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When rainwater harvesting is utilized as an alternative water resource in buildings, a combination of municipal water and rainwater is typically required to meet water demands. Altering source water chemistry can disrupt pipe scale and biofilm and negatively impact water quality at the distribution level. Still, it is unknown if similar reactions occur within building plumbing following a transition in source water quality. The goal of this study was to investigate changes in water chemistry and microbiology at a green building following a transition between municipal water and rainwater. We monitored water chemistry (metals, alkalinity, and disinfectant byproducts) and microbiology (total cell counts, plate counts, and opportunistic pathogen gene markers) throughout two source water transitions. Several constituents including alkalinity and disinfectant byproducts served as indicators of municipal water remaining in the system since the rainwater source does not contain these constituents. In the treated rainwater, microbial proliferation and Legionella spp. gene copy numbers were often three logs higher than those in municipal water. Because of differences in source water chemistry, rainwater and municipal water uniquely interacted with building plumbing and generated distinctively different drinking water chemical and microbial quality profiles.

The rising trend in water conservation awareness has given rise to the use of water-efficient appliances and fixtures for residential potable water systems. This study characterized the microbial dynamics at a water-efficient residential building over the course of one year (58 sampling events) and examined the effects of water stagnation, season, and changes in physicochemical properties on the occurrence of opportunistic pathogen markers. Mean heterotrophic plate counts (HPC) were typically lowest upon entering the building at the service line, but increased by several orders of magnitude at the furthest location in the building plumbing. Legionella spp. and Mycobacterium spp. were detected in the plumbing, with the highest detection occurring in the summer months. Log-transformed HPC were significantly correlated with total cell counts (TCC) (rs = 0.714, p < 0.01), Legionella spp. (rs = 0.534, p < 0.01), and Mycobacterium spp. occurrence (rs = 0.458, p < 0.01). Reduced water usage induced longer stagnation times and longer stagnation times were weakly correlated with an increase in Legionella spp. (rs = 0.356, p < 0.001), Mycobacterium spp. (rs = 0.287, p < 0.001), TCC (rs = 0.216, p < 0.001) and HPC (rs = 0.395, p < 0.001). Interrelationships between seasonal shifts in water chemistry and genus-level genetic markers for opportunistic pathogens were revealed. This study highlights how drinking water microbiology varies seasonally and spatially throughout a low-flow plumbing building and highlights the possible unintended consequences associated with reduced water usage and increases in stagnation.

Maximizing crop production with minimal resources such as water and energy is the primary focus of sustainable agriculture. Subsurface water retention technology (SWRT) is a stable approach that preserves water in sandy soils using water saving membranes. An optimal use of SWRT depends on its shape, location and other factors. In order to predict crop yield for different irrigation schedule, we require at least two computational processes: (i) a crop growth modeling process and (ii) a water and nutrient permeation process through soil to the root system. Validation of software parameters to suit properties of specific field becomes increasingly hard since they involve a coordination with field data and coordination between two software. In this paper, we propose a computationally fast approach that utilizes HYDRUS-2D software for water and nutrient flow simulation and DSSAT crop simulation software with an evolutionary multi-objective optimization (EMO) procedure in a coordinated manner to minimize water utilization and maximize crop yield prediction. Our proposed method consists of training one-dimensional crop model (DSSAT) on data generated by two dimensional model calibrates and validates (HYDRUS-2D), that accounts for water accumulation in the SWRT membranes. Then we used DSSAT model to find the best irrigation schedules for maximizing crop yield with the highest plant water use efficiency (Tambussi et al., 2007; Blum, 2009) using for the EMO methodology. The optimization procedure minimizes water usage with the help of rainfall water and increases corn yield prediction as much as six times compare to a non-optimized and random irrigation schedule without any SWRT membrane. Our framework also demonstrates an integration of latest computing software and hardware technologies synergistically to facilitate better crop production with minimal water requirement.

As non-point sources of pollution begin to overtake point sources in watersheds, source identification and complicating variables such as rainfall are growing in importance. Microbial source tracking (MST) allows for identification of fecal contamination sources in watersheds; when combined with data on land use and co-occuring variables (e.g., nutrients, sediment runoff) MST can provide a basis for understanding how to effectively remediate water quality. To determine spatial and temporal trends in microbial contamination and correlations between MST and nutrients, water samples (n = 136) were collected between April 2017 and May of 2018 during eight sampling events from 17 sites in 5 mixed-use watersheds. These samples were analyzed for three MST markers (human - B. theta; bovine - CowM2; porcine - Pig2Bac) along with E. coli, nutrients (nitrogen and phosphorus species), and physiochemical paramaters. These water quality variables were then paired with data on land use, streamflow, precipitation and management practices (e.g., tile drainage, septic tank density, tillage practices) to determine if any significant relationships existed between the observed microbial contamination and these variables. The porcine marker was the only marker that was highly correlated (p value0.05) with nitrogen and phosphorus species in multiple clustering schemes. Significant relationships were also identified between MST markers and variables that demonstrated temporal trends driven by precipitation and spatial trends driven by septic tanks and management practices (tillage and drainage) when spatial clustering was employed.

