Decreasing trends in total suspended solids and cumulative effects of nonpoint source projects in the Cuyahoga River Watershed, OH.

Using the US EPA's Grants Reporting and Tracking System (GRTS), we test if completion of best management practices (BMPs) through the Clean Water Act Section (§)319 National Nonpoint Source Program was associated with a decreasing trend in total suspended solids (TSS) load (metric tons/year). The study area chosen had 21 completed projects in the Cuyahoga River watershed in northeastern Ohio from 2000 to 2018. The §319 projects ranged from dam removal, floodplain/wetland restoration to stormwater projects. There was an overall decreasing trend in TSS loads. We identified three phases of project implementation and completion, where phase 1 had ongoing projects, but none completed (2000-2004). The steepest decrease in loads, identified as phase 2 (2005-2011), was associated with completion of low-head dam modification and removal projects on the mainstem of the Cuyahoga River. A likely decreasing trend was associated with projects completed in the tributaries, such as natural channel design restoration and stormwater green infrastructure (phase 3). Pairing sediment reduction estimates from projects with the river's flow normalized TSS loading trend, we estimated that the §319 effort may account for a small fraction of the TSS load reduction. Other stream restoration projects (non-§319) have also been done in the Cuyahoga watershed by other organizations. However, trying to compile these other projects is challenging in larger watersheds having multiple municipalities, agencies, and nonprofits doing restoration without better coordinated record keeping and monitoring. While a decreasing trend in a pollutant load is a desirable water quality outcome, determining what contributed to that trend remains difficult.

Linking Water Quality to Drinking Water Treatment Costs Using Time Series Analysis: Examining the Effect of a Treatment Plant Upgrade in Ohio.

We estimate a cost function for a water treatment plant in Ohio to assess the avoided-treatment costs resulting from improved source water quality. Regulations and source water concerns motivated the treatment plant to upgrade its treatment process by adding a granular activated carbon building in 2012. The cost function uses daily observations from 2013 to 2016; this allows us to compare the results to a cost function estimated for 2007-2011 for the same plant. Both models focus on understanding the relationship between treatment costs per 1,000 gallons (per 3.79 m3) of produced drinking water and predictor variables such as turbidity, pH, total organic carbon, deviations from target pool elevation, final production, and seasonal variables. Different from the 2007-2011 model, the 2013-2016 model includes a harmful algal bloom toxin variable. We find that the new treatment process leads to a different cost model than the one that covers 2007-2011. Both total organic carbon and algal toxin are important drivers for the 2013-2016 treatment costs. This reflects a significant increase in cyanobacteria cell densities capable of producing toxins in the source water between time periods. The 2013-2016 model also reveals that positive and negative shocks to treatment costs affect volatility, the changes in the variance of costs through time, differently. Positive shocks, or increased costs, lead to higher volatility compared to negative shocks, or decreased costs, of similar magnitude. After quantifying the changes in treatment costs due to changes in source water quality, we discuss how the study results inform policy-relevant decisions.

Increasingly severe cyanobacterial blooms and deep water hypoxia coincide with warming water temperatures in reservoirs.

Cyanobacterial blooms are expected to intensify and become more widespread with climate change and sustained nutrient pollution, subsequently increasing threats to lentic ecosystems, water quality, and human health. However, little is known about their rates of change because long-term monitoring data are rare, except for some well-studied individual lakes, which typically are large and broadly dispersed geographically. Using monitoring data spanning 1987-2018 for 20 temperate reservoirs located in the USA, we found that cyanobacteria cell densities mostly posed low-to-moderate human health risks until 2003-2005, after which cell densities rapidly increased. Increases were greatest in reservoirs with extensive agriculture in their watersheds, but even those with mostly forested watersheds experienced increases. Since 2009, cell densities posing high human health risks have become frequent with 75% of yearly observations exceeding 100,000 cells ml-1 , including 53% of observations from reservoirs with mostly forested watersheds. These increases coincided with progressively earlier and longer summer warming of surface waters, evidence of earlier onset of stratification, lengthening durations of deep-water hypoxia, and warming deep waters in non-stratifying reservoirs. Among years, higher cell densities in stratifying reservoirs were associated with greater summer precipitation, warmer June surface water temperatures, and higher total Kjeldahl nitrogen concentrations. These trends are evidence that expected increases in cyanobacterial blooms already are occurring as changing climate conditions in some regions increasingly favor their proliferation. Consequently, their negative effects on ecosystems, human health, and socioeconomic wellbeing could increase and expand if warming trends and nutrient pollution continue.

