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Nitrate pollution is of considerable global concern as a threat to human health and aquatic ecosystems. Nowadays, δ 15 N/δ 18 O-NO 3 - combined with a Bayesian-based SIAR model are widely used to identify riverine nitrate sources. However, little is known regarding the effect of variations in pollution source isotopic composition on nitrate source contributions. Herein, we used δ 15 N/δ 18 O-NO 3 - , SIAR modeling, probability statistical analysis and a perturbing method to quantify the contributions and uncertainties of riverine nitrate sources in the Wen-Rui Tang River of China and to further investigate the model sensitivity of each nitrate source. The SIAR model confirmed municipal sewage (MS) as the major nitrate source (58.5-75.7%). Nitrogen fertilizer (NF, 8.6-20.9%) and soil nitrogen (SN, 7.8-20.1%) were also identified as secondary nitrate sources, while atmospheric deposition (AD, <0.1-7.9%) was a minor source. Uncertainties associated with NF (UI 90 = 0.32) and SN (UI 90 = 0.30) were high, whereas those associated with MS (UI 90 = 0.14) were moderate and AD low (UI 90 = 0.0087). A sensitivity analysis was performed for the SIAR modeling and indicated that the isotopic composition of the predominant source (i.e., MS in this study) had the strongest effect on the overall riverine nitrate source apportionment results., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Metal contamination in aquatic environments is a severe global concern to human health and aquatic ecosystems. This study used several risk assessment indices, to evaluate metal (Cu, Zn, Pb, Cd and Cr) environmental risk of riverine surface sediments across the rural-urban interface of the rapidly developing Wen-Rui Tang River watershed in eastern China. Risk assessments were determined for 38 sites based on the potential ecological risk index (RI), consensus-based sediment quality guidelines (SQGs) and risk assessment code (RAC). Land-use cluster analysis showed that sediments were severely contaminated, especially for Cd, whose concentrations were ∼100 times higher than background levels and had a high proportion in the bioaccessible fraction. According to RI, E r Cd was identified with extremely high risk potential, resulting in the highest ecological risk of Cluster 4 (industrial). Similarly, risk within Cluster 4 (industrial) was also ranked highest by SQGs assessment due to the high proportion of industrial land use. Zinc was determined with high risk due to its high concentration compared to its effect range medium (ERM) value. Discrepancies in predicting environmental risks from metals among the three indices were mainly attributed to the contrasting definitions of these metrics. Environmental risk uncertainty derived from spatial variation was further estimated by Monte Carlo simulation and ranked as: Zn > Cd > Cr > Pb > Cu. This comprehensive environmental risk assessment provides important information to guide remediation strategies for management of metal contamination at the watershed scale., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Microplastics are a global concern for their threat to marine ecosystems. Recent studies report a lack of smaller microplastics (<300 μm) in oceans attributed to a "loss in ocean". Several hypotheses have been proposed to explain the absence of smaller microplastics, but their fate and transport remain an enigma. Our study recovered high concentrations of microplastics (32947 ± 15342 items kg -1 dry sediment) from sediments of a coastal plain river network in eastern China, with the <300 μm fraction accounting for ∼85% of total microplastic particles. Microplastic concentrations were generally higher in sediments from tributary streams and streams surrounded by industrial land use. The high variability of microplastics within the watershed indicates that the distribution of microplastics is regulated by several factors, such as distance to source(s), river flow characteristics, buoyancy behavior, degradation, etc. Fragment and foam forms dominated the small microplastics, while fibers were less prevalent in the <300 μm fraction and more abundant in downstream sites. The dominance of small microplastics in riverine sediments in this study provides a possible mechanism to explain the relative absence of small microplastics in the ocean, and advocates for quantification of the whole size spectrum of microplastics in future studies of riverine microplastic fluxes., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Heavy metal pollution of aquatic environments in rapidly developing industrial regions is of considerable global concern due to its potential to cause serious harm to aquatic ecosystems and human health. This study assessed heavy metal contamination of sediments in a highly industrialized urban watershed of eastern China containing several historically unregulated manufacturing enterprises. Total concentrations and solid-phase fractionation of Cu, Zn, Pb, Cr and Cd were investigated for 39 river sediments using multivariate statistical analysis and geographically weighted regression (GWR) methods to quantitatively examine the relationship between land use and heavy metal pollution at the watershed scale. Results showed distinct spatial patterns of heavy metal contamination within the watershed, such as higher concentrations of Zn, Pb and Cd in the southwest and higher Cu concentration in the east, indicating links to specific pollution sources within the watershed. Correlation and PCA analyses revealed that Zn, Pb and Cd were dominantly contributed by anthropogenic activities; Cu originated from both industrial and agricultural sources; and Cr has been altered by recent pollution control strategies. The GWR model indicated that several heavy metal fractions were strongly correlated with industrial land proportion and this correlation varied with the level of industrialization as demonstrated by variations in local GWR R 2 values. This study provides important information for assessing heavy metal contaminated areas, identifying heavy metal pollutant sources, and developing regional-scale remediation strategies., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Accurate quantification of dissolved oxygen (DO) is critically important for managing water resources and controlling pollution. Artificial intelligence (AI) models have been successfully applied for modeling DO content in aquatic ecosystems with limited data. However, the efficacy of these AI models in predicting DO levels in the hypoxic river systems having multiple pollution sources and complicated pollutants behaviors is unclear. Given this dilemma, we developed a promising AI model, known as support vector machine (SVM), to predict the DO concentration in a hypoxic river in southeastern China. Four different calibration models, specifically, multiple linear regression, back propagation neural network, general regression neural network, and SVM, were established, and their prediction accuracy was systemically investigated and compared. A total of 11 hydro-chemical variables were used as model inputs. These variables were measured bimonthly at eight sampling sites along the rural-suburban-urban portion of Wen-Rui Tang River from 2004 to 2008. The performances of the established models were assessed through the mean square error (MSE), determination coefficient (R 2 ), and Nash-Sutcliffe (NS) model efficiency. The results indicated that the SVM model was superior to other models in predicting DO concentration in Wen-Rui Tang River. For SVM, the MSE, R 2 , and NS values for the testing subset were 0.9416 mg/L, 0.8646, and 0.8763, respectively. Sensitivity analysis showed that ammonium-nitrogen was the most significant input variable of the proposal SVM model. Overall, these results demonstrated that the proposed SVM model can efficiently predict water quality, especially for highly impaired and hypoxic river systems.

