Weighted Regressions On Time, Discharge, And Season Research Articles

A Tale of Two Tributaries: Source Delineation of Chloride in a Distressed Watershed (Grand Lake St. Marys, Ohio)

Various sources of pollution have been assigned as contributing to the Freshwater Salinization Syndrome (FSS), by which water bodies are undergoing concurrent salinization and alkalinization. In many urban areas that receive substantial snowfall, road salt application has been ascribed as the main source of chloride driving the FSS. In rural areas, however, inorganic (e.g. chemical) and organic (e.g. manure) fertilizer applications have been found to be the most important sources of chloride. Herein, we compared daily mean concentrations of chloride over the past decade of time between Coldwater Creek and Chickasaw Creek, two tributaries of Grand Lake St. Marys, the largest reservoir in Ohio. We also used Weighted Regressions on Time, Discharge, and Season (WRTDS) analyses to visualize trends in chloride data and compared chloride vs. nitrate levels to delineate likely sources of chloride for the two streams. We found that road salt application increased over time in both subwatersheds and that 37% and 25% of the chloride could be apportioned to road salt as a source in Coldwater Creek and Chickasaw Creek, respectively. Additionally, in Coldwater Creek, 37% of the chloride was apportioned to animal or septic sources, while 25% was apportioned to inorganic fertilizers, in comparison with 30% and 42% for Chickasaw Creek. Monitoring and assessing salinized streams for both chemical and biological water quality is important, particularly since the FSS has become increasingly linked to declines in water quality (e.g. harmful algal blooms, including recent upticks in Prymnesium parvum blooms) and is expected to be exacerbated with global climate change (e.g. increased precipitation causing increased runoff of chloride from the land).

Potential Climate and Human Water-Use Effects on Water-Quality Trends in a Semiarid, Western U.S. Watershed: Fountain Creek, Colorado, USA

Nutrients, total dissolved solids (TDS), and trace elements affect the suitability of water for human and natural needs. Here, trends in such water-quality constituents are analyzed for 1999–2022 for eight nested monitoring sites in the 24,000 km2 Fountain Creek watershed in Colorado, USA, by using the weighted regressions on time, discharge, and season (WRTDS) methodology. Fountain Creek shares characteristics with other western U.S. watersheds: (1) an expanding but more water-efficient population, (2) a heavy reliance on imported water, (3) a semiarid climate trending towards warmer and drier conditions, and (4) shifts of water from agricultural to municipal uses. The WRTDS analysis found both upward and downward trends in the concentrations of nutrients that reflected possible shifts in effluent management, instream uptake, and water conservation by a watershed population that grew by about 40%. Selenium, other trace elements, and TDS can pose water-quality challenges downstream and their concentrations were found to have a downwards trend. Those trends could be driven by either a warming and drying of the local climate or decreased agricultural irrigation, as both would reduce recharge and subsequent mobilization from natural geologic sources via groundwater discharge. The patterns illustrate how changes in climate and water use may have affected water quality in Fountain Creek and demonstrate the patterns to look for in other western watersheds.

A random forest approach to improve estimates of tributary nutrient loading

Estimating constituent loads from discrete water quality samples coupled with stream discharge measurements is critical for management of freshwater resources. Nutrient loads calculated based on discharge-concentration relationships form the basis of government nutrient load targets and scientific studies of the response of receiving waters to external loads. In this study, a new model is developed using random forests and applied to estimate concentrations and loads of total phosphorus, dissolved phosphorus, total nitrogen, and chloride, using data from 17 tributaries to Lake Champlain monitored from 1992 to 2021. I benchmark this model against one of the most widespread models currently used to estimate nutrient loads, Weighted Regressions on Time, Discharge, and Season (WRTDS). The random forest model outperformed both the base WRTDS model and an extension of the WRTDS model using Kalman filtering in the great majority of cases, likely due to the inclusion of rate-of-change in discharge and antecedent discharge over different leading windows as predictors, and to the flexibility of the random forest to model predictor-response relationships. The random forest also had useful visualization capabilities which provided important process insights. WRTDS remains a useful model for many applications, but this study represents a promising new approach for load estimation which can be applied easily to existing datasets, and which is easy to customize for different applications.