Agricultural nonpoint source pollution is the leading source of water quality degradation in United States, which has led to the development of programs that aim to mitigate this pollution. One common approach to mitigating nonpoint source pollution is the use of best management practices (BMPs). However, it can be challenging to evaluate the effectiveness of implemented BMPs due to polices that limit data sharing. In this study, the uncertainty introduced by data sharing limitations is quantified through the use of a watershed model. Results indicated that BMP implementation improved the overall water quality in the region (up to ∼15% pollution reduction) and that increasing the area of BMP implementation resulted in higher pollution reduction. However, the model outputs also indicated that uncertainty caused by data sharing limitations resulted in variabilities ranging from −160% to 140%. This shows the importance of data sharing among agencies to better guide current and future conservation programs.

Residential plumbing is critical for the health and safety of populations worldwide. A case study was conducted to understand fixture water use, drinking water quality and their possible link, in a newly plumbed residential green building. Water use and water quality were monitored at four in-building locations from September 2015 through December 2015. Once the home was fully inhabited average water stagnation periods were shortest at the 2nd floor hot fixture (90 percentile of 0.6–1.2 h). The maximum water stagnation time was 72.0 h. Bacteria and organic carbon levels increased inside the plumbing system compared to the municipal tap water entering the building. A greater amount of bacteria was detected in hot water samples (6–74,002 gene copy number/mL) compared to cold water (2–597 gene copy number/mL). This suggested that hot water plumbing promoted greater microbial growth. The basement fixture brass needle valve may have caused maximum Zn (5.9 mg/L), Fe (4.1 mg/L), and Pb (23 μg/L) levels compared to other fixture water samples (Zn ≤ 2.1 mg/L, Fe ≤ 0.5 mg/L and Pb ≤ 8 μg/L). At the basement fixture, where the least amount of water use events occurred (cold: 60–105, hot: 21–69 event/month) compared to the other fixtures in the building (cold: 145–856, hot: 326–2230 event/month), greater organic carbon, bacteria, and heavy metal levels were detected. Different fixture use patterns resulted in disparate water quality within a single-family home. The greatest drinking water quality changes were detected at the least frequently used fixture.

The reliability and applicability of hydrological models within ecohydrological frameworks are major concerns. Two multi-objective model calibration strategies were formulated to achieve a balanced representation of ecologically relevant hydrologic indices. Two approaches were employed: 1) a performance-based method constraining the targeted hydrologic indices and 2) an unconstrained signature-based method explicitly incorporating the targeted hydrologic indices into multiple objective functions. Both strategies were successful in representing most of the selected hydrologic indices within a ±30% relative error acceptability threshold while yielding consistent runoff predictions in a watershed. The performance-based strategy was preferred since it showed a lower dispersion of near-optimal Pareto solutions when representing the selected indices based on water balance and Flow Duration Curve characteristics. Still, the overall representation of low flow magnitude and timing, rise and fall rates, and duration and frequency of extreme flows was limited in terms of interannual variability due to the hydrological model structural inadequacies.

Land-based wastewater treatment systems (onsite or decentralized) have been used for many years because of their low cost, energy use, and maintenance requirements compared to conventional ...

Several factors can affect virus behavior and persistence in water sources. Historically linear models have been used to describe persistence over time; however, these models do not consider all of the factors that can affect inactivation kinetics or the observed patterns of decay. Meanwhile, applying the appropriate persistence model is critical for ensuring that decision makers are minimizing human health risk in the event of contamination and exposure to contaminated groundwater. Therefore, to address uncertainty in predictions of decay or virus concentrations over time, this study fit seventeen different linear and nonlinear mathematical models to persistence data from a previously conducted sampling study on drinking water wells throughout the United States. The models were fit using Maximum Likelihood Estimation and the best fitting models were determined by the Bayesian Information Criterion. The purpose of the study was to identify the best model for estimating decay of viruses in groundwater and to determine if model uncertainty contributes to erroneous predictions of viral contamination when only conventional models are considered. For the datasets analyzed in this study, the Juneja and Marks models and the exponential damped model were more representative of the persistence of viruses in groundwater than the traditionally used linear models. The results from this study were then evaluated with classification trees in order to identify more relevant modeling methodology for future research. The classification trees aid in narrowing the scope of appropriate persistence models based on characteristics of the experimental conditions and water sampled.

