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No‐till is one of the common conservation practices implemented in the Fort Cobb Reservoir Watershed (FCREW) located in central Oklahoma to improve soil and water resources while ensuring sustainable crop production. In this study, we used the recently developed Multi‐Objective Evolutionary Algorithm for the Soil and Water Assessment Tool model (SWAT‐MEA) to determine the optimal spatial placement of crop management systems when converting from conventional tillage to no‐till in the FCREW. A previously calibrated and validated Soil Water and Assessment Tool (SWAT) model for the FCREW for conventionally tilled land under winter wheat (Triticum aestivum L.), sorghum (Sorghum bicolor L.), and peanuts (Arachis hypogaea L.) cropping system was used. The objective functions for this study were maximizing crop yield ranks while minimizing sediment, total P (TP), and total N (TN) yield loads. Four modifications made in the SWAT‐MEA in order to achieve the defined goal of this study include an automated process of conversion from conventional to no‐till, ability to use crop yield ranks in the optimization process and to sum up species of N and P to TN and TP, and creation of a public domain standalone executable. Results indicated that converting from a conventionally tilled to no‐till system under optimal placement of crop management systems reduced TN, TP, and sediment yield losses by 45, 32, and 65%, respectively, while crop yields were not significantly affected. Overall, this case study shows that the SWAT‐MEA was able to successfully identify the optimal placement of crop management systems under no‐till condition and can be used for decision making. Core Ideas: The SWAT‐Multi‐Objective Evolutionary Algorithm was evaluated for optimal crop placement decisions.Four modifications were made in SWAT‐MEA in order to accomplish study objective.Modified SWAT‐MEA successfully identified optimal placement of five crop management systems.No‐till improved water quality while maintaining yields for optimally placed crops. [ABSTRACT FROM AUTHOR]

Understanding drought effects on vegetative health for crop producing locations, such as the Seferihisar‐Kavakdere region in western Turkey, is vital for water and crop management. This study used Landsat‐derived spectral vegetation indices, specifically normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and land surface water index (LSWI) to evaluate and compare their performance and sensitivity for three major crops (citrus, grape, and olive) under dry (2008), wet (2010), and normal (2015) climatic conditions. Results showed that all three crops were affected by drought despite two (citrus and grape) being irrigated. However, grape and olive were more affected by drought than citrus. Drought conditions were exacerbated during September and October because of lack of precipitation and because irrigation is not a common practice during these months in this region. Therefore, this study aimed to use and compare drought sensitivities of Landsat‐derived spectral vegetation indices. All studied indices were closely related (.48 ≤ R2 ≤.98) for citrus, grape, and olive. The EVI and LSWI were greater than NDVI when NDVI values were greater than 0.65 for citrus, and LSWI values were larger than NDVI when NDVI values were greater than 0.6 for grape and olive during the wet year. The results showed that different vegetation indices are needed to detect drought from one crop to another under different climatic conditions. Our findings indicate that the irrigation applications in 2008 (drought year) were not sufficient to alleviate the severe drought stress experienced throughout the winter and growing seasons. [ABSTRACT FROM AUTHOR]

Erosion and sedimentation pose serious threats to soil and water quality worldwide, including in the U.S. southern Great Plains. To better understand these processes in agricultural landscapes, eight 1.6‐ha watersheds were established and instrumented in 1976 at the USDA‐ARS Grazinglands Research Laboratory, ∼50 km west of Oklahoma City near El Reno, OK, to measure precipitation and surface runoff quantity and quality. Prior to construction, all watersheds were in native grass, primarily big bluestem (Andropogon gerardii Vitman.), little bluestem [Schizachyrium scoparium (Michx.) Nash], and Indiangrass [Sorghastrum nutans (L.) Nash]; afterwards, four of the eight watersheds were cropped initially into winter wheat (Triticum aestivum L.) (two conventionally tilled and two minimally or no‐till). Although there have been many peer‐reviewed papers from the Water Resources and Erosion (WRE) watersheds, none included all the datasets collected during the period 1977–1999. The objectives of this paper were (a) to present and discuss all archived historical data, including methods of collection and analysis, (b) to provide summary analyses of the variability in each dataset, and (c) to provide details about how to access these datasets. These datasets are valuable resources to improve modeling in relation to land use and management changes, climate variability, and other environmental factors and may be useful in developing strategies to mitigate environmental impacts of agricultural systems. They are available at https://doi.org/10.15482/USDA.ADC/1518421. [ABSTRACT FROM AUTHOR]