Recent, widespread nitrate decreases may be linked to persistent dissolved organic carbon increases in headwater streams recovering from past acidic deposition

Long-term monitoring of water quality responses to natural and anthropogenic perturbation of watersheds informs policies for managing natural resources. Dissolved organic carbon (DOC) and nitrate (NO3−) in streams draining forested landscapes provide valuable information on ecosystem function due to their biogeochemical reactivity and solubility in water. Here we evaluate a 20-year record (2001−2021) of biweekly stream-water samples (n > 3000) and continuous discharge in three forested catchments in the Adirondack region of New York to investigate and interpret long-term trends in DOC and NO3− concentrations. Results from the intensively monitored catchments were compared with data from synoptic surveys of streams throughout the Adirondack region. A weighted regressions on time, discharge, and season (WRTDS) model, used to estimate daily flow-normalized concentrations, determined that DOC increased by ~30 to 50 % while NO3− decreased by ~50 to 70 % over the study period. The large amount of data from catchments with different soil properties permitted us to assess the relative effects of hydrology, season, and land cover factors on temporal trends in DOC and NO3− concentrations. We found weak evidence of climatic forcing of long-term increases in DOC, and instead contend that declining ionic strength in precipitation linked to declining anthropogenic acid deposition is driving DOC trends in stream waters. Nitrate concentrations were more variable but clearly decreased in recent years possibly related to declining N deposition. The recent increase in DOC:NO3− in all catchments indicates a major shift in stream stoichiometry that reflects changes in ecosystem functioning that may have important biogeochemical implications for terrestrial as well as aquatic ecosystems.

Твердый сток реки Дон и поступление взвеси в дельту при нагонах: статистическое моделирование и сопоставление в период маловодья

Statistical modeling of the Don River solid runoff based on water discharge and turbidity measurements obtained at the hydrological station in the village of Razdorskaya for the twelve-year period 2009–2020 was carried out. The WRTDS (Weighted Regressions on Time, Discharge, and Season) and WRTDSKalman (WRTDS with Kalman filtering) methods were applied. The developed statistical model is aimed at solving the problem of the imbalance between the regularity of collecting data on water discharge and data on the concentration of suspended matter by “restoring” the concentration values on days without measurements based on data on the most “similar” days with measurements in terms of time, discharge and season and does not claim to describe a relationship between the concentration of the constituent of interest and discharge. The quality of the developed statistical model and its modification were checked. The average daily concentrations and fluxes of suspended matter were compared with estimates of the volumes of suspended material deposited during periods of recurring strong surge phenomena. A comparative assessment of sea and river factors contributions to the transport and sedimentation of suspended matter in the Don River delta was fulfilled.

Nutrient and suspended solid concentrations, loads, and yields in rivers across the Lake Winnipeg Basin: A twenty year trend assessment

Study regionThe Lake Winnipeg Basin in the northern Great Plains of North America Study focusAssessment of trends in total nitrogen (TN), total phosphorus (TP) and total suspended solids (TSS) for 18 river stations in the Lake Winnipeg Basin for the period of 1996–2016 using a Weighted Regressions on Time, Discharge and Season (WRTDS) modeling approach. New hydrological insights for the regionWe observed rapidly increasing concentrations, loads, and yields of TN, TP and TSS at most of the evaluated river monitoring stations in the eastern half of the basin. In contrast, nutrient and suspended solid loads tended to exhibit decreasing or stationary trends at most of the more western stations. Trends in nutrients and suspended solids typically corresponded to discharge, particularly in the Red-Assiniboine subdrainage where rapidly increasing nutrient loads were almost exclusively associated with runoff patterns. Our findings will serve as a baseline against which future trend assessments can be compared. Moreover, our findings suggest that land management practices aimed at reducing nutrient loads to Lake Winnipeg should be prioritized towards the rivers of the eastern subdrainages of the Lake Winnipeg basin.

