Modeling the impact of measured and projected climate and management systems on agricultural fields: Surface runoff, soil moisture, and soil erosion.

As global climate change poses a challenge to crop production, it is imperative to prioritize effective adaptation of agricultural systems based on a scientific understanding of likely impacts. In this study, we applied an integrated watershed modeling framework to examine the impacts of projected climate on runoff, soil moisture, and soil erosion under different management systems in Central Oklahoma. The proposed model uses measured climate data and three downscaled ensembles from the Coupled Model Intercomparison Project Phase 6 (CMIP6) at the water resources and erosion watershed to understand the impact of climate change and various climate conditions under three management systems: (1) continuous winter wheat (Triticum aestivum) under conventional tillage (WW-CT; baseline system), (2) continuous winter wheat under no-till (WW-NT), and (3) cool and warm season forage cover crop mixes under no-till (CC-NT). The study indicates that the occurrence of agricultural drought is projected to increase while erosion rates will remain unchanged under the WW-CT. In contrast, climate simulations imposed on the WW-NT and CC-NT systems significantly reduce runoff and sediment while preserving soil moisture levels. Especially, implementing the CC-NT system can bolster food security and foster sustainable farming practices in Central Oklahoma in the face of a changing climate.

Uncertainty in phosphorus fluxes and budgets across the US long-term agroecosystem research network.

Phosphorus (P) budgets can be useful tools for understanding nutrient cycling and quantifying the effectiveness of nutrient management planning and policies; however, uncertainties in agricultural nutrient budgets are not often quantitatively assessed. The objective of this study was to evaluate uncertainty in P fluxes (fertilizer/manure application, atmospheric deposition, irrigation, crop removal, surface runoff, and leachate) and the propagation of these uncertainties to annual P budgets. Data from 56 cropping systems in the P-FLUX database, which spans diverse rotations and landscapes across the United States and Canada, were evaluated. Results showed that across cropping systems, average annual P budget was 22.4 kg P ha-1 (range = -32.7 to 340.6 kg P ha-1 ), with an average uncertainty of 13.1 kg P ha-1 (range = 1.0-87.1 kg P ha-1 ). Fertilizer/manure application and crop removal were the largest P fluxes across cropping systems and, as a result, accounted for the largest fraction of uncertainty in annual budgets (61% and 37%, respectively). Remaining fluxes individually accounted for <2% of the budget uncertainty. Uncertainties were large enough that determining whether P was increasing, decreasing, or not changing was inconclusive in 39% of the budgets evaluated. Findings indicate that more careful and/or direct measurements of inputs, outputs, and stocks are needed. Recommendations for minimizing uncertainty in P budgets based on the results of the study were developed. Quantifying, communicating, and constraining uncertainty in budgets among production systems and multiple geographies is critical for engaging stakeholders, developing local and national strategies for P reduction, and informing policy.

Use of archived data to derive soil health and water quality indicators for monitoring shifts in natural resources.

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-H2 O) (0.79; P < 0.01), and water nitrates (NO3 -H2 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.

Runoff water quantity and quality data from native tallgrass prairie and crop-livestock systems in Oklahoma between 1977 and 1999.

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.

Field scale nitrogen load in surface runoff: Impacts of management practices and changing climate.

The use of Nitrogen (N) fertilizer boosted crop production to accommodate 7 billion people on Earth in the 20th century but with the consequence of exacerbating N losses from agricultural landscapes. Land management practices that can prevent high N load are constantly being sought for mitigation and conservation purposes. This study was aimed at evaluating the impacts of different land management practices under projected climate scenarios on surface runoff linked N load at the field scale level. A framework to analyze changes in N load at a high spatiotemporal resolution under high greenhouse emission climate projections was developed using the Pesticide Root Zone Model (PRZM) for the Willow Creek Watershed in the Fort Cobb Experimental Watershed in Oklahoma. Specifically, 12 combinations of land management and climate scenarios were evaluated based on their N load via surface runoff from 2020 to 2070. Results showed that crop rotation practices lowered both the N load and the probability of high N load events. Spring application reduced the negative effects in summer and fall from other land management practices but at the risk of increased probability of generating high N load in April and May. The fertilizer application rate was found to be the most critical factor that affected the amount and the probability of high N load events. By adopting a target application management approach, the monthly maximum N can be decreased by 13% while the annual mean N load by 6%. The model framework and analysis method developed in this research can be used to analyze tradeoffs between environmental welfare and economic benefits of N fertilizer at the field scale level.

Performance Assessment of Five Different Soil Moisture Sensors under Irrigated Field Conditions in Oklahoma.

Meeting the ever-increasing global food, feed, and fiber demands while conserving the quantity and quality of limited agricultural water resources and maintaining the sustainability of irrigated agriculture requires optimizing irrigation management using advanced technologies such as soil moisture sensors. In this study, the performance of five different soil moisture sensors was evaluated for their accuracy in two irrigated cropping systems, one each in central and southwest Oklahoma, with variable levels of soil salinity and clay content. With factory calibrations, three of the sensors had sufficient accuracies at the site with lower levels of salinity and clay, while none of them performed satisfactorily at the site with higher levels of salinity and clay. The study also investigated the performance of different approaches (laboratory, sensor-based, and the Rosetta model) to determine soil moisture thresholds required for irrigation scheduling, i.e., field capacity (FC) and wilting point (WP). The estimated FC and WP by the Rosetta model were closest to the laboratory-measured data using undisturbed soil cores, regardless of the type and number of input parameters used in the Rosetta model. The sensor-based method of ranking the readings resulted in overestimation of FC and WP. Finally, soil moisture depletion, a critical parameter in effective irrigation scheduling, was calculated by combining sensor readings and FC estimates. Ranking-based FC resulted in overestimation of soil moisture depletion, even for accurate sensors at the site with lower levels of salinity and clay.