As an important regulator of pollutants in overland flow and interflow, land use has become an essential research component for determining the relationships between surface water quality and pollution sources. This study investigated the use of ordinary least squares (OLS) and geographically weighted regression (GWR) models to identify the impact of land use and population density on surface water quality in the Wen-Rui Tang River watershed of eastern China. A manual variable excluding-selecting method was explored to resolve multicollinearity issues. Standard regression coefficient analysis coupled with cluster analysis was introduced to determine which variable had the greatest influence on water quality. Results showed that: (1) Impact of land use on water quality varied with spatial and seasonal scales. Both positive and negative effects for certain land-use indicators were found in different subcatchments. (2) Urban land was the dominant factor influencing N, P and chemical oxygen demand (COD) in highly urbanized regions, but the relationship was weak as the pollutants were mainly from point sources. Agricultural land was the primary factor influencing N and P in suburban and rural areas; the relationship was strong as the pollutants were mainly from agricultural surface runoff. Subcatchments located in suburban areas were identified with urban land as the primary influencing factor during the wet season while agricultural land was identified as a more prevalent influencing factor during the dry season. (3) Adjusted R 2 values in OLS models using the manual variable excluding-selecting method averaged 14.3% higher than using stepwise multiple linear regressions. However, the corresponding GWR models had adjusted R 2 ~59.2% higher than the optimal OLS models, confirming that GWR models demonstrated better prediction accuracy. Based on our findings, water resource protection policies should consider site-specific land-use conditions within each watershed to optimize mitigation strategies for contrasting land-use characteristics and seasonal variations., (Copyright © 2016 Elsevier B.V. All rights reserved.)