QUADICA: water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany

Abstract. Environmental data are the key to defining and addressing water quality and quantity challenges at the catchment scale. Here, we present the first large-sample water quality data set for 1386 German catchments covering a large range of hydroclimatic, topographic, geologic, land use, and anthropogenic settings. QUADICA (water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany) combines water quality with water quantity data, meteorological and nutrient forcing data, and catchment attributes. The data set comprises time series of riverine macronutrient concentrations (species of nitrogen, phosphorus, and organic carbon) and diffuse nitrogen forcing data (nitrogen surplus, atmospheric deposition, and fixation) at the catchment scale. Time series are generally aggregated to an annual basis; however, for 140 stations with long-term water quality and quantity data (more than 20 years), we additionally present monthly median discharge and nutrient concentrations, flow-normalized concentrations, and corresponding mean fluxes as outputs from Weighted Regressions on Time, Discharge, and Season (WRTDS). The catchment attributes include catchment nutrient inputs from point and diffuse sources and characteristics from topography, climate, land cover, lithology, and soils. This comprehensive, freely available data collection with a large spatial and temporal coverage can facilitate large-sample data-driven water quality assessments at the catchment scale as well as mechanistic modeling studies. QUADICA is available at https://doi.org/10.4211/hs.0ec5f43e43c349ff818a8d57699c0fe1 (Ebeling et al., 2022b) and https://doi.org/10.4211/hs.88254bd930d1466c85992a7dea6947a4 (Ebeling et al., 2022a).

Trends in phosphorus fluxes are driven by intensification of biosolids applications in the Upper St. Johns River Basin (Florida, United States)

Canion A, Hoge V, Hendrickson J, Jobes T, Dobberfuhl D. 2022. Trends in phosphorus fluxes are driven by intensification of biosolids applications in the Upper St. Johns River Basin (Florida, United States). Lake Reserv Manage. XX:XX–XX. Biosolids are beneficially used in agricultural production, but the potential for nutrient enrichment, primarily phosphorus (P), in runoff water remains a concern. This study provides strong correlative evidence that intensified Class B biosolids applications led to increases in total P (TP) and total nitrogen (TN) fluxes in the Upper St. Johns River Basin (USJRB). In 2013, new state regulations resulted in the elimination of Class B biosolids applications in 3 watersheds encompassing most of southern Florida. Most of the applications from these watersheds were shifted into the USJRB, which received 78% of statewide Class B biosolids applications by 2019. Weighted regressions on time, discharge, and season (WRTDS) were used to evaluate the relationship between long-term (1995–2020) trends in tributary TP and TN concentrations and fluxes and the timing and magnitude of biosolids applications in 8 USJRB watersheds. No significant land use change occurred that could account for water quality trends. Flow-normalized concentrations and fluxes were generally stable from 1995 to 2012, but after intensification of applications in 2013, significant increases occurred in 6 and 4 watersheds for TP and TN, respectively. P fluxes increased by 0.9–16.4 metric tons (MT; 40–200%) and N fluxes increased by 1.6–19.7 MT (5–20%). The magnitude of P and N flux increases were between 0.5% and 2.0% of land-applied biosolids P and N, which suggests that small losses of P and N from the landscape were required to produce the observed trends.