DNA metabarcoding effectively quantifies diatom responses to nutrients in streams.

Nutrient pollution from human activities remains a common problem facing stream ecosystems. Identifying ecological responses to phosphorus and nitrogen can inform decisions affecting the protection and management of streams and their watersheds. Diatoms are particularly useful because they are a highly diverse group of unicellular algae found in nearly all aquatic environments and are sensitive responders to increased nutrient concentrations. Here, we used DNA metabarcoding of stream diatoms as an approach to quantifying effects of total phosphorus (TP) and total nitrogen (TN). Threshold indicator taxa analysis (TITAN) identified operational taxonomic units (OTUs) that increased or decreased along TP and TN gradients along with nutrient concentrations at which assemblages had substantial changes in the occurrences and relative abundances of OTUs. Boosted regression trees showed that relative abundances of gene sequence reads for OTUs identified by TITAN as low P, high P, low N, or high N diatoms had strong relationships with nutrient concentrations, which provided support for potentially using these groups of diatoms as metrics in monitoring programs. Gradient forest analysis provided complementary information by characterizing multi-taxa assemblage change using multiple predictors and results from random forest models for each OTU. Collectively, these analyses showed that notable changes in diatom assemblage structure and OTUs began around 20 µg TP/L, low P diatoms decreased substantially and community change points occurred from 75 to 150 µg/L, and high P diatoms became increasingly dominant from 150 to 300 µg/L. Diatoms also responded to TN with large decreases in low N diatoms occurring from 280 to 525 µg TN/L and a transition to dominance by high N diatoms from 525-850 µg/L. These diatom responses to TP and TN could be used to inform protection efforts (i.e., anti-degradation) and management goals (i.e., nutrient reduction) in streams and watersheds. Our results add to the growing support for using diatom metabarcoding in monitoring programs.

EXPLORING NON-TRADITIONAL PARTICIPATION AS AN APPROACH TO MAKE WATER QUALITY TRADING MARKETS MORE EFFECTIVE.

Water quality trading (WQT) has potential to be a low-cost means for achieving water quality goals. WQT allows regulated wastewater treatment plants (WWTPs) facing discharge limits the flexibility to either reduce their own discharge or purchase pollution control from other WWTPs or nonpoint sources (NPSs) such as agricultural producers. Under this limited scope, programs with NPSs have been largely unsuccessful at meeting water quality goals. The decision to participate in trading depends on many factors including the pollution control costs, uncertainty in pollution control, and discharge limits. Current research that focuses on making WQT work tends to identify how to increase participation by traditional traders such as WWTPs and agricultural producers. As an alternative, but complementary approach, we consider whether augmenting WQT markets with non-traditional participants would help increase the number of trades. Determining the economic incentives for these potential participants requires the development of novel benefit functions requiring not only economic considerations, but also accounting for ecological and engineering processes. Existing literature on non-traditional participants in environmental markets tends to center on air quality and only increasing citizen participation as buyers. Here, we consider the issues for broadening participation (both buyers and sellers) in WQT and outline a multidisciplinary approach to begin evaluating feasibility.

IMPROVING PREDICTIVE MODELS OF IN-STREAM PHOSPHORUS CONCENTRATION BASED ON NATIONALLY-AVAILABLE SPATIAL DATA COVERAGES.