Droughts affect many sectors, such as agriculture, economic, social, human health, and ecosystems. Many drought indices have been developed; yet, none of them quantifies the impacts of drought on stream health. The purpose of this study is to define a new drought index capable of assessing fish vulnerability. To accomplish this, a hydrological model, called the Soil and Water Assessment Tool (SWAT), and the Regional-scale Habitat Suitability model were integrated in order to understand the state of drought within 13,831 stream segments within the Saginaw Bay Watershed. The ReliefF algorithm was used as the variable selection method, and partial least squared regression was used to develop two sets of predictor models capable of determining current and future drought severities. Forty-seven different climate scenarios were used to investigate drought model predictability of future climate scenarios. The results indicated that the best drought model has a high capability for predicting future drought conditions with R2 values ranging from 0.86 to 0.89. In general, the majority of reaches (94%) will experience higher drought probability under future climate scenarios compared to current conditions. The procedure introduced in this study is transferable to other watersheds with regional standards for environmental flow to measure the impacts of drought on stream health.

Effective watershed management requires the evaluation of agricultural best management practice (BMP) scenarios which carefully consider the relevant environmental, economic, and social criteria involved. In the Multiple Criteria Decision-Making (MCDM) process, scenarios are first evaluated and then ranked to determine the most desirable outcome for the particular watershed. The main challenge of this process is the accurate identification of the best solution for the watershed in question, despite the various risk attitudes presented by the associated decision-makers (DMs). This paper introduces a novel approach for implementation of the MCDM process based on a comparative neutral risk/risk-based decision analysis, which results in the selection of the most desirable scenario for use in the entire watershed. At the sub-basin level, each scenario includes multiple BMPs with scores that have been calculated using the criteria derived from two cases of neutral risk and risk-based decision-making. The simple additive weighting (SAW) operator is applied for use in neutral risk decision-making, while the ordered weighted averaging (OWA) and induced OWA (IOWA) operators are effective for risk-based decision-making. At the watershed level, the BMP scores of the sub-basins are aggregated to calculate each scenarios' combined goodness measurements; the most desirable scenario for the entire watershed is then selected based on the combined goodness measurements. Our final results illustrate the type of operator and risk attitudes needed to satisfy the relevant criteria within the number of sub-basins, and how they ultimately affect the final ranking of the given scenarios. The methodology proposed here has been successfully applied to the Honeyoey Creek-Pine Creek watershed in Michigan, USA to evaluate various BMP scenarios and determine the best solution for both the stakeholders and the overall stream health.

Sustainability and the health of freshwater ecosystems are vital to insure their safe and continued use. This study introduces a new approach to improve stream health to a desirable condition at the lowest cost by optimizing the best management practice (BMP) implementation plan. Several hydrological models including the Soil and Water Assessment Tool (SWAT) and Hydrologic Index Tool were integrated and the results were used to develop a stream health model. SWAT model was calibrated and validated against daily streamflow data from nine US geological gauging stations for a 10-year-period while the stream health model was calibrated and validated against 193 biological monitoring sites operated by the Michigan Department of Natural Resources. The stream health model was guided by a genetic algorithm to design the watershed-scale management strategies that included five BMPs. Out of 182 BMP implementation scenarios, eight unique scenarios resulted in an overall excellent stream health for the Honeyoey Creek-Pine Creek Watershed in Michigan. In addition, no tillage was the most selected BMP in three of the eight implementation scenarios. The BMP implementation costs for these eight scenarios ranged from 4.28 to 6.41 million dollars. Therefore, the integration of genetic algorithm techniques in stream health modeling resulted in a savings of over 2 million dollars. In addition, the implementation of the lowest costing scenario resulted in a 52% improvement and 36% reduction in stream health scores, with respect to stream length, compared to current conditions. The technique introduced here can be successfully adapted in different regions to identify the optimal solution from both environmental and economic points of view.