Long-term riverine nitrogen dynamics reveal the efficacy of water pollution control strategies

Identification of long-term water quality trends in response to watershed anthropogenic interventions is crucial for developing and adapting water pollution control strategies. This study represents the first use of the Weighted Regressions on Time, Discharge, and Season (WRTDS) model to evaluate trends and sources of riverine nitrogen (N) levels over the 1980–2019 period in the Yongan River watershed of eastern China. The WRTDS model showed satisfactory accuracies for predicting daily riverine total N (TN), NH4+ and NO3− concentrations/loads (R2 > 0.55, n = 366). Modeled flow-normalized riverine NH4+ concentration increased by 789% from 1980 to 2009 and then decreased by 63% in 2010–2019. This changing trend for riverine NH4+ concentration was mainly attributed to a 43% decrease of wastewater NH4+ discharge load in 2010–2019 due to establishment of three new WWTPs in urban areas and enhanced rural domestic sewage collection/treatment. Although chemical N fertilizer use decreased by 49% and domestic animal numbers decreased by 73% in 2000–2019, flow-normalized riverine TN and NO3− concentrations progressively increased by 161% and 232% in 1980–2019, respectively. The paradox between decreasing N inputs and increasing riverine TN/NO3− concentrations is attributed to inputs of legacy N from soil and groundwater. This is supported by the 3.8-fold increase of riverine NO3− concentration in 1980–2019 (86% increase in 2000–2019) following 10-days with no-precipitation (representing groundwater contributions to baseflow) and a 4.1-fold increase of riverine NO3− concentration in 1980–2019 (91% increase in 2000–2019) following the first rainstorm after 10-days of no-precipitation (representing soil flushing). These results document that point-source pollution control efforts were effective, whereas benefits from nonpoint-source pollution control were masked by inputs from legacy N pollution. The WRTDS model was demonstrated to be a useful tool for assessing long-term riverine N pollution dynamics and sources, thereby providing decision-makers with critical information to guide watershed N pollution control strategies.

Insights from an Evaluation of Nitrate Load Estimation Methods in the Midwestern United States

This study investigated the accuracy and suitability of several methods commonly used to estimate riverine nitrate loads at eight watersheds located southwest of Lake Erie in the Midwestern United States. This study applied various regression methods, including a regression estimator with five, six, and seven parameters, an estimator enhanced by composite, triangular, and rectangular error corrections with residual and proportional adjustment methods, the weighted regressions on time, discharge, and season (WRTDS) method, and a simple linear interpolation (SLI) method. Daily discharge and nitrate concentration data were collected by the National Center for Water Quality Research. The methods were compared with subsampling frequencies of 6, 12, and 24 times per year for daily concentrations, daily loads, and annual loads. The results indicate that combinations of the seven-parameter regression method with composite residual and rectangular residual adjustments provided the best estimates under most of the watershed and sampling frequency conditions. On average, WRTDS was more accurate than the regression models alone, but less accurate than those models enhanced by residual adjustments, except for the most urbanized watershed, Cuyahoga. SLI was the most accurate in the Vermilion and Maumee watersheds. The results also provide some information about the effects of rating curve shape and slope, land use, and record length on model performance.

Lake Erie tributary nutrient trend evaluation: Normalizing concentrations and loads to reduce flow variability

Establishing tributary load (i.e., the mass exported over a period of time) targets to reduce anthropogenic nutrient inputs to receiving waters — and thus eutrophication — is a common mitigation strategy in freshwater and coastal ecosystems. However, detecting and quantifying trends can be difficult because annual precipitation strongly influences tributary flow (e.g., average daily stream discharge). This may obscure trends as wet years tend to produce high tributary loads despite management activities to reduce nutrient export, and dry years typically generate low loads, even without management of nutrients. Furthermore, flow and nutrient concentrations are often correlated. Earlier efforts to reduce the effect of flow variability on tributary nutrient assessment were limited by computational and methodological constraints, until the weighted regressions on time, discharge, and season (WRTDS) method was introduced in 2010. Here we use WRTDS to assess nutrient concentration and load changes from 1982 to 2018 in three tributaries to the western basin of Lake Erie, of the Laurentian Great Lakes. Generally, trends revealed by flow-normalization do not contradict those of non-normalized metrics; however flow-normalization made the patterns more perceptible than in non-normalized metrics and reduced the influence of a particularly wet or dry period at the end of records on long-term trend analysis. We demonstrate that using WRTDS for flow-normalization removed the noise arising from annual precipitation variability and makes tributary nutrient trend evaluation more straightforward.