Long‐term simulations of agricultural watersheds have often been done assuming constant land use over time, but this is not a realistic assumption for many agricultural regions. This paper presents the soil and water assessment tool (SWAT)‐Landuse Update Tool (LUT), a standalone, user‐friendly desktop‐based tool for updating land use in the SWAT model that allows users to process multi‐year land use data. SWAT‐LUT is compatible with several SWAT model interfaces, provides users with several options to easily prepare and incorporate land use changes (LUCs) over a simulation period, and allows users to incorporate past or emerging land use categories. Incorporation of LUCs is expected to provide realistic model parameterization and scenario simulations. SWAT‐LUT is a public domain interface written in Python programming language. Two applications at the Fort Cobb Reservoir Experimental Watershed located in Oklahoma and pertinent results are provided to demonstrate its use. Incorporating LUCs related to implementation of recommended conservation practices over the years reduced discharge, evapotranspiration, sediment, total nitrogen, and total phosphorus loads by 59%, 9%, 68%, 53%, and 88%, respectively. The user's manual is included in this article as Supporting Information. The SWAT‐LUT executable file and an example SWAT project with three land use rasters and the user's manual are available at the United States Department of Agriculture‐Agricultural Research Service Grazinglands Research Laboratory website under Software. Editor's note: This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series. Research Impact Statement: SWAT‐LUT, a standalone user‐friendly graphical interface for updating land use in SWAT, simplifies incorporation of multiple land use maps during the simulation period of modeling studies. [ABSTRACT FROM AUTHOR]

Global sensitivity analysis can be used for assessing the relative importance of model parameters on model outputs. The sensitivity of parameters usually indicates a temporal variation due to variation in the environmental conditions (e.g., variation in weather or plant growth). In addition, the size of averaging window by which the outputs of a model are aggregated or averaged may impact parameter sensitivities. In this study, temporal variation of parameters sensitives, model performance, as well as the impact of the size of time‐averaging window on evapotranspiration (ET) prediction using the Agricultural Policy/Environmental eXtender (APEX) model are investigated. To achieve these goals, an open‐source package named PARAPEX was developed in R and used to perform dynamic sensitivity and model performance analysis of APEX using parallel computation. PARAPEX reduced the computation time from 5,939 to 379 s (using 20 and 1 computation nodes, respectively). The sensitivity analysis results indicated the parameters accounting for the reducing effect of plant cover on evaporation from the soil surface, the effect of soil on the plant root growth, and the effect of cycling and transformation dynamics of organic matter at the top soil layer as the top sensitive parameters based on the mean daily simulated ET and the Nash–Sutcliffe model performance measure. The dynamic performance analysis indicated poor ET predictions by APEX during the growing seasons. Editor's note: This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series. Research Impact Statement: An open‐source parallel computation package was developed in R for capturing the dynamics of sensitivity and model performance indices in APEX. [ABSTRACT FROM AUTHOR]

Accurate estimation of evapotranspiration (ET) is essential to improve water use efficiency of crop production systems managed under different water regimes. The Agricultural Policy/Environmental eXtender (APEX) model was used to simulate ET using four potential ET (ETp) methods. The objectives were to determine sensitive ET parameters in dryland and irrigated cropping systems and compare ET simulation in the two systems using multiple performance criteria. Measured ET and crop yield data from lysimeter fields located in the United States Department of Agriculture‐Agricultural Research Service Bushland, Texas were used for evaluation. The number of sensitive parameters was higher for dryland (11–14) than irrigated cropping systems (6–8). Only four input parameters: soil evaporation plant cover factor, root growth soil strength, maximum rain intercept, and rain intercept coefficient were sensitive in both cropping systems. Overall, it is possible to find a set of robust parameter values to simulate ET accurately in APEX in both cropping systems using any ETp method. However, more computation time is required for dryland than irrigated cropping system due to a relatively larger number of sensitive input parameters. When all inputs are available, the Penman–Monteith method takes the shortest computation time to obtain one model run with robust parameter values in both cropping systems. However, in areas with limited datasets, one can still obtain reasonable ET simulations using either Priestley–Taylor or Hargreaves. Editor's note: This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series. Research Impact Statement: Parameter values to simulate ET accurately in APEX in dryland and irrigated cropping systems can be obtained using any potential ET method but easiest in the irrigated system using Penman–Monthieth. [ABSTRACT FROM AUTHOR]