Watershed models are scarcely used by watershed managers due to their complexity. This study facilitates information transfer by introducing simpler techniques related to easily obtained watershed characteristics, including distance to the watershed outlet and stream order. The Soil and Water Assessment Tool (SWAT) was calibrated for the Saginaw River Watershed, Michigan. Five agricultural best management practices (BMPs) were implemented in SWAT one at a time in each subbasin. Five statistical models were used to determine the pollution reduction at the watershed outlet using distance and BMP type, with results suggesting that a mixed effects model (model 5) was optimal. This model included subbasin as a random effect, while distance to watershed outlet and BMP type were fixed effects. Native grass and strip cropping were the most effective BMPs for reducing sediment and nutrient transport. Subbasins containing stream orders 1-3 were ideal for BMP implementation throughout the watershed. Novel techniques identify the best location for conservation practice installation.Trellis plots help to determine the optimal distance to maximize pollution reduction.Surface plots were created to visualize watershed response to pollution reduction.This study helps decision makers and stakeholders in local and watershed-scale planning.

In this study, spatial clustering techniques were used in combination with Structural Equation Modeling (SEM) to characterize the relationships between in-stream health indicators and socioeconomic measures of communities. The study area is the Saginaw River Watershed in Michigan. Four measures of stream health were considered: the Index of Biological Integrity, Hilsenhoff Biotic Index, Family Index of Biological Integrity, and number of Ephemeroptera, Plecoptera, and Trichoptera taxa. The stream health indicators were predicted using nine socioeconomic variables that capture vulnerability in population. The results of spatial clustering showed that incorporating clustering configuration improves the model prediction. A total of 510 Confirmatory Factor Analysis (CFAs) and 85 multivariate regression models were developed for each spatial cluster within the watershed and compared with the model performance without spatial clustering (at the watershed level). In general, watershed level CFAs outperformed cluster level CFAs, while the reverse was true for the regression models. Relationships between stream health and socioeconomic measures were studied.Spatial clustering techniques and Structural Equation Modeling were integrated.Spatial clustering improved regression model prediction.Confirmatory Factor Analysis performed well at the watershed level.

Variable selection is a critical step in development of empirical stream health prediction models. This study develops a framework for selecting important in-stream variables to predict four measures of biological integrity: total number of Ephemeroptera, Plecoptera, and Trichoptera (EPT) taxa, family index of biotic integrity (FIBI), Hilsenhoff biotic integrity (HBI), and fish index of biotic integrity (IBI). Over 200 flow regime and water quality variables were calculated using the Hydrologic Index Tool (HIT) and Soil and Water Assessment Tool (SWAT). Streams of the River Raisin watershed in Michigan were grouped using the Strahler stream classification system (orders 1–3 and orders 4–6), k-means clustering technique (two clusters: C1 and C2), and all streams (one grouping). For each grouping, variable selection was performed using Bayesian variable selection, principal component analysis, and Spearman's rank correlation. Following selection of best variable sets, models were developed to predict the measures of biological integrity using adaptive-neuro fuzzy inference systems (ANFIS), a technique well-suited to complex, nonlinear ecological problems. Multiple unique variable sets were identified, all which differed by selection method and stream grouping. Final best models were mostly built using the Bayesian variable selection method. The most effective stream grouping method varied by health measure, although k-means clustering and grouping by stream order were always superior to models built without grouping. Commonly selected variables were related to streamflow magnitude, rate of change, and seasonal nitrate concentration. Each best model was effective in simulating stream health observations, with EPT taxa validation R2 ranging from 0.67 to 0.92, FIBI ranging from 0.49 to 0.85, HBI from 0.56 to 0.75, and fish IBI at 0.99 for all best models. The comprehensive variable selection and modeling process proposed here is a robust method that extends our understanding of watershed scale stream health beyond sparse monitoring points.

Wetlands have many environmental, social, and economic values. However, due to accelerated land use change and lack of understanding of the functions of wetland ecosystems, they have deteriorated, if not been lost in many areas worldwide. Meanwhile, current functional wetland assessment techniques only provide rough estimations, and are in most cases site specific and qualitative. The overall goal of this project is to examine the sediment reduction benefit of wetland implementation scenarios both at subbasin and watershed scales. Two sets of models were used to accomplish this goal. First, a watershed model – the Soil and Water Assessment Tool (SWAT), was employed to estimate sediment load at the subbasin scale. However, due to limitations of wetland functions of SWAT, a second model – the System for Urban Stormwater Treatment and Analysis Integration (SUSTAIN) was used. The sediment load generated for each subbasin was incorporated in the SUSTAIN model. This allows for evaluating sediment reduction capability of wetlands at subbasin level. Next, a portion of sediment not treated by a wetland was fed back to the SWAT model and routed to the watershed outlet. The impacts of four different wetland surface areas (0.40, 0.81, 2, and 4 ha) on sediment load mitigation were examined one-at-a-time for all subbasins within the River Raisin watershed located in southeastern Michigan and northeastern Ohio. Comparison of the sediment reductions due to different wetland restoration scenarios reveals the importance of wetland placement in a watershed. In general, the rate of streamflow reduction resulting from wetland implementation is higher than sediment reduction at the subbasin level but more comparable at the watershed level. In addition, clusters of wetlands installed at the distance of 150–200 stream km from the outlet outperformed other clustered wetlands at closer and farther distances. Wetlands associated with 1st order streams performed better at the subbasin level, while wetlands located at 4th order streams performed better at the watershed level. Considering environmental and economic issues of wetland restoration scenarios revealed that the 0.4 ha wetlands were the most suitable for subbasin and watershed level implementation due to its sediment reduction efficiency and significantly lower cost of installation and maintenance.

