Search

The Agricultural Policy/Environmental eXtender (APEX) is one of the hydrologic and water quality models being used in the USDA-ARS Long-Term Agroecosystem Research (LTAR) to evaluate current and develop sustainable agricultural production systems throughout the United States. Tillage practices used affect agricultural production, soil erosion, and evapotranspiration (ET), which is a major component of the hydrologic cycle. However, there is limited literature on the impact of tillage practices on parameterization and performance of APEX to simulate ET. The goals of this study were to 1) determine model parameters that affect ET simulation in APEX model for winter wheat (Triticum aestivum L.) fields managed under conservation and conventional tillage systems, and 2) evaluate the ability of APEX to simulate ET and daily biomass for each of these tillage systems in central Oklahoma. Tillage system had an impact on the number and type of APEX parameters sensitive for simulation of ET. Therefore, it is essential to perform a sensitivity analysis for study areas with different tillage management practices to determine appropriate parameters for inclusion in the calibration process. Overall, APEX simulated daily, biweekly, and monthly ET and daily biomass relatively well for both tillage systems. The Nash-Sutcliffe efficiency (NSE) for the simulated monthly ET of the calibrated model was 0.98 and 0.96 for conservation and conventional tillage fields, respectively. The percent bias (PBIAS) values for the ET simulation were within 3%, while PBIAS values for daily biomass simulation were within 25%, which indicates that the average simulated ET and daily biomass values were within 3% and 25% of the measured ET and daily biomass, respectively, in both tillage fields. This result indicates that a well-calibrated APEX model using appropriate sensitive parameters can simulate ET and biomass satisfactorily in both tillage systems.

Reliable estimation of ET is especially important in semi-arid conditions where ET prediction is instrumental in cost-effective management of scarce water resources and crop production. In addition to the role of climate variables and soil characteristics, the actual ET is influenced by dynamic crop characteristics during the growing season. Therefore, accounting for the interaction between crop growth and actual ET can significantly improve the performance of models. In this study, an efficient approach is presented for simultaneous calibration of ET and crop growth parameters for the Agricultural Policy/Environmental eXtender (APEX) model for daily, weekly, and monthly ET. The proposed approach involves the development of an objective function based on a compounded time series comprising of scaled annual ET and crop yield data. An efficient search algorithm based on the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm was implemented in R language to find the parameter values that minimizes the defined objective function. The simultaneous calibration approach, which utilized annual data, improved ET prediction for annual and finer time scales (25% in RMSE reduction for 3-month, 18% for monthly, 17% for 2-week, 19% for weekly, 17% for 3-day, and 13% for daily time scales). The average of absolute relative error for crop yield predictions was also reduced from 43% to 16% for the calibrated model. The simulated leaf area index (LAI) for the calibrated model (i.e. calibrated using annual ET and crop yield data) was also consistent with the measured LAI values, confirming the validity of the calibrated parameter values.

Evapotranspiration (ET), is a major component of the hydrologic budget and therefore it requires accurate estimation. The Agricultural Policy/Environmental eXtender (APEX), a hydrologic and water quality model developed for evaluating the effect of agricultural production management practices on the environment. It has five different methods to simulate ET. The objectives of this study were to: determine the impact of using different ET methods in APEX on sensitive ET parameters in semi-arid environments, determine reasonable range of values for sensitive parameters, and compare performance of these methods using multiple criteria. ET and crop yield data measured in the Lysimeter fields located in the USDA-ARS Conservation and Production Research Laboratory, Bushland, Texas were used to calibrate and validate the model. Results indicated that selection of statistical model performance measure and ET method affects the number of sensitive parameters and parameter rank. The number of sensitive parameters remained relatively stable among ET simulation methods but there was large variability in parameter sensitivity ranks. It is also important that users carefully choose appropriate statistical measure to use depending on the goals of their study. With the exception of maximum rainfall intercept, exponent coefficient rainfall, SCS index coefficient, rain intercept coefficient, and root growth soil strength, all methods had similar ranges of values for the sensitive parameters. In general there were no large differences in the performance of ET methods. However, Penman-Monteith method simulated ET relatively better than the other methods, which may be explained by the fact that it is a physically-based method with many weather variables whose data was available for the study area. However, the study findings indicate that the other ET methods can provide satisfactory results in regions with limited weather data.

