Using watershed characteristics to enhance fecal source identification.

Fecal pollution is one of the most prevalent forms of pollution affecting waterbodies worldwide, threatening public health and negatively impacting aquatic environments. Microbial source tracking (MST) applies polymerase chain reaction (PCR) technology to help identify the source of fecal pollution. In this study, we combine spatial data for two watersheds with general and host-associated MST markers to target human (HF183/BacR287), bovine (CowM2), and general ruminant (Rum2Bac) sources. Concentrations of MST markers in samples were determined with droplet digital PCR (ddPCR). The three MST markers were detected at all sites (n = 25), but bovine and general ruminant markers were significantly associated with watershed characteristics. MST results, combined with watershed characteristics, suggest that streams draining areas with low-infiltration soil groups and high agricultural land use are at an increased risk for fecal contamination. Microbial source tracking has been applied in numerous studies to aid in identifying the sources of fecal contamination, but these studies usually lack information on the involvement of watershed characteristics. Our study combined watershed characteristics with MST results to provide more comprehensive insight into the factors that influence fecal contamination in order to implement the most effective best management practices.

Optimization of bioenergy crop selection and placement based on a stream health indicator using an evolutionary algorithm.

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.

Modeling the hydrological significance of wetland restoration scenarios.

Wetlands provide multiple socio-economic benefits, among them mitigating flood through short- and long-term water storage functions and assisting with reduction of downstream flood peaks. However, their effectiveness in controlling floods is dictated by wetland size and distribution within a watershed. Due to the complexity of wetland hydrological processes at the watershed scale, the Soil and Water Assessment Tool (SWAT) was used to study the impact of wetland restoration on streamflow rates and peaks in the Shiawassee River watershed of Michigan. Wetland restoration scenarios were developed based on combinations of wetland area (50, 100, 250, and 500 ha) and wetland depth (15, 30, 61, and 91 cm). Increasing wetland area, rather than depth, had a greater impact on long-term average daily streamflow. Wetland implementation resulted in negligible reductions in daily peak flow rates and frequency of peak flow events at the watershed outlet. In developing high impact areas for wetland restoration, similar locations were identified for reduction of subbasin and watershed outlet streamflow. However, the best combinations of area/depth differed depending on the goal of the restoration plan.

Evaluating the impact of field-scale management strategies on sediment transport to the watershed outlet.

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.

Evaluating the capabilities of watershed-scale models in estimating sediment yield at field-scale.

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.

Linking biological integrity and watershed models to assess the impacts of historical land use and climate changes on stream health.

Land use change and other human disturbances have significant impacts on physicochemical and biological conditions of stream systems. Meanwhile, linking these disturbances with hydrology and water quality conditions is challenged due to the lack of high-resolution datasets and the selection of modeling techniques that can adequately deal with the complex and nonlinear relationships of natural systems. This study addresses the above concerns by employing a watershed model to obtain stream flow and water quality data and fill a critical gap in data collection. The data were then used to estimate fish index of biological integrity (IBI) within the Saginaw Bay basin in Michigan. Three methods were used in connecting hydrology and water quality variables to fish measures including stepwise linear regression, partial least squares regression, and fuzzy logic. The IBI predictive model developed using fuzzy logic showed the best performance with the R (2) = 0.48. The variables that identified as most correlated to IBI were average annual flow, average annual organic phosphorus, average seasonal nitrite, average seasonal nitrate, and stream gradient. Next, the predictions were extended to pre-settlement (mid-1800s) land use and climate conditions. Results showed overall significantly higher IBI scores under the pre-settlement land use scenario for the entire watershed. However, at the fish sampling locations, there was no significant difference in IBI. Results also showed that including historical climate data have strong influences on stream flow and water quality measures that interactively affect stream health; therefore, should be considered in developing baseline ecological conditions.

Evaluation of targeting methods for implementation of best management practices in the Saginaw River Watershed.

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.

Quantifying the potential impacts of climate change on irrigation demand, crop yields, and green water scarcity in the New Jersey Coastal Plain.

Climate change has significant implications for irrigated agriculture and global food security. Understanding how altered precipitation patterns and magnitudes, coupled with rising growing season temperatures, affect irrigation demand and crop production is a prerequisite for formulating effective water resources management strategies. This study evaluated the effects of near-term climate change (centered on 2035) on irrigation demand, green water scarcity, and row crop yields in a major agricultural watershed in southern New Jersey, USA. Downscaled precipitation and temperature from six General Circulation Models (GCMs) for two representative concentration pathways (RCP-4.5 and 8.5) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) were used to drive the Soil and Water Assessment Tool hydrological model. Temperature and precipitation increases resulted in greater surface runoff, lateral flow, groundwater recharge, and total streamflow. Seasonal ET for corn is projected to alter between -3.0 % to 0.5 %, with irrigation demand between -17 % to -1 %, and yield ranges between -4 % to +9 % depending on the GCMs in the RCP-4.5 scenario, with similar patterns projected by RCP-8.5 scenario. For soybean, the simulation also indicates a declining trend of ET and irrigation demand while increasing yield. Increasing yield for both crops is attributed to changes in agronomic management practices combined with genetically improved cultivars and higher soil fertility due to CO2 fertilization. Green water scarcity analysis under future climate change for corn and soybean display a decreased soil moisture stress due to increased water use efficiency resulting from reduced stomatal conductance under elevated CO2 concentration.

A coupled hydrodynamic (HEC-RAS 2D) and water quality model (WASP) for simulating flood-induced soil, sediment, and contaminant transport.