An approach for decomposing river water-quality trends into different flow classes

A number of statistical approaches have been developed to quantify the overall trend in river water quality, but most approaches are not intended for reporting separate trends for different flow conditions. We propose an approach called FN2Q, which is an extension of the flow-normalization (FN) procedure of the well-established WRTDS (“Weighted Regressions on Time, Discharge, and Season”) method. The FN2Q approach provides a daily time series of low-flow and high-flow FN flux estimates that represent the lower and upper half of daily riverflow observations that occurred on each calendar day across the period of record. These daily estimates can be summarized into any time period of interest (e.g., monthly, seasonal, or annual) for quantifying trends. The proposed approach is illustrated with an application to a record of total nitrogen concentration (632 samples) collected between 1985 and 2018 from the South Fork Shenandoah River at Front Royal, Virginia (USA). Results show that the overall FN flux of total nitrogen has declined in the period of 1985–2018, which is mainly attributable to FN flux decline in the low-flow class. Furthermore, the decline in the low-flow class was highly correlated with wastewater effluent loads, indicating that the upgrades of treatment technology at wastewater treatment facilities have likely led to water-quality improvement under low-flow conditions. The high-flow FN flux showed a spike around 2007, which was likely caused by increased delivery of particulate nitrogen associated with sediment transport. The case study demonstrates the utility of the FN2Q approach toward not only characterizing the changes in river water quality but also guiding the direction of additional analysis for capturing the underlying drivers. The FN2Q approach (and the published code) can easily be applied to widely available river monitoring records to quantify water-quality trends under different flow conditions to enhance understanding of river water-quality dynamics.

Comparison of Long Short-Term Memory and Weighted Regressions on Time, Discharge, and Season Models for Nitrate-N Load Estimation

The long short-term memory (LSTM) model has been widely used for a broad range of applications entailing the estimation of variables in different fields to improve water quality management in rivers. The main objectives of this study are (1) to develop a novel LSTM-based model for the estimation of nitrate-N loads, which adversely affect water resources, and (2) to evaluate the performance of the model by comparing it with that of Monte Carlo sub-sampling and the weighted regressions on time discharge and season (WRTDS) model. We evaluated the model performance using various numbers of hidden layers, ranging from one to four, in the LSTM model to determine the appropriate number of hidden layers; furthermore, we applied the sampling frequencies of 6, 12, and 24 to assess their impact. Seven polluted river basins in the United States were used for analysis, and the relative root mean squared error (rRMSE) and the mean percentage error (MPE) metrics were applied for the validation of the model estimates. The proposed model achieved accurate nitrate-N load estimates using three to four hidden layers, and improved model performance was observed when the sampling frequency was increased. The differences among the results obtained using the LSTM model were examined based on a binning technique via a log-log plot of nitrate-N concentration against discharge. The binning analysis showed that the slope obtained from the average rates of discharge and low discharge values apparently influenced the estimates. Furthermore, box plot analyses of the statistical indices such as rRMSE and MPE demonstrate that the LSTM model seems to exhibit better performance than the WRTDS model. The results of the examination demonstrate that the LSTM model may be a good alternative with regard to estimating nitrate-N loads for the control of water quality constituents.