Eddy fluxes collected during 2016 to 2019 from eight production-scale multi-purpose winter wheat fields (grain only, graze-grain, and graze-out), managed under conventional till (CT) and no-till (NT), were synthesized to determine seasonality, daily magnitudes, seasonal, and annual budgets of carbon dioxide (CO2) fluxes and evapotranspiration (ET), and to investigate spatio-temporal variability of the fluxes. Maximum daily net ecosystem CO2 exchange (NEE), gross primary production (GPP), and ecosystem respiration (ER) approximated -11, 19, and 12 g C m−2, respectively, and daily ET approximated 7 mm. Wheat fields, including graze-out, were large sinks of CO2 (ranged from -149 ± 8 to -564 ± 9 g C m−2) during growing seasons (October–May). Wheat fields, left fallow during summer, were from near neutral to large sinks of CO2 at annual (calendar year) scales. Cumulative annual NEE was up to -242 ± 12 g C m−2 in a NT and -183 ± 12 g C m−2 in a CT field, which had grain-only wheat in spring followed by graze-grain wheat in fall. Cumulative seasonal ET ranged from 260 mm to 521 mm, and maximum annual ET approximated 800 mm. In general, ET was smaller under NT than CT. Eddy fluxes showed stronger relationships with remotely-sensed enhanced vegetation index and in-situ biometric variables in grain-only fields than grazed fields. Across-site analysis for grain-only wheat showed biomass and leaf area index alone explained >80% of variations in NEE, GPP, and ET, and ∼70% of variations in ER. Similarly, Canopy coverage explained >80% of variations in NEE and GPP, and ∼60% of variations in ER and ET. Strong relationships of biometric observations with the fluxes demonstrated their potential to model and explain spatio-temporal variability of CO2 fluxes and ET. Results also indicated huge implications of management practices on carbon and water budgets by altering vegetative properties.

Although the hydrologic cycle is a continuously renewable resource, the natural rate of water delivery is highly variable. Water is made available to our society on a consistent and reliable basis largely due to flow regulation by storage reservoirs. However, under current management, the reservoir storage capacity needed for flow regulation is a non-renewable resource because this capacity is steadily being lost to sedimentation. Today’s reservoirs occupy unique sites and may be considered largely irreplaceable, making the nation dependent on a non-sustainable resource. Sedimentation is steadily depleting storage capacity and progressively degrading the ability of reservoirs to fulfill their designated purposes. Sedimentation is also causing environmental impacts upstream and downstream of reservoirs. In the United States, the combined impacts of sedimentation and population growth have resulted in an estimated 35% decline in storage capacity per capita since this value peaked around 1970. In absolute terms, the estimated total reservoir storage capacity in the U.S. has dropped from a peak of 850 Gm3 in the late 1980s to 810 Gm3 today. Yet, sustaining the nation’s long-term reservoir storage capacity has not been a priority for many public or private dam owners, especially when they lack a reservoir sedimentation monitoring policy. In many regions, future reservoir storage will have to serve an important role in the mitigation of climate change to help ensure water, food, and energy, and the reduction of flood risk. There is an imperative need to preserve existing reservoir storage capacity due to rising demands associated with population growth, and increasing hydrologic variability associated with climate change, and the challenges and costs associated with either expanding existing capacity or decommissioning and developing new storage capacity. The trapping of sediment behind dams has also contributed to the decline of freshwater and coastal environments downstream of dams. Reversing these dangerous trends in storage capacity and environmental integrity will require increased monitoring of reservoirs, application of both established and emerging sediment management technologies, and a new paradigm for sustainable reservoir design and management. It requires moving from the traditional design life (reservoir life expectancy) approach to the adoption of sustainable use as the appropriate criteria for reservoir design and operation, achieving a sediment balance across reservoirs to permit the indefinite operation of this critical infrastructure.