Increased intensity and frequency of floods raise concerns about the release and transport of contaminated soil and sediment to and from rivers and streams. To model these processes during flooding events, we developed an External Coupler in Python to link the Hydrologic Engineering Center-River Analysis System (HEC-RAS) 2D hydrodynamic model to the Water Quality Analysis Simulation Program (WASP). Accurate data transfer from a hydrodynamic model to a water quality model is critical. Our test results showed the External Coupler successfully linked HEC-RAS and WASP and addressed technical challenges in aggregating flow data and conserving mass during the flood event. We ran the coupled models for a 100-year flood event to calculate flood-induced transport of sediment-associated arsenic in Woodbridge Creek, NJ. Change in surface sediment and arsenic at the end of 48-h flood simulation ranged from a net loss of 13.5 cm to a net gain of 11.6 cm, and 16.2 to 2.9 mg/kg, respectively, per model segment, which demonstrates the capability of the coupled model for simulating sediment and contaminant transport in flood.

Changes in event-based streamflow magnitude and timing after suburban development with infiltration-based stormwater management.

Green stormwater infrastructure implementation in urban watersheds has outpaced our understanding of practice effectiveness on streamflow response to precipitation events. Long-term monitoring of experimental suburban watersheds in Clarksburg, Maryland, USA, provided an opportunity to examine changes in event-based streamflow metrics in two treatment watersheds that transitioned from agriculture to suburban development with a high density of infiltration-focused stormwater control measures (SCMs). Urban Treatment 1 has predominantly single family detached housing with 33% impervious cover and 126 SCMs. Urban Treatment 2 has a mix of single family detached and attached housing with 44% impervious cover and 219 SCMs. Differences in streamflow-event magnitude and timing were assessed using a before-after-control-reference-impact design to compare urban treatment watersheds with a forested control and an urban control with detention-focused SCMs. Streamflow and precipitation events were identified from 14 years of sub-daily monitoring data with an automated approach to characterize peak streamflow, runoff yield, runoff ratio, streamflow duration, time to peak, rise rate, and precipitation depth for each event. Results indicated that streamflow magnitude and timing were altered by urbanization in the urban treatment watersheds, even with SCMs treating 100% of the impervious area. The largest hydrologic changes were observed in streamflow magnitude metrics, with greater hydrologic change in Urban Treatment 2 compared with Urban Treatment 1. Although streamflow changes were observed in both urban treatment watersheds, SCMs were able to mitigate peak flows and runoff volumes compared with the urban control. The urban control had similar impervious cover to Urban Treatment 2, but Urban Treatment 2 had more than twice the precipitation depth needed to initiate a flow response and lower median peak flow and runoff yield for events less than 20 mm. Differences in impervious cover between the Urban Treatment watersheds appeared to be a large driver of differences in streamflow response, rather than SCM density. Overall, use of infiltration-focused SCMs implemented at a watershed-scale did provide enhanced attenuation of peak flow and runoff volumes compared to centralized-detention SCMs.

Sediment retention by natural landscapes in the conterminous United States.

Soils provide vital ecosystem services, from sequestering carbon to providing food and moderating floods. Soil erosion threatens the provisioning of these services and degrades downstream water quality. Vegetation plays an important role in soil retention: by holding it in place, soil can continue to provide ecosystem goods and services and protect water resources. The aims of this study were to: (1) develop a 30-meter resolution map of erosion in the conterminous United States, and (2) quantify the soil retention service of natural vegetation. Using the Revised Universal Soil Loss Equation and physiographic and remote sensing datasets, we estimated sheet and rill erosion. We also developed a map of sediment delivery ratio to connect erosion to downstream delivery using hydrologic connectivity. The estimated sheet and rill erosion in the conterminous United States was 1.55 Pg yr-1, of which 0.52 Pg yr-1 reached waterbodies. Natural land cover prevents 12.3 Pg yr-1 of sheet and rill erosion and 5.1 Pg yr-1 in delivery to waterbodies. The value of natural land cover in retaining sediment is a function of the land cover, physiographic characteristics, and spatial context. This study has implications for spatial prioritization of natural land cover preservation and agricultural land management to minimize sediment erosion and delivery.

Development of a spatially complete floodplain map of the conterminous United States using random forest.

Floodplains perform several important ecosystem services, including storing water during precipitation events and reducing peak flows, thus reducing flooding of downstream communities. Understanding the relationship between flood inundation and floodplains is critical for ecosystem and community health and well-being, as well as targeting floodplain and riparian restoration. Many communities in the United States, particularly those in rural areas, lack inundation maps due to the high cost of flood modeling. Only 60% of the conterminous United States has Flood Insurance Rate Maps (FIRMs) through the U.S. Federal Emergency Management Agency (FEMA). We developed a 30-meter resolution flood inundation map of the conterminous United States (CONUS) using random forest classification to fill the gaps in the FIRM. Input datasets included digital elevation model (DEM)-derived variables, flood-related soil characteristics, and land cover. The existing FIRM 100-year floodplains, called the Special Flood Hazard Area (SHFA), were used to train and test the random forests for fluvial and coastal flooding. Models were developed for each hydrologic unit code level four (HUC-4) watershed and each 30-meter pixel in the CONUS was classified as floodplain or non-floodplain. The most important variables were DEM-derivatives and flood-based soil characteristics. Models captured 79% of the SFHA in the CONUS. The overall F1 score, which balances precision and recall, was 0.78. Performance varied geographically, exceeding the CONUS scores in temperate and coastal watersheds but were less robust in the arid southwest. The models also consistently identified headwater floodplains not present in the SFHA, lowering performance measures but providing critical information missing in many low-order stream systems. The performance of the random forest models demonstrates the method's ability to successfully fill in the remaining unmapped floodplains in the CONUS, while using only publicly available data and open source software.

Ecohydrological modeling for large-scale environmental impact assessment.

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.

Ecohydrological model parameter selection for stream health evaluation.

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.

Modeling the effects of conservation practices on stream health.

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.