River Water‐Quality Concentration and Flux Estimation Can be Improved by Accounting for Serial Correlation Through an Autoregressive Model

AbstractAccurate quantification of riverine water‐quality concentration and flux is challenging because monitoring programs typically collect concentration data at lower frequencies than discharge data. Statistical methods are often used to estimate concentration and flux on days without observations. One recently developed approach is the Weighted Regressions on Time, Discharge, and Season (WRTDS), which has been shown to provide among the most accurate estimates compared to other common methods. The main objective of this work was to improve WRTDS estimation by accounting for the autocorrelation structure of model residuals using the first‐order autoregressive model (AR1). This modified approach, called WRTDS‐Kalman Filter (WRTDS‐K), was compared with WRTDS for six constituents including nitrate‐plus‐nitrite (NOx), total phosphorus, total Kjeldahl nitrogen, soluble reactive phosphorus, suspended sediment, and chloride. Near‐daily concentration records at nine sites were used to generate subsets through Monte Carlo sampling for five different sampling scenarios. Results show that WRTDS‐K provided generally better daily estimates of concentration and flux than WRTDS under these sampling scenarios for all constituents, especially NOx. The degree of improvement is strongly affected by the underlying sampling scenario, with WRTDS‐K gaining more advantage when more samples are available, and hence more residuals can be exploited. The performance of WRTDS‐K depends on the AR1 coefficient (ρ) and that relationship varies with constituents and sampling scenarios. These results provided recommendations on the optimal ρ for each constituent and sampling scenario. Overall, WRTDS‐K has the potential for broad applications to monitoring records elsewhere, as demonstrated by a pilot application to Chesapeake Bay tributaries.

Comment on “Suspended-sediment concentrations and loads in the lower Mississippi and Atchafalaya Rivers decreased by half between 1980 and 2015” by Scott V. Mize, Jennifer C. Murphy, Timothy H. Diehl, and Dennis K. Demcheck, Journal of Hydrology 564 (2018) 1–11

The author read with much interest the publication by Mize et al. (2018) on the decrease in suspended-sediment concentrations and loads in the lower Mississippi and Atchafalaya Rivers between 1980 and 2015. Researchers interested in sediment dynamics in the lower Mississippi and Atchafalaya Rivers also should be aware that sediment data previously reported for the lower Mississippi-Atchafalaya River Basin by the U.S. Geological Survey (USGS) and U.S. Army Corps of Engineers (USACE) were affected by a systematic error in the fines fraction (particle sizes less than 0.063 millimeter) of the suspended-sediment concentration. This error affected results reported from October 1973 through February 2015 for two sites on the main stem of the Mississippi River, three sites on the Atchafalaya River, one site on the Old River Outflow Channel, and one site on the Red River. The USGS and USACE identified the error and have revised the results for samples collected from October 1989 through February 2015, as reported by Norton et al. (2019a) and Norton et al. (2019b). The significant conclusions of Mize et al. (2018) and sediment loads modeled using the Weighted Regressions on Time, Discharge, and Season (WRTDS) method were unaffected by the systematic error because the models used data from unaffected sites; however, the background information provided in tables 1 and 2 of Mize et al. (2018) included references to several previous studies that contained results affected by the error. This comment paper identifies which references in Mize et al. (2018) were affected by the error and alerts readers to implications of the data revision.

Estimation Bias in Water-Quality Constituent Concentrations and Fluxes: A Synthesis for Chesapeake Bay Rivers and Streams

