristics were reported. Most articles used algal biomass or chlorophyll as modeling endpoints, with a quarter using novel or unique endpoints. Algal toxins motivated model development in 23% of the articles, however just 5% used algal toxins as an endpoint. Only 6% of the articles modeled benthic HABs; the rest focused on pelagic HABs. There was no standard model used for modeling river HABs. Process-based models were more common (59%) than data-driven approaches (37%), with model formulations ranging from simple to complex, which contrasts with a lake-focused literature review of HAB models that found data-driven models were more common. Models in river settings shared similar input variables as those previously identified for lakes, such as water temperature, nutrients, and light availability. However, streamflow and other transport metrics took prominence in river models compared to lake models. Algal cell physiology (such as growth, predation, and motility) was routinely included as input data or as mathematical formulations in process-based models and these processes were frequently identified as an important predictor by the articles’ authors. Conversely, data-driven models rarely included these processes, instead using predictors related to environmental conditions, such as nutrients, water quality, water temperature, and streamflow. These important proxy predictors have apparent success with modeling overall algal biomass (irrespective of taxa) whereas other factors, such as those related to algal physiology and other biological processes, are likely responsible for more subtle shifts in community composition. These differences highlight the influence of data availability, especially for processes that are difficult, time-consuming, or expensive to measure, on model development and model outcomes, raising questions about the selection of modeling inputs and endpoints. Challenges to advancing river HAB modeling include the lack of site-specific model inputs representing key processes (e.g., photosynthetic parameters and predation rates), overlooked riverine environments like the benthos and side/back-channel areas, lack of information on environmental settings, and poorly reported model performance metrics. This review emphasizes opportunities for advancing river HAB modeling by learning from well-honed estuarine models, supporting current forecasting and operationalization efforts, and developing common datasets for river HAB model development and evaluation.

This report describes a cooperative study by the U.S. Geological Survey and Missouri Department of Natural Resources that evaluated temporal changes in total nitrogen (TN) and total phosphorus (TP) concentrations in the Lower Grand River hydrologic unit. The study focused on trends since 2010, when the basin was designated as a priority drainage basin of the Mississippi River Basin Healthy Watersheds Initiative (MRBI). At three local drainage basins within the Lower Grand hydrological unit (MRBI sites), stream nutrient trends were evaluated using flow-adjusted (FA) TN and TP concentrations for water years 2011 through 2023. FATN concentration trends were not statistically significant for any MRBI site. One site (site 2) showed a statistically significant increasing trend in FATP concentration, indicating a possible increase in phosphorus sources in parts of the basin. Overall, streamflow variability appeared to be the dominant factor affecting nutrient concentrations at MRBI sites. At five regional drainage basins, including the Grand River and nearby rivers with data from 1994 through 2023 (long-term sites), annual flow-normalized (FN) TN and TP concentrations were evaluated for trends before (water years 2000–10) and during (water years 2010–23) the MRBI. For water years 2010 through 2023, annual FNTN and FNTP concentrations decreased in the Grand River, as well as in the Nodaway and Chariton Rivers, which were not targeted by the MRBI. The Grand River (site 9) reversed from increasing to decreasing FNTP concentrations after 2010, with a 26-percent reduction. Annual FNTN and FNTP concentrations also decreased at the Missouri River sites. While nutrient reductions in the Grand River may reflect the effects of implemented conservation practices, similar trends in nearby, nontargeted rivers and the absence of strong decreasing trends at MRBI sites suggest that broader regional factors, instead of or in addition to MRBI efforts, may have contributed to nutrient reductions in the Grand River.

The concentration of chlorophyll a in phytoplankton and periphyton represents the amount of algal biomass. We compiled an 18-year record (2005–2022) of pigment data from water bodies across the United States (US) to support efforts to develop process-based, machine learning, and remote sensing models for prediction of harmful algal blooms (HABs). To our knowledge, this dataset of nearly 84,000 sites and over 1,374,000 pigment measurements is the largest compilation of harmonized discrete, laboratory-extracted chlorophyll data for the US. These data were compiled from the Water Quality Portal (WQP) and previously unpublished U.S. Geological Survey’s National Water Quality Laboratory (NWQL) data. Data were harmonized for reporting units, pigment type, duplicate values, collection depth, site name, negative values, and some extreme values. Across the country, data show great variation by state in sampling frequency, distribution, and methods. Uses for such data include the calibration of models, calibration of field sensors, examination of relationship to nutrients and other drivers, evaluation of temporal trends, and other applications addressing local to national scale concerns.

Measurement of planktonic chlorophyll‐ a —a proxy for algal biomass—in rivers may represent local production or algae transported from upstream, confounding understanding of algal bloom development in flowing waters. We modeled 3 years of chlorophyll‐ a transport through a 394‐km portion of the Illinois River and found that although algal biomass is longitudinally widespread, most net production occurs at river control points in the upper reaches (up to 3.7 Mg chlorophyll‐ a y ⁻¹ km ⁻¹ ). Up to 69% of the algal biomass in the upper river was a result of within‐reach production, with the remainder recruited from headwaters and tributaries. High chlorophyll‐ a measured farther downstream was largely because of transport from source‐area control points, with substantial net losses of algal biomass occurring in the lower river. Modeling the often‐overlooked river transport component is necessary to characterize where, when, and why planktonic algae grow and predict how far and fast they move downstream.