Spatial data are playing an increasingly important role in watershed science and management. Large investments have been made by government agencies to provide nationally-available spatial databases; however, their relevance and suitability for local watershed applications is largely unscrutinized. We investigated how goodness of fit and predictive accuracy of total phosphorus (TP) concentration models developed from nationally-available spatial data could be improved by including local watershed-specific data in the East Fork of the Little Miami River, Ohio, a 1290 km2 watershed. We also determined whether a spatial stream network (SSN) modeling approach improved on multiple linear regression (nonspatial) models. Goodness of fit and predictive accuracy were highest for the SSN model that included local covariates, and lowest for the nonspatial model developed from national data. Septic systems and point source TP loads were significant covariates in the local models. These local data not only improved the models but enabled a more explicit interpretation of the processes affecting TP concentrations than more generic national covariates. The results suggest that SSN modeling greatly improves prediction and should be applied when using national covariates. Including local covariates further increases the accuracy of TP predictions throughout the studied watershed; such variables should be included in future national databases, particularly the locations of septic systems.

High methane emissions from a midlatitude reservoir draining an agricultural watershed.

Reservoirs are a globally significant source of methane (CH4), although most measurements have been made in tropical and boreal systems draining undeveloped watersheds. To assess the magnitude of CH4 emissions from reservoirs in midlatitude agricultural regions, we measured CH4 and carbon dioxide (CO2) emission rates from William H. Harsha Lake (Ohio, U.S.A.), an agricultural impacted reservoir, over a 13 month period. The reservoir was a strong source of CH4 throughout the year, emitting on average 176 ± 36 mg C m(-2) d(-1), the highest reservoir CH4 emissions profile documented in the United States to date. Contrary to our initial hypothesis, the largest CH4 emissions were during summer stratified conditions, not during fall turnover. The river-reservoir transition zone emitted CH4 at rates an order of magnitude higher than the rest of the reservoir, and total carbon emissions (i.e., CH4 + CO2) were also greater at the transition zone, indicating that the river delta supported greater carbon mineralization rates than elsewhere. Midlatitude agricultural impacted reservoirs may be a larger source of CH4 to the atmosphere than currently recognized, particularly if river deltas are consistent CH4 hot spots. We estimate that CH4 emissions from agricultural reservoirs could be a significant component of anthropogenic CH4 emissions in the U.S.A.

Using DNA metabarcoding to characterize national scale diatom-environment relationships and to develop indicators in streams and rivers of the United States.

Recent advancements in DNA techniques, metabarcoding, and bioinformatics could help expand the use of benthic diatoms in monitoring and assessment programs by providing relatively quick and increasingly cost-effective ways to quantify diatom diversity in environmental samples. However, such applications of DNA-based approaches are relatively new, and in the United States, unknowns regarding their applications at large scales exist because only a few small-scale studies have been done. Here, we present results from the first nationwide survey to use DNA metabarcoding (rbcL) of benthic diatoms, which were collected from 1788 streams and rivers across nine ecoregions spanning the conterminous USA. At the national scale, we found that diatom assemblage structure (1) was strongly associated with total phosphorus and total nitrogen concentrations, conductivity, and pH and (2) had clear patterns that corresponded with differences in these variables among the nine ecoregions. These four variables were strong predictors of diatom assemblage structure in ecoregion-specific analyses, but our results also showed that diatom-environment relationships, the importance of environmental variables, and the ranges of these variables within which assemblage changes occurred differed among ecoregions. To further examine how assemblage data could be used for biomonitoring purposes, we used indicator species analysis to identify ecoregion-specific taxa that decreased or increased along each environmental gradient, and we used their relative abundances of gene reads in samples as metrics. These metrics were strongly correlated with their corresponding variable of interest (e.g., low phosphorus diatoms with total phosphorus concentrations), and generalized additive models showed how their relationships compared among ecoregions. These large-scale national patterns and nine sets of ecoregional results demonstrated that diatom DNA metabarcoding is a robust approach that could be useful to monitoring and assessment programs spanning the variety of conditions that exist throughout the conterminous United States.

Understanding the effects of phosphorus on diatom richness in rivers and streams using taxon-environment relationships.

