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Current gaps impeding researchers from developing a soil and watershed health nexus include design of long-term field-scale experiments and statistical methodologies that link soil health indicators (SHI) with water quality indicators (WQI). Land cover is often used to predict WQI but may not reflect the effects of previous management such as legacy fertilizer applications, disturbance, and shifts in plant populations) and soil texture. Our research objectives were to use nonparametric Spearman rank-order correlations to identify SHI and WQI that were related across the Fort Cobb Reservoir experimental watershed (FCREW); use the resulting rho (r) and p values (P) to explore potential drivers of SHI-WQI relationships, specifically land use, management, and inherent properties (soil texture, aspect, elevation, slope); and interpret findings to make recommendations regarding assessment of the sustainability of land use and management. The SHI values used in the correlation matrix were weighted by soil texture and land management. The SHI that were significantly correlated with one or more WQI were available water capacity (AWC), Mehlich III soil P, and the sand to clay ratio (S:C). Mehlich III soil P was highly correlated with three WQI: total dissolved solids (TDS) (0.80; P < 0.01), electrical conductivity of water (EC-H 2 O) (0.79; P < 0.01), and water nitrates (NO 3 -H 2 O) (0.76; P < 0.01). The correlations verified that soil texture and management jointly influence water quality (WQ), but the size of the soils dataset prohibited determination of the specific processes. Adoption of conservation tillage and grasslands within the FCREW improved WQ such that water samples met the U.S. Environmental Protection Agency (EPA) drinking water standards. Future research should integrate current WQI sampling sites into an edge-of-field design representing all management by soil series combinations within the FCREW., (© 2023 The Authors. Journal of Environmental Quality published by Wiley Periodicals LLC on behalf of American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

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]

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]

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 Agricultural Policy Environmental eXtender (APEX) model is the scientific basis for the Nutrient Tracking Tool (NTT). NTT is an enhanced version of the Nitrogen Trading Tool, a user-friendly web-based computer program originally developed by the USDA. NTT was developed to estimate reductions in nutrient losses to the environment associated with alternative practices. The relatively easy access and ease with which the interface can be used has provided opportunities to demonstrate NTT in locations throughout the country; however, the absence of a clearly defined, consistent approach to parameterization and validation has raised questions over the reliability and consistency of simulated results. In this study: guidelines for parameterization and validation of APEX were developed based on literature review findings and the authors’ experience; and a case study was provided to illustrate how the developed guidelines are applied. The developed guidelines are in the form of recommendations covering essential phases of model simulation studies as well as a clear interpretation of model performance evaluation criteria thresholds and model simulation performance results. These guidelines were successfully applied in the central Ohio case study. The most sensitive water yield parameters and their respective reasonable range of values were determined. Simulated monthly and annual water yield values were within 5% and 15% of observed values during the calibration and validation periods, respectively. Overall, the developed guidelines together with the illustrative case study example are intended to serve as the framework to parameterize and validate APEX to support nation-wide deployment of NTT. This framework can be easily modified and used in additional APEX and other modeling studies. [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]

To provide a common background and platform for consensual development of calibration and validation guidelines, model developers and/or expert users of the commonly used hydrologic and water quality models globally were invited to write technical articles recommending calibration and validation procedures specific to their model. This article introduces a special collection of 22 research articles that present and discuss calibration and validation concepts in detail for 25 hydrologic and water quality models. The main objective of this introductory article is to introduce and summarize key aspects of the hydrologic and water quality models presented in this collection. The models range from field to watershed scales for simulating hydrology, sediment, nutrients, bacteria, and pesticides at temporal scales varying from hourly to annually. Individually, the articles provide model practitioners with detailed, model-specific guidance on model calibration, validation, and use. Collectively, the articles in this collection present a consistent framework of information that will facilitate development of a proposed set of ASABE model calibration and validation guidelines. [ABSTRACT FROM AUTHOR]

This article presents an overview of the Agricultural Drainage and Pesticide Transport (ADAPT) model and a case study to illustrate the calibration and validation steps for predicting subsurface drainage and nitrate-N losses from an agricultural system. The ADAPT model is a daily time step, field-scale water table management model that was developed as an extension of the GLEAMS model. The GLEAMS algorithms were augmented with algorithms for subsurface drainage, subsurface irrigation, deep seepage, and related water quality processes. Recently, a frost depth algorithm was incorporated to enhance the model's capability to predict flow during spring and fall months. In addition to the normal GLEAMS output, ADAPT gives estimates of pesticides and nutrients in drainage. The model has four components: hydrology, erosion, nutrient transport, and pesticide transport. Predictions of surface runoff and subsurface drainage by ADAPT are very sensitive to hydrology input parameters, such as NRCS curve number, hydraulic conductivity, depth of the impeding layer, and hydraulic conductivity of the impeding layer. In the erosion component, slope, hydraulic length, and crop management are the most sensitive factors. Nutrients generally follow the trends in surface runoff and subsurface drainage. In addition, nitrogen and phosphorus concentrations in soil horizons are sensitive to nutrient losses. Recently, the ADAPT model was further calibrated and validated in southern Minnesota to evaluate impacts of subsurface drain spacing and depth, rate and timing of nitrogen application, and precipitation changes on water quality. ADAPT is written in FORTRAN, and the source code is available to interested model users. Considering the limited technical support and text editor-based input files, development of a user-friendly interface to create input files would greatly enhance ADAPT's acceptability by users involved in modeling agricultural systems equipped with subsurface drains. [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.

• Our framework for uncertainty analysis uses a range of calibrated parameters. • We explored the uncertainty range of the runoff parameters in grazing activities. • This study employed the calibrated APEXgraze model based on the Moriasi Criteria. • A holistic approach captured the internal dynamics of hydrological processes. • This work opens avenues to conduct uncertainty and scenario analyses of any model. Researchers in hydrological sciences have developed agro-hydrological models to study water quantity and quality in small-scale watersheds. These models, however, often exhibit significant uncertainty in both parameters and response variables. The study aims to address limited research on the uncertainty range of runoff-related parameters in watershed models, particularly those analyzing the impact of grazing operations. It also seeks to improve existing uncertainty analysis protocols because these protocols rely on parameter distributions, which are often difficult to determine. A generalized uncertainty analysis protocol that statistically considers multiple acceptable solutions from calibrated agro-hydrological models was developed. This approach employed a variant of the Agricultural Policy eXtender (APEX) model with an expanded grazing module called APEXgraze to perform uncertainty analysis of runoff and sediment-related parameters. Four small-scale watershed models were developed for calibration: a) native prairie, b) native prairie under grazing operations, c) cereals (winter wheat and one season of oats), and d) the same cereals under grazing operations in a semi-arid region of Oklahoma, United States. This work demonstrated that a simplified uncertainty analysis approach effectively captured the internal dynamics of hydrological processes within a statistically significant range of parameters. This observation was evidenced by a small range of water balance in both magnitude and percentage. The procedure also helped identify redundant parameters in sensitivity and uncertainty analyses. The proposed generalized uncertainty analysis protocol offers a reliable method for assessing hydrological models' internal dynamics and identifying critical parameters. This approach can enhance the accuracy of watershed models, particularly in regions with grazing operations. [Display omitted] [ABSTRACT FROM AUTHOR]

Highlights: [•] DRAINMOD-NII and ADAPT performed equally well in predicting monthly subsurface drainage. [•] DRAINMOD-NII performed slightly better than ADAPT in predicting monthly nitrate losses and annual N budget. [•] DRAINMOD-NII prediction of denitrification rate was better than ADAPT prediction. [ABSTRACT FROM AUTHOR]