Drinking water chemical quality can deteriorate after water enters building plumbing. This study aimed to better understand seasonal and spatial water quality differences in a highly monitored net-zero energy residential building. Water flow rate and temperature were monitored for one year at the service line and at every fixture throughout the crosslinked polyethylene plumbing. Discrete water sampling events (58) were conducted at the service line, 1st floor kitchen sink, 2nd floor bathroom sink, the water heater, and 2nd floor shower. More than 2.4 billion online monitoring records were collected for fixture flow and temperature. In-building water stagnation time varied seasonally and across fixtures. Significant spatial and temporal water chemical quality variations were found. Average seasonal variability was found for service line temperature (15–23 °C) for the total chlorine residual (0.4–0.9 mg/L-Cl2), NH3 (

Non-point source pollution from agricultural lands is a significant contributor of sediment pollution in United States lakes and streams. Therefore, quantifying the impact of individual field management strategies at the watershed-scale provides valuable information to watershed managers and conservation agencies to enhance decision-making. In this study, four methods employing some of the most cited models in field and watershed scale analysis were compared to find a practical yet accurate method for evaluating field management strategies at the watershed outlet. The models used in this study including field-scale model (the Revised Universal Soil Loss Equation 2 - RUSLE2), spatially explicit overland sediment delivery models (SEDMOD), and a watershed-scale model (Soil and Water Assessment Tool - SWAT). These models were used to develop four modeling strategies (methods) for the River Raisin watershed: Method 1) predefined field-scale subbasin and reach layers were used in SWAT model; Method 2) subbasin-scale sediment delivery ratio was employed; Method 3) results obtained from the field-scale RUSLE2 model were incorporated as point source inputs to the SWAT watershed model; and Method 4) a hybrid solution combining analyses from the RUSLE2, SEDMOD, and SWAT models. Method 4 was selected as the most accurate among the studied methods. In addition, the effectiveness of six best management practices (BMPs) in terms of the water quality improvement and associated cost were assessed. Economic analysis was performed using Method 4, and producer requested prices for BMPs were compared with prices defined by the Environmental Quality Incentives Program (EQIP). On a per unit area basis, producers requested higher prices than EQIP in four out of six BMP categories. Meanwhile, the true cost of sediment reduction at the field and watershed scales was greater than EQIP in five of six BMP categories according to producer requested prices.

Many watershed model interfaces have been developed in recent years for predicting field-scale sediment loads. They share the goal of providing data for decisions aimed at improving watershed health and the effectiveness of water quality conservation efforts. The objectives of this study were to: 1) compare three watershed-scale models (Soil and Water Assessment Tool (SWAT), Field_SWAT, and the High Impact Targeting (HIT) model) against calibrated field-scale model (RUSLE2) in estimating sediment yield from 41 randomly selected agricultural fields within the River Raisin watershed; 2) evaluate the statistical significance among models; 3) assess the watershed models' capabilities in identifying areas of concern at the field level; 4) evaluate the reliability of the watershed-scale models for field-scale analysis. The SWAT model produced the most similar estimates to RUSLE2 by providing the closest median and the lowest absolute error in sediment yield predictions, while the HIT model estimates were the worst. Concerning statistically significant differences between models, SWAT was the only model found to be not significantly different from the calibrated RUSLE2 at α = 0.05. Meanwhile, all models were incapable of identifying priorities areas similar to the RUSLE2 model. Overall, SWAT provided the most correct estimates (51%) within the uncertainty bounds of RUSLE2 and is the most reliable among the studied models, while HIT is the least reliable. The results of this study suggest caution should be exercised when using watershed-scale models for field level decision-making, while field specific data is of paramount importance.