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.

The Missouri River system has a large water storage capacity, where baseflow plays an important role. Understanding historical baseflow characteristics with respect to climate and land use impacts is essential for effective planning and management of water resources in the Missouri River Basin (MORB). This study evaluated statistical trends in baseflow and precipitation for 99 MORB minimally disturbed watersheds during 1950–2014. Elasticity of baseflow to climate variability and agricultural land use change were quantified for the 99 studied watersheds. Baseflow was derived from daily streamflow records with a recursive digital filter method. The results showed that baseflow varied between 38 and 80% (0 and 331 mm/year) of total streamflow with an average of 60%, indicating that more than half of streamflow in the MORB is derived from baseflow. The trend analysis revealed that precipitation increased during the study period in 78 out of 99 watersheds, leading to 1–3.9% noticeable increase in baseflow for 68 of 99 watersheds. Although the changes in baseflow obtained in this study were a result of the combined effects of climate and land use change across the basin, upward trends in baseflow generally coincide with increased precipitation and agricultural land use trends in the basin. Agricultural land use increase mostly led to a 0–5.7% decrease in annual baseflow in the basin, except toward east of the basin where baseflow mostly increased with agricultural land use increase (0.1–2.0%). In general, a 1% increase in precipitation and a 1% increase in agricultural land use resulted in 1.5% increase and 0.2% decrease in baseflow, respectively, during the study period. These results are entirely dependent on the quality of data used; however, they provide useful insight into the relative influence of climate and land use change on baseflow conditions in the Great Plains region of the USA.

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.

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.

We review over twenty years of publications on Soil and Water Assessment Tool (SWAT) applications in arid/semi-arid irrigated agricultural watersheds. Our review reveals strict dominance of the model's use for better understanding water quantity aspects of water management. While this is to be expected given the reality of water scarcity and associated challenges for agricultural production systems in arid/semi-arid regions, the capabilities of SWAT to model water quality have been underutilized. The main modeling challenges are lack of observational data, poor data quality, concerns about simulation accuracy, and technical limitations of the model despite numerous advancements in the last two decades. To deal with these challenges, modelers (i) combined data from different sources with those in existing SWAT databases, (ii) used supplemental tools to estimate missing data and evaluate model performance, (iii) traded off simulation accuracy when the major aim of the study was not undermined, and (iv) developed modular codes, tools, and algorithms to expand the model’s capabilities and improve process representations. A realistic simulation of regional hydrologic fluxes and agricultural water management practices that affect them is essential for meaningful SWAT applications in irrigated dry regions. Modelers are encouraged to apply multi-component calibration (e.g., streamflow, evapotranspiration, crop yield, and groundwater recharge) and report limitations and regional relevance of parameter values to contextualize model performance. We anticipate that model advancements such as conjunctive use of surface water and groundwater, explicit simulation of different irrigation systems, dynamic land use, and impacts of soil and water salinity on crop growth would significantly enhance the utility of SWAT in arid/semi-arid irrigated areas.

Assessment of long-term anthropogenic impacts on agro-ecosystems requires comprehensive modelling capabilities to simulate water interactions between the surface and groundwater domains. To address this need, a modelling framework, called "SWATmf", was developed to link and integrate the Soil Water Assessment Tool (SWAT), a widely used surface watershed model with the MODFLOW, a groundwater model. The SWATmf is designed to serve as a project manager, builder, and model performance evaluator, and to facilitate dynamic interactions between surface and groundwater domains at the watershed scale, thus providing a platform for simulating surface and groundwater interactions. Using datasets from the Fort Cobb Reservoir experimental watershed (located in Oklahoma, USA), the SWATmf to facilitate linkage and dynamic simulation of SWAT and MODFLOW models. Simulated streamflow and groundwater levels generally agreed with observations trends showing that the SWATmf can be used for simulating surface and groundwater interactions. A modelling framework integrating SWAT and MODFLOW models based on structure grids and a unified spatio-temporal frame.Implementation of model capabilities for simulating multiple surface watersheds contributing to single aquifer domain.Simulation of daily groundwater from irrigation needs and distributed percolation fluxes in the watershed.Assessment of linkages between groundwater levels and extracted volumes during the 2011 and 2012 dry period in Oklahoma.