Flux quantification for riverine water-quality constituents has been an active area of research. Statistical approaches are often employed to make estimation for days without observations. One such approach is the Weighted Regressions on Time, Discharge, and Season (WRTDS) method. While WRTDS has been used in many investigations, there is a general lack of effort to identify factors that influence its estimation bias. This work was aimed to (1) synthesize and compare WRTDS estimation bias for constituent concentrations and fluxes for rivers and streams in the Chesapeake Bay watershed (including headwater sites) and (2) identify controlling factors from five broad categories (watershed size, sampling practice, concentration and discharge conditions, land use, and geology). Five major constituents were considered, namely, suspended sediment (SS), total phosphorus (TP), total nitrogen (TN), orthophosphate (PO4), and nitrate-plus-nitrite (NOx). For both concentration and flux, estimation bias follows the general order of SS > TP > PO4 > TN ≈ NOx. Median TN and NOx bias statistics were near zero, with an equal distribution of small positive and negative bias. TP, PO4, and SS each showed a median positive bias across sites of less than 18% for flux and less than 7% for concentration. Particulate constituents, especially SS, tend to have larger bias at sites with smaller sampling frequencies, shorter sampling record lengths, and smaller watershed sizes. Results of multivariate models showed that both flux and concentration biases are most affected by concentration and discharge variabilities and the length of concentration record. In comparison, flux bias of particulate constituents is more affected by flow variability, whereas flux bias of dissolved constituents is more affected by concentration variability. Moreover, analysis using classification and regression trees provided additional information on how the factors affected flux bias: when all site-constituent combinations are considered, large flux biases are more likely associated with sites that have large concentration and discharge variabilities, small lengths of concentration record, and small sampling frequencies. These results may be useful for identifying sites with large biases, modifying monitoring practice at existing sites to reduce those biases, and choosing new monitoring locations in the Chesapeake watershed and beyond.

Tracking changes in nutrient delivery to western Lake Erie: Approaches to compensate for variability and trends in streamflow

Abstract Tracking changes in stream nutrient inputs to Lake Erie over multidecadal time scales depends on the use of statistical methods that can remove the influence of year-to-year variability of streamflow but also explicitly consider the influence of long-term trends in streamflow. The methods introduced in this paper include an extended version of Weighted Regressions on Time, Discharge, and Season (WRTDS) modeling that explicitly considers nonstationary streamflow by incorporating information on changes in the frequency distribution of daily measured streamflow (discharge) over time. Soluble reactive phosphorus (SRP) trends in annual flow-normalized fluxes (loads) at five long-term monitoring sites in the western Lake Erie drainage basin show increases of 109 to 322% over the period 1995 to 2015. About one-third of the increase appears attributable to increasing discharge trends, while the remaining two-thirds appears to be driven by changes in concentration versus discharge relationships reflecting higher concentrations for any given discharge during recent years. Trends in total phosphorus and three nitrogen parameters (total nitrogen, nitrate-nitrite, and total Kjeldahl nitrogen) at the 10 sites analyzed were much less pronounced, and commonly show decreases in concentration-discharge relationships accompanied by increases in discharge, resulting in little net change in total flux. Trends in monthly SRP fluxes and discharge, dissolved versus particulate fractions of nutrients, and N:P flux ratios were also evaluated. The methods described here provide tools to more clearly discern the effectiveness of nutrient-control strategies and can serve as ongoing measures of progress, or lack of progress, towards nutrient-reduction goals.

Overlapping anthropogenic effects on hydrologic and seasonal trends in DOC in a surface water dependent water utility

Drinking water supplies are increasingly affected by overlapping anthropogenic global change processes. As a key currency of ecosystem function in aquatic ecosystems, dissolved organic carbon (DOC) concentration and composition is sensitive to many global change processes. However, DOC must also be removed to avoid the production of harmful disinfection byproducts as water is processed. Thus, understanding the effects of global change processes on the seasonal and long-term dynamics of DOC composition and concentration is critical for ensuring the sustainability of drinking water supplies. To understand these dynamics, we analyzed a novel 11-year time series of stream water DOC concentration and composition using Weighted Regressions on Time Discharge and Season (WRTDS) to understand the influences of co-occurring changes in climate and atmospheric deposition. We also discuss the implications for water supply provision and management. We found that, during our study period, overlapping global change processes in the watershed had the net effect of increasing the DOC aromaticity, as measured by SUVA254, at moderate to high discharge levels during the late spring and early summer and the autumn and early winter. However, changes in DOC concentration were more dynamic and we observed both increasing and decreasing trends depending on season and hydrologic state. During summer, at low to moderate flow levels we observed a significant (p < 0.05) increase in DOC concentration. During autumn, at moderate to high flow levels we observed a significant (p < 0.05) decrease in DOC concentration and an increase in SUVA254. For drinking water providers, our results suggest that close monitoring of source waters must be coupled with the development of plans accounting for season- and hydrology-specific long-term changes.