Changes in phosphorus concentrations affect periphytic diatom composition in streams, yet we rarely observe strong relationships between diatom richness and phosphorus. In contrast, changes in conductivity are strongly associated with differences in both diatom composition and richness. We hypothesised that we could better understand the mechanisms that control the phosphorus-richness relationship by examining relationships between phosphorus and the occurrence of individual diatom taxa, comparing these with relationships between conductivity and taxon occurrence, and documenting how niche breadths of taxa affect richness patterns. We estimated relationships between phosphorus and taxon occurrence using DNA metabarcoding data of diatoms collected from 1,811 sites distributed across the conterminous U.S.A. and contrasted patterns in these relationships with those between conductivity and taxon occurrence. The distribution of taxon optima for phosphorus was bimodal, with most optima located at either the maximum or minimum observed phosphorus concentration. The distribution of taxon optima for conductivity was unimodal. Niche breadths of taxa for phosphorus and for conductivity both generally increased with optimum values. The distribution of conductivity optima gave rise to a prominent hump-shaped relationship between richness and conductivity. The relationship between richness and phosphorus was also slightly hump-shaped, but this relationship would not be expected from the bimodal distribution of optima. Instead, we determined that broad niche breadths caused the hump-shaped relationship between richness and phosphorus. Our results highlight the nuanced effects that increased P loadings exert on diatom assemblages in rivers and streams and identify reasons that weak relationships between taxon richness and increased phosphorus have been observed. These findings allow us to better describe how excess phosphorus and subsets of taxa and their niche breadths contribute to patterns of taxa richness in diatom assemblages, and to improve the tools used to manage phosphorus pollution.

Using Single-Species and Whole Community Stream Mesocosm Exposures for Identifying Major Ion Effects in Doses Mimicking Resource Extraction Wastewaters.

Wastewaters and leachates from various inland resource extraction activities contain high ionic concentrations and differ in ionic composition, which complicates the understanding and effective management of their relative risks to stream ecosystems. To this end, we conducted a stream mesocosm dose-response experiment using two dosing recipes prepared from industrial salts. One recipe was designed to generally reflect the major ion composition of deep well brines (DWB) produced from gas wells (primarily Na+, Ca2+, and Cl-) and the other, the major ion composition of mountaintop mining (MTM) leachates from coal extraction operations (using salts dissociating to Ca2+, Mg2+, Na+, SO42- and HCO3-)-both sources being extensive in the Central Appalachians of the USA. The recipes were dosed at environmentally relevant nominal concentrations of total dissolved solids (TDS) spanning 100 to 2000 mg/L for 43 d under continuous flow-through conditions. The colonizing native algal periphyton and benthic invertebrates comprising the mesocosm ecology were assessed with response sensitivity distributions (RSDs) and hazard concentrations (HCs) at the taxa, community (as assemblages), and system (as primary and secondary production) levels. Single-species toxicity tests were run with the same recipes. Dosing the MTM recipe resulted in a significant loss of secondary production and invertebrate taxa assemblages that diverged from the control at all concentrations tested. Comparatively, intermediate doses of the DWB recipe had little consequence or increased secondary production (for emergence only) and had assemblages less different from the control. Only the highest dose of the DWB recipe had a negative impact on certain ecologies. The MTM recipe appeared more toxic, but overall, for both types of resource extraction wastewaters, the mesocosm responses suggested significant changes in stream ecology would not be expected for specific conductivity below 300 µS/cm, a published aquatic life benchmark suggested for the region.

Quantifying spatial and temporal relationships between diatoms and nutrients in streams strengthens evidence of nutrient effects from monitoring data.

Observational data are frequently used to better understand the effects of changes in P and N on stream biota, but nutrient gradients in streams are usually associated with gradients in other environmental factors, a phenomenon that complicates efforts to accurately estimate the effects of nutrients. Here, we propose a new approach for analyzing observational data in which we compare the effects of changes in nutrient concentrations in time within individual sites and in space among many sites. Covarying relationships between other, potentially confounding environmental factors and nutrient concentrations are unlikely to be the same in both time and space, and, therefore, estimated effects of nutrients that are similar in time and space are more likely to be accurate. We applied this approach to diatom rbcL metabarcoding data collected from streams in the East Fork of the Little Miami River watershed, Ohio, USA. Changes in diatom assemblage composition were consistently associated with changes in the concentration of total reactive P in both time and space. In contrast, despite being associated with spatial differences in ammonia and urea concentrations, diatom assemblage composition was not associated with temporal changes in these nitrogen species. We suggest that the results of this analysis provide evidence of a causal effect of increased P on diatom assemblage composition. We further analyzed the effects of temporal variability in measurements of total reactive P and found that averaging periods greater than ~1 wk prior to sampling best represented the effects of P on the diatom assemblage. Comparisons of biological responses in space and time can sharpen insights beyond those that are based on analyses conducted on only 1 of the 2 dimensions.