Riparian erosion vulnerability model based on environmental features.

Riparian erosion is one of the major causes of sediment and contaminant load to streams, degradation of riparian wildlife habitats, and land loss hazards. Land and soil management practices are implemented as conservation and restoration measures to mitigate the environmental problems brought about by riparian erosion. This, however, requires the identification of vulnerable areas to soil erosion. Because of the complex interactions between the different mechanisms that govern soil erosion and the inherent uncertainties involved in quantifying these processes, assessing erosion vulnerability at the watershed scale is challenging. The main objective of this study was to develop a methodology to identify areas along the riparian zone that are susceptible to erosion. The methodology was developed by integrating the physically-based watershed model MIKE-SHE, to simulate water movement, and a habitat suitability model, MaxEnt, to quantify the probability of presences of elevation changes (i.e., erosion) across the watershed. The presences of elevation changes were estimated based on two LiDAR-based elevation datasets taken in 2009 and 2012. The changes in elevation were grouped into four categories: low (0.5 - 0.7 m), medium (0.7 - 1.0 m), high (1.0 - 1.7 m) and very high (1.7 - 5.9 m), considering each category as a studied "species". The categories' locations were then used as "species location" map in MaxEnt. The environmental features used as constraints to the presence of erosion were land cover, soil, stream power index, overland flow, lateral inflow, and discharge. The modeling framework was evaluated in the Fort Cobb Reservoir Experimental watershed in southcentral Oklahoma. Results showed that the most vulnerable areas for erosion were located at the upper riparian zones of the Cobb and Lake sub-watersheds. The main waterways of these sub-watersheds were also found to be prone to streambank erosion. Approximatively 80% of the riparian zone (streambank included) has up to 30% probability to experience erosion greater than 1.0 m. By being able to identify the most vulnerable areas for stream and riparian sediment mobilization, conservation and management practices can be focused on areas needing the most attention and resources.

Long-term environmental research: the upper washita river experimental watersheds, oklahoma, USA.

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.

Upper washita river experimental watersheds: reservoir, groundwater, and stream flow data.

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 (), stream gauges, and groundwater wells and their corresponding stream flow (; LWREW ARS 522-526 stream gauges) and groundwater level data (SGPRW groundwater levels data; LWREW groundwater data; ; ), 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.

Upper washita river experimental watersheds: data screening procedure for data quality assurance.

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 () 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 ( and -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.

Upper washita river experimental watersheds: physiography data.

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 (), (ii) available soils data ( and ) and associated error analysis based on limited measured data, (iii) geologic formations in the LWREW and FCREW ( and ), and (iv) available rapid geomorphic assessment measurements () 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.

Upper washita river experimental watersheds: multiyear stability of soil water content profiles.

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.

Evaluation of the hooghoudt and kirkham tile drain equations in the soil and water assessment tool to simulate tile flow and nitrate-nitrogen.

Subsurface tile drains in agricultural systems of the midwestern United States are a major contributor of nitrate-N (NO-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. The Hooghoudt and Kirkham tile drain equations in the Soil and Water Assessment Tool have not been rigorously tested for predicting tile flow and the corresponding NO-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 flows and associated NO-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). The SWAT and Revised SWAT models were calibrated and validated for tile flow and associated NO-N losses. Results indicated that, on average, Revised SWAT predicted monthly tile flow and associated NO-N losses better than SWAT by 48 and 28%, respectively. For the calibration period, the Revised SWAT model simulated tile flow and NO-N losses within 4 and 1% of the observed data, respectively. For the validation period, it simulated tile flow and NO-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 NO-N management practices.

Sediment measurement and transport modeling: impact of riparian and filter strip buffers.

Well-calibrated models are cost-effective tools to quantify environmental benefits of conservation practices, but lack of data for parameterization and evaluation remains a weakness to modeling. Research was conducted in southwestern Oklahoma within the Cobb Creek subwatershed (CCSW) to develop cost-effective methods to collect stream channel parameterization and evaluation data for modeling in watersheds with sparse data. Specifically, (i) simple stream channel observations obtained by rapid geomorphic assessment (RGA) were used to parameterize the Soil and Water Assessment Tool (SWAT) model stream channel variables before calibrating SWAT for streamflow and sediment, and (ii) average annual reservoir sedimentation rate, measured at the Crowder Lake using the acoustic profiling system (APS), was used to cross-check Crowder Lake sediment accumulation rate simulated by SWAT. Additionally, the calibrated and cross-checked SWAT model was used to simulate impacts of riparian forest buffer (RF) and bermudagrass [ (L.) Pers.] filter strip buffer (BFS) on sediment yield and concentration in the CCSW. The measured average annual sedimentation rate was between 1.7 and 3.5 t ha yr compared with simulated sediment rate of 2.4 t ha yr Application of BFS across cropped fields resulted in a 72% reduction of sediment delivery to the stream, while the RF and the combined RF and BFS reduced the suspended sediment concentration at the CCSW outlet by 68 and 73%, respectively. Effective riparian practices have potential to increase reservoir life. These results indicate promise for using the RGA and APS methods to obtain data to improve water quality simulations in ungauged watersheds.