In the context of water resource management and pollution control, the characterization of water quality impairments and identification of dominant pollutants are of critical importance. In this study, water quality impairment was assessed on the basis of 7 hydrochemical variables that were monitored bimonthly at 17 sites in 2010 along the rural-suburban-urban portion of the Wen-Rui Tang River in eastern China. Seven methods were used to assess water quality in the river system. These methods included single-factor assessment, water quality grading, comprehensive pollution index, the Nemerow pollution index, principle component analysis, fuzzy comprehensive evaluation, and comprehensive water quality identification index. Our analysis showed that the comprehensive water quality identification index was the best method for assessing water quality in the Wen-Rui Tang River due to its ability to effectively characterize highly polluted waters with multiple impairments. Furthermore, a guideline for the applications of these methods was presented based on their characteristics and efficacy. Results indicated that the dominant pollutant impairing water quality was total nitrogen comprised mainly of ammonium. The temporal variation of water quality was closely related to precipitation as a result of dilution. The spatial variation of water quality was associated with anthropogenic influences (urban, industrial, and agriculture activities) and water flow direction (downstream segments experiencing cumulative effects of upstream inputs). These findings provide valuable information and guidance for water pollution control and water resource management in highly polluted surface waters with multiple water quality impairments in areas with rapid industrial growth and urbanization.

Identification of representative sampling sites is a critical issue in establishing an effective water quality monitoring program. This is especially important at the urban-agriculture interface where water quality conditions can change rapidly over short distances. The objective of this research was to optimize the spatial allocation of discrete monitoring sites for synoptic water quality monitoring through analysis of continuous longitudinal monitoring data collected by attaching a water quality sonde and GPS to a boat. Sampling was conducted six times from March to October 2009 along a 6.5 km segment of the Wen-Rui Tang River in eastern China that represented an urban-agricultural interface. When travelling at a velocity of ∼2.4 km h(-1), this resulted in water quality measurements at ∼20 m interval. Ammonia nitrogen (NH(4)(+)-N), electrical conductivity (EC), dissolved oxygen (DO), and turbidity data were collected and analyzed using Cluster Analysis (CA) to identify optimal locations for establishment of long-term monitoring sites. The analysis identified two distinct water quality segments for NH(4)(+)-N and EC and three distinct segments for DO and turbidity. According to our research results, the current fixed-location sampling sites should be adjusted to more effectively capture the distinct differences in the spatial distribution of water quality conditions. In addition, this methodology identified river reaches that require more comprehensive study of the factors leading to the changes in water quality within the identified river segment. The study demonstrates that continuous longitudinal monitoring can be a highly effective method for optimizing monitoring site locations for water quality studies.

Water quality has degraded dramatically in Wen-Rui Tang River watershed, Zhejiang, China, especially due to rapid economic development since 1995. This paper aims to assess spatial and temporal variations of the main pollutants (NH⁺₄-N, TN, BOD(5), COD(Mn), DO) of water quality in Wen-Rui Tang River watershed, using the geographic information system, cluster analysis (CA) and principal component analysis (PCA). Results showed that concentrations of BOD(5), COD(Mn), NH⁺₄-N, and TN were significantly higher in tertiary rivers than in primary and secondary rivers. From April 2006 to March 2007, the concentrations of NH⁺ ₄-N (2.25-57.9 mg/L) and TN (3.78-70.4 mg/L) in all samples exceeded Type V national water quality standards (≥2 mg/L), while 5.3% of all COD(Mn) (1.83-27.5 mg/L) and 33.6% of all BOD(5) (0.34-50.4 mg/L) samples exceeded Type V national water quality standards (COD(Mn) ≥ 15 mg/L, BOD(5) ≥ 10 mg/L). Monthly changes of pollutant concentrations did not show a clear pattern, but correlation analysis indicated that NH⁺₄-N and TN in tertiary rivers had a significant negative correlation with 5-day cumulative rainfall and monthly rainfall, while there were no significant correlations in primary and secondary rivers. The results of CA and spatial analysis showed that the northern part of Wen-Rui Tang River watershed was the most seriously polluted. This region is characterized by the high population density and industrial and commercial activities. The PCA and spatial analysis indicated that the degraded water quality is caused by anthropogenic activities and poor wastewater management.