Development of a Risk Characterization Tool for Harmful Cyanobacteria Blooms on the Ohio River.

A data-driven approach to characterizing the risk of cyanobacteria-based harmful algal blooms (cyanoHABs) was undertaken for the Ohio River. Twenty-five years of river discharge data were used to develop Bayesian regression models that are currently applicable to 20 sites spread-out along the entire 1579 km of the river's length. Two site-level prediction models were developed based on the antecedent flow conditions of the two blooms that occurred on the river in 2015 and 2019: one predicts if the current year will have a bloom (the occurrence model), and another predicts bloom persistence (the persistence model). Predictors for both models were based on time-lagged average flow exceedances and a site's characteristic residence time under low flow conditions. Model results are presented in terms of probabilities of occurrence or persistence with uncertainty. Although the occurrence of the 2019 bloom was well predicted with the modeling approach, the limited number of events constrained formal model validation. However, as a measure of performance, leave-one-out cross validation returned low misclassification rates, suggesting that future years with flow time series like the previous bloom years will be correctly predicted and characterized for persistence potential. The prediction probabilities are served in real time as a component of a risk characterization tool/web application. In addition to presenting the model's results, the tool was designed with visualization options for studying water quality trends among eight river sites currently collecting data that could be associated with or indicative of bloom conditions. The tool is made accessible to river water quality professionals to support risk communication to stakeholders, as well as serving as a real-time water data monitoring utility.

Characterizing temporal variability in streams supports nutrient indicator development using diatom and bacterial DNA metabarcoding.

Interest in developing periphytic diatom and bacterial indicators of nutrient effects continues to grow in support of the assessment and management of stream ecosystems and their watersheds. However, temporal variability could confound relationships between indicators and nutrients, subsequently affecting assessment outcomes. To document how temporal variability affects measures of diatom and bacterial assemblages obtained from DNA metabarcoding, we conducted weekly periphyton and nutrient sampling from July to October 2016 in 25 streams in a 1293 km2 mixed land use watershed. Measures of both diatom and bacterial assemblages were strongly associated with the percent agriculture in upstream watersheds and total phosphorus (TP) and total nitrogen (TN) concentrations. Temporal variability in TP and TN concentrations increased with greater amounts of agriculture in watersheds, but overall diatom and bacterial assemblage variability within sites-measured as mean distance among samples to corresponding site centroids in ordination space-remained consistent. This consistency was due in part to offsets between decreasing variability in relative abundances of taxa typical of low nutrient conditions and increasing variability in those typical of high nutrient conditions as mean concentrations of TP and TN increased within sites. Weekly low and high nutrient diatom and bacterial metrics were more strongly correlated with site mean nutrient concentrations over the sampling period than with same day measurements and more strongly correlated with TP than with TN. Correlations with TP concentrations were consistently strong throughout the study except briefly following two major precipitation events. Following these events, biotic relationships with TP reestablished within one to three weeks. Collectively, these results can strengthen interpretations of survey results and inform monitoring strategies and decision making. These findings have direct applications for improving the use of diatoms and bacteria, and the use of DNA metabarcoding, in monitoring programs and stream site assessments.

Developing Indicators of Nutrient Pollution in Streams Using 16S rRNA Gene Metabarcoding of Periphyton-Associated Bacteria.