Although surface waterbodies are water sources for socio‐economic activities and ecosystems, their functions are threatened by sedimentation. Sedimentation of lakes and reservoirs can result in a loss of storage capacity and altered water quality. The present study assessed the sedimentation status of Lake Naivasha, Kenya, based on sediment distribution and accumulation over the past 50 years, using a Bathymetric Survey System (BSS). The BSS uses multi‐frequency Acoustic Profiling System (APS) to map recently deposited sediments. Sediment core samples were collected with a vibe‐ coring device and dated. Sediment layers corresponding to a period of the past 20 and 50 years were identified. Sediment cores and acoustic images were subsequently used to determine sediment thickness within the lake. The collected depth data from multi‐frequency APS, and dated cores were processed in DepthPic and Surfer software. The sediment depth was extracted in DepthPic, while the sediment volume and distribution were generated from Surfer software. The results from present study indicated that sediment distribution varied from one part of the lake to another for the past 20 and 50 years. High sediment thickness observed in the south‐west and eastern parts of the lake. Between 1996–2016 and 1966–2016 periods, the maximum accumulated sediment thickness was found to be about 0.55 and 1.9 m, with an average sediment thickness of 0.25 and 0.56 m, respectively. The mean sediment load corresponding to the 1966–1996 and 1996–2016 periods was 2.78 × 105 and 4.61 × 105 t/year, respectively. It was found that sediment load into Lake Naivasha has been increasing in the recent past. Based on the present the study, it was found that combined use of BSS, sediment cores and dating can be adopted in many lakes and reservoirs to determine sediment thicknesses even where no prior bathymetric surveys exist for comparison. [ABSTRACT FROM AUTHOR]

Abstract: Forty‐five flood control reservoirs, authorized in the Watershed Protection and Flood Prevention Act 1954, were installed by United States Department of Agriculture (USDA) between 1969 and 1982 in the Little Washita River Experimental Watershed (LWREW), located in central Oklahoma. Over time, these reservoirs have lost sediment and flood storage capacity due to sedimentation, with rates dependent on upstream land use and climate variability. In this study, sedimentation rates for 12 reservoirs representing three major land use categories within LWREW were measured based on bathymetric surveys that used acoustic profiling system. Physiographic and climate attributes of drainage area of surveyed reservoirs were extracted from publicly available data sources including topographic maps, digital elevation models, USDA Natural Resource Conservation Service soils, and weather station databases. Correlation, principal component analysis, and stepwise regression were utilized to analyze the relationship between normalized reservoir sedimentation rates (ReSRa) and the drainage area characteristics to determine the major variables controlling sedimentation within the LWREW. Percent of drainage area with extreme slopes, saturated hydraulic conductivity, and maximum daily rainfall event recorded in spring explained most of the variability in ReSRa. It was also found that percent reduction in reservoir surface area can be used as a surrogate for estimating ReSRa. The implications of the results are discussed. [ABSTRACT FROM AUTHOR]

Due to resource constraints, long-term monitoring data for calibration and validation of hydrologic and water quality models are rare. As a result, most models are calibrated and, if possible, validated using limited measured data. However, little research has been done to determine the impact of length of available calibration data on model parameterization and performance. The main objective of this study was to evaluate the impact of length of calibration data ( LCD) on parameterization and performance of the Agricultural Policy Environmental eXtender model for predicting daily, monthly, and annual streamflow. Long-term (1984-2015) measured daily streamflow data from Rock Creek watershed, an agricultural watershed in northern Ohio, were used for this study. Data were divided into five Short (5-year), two Medium (15-year), and one Long (25-year) streamflow calibration data scenarios. All LCD scenarios were calibrated and validated at three time steps: daily, monthly, and annual. Results showed LCD affected the ability of the model to accurately capture temporal variability in simulated streamflow. However, overall average streamflow, water budgets, and crop yields were simulated reasonably well for all LCD scenarios. [ABSTRACT FROM AUTHOR]

