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

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.

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.

Statistical models for evaluating suspected artefacts in long-term environmental monitoring data

Long-term water quality monitoring is of high value for environmental management as well as for research. Artificial level shifts in time series due to method improvements, flaws in laboratory practices or changes in laboratory are a common limitation for analysis, which, however, are often ignored. Statistical estimation of such artefacts is complicated by the simultaneous existence of trends, seasonal variation and effects of other influencing factors, such as weather conditions. Here, we investigate the performance of generalised additive mixed models (GAMM) to simultaneously identify one or more artefacts associated with artificial level shifts, longitudinal effects related to temporal trends and seasonal variation, as well as to model the serial correlation structure of the data. In the same model, it is possible to estimate separate residual variances for different periods so as to identify if artefacts not only influence the mean level but also the dispersion of a series. Even with an appropriate statistical methodology, it is difficult to quantify artificial level shifts and make appropriate adjustments to the time series. The underlying temporal structure of the series is especially important. As long as there is no prominent underlying trend in the series, the shift estimates are rather stable and show less variation. If an artificial shift occurs during a slower downward or upward tendency, it is difficult to separate these two effects and shift estimates can be both biased and have large variation. In the case of a change in method or laboratory, we show that conducting the analyses with both methods in parallel strongly improves estimates of artefact effects on the time series, even if certain problems remain. Due to the difficulties of estimating artificial level shifts, posterior adjustment is problematic and can lead to time series that no longer can be used for trend analysis or other analysis based on the longitudinal structure of the series. Before carrying out a change in analytic method or laboratory, it should be considered if this is absolutely necessary. If changes cannot be avoided, the analysis of the two methods considered, or the two laboratories contracted, should be run in parallel for a considerable period of time so as to enable a good assessment of changes introduced to the data series.

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.

Evaluating the impacts of agricultural land management practices on water resources: A probabilistic hydrologic modeling approach

Evaluating the effectiveness of agricultural land management practices in minimizing environmental impacts using models is challenged by the presence of inherent uncertainties during the model development stage. One issue faced during the model development stage is the uncertainty involved in model parameterization. Using a single optimized set of parameters (one snapshot) to represent baseline conditions of the system limits the applicability and robustness of the model to properly represent future or alternative scenarios. The objective of this study was to develop a framework that facilitates model parameter selection while evaluating uncertainty to assess the impacts of land management practices at the watershed scale. The model framework was applied to the Lake Creek watershed located in southwestern Oklahoma, USA. A two-step probabilistic approach was implemented to parameterize the Agricultural Policy/Environmental eXtender (APEX) model using global uncertainty and sensitivity analysis to estimate the full spectrum of total monthly water yield (WYLD) and total monthly Nitrogen loads (N) in the watershed under different land management practices. Twenty-seven models were found to represent the baseline scenario in which uncertainty of up to 29% and 400% in WYLD and N, respectively, is plausible. Changing the land cover to pasture manifested the highest decrease in N to up to 30% for a full pasture coverage while changing to full winter wheat cover can increase the N up to 11%. The methodology developed in this study was able to quantify the full spectrum of system responses, the uncertainty associated with them, and the most important parameters that drive their variability. Results from this study can be used to develop strategic decisions on the risks and tradeoffs associated with different management alternatives that aim to increase productivity while also minimizing their environmental impacts.

Seasonal sediment and nutrient transport patterns

It is essential to understand sediment and nutrient sources and their spatial and temporal patterns to design effective mitigation strategies. However, long-term data sets to determine sediment and nutrient loadings are scarce and expensive to collect. The goal of this study was to determine seasonal patterns of suspended sediment (SS), total N (TN), and total P (TP) concentrations and loadings for three USGS gauge sites located at the Fort Cobb Reservoir Experimental watershed (FCREW) located in southwestern Oklahoma. Measured instantaneous discharge, SS, TN, and TP concentration data were used to develop lognormal water quality-discharge relationships. The water quality-discharge relationships were used to generate estimated seasonal concentrations and loads based on hourly or 30-min interval discharge. The estimated concentrations and loads were used to determine seasonal patterns for SS, TN, and TP relative to the respective state water quality criteria. Decreasing and increasing monotonic trends were observed for the seasonal time series loads for all three sites, but they were insignificant based on the Spearman test (α = 0.05). The largest loads were estimated during the wet springs and summers. The study SS, TN, and TP target concentrations were exceeded in one season or another. The study results showed that the priority locations to implement the TN and TP conservation practices were the Lake Creek and Willow Creek subwatersheds during the winter and spring seasons. Common practices to mitigate nutrients and suspended sediments include nutrient management, no-till, conversion of cultivated land to pasture, riparian buffers, and animal exclusion.

Upper washita river experimental watersheds: meteorologic and soil climate measurement networks

Hydrologic, watershed, water resources, and climate-related research conducted by the USDA-ARS Grazinglands Research Laboratory (GRL) are rooted in events dating back to the 1930s. In 1960, the 2927-km Southern Great Plains Research Watershed (SGPRW) was established to study the effectiveness of USDA flood control and soil erosion prevention programs. The size of the SGPRW was scaled back in 1978, leaving only the 610-km Little Washita River Experimental Watershed (LWREW) to be used as an outdoor hydrologic research laboratory. Since 1978, the number of measurement sites and types of instruments used to collect meteorologic and soil climate data have changed on the LWREW. Moreover, a second research watershed, the 786-km Fort Cobb Reservoir Experimental Watershed (FCREW), was added in 2004 to the GRL's outdoor research laboratories to further study the effects of agricultural conservation practices on selected environmental endpoints. We describe the SGPREW, FCREW, and LWREW and the meteorologic measurement network (historic and present) deployed on them, provide descriptions of measurements, including information on accuracy and calibration, quality assurance measures (where known), and data archiving of the present network, give examples of data products and applications, and provide information for the public and research communities regarding access and availability of both the historic and recent data from these watersheds.

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.