Indicators based on nutrient-biota relationships in streams can inform water quality restoration and protection programs. Bacterial assemblages could be particularly useful indicators of nutrient effects because they are species-rich, important contributors to ecosystem processes in streams, and responsive to rapidly changing conditions. Here, we sampled 25 streams weekly (12-14 times each) and used 16S rRNA gene metabarcoding of periphyton-associated bacteria to quantify the effects of total phosphorus (TP) and total nitrogen (TN). Threshold indicator taxa analysis identified assemblage-level changes and amplicon sequence variants (ASVs) that increased or decreased with increasing TP and TN concentrations (i.e., low P, high P, low N, and high N ASVs). Boosted regression trees confirmed that relative abundances of gene sequence reads for these four indicator groups were associated with nutrient concentrations. Gradient forest analysis complemented these results by using multiple predictors and random forest models for each ASV to identify portions of TP and TN gradients at which the greatest changes in assemblage structure occurred. Synthesized statistical results showed bacterial assemblage structure began changing at 24 µg TP/L with the greatest changes occurring from 110 to 195 µg/L. Changes in the bacterial assemblages associated with TN gradually occurred from 275 to 855 µg/L. Taxonomic and phylogenetic analyses showed that low nutrient ASVs were commonly Firmicutes, Verrucomicrobiota, Flavobacteriales, and Caulobacterales, Pseudomonadales, and Rhodobacterales of Proteobacteria, whereas other groups, such as Chitinophagales of Bacteroidota, and Burkholderiales, Rhizobiales, Sphingomonadales, and Steroidobacterales of Proteobacteria comprised the high nutrient ASVs. Overall, the responses of bacterial ASV indicators in this study highlight the utility of metabarcoding periphyton-associated bacteria for quantifying biotic responses to nutrient inputs in streams.

Combining land use information and small stream sampling with PCR-based methods for better characterization of diffuse sources of human fecal pollution.

Diffuse sources of human fecal pollution allow for the direct discharge of waste into receiving waters with minimal or no treatment. Traditional culture-based methods are commonly used to characterize fecal pollution in ambient waters, however these methods do not discern between human and other animal sources of fecal pollution making it difficult to identify diffuse pollution sources. Human-associated quantitative real-time PCR (qPCR) methods in combination with low-order headwatershed sampling, precipitation information, and high-resolution geographic information system land use data can be useful for identifying diffuse source of human fecal pollution in receiving waters. To test this assertion, this study monitored nine headwatersheds over a two-year period potentially impacted by faulty septic systems and leaky sanitary sewer lines. Human fecal pollution was measured using three different human-associated qPCR methods and a positive significant correlation was seen between abundance of human-associated genetic markers and septic systems following wet weather events. In contrast, a negative correlation was observed with sanitary sewer line densities suggesting septic systems are the predominant diffuse source of human fecal pollution in the study area. These results demonstrate the advantages of combining water sampling, climate information, land-use computer-based modeling, and molecular biology disciplines to better characterize diffuse sources of human fecal pollution in environmental waters.

Temporal dynamics of periphyton exposed to tetracycline in stream mesocosms.

Significant amounts of antibiotics enter the environment via point and nonpoint sources. We examined the temporal dynamics of tetracycline exposure to stream periphyton and associated organisms across a logarithmically dosed-series of experimental mesocosms, designed to mimic natural conditions. Target in-stream tetracycline exposures were based on environmentally relevant concentrations in aquatic ecosystems throughout the United States (<1-100 µg L(-1)). Significant changes in the stream biotic community were observed within 7 days with in-stream tetracycline concentrations as low as 0.5 µg L(-1), including significant changes in antibiotic resistance, bacteria abundance and productivity, algae biomass, cyanobacteria, organic biomass, and nematodes. These effects were magnified with increased exposure time and dosing concentration. Recovery of the periphyton community after 28 days of exposure was dependent upon the tetracycline dose. At the highest doses, 10 and 100 µg L(-1), bacteria productivity recovered; however, bacteria, algae, and nematode abundance did not recover at the same rate and remained low even after a 28-day recovery period (of nondosing). This study demonstrates that tetracycline exposure under near-natural conditions and at concentrations currently observed in aquatic environments may have important consequences for the structure and function of stream periphyton and, potentially, public health via increasing resistance of naturally occurring bacteria.

A Novel Thin-Film Technique to Improve Accuracy of Fluorescence-Based Estimates for Periphytic Biofilms.