The Soil Conditioning index (SCI) and Soil management assessment Framework (SMAF) are two different but complementary methods for evaluating soil quality. Both tools have been widely used, but little is known regarding how they compare and if they provide similar results when the same agricultural management practices are compared. This SCI and SMAF soil quality index (SQI) comparison was conducted on the Fort Cobb Reservoir experimental Watershed (FCREW) in oklahoma. Forty-one loamy and sandy surface soil sites were sampled on the FCREW under (i) annual cropping with conventional tillage (conventional), (ii) annual cropping with either conservation tillage or no-till (conservation), (iii) cropland that had been converted to perennial grass (managed grass), and (iv) native grass. The SCI and SMAF SQI gave similar assessments, indicating that soil quality within conventional and conservation systems was similar but lower than in either managed or native grassland systems. Simple comparisons of soil properties by textural class showed no significant effects on most soil quality indicator values, and although texture by management subgroups was examined, no clear relationships were detected because of the limited number of sampling sites. The SMAF and SCI indicators and scores were correlated in tilled systems with limited vegetative cover but not for no-till or forage-based pasture or grassland systems that were in place for at least 10 yr. The SMAF provided more resolution when evaluating agroecosystem management effects on soil quality, including soil organic C enrichment, especially within forage-based systems. Recognizing that the SMAF currently does not account for soil loss, we conclude with recommendations for improving the tool, particularly for tilled systems. [ABSTRACT FROM AUTHOR]

Scaling in situ soil water content time series data to a large spatial domain is a key element of watershed environmental monitoring and modeling. The primary method of estimating and monitoring large-scale soil water content distributions is via in situ networks. It is critical to establish the stability of in situ networks when deploying them to study hydrologic systems. Two watersheds in Oklahoma, the Little Washita River Experimental Watershed (LWREW) and the Fort Cobb Reservoir Experimental Watershed (FCREW), are two prime examples of well-equipped research watersheds that provide long-term measurements of atmospheric and soil water content from in situ networks. The soil water content measurement network on the LWREW has been in operation since 2002, with 20 stations available for investigating soil water dynamics at the watershed scale. Temporal stability analysis of the network is complicated by the changing configuration of the network, but it is possible to determine a singular long-term average for the network. The FCREW consists of 15 soil water content stations and began operation in 2007, providing detailed information across a mixed agricultural domain and was determined to be stable and representative of the region. This study reinforces the applicability of temporal stability analysis to very long time scales, which are now becoming available for soil moisture monitoring. Each of these networks is temporally stable with respect to soil water content at each depth on a spatial basis. The LWREW has a persistent pattern through the root zone profile, but the FCREW does not, which requires further investigation. [ABSTRACT FROM AUTHOR]

Physiographic data such as digital elevation models (DEMs), soils, geology, stream channel network characteristics, channel stability, and land use data are essential for understanding the complex hydrologic cycle and chemical transport processes of any given study area. We describe the physiographic data available in the Little Washita River Experimental Watershed (LWREW) and Fort Cobb Reservoir Experimental Watershed (FCREW) in Oklahoma. Specifically, we describe (i) available raw and post-processed DEM products (ftp://164.58.150.21/DEM/), (ii) available soils data (ftp://164.58.150.21/FT_Cobb_Reservoir_Watershed_2005-2012/GIS-DataSets/Physiography/Soils/ and ftp://164.58.150.21/Little_Washita_River_Research_Watershed_Other/GIS_Datasets/Physiography/Soils) and associated error analysis based on limited measured data, (iii) geologic formations in the LWREW and FCREW (ftp://164.58.150.21/FT_Cobb_Reservoir_Watershed_2005-2012/GISDataSets/Physiography/Geology/FCREW_Geology/ and ftp://164.58.150.21/Little_ Washita_River_Research_Watershed_Other/GIS_Datasets/Physiography/Geology/), and (iv) available rapid geomorphic assessment measurements (ftp://164.58.150.21/FT_Cobb_Reservoir_Watershed_2005-2012/GIS-DataSets/Physiography/ RGA/) and their uses. Data collection is a collaborative effort among USGS, NRCS, and ARS. These data sets have been used for several research applications by USDA-ARS scientists and researchers from other institutions and agencies. Plans for detailed geomorphic assessment of stream channel networks in the FCREW are underway in collaboration with Oklahoma State University in Stillwater. The collected data will enable updating of the channel stability stage condition since there have been several major rainfall events in the watershed since the last geomorphic assessment. [ABSTRACT FROM AUTHOR]