The multi-metric assessment of model performance in a dominantly single-domain modeling approach (i.e., surface) may not be sufficient to gauge the validity of the model to represent the hydrologic system. Consequently, rating metrics can mathematically validate model results as satisfactory even when some of the simulated hydrologic processes are incorrectly represented during the calibration process. In this paper, we argue that to properly represent the hydrologic system, calibration tasks focused on modifying model parameters should account for equifinality, model inadequacy, and constraint inadequacy. To demonstrate our argument, we conducted a traditional calibration using the Soil and Water Assessment Tool (SWAT) coupled to the Modular Finite-difference Flow Model (MODFLOW) to simulate the hydrologic processes in the Fort Cobb Reservoir Experimental Watershed (FCREW) in central western Oklahoma. The results indicated that model calibration based on metrics from a single domain, in this case using SWAT, did not necessarily guarantee an appropriate representation of water circulation. Moreover, unsupervised calibration techniques based on the objective-function optimization (e.g., inverse calibration using SWAT-CUP SUFI2 algorithm) may not be sufficient if model constraints in multiple domains are not properly set, resulting in distorted parameters. This practice can result in the misrepresentation of water circulation and can have adverse impacts on environmental model results used to support policies and decisions.

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.

Recently, the Soil and Water Assessment Tool (SWAT) was revised to improve the partitioning of runoff and tile drainage in poorly drained soils by modifying the algorithm that computes the soil moisture retention parameter. In this study, the Revised SWAT model was used to evaluate the sensitivity and long-term effects of different nitrogen (N) application rates and tile drain spacing (SDRAIN) and depths (DDRAIN) on nitrate-nitrogen (NO3-N) losses through tile drains. Monitoring data for the 1983–1996 period measured on three experimental plots on a poorly drained Webster clay loam soil (fine-loamy, mixed, superactive, mesic Typic Endoaquoll) in southern Minnesota was used. Sensitivity analysis covered the 1983–1996 period and long-term simulations were made for the 1915–1996 period. Sensitivity analysis showed a decrease in tile flow as DDRAIN decreased and/or SDRAIN increased. The predicted NO3-N losses in tile drain decreased by 16% (from 33.8 to 28.4 kg ha−1) and 14% (from 34.0 to 29.4 kg ha−1) when SDRAIN was increased by 122% (from 27 to 60 m) and 40% (from 1.5 to 0.9 m), respectively. However, NO3-N losses were decreased by 67% (from 33.8 to 11.1 kg ha−1) when N application rate was decreased by 50% (from 200 to 100 kg ha−1). Long-term simulations results indicated that much greater reductions in NO3-N losses can be achieved with reduction in the N application rates than with changing the tile drain spacing and depth. Reductions in NO3-N losses were consistent with the results reported using the Agricultural Drainage and Pesticide Transport (ADAPT) model, which was developed specifically for understanding effects of tile drainage on water quality in the Upper Midwest U.S. Overall, results from sensitivity analysis and long term simulation indicated that Revised SWAT can be used to adequately evaluate the effects of tile drain configurations on drainage and associated NO3-N losses.