Water quantity and quality joint operation is a new mode in the present dams' operation research. It has become a hot topic in governmental efforts toward integrated basin improvement. This paper coupled a water quantity and quality joint operation model (QCmode) and genetic algorithm with Soil and Water Assessment Tool (SWAT). Together, these tools were used to explore a reasonable operation of dams and floodgates at the basin scale. Wenyu River Catchment, a key area in Beijing, was selected as the case study. Results showed that the coupled water quantity and quality model of Wenyu River Catchment more realistically simulates the process of water quantity and quality control by dams and floodgates. This integrated model provides the foundation for research of water quantity and quality optimization on dam operation in Wenyu River Catchment. The results of this modeling also suggest that current water quality of Wenyu River will improve following the implementation of the optimized operation of the main dams and floodgates. By pollution control and water quantity and quality joint operation of dams and floodgates, water quality of Wenyu river will change significantly, and the available water resources will increase by 134%, 32%, 17%, and 82% at the downstream sites of Sha River Reservoir, Lutong Floodgate, Xinpu Floodgate, and Weigou Floodgate, respectively. The water quantity and quality joint operation of dams will play an active role in improving water quality and water use efficiency in Wenyu River Basin. The research will provide the technical support for water pollution control and ecological restoration in Wenyu River Catchment and could be applied to other basins with large number of dams. Its application to the Wenyu River Catchment has a great significance for the sustainable economic development of Beijing City.

The development of the leather industry in the Aojiang watershed of Zhejiang province increased the release of waste water. In the waste water, ammonium nitrogen (NH(+) (4)-N) and germanium (Ge) are the main pollutants. In recent years, literature has documented that the intake of high concentrations of NH(+) (4)-N and Ge harms human health and biological species. This paper focuses on assessing the trends of NH(+) (4)-N and Ge concentrations in the released waste water in Aojiang watershed and on understanding their relationships with the released waste water using regression and correlation statistics. The paper also utilizes the integrated pollution index to evaluate the water quality in the watershed. Preliminary results show that, from 1992 to 1998, the concentrations of NH(+) (4)-N and total Ge increased 13 and 14 times, respectively, and they decreased somewhat after 1998. The concentrations of NH(+) (4)-N and total Ge are positively correlated to the amount of released waste water. These concentrations of NH(+) (4)-N and Ge, respectively, exceed 12 and 3 times, of the water standards. The water quality in the watershed degraded from Type III in 1992 to over Type V in 2003 when they were compared with the national water quality standards. It appeared that the pollution had positive correlation with leather industry production. The degraded water has no doubt affected human health and the ecosystem health. These results can provide scientific information for the local government to reasonably adjust the industry structure and reduce the pollution to protect the environment.

Nitrate pollution is of considerable global concern as a threat to human health and aquatic ecosystems. Nowadays, δ15N/δ18O-NO3- combined with a Bayesian-based SIAR model are widely used to identify riverine nitrate sources. However, little is known regarding the effect of variations in pollution source isotopic composition on nitrate source contributions. Herein, we used δ15N/δ18O-NO3-, SIAR modeling, probability statistical analysis and a perturbing method to quantify the contributions and uncertainties of riverine nitrate sources in the Wen-Rui Tang River of China and to further investigate the model sensitivity of each nitrate source. The SIAR model confirmed municipal sewage (MS) as the major nitrate source (58.5-75.7%). Nitrogen fertilizer (NF, 8.6-20.9%) and soil nitrogen (SN, 7.8-20.1%) were also identified as secondary nitrate sources, while atmospheric deposition (AD,

Metal contamination in aquatic environments is a severe global concern to human health and aquatic ecosystems. This study used several risk assessment indices, to evaluate metal (Cu, Zn, Pb, Cd and Cr) environmental risk of riverine surface sediments across the rural-urban interface of the rapidly developing Wen-Rui Tang River watershed in eastern China. Risk assessments were determined for 38 sites based on the potential ecological risk index (RI), consensus-based sediment quality guidelines (SQGs) and risk assessment code (RAC). Land-use cluster analysis showed that sediments were severely contaminated, especially for Cd, whose concentrations were ∼100 times higher than background levels and had a high proportion in the bioaccessible fraction. According to RI, ErCd was identified with extremely high risk potential, resulting in the highest ecological risk of Cluster 4 (industrial). Similarly, risk within Cluster 4 (industrial) was also ranked highest by SQGs assessment due to the high proportion of industrial land use. Zinc was determined with high risk due to its high concentration compared to its effect range medium (ERM) value. Discrepancies in predicting environmental risks from metals among the three indices were mainly attributed to the contrasting definitions of these metrics. Environmental risk uncertainty derived from spatial variation was further estimated by Monte Carlo simulation and ranked as: Zn > Cd > Cr > Pb > Cu. This comprehensive environmental risk assessment provides important information to guide remediation strategies for management of metal contamination at the watershed scale.