Surface and groundwater quantity and quality data are essential in many hydrologic applications and to the development of hydrologic and water quality simulation models. We describe the hydrologic data available in the Little Washita River Experimental Watershed (LWREW) of the Southern Great Plains Research Watershed (SGPRW) and Fort Cobb Reservoir Experimental Watershed (FCREW), both located in southwest Oklahoma. Specifically, we describe the flood retarding structures and corresponding stage, discharge, seepage, and consumptive use data (http://www.usbr.gov/gp-bin/arcweb_cobb.pl), stream gauges, and groundwater wells and their corresponding stream flow (http://waterdata.usgs.gov/ok/nwis/sw; LWREW ARS 522-526 stream gauges) and groundwater level data (SGPRW groundwater levels data; LWREW groundwater data; http://waterdata.usgs.gov/ok/nwis/inventory; http://nwis.waterdata. usgs.gov/ok/nwis/gwlevels), respectively. Data collection is a collaborative effort between federal and state agencies. Stage, discharge, seepage, and consumptive use data for the Fort Cobb Reservoir are available from the Bureau of Reclamation and cover a period of 1959 to present. There are 15 stream gauges in the LWREW and six in the FCREW with varying data records. There were 479 observation wells with data in the SGPRW and 80 in the LWREW, with the latest records collected in 1992. In addition, groundwater level data are available from five real-time monitoring wells and 34 historical wells within the FCREW. These data sets have been used for several research applications. Plans for detailed groundwater data collection are underway to calibrate and validate the linked Soil and Water Assessment Tool (SWAT)-MODFLOW model. Also, plans are underway to conduct reservoir bathymetric surveys to determine the current reservoir capacity as affected by land use/land cover and overland and stream channel soil erosion. [ABSTRACT FROM AUTHOR]

The presence of non-stationary conditions in long-term hydrologic observation networks is associated with natural and anthropogenic stressors or network operation problems. Detection and identification of network operation drivers is fundamental in hydrologic investigation due to changes in systematic errors that can exacerbate modeling results or bias research conclusions. We applied a data screening procedure to the USDA-ARS experimental watersheds data sets (ftp://164.58.150.21) in Oklahoma. Detection of statistically significant monotonic trends and changes in mean and variance were used to investigate non-stationary conditions with network operation drivers to assess the impact of changes in the amount of systematic error. Detection of spurious data, filling in missing data, and data screening procedures were applied to >1000 time series, and processed data were made publicly available. The SPELLmap application was used for data processing and statistical tests on watershed segregated data sets and temporally aggregated data. A test for independency (Anderson test), normality, monotonic trend (Spearman test), detection of change point (Pettitt test), and split record test (F and t-tests) were used to assess non-stationary conditions. Statistically significant (95% confidence limit) monotonic trends and changes in mean and variance were detected for annual maximum air temperature, rainfall, relative humidity, and solar radiation and in maximum and minimum soil temperature time series. Network operation procedures such as change in calibration protocols and sensor upgrades as well as natural regional weather trends were suspected as driving the detection of statistically significant trends and changes in mean and variance. We concluded that a data screening procedure that identifies changes in systematic errors and detection of false non-stationary conditions in hydrologic problems is fundamental before any modeling applications. [ABSTRACT FROM AUTHOR]