Adequate knowledge on the movement of nitrate-nitrogen (NO 3 -N) under different subsurface (tile) drain configurations and management practices in the U.S. Midwest is essential for developing remedial measures for reducing hypoxic conditions in the Gulf of Mexico. In this study, DRAINMOD-NII, a daily time-step soil carbon (C) and N model, was calibrated and validated for subsurface drainage and associated NO 3 -N losses, and crop yield. Long term (1983–1996) monitoring data measured on three experimental plots under continuous corn ( Zea mays L.) with conventional tillage practice at the University of Minnesota's Southern Research and Outreach Center near Waseca, southern Minnesota was used for this purpose. Nash-Sutcliffe efficiency (NSE), Percent Error (PE) and Index of agreement ( d ) were used for assessing the model performance. DRAINMOD-NII predicted monthly subsurface drainage matched well with measured data during calibration (NSE = 0.81, PE = −7.8% and d = 0.94) and validation (NSE = 0.67, PE = −0.7% and d = 0.88) periods. Performance of DRAINMOD-NII for predicting monthly NO 3 -N losses in subsurface drainage was also good for both calibration (NSE = 0.64, PE = 0.8%, and d = 0.85) and validation (NSE = 0.62, PE = −5.3%, and d = 0.83) periods. DRAINMOD-NII predicted average (1983–1992) annual corn relative yield (93%), a ratio of crop yield in a year to the long-term average crop yield, was close to the observed relative yield (92.5%). DRAINMOD-NII simulation results were also compared and contrasted with those obtained by the Agricultural Drainage and Pesticide Transport (ADAPT) model with the same dataset. Both models performed equally well in predicting monthly subsurface drainage. However, DRAINMOD-NII performed slightly better in predicting monthly NO 3 -N losses and annual N budget, in addition to showing potential to simulate the effects of excess and deficit water stresses on crop yield. Studies comparing performances of different drainage models in the U.S. Midwest are useful to select an appropriate model for devising various strategies for reducing NO 3 -N losses from subsurface drainage systems, and thereby minimizing hypoxic conditions in the Gulf of Mexico.

There is a need in water sciences for computational tools to integrate large spatially distributed datasets to provide insight into the spatial and temporal domains of the data while allowing visualization, analysis in the spatial and temporal dimensions, data metrics, and pattern recognition in the same application. Spatial and temporal variability of hydrological processes as well as the associated phenomena transport is better represented in high spatio-temporal resolution datasets. A conceptual data model and analysis tool, SPELLmap, was developed at the USDA Agricultural Research Service, Grazinglands Research Laboratory using the Delphi programming language to rapidly process, manipulate, analyze, and visualize large geo-located datasets. SPELLmap integrates the spatial and temporal domains of hydrological data to perform analyses in space and time while providing data metrics. SPELLmap has the capacity to represent three or four dimensional problems using a layer data structure. Three examples to illustrate SPELLmap functionalities were provided for the raster and raster-to-network domains. SPELLmap can be used for data interpolation, visualization, gridding, pattern recognition, and data metrics in integrated environmental modeling problems.

This paper examines the potential for improving Soil and Water Assessment Tool (SWAT) hydrologic predictions of root-zone soil moisture, evapotranspiration, and stream flow within the 341 km2 Cobb Creek Watershed in southwestern Oklahoma through the assimilation of surface soil moisture observations using an Ensemble Kalman filter (EnKF). In a series of synthetic twin experiments assimilating surface soil moisture is shown to effectively update SWAT upper-layer soil moisture predictions and provide moderate improvement to lower layer soil moisture and evapotranspiration estimates. However, insufficient SWAT-predicted vertical coupling results in limited updating of deep soil moisture, regardless of the SWAT parameterization chosen for root-water extraction. Likewise, a real data assimilation experiment using ground-based soil moisture observations has only limited success in updating upper-layer soil moisture and is generally unsuccessful in enhancing SWAT stream flow predictions. Comparisons against ground-based observations suggest that SWAT significantly under-predicts the magnitude of vertical soil water coupling at the site, and this lack of coupling impedes the ability of the EnKF to effectively update deep soil moisture, groundwater flow and surface runoff. The failed attempt to improve stream flow prediction is also attributed to the inability of the EnKF to correct for existing biases in SWAT-predicted stream flow components.