Microplastics are a global concern for their threat to marine ecosystems. Recent studies report a lack of smaller microplastics (

Heavy metal pollution of aquatic environments in rapidly developing industrial regions is of considerable global concern due to its potential to cause serious harm to aquatic ecosystems and human health. This study assessed heavy metal contamination of sediments in a highly industrialized urban watershed of eastern China containing several historically unregulated manufacturing enterprises. Total concentrations and solid-phase fractionation of Cu, Zn, Pb, Cr and Cd were investigated for 39 river sediments using multivariate statistical analysis and geographically weighted regression (GWR) methods to quantitatively examine the relationship between land use and heavy metal pollution at the watershed scale. Results showed distinct spatial patterns of heavy metal contamination within the watershed, such as higher concentrations of Zn, Pb and Cd in the southwest and higher Cu concentration in the east, indicating links to specific pollution sources within the watershed. Correlation and PCA analyses revealed that Zn, Pb and Cd were dominantly contributed by anthropogenic activities; Cu originated from both industrial and agricultural sources; and Cr has been altered by recent pollution control strategies. The GWR model indicated that several heavy metal fractions were strongly correlated with industrial land proportion and this correlation varied with the level of industrialization as demonstrated by variations in local GWR R2 values. This study provides important information for assessing heavy metal contaminated areas, identifying heavy metal pollutant sources, and developing regional-scale remediation strategies.

Heavy metal pollution is a major concern in China because of its serious effects on human health. To assess potential human health and ecological risks of heavy metal pollution, concentration data for seven heavy metals (As, Pb, Cd, Cr, Hg, Cu, Zn) from 14 sites spanning the rural-urban interface of the Wen-Rui Tang River watershed in southeast China were collected from 2000 to 2010. The heavy metal pollution index (HPI), hazard index (HI) and carcinogenic risk (CR) metrics were used to assess potential heavy metal risks. Further, we evaluated the uncertainty associated with the risk assessment indices using Monte Carlo analysis. Results indicated that all HPI values were lower than the critical level of 100 suggesting that heavy metal levels posed acceptable ecological risks; however, one site having an industrial point-source input reached levels of 80-97 on several occasions. Heavy metal concentrations fluctuated over time, and the decrease after 2007 is due to increased wastewater collection. The HI suggested low non-carcinogenic risk throughout the study period (HI  10-4). While most metrics for human health risk did not exceed critical values for heavy metals, there is still a potential human health risk from chronic exposure to low heavy metal concentrations due to long-term exposure and potential metal interactions. Results of this study inform water pollution remediation and management efforts designed to protect public health in polluted urban area waterways common in rapidly developing regions.

As an important regulator of pollutants in overland flow and interflow, land use has become an essential research component for determining the relationships between surface water quality and pollution sources. This study investigated the use of ordinary least squares (OLS) and geographically weighted regression (GWR) models to identify the impact of land use and population density on surface water quality in the Wen-Rui Tang River watershed of eastern China. A manual variable excluding-selecting method was explored to resolve multicollinearity issues. Standard regression coefficient analysis coupled with cluster analysis was introduced to determine which variable had the greatest influence on water quality. Results showed that: (1) Impact of land use on water quality varied with spatial and seasonal scales. Both positive and negative effects for certain land-use indicators were found in different subcatchments. (2) Urban land was the dominant factor influencing N, P and chemical oxygen demand (COD) in highly urbanized regions, but the relationship was weak as the pollutants were mainly from point sources. Agricultural land was the primary factor influencing N and P in suburban and rural areas; the relationship was strong as the pollutants were mainly from agricultural surface runoff. Subcatchments located in suburban areas were identified with urban land as the primary influencing factor during the wet season while agricultural land was identified as a more prevalent influencing factor during the dry season. (3) Adjusted R2 values in OLS models using the manual variable excluding-selecting method averaged 14.3% higher than using stepwise multiple linear regressions. However, the corresponding GWR models had adjusted R2 ~59.2% higher than the optimal OLS models, confirming that GWR models demonstrated better prediction accuracy. Based on our findings, water resource protection policies should consider site-specific land-use conditions within each watershed to optimize mitigation strategies for contrasting land-use characteristics and seasonal variations.