Water is central to life and earth processes, connecting physical, biological, chemical, ecological, and economic forces across the landscape. The vast scope of hydrologic sciences requires research efforts worldwide and across a wide range of disciplines. While hydrologic processes and scientific investigations related to sustainable agricultural systems are based on universal principles, research to understand processes and evaluate management practices is often site-specific to achieve a critical mass of expertise and research infrastructure to address spatially, temporally, and ecologically complex systems. In the face of dynamic climate, market, and policy environments, long-term research is required to understand and predict risks and possible outcomes of alternative scenarios. This special section describes the USDA-ARS's long-term research (1961 to present) in the Upper Washita River basin of Oklahoma. Data papers document datasets in detail (weather, hydrology, physiography, land cover, and sediment and nutrient water quality), and associated research papers present analyses based on those data. This living history of research is presented to engage collaborative scientists across institutions and disciplines to further explore complex, interactive processes and systems. Application of scientific understanding to resolve pressing challenges to agriculture while enhancing resilience of linked land and human systems will require complex research approaches. Research areas that this watershed research program continues to address include: resilience to current and future climate pressures; sources, fate, and transport of contaminants at a watershed scale; linked atmospheric-surface-subsurface hydrologic processes; high spatiotemporal resolution analyses of linked hydrologic processes; and multiple-objective decision making across linked farm to watershed scales. [ABSTRACT FROM AUTHOR]

Subsurface tile drains in agricultural systems of the midwestern United States are a major contributor of nitrate-N (NO3-N) loadings to hypoxic conditions in the Gulf of Mexico. Hydrologic and water quality models, such as the Soil and Water Assessment Tool, are widely used to simulate tile drainage systems. he Hooghoudt and Kirkham tile drain equations in the Soil and Water Assessment Tool have not been rigorously tested for predicting tile low and the corresponding NO3-N losses. In this study, long-term (1983-1996) monitoring plot data from southern Minnesota were used to evaluate the SWAT version 2009 revision 531 (hereafter referred to as SWAT) model for accurately estimating subsurface tile drain lows and associated NO3-N losses. A retention parameter adjustment factor was incorporated to account for the effects of tile drainage and slope changes on the computation of surface runoff using the curve number method (hereafter referred to as Revised SWAT). he SWAT and Revised SWAT models were calibrated and validated for tile low and associated NO3-N losses. Results indicated that, on average, Revised SWAT predicted monthly tile low and associated NO3-N losses better than SWAT by 48 and 28%, respectively. For the calibration period, the Revised SWAT model simulated tile low and NO3-N losses within 4 and 1% of the observed data, respectively. For the validation period, it simulated tile low and NO3-N losses within 8 and 2%, respectively, of the observed values. Therefore, the Revised SWAT model is expected to provide more accurate simulation of the effectiveness of tile drainage and NO3-N management practices. [ABSTRACT FROM AUTHOR]

The Fort Cobb Watershed in Oklahoma has diverse biogeophysical settings and provides an opportunity to explore the association of water quality with a diverse set of landscapes during both wet (April 2007-December 2009) and dry (January 2005-March 2007) periods. The objective of this work was to identify spatial patterns in phosphorus (P) (soluble reactive P [ SRP] and bioavailable P [ BAP]) associated with landscape metrics for two distinct streamflow regimes. Spatial autocorrelation of P was evaluated using contiguous (side-by-side) and upstream (upstream:downstream) connectivity matrices. Biogeophysical metrics were compiled for each contributing area, and were partitioned based on association to P concentrations. Results for both SRP and BAP indicated that spatial autocorrelation was present ( p < 0.05). There was more spatial autocorrelation and stream P concentrations were three to five times higher in the Wet phase than in the Dry phase ( p < 0.05). Analysis with recursive partitioning resulted in higher R2 with spatial autocorrelation than without spatial autocorrelation and indicated that lateral metrics (topography, soil, geology, management) were better predictors for SRP than instream metrics. During Wet phase, lateral metrics indicative of rapid surface and subsurface water movement were associated with higher P stream concentrations. This research demonstrated that we can detect landscapes more vulnerable to P losses and/or contaminations in either drought or very wet periods. [ABSTRACT FROM AUTHOR]

The article presents a study on the development of cost-effective methods to collect parameterization and evaluation data for watershed modeling and to use the calibrated model to determine the impact of conservation practices on water quality in Oklahoma. It notes that the research was conducted within the Cobb Creek subwatershed (CCSW) to develop cost-effective methods to collect stream channel parameterization and evaluation data for modeling in watersheds with thin data. It mentions the use of the calibrated and cross-checked Soil and Water Assessment Tool (SWAT) model to stimulate impacts of riparian forest buffers (RF) and bermudagrass filter strip buffer (BFS) on sediment yield and concentration in the CCSW.