Freshwater resources are vital for human and natural systems. However, anthropogenic activities, such as agricultural practices, have led to the degradation of the quality of these limited resources through pollutant loading. Agricultural Best Management Practices (BMPs), such as wetlands, are recommended as a valuable solution for pollutant removal. However, evaluation of their long-term impacts is difficult and requires modeling since performing in-situ monitoring is expensive and not feasible at the watershed scale. In this study, the impact of natural wetland implementation on total phosphorus reduction was evaluated both at the subwatershed and watershed levels. The study area is the Saginaw River Watershed, which is largest watershed in Michigan. The phosphorus reduction performances of four different wetland sizes (2, 4, 6, and 8 ha) were evaluated within this study area by implementing one wetland at a time in areas identified to have the highest potential for wetland restoration. The subwatershed level phosphorus loads were obtained from a calibrated Soil and Water Assessment Tool (SWAT) model. These loads were then incorporated into a wetland model (System for Urban Stormwater Treatment and Analysis IntegratioN-SUSTAIN) to evaluate phosphorus reduction at the subwatershed level and then the SWAT model was again used to route phosphorus transport to the watershed outlet. Statistical analyses were performed to evaluate the spatial impact of wetland size and placement on phosphorus reduction. Overall, the performance of 2 ha wetlands in total phosphorus reduction was significantly lower than the larger sizes at both the subwatershed and watershed levels. Regarding wetland implementation sites, wetlands located in headwaters and downstream had significantly higher phosphorus reduction than the ones located in the middle of the watershed. More specifically, wetlands implemented at distances ranging from 200 to 250 km and 50–100 km from the outlet had the highest impact on phosphorus reduction at the subwatershed and watershed levels, respectively. A multi criteria decision making (MCDM) method named VIKOR was successfully executed to identify the most suitable wetland size and location for each subwatershed considering the phosphorus reduction and economic cost associated with wetland implementation. The methods introduced in this study can be easily applied to other watersheds for selection and placement of wetlands while considering environmental benefits and economic costs.

Droughts are known as the world's costliest natural disasters impacting a variety of sectors. Despite their wide range of impacts, no universal drought definition has been defined. The goal of this study is to define a universal drought index that considers drought impacts on meteorological, agricultural, hydrological, and stream health categories. Additionally, predictive drought models are developed to capture both categorical (meteorological, hydrological, and agricultural) and overall impacts of drought. In order to achieve these goals, thirteen commonly used drought indices were aggregated to develop a universal drought index named MASH. The thirteen drought indices consist of four drought indices from each meteorological, hydrological, and agricultural categories, and one from the stream health category. Cluster analysis was performed to find the three closest indices in each category. Then the closest drought indices were averaged in each category to create the categorical drought score. Finally, the categorical drought scores were simply averaged to develop the MASH drought index. In order to develop predictive drought models for each category and MASH, the ReliefF algorithm was used to rank 90 variables and select the best variable set. Using the best variable set, the adaptive neuro-fuzzy inference system (ANFIS) was used to develop drought predictive models and their accuracy was examined using the 10-fold cross validation technique. The models' predictabilities ranged from R2 = 0.75 for MASH to R2 = 0.98 for the hydrological drought model. The results of this study can help managers to better position resources to cope with drought by reducing drought impacts on different sectors.

Anthropogenic activities such as agricultural practices can have large effects on the ecological components and overall health of stream ecosystems. Therefore, having a better understanding of those effects and relationships allows for better design of mitigating strategies. The objectives of this study were to identify influential stream variables that correlate with macroinvertebrate indices using biophysical and statistical models. The models developed were later used to evaluate the impact of three agricultural management practices on stream integrity. Our study began with the development of a high-resolution watershed model for the Saginaw River watershed in Michigan for generating in-stream water quality and quantity data at stream reaches with biological sampling data. These in-stream data were then used to explain macroinvertebrate measures of stream health including family index of biological integrity (FamilyIBI), Hilsenhoff biotic index (HBI), and the number of Ephemeroptera, Plecoptera , and Trichoptera taxa (EPTtaxa). Two methods (stepwise linear regression and adaptive neuro-fuzzy inference systems (ANFIS)) were evaluated for developing predictive models for macroinvertebrate measures. The ANFIS method performed the best on average and the final models displayed the highest R(2) and lowest mean squared error (MSE) for FamilyIBI (R(2)=0.50, MSE=29.80), HBI (R(2)=0.57, MSE=0.20), and EPTtaxa (R(2)=0.54, MSE=6.60). Results suggest that nutrient concentrations have the strongest influence on all three macroinvertebrate measures. Consistently, average annual organic nitrogen showed the most significant association with EPTtaxa and HBI. Meanwhile, the best model for FamilyIBI included average annual ammonium and average seasonal organic phosphorus. The ANFIS models were then used in conjunction with the Soil and Water Assessment Tool to forecast and assess the potential effects of different best management practices (no-till, residual management, and native grass) on stream integrity. Based on the model predictions, native grass resulted in the largest improvement for all macroinvertebrate measures.