The authors developed a simple screening-level model of exposure of aquatic species to pyrethroid insecticides for the lower American River watershed (California, USA). The model incorporated both empirically derived washoff functions based on existing, small-scale precipitation simulations and empirical data on pyrethroid insecticide use and watershed properties for Sacramento County, California, USA. The authors calibrated the model to in-stream monitoring data and used it to predict daily river pyrethroid concentration from 1995 through 2010. The model predicted a marked increase in pyrethroid toxic units starting in 2000, coincident with an observed watershed-wide increase in pyrethroid use. After 2000, approximately 70% of the predicted total toxic unit exposure in the watershed was associated with the pyrethroids bifenthrin and cyfluthrin. Pyrethroid applications for aboveground structural pest control on the basis of suspension concentrate categorized product formulations accounted for greater than 97% of the predicted total toxic unit exposure. Projected application of mitigation strategies, such as curtailment of structural perimeter band and barrier treatments as recently adopted by the California Department of Pesticide Regulation, reduced predicted total toxic unit exposure by 84%. The model also predicted that similar reductions in surface-water concentrations of pyrethroids could be achieved through a switch from suspension concentrate-categorized products to emulsifiable concentrate-categorized products without restrictions on current-use practice. Even with these mitigation actions, the predicted concentration of some pyrethroids would continue to exceed chronic aquatic life criteria.

Trend and change-point analyses of water quality time series data have important implications for pollution control and environmental decision-making. This paper developed a new approach to assess trends and change-points of water quality parameters by integrating locally weighted polynomial regression (LWPR) and segmented regression (SegReg). Firstly, LWPR was used to pretreat the original water quality data into a smoothed time series to represent the long-term trend of water quality. Then, SegReg was used to identify the long-term trends and change-points of the smoothed time series. Finally, statistical tests were applied to determine the significance of the long-term trends and change-points. The efficacy of this approach was validated using a 10-year record of total nitrogen (TN) and chemical oxygen demand (COD) from Shanxi Reservoir watershed in eastern China. Results showed that this approach was straightforward and reliable for assessment of long-term trends and change-points on irregular water quality datasets. The reliability was verified by statistical tests and practical considerations for Shanxi Reservoir watershed. The newly developed integrated LWPR-SegReg approach is not only limited to the assessment of trends and change-points of water quality parameters but also has a broad application to other fields with long-term time series records. [ABSTRACT FROM AUTHOR]

Many different approaches have been suggested for the assessment of organic pollution and related water quality variables. However, many methods used to assess organic pollution cannot qualitatively estimate the degree of importance of each pollutant. Water quality identification indices can provide comprehensive water quality information, which is both qualitative and quantitative, especially for areas worse than Grade V of the National Surface Water Quality Standard of China. However, this method considers every indicator equally, without considering the degree of importance of each parameter. Consequently, in this study, the entropy weight method was combined with the water quality identification index method. The improved comprehensive water quality identification index was applied to a water quality data-set for the Wen-Rui Tang River in China, which was generated in 2009 during six months of monitoring at 16 different sites and included seven parameters. The results indicated that the improved comprehensive water quality identification index provided an objective evaluation of each water quality indicator. Understanding the significance of each water quality parameter will help to implement more effective water quality improvement plans. [ABSTRACT FROM AUTHOR]

The Soil and Water Assessment Tool (SWAT) was calibrated for hydrology conditions in an agricultural watershed of Orestimba Creek, California, and applied to simulate fate and transport of two organophosphate pesticides chlorpyrifos and diazinon. The model showed capability in evaluating pesticide fate and transport processes in agricultural fields and instream network. Management-oriented sensitivity analysis was conducted by applied stochastic SWAT simulations for pesticide distribution. Results of sensitivity analysis identified the governing processes in pesticide outputs as surface runoff, soil erosion, and sedimentation in the study area. By incorporating sensitive parameters in pesticide transport simulation, effects of structural best management practices (BMPs) in improving surface water quality were demonstrated by SWAT modeling. This study also recommends conservation practices designed to reduce field yield and in-stream transport capacity of sediment, such as filter strip, grassed waterway, crop residue management, and tailwater pond to be implemented in the Orestimba Creek watershed. [Copyright &y& Elsevier]