Four decades of water quality change in the upper San Francisco Estuary

Quantitative descriptions of chemical, physical, and biological characteristics of estuaries are critical for developing an ecological understanding of drivers of change. Historical trends and relationships between key species of dissolved inorganic nitrogen (ammonium, nitrate/nitrite, total) from the Delta region of the San Francisco Estuary were modelled with an estuarine adaptation of the Weighted Regressions on Time, Discharge, and Season (WRTDS). Analysis of flow-normalized data revealed trends that were different from those in the observed time-series. Flow-normalized data exhibited changes in magnitude and even reversal of trends relative to the observed data. Modelled trends demonstrated that nutrient concentrations were on average higher in the last twenty years relative to the earlier periods of observation, although concentrations have been slowly declining since the mid-1990s and early 2000s. We further describe mechanisms of change with two case studies that evaluated 1) downstream changes in nitrogen following upgrades at a wastewater treatment plant, and 2) interactions between biological invaders, chlorophyll, macro-nutrients (nitrogen and silica), and flow in Suisun Bay. WRTDS results for ammonium trends showed a distinct signal as a result of upstream wastewater treatment plant upgrades, with specific reductions observed in the winter months during low-flow conditions. Results for Suisun Bay showed that chlorophyll a production in early years was directly stimulated by flow, whereas the relationship with flow in later years was indirect and influenced by grazing pressure. Although these trends and potential causes of change have been described in the literature, results from WRTDS provided an approach to test alternative hypotheses of spatiotemporal drivers of nutrient dynamics in the Delta.

Suspended-sediment concentrations and loads in the lower Mississippi and Atchafalaya rivers decreased by half between 1980 and 2015

The Weighted Regressions on Time, Discharge, and Season (WRTDS) model was used to derive estimates of suspended-sediment concentration (SSC) and suspended-sediment load (SSL), their dependence on discharge, and their trends with confidence intervals, for one site each on the lowermost Mississippi and Atchafalaya Rivers. The WRTDS model reduces uncertainty in SSCs related to variable streamflow conditions. Flow-normalized SSCs in each river were similar, and decreased from about 260 mg/L to 130 mg/L from 1980 through 2015; combined annual SSL in the two rivers decreased from about 200 Megatons per year (MT/y) to about 100 MT/y. Declines in SSC and SSL were more gradual from 2005 through 2015 and show signs of stabilizing. Our estimates of SSL in 2015 differ markedly from several recently published estimates of current and projected future Mississippi River SSLs, which were generally around 200 MT/y. However, these values came mostly from a different site upstream on the Mississippi River. The relationship between SSC and streamflow differed in an important way between the two rivers. SSC increased as streamflow increased for the entire range of observed streamflow in the Atchafalaya River. In the Mississippi River, SSC followed the same pattern during low and moderate streamflow but decreased at the highest streamflow that tended to occur between January and July. Since much of the water flowing in the Atchafalaya originates from the Mississippi River, the difference suggests a within-basin source of suspended sediment for the Atchafalaya River that is absent in the lower Mississippi River. These findings have important implications for the restoration of deltaic wetlands in coastal Louisiana. Accurate estimates of the SSL available in each river are crucial for understanding how effective diversions of river water into adjacent estuaries will be in sustaining these wetlands. Our study demonstrates that there might be far less sediment available than previously reported. Further, the difference in the relationship between SSC and streamflow in the two rivers is highly relevant to the ongoing discussion of coastal restoration strategies because the delta building that is occurring at the mouth of the Atchafalaya River is frequently used as a model of what could be expected with controlled diversions in the lower Mississippi River delta. The differences in the SSC behavior with changes in streamflow between the two rivers needs to be considered when results from the Atchafalaya River system are projected to those of the Mississippi River.