Increasing concerns regarding water quality in the Great Lakes region are mainly due to changes in urban and agricultural landscapes. Both point and non-point sources contribute pollution to Great Lakes surface waters. Best management practices (BMPs) are a common tool used to reduce both point and non-point source pollution and improve water quality. Meanwhile, identification of critical source areas of pollution and placement of BMPs plays an important role in pollution reduction. The goal of this study is to evaluate the performance of different targeting methods in 1) identifying priority areas (high, medium, and low) based on various factors such as pollutant concentration, load, and yield, 2) comparing pollutant (sediment, total nitrogen-TN, and total phosphorus-TP) reduction in priority areas defined by all targeting methods, 3) determine the BMP relative sensitivity index among all targeting methods. Ten BMPs were implemented in the Saginaw River Watershed using the Soil and Water Assessment Tool (SWAT) model following identification of priority areas. Each targeting method selected distinct high priority areas based on the methodology of implementation. The concentration based targeting method was most effective at reduction of TN and TP, likely because it selected the greatest area of high priority for BMP implementation. The subbasin load targeting method was most effective at reducing sediment because it tended to select large, highly agricultural subbasins for BMP implementation. When implementing BMPs, native grass and terraces were generally the most effective, while conservation tillage and residue management had limited effectiveness. The BMP relative sensitivity index revealed that most combinations of targeting methods and priority areas resulted in a proportional decrease in pollutant load from the subbasin level and watershed outlet. However, the concentration and yield methods were more effective at subbasin reduction, while the stream load method was more effective at reducing pollutants at the watershed outlet. The results of this study indicate that emphasis should be placed on selection of the proper targeting method and BMP to meet the needs and goals of a BMP implementation project because different targeting methods produce varying results.

In this study, the environmental impacts of large scale bioenergy crops were evaluated using the Soil and Water Assessment Tool (SWAT). Daily pesticide concentration data for a study area consisting of four large watersheds located in Michigan (totaling 53,358 km2) was estimated over a six year period (2000–2005). Model outputs for atrazine, bromoxynil, glyphosate, metolachlor, pendimethalin, sethoxydim, triflualin, and 2,4-D model output were used to predict the possible long-term implications that large-scale bioenergy crop expansion may have on the bluegill (Lepomis macrochirus) and humans. Threshold toxicity levels were obtained for the bluegill and for human consumption for all pesticides being evaluated through an extensive literature review. Model output was compared to each toxicity level for the suggested exposure time (96-hour for bluegill and 24-hour for humans). The results suggest that traditional intensive row crops such as canola, corn and sorghum may negatively impact aquatic life, and in most cases affect the safe drinking water availability. The continuous corn rotation, the most representative rotation for current agricultural practices for a starch-based ethanol economy, delivers the highest concentrations of glyphosate to the stream. In addition, continuous canola contributed to a concentration of 1.11 ppm of trifluralin, a highly toxic herbicide, which is 8.7 times the 96-hour ecotoxicity of bluegills and 21 times the safe drinking water level. Also during the period of study, continuous corn resulted in the impairment of 541,152 km of stream. However, there is promise with second-generation lignocellulosic bioenergy crops such as switchgrass, which resulted in a 171,667 km reduction in total stream length that exceeds the human threshold criteria, as compared to the base scenario. Results of this study may be useful in determining the suitability of bioenergy crop rotations and aid in decision making regarding the adaption of large-scale bioenergy cropping systems.

The emission of greenhouse gases continues to amplify the impacts of global climate change. This has led to the increased focus on using renewable energy sources, such as biofuels, due to their lower impact on the environment. However, the production of biofuels can still have negative impacts on water resources. This study introduces a new strategy to optimize bioenergy landscapes while improving stream health for the region. To accomplish this, several hydrological models including the Soil and Water Assessment Tool, Hydrologic Integrity Tool, and Adaptive Neruro Fuzzy Inference System, were linked to develop stream health predictor models. These models are capable of estimating stream health scores based on the Index of Biological Integrity. The coupling of the aforementioned models was used to guide a genetic algorithm to design watershed-scale bioenergy landscapes. Thirteen bioenergy managements were considered based on the high probability of adaptation by farmers in the study area. Results from two thousand runs identified an optimum bioenergy crops placement that maximized the stream health for the Flint River Watershed in Michigan. The final overall stream health score was 50.93, which was improved from the current stream health score of 48.19. This was shown to be a significant improvement at the 1% significant level. For this final bioenergy landscape the most often used management was miscanthus (27.07%), followed by corn-soybean-rye (19.00%), corn stover-soybean (18.09%), and corn-soybean (16.43%). The technique introduced in this study can be successfully modified for use in different regions and can be used by stakeholders and decision makers to develop bioenergy landscapes that maximize stream health in the area of interest.