Recent studies suggest that photophysiological parameters for intact substrates with depth (e.g., periphytic biofilms, microphytobenthos) are overestimated by pulse-amplitude modulated (PAM) fluorometry. This overestimation results from depth-integration effects, following the activation of deeper photosynthesizing layers by an attenuated light signal. To mitigate this error, we propose a novel slide-based thin-film technique in which fluorescence is measured on a vertically representative subsample of the biofilm, spread evenly on a microscope slide. We compared bias and precision for photosynthetic parameters estimated through conventional PAM fluorometry on intact biofilms and through our novel slide-based technique, both theoretically and empirically. Numerical simulations confirmed the consistent overestimation of key parameters for intact biofilms, with relative errors up to 145%, compared to, at most, 52% on thin films. Paired empirical observations likewise demonstrated that estimates based on intact biofilms were consistently higher (up to 248%, p < 0.001) than estimates from thin films. Numerical simulation suggested greater precision with the slide-based technique for homogeneous biofilms, but potentially less precision for heterogeneous biofilms with improper subsampling. Our empirical comparison, however, demonstrated some improvement in precision with the slide-based technique (e.g., the coefficient of variation for the maximum electron transport rate was reduced 30%, p = 0.009). We recommend the use of the slide-based technique, particularly for biofilms that are thick or have small light attenuation coefficients. Care should be taken, however, to obtain vertically representative subsamples of the biofilm for measurement.

Empirically-based modeling and mapping to consider the co-occurrence of ecological receptors and stressors.

Part of the ecological risk assessment process involves examining the potential for environmental stressors and ecological receptors to co-occur across a landscape. In this study, we introduce a Bayesian joint modeling framework for use in evaluating and mapping the co-occurrence of stressors and receptors using empirical data, open-source statistical software, and Geographic Information Systems tools and data. To illustrate the approach, we apply the framework to bioassessment data on stream fishes and nutrients collected from a watershed in southwestern Ohio. The results highlighted the joint model's ability to parse and exploit statistical dependencies in order to provide empirical insight into the potential environmental and ecotoxicological interactions influencing co-occurrence. We also demonstrate how probabilistic predictions can be generated and mapped to visualize spatial patterns in co-occurrences. For practitioners, we believe that this data-driven approach to modeling and mapping co-occurrence can lead to more quantitatively transparent and robust assessments of ecological risk.

Drainage area characterization for evaluating green infrastructure using the Storm Water Management Model.

Urban stormwater runoff quantity and quality are strongly dependent upon catchment properties. Models are used to simulate the runoff characteristics, but the output from a stormwater management model is dependent on how the catchment area is subdivided and represented as spatial elements. For green infrastructure modeling, we suggest a discretization method that distinguishes directly connected impervious area from the total impervious area. Pervious buffers, which receive runoff from upgradient impervious areas should also be identified as a separate subset of the entire pervious area. This separation provides an improved model representation of the runoff process. With these criteria in mind, an approach to spatial discretization for projects using the U.S. Environmental Protection Agency's Storm Water Management Model (SWMM) is demonstrated for the Shayler Crossing watershed, a well-monitored, residential suburban area occupying 100 ha, east of Cincinnati, Ohio. The model relies on a highly resolved spatial database of urban land cover, stormwater drainage features, and topography. To verify the spatial discretization approach, a hypothetical analysis was conducted. Six different representations of a common urban scape that discharges runoff to a single storm inlet were evaluated with eight 24 h synthetic storms. This analysis allowed us to select a discretization scheme that balances complexity in model set-up with presumed accuracy of the output with respect to the most complex discretization option considered. The balanced approach delineates directly and indirectly connected impervious areas, buffering pervious area receiving impervious runoff, and the other pervious area within a SWMM subcatchment. It performed well at the watershed scale with minimal calibration effort (Nash-Sutcliffe coefficient = 0.852; R 2 = 0.871). The approach accommodates the distribution of runoff contributions from different spatial components and flow pathways that would impact green infrastructure performance. A developed SWMM model using the discretization approach is calibrated by adjusting parameters per land cover component, instead of per subcatchment, and, therefore, can be applied to relatively large watersheds if the land cover components are relatively homogeneous and/or categorized appropriately in the GIS that supports the model parameterization. Finally, with a few model adjustments, we show how the simulated stream hydrograph can be separated into the relative contributions from different land cover types and subsurface sources, adding insight to the potential effectiveness of planned green infrastructure scenarios at the watershed scale.