Ecohydrological models are frequently used to assess the biological integrity of unsampled streams. These models vary in complexity and scale, and their utility depends on their final application. Tradeoffs are usually made in model scale, where large-scale models are useful for determining broad impacts of human activities on biological conditions, and regional-scale (e.g. watershed or ecoregion) models provide stakeholders greater detail at the individual stream reach level. Given these tradeoffs, the objective of this study was to develop large-scale stream health models with reach level accuracy similar to regional-scale models thereby allowing for impacts assessments and improved decision-making capabilities. To accomplish this, four measures of biological integrity (Ephemeroptera, Plecoptera, and Trichoptera taxa (EPT), Family Index of Biotic Integrity (FIBI), Hilsenhoff Biotic Index (HBI), and fish Index of Biotic Integrity (IBI)) were modeled based on four thermal classes (cold, cold-transitional, cool, and warm) of streams that broadly dictate the distribution of aquatic biota in Michigan. The Soil and Water Assessment Tool (SWAT) was used to simulate streamflow and water quality in seven watersheds and the Hydrologic Index Tool was used to calculate 171 ecologically relevant flow regime variables. Unique variables were selected for each thermal class using a Bayesian variable selection method. The variables were then used in development of adaptive neuro-fuzzy inference systems (ANFIS) models of EPT, FIBI, HBI, and IBI. ANFIS model accuracy improved when accounting for stream thermal class rather than developing a global model.

In this study an analytical hierarchy process (AHP) was used for ranking best management practices (BMPs) in the Saginaw River Watershed based on environmental, economic and social factors. Three spatial targeting methods were used for placement of BMPs on critical source areas (CSAs). The environment factors include sediment, total nitrogen, and total phosphorus reductions at the subbasin level and the watershed outlet. Economic factors were based on total BMP cost, including installation, maintenance, and opportunity costs. Social factors were divided into three favorability rankings (most favorable, moderately favorable, and least favorable) based on area allocated to each BMP. Equal weights (1/3) were considered for the three main factors while calculating the BMP rank by AHP. In this study three scenarios were compared. A comprehensive approach in which environmental, economic, and social aspects are simultaneously considered (Scenario 1) versus more traditional approaches in which both environmental and economic aspects were considered (Scenario 2) or only environmental aspects (sediment, TN, and TP) were considered (Scenario 3). In Scenario 1, only stripcropping (moderately favorable) was selected on all CSAs at the subbasin level, whereas stripcropping (49-69% of CSAs) and residue management (most favorable, 31-51% of CSAs) were selected by AHP based on the watershed outlet and three spatial targeting methods. In Scenario 2, native grass was eliminated by moderately preferable BMPs (stripcropping) both at the subbasin and watershed outlet levels due the lower BMP implementations cost compared to native grass. Finally, in Scenario 3, at subbasin level, the least socially preferable BMP (native grass) was selected in 100% of CSAs due to greater pollution reduction capacity compared to other BMPs. At watershed level, nearly 50% the CSAs selected stripcropping, and the remaining 50% of CSAs selected native grass and residue management equally.

Manure-borne pathogens are a threat to water quality and have resulted in disease outbreaks globally. Land application of livestock manure to croplands may result in pathogen transport through surface runoff and tile drains, eventually entering water bodies such as rivers and wetlands. The goal of this study was to develop a robust model for estimating the pathogen removal in surface flow wetlands under pulse loading conditions. A new modeling approach was used to describe Escherichia coli removal in pulse-loaded constructed wetlands using adaptive neuro-fuzzy inference systems (ANFIS). Several ANFIS models were developed and validated using experimental data under pulse loading over two seasons (winter and summer). In addition to ANFIS, a mechanistic fecal coliform removal model was validated using the same sets of experimental data. The results showed that the ANFIS model significantly improved the ability to describe the dynamics of E. coli removal under pulse loading. The mechanistic model performed poorly as demonstrated by lower coefficient of determination and higher root mean squared error compared to the ANFIS models. The E. coli concentrations corresponding to the inflection points on the tracer study were keys to improving the predictability of the E. coli removal model.